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Will AI & ML Draw The New-Age Skill For India? New-age skills are gaining prominence in every sector and this includes the entertainment industry as well. Hence, when Finance Minister Nirmala Sitharaman in her maiden Budget speech said that the government will focus on new-age skills like Artificial Intelligence (AI), Internet of Things, Big Data, 3D Printing, Virtual Reality and Robotics, it was welcomed by the industry players. According to them, these skills will lead to newer job opportunities and even offer higher remuneration to employees. But if the question the use of these technologies these sectors, then there are various cases. For example, AI is being used significantly in meta data generation.
If there is a video clip online, the more data there is about that video clip, the greater the ability to use that video clip in different situations. With the help of AI engines and software, specific elements are automatically identified in a particular scene. There are few startups too that are trying to specialize in this field.
The other aspect where AI is used a lot in India is embedded with data analytics. Data is being used as a powerful tool for marketing to audiences. People’s comments, mentions and shares are analysed on social media. For example, a marketer takes note of positive sentiments in comments like thank you or a thumbs up for a film’s marketing content.
And uses this to mark territories where the probability of the film doing well is more. Social media analysis is also used to determine screen count across territories. Online video platforms are also using AI aggressively to personalise content for every individual. Platforms using AI have algorithms that learn over time what users want to watch and recommend similar content. Even Bollywood has resorted to AI to maximise reach of films. Producers of 2017 release Shaadi Mai Zaroor Aana collaborated with Infinite Analytics, a predictive analytics company that started operations in 2012, to apply analytics to rope in audiences for the film. Infinite Analytics identified the lead actor Rajkumar Rao’s films, music, food, books and recommended the same for the film. Then social media was analysed to find out people with similar preferences. This data was then given to the filmmakers who kept in mind preferences of the people like the language or clothing while making the trailer of the film. As a result of successful implementation of data analytics, the trailer of the film reached a million hits within five days of its release. Companies are training machine learning algorithms to help develop film and television promos, which will bring down the entire process of making trailers to 24 to 48 hours, down from the minimum two weeks required currently. This is why employees with new-age skills will make them more relevant, lucrative and more employable. In addition, "removal of all foreign direct investment (FDI) cap will help the industry push technology in areas of Big Data, AI, Machine Learning and Robotics," believes Rahul Puri, MD of Mukta Arts A2 Cinemas. In the current environment where the entire IT landscape of enterprises is changing, India will once again be the powerhouse for solving AI and ML and Data Science needs of the Fortune 2000 businesses. Conventional service lines offered by IT services leaders are being transformed to AI and Digital service lines by these companies. So it has a great future in Indian IT industry.
Likewise several startups are emerging in areas of business enabled by AI/ML like healthcare, ecommerce etc. Recently Flipkart announced an initiative for AIFORINDIA. Another pioneering effort is by government which wants to embrace data based governance. Look at Open Government Data (OGD) Platform India the repository maintained by Government of India. @JitendraSagar15
So future of AI ML Data Science is bright in India. We sincerely hope you enjoy reading the October issue of TimesTech.
Jitendra K Sagar Editor jitendra@timestech.in
04 October 2019
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16 Machine Learning IMUs: Let Your Sleep with On-Board
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20 Tech Focus New Applications for Energy Harvesting
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A deeper dive into AI at the Edge
30 Cloud & Data 5G Demands Comprehensive Cloud Data Management
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Editor, Publisher, Printer and Owner make every effort to ensure high quality and accuracy of the content published. However he cannot accept any responsibility for any effects from errors or omissions. The views expressed in this publication are not necessarily those of the Editor and publisher. The information in the content and advertisement published in the Magazine are just for reference of the readers. However, readers are cautioned to make inquiries and take their decision on purchase or investment after consulting experts on the subject. TimesTech Print Media holds no responsibility for any decision taken by readers on the basis of the information provided herein. Any unauthorised reproduction of TimesTech Magazine content is strictly forbidden. Copyright Š 2019..... All right reserved. Reproduction in any manner is prohibited. Subject to Hapur Jurisdiction.
Tech News
India has Potential to Become A Solar Power Battery Hub: PM Modi If solar power was used for cooking in India, there was scope for 250 million batteries, which in turn could benefit the electric vehicle market through crosssubsidy. Amid the thrust on renewable energy, Prime Minister Narendra Modi wondered whether India could become a hub of solar power battery manufacturing, which can play a major role in the march towards clean energy. He stated this while giving the example of mobile phones, saying their popularity increased manifold as the size of their batteries decreased. Modi was responding when asked for his comments on the global challenge of climate change after he addressed the Eastern Economic Forum (EEF) here in presence of Russian President Vladimir Putin, Japanese Prime Minister Shinzo Abe, Malaysian Prime Minister Mahathir Mohamed and Mongolian President Kjhaltmaagin Battulga. He said India, as part of its aspiration to generate 175 GW of clean energy by 2022, is focusing on solar
power in a big way. Expressing confidence that India will achieve the target of 175 GW of renewable energy, the Prime Minister wondered whether India could become a hub of solar power battery manufacturing. He said he has invited companies dealing in it to discuss the matter. He said if solar power was used for cooking in the country, there was scope for 250 million batteries, which in turn could benefit the electric vehicle market through cross-subsidy.
Auto Component Industry Should Produce Electronics and Key Parts in India to Cut Imports: Maruti Renowned carmaker Maruti Suzuki India asked the components makers to start manufacturing vehicle electronics and certain key parts in India in order to cut imports of such articles. The local manufacturing of such parts would not only help Maruti Suzuki India (MSI), but also support the government’s Make in India initiative, MSI MD and CEO Kenichi Ayukawa said while speaking at the ACMA annual convention, reported PTI. Ayukawa stated that a Maruti Suzuki car is over 90 percent local, component-wise. But some key parts and electronics are areas where the automaker still needs to import. He expressed his strong desire to completely go Make-in-India. While addressing his challenge, he extended an invitation to the component industry by saying that anybody can make electronic components and some key parts in India with quality and reliability. He was optimistic that it will not only help Maruti Suzuki but the entire Indian automobile industry.
lies in developing in-house research and development (R&D) capability. He also emphasised on the virtues of quality manufacturing of auto components. On government policy, Ayukawa said that if the government sets targets on the end-goals and allows freedom to the industry players to choose the technology, it would be best suited to achieve the endgoals.
Ayukawa said the best opportunity to win in the future
08 October 2019
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Electronics Manufacturers Urges The Government to Take Steps to Boost Exports Ÿ Nearly 60 leading electronics manufacturers will
meet with the government today Ÿ MeitY will hear input and feedback from the industry
to understand the opportunities available and the conditions needed to achieve more manufacturing in India Electronic product manufacturers said that the government should develop policies and incentives to attract the world’s largest electronics manufacturing. Nearly 60 leading electronics manufacturers will meet with the government today and will raise this issue, including this issue, to promote the country’s industrial development. At a recently organised discussion by the Chennai International Centre, George Paul, chief executive officer of Manufacturers Association for Information Technology (MAIT) said that the electronics manufacturing industry globally has seen a change. He was of the opinion that because of this change, many of the companies are looking at de-risking their operations by having their facilities in new
geographies. Paul stated that this is the right time for India to attract some of the big manufacturers in India, which will help to grow exports. Paul has said that the Ministry of Electronics and Information Technology has called for a meeting with the industry to hear input and feedback from industry representatives, to understand the opportunities available and the conditions needed to achieve more manufacturing in India.
element14 Sales reach 15 Million Raspberry Pi Computer in the world element14, Raspberry Pi single-board computer (SBC), which it has sold to electronic design engineers, educators, hobbyists and makers since its launch in 2012. element14’s sales alone, which are supported by its holistic offering including a diverse ecosystem of accessories and support through its element14 Community, have now reached the remarkable total of 15 million devices worldwide. Raspberry Pi’s most recent launch, the Raspberry Pi 4 Model B computer, has driven a significant increase in sales. This new board is the most powerful Raspberry Pi to date: three times faster than the previous model with a range of memory options and improved interfacing and connectivity to extend capabilities for all users. Raspberry Pi 4 can be used as a high-performance embedded platform, or as a full-featured desktop computer. element14 stocks all Raspberry Pi models, alongside its diverse ecosystem of accessories, which supports users as they build devices for home, education or commercial applications. element14 has championed Raspberry Pi through its element14
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Community, creating a forum for its 650,000 members to access informative and educational threads, be inspired by fellow members’ ideas, and get involved in Raspberry Pi-based design challenges. The rising complexity of AI and machine learning systems is driving an ever greater need to complement processing power with more memory and storage for faster data processing. Increased amounts of memory and storage help avoid performance and throughput bottlenecks while transforming vast amounts of data into insights. TIMESTech.in
Tech News
Keysight Technologies Hosted Keysight World 2019 in India
Keysight Technologies, hosted Keysight World 2019 in Delhi and Bangalore last week. Keysight World brings together Keysight thought leaders, technological advances, breakthrough design/test/optimization strategies, and leading-edge solutions on a global scale to inspire, enable, and accelerate the realization of your innovation. Keysight World serves as an excellent environment for industry leaders to connect with stakeholders involved with all aspects of the electronic and semiconductor value chain; address test and measurement challenges; and experience realworld solutions.
In today’s global economy, success demands an integrated and agile approach to innovation. Delivering the next big thing requires the perfect blend of inspiration, technology, solutions, and partners from around the world. That’s the driving idea behind Keysight World. As the world’s premier electronic measurement company, Keysight offers a full portfolio of test and design solutions that span the entire lifecycle. From simulation to R&D, design verification and preconformation, conformation, manufacturing, deployment, and service assurance, Keysight continues to be at the forefront. ‘’Since customers play an integral role in the success of Keysight, Keysight World helped in bringing our customers together and served as a platform for them to network and exchange ideas. At Keysight,
Yuba Dozono appointed as new President of Vicor KK Vicor Corporation appointment of Yuba Dozono as President of Vicor KK (Headquarter: Shinagawa ward, Tokyo), effective July 1, 2019. Mr. Dozono will oversee Vicor’s Japan business as Vice president for Sales & Marketing of Vicor Corporation. Commenting on the appointment, Vicor’s Corporate Vice President Global Sales and Marketing Mr. Philip D. Davies commented “Japan is a key market for Vicor. Mr.Dozono brings extensive experience for deploying our global strategy into play and strengthens and expands stronger ties with our customer.” Prior to joining Vicor, Mr. Dozono lead Japan’s automotive segment at Integrated Device Technology. Prior to IDT, Mr. Dozono was at Microchip and Macnica corporation. TIMESTech.in
we believe that these seminars add value to our customers and their businesses,’’ said Sudhir Tangri, VP & Country General Manager India at Keysight Technologies. Sandeep Kapoor, Director Marketing (Europe, MEA and India), Keysight Technologies said, ‘’the program was designed to provide experiences through realworld product demonstrations, technical presentations and networking among industry experts. Keysight World was a great platform to learn from industry leaders and technical experts about the latest industry directions and technologies. We saw a lot of interest from our customers and industry experts in Keysight World. We had a strong panel of speakers who shed light on innovative solutions and topics that are key for our stakeholders.”
Dynamic Cables and SunAlpha team up to commission 1200 kW solar project Rajasthan-based Dynamic Cables Ltd, a global manufacturer and supplier of cables & conductors, has partnered with SunAlpha Energy to set up 1200kW of rooftop solar power project at its manufacturing units in Jaipur and Reengus in Rajasthan. Dynamic Cables will meet about 30% of its energy requirement through a rooftop solar project and will be able to generate approximately 17, 00,000 kWh of clean energy annually, cutting down 42,000 metric tonnes of carbon emissions. The project was commissioned under the Rajasthan solar net-metering policy. Dynamic Cables is a flagship company of renowned MANGAL GROUP OF COMPANIES based at Jaipur (a Partnership firm DYNAMIC ENGINEERS formed in 1986 was converted into a private limited company in 2007) is engaged in manufacturing of all types of Electrical Cables and Overhead Conductors to meet the market needs in India and Overseas as well. October 2019
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AI&ML Evolution Concept and Architecture of AI &ML Artificial Intelligence (AI) we refer to a subfield of Computer Science. Artificial Intelligence is acted by machines, computers and mainly software. Machines mimic, here we see why it is called artificial, some kind of cognitive function based on environment, observations, rewards and learning process. Few debate that During the Second World War, noted British computer scientist Alan Turing worked to crack the 'Enigma' code which was used by German forces to send messages securely. Alan Turing and his team created the Bombe machine that was used to decipher Enigma's messages. The Enigma and Bombe Machines laid the foundations for Machine Learning. According to Turing, a machine that could converse with humans without the humans knowing that it is a machine would win the “imitation game” and could be said to be “intelligent”. In 1951, an machine known as Ferranti Mark 1 successfully used an algorithm to master checkers. Subsequently, Newell and Simon developed General Problem Solver algorithm to solve mathematical problems. Also in the 50s John McCarthy, often known as the father of AI, developed the LISP programming language which became important in machine learning.
12 October 2019
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Machine Learning Machine learning is a subset of AI, where we make use of algorithms and technologies like sensors and connected devices to teach the system to learn autonomously. Instead of feeding hundreds of instances to make a decision, we give machines the ability to filter out wrong decisions and choose the most appropriate ones (again like humans). But that is not as easy as it sounds. The working behind virtual voice assistants and any other technology we have talked about earlier are based on machine learning. To give the right response, Alexa has to understand if your question is genuine or sarcastic, filter out on replies that sound dumb and respond in a way that matches the tone of your question.
Designing Future Chips With PCs and mobile phones, the game-changing innovations that defined this era, the architecture and software layers of the technology stack enabled several important advances. In this environment, semiconductor companies were in a difficult position. Although their innovations in chip design and fabrication enabled nextgeneration devices, they received only a small share of the value coming from the technology stack—about 20 to 30 percent with PCs and 10 to 20 percent with mobile, reports McKinsey.
The report also suggested on how will new revenue and sales com for the semiconductor companies – They said, AI & ML. v AI could allow semiconductor companies to capture 40 to 50 percent of total value from the technology stack, representing the best opportunity they've had in decades. v Storage will experience the highest growth, but semiconductor companies will capture most value in compute, memory, and networking. v To avoid mistakes that limited value capture in the past, semiconductor companies must undertake a new valuecreation strategy that focuses on enabling customized, end-to-end solutions for specific industries, or “microverticals.”
Opportunities for Semiconductor Companies AI has made significant advances since its emergence in the 1950s, but some of the most important developments have occurred recently as developers created sophisticated machine-learning (ML) algorithms that can process large data sets, “learn” from experience, and improve over time. The greatest leaps came in the 2010s because of advances in deep learning (DL), a type of ML that can process a wider range of data, requires less data preprocessing by human operators, and often produces more accurate results.
The technology stack for artificial intelligence (AI) contains nine layers. Technology Services
Stack Solution and use case
Definition Integrated solutions that include training data, models, hardware, and other components (eg, voice-rec-ognition systems)
Training
Data types
Data presented to AI systems for analysis
Platform
Methods
Techniques for optimizing weights given no model inputs
Architecture
Structured approach to extract features from data eg, convolutional or recurrent neural networks)
Electronic repository for long-term storage of large data sets
Algorithm
A set of rules that gradually modifies the weights given to certain model inputs within the neural network during training to optimize inference
Storage typically consists of NAND
Interface
hardware
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Framework
Software packages to define architectures and invoke algorithms on the hardware through the interface
Interface Systems
Systems within framework that determine and facilitate communication pathways between software and underlying hardware
Head node
Hardware unit that orchestrates and coordinates computations among accelerators
Accelerator
Silicon chip designed to perform highly parallel operations required by AI; also enables simultaneous computations
Memory Electronics data repository for short-term storage during processing Memory typically consists of DRAM Storage
Logic Processor optimized to calculate neural network operations, ie, convolution and matrix multiplication Logic devices are typically CPU, GPU, FPGA, and/or ASIC3 Networking Switches, routers, and other equipment used to link servers in the cloud and to connect edge devices
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Artificial Intelligence
Machine Learning
AI stands for Artificial intelligence, where intelligence is defined acquisition of knowledge intelligence is defined as a ability to acquire and apply knowledge
ML stands for Machine Learning which is defined as the acquisition of knowledge or skill
The aim is to increase chance of success and not accuracy.
The aim is to increase accuracy, but it does not care about success
It work as a computer program that does smart work.
It is a simple concept machine takes data and learn from data.
The goal is to simulate natural intelligence to solve complex problem
The goal is to learn from data on certain task to maximize the performance of machine on this task.
AI is decision making.
ML allows system to learn new things from data.
It leads to develop a system to mimic human to respond behave in a circumstances.
It involves in creating self learning algorithms.
AI will go for finding the optimal solution.
ML will go for only solution for that whether it is optimal or not.
AI leads to intelligence or wisdom.
ML leads to knowledge.
“Between 2018 and 2019, organizations that have deployed artificial intelligence (AI) grew from 4% to 14%, according to Gartner's 2019 CIO Agenda survey.�
Gartner Hype Cycle for Artificial Intelligence, 2019
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Intelligent applications
been an increase in learning how to deal with biased data.
Most organizations' preference for acquiring AI capabilities is shifting in favor of getting them in enterprise applications. Intelligent applications are enterprise applications with embedded or integrated AI technologies to support or replace human-based activities via intelligent automation, data-driven insights, and guided recommendations to improve productivity and decision making, writes Garter.
Neural networks
Today, enterprise application providers are embedding AI technologies within their offerings as well as introducing AI platform capabilities — from enterprise resource planning to customer relationship management to human capital management to workforce productivity applications.
Quantum Computing On the onset, let me dispel any myths that we would make quantum computing incrementally better in 2019. Instead we would just be pushing a little in the right direction towards building better quantum computing devices. Although it would be small increment, it would still be a huge focal point in the area of AI. Quantum computers use quantum physics to compute calculations faster than any supercomputer today. We are well aware of how computers use bits and bytes. However unlike a regular computer, quantum computers use qubits (Quantum bits) to store information. We have a long way to go in terms of dealing with the challenges of quantum computing like maintaining coherence of the qubits or removing the unnecessary and noisy computations.
Biased data: This topic is becoming increasingly important as machine learning models are being used for decision-making such as hiring, mortgage loans, prisoners released from parole or the type of social service benefits. For example, consider a fictitious case of the decision of promoting a woman. Historical data on employment may show women getting less promoted than men and hence create discriminatory AI applications. Many more examples have led to an increased emphasis of dealing with biased data in AI applications. As the usage of AI applications increases in 2019, there has
To put it briefly, neural networks or artificial neural networks emulate human brain. They store all data in a digital format — sensory, text or time and use it to classify and group the information. For example, reading someone's handwriting comes easily and unconsciously to us whereas in order to teach an algorithm we feed it with vast amounts of handwritten data to recognize patterns in it. There is a huge demand of neural networks in robotics, to improve order fulfillment, prediction of the stock market, and diagnosis of medical problems or even to compose music! What can machine learning do to automate these complex problems? Quite a lot, but the actual application and techniques being used depend highly on the problem space. As an example, while there technically is “a lot of data to mine,” in reality the amount of useful data to train a predictive model is quite limited. Unlike social media where data that can be harvested seemingly without limits, chip design data is available only in a fractured environment, and technology is constantly changing. Imagine a self-driving car that that would have to recognize new rules or road signs every few months and is only allowed to train on portions of the road at a time. In chip design, the training of machine learning models is likely to happen in each customer environment independently at the design level, and for each foundry ecosystem at the technology node level. Conclusion But there also is a level of risk associated with AI, depending upon the application and the level of precision. The design of electronic systems in the past has been based upon the complete predictability of logic, much of which has been hard-wired. AI replaces computational precision with distributions of acceptable behavior, and there is a lot of discussion at conferences about what that means for design sign-off. It's not clear whether existing tools or methodologies will provide the same level of confidence. There are many promising technologies that have the power to completely transform the way chips are being designed. Although AI will bring groundbreaking potential, it will be empowering to designers rather than replace them.
Complexity of Chip Design
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Machine Learning
IMUs:
Let Your Host Sleep with On-Board
Rich Miron Applications Engineer at Digi-Key Electronics
Inertial measurement units (IMUs) are widely used to provide a steady stream of multi-axis position information from accelerometers, gyroscopes, and other sensors. With the many degrees of freedom (DOF) all generating data, the merged data streams from these devices can keep system processors in constant wake mode and tax them as they sift through the raw IMU data to extract useful gesture and system location information. What designers need is a way to offload this sifting function from the main processor. Machine learning may be the answer. After a brief overview of IMU use, this article introduces the 6DOF LSM6DSO from STMicroelectronics. It then uses this device to show how the addition and integration of machine learning and decision tree processing into IMUs can offload real-time position and movement processing from the host application processor and how these features can be used in real applications.
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MACHINE LEARNING A quick IMU review IMUs integrate a number of motion sensors into one device and can provide high accuracy positioning information. They can be used for a variety of applications including consumer (mobile phones), medical (imaging), industrial (robotics), and military (head tracking). They react to the motion of the sensor and incorporate one or more of the following motion sensor types: • Gyroscope sensors measure angular position changes, usually expressed in degrees per second. Integrating angular rate over time results in a measured angle of travel that can be used to track changes in orientation. Gyroscopes track relative movement independently from gravity, so errors from sensor bias or integration result in a position error called “drift,” which can be compensated for with software. • Accelerometers measure linear acceleration, including acceleration components caused by device motion and acceleration due to gravity. The acceleration unit of measurement is g, where 1 g = the earth’s gravitational force = 9.8 meters/second2. Accelerometers are available with one, two, or three axes, which define an X, Y, Z coordinate system. • Magnetic sensors measure magnetic field strength, typically in units of microTeslas (µT) or Gauss (100 µT = 1 Gauss). The most common magnetic sensor used for mobile electronics is a three-axis Hall effect magnetometer. By computing the angle of the detected earth’s magnetic field, and comparing that measured angle to gravity as measured by an accelerometer, it is possible to measure a device’s heading with respect to magnetic north with high accuracy. Motion tracking using IMUs employs sensor fusion to derive a single, high accuracy estimate of relative device orientation and position from a known starting point and orientation. Sensor fusion usually employs software to combine the IMU’s various motion sensor outputs using complex mathematical algorithms developed either by the IMU manufacturer or the application developer. Position calculations using sensor fusion can produce the following measurements: • Gravity – Specifically the earth’s gravity, which excludes the acceleration caused by the motion being experienced by the device. An accelerometer
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Machine Learning
measures the gravity vector when the IMU is stationary. When the IMU is in motion, the gravity measurement requires fusing data from an accelerometer and a gyroscope and subtracting out the acceleration caused by motion. • Linear acceleration – Equivalent to the acceleration of the device as measured by the accelerometer, but with the gravity vector subtracted using software. IMU linear acceleration can be used to measure movement in threedimensional space. • Orientation (attitude) – The set of Euler angles including yaw (azimuth), pitch, and roll, as measured in units of degrees. • Rotation vector – Derived from a combination of data from accelerometer, gyroscope, and magnetometer sensors. The rotation vector represents a rotation angle around a specified axis.
Sources of IMU error Gyroscopes sense orientation through angular velocity changes, but they tend to drift over time because they only sense changes and have no fixed frame of reference. Adding accelerometer data to the gyroscope data allows software to minimize gyroscope bias for a more accurate location estimate. Accelerometers sense changes in direction with respect to gravity, and that data can be used to orient a gyroscope. Accelerometers are more accurate for static (as opposed to dynamic) calculations. Gyroscopes are better at detecting orientation when the system is already in motion. Accelerometers react quickly, so accelerometer jitter and noise produce accumulated error when that data is used alone. Additionally, accelerometers tend to distort accelerations due to external forces such as gravitational forces, which also accumulate in the system as noise. Filtering this data improves accuracy. Combining a gyroscope’s short-term accuracy with an accelerometer’s long-term accuracy results in more precise orientation readings by relying on each sensor’s strengths to cancel or at least reduce the other sensor’s weaknesses. The two sensor types complement each other to help reduce errors, but there are other ways by which errors are reduced.
Fused filtering needed to reduce error
generate an interrupt that awakens the host processor from sleep mode so that it can initiate processing or take some action as a result of the interrupt. To enable this capability, some IMU vendors are starting to incorporate processing and decision making features in their IMUs.
Let the IMU do the thinking The 6DOF LSM6DSO from STMicroelectronics is one such IMU. It incorporates three microelectromechanical systems (MEMS) gyroscopes and three MEMS accelerometers and can detect orientation changes and gestures without oversight or assistance from a host processor, all using on board processing. The IMU consumes 0.55 milliamps (mA) running in its highest performance mode. In this mode, the LSM6DSO can continuously monitor its own attitude and movement in space and can generate an interrupt upon a prearranged condition that awakens the host processor to perform additional processing on the sensor stream. Using a low-power IMU that can always remain operational is beneficial because it lets the host processor sleep, awakening it only when necessary. This is a tried and trusted means of saving energy in battery-powered systems. In addition to its gyroscope and accelerometer sensors, the LSM6DSO IMU contains a signal conditioning and filter block, a finite state machine (FSM) that can run as many as 16 programs —all sharing a common, configurable output data rate—and a machine learning core. Used together, these resources can generate event detection interrupts for the following conditions: • • • • • •
Free fall Wakeup 6DOF orientation Single-click and double-click sensing Activity/inactivity recognition Stationary/motion detection
The signal conditioning block applies conversion factors stored in its sensitivity registers to scale the raw sensor data. It then converts the raw IMU sensor data stream into a 16-bit, half-precision floating point (HFP) byte format that the FSM can understand. The IMU’s MEMS sensors (the accelerometers and gyroscopes) along with the two analogto-digital converters (ADCs) and four filter blocks are shown in Figure 1. The filter blocks are used to convert the analog MEMS sensor signals into filtered digital data streams.
IMU software uses filtering to minimize positioning error from IMU data. Several filtering methods for fusing sensor data are available, each with varying degrees of complexity. A complementary filter combines a high pass gyroscope filter and low pass accelerometer filter. High frequency noise in the accelerometer data is therefore filtered out in the short term and smoothed by the gyroscope data. The computational horsepower needed to perform all of this sensor processing, filtering, and fusing consumes energy, which can be a problem in battery-powered systems, especially when the IMU information is not needed as a continuous stream. For many embedded applications, significant power savings can be realized if the IMU can
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Figure 1: The LSM6DSO IMU uses two ADCs to convert analog signals from its internal MEMS accelerometers and gyroscopes into digital streams. The ADCs are followed by four digital filters to condition the signals for decision making by the internal FSM and machine learning core and by the host processor. (Image source: STMicroelectronics)
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Machine Learning
The programmable FSM consists of a configuration block and sixteen program blocks. The FSM’s configuration block configures and controls the entire FSM. Each of the FSM’s sixteen program blocks consists of an input selector block and a code block (Figure 2). Both of these blocks are controlled by values written to registers within the IMU.
Figure 2: Each of the FSM’s sixteen program blocks in the LSM6DSO IMU consists of an input selector block and a code block. (Image source: STMicroelectronics)
The Input Selector block routes the selected input data from one of the IMU’s internal sensors or from an external sensor connected to the IMU’s sensor hub to the code block. The IMU’s sensor hub can accommodate as many as four additional external sensors such as magnetometers, which are connected to the IMU via an I2C port.
these comparisons. Because it’s a simple machine, it’s programmed directly with the FSM opcodes. There is no high level language compiler for the FSM, but the programs are generally so simple that no compiler is needed.
Using the FSM The LSM6DSO IMU’s FSM can be programmed to generate interrupt signals activated by predefined motion patterns. The FSM can run as many as 16 simultaneous, independent programs to detect motion. Each FSM program consists of a sequence of if-then-else steps and uses the sensor streams from the LSM6DSO’s accelerometers and gyroscopes as inputs. If any of the FSM programs detects a match to its preprogrammed pattern, the FSM can generate an interrupt to the host processor. Each of the sixteen possible FSM programs contains three memory sections for fixed data, variable data, and instructions. A single FSM program block diagram is shown in Figure 3.
The FSM’s code block contains one program for the state machine. The program block’s data section’s fixed portion consists of six bytes that define the number of thresholds, hysteresis, mask, and timer settings for the program. The program block’s variable data section holds the actual threshold, hysteresis, mask, and timer settings for each program as defined by the values stored in the fixed part of the data section. The fixed portion of the data section also defines the size of the variable portion of the code block’s memory footprint, a programmable reset vector, and a program counter. Because these are all 8-bit values, each FSM program is limited to 256 bytes. The program block’s instruction section contains the actual FSM program. Program instructions include opcodes for checking sensor inputs against thresholds, checking for zero crossings, and checking timer values for timeout comparisons. The opcodes specify the condition needed to pass from the current FSM state to the next. In addition, there are command opcodes for selecting thresholds and masks stored in the program’s variable data section; for setting the IMU’s sensor hub multiplex selector to connect to one of the four possible external sensors; and for asserting an interrupt. Each FSM program can generate an interrupt and can modify the contents of a corresponding register value based on the selected input signal. These register values are used to pass data from the IMU to the host processor. It’s useful to think of the FSM as a microprocessor minus the arithmetic logic unit. The FSM can make selections, perform comparisons, and can make decisions about its next state based on those comparisons. It does not compute values other than the Boolean results from the comparisons. The FSM is not a microprocessor. It can make comparisons and perform simple changes to the program flow based on
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Figure 3: The FSM in the STMicroelectronics LSM6DSO IMU incorporates sixteen code blocks, each of which contains three memory sections for fixed data, variable data, and instructions. (Image source: STMicroelectronics)
The structure of a single program in a code block consists of three sections in a block of memory: • A fixed data section, which has the same size for all the FSM programs • A variable data section, which can vary in size • An instruction section, which contains conditions and commands Programming each FSM code block involves loading the three memory sections with programing values that determine the FSM’s behavior. STMicroelectronics provides an FSM programming tool within its downloadable Unico evaluation development software and development environment. STMicroelectronics has also included several sample FSM programs with the Unico development tools as an aid in learning how to program the FSM. These sample programs demonstrate several IMU based interrupt scenarios including: • • • • •
A basic pedometer System in free fall Simple motion detection System has been picked up System has been shaken
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Machine Learning
• System has stopped moving (stationary) • Wrist tilt The sample FSM program examples demonstrate the use of the various FSM features. Any of these sample programs can be installed into an IMU demo platform like the STEVALMKI109V3 eMotion STM32 eval board, which has a 28-pin socket that accepts the LSM6DSO STEVAL-MKI197V1 IMU adapter board. Programming the STEVAL-MKI109V2 board with one of the example programs requires only a few clicks in the Unico development environment. However, there’s a bit more to the LSM6DSO than meets the eye.
The machine learning core The LSM6DSO IMU also incorporates a more sophisticated and programmable pattern matching engine called the machine learning core. This can identify classes of movement using the multiple sensor data streams from the internal IMU sensors and any attached external sensors. Recognizable event classes include stationary (no movement), walking, jogging, biking, and driving. Classification takes the form of decision trees within the machine learning core. The machine learning core consists of three blocks: the sensor data block, the computation block, and the decision tree (Figure 4). The machine learning core’s sensor data block aggregates data streams from the IMU’s internal accelerometers and gyroscopes and from any external sensors attached to the IMU through the I2C interface. The computation block can filter the sensor data using predefined filtering parameters, and compute windowed statistics including the mean, variance, peak-to-peak amplitude, minimum, maximum, and zero crossing for the sensor data. The decision tree compares the computed sensor data statistics against thresholds to classify the input data.
The downloadable STMicroelectronics X-CUBE-MEMS1 software pack for the company’s STM32Cube development system includes the following example software routines: • Activity recognition – Provides information on the type of activity being performed by the user including holding still, walking, fast walking, jogging, biking, or driving. This algorithm might typically be used in a mobile phone or some sort of wearable device. • Motion duration detection – When combined with pedometer data, motion duration detection can be used to determine the number of seconds that a user is active. This algorithm might typically be used in a wearable device for fitness or health tracking. • Vibration or motion intensity detection – Provides information about the intensity of user motion and can distinguish motion intensity in a range from 0 (still) to 10 (sprinting). This algorithm might typically be used in a mobile phone or some sort of wearable fitness device. • Carrying position recognition – Provides information about how the user is carrying a device and can distinguish among the following positions: on a desk, in a hand, near the head, in a shirt pocket, in a trouser pocket, in a jacket pocket, and held in a swinging arm. This algorithm might typically be used in a mobile phone or some other sort of carried device for activity related context detection. Conclusion The need to keep a host processor running to maintain a position fix and to detect movement and gestures from IMU data can be a difficult goal to achieve with battery-powered embedded designs because of the host processor’s relatively high power consumption. However, a new generation of low-power IMUs with sufficient on board processing to perform machine learning can solve this problem by allowing the host processor to sleep in a low current mode until it’s needed.
Figure 4: The machine learning core in the STMicroelectronics LSM6DSO IMU consists of three blocks: a sensor data block that aggregates data streams from internal and external sensors, a computation block that filters the sensor data and computes statistics on that sensor data, and a decision tree that classifies events based on the computed statistics. (Image source: STMicroelectronics)
As with the LSM6DSO’s FSM, a dedicated tool in the Unico development environment is used to program the IMU’s machine learning core. The finite state machine and the machine learning core can also be used in conjunction with a host processor to implement more sophisticated position tracking algorithms.
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October 2019
19
Tech Focus
New Applications for John Donovan Mouser Electronics
Energy Harvesting
Blame it on the cell phone As mobile phones morphed from wireless analog telephones to handheld computers, users kept demanding more and more energy-consuming features, such as web browsing, videos, gaming, and email, while still requiring extended battery life. Since battery makers were not much help, semiconductor manufacturers devised numerous energy-saving techniques to make it all possible. They have been wildly successful. Low power has been the most important electronic design criterion for at least the last ten years. Thanks to Moore’s Law and a lot of smart engineers, semiconductor power levels have dropped dramatically, often consuming milliwatts in run mode and nanowatts in standby mode. As a direct result, ultra-low-power wireless sensorless networks finally became possible and their adoption has been dramatic. Now, sensors stand alone in remote or hard-to-reach areas to warn of building and bridge stresses, air pollution, forest fires, pending landslides, worn bearings, and wing vibration. Low-power wireless sensor networks are at the heart of numerous industrial, medical, and commercial applications.
ambient energy sources for power. The combination of ultra-low-power MCUs and energy harvesting have given rise to a wealth of applications that previously were not possible. The energy harvesting market is large and growing rapidly. According to analysts at IDTechEx, energy harvesting was a $0.7 billion market in 2012 and is expected to exceed $5 billion by 2022; by then 250 million sensors will be powered by energy harvesting sources. The market for thermoelectric energy harvesting alone will reach $865 million by 2023.
Current technologies and applications There are several energy harvesting technologies in common use, with some innovative techniques just over the horizon. The most common energy sources are light, heat, vibration, and RF. Short of rooftop solar panels none of them generate a great deal of energy (see Figure 1), but one or more of them may be more than adequate to power low-power devices in a particular environment.
However, off-grid, as well as portable sensor nodes, rely on batteries for power and face the same problem as cell phones. In such cases, it is advisable to prolong battery life by harvesting environmental energy sources – most often available as light, heat, vibration, motion, or ambient RF. If a device’s energy requirement is low enough and battery replacement would be difficult or expensive, it may be possible to dispense with the battery altogether and rely exclusively on harvesting
20 October 2019
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Tech Focus
Seebeck effect. The Peltier effect is the basis for thermoelectric heat pumps.
Piezoelectric Piezoelectric transducers generate electricity when stressed, which make them good candidates for vibration sensors when they are used in energy harvesting modules that detect motor bearing noise and the vibration of aircraft wings. The Midé Volture™ V-20W Vibration Energy Harvester employs a cantilever that attaches to a piezoelectric crystal. When vibrations set the cantilever in motion it generates an AC output voltage that is rectified, regulated, and stored in a supercapacitor or thin-film battery. Figure 1: Power available from energy harvesting sources
Solar There is hardly a home or office that does not have at least one solar-powered calculator – actually, a calculator with a coin-cell battery and a small front panel photovoltaic (PV) cell to top it up. These polycrystalline silicon or thin-film cells convert photons to electrons with a typical efficiency of about 15 to 20% for polycrystalline and 6 to 12% for thin film cells. Since the power available from indoor lighting is typically only about 10 µW/cm², their usefulness depends on the size of the module plus the spectral composition of the light. Small solar cells are frequently used in consumer and industrial applications, including toys, watches, calculators, street lighting controls, portable power supplies, and satellites. Since light sources tend to be intermittent, solar cells are used to charge batteries and/or supercapacitors to provide a stable energy source.
Thermoelectric Thermoelectric harvesters exploit the Seebeck effect, where a voltage is created when a temperature differential exists at the junction of two dissimilar metals. Thermoelectric generators (TEGs) consist of an array of these thermocouples connected together in series to a common heat source such as an engine, water heater, or even the back of a solar panel. Output depends on the size of the TEG and the temperature differential that can be maintained. TEGs are typically used to power wireless sensor nodes in high-temperature environments such as industrial heating systems. A TEG mounted between a power transistor and its heatsink can recycle some of the energy that would otherwise be lost as heat. Micropelt’s TE-CORE7 Thermal Energy Harvesting Modules convert locally available waste heat to provide long-life operation for low-power devices. The TE-CORE TEG converts heat to an electrical charge which is then boosted, stored in a 100µF capacitor, and regulated to supply up to 5.5V. Running at 50°C the TE-CORE7 can deliver 6.424mAh annually, the equivalent of three to four AA batteries – at that rate the batteries would need to be changed every few months. Forcing a current to flow through the junction of dissimilar metals will cause heat to transfer from the hot to the cool junction – the Peltier effect, essentially the opposite of the
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Figure 2: Midé Volture™ piezoelectric energy harvester (Courtesy of Midé)
Radio Frequency - RF RFID works by rectifying a strong local signal (not ambient RF) aimed directly at the sensor. Similarly Powercast’s P2110 RF Powerharvester™ receiver converts low-frequency RF signals to 5.25V, providing up to 50mA output current. In conjunction with a low-power MCU, sensors, and a radio module, the P2110 can provide a complete, battery-free wireless sensor node that can operate with as little as 11.5dBm RF input. Applications for the device include battery-free wireless sensors for industrial monitoring, building automation, smart grid, agriculture, and defense applications. Mouser carries Powercast development kits for battery charging and wireless sensors.
Figure 3: Powercast P2110 in a batteryless wireless sensor (Courtesy of Powercast).
Innovative techniques and technologies Some very interesting energy harvesting technologies that are still in the laboratory could change the face of the energy harvesting industry over the next few years.
Medical and Fitness Devices Some novel uses for piezoelectric energy harvesting are starting to emerge. Researchers at the University of Michigan have developed a device that harvests energy from the
October 2019
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Tech Focus
reverberation of heartbeats through the chest and converts it to electricity to run a pacemaker or an implanted defibrillator, hopefully obviating the need for periodic battery replacement. Research is also under way looking for ways to scavenge body heat, movement, and vibration to power other implantable devices. RF is already being used experimentally to recharge the batteries in pacemakers and implanted transcutaneous electrical nerve stimulation (TENS) devices. The patient sits in a chair that contains a low-frequency RF source whose output is received, rectified, and stored by the device. Researchers at MIT and Harvard have developed a chip that can be implanted into the inner ear, with power provided by harvesting the energy in sound waves. The chip is designed to monitor biological activity in the ears of people with hearing or balance impairments. Fitness buffs will be happy to learn that they can recapture some of the energy they expend at the gym. Three British universities have teamed up to develop a piezoelectric energy harvesting device that attaches to the knee, generating power as they walk or run on the treadmill. Riga Technical University offers a mechanical energy harvester that requires magnets to be sewn into the sleeves and coils into the pockets of a jacket; swinging the arms past the pockets while walking generates a current that can be stored in a battery. Anything to keep that iPhone charged!
MEMS pyroelectric generator Oak Ridge National Laboratories has developed a unique pyroelectric generator that can cool electronic devices, photocells, computers, and even large waste-heat producing systems while generating electricity. The device is based on a MEMS pyroelectric capacitor at the end of a bimetal cantilever that oscillates between hot and cold surfaces. The tip of the hot cantilever comes into contact with a cold surface, the heat sink, where it rapidly loses its heat and causes the cantilever to move back and make contact with the hot surface. The oscillation continues as long as long as there is a sufficient temperature differential – anywhere from a few degrees to several hundred degrees – between the two surfaces.
The cantilever structures are only about 1 mm² and generate 1 to 10mW per device; however 1,000 of them can be attached to a one square inch substrate, creating a relatively high output power source. Due to the fast cycle time of the cantilever, the developer projects 10 to 30% efficiency – far better than current thermoelectric and piezoelectric energy harvesting devices.
Nantennas Photovoltaic cells are the most widely used energy harvesting source, but they are not very efficient. The best monocrystalline PV cells – with a theoretical maximum efficiency of 30% – do well to top 20% efficiency. Now scientists at the University of Missouri and the Idaho National Laboratory have developed a flexible solar film that can theoretically achieve 90% efficiency. In contrast to conventional photovoltaic cells, the film is essentially an array of nanoantennas (or “nantennas”), each tuned to a specific frequency of light. Rather than generating single electron-hole pairs, as in the case of PVs, the incoming electromagnetic field from the sun induces a current in the antenna that is then collected at the feed point, rectified, and stored. Nanoelectronic electromagnetic collectors (NECs) can be configured as frequency selective surfaces to efficiently absorb the entire solar spectrum. Or NECs can be configured as a reflective bandpass filter centered at a wavelength of 6.5µm; this would enable them to absorb infrared rays, thus recycling waste heat from engines, furnaces, and other high-temperature power sources. NEC devices have been successfully prototyped on both silicon and polyethylene substrates, however developing economical mass production processes will require further funding and time. The researchers foresee a product that complements conventional PV solar panels by capturing currently unused infrared energy. As a film, it could be incorporated into building materials and infrastructure. NECs can be integrated into polymer materials so they might also be incorporated into the skin of consumer electronic devices to continuously charge batteries.
Looking Forward The development of ultra-low-power MCUs has created a huge and rapidly expanding energy harvesting market on which they are becoming increasingly dependent. The first wave of energy harvesting has given rise to low-power wireless sensors, which are turning up seemingly everywhere. But the ripple effect will continue throughout consumer, industrial, and medical markets, creating new applications that we can only begin to imagine. Whether planning portable battery-powered devices or the desire to improve the energy efficiency of larger ones, all design engineers should consider incorporating energy harvesting techniques into their products. Investigate a wide range of energy harvesting devices at Mouser Electronics’ Energy Harvesting site to find out more.
Figure 4: MEMS pyroelectric generator (Courtesy of Oak Ridge National Laboratories).
22 October 2019
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Automotive
TRUSTED PLATFORM MODULES PROVIDE SECURITY FOR
e-Mobility Martin Brunner Principal Automotive Security at Infineon Technologies
The continued adoption of electric vehicles and the subsequent electrification of the drivetrain have huge implications on the entire industry. It is not appropriate to see electricity as simply an alternative form of fuel for vehicles; it represents an entirely new paradigm in mobility. E-mobility, as it is referred to, encompasses fundamental changes to the design, ownership and use of vehicles. For example: while autonomy is often held up as the prime use-case for connected cars, also the infrastructure required to support e-mobility
24 October 2019
dictates that vehicles become accessible in ways never before conceived. Therefore, while the industry is still developing solutions to implement automation levels 3 through to 5, the need for fully connected vehicles has already arrived with the electric car. The most apparent reason is perhaps the necessary interconnectivity between the vehicle and the charging infrastructure now being put in place across towns and cities worldwide. Charging cars will be one of many examples in an era that sees vehicles as a service and communications hub.
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Making security part of the architecture As a society, we are comfortable with accessing services and often assured by the (perhaps perceived) levels of security provided. The same is true in industry, where the concept of Platforms as a Service (PaaS) is already becoming the norm. Here, the use of security is implicit, established by special interest groups such as the Trusted Computing Group (TCG), whose standards are often adopted by committees and organizations including the IEC and ISO. As vehicles turn into service platforms, car manufacturers must also explore the potential risks associated with increased connectivity and the solutions now available to them. The Trusted Platform Module (TPM) has emerged as the most appropriate form of delivering e-mobility in a secured way.
course changed immeasurably since the earliest examples, ultimately the ICE still operates in much the same way. The majority of developments made over the course of time have been aimed at improving fuel efficiency in what is essentially a closed system. Fuel is stored and consumed within the vehicle and the nature of liquid fuel means it has always been relatively simple to refuel. Replacing liquid fuel with electric charge is clearly changing that. Fuel in the form of electricity is now effectively less regulated, as it is no longer necessary to obtain it from a licensed supplier. Any electric vehicle owner can typically charge their vehicle from any electrical outlet, but in terms of scale it becomes necessary to impose control with the fewest possible restrictions. This combination of ease of access coupled with protection is very well established in the computing world and it is here the automotive industry is turning, in order to establish standards that can be applied to e-mobility. The Trusted Computing Group has driven the development and acceptance of the specification referred to as the Trusted Platform Module (TPM) to further the goal of protecting while still providing ease of access. This specification is now being implemented using dedicated semiconductors by integrated device manufacturers, referred to as discrete TPMs to differentiate them from implementations that form part of another integrated device or are implemented purely in software; the TCG sees discrete TPMs as the most secure. If certified according to Common Criteria (ISO/IEC 15408) to its adopted standard defining TPMs (ISO/IEC 11889), devices that meet this specification are resistant to physical attacks and implement security features including authentication, encryption and cryptography that help secure connected systems using protected keys. TPM 2.0 is the latest iteration of the specification and it provides a more flexible approach to developing a solution.
Fig. 1: Reference architecture of a connected vehicle as a communications and service platform
If e-mobility is to be successful, it needs to provide and maintain an optimal and verifiable level of security for transactions between the vehicle and the charge point. The nature of an open market means that there will be many competing suppliers of charge points, but for the consumer this must not be allowed to become a barrier to adoption. This only escalates the challenge, because this essential service now becomes a primary attack surface. The charging infrastructure must be able to support the negotiation and communication between vehicles and charge stations, supplied and maintained by various manufacturers and providers. The vehicle as we perceive it has been a part of our lives for well over 100 years now and while its capabilities have of
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Secured microcontrollers that comply with TPM 2.0 offer levels of tamper-resistance that simply aren’t included in general purpose microcontrollers, developed in accordance with the use-cases being developed for e-mobility, for example charging. One form of protection includes adding a root of trust to implement secured boot at power-up, which uses authentication to verify that the code/data stored in an external memory hasn’t been tampered with before it is loaded into the processor’s main memory. Other forms of intrusion include so-called ‘side-channel’ attacks, which exploit easily accessible information about the system to gain insights. This may include using non-invasive techniques, such as differential power analysis, which has been shown to be effective in the reconstruction of data. This is specifically important, since as there is physical access to both vehicle and charging station, physical attacks must be considered in the attacker model. As well as securing the access points in a connected vehicle, it will also be necessary to use a TPM to secure sensitive data generated by modern vehicles. This may include but is not limited to data attributed to vehicle operation and maintenance as well as data attributed to the driver or owner (containing Personally Identifiable Information (PII), accounting and billing details, etc.) whose integrity,
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Automotive
authenticity – and in some cases also confidentiality and/or non-repudiation – needs to be protected. The benefits of choosing a TPM based on a discrete secured microcontroller include protection against physical and logical attacks, both malicious and those that may be benign but potentially disruptive.
E-Mobility interfaces and reference architecture The primary actors in the e-mobility charging infrastructure include the electric vehicle (EV) and the charge point, referred to as the Electric Vehicle Supply Equipment, or EVSE. Within the EV, an Electric Vehicle Communication Controller (EVCC) will negotiate with the Supply Equipment Communication Controller (SECC) over a connection compliant with the ISO/IEC 15118 specification.
to the driver’s smart phone, or WiFi for the other occupants, it becomes clear that the potential security risks and attack points need to be routed through a central security ECU, equipped with hardware-assisted security, such as a TPM. The process of charging an electric vehicle using a publicly accessible charging point perfectly encapsulates the total threat associated with a connected society. The technical requirements of such as system are already numerous, involving high power, highly efficient semiconductors and passive components designed to handle hundreds of volts. In this respect, it will reshape the way vehicles are designed, but coupled with this are the requirements to be able to identify, authenticate and safeguard the information that will necessarily be passed between the vehicle and the infrastructure in order to facilitate public charging points. The cryptography involved will need to protect not only the charging infrastructure but also the vehicles using it. At a system level, a charging station is an access port to the network, which could potentially allow access between any devices connected to the same network. In this respect the electric grid can be seen as the largest of all networks, access to which is not controlled by physical access. If it were a data center it would be protected from malicious intent by placing it within a secured building, surrounded by a security fence and surveillance system, along with human guards. When considered in this respect, it becomes clear that the requirement for highly secured systems within each vehicle accessing the grid is paramount.
Fig. 2: Beside the vehicle itself and the charging infrastructure, emobility involves further entities.
Within the EV the EVCC will control the on-board charging circuit, provide feedback to the vehicle user through an HMI, and remain in close negotiation with the vehicle’s ECU(s). On the EVSE side, the SECC will negotiate with its own electric energy meter and pass data generated by that to the paying unit, as well as have final control over the physical delivery of the electricity drawn by the EV. It will also typically feature an HMI to inform the vehicle user of each stage of the process. At the interface of each of these discrete functions, it will be essential to provide security through state of the art cryptography to safeguard the user’s data and the infrastructure’s integrity.
Security implications There are numerous examples of how modern vehicles are being compromised through new communication channels. Even technology provided by Third Parties intended to secure these valuable assets has shown to be susceptible to cyberattacks, allowing criminals to remotely take control of a vehicle, by disabling it even while the owner is driving the car. The potential attack surfaces increase significantly when considering the e-mobility reference architecture and its various stakeholders, interfaces and communication paths, as outlined above. When other forms of seemingly unrelated forms of communication are included, such as a Bluetooth connection
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Fig. 3: When charging an electric vehicle the charging station becomes a hub to the energy grid.
As part of the ISO 15118 international standard comes the concept of Plug & Charge. Intended to be robust enough to withstand the immediate and future needs of e-mobility, it can be expressed as enabling a secured and convenient way of charging an electric vehicle, covering both wired and wireless charging technologies based on AC and DC subsystems. At its core, Plug & Charge is intended to ensure confidentiality, data integrity and authenticity, and it achieves this through the algorithms defined by ISO 15118 for symmetric and asymmetric cryptography. Symmetric cryptography describes the process of using a single key for both the encryption and decryption of information and it is one of the oldest known forms of
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cryptography. Any system that implements symmetric cryptography dictates that the sender and receiver must both agree on the single key used on both sides of the secured channel. This is used to achieve the confidential exchange of data in a Plug & Charge system. Conversely, asymmetric cryptography uses two different keys; one for encryption and another for decryption, and this technique is used to provide data integrity and authentication within Plug & Charge. Asymmetric cryptography uses what is normally termed a Public key for encryption and a Private key for decryption. There is no intrinsic difference between the two keys, the term Public is applied because it is not critical that the key is kept secret. If the Public key is discovered it can be used to encrypt a message but it cannot be used to recover, or decrypt, the same message. In this respect only the Private key must be kept secret. Implemented as a tamper resistant, secured and certified microcontroller using advanced hardware security technology a TPM is able to securely store Private keys and it also includes a true random number generator in order to generate such cryptographic keys.
which involves Elliptic Curve Diffie-Hellman algorithms, is high relative to symmetric cryptography, so the use of both forms of encryption provides the appropriate levels of security without becoming a processing burden. The entire process is governed by the use of digital certificates, as outlined in ISO 15118 and based on a Public Key Infrastructure (PKI). This describes the way in which digital certificates are created, stored, distributed and eventually revoked by what is termed Certificate Authorities, or CAs. The digital certificates used in Plug & Charge are used in the authentication and authorization of the agents involved with the electric vehicle charging infrastructure, comprising the Charge Point Operator, the Certificate Provisioning Service (CPS), the Mobility Operator (MO) and the Car Manufacturer, or OEM. In order to protect the authenticity of these entities involved in the EV charging infrastructure, the integrity of the thereby exchanged data and the confidentiality of sensitive information a tamper resistant, secured and certified microcontroller, such as one certified to TPM 2.0, is an essential building block to provide the security features needed to protect EV charging use cases and thus enable trusted e-mobility. The OPTIGA TPM SLI 9670 is Infineon Technologies’ AEC Q100 qualified Trusted Platform Module, based on a tamper resistant, secured and certified microcontroller. As a turnkey solution it is supplied with firmware compliant with TCG specifications and is designed for use in telematics control units, connected gateways and any ECU that requires strong security.
It is the nature of properly implemented asymmetric cryptography that a Private key cannot be derived from a Public key or the data it encrypts, and only the Private key associated to a certain Public key can be used to decrypt a message. In general, when implementing secured communications, plain text will be encrypted using a Public key and decrypted by a Private key, while this procedure is inverted for the process of authentication using a digital signature. That is, only the Private key can be used for creating the signature while the associated Public key is used to verify the signature.
Conclusion
In a Plug & Charge application, asymmetric cryptography would be used to establish a secured connection, authenticated using digital signatures and allowing a common key to be agreed. At that point, symmetric cryptography can be used for all other message exchanges during the charging session. This is because the computational effort required for asymmetric cryptography,
As EVs and the infrastructure needed to support e-mobility continue to develop it is clear that the Trusted Platform Module will become an essential technology in its delivery. Through the use of TPMs both consumers and manufacturers can anticipate a safe and secure experience, as we as a society make the evolutionary step towards full electric and fully autonomous mobility.
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27
Artificial Intelligence
A deeper dive into
AI
at the Edge
- Avnet
Artificial intelligence (AI) might feel far away, but many of us experience AI every single day in applications like speech to text virtual assistance or fingerprint recognition on smartphones. AI capabilities in IoT applications help to identify patterns and detect variations in IoT edge devices carrying sensors for environmental parameters like temperature or pressure. Traditionally, simple embedded edge devices collect this data from sensors in the application environment and stream the data to AI systems built on cloud infrastructures to perform analytics and draw inferences. Yet, as the need for real-time decision making in IoT implementations grows, so do connectivity or processing needs—and it is not always possible to stream all data to the cloud for AI processing. This paper will discuss how deploying AI at the edge can improve the efficiency and cost-effectivness of IoT implementations.
Exploring AI in an IoT solution
AI technology comprises of several variants like machine learning, predictive analytics and neural networks. Data collected from the edge devices are labeled, and then data engineers who have specialized skills in creating software solutions around big data prepare pipelines to feed into the data models. Data scientists with skills across mathematics, statistics and programming languages like C and C++ create AI models using machine learning algorithms finetuned for various known applications. These models can be finally expressed in different ways, like neural networks, decision trees or inference rule sets. Machine learning is either supervised or unsupervised learning. While unsupervised learning (based on inputs without output variables) can help developers learn more about the data, supervised learning is the basis for most applied machine learning. In the training phase of supervised machine learning, huge data streams are mined to look out for meaningful patterns or inferences using multiple computations to arrive at a prediction. At the AI application stage, data collected from the edge devices are fed to models selected from the available data models using standard library frameworks like Tensorflow. The modeling step requires a considerable amount of processing power, usually available in a central location which can be a cloud site or a large data center. In the deploy phase, things get interesting. For instance, the software packages with the dependencies for the chosen models can be accessed by edge devices from a shared repository without as much reliance on the cloud. In areas
28 October 2019
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Artificial Intelligence
Apart from being highly responsive in real time, edge-based AI has significant advantages such as greater security built into the edge devices and lesser data flowing up and down the network. It is highly flexible, as customized solutions are built for each application. Since the inferences are pre-built into the edge devices, it needs fewer skills to operate and maintain.
like health monitoring, wearable devices which need unsupervised machine learning adapted to the user can benefit immensely from edge computing. Plus, applications that are customized to take inferences on the spot without prior learning often need very high processing power, a need well-suited for AI at the edge. In most cases, technical or energy constraints make it impossible to stream all the data to the cloud where the AI resides. There are use cases like audio or video recognition where patterns and inferences have to be recognized instantaneously, and the communication latency is prohibitive. There are instances where the deployment does not provide stable connectivity. Therefore, there needs to be a scalable hybrid architecture where the required models are built on the cloud, and the inference task is performed at the edge. This approach sends fewer data to the central location making it bandwidth efficient while improving latency and responsiveness.
How to deploy edge AI The basic components of a typical edge AI model include both the hardware and software for capturing sensor data, software for training the model for application scenarios as well as the application software that runs the AI model on the IoT device. A micro-service software that is running on the edge device is responsible for initiating the AI package residing on the edge device upon request by the user. Within the edge device, the feature selections and transformations defined during the training phase are used. The models are customized to the appropriate feature set, which can be extended to include aggregations and engineered features. Intelligent edge devices are deployed in battery operated applications in areas with low bandwidth and intermittent network connectivity. Edge device manufacturers are building sensors with integrated processing and memory capabilities and widely used low-speed communication protocols like BLE, Lora, and NB-IoT in tiny footprints and low power consumption.
The benefits of AI at the edge
Edge computing also allows developers to distribute computing across the network by transferring some sophisticated activities to edge processors in the local network like routers, gateways, and servers. They provide very good operational reliability as data is stored and intelligence is derived locally helping deployment in areas of intermittent connectivity or without a network connection. Ordinarily, building a machine learning model to solve a challenge is complex. Developers have to manage vast amounts of data for model training, choose the best algorithm to implement, and manage the cloud services to train the model. Application developers then deploy the model into a production environment using programming languages like Python. The smart edge device manufacturer will find it extremely difficult to invest in resources to execute an AI implementation on edge from scratch. However, devices like Avnet's SmartEdge Agile have various types of sensors attached, with built-in AI software stacks. The associated software platforms and development studios, like Branium and Microsoft's Azure Sphere, are capable of supervised and unsupervised machine learning with a database of ready AI algorithms to choose from and deploy models to the device without writing a single line of code. The users also can create multiple widgets where they can view the values from the sensors in real-time and also can save these data for future use. It's true that artificial intelligence adds complexity to an already complex space with the Internet of Things. Double that when you add edge AI. However, with the right platforms and partners, developers can navigate this complexity and bring innovation that leaves speech to text and fingerprint recognition in the dust. Dig deeper into what AI at the edge has to offer developers—and see if the technology is right for your deployment. Article Courtesy: Avnet Avnet is a global technology solutions provider with an extensive ecosystem delivering design, product, marketing and supply chain expertise for customers at every stage of the product lifecycle. We transform ideas into intelligent solutions, reducing the time, cost and complexities of bringing products to market. For nearly a century, Avnet has helped its customers and suppliers around the world realize the transformative possibilities of technology.
While the complexity of such designs may make the edge expensive, the benefits far outweigh the related costs.
TIMESTech.in
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29
Cloud & Data
5G Demands Comprehensive Cloud Data Management
Aman Brar VP - Global Alliances, Openwave Mobility, an Enea company.
A number of operators have started implementing 5G networks. They are however increasingly realizing that the data management needs of 5G are drastically different than those of earlier network generations. 5G demands that operators move from bare metal to cloudbased stateless applications, where more efficient managing and processing of data is needed to manage and process larger data volumes, also ensuring that authorized applications have rapid access without downtime. The solution is Cloud Data Management (CDM) in which each 5G Network Function (NF) is treated as a service. CDM provides a data layer that stores information that NF's need, including fast-changing session/state data, subscriptions, policy and configuration data. CDM replicates data as required, easing the creation of 5G slices and stateless core services, while also delivering data at the edge for performance-sensitive, ultra-reliable low latency applications. The data replication is also key to providing six-nines of service availability on virtual infrastructure that typically provides three-nines of availability at infrastructure level.
IT provides a glimpse of CDM's 5G benefits A recent survey of 116 business and technical managers responsible for modernizing data environments, illustrates the importance of CDM to the IT networks:
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A wide range of business functions are supported by CDM the most important of which include analytics (64%), data warehousing (52%) and reporting (47%)
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The top CDM benefits are seen as scalability for data
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Cloud & Data
storage and integration workloads (51%), automatic and elastic resource management (44%) and affordably enabling advanced analytics at scale (35%) v
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More than 85 percent of respondents say implementing CDM is either extremely important (39%) or moderately important (47%) to the success of their data strategy The “must have” features of CDM include core data management functions for data integration (70%), data prep (57%) and data quality (55%)
While the report is focused on IT infrastructure, it mirrors the benefits CDM can deliver for 5G core networks and provides a critical advantage for operators. CDM provides the key infrastructure components needed to enable new 5G core Service Based Architecture (SBA).
The 5G core difference The 5G core is composed into service-based architecture (SBA) elements. They are designed to separate the control plane (CP) and user plane (UP) and relies on an external Data Layer to make the control plane and user plane stateless. The 5G core is made up of virtualized, softwarebased NFs (or services), creating a new architecture that gives operators the flexibility to meet diverse network requirements for 5G use cases. Because 5G core architecture is cloud native, it's important for a 5G CDM offering to integrate cloud-ready capabilities for the 5G mobile core. These include:
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Unified Data Management (UDM): Supports the generation of authentication and key agreement (AKA) credentials; user identification handling; access authorization; subscription management
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User Data Repository (UDR): Stores information about subscribers, application-specific data and policy data
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Authentication Server Function (ASF or AUSF): Implements the extensible authentication protocol (EAP) authentication server and stores keys
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Policy Control Function (PCF): Provides policy rules for control plane functions, including network slicing, roaming and mobility management. It accesses subscription information for policy decisions taken by the UDR and supports the new 5G quality of service (QoS) and charging functions
Other 5G network functions like NRF, NEF, CHF, AMF and SMF benefit from the UDSF portion of CDM which enables these stateless network functions to store the fast changing session data. Openwave Mobility, an Enea company, is in a unique position to provide operators with a single CDM solution that provides all of these features. In March, Openwave's parent company, Enea AB, acquired a business unit from Atos Convergence Creators, enabling us to add cloud-ready features to our 5G core CDM solution – Stratum. These features include policy management, authentication and subscriber data management.
India's # 1 B2B Tech Website
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October 2019
31
Industry Focus
Growth of ever evolving
Indian logistic Industry
Sumit Sharma Co-founder of GoBOLT In today's economy logistics is the most rapidly evolving industry, it is the trade of infrastructure, technology and new types of service providers. It is considered as the backbone of the economy, it is the mixture of infrastructure, technology and new types of service providers, which defines whether the logistic industry is able to help its consumers reduce their costs in logistic sector and provide effective services. From last couple of years, it has seen significant development which is reflected in the global rankings. According to the reports of Global Ranking of the World Bank's 2018 Logistics Performance Index, India jumped to
32 October 2019
35th rank in 2018 from 54th rank in 2016 in terms of overall logistics performance. Indian logistics sector is currently around $160 billion and estimated to be of $215 billion by 2020. This phenomenal growth is driven by emerging ecommerce retailers especially in Tier II, a corresponding increase in demand and the entry of more multinational companies in the FMCG segment. This has further resulted in the rapid advancement of retail channels requiring efficient inventory management and warehousing solutions. Many companies have started engaging with logistics service providers for
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Industry Focus
intelligence, internet of things and machine learning will disrupt the conventional workings of the country's logistics sector. The impact of these technologies is anticipated to enhance productivity across the logistics industry and streamline operational processes. These technologies will largely play a vital role in boosting efficiencies of supply networks, reduce wastages and lead to supply chain optimization. The speed and scale with which we align our supply chain strategies to tackle the complexities of a changing global trade order will be key to determining our position in the global logistics ranking.
Some solutions to prevailing challenges catering the customized demands of the consumers, in turn, resulting the global logistics market to register a CAGR of over 7% by the end of 2022. The main areas for creating a sustainable roadmap for the growth of the Indian logistics industry are:
Infrastructure Development The government has reiterated its firm commitment on modernizing the functionalities of Indian logistics with a key focus on infrastructure development. With a view to improving supply chain efficiencies and enhancing connectivity to support logistics players tap the under leveraged markets in the country's hinterlands, key infrastructure developmental projects have been rolled out. Its further development depends upon its soft infrastructure like education, training and policy framework as much as the hard infrastructure. Growth of the international goods transportation as the ecommerce continues to thrive. International cooperation is also required to make logistics sector grow.
Policy & regulatory boost Key reform measures and policy interventions like the unveiling of the Goods and Services Tax, (GST), relaxed FDI regulations and granting of infra status has boosted the core competencies of the Indian logistics industry. GST was a game-changer for Indian logistics. It laid the foundation for the setting up of large format multi-modal logistics parks along key consumption and industrial centres which can function as freight aggregation and distribution hubs. With the implementation of GST, the logistics companies, which are currently forced to set up many small warehouses across multiple cities can set up just a few, big warehouses region and different model for freight movement from the warehouses to the different manufacturing plants, wholesale outlets, retail outlets and the various POS. This growth is backed by the growth in the e-commerce sector and expansionary policies of the FMCG firms. The Freight transport between the smaller delivery companies will expand the service areas, improve service quality, increase loads on the trips and reduce the costs of delivery.
It is necessary to realize that the benefits which can bestly be practiced in logistics industry can be brought about by the companies by establishing training intuitions. Proper and safe storage and Warehousing facilities are important for the growth of the logistics industry. By solving the problem of automatic stockpiling, inventory removal and maintenance of the available stock portfolio, we have the opportunity to develop new cost-cutting processes. Warehousing is required for changing dynamics of manufacturing, global procurement and new models of sales and distribution. It is also important to enhance the research and development because it encourages the use of indigenous technology, which can make the industry competitive in respect to cost and bring improvement in the service attributes.
Future prospects The global outlook, indeed that of India is expected to significantly improve as India Inc begins to tackle the economic downturn. Future Cooperation between companies will reduce logistics costs and make use of the transport capacities that are available that will help this industry to grow and become the most rapid growing sector in the market. In the coming future specialized services for logistics delivery will be in demand to cope with delicate products such as fresh food and computer chips. These logistic centers will help to keep distances shorted between production and marketing. The industry has moved from just a service provider to the position which provides end to end solutions to their customers. Thus, all these developments and growth has paved the way for Logistics industry in the coming years.
New tech leverage The emergence of the newage technologies like artificial
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October 2019
33
Test & Measurement
Anritsu’s MT8000A Supports Performance Validation of Qualcomm Snapdragon 5G Modem-RF Systems
Anritsu MT8000A Radio Communication Test Station has been adopted to verify the performance of key 5G features, such as Dynamic Spectrum Sharing (DSS) in the FR1 FDD mode, in the Snapdragon X55 5G Modem-RF System. This continued collaboration between Anritsu and Qualcomm will help facilitate early rollout and adoption of 5G devices and services. 5G non-standalone (NSA) services have already been introduced in several parts of the world in 2019 and 5G standalone (SA) services are expected to be deployed globally starting in 2020, which in turn is accelerating the penetration of 5G technology. Our collaboration with
Qualcomm Technologies is focused on testing of key features required by these services in both SA and NSA modes. DSS is a key technology for flexibly allocating the resources of a single frequency band to LTE and 5G according to demand. Using DSS at introduction of 5G services with a mixture of 5G and LTE terminal users helps provide LTE users with continued good communications while offering 5G users advanced services over the same frequency band – allowing for a smooth transition from 4G to 5G as well as efficient use of frequency resources. DSS also allows operator to rapidly deploy 5G services in bands currently used by LTE without having to wait for frequency re-farming. In addition to supporting DSS, the MT8000A can also test several other advanced 5G features included in the Snapdragon X55 5G Modem-RF System such as Massive MIMO and Service Data Adaptation Protocol (SDAP), the 5G core network slicing technology.
Radio Communication Test Station MT8000A The MT8000A is an all-in-one test platform for RF, Protocol, and Beam tests, as well as for beam-forming evaluations. In addition to supporting functions for simulating base-station NSA and SA modes required for development of 5G chipsets and terminals, it also supports the latest technologies, such as 4×4 MIMO for increasing data transmission speeds in the Sub-6 GHz band as well as 8CC for implementing wideband mmWave. Moreover, it covers the key frequency bands used by the first 5G services, such as the Sub-6 GHz band (FR1) frequencies of 600 MHz, 2.5 GHz, 3.5 GHz, 4.5 GHz, etc., and the mmWave band (FR2) frequencies of 28 GHz, 39 GHz, etc. With an easy-to-use GUI and software for setting various test parameters, it helps easy and flexible configuration of an efficient and cost-effective test environment.
VIAVI to Unveil Advanced Test Solutions at ECOC 2019 VIAVI Solutions announced the slate of new test automation, measurement and monitoring solutions it will feature at ECOC 2019 in Dublin, Ireland, September 23-25. VIAVI is introducing new offerings to enhance the industry’s most comprehensive portfolio of test and measurement solutions for lab, production, manufacturing and field environments. T-BERD/MTS 400G Network Tester T-BERD/MTS 400G Network Tester equips field technicians to fully address metro/core, data center interconnect, and business services. It provides the latest technology with interfaces such as QSFP-DD and SFP-DD. Beyond advanced tests like Optics Self-Test for QSFPx and SFPx pluggables, high-speed cable testing for data centers, RFC 2544/Y.1564, and OTN Check, the 400G tester includes a new intuitive design, test process automation and cloud-based
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October 2019
management that together improve workforce efficiency even as networks get more complex. MAP-300 Test Automation Platform The MAP-300 photonic metrology and test automation platform is the third generation of a measurement platform recognized for setting the industry standard. The MAP-300 delivers the precision required for laboratory testing, combined with configurability to meet the demands of the manufacturing process. Remote automation functionality, along with multi-user architecture, helps increase capital utilization and dramatically reduce the cost of testing. ONMS (Optical Network Management Solutions) The ONMS family offers the modular OTU-8000 Optical Test Unit with a tunable DWDM OTDR and the ultra-compact OTU-5000 rack-mounted OTDR for rapid, automatic identification of fiber events such as bends, crushes, breaks and malicious tapping. A single optical test head can test hundreds of fiber links, and auto reports the GPS location of a fault within minutes, dramatically reducing the time and cost of construction and repair.
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Test & Measurement
Rohde & Schwarz and MESIT jointly invest into secure communications solation from DICOM communications system. SOVERON stands for fully integrated, highperformance and scalable trusted communications solutions for all branches of the armed forces. SOVERON strengthens the customers’ digital sovereignty and gives them the greatest possible independence from manufacturers.
Rohde & Schwarz and MESIT have jointly established a new joint venture as the majority shareholder. The strategic purpose is to further enhance the trusted, interoperable SOVERON
The JV is named DICOM, s.r.o. and has commercial operations in Uherské Hradiště in the Czech Republic. DICOM will be active as an advanced research & development (R&D) undertaking. It will initially focus on the new SOVERON lightweight handheld software defined
Keysight, OPPO set up joint 5G test lab in Shenzhen, China
radio (SDR) with networking and multiband capability in the UHF/VHF frequency range. “Rohde & Schwarz cutting edge solutions are in operation with many defense forces around the world. We are looking forward to expanding our presence in the Czech Republic. This new JV will create new technological advances, allowing us to provide digital sovereignty to our customers,” Bosco Novak, Executive Vice President, Secure Communications Division, Rohde & Schwarz, concludes.
Teledyne LeCroy New High Channel Count, Oscilloscopes and Motor Drive Analyzers
Keysight Technologies joint 5G test laboratory in Shenzhen, China with OPPO, one of the world’s top five mobile device manufacturers, further extending the collaboration between the two companies. The new lab uses Keysight’s 5G platform to help verify the performance of new 5G new radio (NR) designs, a key activity that will help the Chinese-based smartphone manufacturer expand its global market presence. Keysight’s solutions, which are widely adopted by leading chipset and device makers, enable OPPO to comprehensively test their 5G multi-mode devices in different form factors. Chipset manufacturers and their mobile device ecosystems use Keysight’s 5G NR platform, which is compliant to the latest 3GPP 5G NR standards, to accelerate development, validation and carrier acceptance. The platform supports both sub-6GHz and mmWave frequencies for conducted and over-the-air (OTA) testing. Common development tools enable users to share design insights gained across each stage of the device lifecycle. As a result, chipset and device manufacturers can accelerate delivery of new 5G NR products to market and capitalize on early commercial revenue opportunities. On November 29, 2018 OPPO used Keysight’s UXM-based 5G NR network emulation solutions to successfully establish a 5G video call, thereby completing one of the world’s first 5G signaling and data connections based on OPPO’s commercially available mobile phone.
Teledyne LeCroy, WaveRunner 8000HD High Definition Oscilloscope (HDO), the MDA 8000HD Motor Drive Analyzer (MDA), and OscilloSYNCTM technology. Designers of high-power inverters and motor drives, such as electric vehicle (EV) propulsion drives, vehicle non-propulsion drives, and solar photovoltaic (PV) inverters require an accurate understanding of system power under varying loads (aka, dynamic power). At the same time, designers of embedded systems found in power conversion systems, consumer electronics, mobile devices, and automotive electronics must validate complex power distribution networks (PDNs). Both designers must correlate a significant number of power, sensor, and control events. The new WaveRunner 8000HD, MDA 8000HD and OscilloSYNC technology will help these design engineers more quickly and accurately characterize system performance in high power inverters, motor drives and embedded systems. Ÿ WaveRunner 8000HD Delivers an Unprecedented
Combination of Capabilities Ÿ MDA 8000HD Provides Insight Across the Entire Motor
Drive System Ÿ OscilloSYNC Doubles Channel Capacity
TIMESTech.in
October 2019
35
New Products
Mouser now stocking PolarFire FPFA Video and Imaging Kit from Microsemi
Mouser Electronics, is now stocking the PolarFire FPGA Video and Imaging Kit from Microsemi. Incorporating a nonvolatile PolarFire field programmable gate array (FPGA), the kit enables highperformance evaluation of 4K image processing and rendering using dualcamera sensors, at 50 percent lower power than other SRAM FPGAs. The Microsemi PolarFire FPGA Video and Imaging Kit, includes a PolarFire Video and Imaging Board with onboard PolarFire FPGA with 300K logic elements, 4 GBytes of DDR4, and 1 GByte of flash memory, plus a dualcamera sensor board. Ideal for midbandwidth (4K/2K) imaging and video applications, the kit offers a rich selection of interfaces and IP, including bidirectional MIPI, HDMI, DSI, and SDI. The kit comes with a complete ecosystem, featuring comprehensive application-specific hardware, optimized intellectual property suite for image processing, sample reference designs, demonstration designs and collateral — providing designers with the hardware and software needed to implement 4K resolution designs targeting PolarFire FPGAs. For more information: www.mouser.in
Vishay introduced FRED Pt Ultrafast recovery rectifiers in MicroSMP package
Vishay Intertechnology, new 200 V FRED Pt Ultrafast recovery rectifiers in
36 October 2019
the eSMP series MicroSMP (DO219AD) package, including the industry’s first such device to offer a current rating to 2 A. Measuring 2.5 mm by 1.3 mm with a low 0.65 mm profile, the 1 A VS-1EQH02HM3 and 2 A VS-2EQH02HM3 provide spacesaving alternatives to rectifiers in the SMA. Also available are commercial versions, the 1 A VS-1EQH02-M3 and 2 A VS-2EQH02-M3. The devices increase power density by providing the high current ratings typically reserved for the SMA package in the smaller MicroSMP, which utilizes 57 % less PCB space. For excellent thermal performance, the VS1EQH02HM3 and VS-2EQH02HM3 feature an asymmetric design with a large metal pad for heat dissipation, while their FRED Pt technology enables ultrafast recovery times down to 13 ns, reduced Qrr to 11 nC, and soft recovery features over the entire working temperature range of -55 °C to +175 °C. The AEC-Q101 qualified devices feature low forward voltage drop down to 0.72 V at 1 A, which reduces power losses and improves efficiency in high frequency inverters, DC/DC converters, freewheeling diodes, and power factor correction in automotive engine control units (ECU), anti-lock braking systems (ABS), and HID and LED lighting, and telecom and industrial power supplies. The VS-1EQH02HM3 and VS2EQH02HM3 offer an MSL moisture sensitivity level of 1, per J-STD-020, LF maximum peak of +260 °C. RoHScompliant and halogen-free, the devices are ideal for automated placement and allow for automated optical inspection (AOI) in automotive systems.
that supports approximately 70 percent of the communications protocols used in industrial network applications. This enables users to immediately launch slave equipment development, such as motor control blocks for compact industrial robots, PLC (programmable logic controller) devices, and remote I/O systems. The sample software includes: EtherCAT, PROFINET RT, Ethernet/IP, Modbus TCP, and OPC UA, as industrial Ethernet software, and PROFIBUS DP, Modbus, RTU/ASCII, CAN open, and DeviceNet, as Field Bus communication software. Industrial network protocols differ extensively between countries and regions, and industrial network equipment deploying globally must support a wide range of protocols. The RX72M solution includes major protocols used in each region, allowing users to evaluate industrial network connectivity equipment immediately. Customers can focus on their own application development efforts, while strengthening their competitive edge for global product deployments. Learn more at renesas.com.
Power Integrations announced Automotive-Qualified 200 V Qspeed Diodes Excel in Audio Amplifiers
For more information: www.vishay.com
Renesas Introduced RX72M Solution for Slave Equipment in Industrial Network Renesas Electronics development of the RX72M Industrial Network Solution to accelerate the development of industrial slave equipment using the RX72M Group of 32-bit industrial Ethernet MCUs. The new RX72M solution includes an evaluation board, an operating system, middleware, and a sample software
Power Integrations, its 200 V Qspeed diodes – LQ10N200CQ and LQ20N200CQ – are now available with AEC-Q101 automotive qualification. Qspeed silicon diodes use merged-PIN technology to offer a unique balance of
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New Products
soft switching and low reverse recovery charge (Qrr). This results in low EMI and reduced output noise, which is especially important for in-vehicle audio systems. The newly qualified 200 V diodes feature the industry’s lowest reverse recovery charge, typically 32.4 nC at TJ of 125°C, and a diode softness ratio of 0.39. This minimizes high-frequency EMI inherent in the Schottky rectifiers often used in Class-D power amplifier output stages. Dual 10 A and 20 A common-cathode diodes are housed in the industry-standard, rugged DPAK TO-252 package. The LQ10N200CQ and LQ20N200CQ diodes are produced in IATF 16949certified facilities. Devices are available now; the LQ10N200CQ and LQ20N200CQ are priced at $0.60 and $0.74 respectively in 10,000 quantities.
ON Semiconductor Supports Increasing Power Demands of IoT Endpoints with PoE Solutions
Avnet RFSoC Development Kit using the Zynq UltraScale+ from Xilinx, enabling system architects to explore the entire signal chain from antenna to digital. Using MATLAB and Simulink from MathWorks, and RF components from Qorvo, the kit enables users to quickly deploy systems for 5G wireless communication, including for aerospace and defense uses, by harnessing the integration of Xilinx Zynq UltraScale+ RFSoCs for direct-RF sampling. Specifically, the kit extends the functionality of the Zynq UltraScale+ RFSoC ZCU111 Evaluation Kit by adding a Qorvo 2×2 Small Cell RF Front-end 1.8GHz Card for over-the-air transmission, plus a native connection to MATLAB and Simulink with Avnet’s RFSoC Explorer application.
Using the new IEEE 802.3bt standard, Power over Ethernet (PoE) can be used to deliver high-speed connectivity up to 90 W of power over Local Area Network (LAN) connections. The new IEEE 802.3bt standard for PoE has the potential to transform every vertical market touched by the IoT, by enabling more sophisticated endpoints operating across larger networks. The IEEE 802.3bt standard optimizes energy management through the new “Autoclass” feature, which enables Powered Devices (PDs) to communicate their specific power needs to the Power Sourcing Equipment (PSE). This in turn allows each PSE to allocate just the right amount of power to each PD, maximizing both the available energy and bandwidth.,/p> With up to 90 W of power available, compared to the 30 W provided by the IEEE 802.at standard (PoE+), IEEE 802.3bt can provide both power and connectivity to new applications that would otherwise require a dedicated and typically off-line power source. PoE will simplify network topologies and provide a more robust ‘plug and play’ user experience. The controllers are complemented by the NCP1566 DC-DC Controller, the FDMC8622 Single MOSFET and the FDMQ8203 and FDMQ8205A GreenBridge Quad MOSFETs, which have been developed to provide a more efficient alternative to a diode bridge in PoE applications. Together, these devices enable highly efficient PoE interfaces with up to the standard limit of 90 Watts of power or to a proprietary 100 W solution if more power is needed.
For more information: www.avnet.com
For more information: www.onsemi.com.
For more information: www.power.com.
Avnet Announced RFSoC Development Kit Accelerates Wireless Design
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RS Components introduces robust embedded power supply series from TDK-Lambda
A new series of AC-DC embedded switch-mode power supplies (SMPS) from TDK-Lambda. The cost-effective RWS-B series of ACDC industrial power supplies comprises 10 models, each coming with an isolated 5V 1A standby voltage option. This latter feature makes the series suitable for markets including industrial, communications, test and measurement, LED signage equipment, and alternative-energy applications. Importantly, the additional isolated 5V standby voltage kicks in even when the main output is inhibited or is in overvoltage or overcurrent condition. This low-power output means the supply of an additional voltage to keep key circuitry active during an energysaving ‘sleep’ mode or during an overload condition – thereby avoiding a complete hard system restart. Offering either 1000W (RWS1000-B) or 1500W (RWS1500-B) output power, the series has a universal 85 to 265V AC input range and includes devices with 12V, 15V, 24V, 36V and 48V DC outputs. Other features of the series include: remote on/off; current share; an isolated DC Good/Fan-Fail signal; and reverse airflow – which can extend operation in ambient temperatures up to +70ºC. Unit dimensions are 127 x 63mm with a height of 198/261mm for the 1000W/1500W models, respectively. For more information: uk.rs-online.com
AAEON announces BOXER-8300AI Series: Powering AI@Edge with Intel Movidius Myriad X AAEON, BOXER-8300AI Series. Including the BOXER-8310AI, BOXER8320AI, and the BOXER-8330AI (Coming Q4 2019), this family of AI@Edge embedded box PCs power
October 2019
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New Products
AI and edge computing thanks to the innovative Intel Movidius Myriad X. At the core of the BOXER-8300AI series is the innovative AI Core X module from AAEON. Each AI Core X module features the Intel Movidius Myriad X VPU. The Intel Movidius Myriad X provides high performance processing, with speeds up to 105 fps (80 fps typical) and 1 TOPS as a dedicated neural network. The BOXER-8300AI Series features improved thermal design, allowing the Intel Movidius Myriad X to operate at higher temperatures without loss of performance. The advanced thermal design of the BOXER-8320AI allows it to operate in conditions from -20°C to 60°C. With the power of the Intel Core i3 processor and two AI Core X modules, the BOXER-8320AI can be used in highperformance AI applications such as smart security, facial recognition, and more.
technology that accepts same electrical inputs as traditional copper cable, but uses optical fiber between the connectors to extend HDMI signals with zero loss or latency. The AOC fiber design allows power to be pulled from the source and transmitted to the display side with no external power required. The BestNet HDMI active optical cables support HDCP and compatible with HDMI 2.0 standard along with third-party equipment’s, including displays and video switches. Enabling transmission of ultra-highbandwidth video and audio the key applications of BestNet HDMI Active Optical Cable include: Digital signage’s, LED signboards, Medical imaging equipment’s, Airplane onboard video systems, Home theatres, Blue-ray, 3D video, Projector, Set-up box, DVR, Game consoles , TV broadcast stations, Conferencing video equipment and Security systems.
Printronix Introduces HighPerformance Thermal Desktop Printer with RFID in India
Eurotech Launches BestNet HDMI Active Optical Cables
38 October 2019
For more information: printronixautoid.com
TSC Launches TDM-30 New Mobile Printer in India
For more information: eurotechindia.com.
For more information, www.aaeon.com.
BestNet HDMI active optical cables that enables transmission of ultra-highbandwidth video and audio. BestNet AOC HDMI 2.0 cables with Ethernet are designed with foremost ingenuity to deliver ultra-high-definition bandwidth of 18 Gbps. The plug & play, active optical cable delivers premium video quality up to 4096x2160p@60Hz and available off-the-shelf in various lengths of 50, 100 and 150 mtrs. Active Optical Cable (AOC) is a cabling
and healthcare are just a few of the industries that use RFID to track products throughout the supply chain, take inventory in real-time, or locate assets within the enterprise. The T800 makes printer setup for encoding and printing of RFID labels fast and easy. Not only can it support labels down to 625 inches in length, but the antenna can be adjusted for non-standard inlay positions. Further, the RFID label calibration function automatically sets the optimal label encoding position within the printer.
Printronix Auto ID, T800, a highperformance thermal desktop printer. The T800 offers enterprise-level productivity, dependable performance, and a suite of versatile features such as RFID, and Wi-Fi with advanced security protocols. A high-performance ARM A7 processor offers fast-time to -first-print and it is easily capable of producing over 1,000 labels per day. Its 300-meter ribbon ensures fewer roll changes and therefore less downtime. One of the key differentiators between traditional desktops and the T800 is its ability to print RFID labels. The T800 with optional UHF RFID was designed to meet the growing demand for encoding and printing RFID labels at an affordable price. Retail, manufacturing,
TDM-30 is Mobile barcode printer; intelligently designed for on-the-go printing. The TDM-30 is equipped to operate across different platforms with multiple connectivity options. It’s lightweight, durable and flexible design, coupled with ease of operation without compromising with the functionality is very essential for retail & mobile point of sale (MPOS) applications. The rugged palm-size design of the TDM-30 is built for long-lasting performance and its real-time printer health status provides two important functions: The TDM-30 has a wealth of tools that guarantees easy integration into your MPOS system such as; Bluetooth is MFi certified and can be connected with an NFC tag; ESC/POS language is supported and has an OPOS driver for POS systems; a smartphone application for both Android and iOS are available for browser-based applications and Software Development Kits (SDKs) and printer languages are available for all other software integration needs. For more information: www.tscprinters.com
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TimesTech.in, India's #1 growing B2B website on Electronics and Technology is exploring the editorial opportunity for companies/firms working in the EMS Industry.
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TimesTech.in is India's leading web portal, which is catering and addressing the industry hunger for electronics and technology information. We are hereby committed to giving our readers exhaustive information on electronics technology and revolutionary innovations in the field that will define the trajectory of coming times.
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