GNU Radio-Based Teaching System

Software Engineering & Web Applications GNU radio-based teaching system

MALCOLM VELLA VIDAL | SUPERVISOR: Prof. Victor Buttigieg COURSE: B.Sc. (Hons.) Computer Engineering

Software-defined radio (SDR) is a growing field that is leading the innovation in communications technology. Reconciling the abstract nature of communications theory to an effective method of teaching it has often been a major challenge for universities. The traditional approach has relied on the use of books, notes, lectures and simulationbased laboratory sessions.

This dissertation proposes SDR as an alternative to the traditional teaching techniques and explores the extent to which it could be a pivotal tool if introduced into undergraduate communications courses. SDR receives real-world signals using an antenna and sends them to the device to which they would be connected, allowing the use of software to process and manipulate these signals in real time. This work environment is portrayed in Figure 1.

GNU Radio Companion (GRC) was the software tool used to process the input signal. It is a free-to-use software that evolved significantly since its inception. GRC contains a library of inbuilt blocks which could be strung together to form a system in the form of a flowgraph (see Figure 2). This dissertation uses a combination of SDR and GRC to build a sequence of these flowgraphs, presenting them in the form of a laboratory sheet that explains certain communications concepts, such as sampling theory and frequency modulation (FM) reception.

A questionnaire was prepared and presented to past and current Computer Engineering students. The goal of this questionnaire was to reach subjective results regarding the students’ view on the possible introduction of SDR and GRC into the syllabus. The results of the questionnaire consolidated the idea that students would value the use of hardware in laboratories, as it tends to provide a more personalised feel to the work they are doing, and in turn motivates them to continue their work at home.

Figure 1. The work environment used throughout this dissertation comprising an antenna, SDR and a laptop.

Figure 2. A simple flowgraph outlining how GRC receives the input signal from the SDR, and displays its waveform through the frequency sink.

A domain-specific language for the internet of things in the retail sector

EMAN ABELA | SUPERVISOR: Prof. Ernest Cachia COURSE: B.Sc. IT (Hons.) Software Development

In recent years, there has been a marked rise in the need for data analysis. This is partly due to the increased pervasiveness and adoption of the internet of things (IoT) and, consequently, the data it generates – contributing big data. Considerable leaps have been made regarding applications and organisations that can conduct data analysis, giving rise to the necessary scientific treatment of data. Profitability for businesses through the valorisation and usability of data is the primary reason for this increase in analytical work. However, in view of the amount of data generated, and the speed at which it is being generated, data analysis is a commodity that not every business can afford or has the resources to undertake effectively. In particular, small to medium-sized enterprises face significant challenges when investing in data analysis, due to high outsourcing costs.

This project discusses how a domain-specific language (DSL) could help small businesses benefit from investing in solutions using smart devices connected as an IoT, and the subsequent data analysis. This was done by implementing a cost-effective solution that would enable small to mediumsized businesses to better manage their data. This study seeks to propose a solution for retail outlets. The solution is made up of 3 parts: 1. An IoT data-collection structure where data collection will be occurring after each purchase transaction; 2. A customised DSL to assist the retail owner in analysing data without having to resort to outsourcing; 3. The use of data analysis through the analytical methods present in the system, i.e.,: descriptive analysis and predictive analysis, which employ machine learning techniques.

A visual representation of the solution described above could be seen in Figure 1. The solution is internally algorithmically scalable, in that it allows the possibility of adding more analytical methods. The ultimate benefit of the solution was shown through the analytical methods embodied within it. In using the proposed solution, the management of the retail outlet would be able to make better use of the generated data targeted at increasing sales and profits, for example: “Which product sells the most between 3pm and 4pm?”. The solution will search for the best method to address that query. This is done by utilising some basic natural-language processing (NLP) on the input.

In part, the artefact was evaluated through its ability to validly handle different datasets. To evaluate the ability of the solution to deal with various datasets, those used to test the system were randomly generated from different years and related to various retail outlets, namely: electronics shops and supermarkets.

When compared to similar market products, the proposed solution appears to be more robust and compares well with its peers in terms of accuracy, subject to the number and type of analytical methods used in this scalable solution. In time, accumulated data could render the outcome from the solution more relevant. In other words, the more the solution is used in a specific context, the higher the accuracy it will exhibit.

Figure 1. Flow diagram of the system

REFERENCES

[1] N. Jones and C. Graham, “Can the IoT Help Small Businesses?”, Bulletin of Science, Technology & Society, vol. 38, no. 1-2, pp. 3-12, 2018. Available: 10.1177/0270467620902365.

[2] “(PDF) Building Competitive Advantage in Retail Industry using Internet of Things (IoT)”, ResearchGate, 2021. [Online]. Available: https:// www.researchgate.net/publication/320237967_Building_Competitive_Advantage_in_Retail_Industry_using_Internet_of_Things_IoT. [Accessed: 12- May- 2021].

[3] Z. Zones, “Editorial Note: IoT Technology for Promoting Multimedia Services”, Multimedia Tools and Applications, vol. 78, no. 5, pp. 51055105, 2019. Available: 10.1007/s11042-019-7266-4.

An event-based approach for resource levelling in IIoT applications

DANIEL AZZOPARDI | SUPERVISOR: Prof. Matthew Montebello COURSE: B.Sc. IT (Hons.) Artificial Intelligence

The Fourth Industrial Revolution (or Industry 4.0) has seen widespread adoption of the Internet of Things (IoT) concept by various industries attempting to optimise logistics and advance supply chain management. These large-scale systems, capable of gathering and analysing vast amounts of data, have come to be known as the industrial internet of things (IIoT).

This study aims to create a scalable tangible-resource allocation tool capable of learning and forecasting resource distributions and handling allocation biases caused by preemptable events. Furthermore, the system should be capable of proposing suitable reallocation strategies across a predefined number of locations.

The physical framework would assume the form of a low-cost resource-tracking platform similar to that seen in the data-collection component of the accompanying diagram. At each location, a node would keep track of the number of items available and items in use, with data being sent periodically to a central server, together with any registered events. However, this goes beyond the purpose of the study and was not pursued further.

In this study, real-world datasets and generated synthetic data were used in order to evaluate the system’s performance, representing the data-collection component of the underlying framework.

In the predictive resource-allocation space models based on long short-term memory (LSTM) [1] and, more recently, gated recurrent unit (GRU) [2] have become popular with state-of-the-art implementations.

The core implementation proposed in this research investigates a cascaded dual-model approach. Taking raw data as input, the first model was trained to generate an initial resource-requirement forecast for each of the predefined locations, assuming spatial dependency. The second model was then trained to predict the error between the distribution predicted by the first model and the true values. Using varied combinations of bi-directional LSTM and GRU layers, the cascaded dual-model approach indicated significant improvements compared to standard LSTM and GRU implementations in initial evaluations. The system would then handle prediction distributions for intervals in which relevant preemptable events with limited sample data and no identifiable time-series correlations could cause highly discernible data variation. Depending on the application space, such events could include concerts, public holidays or an incoming influx of patients from a major accident.

Despite the trained models being capable of augmentation for the generation of long-term allocation forecasts, the proposed system was evaluated on shortterm predictions generated using a one-step lookahead. Once the event biases affecting the initial prediction had been handled, further post-processing would adjust the final allocation across the various nodes, depending on the importance of each location and the availability of limited resources using the forecasted requirements. Using a priority-based double-standard repositioning model [3] the system would then suggest a resource-relocation strategy. This approach preemptively moves resources as per the final prediction. Should the number of resources at prediction not be enough to satisfy the forecasted allocation requirement for each node, the system would maintain a queue for moving resources as soon as they become available.

Figure 1. System-component diagram

REFERENCES

[1] S. Hochreiter and J. Schmidhuber, ‘Long Short-Term Memory’, Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/ neco.1997.9.8.1735.

[2] K. Cho et al., ‘Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation’, arXiv:1406.1078 [cs, stat], Sep. 2014, Accessed: May 10, 2021. [Online]. Available: http://arxiv.org/abs/1406.1078

[3] V. Bélanger, Y. Kergosien, A. Ruiz, and P. Soriano, ‘An empirical comparison of relocation strategies in real-time ambulance fleet management’, Computers & Industrial Engineering, vol. 94, pp. 216–229, Apr. 2016, doi: 10.1016/j.cie.2016.01.023.