Promote the Educational Reform in Electronics by Developing Extracurricular Scientific Activities

International Journal of Modern Education Forum (IJMEF) Volume 3 Issue 4, November 2014 DOI: 10.14355/ijmef.2014.0304.08

www.ijmef.org

Promote the Educational Reform in Electronics by Developing Extracurricular Scientific Activities Danfeng Hu*1, Min Qian2 School of Electronic and Information, College of Physics, Optoelectronics and Energy of Soochow University No.1 Shizi Street, Suzhou, China hdf78@suda.edu.cn; 2mqian@suda.edu.cn

*1

Abstract With the rapid development of science and technology, the practical ability of college students needs to be enhanced in order to fit the demands of the work position. Meanwhile, providing university students with high quality training is an important mission of the higher education. As for the students majoring in electronics, they can consolidate the knowledge learned from class through attending extracurricular scientific activities. From these activities, the deficiency of education system can be found and solved by promoting the educational reform. The principles, methods and examples are proposed in this paper.

Keywords Extracurricular Scientific Activities; Electronics; Educational Reform

Introduction Training qualified scientists and technicians is the basic task of higher engineering education. At present, the development of science and technology is very rapid. It needs a large number of engineers with practice and innovative ability to keep this progress.

Colleges and universities should put a lot to efforts into promoting the educational reform. So the graduates can serve the social economic development better.

College students in electronics major have learned several foundation courses in the first two years, such as advanced mathematics, general physics, circuit analysis, etc. What they lacked is the ability to use them. The extracurricular scientific activities play a major role in proving students’ practical abilities.

During this process, the deficiency of education system can be found and solved by promoting the educational reform. Under such guidelines, several extracurricular scientific activities were performed, for example, national undergraduate electronic design

contest, national student innovation training project, "Challenge Cup" national undergraduate curricular academic science and technology works, etc.

Extracurricular Scientific Activities and Theory Courses In engineering professional students’ opinion, theory courses are dull and dry. They feel boring in the class.

What’s more, they do not know the usefulness of those theories. Under this circumstance, students cannot sense the pleasure of learning, so they will not concentrate on the study. They just memorize the formulas and theorems mechanically. After the final examination, most of that knowledge is forgotten.

Students are confused why they must learn these useless things.

The Course of Automation Control For example, there are a lot of theorems in the course of automation control, which needs to spend pretty much time understanding them. Even those students who get high scores in this course may not know how to design a control system. So the purpose of this course cannot be achieved by teaching in classroom only.

The extracurricular scientific activities can exactly settle this problem. In the national undergraduate electronic design contest, there is a subject about designing a constant water temperature control system. The water temperature can be heated by resistance wires, cooled by electric fans and measured by transducers. The most important part in this system is the control algorithm. Students can consolidate the knowledge they have learned from class.

At first, they must build a architecture of the system, which is so called “closed loop system” in the course.

135

www.ijmef.org

International Journal of Modern Education Forum (IJMEF) Volume 3 Issue 4, November 2014

As Fig .1. showing,

FIG. 1. DIAGRAM OF CLOSED-LOOP SYSTEM

In this case, the reference input is the temperature set by potentiometer or digital keys, the error signal is detected by operational amplifier or MCU (microprogrammed control unit), the control elements is a analog circuit or digital algorithm implemented by MCU, the disturbance is the variation in load, the open loop system is the whole water tank and the feedback signal is the temperature acquired by the sensors, such as thermoelectric couple, thermal resistance, DS18B20, etc.

Secondly, the students should do some simulation in MATLAB. The system model can be built, the control algorithms can be test, the output results can be shown by virtual instruments. The system parameters (rising time, peak time, overshoot, and steady time) can be calculated. Then we can compare the effect of several algorithms such as PID control, fuzzy control, artificial intelligence control, etc.

At last, based on the result of simulation, the program running on the MCU is designed with C language or ASM. Also the control parameters tuning is implemented according to the system testing.

When the students accomplish all above, they can know the control algorithms better, by transfering the theory to reality. Hence, the reform of the course of automation control is introudcing more control system designing work.

synthesize square wave signals. The students must know the Fourier transform theory before design the circuit. In mathematics, the Fourier transform is the operation that decomposes a signal into its constituent frequencies.

The original signal depends on time; therefore, it is called the time domain representation of the signal.

Whereas the Fourier transform depends on frequency, so it is called the frequency domain representation of the signal. The term Fourier transform refers both to the frequency domain representation of the signal and the process that transforms the signal to its frequency domain representation. The Fourier transform and its generalizations are the subject of Fourier analysis. In this specific case, both time and frequency domains are unbounded linear continua. It is possible to define the Fourier transform of a function of several variables, which is important for students’ study, for instance in the physical study of wave motion and optics. It is also possible to generalize the Fourier transform on discrete structures such as finite groups. The efficient computation of such structures, by fast Fourier transform, is essential for high-speed computing.

According to the Fourier transform theory, a square wave signal can be decomposed into a combination of simple sine waves. It decomposes any periodic function or periodic signal into the sum of a (possibly infinite) set of simple oscillating functions, namely sines and cosines (or, equivalently, complex exponentials). The formulation is shown in equation 1. f (t ) 

a0    (an cos nt  bn sin nt ) 2 n 1

(1)

coefficient,

a0 denotes the DC component 2 an denotes the cosines components

coefficients,

bn

In

equation1,

denotes

the

sines

components

coefficients. t T a0 t f (t )dt 1 t T  t T 2  t f (t )dt  f (t ) 2 T t 1 dt 1

1

1

1

1

(2)

1

The Course of Signal and System The other similar example is the course of signal and system. In this course, Fourier transform is an important part and it involves a lot of derivation. It is hard for students to know the practical significance of it only by learning from the textbooks.

The extracurricular scientific activities can be the complementary part. There is a subject about designing a circuit which can decompose and

136

an 

2 t T f (t ) cos(nt )dt  T t

(3)

bn 

2 t T f (t )sin(nt )dt  T t

(4)

1

1

1

1

Conversely, the sum of sines and cosines can synthesize into square wave. The figure of square wave combination is shown in Fig. 2.

The students can design filter circuits and adder

International Journal of Modern Education Forum (IJMEF) Volume 3 Issue 4, November 2014

circuits, to testify the theory. Because of this, the reform of the course of signal and systems is introducing more theory realization work.

FIG. 2. SQUARE WAVE COMBINATION

Extracurricular Scientific Activities and Experimental Courses In traditional mode of experimental courses, students just follow the instruction books, they learn passively and inefficiently. The reason is that most content of verification experiments are obsolete and limit the development of students’ practical and innovative ability. So developing extracurricular scientific activities is to make the experiments more practical and interesting.

For example, in the course of microcomputer principles, the experiments may combine with real demands instead of verifing the assembly language. In a China student innovation training project, a step motor control system needs to be designed. The system diagram is shown in Fig. 3.

FIG. 3. THE SYSTEM DIAGRAM

This project can be introduced into the experimental course. Students can design the circuits and program according to the system demand. There is no routine operation and limits here. The students is familiar with the command in the real design. Making the step motor work will give them a sense of achievement.

www.ijmef.org

Extracurricular Scientific Activities and Experimental Courses The content of courses may way behind the latest development. The teacher should add some new knowledge into the course. The students may encounter problems that they never learned in the extracurricular scientific activities, so the teacher should introduce some new knowledge into the course.

For example, in a China student innovation training project, students do some research in photoacoustic tomography (PAT). PAT is an emerging noninvasive biomedical imaging technique. This technique is based on the photoacoustic effect which is discovered by Bell in 1880: when a photon is absorbed by the object and then converted into heat, the temperature of the object will increase rapidly, which causes thermal expansion, thereby generating wideband ultrasonic waves transmitted to the boundaries. With the ultrasound signals acquired by one or some ultrasonic transducers, the initial pressure distribution in the tissue can be imaged. In photoacoustic imaging, a pulsed broad laser beam illuminates the biological tissue to which causes rapid temperature increases. The resultant thermoelastic expansion leads to the emission of shortwavelength pulsed ultrasonic waves. When the temporal width of the exciting laser pulse is sufficiently short, the generation and propagation of photoacoustic waves in a lossless medium can be described by the photoacoustic wave equation:

 2 p(r , t ) 

1 2   p(r , t )   A(r ) I (t ) 2 2 Cp t c t

(5)

The filtered back-projection (FBP) method is widely used in the photoacoustic image reconstruction. It needs intensive and full-scanned photoacoustic data to balance the unknown numbers for achieving superior image quality. However, in actual imaging systems, due to the hardware conditions and spatial location restrictions, only incomplete set of measurement data can be obtained, which, therefore, degrades the quality of the reconstructed images. The key point of applying the compressed sensing theory in signal compression is to find a set of bases which spans a space, so that most of the energies of the signal concentrate on a small number of coefficients is in such a space.

Therefore, the original signal can be approximately represented with a small number of coefficients with larger values in the transformed domain and hence the signal can be compressed. On the contrary, given the

137

www.ijmef.org

International Journal of Modern Education Forum (IJMEF) Volume 3 Issue 4, November 2014

compressed signals in the transformed domain, the original signal can be recovered by padding zeros to the compressed signals and performing inverse transform. Under these considerations, it will be possible to recover complete data from the incomplete data which are obtained with some hardware and spatial location restrictions in the framework of CS theory, if a proper set of bases are found. With regard to this, the quality of the reconstructed image can be significantly improved, if the CS is employed for completing data recovery.

Then our problem is to recover the complete data from the actual incomplete data . In our simulated experiment the incomplete data are obtained from a random measurement matrix , i.e. y  Kx

(6)

Generally, the dimension of y is much smaller than that of x , therefore, directly recovering x from y is a severe underdetermined problem. In order to resolve this problem in the framework of CS, we try to find an appropriate transform  , so that x is sparse in the transformed domain, i.e.,  :    x and 

l0

M ,

where M is the number of components in complete data x ,  is a compressed version of x upon performing transform  . In this way, recovering

is determined as the point of intersection between the solution line and the isoline with the smallest value.

FIG.4. SCHEMATIC ILLUSTRATION OF SPARSE SOLUTION

The theories mentioned above are not in the traditional courses. So the teacher can put them into related courses.

Conclusions The method of educational reform is proposed. It can be combined with the extracurricular scientific activities. It proved that this approach has a favorable effect on practical applications.

ACKNOWLEDGEMENT

Funded by research project of teaching reform in higher education, Soochow University in 2013. No. 5731504813.

complete data x from incomplete data y can be

REFERENCES

converted into recovering it from its compressed version  if  has been found. According to the

Azemi, A., & R. Toto. “Work in Progress: Enhancement of

principles of the compressed sensing theory, the transform  can be determined by solving the

Student Learning via Recorded Worked-out Examples

following constrained optimization problem:

of the 42nd Frontiers in Education Conference, Seattle

min 

and In-Class Team Based Problem Solving”. Proceedings

WA, 2012. l1

s.t. y  K 1

(7)

Danfeng Hu, Jiajun Wang, Erxi Fang, Wei Zhou, Yue Zhou.

“The application of compressed sensing method in

Since the l0 norm is a NP-hard problem and can’t be

photoacoustic image reconstruction”. Proceedings of the

solved efficiently, it has been replaced with the l1

Fourth IEEE International Conference on Information

norm in (7). Such a substitution is plausible because the solution  to (7) is sparse enough. Let A  K 1 ,

Science and Technology, Shenzhen, China, 2014.

we can write problem (7) as min 

Mason, G.S., & T.R. Shuman. “ Comparing the Effectiveness of an Inverted Classroom to a Traditional Classroom in

l1

s.t. y  A

(8)

an

Upper-Division

Engineering

Course”.

IEEE

Transaction on Education, 2013.

The constraint y  A in (8) is an underdetermined

Toto, R., & H. Nguyen. “Flipping the work design in an

linear system that has more unknowns than equations.

So this is an ill-posed problem. In Fig. 4. for 2D case, both the isoline for  l1  1  2 and the line for

industrial engineering course”. Proceedings of the 39th

solutions to y  A are given. Then the sparse solution

138

Frontiers in Education Conference, San Antonio, 2009.

Yuan Zhengguang.” Voice of the Era – SRTP Experts’ Talks at the Turn of the Century”. The Reform Press, 2000.