M.Tech Thesis Directions: Choosing a Research Topic

M.Tech Thesis/dissertation seems to be a formidable task to the students as it requires selection of unique topic, implementation of the base paper, doing research in the base paper, writing a scholarly report, paper publication etc.

With limited resources made available by the academics, most of the students hire an external guide or tutor for the research guidance to help them accomplish their dissertation work.

 

The selection of research topic for the M.Tech thesis is the most crucial step for beginning the thesis work.  

Here are a few guidelines for the selection for Research topic for M.Tech/PhD Thesis:

•        It’s almost impossible to come up with a completely new idea. Every discovery or research depends on existing methodologies or technology. The existing technology or theory can be rolled in a new way to make it applicable for the new problem. So, never pick up any hypothetical topic for the research.
•        Aim for a specific result that nobody has achieved ever before as Watson and Crick aimed to discover DNA.
•      Apply a new methodology in the existing technology to contribute to the technology. The methodology should be new to the problem statement addressed in the research.
•        Choose a specific subject for the research i.e. VLSI from ECE, Digital image processing form CSE etc.
•        Read some standard papers from reputed journals such as IEEE or Sciencedirect to find about the work going on in the subject selected by you.
•        Be aware of the resources and tools you have for your research.
•        Never sit and wait for one specific idea, instead gather several ideas no matter if this seems to be crazy at first.  Once you are opted with a number of ideas you can choose a specific out of those.
•        Gather good data before going for implementation and facilitate yourself with the resources in the initial phase that you are going to use in the implementation.
•      Consider multiple ideas for the topic selection for your M.tech thesis, eliminate the outdated after studying the research papers and go for the unique one before initiating the implementation.

Some Crucial Steps to Pick a PhD Project

This article is focused on conventional as well as modern ways to choose a PhD project. There are several PhD projects that will make you confuse such as VLSI, Digital Signal Processing, and Digital Image Processing when you start researching what is out there, though having an idea in advance about the most significant areas of research and development will help to choose a better research area. Silicon Mentor understands the need of current researchers and students and provides them full support in terms of best real time research area selection and complete guidance up to the project completion. Silicon Mentor believes in the theory of knowledge sharing, to accomplish this it provides a new category of professors and mentors.

Silicon Mentors usually recommends to make a list of topics and general areas within your area of interest. There is another way to find some of the unique PhD projects to search online. There are many numbers of websites which provides a list of standard and unique topics of PhD research areas in all over the world. However, you will find a number of PhDs in the general research area, specific subject areas such as VLSI, DSP and DIP will not mentioned on these-websites.

Therefore one should use general search as well some specific search techniques. The search should contain the university’s prospective supervisors search or general research direction of particular university. Some Times it is worthwhile to contact prospective supervisors directly and ask him for their ability to offer you a sufficient environment for your PhD.

Interplay Between Biology and Technology

We know that biology is the study of life and living organisms with their evolution, function and growth and technology is the collection of the techniques, processes and methods used in the production of any goods such as scientific investigation.  Biology mainly is an investigation to human health related processes. In today’s world, human birth rate is more than the death rate and it is only due to the merging of biology and technology because technology processes the human biology in a better and safer way. So, biotechnology (biology + technology) is the use of living things in any technical application or use of the technical things in particular application of human life. People have used biotechnology in many fields such as food production, agriculture, medicine, genetic engineering and in industries to make chemicals, textiles, papers and Biofuels. 

About biotechnology, we can say that it is the combination of human technology with human intelligence. Biomedical technology is also the part of biotechnology in which technology plays main role as medicine to check and enhance human health with or without the help of a doctor. Our body parts, which are the part of biological study, are tested by the technical equipment using different biomedical signals (i.e. electrocardiogram, electroencephalogram, electrooculography, carotid pulse, phonocardiogram, and speech signals) that are generated by our body. Nanotechnology (Nano Science + Technology) also play a vital role in developing medicines and equipment which increase the human health rate.

Today’s bio-technical devices (healthcare devices) are easily available in the market due to their low cost and effectiveness in measurement of every health related problems. According to the healthcare device indications, our body can be checked up by the doctor when required otherwise we can take required medicines to overcome our body issues. In these days, problems in the human health are increasing due to pollution and low nutrient food. Only due to the advancement in the biotechnology, average age of human living is increased that makes biotechnology more important in the lives of people.

An Edge to VLSI Implementation of Digital Signal Processing Algorithms

The implementation of digital signal processing algorithms and availability of the digital systems have become more widespread since last two decades due to easy availability of digital systems. Earlier analog processors were used to perform the signal processing due to unavailability of digital processors.

The digital signal processing became feasible to be performed in real time in the recent times due to hardware implementation of the algorithms developed in signal processing. It is all because of the requirement of higher level computations in the signal processing especially for the real time applications.

The demands for the high level computation systems combined with the performance of the VLSI architectures which led to the development of VLSI Digital signal processors such as TMS320(1982) and DSP56001(1987). The developers are left with enormous specific architectures of DSP with the ease in development of VLSI designs.

The most common and usually employable DSP techniques are the FFT computing, FIR and IIR digital filters. These techniques require the three basic operations i.e. multiplication, storage and addition and these operations can easily be performed with the VLSI oriented architectures for Digital signal processing architectures. 

The architectures developed through the VLSI implementation for DSP applications generally make use of parallel processing, multiprocessing, array processors, RISC i.e. Reduced instruction set and  pipelining for the very high processing.

The architectures developed for the Digital signal processing applications are tested and brought to the real time implementation through VLSI only. The most commonly and preferably used hardware for the implementation of DSP algorithms in VLSI is the FPGA. FPGA implementation of the digital signal processing algorithms makes it possible to develop a VLSI architecture for the high computation processing and multiprocessing at the real time.

TOWARDS THE LOW POWER SYSTEM FOR NEURAL NETWORKS

Requirement of low power system in the field of neural network prompts the evolution of new technology to tackle with the problem of significant power consumption.

When large number of neurons in a circuit deals with the particular application then power consumption is high enough to scath the circuit. So a low energy consumption circuit for neural networks has been developed by using ferroelectric memristor. First ferroelectric memristor was developed by Panasonic.

Beauty of the devices using this technique is to treat digital signal as analog data. This analog data is stored continuously as resistance on a CMOS circuit. As analog data is processed directly over here so it is obvious that power consumption diminute drastically.

Generally data storing templates always exist in neural networks so there is requirement of some kind of memory element, this need is undeniably accomplish by ferroelectric memristor.

Continuous change in the value of resistance of ferroelectric memristor occurs by accordingly change in the applied voltage. It behaves as a memory device when there is no voltage applying which leads a maintained resistance. And this fixed resistance acts as stored templates. As analog signal is processed in this technology so any intermediate value can be stored as data rather than binary value 1/0 due to this fact it is capable to store more information than the digital memory.

According to the large amount of information stored in memristor it is possible to lessen the power consumption as the dealing with analog signal proven its vitality in up surging the amount of data to be processed. This innovative development leads to an  efficient neural network in terms of accuracy and power consumption.

Bio-Medical Signal Processing at a Glance

ECG signal:

ECG signal, also known as EKG signal, is a diagnostic tool which makes the electrical and muscular function of the heart accessible for analysis. The continuous pumping of blood by heart from lungs to various parts of body is responsible for generation of ECG signal.  The heart is a two stage electrical pump and its electrical activity can be measured by placing the electrodes on the chest or by using some special bands. The electrocardiogram is used to measure the rate and rhythm of the heartbeat, as well as it provides an evidence of blood flow to the heart muscles.

                              

                                                                                       Fig1 ECG signal

Introduction:

For ECG signal analysis we perform Signal Processing on it. With the help of the signal processing we can extract that information from the ECG signal that we cannot extract by simply visualizing it. Many noises may add into our ECG signal and elimination of these noises is also the important objective of the Signal processing. And another main objective of the signal processing is to do the compression of the data. The following diagram shows the algorithm for basic ECG signal processing:

             

                                                          Fig2 Algorithm for basic ECG signal processing

After getting the information by the signal processing, we can use this information in many applications.

ECG Pre-Processing:

Filtering is done in Pre-Processing part of the ECG signal and after filtering, various analyses are performed on the ECG signal. Filters are mainly designed for the removal of the following:

• 1      Baseline wander

• 2      Power line interference

Baseline wander:

The main factor require for designing the linear, time-invariant, high pass filters for removal of baseline wander is cutoff frequency and phase response characteristics In definite situations, baseline wander becomes commonly well-defined at higher heart rates such as during the final stages of a stress test when the workload increases. Then, it may be advantageous to couple the cut-off frequency to the general heart rate, rather than to the lowest possible heart rate, to further improve baseline removal.

Figure 3(a) shows Electrocardiographic baseline wander because of sudden body movements. The amplitude of the baseline wander is considerably larger than that of the QRS complexes .Figure3(b)  a close-up in time (10 x) of the ECG signal framed in (a)

Power line Interference:

Electromagnetic fields generated by a power line signify a common noise source in the ECG that is characterize by 50 or 60 Hz sinusoidal interference, probably accompany by a number of harmonics. Such narrow band noise makes the analysis and interpretation of the ECG more difficult, as the description of low-amplitude waveforms becomes unpredictable and fake waveforms may be introduced.

4 QRS Detection:

The information content present in any ECG signal is its existence and its time of occurrence. The QRS complex present in ECG signal indicates the existence of beat in the signal. Also many other analyses of human body like pulse rate, blood pressure measurement, physical and mental status etc can be performed after detection of QRS peak. Thus, proper detection is of utmost requirement which ensures that refined and distortion less signal will be further used by system for human body analysis. The poor detection can lead to limitation in performance of whole system.

The two problems frequently faced in QRS detection are that the signal either remains undetected or signal is detected falsely. The problem of more concern is no detection, because the information content from that part is lost and cannot be recovered in later stages of the system. For false detection of signal, there are various methods to resolve it like performing classification of QRS morphologies. The detector must be capable of detecting different morphologies to allow sudden changes in the output i.e. it should not lock onto certain types of rhythm, but treat each event as if it could occur at almost any time. The noises also accompany the detected signal. These noises may be transient in behavior or persistent. 

                                     

                                                          Fig Block diagram of QRS Detector

The above figure shows the block diagram of a commonly used QRS detector. The input to system is the ECG signal, and the output is a series of occurrence times of the QRS complex signal. It is a must requirement to improve the resolution using an algorithm which is responsible for time alignment of the detected signal. Time alignment helps in elimination of smearing which occurs during computation of the ensemble average of several detected beats.