Saira Joseph; Dr. Binu Paul(Cochin University of Science and Technology, November 20, 2016)
[+]
[-]
Abstract:
Over the past decade, there is a great demand for permission to transmit large bandwidth
concurrent with existing narrowband signals. In 2002, the federal communications
commission (FCC) decided to permit use of ultrawideband (UWB) systems.
UWB systems are unique in their large instantaneous bandwidth and potential for lowcost
digital design that enables a single system to operate in different modes as a
communication device, radar etc. UWB systems aim at covering the frequency band of
3.1-10.6 GHz defined by the FCC. The addition of more and more features in each new
generation communication system demands universal antennas suitable for operation in
multiple bands. In this regard, designing a multiband antenna which also covers the
UWB range without deteriorating the UWB performance is of great interest. Also, a
significant issue in communication systems is to miniaturize the antenna size while
providing good performance over the operation bands.
The thesis presents three different designs of CPW-fed multiband antennas. The first
design uses a fractal concept to achieve multiband operation. In the second and third
designs, a slotted circular monopole and a spiral monopole are used respectively for
multiband operation with UWB applications. For both these cases, variations from the
basic antenna geometry are also proposed in which the frequency of operation is
lowered without increase in overall antenna size. Design guidelines are developed for
all antennas to facilitate design on substrates of different permittivity and thickness. The
antennas are analyzed in both frequency and time domain for complete characterization
Raphika, P. M.; Dr. Abdulla P.(Cochin University of Science and Technology, May 3, 2016)
[+]
[-]
Abstract:
Filters are one of the essential components in the RF and wireless
communication systems. Small sized planar lowpass filters with good electrical
characteristics along with low cost, light weight and ease of fabrication are highly
desirable for the front end of modern communication systems to suppress harmonics
and spurious signals. Design of compact lowpass filters with improved performance
and diverse specifications for numerous applications is a huge challenge.
In this thesis, high performance planar compact lowpass filters using multiple
patch resonators on high impedance transmission line are developed. Design techniques
of different types of patch resonators and their modifications to enhance the
performance of the filters are presented.Patch resonators are designed by using high impedance short circuited stubs
and low impedance open circuited patches. In the first stage of filter realization,
compact lowpass filter having sharp roll-off using triangular and funnel patch
resonators is presented. The structure is modified further to enhance the relative
stopband bandwidth of the filter. In the third stage, another resonator has been
introduced near the feed line to achieve sharp roll-off for the same cutoff frequency,
stopband bandwidth and suppression level. To obtain compactness, high suppression
level and wide stopband in filter design, low thickness substrate is tested and proved in
the fourth stage.
Realizations of planar compact lowpass filter with very sharp roll-off near the
cutoff frequency have been presented using stepped impedance polygonal patch
resonators. By increasing the patch size and number of resonators, the stopband
bandwidth and suppression level have to be enhanced to a great extent. Enhancement
of performance characteristics of lowpass filter design is continually being extended.
By using high value capacitance patch, the stopband suppression level of the filter with
sharp roll-off rate is achieved. Elliptic function lowpass filter with ultra-sharp roll-off is
also developed using elliptic shaped patch resonators.
Throughout the study, low cost substrate having permittivity 4.4 is used for the
filter design. All the designed filters have been fabricated and predicted results are
validated by the measurements.
Baby Paul; Dr. P. Mythili(Cochin University of Science and Technology, October 8, 2015)
[+]
[-]
Abstract:
Electrocardiogram gives the information regarding the health of
the patients by monitoring the bioelectric potentials generated by the
sinoatrial node in the heart. These signals can be collected by using
electrodes suitably placed on the body of a patient. The normal human
ECG lie in the frequency range of 0.05-100 Hz and the most useful
information is contained in the range of 0.5-45 Hz. Even though a large
amount of work has already been done in the field of ECG classification,
no classification system has made an attempt in identifying the isolated
abnormalities which pose a silent threat to patients.
An adaptive filtering technique for denoising the ECG which is
based on Genetic Algorithm (GA) tuned Sign-Data Least Mean Square
(SD-LMS) algorithm is proposed. This algorithm gave an average
signal to noise ratio improvement of 10.75 dB for baseline wander and
24.26 dB for power line interference. It is seen that the step size ‘μ’
optimized with GA helps in obtaining better SNR value without causing
any damage to the information content in the ECG.
A new wavelet for automatic classification of arrhythmias
from electrocardiogram is proposed. This new wavelet is formed as a
sum of shifted Gaussians so that it resembles a normal ECG. This shape
has been chosen with the aim of extracting maximum information from
the ECG under analysis. The classification performance was studied
using the most commonly used database, the MIT-BIH Arrhythmia
database. The shifted and summed Gaussian wavelet was then
optimized using GA. The optimum wavelet for classification was
obtained after several runs of the GA algorithm. The ECG class
labeling was done according to the Association for the Advancement of
Medical Instrumentation (AAMI). The wavelet scales corresponding to
the different frequency levels giving maximum classification
performance were identified by selecting finer scales. Probabilistic
Neural Network classifier was used for classification purpose. The
proposed classification system offered better results than that reported
in literature by giving an overall sensitivity of 97.01% for Normal beats,
75.20% for Supraventricular beats and 93.06% for Ventricular beats.
As mentioned above this technique could exclusively identify some of
the isolated abnormalities present in the patient records.
Hari, V S; Dr. Jagathy Raj, V P; Dr.Gopikakumari, R(Cochin University of Science And Technology, June , 2013)
[+]
[-]
Abstract:
The basic concepts of digital signal processing are taught to the students
in engineering and science. The focus of the course is on linear,
time invariant systems. The question as to what happens when the
system is governed by a quadratic or cubic equation remains unanswered
in the vast majority of literature on signal processing. Light has
been shed on this problem when John V Mathews and Giovanni L Sicuranza
published the book Polynomial Signal Processing. This book
opened up an unseen vista of polynomial systems for signal and image
processing. The book presented the theory and implementations
of both adaptive and non-adaptive FIR and IIR quadratic systems
which offer improved performance than conventional linear systems.
The theory of quadratic systems presents a pristine and virgin area of
research that offers computationally intensive work. Once the area of
research is selected, the next issue is the choice of the software tool to
carry out the work. Conventional languages like C and C++ are easily
eliminated as they are not interpreted and lack good quality plotting
libraries. MATLAB is proved to be very slow and so do SCILAB and
Octave. The search for a language for scientific computing that was
as fast as C, but with a good quality plotting library, ended up in
Python, a distant relative of LISP. It proved to be ideal for scientific
computing. An account of the use of Python, its scientific computing
package scipy and the plotting library pylab is given in the appendix Initially, work is focused on designing predictors that exploit the polynomial
nonlinearities inherent in speech generation mechanisms. Soon,
the work got diverted into medical image processing which offered
more potential to exploit by the use of quadratic methods. The major
focus in this area is on quadratic edge detection methods for retinal
images and fingerprints as well as de-noising raw MRI signals
Description:
School of Engineering
Cochin University of Science and Technology
Babita Roslind, Jose; Dr.Mythili, P(Cochin University of Science & Technology, January , 2010)
[+]
[-]
Abstract:
Analog-to digital Converters (ADC) have an important impact on the overall performance of signal processing system. This research is to explore efficient techniques for the design of sigma-delta ADC,specially for multi-standard wireless tranceivers. In particular, the aim is to develop novel models and algorithms to address this problem and to implement software tools which are avle to assist the designer's decisions in the system-level exploration phase. To this end, this thesis presents a framework of techniques to design sigma-delta analog to digital converters.A2-2-2 reconfigurable sigma-delta modulator is proposed which can meet the design specifications of the three wireless communication standards namely GSM,WCDMA and WLAN. A sigma-delta modulator design tool is developed using the Graphical User Interface Development Environment (GUIDE) In MATLAB.Genetic Algorithm(GA) based search method is introduced to find the optimum value of the scaling coefficients and to maximize the dynamic range in a sigma-delta modulator.
Description:
School of Engineering, Cochin University of Science and Technology
Rajesh, M V; Dr.Gopikakumari, R; Dr.Unnikrishnan, A(Cochin University of Science & Technology, December , 2010)
[+]
[-]
Abstract:
Identification and Control of Non‐linear dynamical systems are challenging problems to the control engineers.The topic is equally relevant in communication,weather prediction ,bio medical systems and even in social systems,where nonlinearity is an integral part of the system behavior.Most of the real world systems are nonlinear in nature and wide applications are there for nonlinear system identification/modeling.The basic approach in analyzing the nonlinear systems is to build a model from known behavior manifest in the form of system output.The problem of modeling boils down to computing a suitably parameterized model,representing the process.The parameters of the model are adjusted to optimize a performanace function,based on error between the given process output and identified process/model output.While the linear system identification is well established with many classical approaches,most of those methods cannot be directly applied for nonlinear system identification.The problem becomes more complex if the system is completely unknown but only the output time series is available.Blind recognition problem is the direct consequence of such a situation.The thesis concentrates on such problems.Capability of Artificial Neural Networks to approximate many nonlinear input-output maps makes it predominantly suitable for building a function for the identification of nonlinear systems,where only the time series is available.The literature is rich with a variety of algorithms to train the Neural Network model.A comprehensive study of the computation of the model parameters,using the different algorithms and the comparison among them to choose the best technique is still a demanding requirement from practical system designers,which is not available in a concise form in the literature.The thesis is thus an attempt to develop and evaluate some of the well known algorithms and propose some new techniques,in the context of Blind recognition of nonlinear systems.It also attempts to establish the relative merits and demerits of the different approaches.comprehensiveness is achieved in utilizing the benefits of well known evaluation techniques from statistics. The study concludes by providing the results of implementation of the currently available and modified versions and newly introduced techniques for nonlinear blind system modeling followed by a comparison of their performance.It is expected that,such comprehensive study and the comparison process can be of great relevance in many fields including chemical,electrical,biological,financial and weather data analysis.Further the results reported would be of immense help for practical system designers and analysts in selecting the most appropriate method based on the goodness of the model for the particular context.
Description:
Division of Electronics Engineering,
School of Engineering,
Cochin University of Science & Technology