Abstract: Many digital signal processing, techniques have been used to automatically distinguish protein coding regions (exons) from non-coding regions (introns) in DNA sequences. In this work, we have characterized these sequences according to their nonlinear dynamical features such as moment invariants, correlation dimension, and largest Lyapunov exponent estimates. We have applied our model to a number of real sequences encoded into a time series using EIIP sequence indicators. In order to discriminate between coding and non coding DNA regions, the phase space trajectory was first reconstructed for coding and non-coding regions. Nonlinear dynamical features are extracted from those regions and used to investigate a difference between them. Our results indicate that the nonlinear dynamical characteristics have yielded significant differences between coding (CR) and non-coding regions (NCR) in DNA sequences. Finally, the classifier is tested on real genes where coding and non-coding regions are well known.
Abstract: The purpose of my research proposal is to
demonstrate that there is a relationship between EEG and
endometrial cancer.
The above relationship is based on an Aristotelian Syllogism;
since it is known that the 14-3-3 protein is related to the electrical
activity of the brain via control of the flow of Na+ and K+ ions and
since it is also known that many types of cancer are associated with
14-3-3 protein, it is possible that there is a relationship between EEG
and cancer. This research will be carried out by well-defined
diagnostic indicators, obtained via the EEG, using signal processing
procedures and pattern recognition tools such as neural networks in
order to recognize the endometrial cancer type. The current research
shall compare the findings from EEG and hysteroscopy performed on
women of a wide age range. Moreover, this practice could be
expanded to other types of cancer. The implementation of this
methodology will be completed with the creation of an ontology.
This ontology shall define the concepts existing in this research-s
domain and the relationships between them. It will represent the
types of relationships between hysteroscopy and EEG findings.
Abstract: This paper proposes an architecture of dynamically
reconfigurable arithmetic circuit. Dynamic reconfiguration is a
technique to realize required functions by changing hardware
construction during operations. The proposed circuit is based on a
complex number multiply-accumulation circuit which is used
frequently in the field of digital signal processing. In addition, the
proposed circuit performs real number double precision arithmetic
operations. The data formats are single and double precision floating
point number based on IEEE754. The proposed circuit is designed
using VHDL, and verified the correct operation by simulations and
experiments.
Abstract: Revolutions Applications such as telecommunications, hands-free communications, recording, etc. which need at least one microphone, the signal is usually infected by noise and echo. The important application is the speech enhancement, which is done to remove suppressed noises and echoes taken by a microphone, beside preferred speech. Accordingly, the microphone signal has to be cleaned using digital signal processing DSP tools before it is played out, transmitted, or stored. Engineers have so far tried different approaches to improving the speech by get back the desired speech signal from the noisy observations. Especially Mobile communication, so in this paper will do reconstruction of the speech signal, observed in additive background noise, using the Kalman filter technique to estimate the parameters of the Autoregressive Process (AR) in the state space model and the output speech signal obtained by the MATLAB. The accurate estimation by Kalman filter on speech would enhance and reduce the noise then compare and discuss the results between actual values and estimated values which produce the reconstructed signals.
Abstract: Financial forecasting is an example of signal processing problems. A number of ways to train/learn the network are available. We have used Levenberg-Marquardt algorithm for error back-propagation for weight adjustment. Pre-processing of data has reduced much of the variation at large scale to small scale, reducing the variation of training data.
Abstract: The feature of HIV genome is in a wide range because
of it is highly heterogeneous. Hence, the infection ability of the virus changes related with different chemokine receptors. From this point,
R5 and X4 HIV viruses use CCR5 and CXCR5 coreceptors respectively while R5X4 viruses can utilize both coreceptors. Recently, in Bioinformatics, R5X4 viruses have been studied to
classify by using the coreceptors of HIV genome.
The aim of this study is to develop the optimal Multilayer
Perceptron (MLP) for high classification accuracy of HIV sub-type viruses. To accomplish this purpose, the unit number in hidden layer
was incremented one by one, from one to a particular number. The statistical data of R5X4, R5 and X4 viruses was preprocessed by the
signal processing methods. Accessible residues of these virus sequences were extracted and modeled by Auto-Regressive Model
(AR) due to the dimension of residues is large and different from each other. Finally the pre-processed dataset was used to evolve MLP with various number of hidden units to determine R5X4
viruses. Furthermore, ROC analysis was used to figure out the optimal MLP structure.
Abstract: In this paper, an innovative watermarking scheme for audio signal based on genetic algorithms (GA) in the discrete wavelet transforms is proposed. It is robust against watermarking attacks, which are commonly employed in literature. In addition, the watermarked image quality is also considered. We employ GA for the optimal localization and intensity of watermark. The watermark detection process can be performed without using the original audio signal. The experimental results demonstrate that watermark is inaudible and robust to many digital signal processing, such as cropping, low pass filter, additive noise.
Abstract: Residue Number System (RNS) is a modular representation and is proved to be an instrumental tool in many digital signal processing (DSP) applications which require high-speed computations. RNS is an integer and non weighted number system; it can support parallel, carry-free, high-speed and low power arithmetic. A very interesting correspondence exists between the concepts of Multiple Valued Logic (MVL) and Residue Number Arithmetic. If the number of levels used to represent MVL signals is chosen to be consistent with the moduli which create the finite rings in the RNS, MVL becomes a very natural representation for the RNS. There are two concerns related to the application of this Number System: reaching the most possible speed and the largest dynamic range. There is a conflict when one wants to resolve both these problem. That is augmenting the dynamic range results in reducing the speed in the same time. For achieving the most performance a method is considere named “One-Hot Residue Number System" in this implementation the propagation is only equal to one transistor delay. The problem with this method is the huge increase in the number of transistors they are increased in order m2 . In real application this is practically impossible. In this paper combining the Multiple Valued Logic and One-Hot Residue Number System we represent a new method to resolve both of these two problems. In this paper we represent a novel design of an OHRNS-based adder circuit. This circuit is useable for Multiple Valued Logic moduli, in comparison to other RNS design; this circuit has considerably improved the number of transistors and power consumption.
Abstract: A new low-voltage floating gate MOSFET (FGMOS)
based squarer using square law characteristic of the FGMOS is
proposed in this paper. The major advantages of the squarer are simplicity,
rail-to-rail input dynamic range, low total harmonic distortion,
and low power consumption. The proposed circuit is biased without
body effect. The circuit is designed and simulated using SPICE in
0.25μm CMOS technology. The squarer is operated at the supply
voltages of ±0.75V . The total harmonic distortion (THD) for the
input signal 0.75Vpp at 25 KHz, and maximum power consumption
were found to be less than 1% and 319μW respectively.
Abstract: Support Vector Machine (SVM) is a statistical
learning tool that was initially developed by Vapnik in 1979 and later
developed to a more complex concept of structural risk minimization
(SRM). SVM is playing an increasing role in applications to
detection problems in various engineering problems, notably in
statistical signal processing, pattern recognition, image analysis, and
communication systems. In this paper, SVM was applied to the
detection of SAR (synthetic aperture radar) images in the presence of
partially developed speckle noise. The simulation was done for single
look and multi-look speckle models to give a complete overlook and
insight to the new proposed model of the SVM-based detector. The
structure of the SVM was derived and applied to real SAR images
and its performance in terms of the mean square error (MSE) metric
was calculated. We showed that the SVM-detected SAR images have
a very low MSE and are of good quality. The quality of the
processed speckled images improved for the multi-look model.
Furthermore, the contrast of the SVM detected images was higher
than that of the original non-noisy images, indicating that the SVM
approach increased the distance between the pixel reflectivity levels
(the detection hypotheses) in the original images.
Abstract: Autofluorescence (AF) bronchoscopy is an
established method to detect dysplasia and carcinoma in situ (CIS).
For this reason the “Sotiria" Hospital uses the Karl Storz D-light
system. However, in early tumor stages the visualization is not that
obvious. With the help of a PC, we analyzed the color images we
captured by developing certain tools in Matlab®. We used statistical
methods based on texture analysis, signal processing methods based
on Gabor models and conversion algorithms between devicedependent
color spaces. Our belief is that we reduced the error made
by the naked eye. The tools we implemented improve the quality of
patients' life.
Abstract: Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recorded from the C3 and C4 locations. Principle features extracted through principle component analysis of the segmented EEG are analyzed using two novel classification algorithms using Elman recurrent neural network and functional link neural network. Performance of both classifiers is evaluated using a particle swarm optimization training algorithm; results are also compared with the conventional back propagation training algorithm. EEG motor imagery recorded from two subjects is used in the offline analysis. From overall classification performance it is observed that the BP algorithm has higher average classification of 93.5%, while the PSO algorithm has better training time and maximum classification. The proposed methods promises to provide a useful alternative general procedure for motor imagery classification
Abstract: In this paper, we propose a fully-utilized, block-based 2D DWT (discrete wavelet transform) architecture, which consists of four 1D DWT filters with two-channel QMF lattice structure. The proposed architecture requires about 2MN-3N registers to save the intermediate results for higher level decomposition, where M and N stand for the filter length and the row width of the image respectively. Furthermore, the proposed 2D DWT processes in horizontal and vertical directions simultaneously without an idle period, so that it computes the DWT for an N×N image in a period of N2(1-2-2J)/3. Compared to the existing approaches, the proposed architecture shows 100% of hardware utilization and high throughput rates. To mitigate the long critical path delay due to the cascaded lattices, we can apply the pipeline technique with four stages, while retaining 100% of hardware utilization. The proposed architecture can be applied in real-time video signal processing.
Abstract: A challenging problem in radar signal processing is to
achieve reliable target detection in the presence of interferences. In
this paper, we propose a novel algorithm for automatic censoring of
radar interfering targets in log-normal clutter. The proposed
algorithm, termed the forward automatic censored cell averaging
detector (F-ACCAD), consists of two steps: removing the corrupted
reference cells (censoring) and the actual detection. Both steps are
performed dynamically by using a suitable set of ranked cells to
estimate the unknown background level and set the adaptive
thresholds accordingly. The F-ACCAD algorithm does not require
any prior information about the clutter parameters nor does it require
the number of interfering targets. The effectiveness of the F-ACCAD
algorithm is assessed by computing, using Monte Carlo simulations,
the probability of censoring and the probability of detection in
different background environments.
Abstract: An array antenna system with innovative signal
processing can improve the resolution of a source direction of arrival
(DoA) estimation. High resolution techniques take the advantage of
array antenna structures to better process the incoming waves. They
also have the capability to identify the direction of multiple targets.
This paper investigates performance of the DOA estimation
algorithm namely; Capon and MUSIC on the uniform linear array
(ULA). The simulation results show that in Capon and MUSIC
algorithm the resolution of the DOA techniques improves as number
of snapshots, number of array elements, signal-to-noise ratio and
separation angle between the two sources θ increases.
Abstract: Higher-order Statistics (HOS), also known as
cumulants, cross moments and their frequency domain counterparts,
known as poly spectra have emerged as a powerful signal processing
tool for the synthesis and analysis of signals and systems. Algorithms
used for the computation of cross moments are computationally
intensive and require high computational speed for real-time
applications. For efficiency and high speed, it is often advantageous
to realize computation intensive algorithms in hardware. A promising
solution that combines high flexibility together with the speed of a
traditional hardware is Field Programmable Gate Array (FPGA). In
this paper, we present FPGA-based parallel architecture for the
computation of third-order cross moments. The proposed design is
coded in Very High Speed Integrated Circuit (VHSIC) Hardware
Description Language (VHDL) and functionally verified by
implementing it on Xilinx Spartan-3 XC3S2000FG900-4 FPGA.
Implementation results are presented and it shows that the proposed
design can operate at a maximum frequency of 86.618 MHz.
Abstract: Small cracks or chips of a product appear very
frequently in the course of continuous production of an automatic
press process system. These phenomena become the cause of not only
defective product but also damage of a press mold. In order to solve
this problem AE system was introduced. AE system was expected to
be very effective to real time detection of the defective product and to
prevention of the damage of the press molds.
In this study, for pick and analysis of AE signals generated from the
press process, AE sensors/pre-amplifier/analysis and processing board
were used as frequently found in the other similar cases. For analysis
and processing the AE signals picked in real time from the good or bad
products, specialized software called cdm8 was used. As a result of
this work it was conformed that intensity and shape of the various AE
signals differ depending on the weight and thickness of metal sheet
and process type.
Abstract: This document details the process of developing a
wireless device that captures the basic movements of the foot (plantar
flexion, dorsal flexion, abduction, adduction.), and the knee
movement (flexion). It implements a motion capture system by using
a hardware based on optical fiber sensors, due to the advantages in
terms of scope, noise immunity and speed of data transmission and
reception. The operating principle used by this system is the detection
and transmission of joint movement by mechanical elements and
their respective measurement by optical ones (in this case infrared).
Likewise, Visual Basic software is used for reception, analysis and
signal processing of data acquired by the device, generating a 3D
graphical representation in real time of each movement. The result is
a boot in charge of capturing the movement, a transmission module
(Implementing Xbee Technology) and a receiver module for
receiving information and sending it to the PC for their respective
processing.
The main idea with this device is to help on topics such as
bioengineering and medicine, by helping to improve the quality of
life and movement analysis.
Abstract: Research on damage of gears and gear pairs using
vibration signals remains very attractive, because vibration signals
from a gear pair are complex in nature and not easy to interpret.
Predicting gear pair defects by analyzing changes in vibration signal
of gears pairs in operation is a very reliable method. Therefore, a
suitable vibration signal processing technique is necessary to extract
defect information generally obscured by the noise from dynamic
factors of other gear pairs.This article presents the value of cepstrum
analysis in vehicle gearbox fault diagnosis. Cepstrum represents the
overall power content of a whole family of harmonics and sidebands
when more than one family of sidebands is present at the same time.
The concept for the measurement and analysis involved in using the
technique are briefly outlined. Cepstrum analysis is used for detection
of an artificial pitting defect in a vehicle gearbox loaded with
different speeds and torques. The test stand is equipped with three
dynamometers; the input dynamometer serves asthe internal
combustion engine, the output dynamometers introduce the load on
the flanges of the output joint shafts. The pitting defect is
manufactured on the tooth side of a gear of the fifth speed on the
secondary shaft. Also, a method for fault diagnosis of gear faults is
presented based on order Cepstrum. The procedure is illustrated with
the experimental vibration data of the vehicle gearbox. The results
show the effectiveness of Cepstrum analysis in detection and
diagnosis of the gear condition.
Abstract: Electromyography (EMG) is the study of muscles function through analysis of electrical activity produced from muscles. This electrical activity which is displayed in the form of signal is the result of neuromuscular activation associated with muscle contraction. The most common techniques of EMG signal recording are by using surface and needle/wire electrode where the latter is usually used for interest in deep muscle. This paper will focus on surface electromyogram (SEMG) signal. During SEMG recording, several problems had to been countered such as noise, motion artifact and signal instability. Thus, various signal processing techniques had been implemented to produce a reliable signal for analysis. SEMG signal finds broad application particularly in biomedical field. It had been analyzed and studied for various interests such as neuromuscular disease, enhancement of muscular function and human-computer interface.