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Signal Processing and Its Applications (CSPA), 2010 6th International Colloquium on

Date 21-23 May 2010

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  • [Front cover]

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  • [Copyright notice]

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  • Preface

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  • Organizing Committee

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  • list-reviewer

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  • Table of contents

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  • Logical effort using Particle Swarm Optimization algorithm — An examination on the 8-stage full adder circuit

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (146 KB) |  | HTML iconHTML  

    The delay reduction of logic architecture leads to the reduction in costs associated with the development time, fabrication (chip area), and power requirements, as well as increased performance. The logical effort technique provides an easy way to compare and select circuit topologies, choose the best number of stages for path and estimate path delay. The Particle Swarm Optimization method is proposed to solve the Logical Effort (LE) problem for electronic circuits. Various optimization parameters, such as swarm size and iterations were tested under different initialization conditions to verify its performance. Results have indicated that the PSO algorithm was an effective method to apply to the LE problem, with high convergence rates. View full abstract»

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  • Optimizing filter parameters using Particle Swarm Optimization

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (125 KB) |  | HTML iconHTML  

    Filter designers often have to calculate the best parameters to suit the filter specifications. Software is typically used to help estimate those values, but sometimes the parameter combination cannot yield perfect results. Calculating the filter parameters using transfer functions would be more challenging with filter that have high orders. This project presents an application of a Particle Swarm Optimization algorithm (PSO) for designing high order filter. The proposed algorithm was successfully applied on a six-order elliptic filter, and has been shown to work well. View full abstract»

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  • Moving vehicle noise classification using backpropagation algorithm

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (976 KB) |  | HTML iconHTML  

    The hearing impaired is afraid of walking along a street and living a life alone. Since, it is difficult for hearing impaired to hear and judge sound information and they often encounter risky situations while they are in outdoors. The sound produced by moving vehicle in outdoor situation cannot be moderate wisely by profoundly deaf people. They also cannot distinguish the type and the distance of any moving vehicle approaching from their behind. Generally the profoundly deaf people do not use any hearing aid which does not provide any benefit. In this paper, a simple system that identifies the type and distance of a moving vehicle using artificial neural network has been proposed. The noises emanated from moving vehicles along the roadside were recorded along with the type and distance of moving vehicles. Simple feature extraction algorithm for extracting the feature from noise emanated by the moving vehicle has been made using frequency analysis approach. A one-third-octave filter bands is used for getting the important signatures from the emanated noise. The extracted features are associated with the type and distance of the moving vehicle and a simple neural network model is developed. The developed neural network model is tested for its validity. View full abstract»

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  • Robot chair control using an asynchronous brain machine interface

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (689 KB) |  | HTML iconHTML  

    Robot chair control using an asynchronous brain machine interface (ABMI) based on motor imagery requires sufficient subject training. This paper proposes a generalized a brain machine interface design to investigate the feasibility of real-time robot chair control by trained subjects. Performance of the real-time experiments conducted for asynchronous navigation is assessed based on completion of a navigation protocol. The performances of the ABMI and its constraints are discussed. View full abstract»

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  • A study on electrode for amperometric measurement of human stress with flow injection analysis biosensing system

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1625 KB) |  | HTML iconHTML  

    This paper presents a study on electrode for the amperometric detection of human stress based on salivary alpha amylase, emulated with different concentration of glucose, with the flow injection analysis biosensing system. Amperometric detection is an electrochemical voltammetric measurement approach, where the current intensity in a detection cell is regarded as a function of the concentration of the analyte. Flow injection analysis is a chemical analysis method that injects a test sample into a flowing carrier stream. The screen printed electrode is used to detect the amount of glucose concentration, which is proportional to the amount of output current. The objective of the study is to conduct a comparative study between the gold and carbon electrode, as a choice for amperometric detection of human stress with flow injection analysis biosensing system. It has been found that, the [initial, steady state current output at first injection of glucose, current output at second injection of glucose] for carbon and gold printed electrode are [0-80, 0-20, 0-20] μA and [0-35, 0-10, 0-5] μA respectively. This shows measurement resolution of the carbon screen printed electrode is better, which results in higher measurement sensitivity. However, it is found that the gold printed electrode exhibits higher rate of reaction with the steeper gradients and the current reaches its steady state values faster. Yet, in view of the cost, the gold printed electrode is about three times more than the carbon printed electrode and also the ease in handling the zero baseline, the latter is thus made as the choice electrode. View full abstract»

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  • Ensemble dual recursive learning algorithms for identifying flow with leakage

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (561 KB) |  | HTML iconHTML  

    In industrial process, pipes and tank may leak and sensors may have biased since corrosion, measuring noise and instrument faults exist. In order to maintain production and to prevent accident from happen it is crucial to develop reliable method of analyses of flammable gas release and dispersion. Relative mass release of the leakage is introduced as the input for the simulation model and the data from the simulation model is taken at real time (on-line) to feed into the recursive algorithms. The objective of this paper is to introduce a combination of advantages of different algorithm scheme into one learning algorithm. For this purpose, three models is developed, first using recursive least square algorithm (RLS), second using recursive instrument variable (RIV) algorithm and lastly using combination of this two algorithm. This paper proposed that, combination of two algorithms into one learning algorithm for predicting mass flow rate of a flow with leakage resulting in a better mass prediction error compare to a model with single learning algorithm. View full abstract»

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  • EEG different frequency sound response identification using neural network and fuzzy techniques

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (727 KB) |  | HTML iconHTML  

    Electroencephalographic (EEG) technology has enabled effective measurement of human brain activity, as functional and physiological changes within the brain may be registered by EEG signals. In this paper, electrical activity of human brain due to sound waves of different frequency, i.e. 40 Hz, 500 Hz, 5000 Hz and 15000 Hz, is studied based on EEG signals. Several signal processing techniques, i.e. Principle Component algorithm, Discrete Wavelet Transform and Fast Fourier Transform, are applied onto the raw EEG signal to extract useful information and specific characteristics from the EEG signals. This research has shown that the characteristics of EEG signals differ with respect to different frequency of sound waves, and hence the EEG signal can be identified with suitable characterization algorithm using artificial intelligent techniques, such as Artificial neural network, fuzzy logic and adaptive neuro-fuzzy system. View full abstract»

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  • Breathalyzer enabled ignition switch system

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (542 KB) |  | HTML iconHTML  

    Breath alcohol detector or better known as breathalyzer plays a vital role in monitoring alcohol concentration in a person's bloodstream. This project involves the design and development of breathalyzer device which controls ignition switch. The hardware modules include the PIC16F877A microcontroller, alcohol sensor, LCD panel and ignition switch circuitry. The software component includes the programming and source code which is implemented via the PIC microcontroller. Upon assembly, the system is able to detect alcohol concentration in a person's breath sample and displays the detected amount in terms of BAC (Blood Alcohol Concentration) percentage on the LCD panel. Then, according to the amount, the system decides whether to enable or disable the ignition switch circuitry. The system can be very useful in enforcing the alcohol limit law. View full abstract»

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  • Grasp hand approach to detect the attentiveness and fatigue of driver via vibration system

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (634 KB) |  | HTML iconHTML  

    The main purpose of this project is to produce a safety system especially for fatigue and sleepy car driver so as to prevent from accidents. These systems encompassed the approach of hand pressure applied on the steering wheel. The steering will be installed with pressure sensors. At the same time these sensors can be used to measure gripping force while driving by pressing the calibration button placed on the steering. When the driver gets fatigue or sleepy during driving the seat will vibrate and both thigh of driver will feel the vibration and also the buzzer will activate synchronously. Wireless communication used to avoid the system looking fibred between steering wheel and motor vibrator inside the seat driver. View full abstract»

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  • Waste of radio frequency signal analysis for wireless energy harvester

    Page(s): 1 - 3
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (542 KB) |  | HTML iconHTML  

    Process by which energy is derived from external sources like thermal energy, wind energy and kinetic energy, captured and stored are called `Energy Harvesting' or `Energy Scavenging'. Normally this method is applied to small autonomous robot, wearable electronic devices and wireless sensor networks. Firstly, radio frequency (RF) radiation is a subset of electromagnetic radiation with a wavelength of 100km to 1mm, which is a frequency of 3 KHz to 300 GHz, respectively. This range of electromagnetic radiation constitutes the radio spectrum and corresponds to the frequency of alternating current electrical signals used to produce and detect radio waves. RF can refer to electromagnetic oscillations in either electrical circuits or radiation through air and space. Like other subsets of electromagnetic radiation, RF travels at the speed of light. The reasons that this project focus on Radio Frequencies Energies is that for starter, radio frequencies itself are electric energy that is transmitting though the air by ionizing the medium on its paths. The energy also can be easily found in our surrounding as it is used widely by many applications like television broadcasting, telecommunication and in microwave too. By using it availability, we are harvesting it and turning it into electrical energy although only small amount of it can be obtained. View full abstract»

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  • PD Fuzzy Logic with non-collocated PID approach for vibration control of flexible joint manipulator

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (701 KB) |  | HTML iconHTML  

    The increased complexity of the dynamics of robots manipulator considering joint elasticity makes conventional model-based control strategies complex and difficult to synthesize. This paper presents investigations into the development of PD type Fuzzy Logic Control (FLC) with non-collocated Proportional Integral Derivative (PID) for trajectory tracking and vibration control of a flexible joint manipulator. To study the effectiveness of the controllers, a PD type Fuzzy Logic Controller is developed for tip angular position control of a flexible joint manipulator. This is then extended to incorporate a non-collocated PID Controller for vibration reduction of the flexible joint system. Simulation results of the response of the flexible joint manipulator with the controllers are presented in time and frequency domains. The performances of the non-collocated PID control schemes are examined in terms of input tracking capability, level of vibration reduction and time response specifications. Finally, a comparative assessment of the control techniques is presented and discussed. View full abstract»

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  • Piezoelectric vibration control through fuzzy logic for direct current converter

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (567 KB) |  | HTML iconHTML  

    This paper describes a piezoelectric vibration control through fuzzy logic for direct current converter, which is the piezoelectric will be placed inside the unique microphone to make it vibrating and produce the direct current. A experiment were made between piezoelectric barium titanate (BT) and piezoelectric lead zirconate titanate (PZT) to compare between those two crystal type which one is able produce energy more electricity and chosen as piezoelectric microphone material. Element effects such as environmental temperature on system also being consider. The piezoelectric that produce highest output was used as the piezoelectric material and it was directly exposed to acoustic waves. Stresses in the crystals, resulting from a sound field, generate an output proportional to the acoustic pressure. The piezoelectric microphone diaphragm thickness is 1mm. The piezoelectric microphone also was connected to an amplifier system to make sure the output can be seen. However the gain of the amplifier is control by the fuzzy logic controller and the gain of the amplifier range is from 1 to 200. View full abstract»

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  • UKM campus bus monitoring system using RFID and GIS

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (228 KB) |  | HTML iconHTML  

    RFID is an expanding technology and has a promising future in monitoring and identification system. This research is carried out to test the RFID and GIS system integration. The developed system is able to monitor the movement of the campus buses and their positions. Accordingly, we have developed a theoretical framework as well as the method and system for bus monitoring system. Experimental process has also been conducted. The result would display the location of buses and their positions in map form as well as timing of the bus travelling in each location. Initially, a GUI is developed to give an idea on how the actual system is going to work. View full abstract»

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  • Structural steel plate damage detection using DFT spectral energy and artificial neural network

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (805 KB) |  | HTML iconHTML  

    In this paper, simple methods for crack identification in steel plates and their classification based on the frame based frequency domain features is presented. Based upon the boundary conditions and experimental modal analysis, two simple experimental methods are designed to measure the vibration at different positions of the steel plate. The plate is excited by an impulse signal and made to vibrate. The propagated vibration signals are then recorded. The signal is transformed into frequency domain by computing the Discrete Fourier Transformation (DFT). The frequency spectral bands are identified and the spectral energy is extracted as features. The condition of the steel plate namely healthy or faulty is associated with the extracted features to form a final feature vector. Two simple neural network models were developed, trained using Backpropagation (BP) and Radial Basis Function (RBF) algorithms. The results and the effectiveness of the system are validated through simulation. View full abstract»

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  • Vehicle noise comfort level indication: A psychoacoustic approach

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (621 KB) |  | HTML iconHTML  

    Nowadays, the studies and researches related to the improvement of the passenger comfort in the car are carried out vigorously. The comfort in the car interior is already become a need for the passengers and the buyers. Due to high competition in car industries, all the car manufacturers are concentrating in improving the interior noise comfort of the car. Vehicle Noise Comfort Index (VNCI) has been developed recently to evaluate the sound characteristics of passenger cars. VNCI indicates the interior vehicle noise comfort using a numeric scale from 1 to 10. Most of the researches are relating the vehicle interior sound quality to psychoacoustics sound metrics such as loudness and sharpness for the frequency between 20 Hz to 20 kHz. In this present paper, a vehicle comfort level indication is proposed to detect the comfort level in cars using artificial neural network. Determination of vehicle comfort is important because continuous exposure to the noise and vibration leads to health problems for the driver and passengers. The database of sound samples from 15 local cars is used. The sound samples are taken from two states, while the car is in stationary condition and while it is moving at a constant speed. Features such as the psychoacoustics criterions are extracted from the signals. The correlation between the subjective and the objective evaluation is also tested. The relationship between the VNCI and the sound metrics is modelled using a feed-forward neural network trained by back-propagation algorithm. View full abstract»

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  • Effect of chaos noise on the learning ability of back propagation algorithm in feed forward neural network

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    In the area of artificial neural networks, the Back Propagation (BP) learning algorithm has proved to be efficient in many engineering applications especially in pattern recognition, signal processing and system control. Although the BP learning has been a significant research area of neural network, it has also been known as an algorithm with a poor convergence rate. Many attempts have been made on the learning algorithm to improve the performance on convergence speed and learning efficiency. In this study, we propose a new modified BP learning algorithm by adding chaotic noise into weight update process during error propagation. The chaotic noise is generated using various chaotic maps such as Logistic map, Skew Tent map and Bernoulli Shift map. By computer simulations, we confirm that our proposed algorithm can give a better convergence rate and can find a good solution in early time compared to the conventional BP learning algorithm. Weight update position, noise amplitude and control parameter of chaos can give a big effect on the learning ability of feed forward neural network. View full abstract»

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  • Brain Machine Interface for physically retarded people using colour visual tasks

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (640 KB) |  | HTML iconHTML  

    A Brain Machine Interface is a communication system which connects the human brain activity to an external device bypassing the peripheral nervous system and muscular system. It provides a communication channel for the people who are suffering with neuromuscular disorders such as amyotrophic lateral sclerosis, brain stem stroke, quadriplegics and spinal cord injury. In this paper, a simple BMI system based on EEG signal emanated while visualizing of different colours has been proposed. The proposed BMI uses the color visual tasks and aims to provide a communication through brain activated control signal for a system from which the required task operation can be performed to accomplish the needs of the physically retarded community. The ability of an individual to control his EEG through the colour visualization enables him to control devices. The EEG signal is recorded from 10 voluntary healthy subjects using the noninvasive scalp electrodes placed over the frontal, parietal, motor cortex, temporal and occipital areas. The obtained EEG signals were segmented and then processed using an elliptic filter. Using spectral analysis, the alpha, beta and gamma band frequency spectrum features are obtained for each EEG signals. The extracted features are then associated to different control signals and a neural network model using back propagation algorithm has been developed. The proposed method can be used to translate the colour visualization signals into control signals and used to control the movement of a mobile robot. The performance of the proposed algorithm has an average classification accuracy of 95.2%. View full abstract»

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  • A phoneme based sign language recognition system using skin color segmentation

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (652 KB) |  | HTML iconHTML  

    A sign language is a language which, instead of acoustically conveyed sound patterns, uses visually transmitted sign patterns. Sign languages are commonly developed for deaf communities, which can include interpreters, friends and families of deaf people as well as people who are deaf or hard of hearing themselves. Developing a sign language recognition system will help the hearing impaired to communicate more fluently with the normal people. This paper presents a simple sign language recognition system that has been developed using skin color segmentation and Artificial Neural Network. The moment invariants features extracted from the right and left hand gesture images are used to develop a network model. The system has been implemented and tested for its validity. Experimental results show that the average recognition rate is 92.85%. View full abstract»

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  • A design, analysis and optimization technique of 1.9 GHz CDMA low noise amplifier

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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (751 KB) |  | HTML iconHTML  

    In this paper, a 1.9GHz CDMA low noise amplifier (LNA) has been designed, analyzed and simulated using GaAs technology with high RF performances. A technique to design this LNA is presented along. The proposed low noise amplifier allows a receiver system to become adaptive to an antenna's array, waveguides and a transmission lines, as well as standard antenna signal powers. It is also suitable for applications in cellular and PCS handsets, and other systems requiring super low noise figure with good intercept in the 0 to 10 GHz frequency range. At 1.9GHz, the amplifier is optimized to provide a max gain of 16.7dB, a noise figure (NF) of only 0.9 dB. The proposed LNA dissipates a power of 20mW at a supply voltage of 2V. Although this paper is based on LNA design with a specific transistor, this general LNA design technique can be used for other transistors and applications. View full abstract»

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