|Title: Computer Correlative Method For Biomedical Image Processing On Base Optoelectronic “Eye-Processor” Device|
|Author(s): Hani Qasem Rashrash Al-Zoubi, Mohammed Al-Maitah|
|Pages: 1-4||Paper ID: 110801-9393 IJVIPNS-IJENS||Published: February, 2011|
Abstract: We present a novel specified method and its application for pattern analysis. Architecture of optoelectronic device of “eye-processor” type has been improved. In paper the device is designed for transformation and storing of images applying automatic processing of images by means of introduction of blocks to carry out the comparison of reference image of the object and image of recognized object and background. It is very important for image processing, especially in real time in dynamic biomedical systems.
|Keywords: Optoelectronic, eye-processor, biomedical Image.|
|Full Text (.pdf) | 316 KB|
|Title: A Review on the Communication Mechanism of Mobile Agent|
|Author(s): Arif Hidayat|
|Pages: 5-09||Paper ID: 113501-8787 IJVIPNS-IJENS||Published: February, 2011|
Abstract: Mobile agent technology is becoming more popular and has been implemented in many areas. Several researches have been conducted to address its challenges, while one of the most important is agent communication. This paper will reobserve the problem of mobile agent communication. It explains the background concept of mobile agent communication as well. Subsequently, various current agent communication mechanisms are discussed. It finally summarizes how these mechanisms answer the challenges found in mobile agent communication including their advantages and disadvantages.
|Keywords: Mobile agent, agent communication, Message Efficiently Forwarding Scheme, Distributed Sendbox Scheme, Virtual Agent-Based Communication|
|Full Text (.pdf) | 242 KB|
|Title: Particle Swarm Optimization with Fuzzy Adaptive Acceleration for Human Object Detection|
|Author(s): Dewi Yanti Liliana, M. Rahmat Widyanto|
|Pages: 10-18||Paper ID: 115801-9292 IJVIPNS-IJENS||Published: February, 2011|
Abstract: In this paper, a new approach to dynamically adapt the acceleration coefficient of particle swarm optimization (PSO) is discussed. The proposed method uses fuzzy inference system to lead the particles movement in exploring and exploiting the search area; therefore it increases the accuracy and reduces the detection time of human object detection system. The performance of the proposed method was tested on real images, artificial images, as well as real-time videos; the result is then compared to that of conventional method. Experiment on testing data using the proposed method improves the accuracy rate 9% better and almost twice faster than the standard window scanning method. The proposed PSO with fuzzy adaptive acceleration gives a promising contribution to solve real-world problems where computational time is critical.
|Keywords: Fuzzy inference system, human object detection, particle swarm optimization|
|Full Text (.pdf) | 492 KB|
|Title: Impact of Ad Hoc Network Parameters and Conditions on Video Conferencing Quality|
|Author(s): Arif Hidayat, Campbell Wilson|
|Pages: 19-25||Paper ID: 116801-7373 IJVIPNS-IJENS||Published: February, 2011|
Abstract: Ad Hoc Wireless Network has limited bandwidth and suffers from errors caused by highly mobile nodes as well as the absence of a dedicated routing device. These may affect its performance in delivering high load packets such as in a video conferencing application. In this paper we observe the effect of various ad hoc wireless network parameters and conditions on the quality of video in video conferencing applications. Experimental scenarios are designed to investigate the changes in transmitted video quality in response to changes in the level of each investigated factor, including bandwidth, congestion, video rate, mobility direction, distance and light. We utilize an objective metric, PSNR, to measure the quality of transmitted videos. We show that the number of dropped frames is responsible for the reduction in video quality delivered to the destination node. We demonstrate that bandwidth, congestion and video rate significantly affect the video quality of video conferencing sessions on Ad Hoc Wireless Networks.
|Keywords: Video conferencing, ad hoc network, video streaming, video quality|
|Full Text (.pdf) | 259 KB|
|Title: Automatic Ear Recognition System using Back Propagation Neural Network.|
|Author(s): Samuel Adebayo Daramola, Oladejo Daniel Oluwaninyo|
|Pages: 26-29||Paper ID: 118001-3232 IJVIPNS-IJENS||Published: February, 2011|
Abstract: This paper presents a new approach for automatic ear recognition system using energy-edge density feature and Back Propagation Neural Network (BPNN). Input ear image is decomposed into four sub-bands using Haar wavelet transform. Thereafter fused feature is extracted from image blocks of each of the detailed sub-bands. The fused feature is used as input to neural network for effective ear image classification. The proposed system has been tested using ear images collected from 350 people. Experimental results have demonstrated the effectiveness of the proposed system in term of recognition accuracy in comparison with previous methods.
|Keywords: Ear image, Haar wavelet transform, Back propagation Neural Network.|
|Full Text (.pdf) | 150 KB|
|Title: Design and Implementation of Encryption Unit Based on Customized AES Algorithm|
|Author(s): Nabil Hamdy, Khaled Shehata, Haitham Eldemerdash|
|Pages: 30-37||Paper ID: 118301-4242 IJVIPNS-IJENS||Published: February, 2011|
Abstract: This encryption unit adopts the AES (Advanced Encryption Standard) as the encryption algorithm because it has been extensively challenged, evaluated, and, it is the most popularly used symmetric key algorithm. In this paper, we propose a customized version of the “AES” block cipher to suit proprietary data encryption applications. We designed the customization of the AES to cover three main AES cryptographic functions, these are: S-box Generation, Mix Column Transformation, and Key Expansion Function. The S-Box generation process results in a new S-Box. The new S-Box is tested to be sure of satisfying the required cryptographic features: algebraic degree, non linearity, propagation criteria, correlation immunity, and balancedness. The customized AES is tested also against statistical randomness properties. The encryption unit is finally designed, implemented, and tested using FPGA technology.
|Keywords: Advanced Encryption Standard (AES), S-Box generation, S-Box testing, Field programmable gate arrays (FPGA).|
|Full Text (.pdf) | 1,323 KB|