International Journal of Information, Control and Computer Sciences

Digital Sites- Performative Views

Year: 2012 Volume: 6 Issue: 4 450 - 454 Pages

Abstract: Webcam systems now function as the new privileged vantage points from which to view the city. This transformation of CCTV technology from surveillance to promotional tool is significant because its'scopic regime' presents, back to the public, a new virtual 'site' that sits alongside its real-time counterpart. Significantly, thisraw 'image' data can, in fact,be co-optedand processed so as to disrupt their original purpose. This paper will demonstrate this disruptive capacity through an architectural project. It will reveal how the adaption the webcam image offers a technical springboard by which to initiate alternate urban form making decisions and subvert the disciplinary reliance on the 'flat' orthographic plan. In so doing, the paper will show how this 'digital material' exceeds the imagistic function of the image; shiftingit from being a vehicle of signification to a site of affect.

Efficacy of Anti-phishing Measures and Strategies - A Research Analysis

Year: 2010 Volume: 4 Issue: 9 1404 - 1410 Pages

Authors:
Gundeep Singh Bindra

Abstract: Statistics indicate that more than 1000 phishing attacks are launched every month. With 57 million people hit by the fraud so far in America alone, how do we combat phishing?This publication aims to discuss strategies in the war against Phishing. This study is an examination of the analysis and critique found in the ways adopted at various levels to counter the crescendo of phishing attacks and new techniques being adopted for the same. An analysis of the measures taken up by the varied popular Mail servers and popular browsers is done under this study. This work intends to increase the understanding and awareness of the internet user across the globe and even discusses plausible countermeasures at the users as well as the developers end. This conceptual paper will contribute to future research on similar topics.

A Comparative Study of Main Memory Databases and Disk-Resident Databases

Year: 2008 Volume: 2 Issue: 2 385 - 388 Pages

Abstract: Main Memory Database systems (MMDB) store their data in main physical memory and provide very high-speed access. Conventional database systems are optimized for the particular characteristics of disk storage mechanisms. Memory resident systems, on the other hand, use different optimizations to structure and organize data, as well as to make it reliable. This paper provides a brief overview on MMDBs and one of the memory resident systems named FastDB and compares the processing time of this system with a typical disc resident database based on the results of the implementation of TPC benchmarks environment on both.

64 bit Computer Architectures for Space Applications – A study

Year: 2009 Volume: 3 Issue: 3 645 - 649 Pages

Abstract: The more recent satellite projects/programs makes extensive usage of real – time embedded systems. 16 bit processors which meet the Mil-Std-1750 standard architecture have been used in on-board systems. Most of the Space Applications have been written in ADA. From a futuristic point of view, 32 bit/ 64 bit processors are needed in the area of spacecraft computing and therefore an effort is desirable in the study and survey of 64 bit architectures for space applications. This will also result in significant technology development in terms of VLSI and software tools for ADA (as the legacy code is in ADA). There are several basic requirements for a special processor for this purpose. They include Radiation Hardened (RadHard) devices, very low power dissipation, compatibility with existing operational systems, scalable architectures for higher computational needs, reliability, higher memory and I/O bandwidth, predictability, realtime operating system and manufacturability of such processors. Further on, these may include selection of FPGA devices, selection of EDA tool chains, design flow, partitioning of the design, pin count, performance evaluation, timing analysis etc. This project deals with a brief study of 32 and 64 bit processors readily available in the market and designing/ fabricating a 64 bit RISC processor named RISC MicroProcessor with added functionalities of an extended double precision floating point unit and a 32 bit signal processing unit acting as co-processors. In this paper, we emphasize the ease and importance of using Open Core (OpenSparc T1 Verilog RTL) and Open “Source" EDA tools such as Icarus to develop FPGA based prototypes quickly. Commercial tools such as Xilinx ISE for Synthesis are also used when appropriate.

EGCL: An Extended G-Code Language with Flow Control, Functions and Mnemonic Variables

Year: 2012 Volume: 6 Issue: 7 878 - 885 Pages

Abstract: In the context of computer numerical control (CNC) and computer aided manufacturing (CAM), the capabilities of programming languages such as symbolic and intuitive programming, program portability and geometrical portfolio have special importance. They allow to save time and to avoid errors during part programming and permit code re-usage. Our updated literature review indicates that the current state of art presents voids in parametric programming, program portability and programming flexibility. In response to this situation, this article presents a compiler implementation for EGCL (Extended G-code Language), a new, enriched CNC programming language which allows the use of descriptive variable names, geometrical functions and flow-control statements (if-then-else, while). Our compiler produces low-level generic, elementary ISO-compliant Gcode, thus allowing for flexibility in the choice of the executing CNC machine and in portability. Our results show that readable variable names and flow control statements allow a simplified and intuitive part programming and permit re-usage of the programs. Future work includes allowing the programmer to define own functions in terms of EGCL, in contrast to the current status of having them as library built-in functions.

Analysis of Textual Data Based On Multiple 2-Class Classification Models

Year: 2008 Volume: 2 Issue: 11 3740 - 3745 Pages

Abstract: This paper proposes a new method for analyzing textual data. The method deals with items of textual data, where each item is described based on various viewpoints. The method acquires 2- class classification models of the viewpoints by applying an inductive learning method to items with multiple viewpoints. The method infers whether the viewpoints are assigned to the new items or not by using the models. The method extracts expressions from the new items classified into the viewpoints and extracts characteristic expressions corresponding to the viewpoints by comparing the frequency of expressions among the viewpoints. This paper also applies the method to questionnaire data given by guests at a hotel and verifies its effect through numerical experiments.

A Distributed Algorithm for Intrinsic Cluster Detection over Large Spatial Data

Year: 2008 Volume: 2 Issue: 9 2983 - 2993 Pages

Abstract: Clustering algorithms help to understand the hidden information present in datasets. A dataset may contain intrinsic and nested clusters, the detection of which is of utmost importance. This paper presents a Distributed Grid-based Density Clustering algorithm capable of identifying arbitrary shaped embedded clusters as well as multi-density clusters over large spatial datasets. For handling massive datasets, we implemented our method using a 'sharednothing' architecture where multiple computers are interconnected over a network. Experimental results are reported to establish the superiority of the technique in terms of scale-up, speedup as well as cluster quality.

An Examination of the Factors Influencing Software Development Effort

Year: 2007 Volume: 1 Issue: 4 934 - 943 Pages

Abstract: Effective evaluation of software development effort is an important aspect of successful project management. Based on a large database with 4106 projects ever developed, this study statistically examines the factors that influence development effort. The factors found to be significant for effort are project size, average number of developers that worked on the project, type of development, development language, development platform, and the use of rapid application development. Among these factors, project size is the most critical cost driver. Unsurprisingly, this study found that the use of CASE tools does not necessarily reduce development effort, which adds support to the claim that the use of tools is subtle. As many of the current estimation models are rarely or unsuccessfully used, this study proposes a parsimonious parametric model for the prediction of effort which is both simple and more accurate than previous models.

An Agent Oriented Approach to Operational Profile Management

Year: 2008 Volume: 2 Issue: 4 1098 - 1108 Pages

Abstract: Software reliability, defined as the probability of a software system or application functioning without failure or errors over a defined period of time, has been an important area of research for over three decades. Several research efforts aimed at developing models to improve reliability are currently underway. One of the most popular approaches to software reliability adopted by some of these research efforts involves the use of operational profiles to predict how software applications will be used. Operational profiles are a quantification of usage patterns for a software application. The research presented in this paper investigates an innovative multiagent framework for automatic creation and management of operational profiles for generic distributed systems after their release into the market. The architecture of the proposed Operational Profile MAS (Multi-Agent System) is presented along with detailed descriptions of the various models arrived at following the analysis and design phases of the proposed system. The operational profile in this paper is extended to comprise seven different profiles. Further, the criticality of operations is defined using a new composed metrics in order to organize the testing process as well as to decrease the time and cost involved in this process. A prototype implementation of the proposed MAS is included as proof-of-concept and the framework is considered as a step towards making distributed systems intelligent and self-managing.

Surrogate based Evolutionary Algorithm for Design Optimization

Year: 2007 Volume: 1 Issue: 10 3079 - 3084 Pages

Authors:
Maumita Bhattacharya

Abstract: Optimization is often a critical issue for most system design problems. Evolutionary Algorithms are population-based, stochastic search techniques, widely used as efficient global optimizers. However, finding optimal solution to complex high dimensional, multimodal problems often require highly computationally expensive function evaluations and hence are practically prohibitive. The Dynamic Approximate Fitness based Hybrid EA (DAFHEA) model presented in our earlier work [14] reduced computation time by controlled use of meta-models to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the meta-model are generated from a single uniform model. Situations like model formation involving variable input dimensions and noisy data certainly can not be covered by this assumption. In this paper we present an enhanced version of DAFHEA that incorporates a multiple-model based learning approach for the SVM approximator. DAFHEA-II (the enhanced version of the DAFHEA framework) also overcomes the high computational expense involved with additional clustering requirements of the original DAFHEA framework. The proposed framework has been tested on several benchmark functions and the empirical results illustrate the advantages of the proposed technique.

Shadow Detection for Increased Accuracy of Privacy Enhancing Methods in Video Surveillance Edge Devices

Year: 2008 Volume: 2 Issue: 10 3347 - 3352 Pages

Abstract: Shadow detection is still considered as one of the potential challenges for intelligent automated video surveillance systems. A pre requisite for reliable and accurate detection and tracking is the correct shadow detection and classification. In such a landscape of conditions, privacy issues add more and more complexity and require reliable shadow detection. In this work the intertwining between security, accuracy, reliability and privacy is analyzed and, accordingly, a novel architecture for Privacy Enhancing Video Surveillance (PEVS) is introduced. Shadow detection and masking are dealt with through the combination of two different approaches simultaneously. This results in a unique privacy enhancement, without affecting security. Subsequently, the methodology was employed successfully in a large-scale wireless video surveillance system; privacy relevant information was stored and encrypted on the unit, without transferring it over an un-trusted network.

Hybrid Intelligent Intrusion Detection System

Year: 2007 Volume: 1 Issue: 11 3477 - 3480 Pages

Abstract: Intrusion Detection Systems are increasingly a key part of systems defense. Various approaches to Intrusion Detection are currently being used, but they are relatively ineffective. Artificial Intelligence plays a driving role in security services. This paper proposes a dynamic model Intelligent Intrusion Detection System, based on specific AI approach for intrusion detection. The techniques that are being investigated includes neural networks and fuzzy logic with network profiling, that uses simple data mining techniques to process the network data. The proposed system is a hybrid system that combines anomaly, misuse and host based detection. Simple Fuzzy rules allow us to construct if-then rules that reflect common ways of describing security attacks. For host based intrusion detection we use neural-networks along with self organizing maps. Suspicious intrusions can be traced back to its original source path and any traffic from that particular source will be redirected back to them in future. Both network traffic and system audit data are used as inputs for both.

Performance Analysis of Load Balancing Algorithms

Year: 2008 Volume: 2 Issue: 2 381 - 384 Pages

Abstract: Load balancing is the process of improving the performance of a parallel and distributed system through a redistribution of load among the processors [1] [5]. In this paper we present the performance analysis of various load balancing algorithms based on different parameters, considering two typical load balancing approaches static and dynamic. The analysis indicates that static and dynamic both types of algorithm can have advancements as well as weaknesses over each other. Deciding type of algorithm to be implemented will be based on type of parallel applications to solve. The main purpose of this paper is to help in design of new algorithms in future by studying the behavior of various existing algorithms.

Nonlinear Model Predictive Control for Solid Oxide Fuel Cell System Based On Wiener Model

Year: 2010 Volume: 4 Issue: 12 1893 - 1898 Pages

Abstract: In this paper, we consider Wiener nonlinear model for solid oxide fuel cell (SOFC). The Wiener model of the SOFC consists of a linear dynamic block and a static output non-linearity followed by the block, in which linear part is approximated by state-space model and the nonlinear part is identified by a polynomial form. To control the SOFC system, we have to consider various view points such as operating conditions, another constraint conditions, change of load current and so on. A change of load current is the significant one of these for good performance of the SOFC system. In order to keep the constant stack terminal voltage by changing load current, the nonlinear model predictive control (MPC) is proposed in this paper. After primary control method is designed to guarantee the fuel utilization as a proper constant, a nonlinear model predictive control based on the Wiener model is developed to control the stack terminal voltage of the SOFC system. Simulation results verify the possibility of the proposed Wiener model and MPC method to control of SOFC system.

An LMI Approach of Robust H∞ Fuzzy State-Feedback Controller Design for HIV/AIDS Infection System with Dual Drug Dosages

Year: 2012 Volume: 6 Issue: 5 610 - 615 Pages

Authors:
Wudhichai Assawinchaichote

Abstract: This paper examines the problem of designing robust H controllers for for HIV/AIDS infection system with dual drug dosages described by a Takagi-Sugeno (S) fuzzy model. Based on a linear matrix inequality (LMI) approach, we develop an H controller which guarantees the L2-gain of the mapping from the exogenous input noise to the regulated output to be less than some prescribed value for the system. A sufficient condition of the controller for this system is given in term of Linear Matrix Inequalities (LMIs). The effectiveness of the proposed controller design methodology is finally demonstrated through simulation results. It has been shown that the anti-HIV vaccines are critically important in reducing the infected cells.

An Improved Preprocessing for Biosonar Target Classification

Year: 2007 Volume: 1 Issue: 1 63 - 66 Pages

Abstract: An improved processing description to be employed in biosonar signal processing in a cochlea model is proposed and examined. It is compared to conventional models using a modified discrimination analysis and both are tested. Their performances are evaluated with echo data captured from natural targets (trees).Results indicate that the phase characteristics of low-pass filters employed in the echo processing have a significant effect on class separability for this data.

Optimization and GIS-Based Intelligent Decision Support System for Urban Transportation Systems Analysis

Year: 2013 Volume: 7 Issue: 3 350 - 375 Pages

Abstract: Optimization plays an important role in most real world applications that support decision makers to take the right decision regarding the strategic directions and operations of the system they manage. Solutions for traffic management and traffic congestion problems are considered major problems that most decision making authorities for cities around the world are looking for. This review paper gives a full description of the traffic problem as part of the transportation planning process and present a view as a framework of urban transportation system analysis where the core of the system is a transportation network equilibrium model that is based on optimization techniques and that can also be used for evaluating an alternative solution or a combination of alternative solutions for the traffic congestion. Different transportation network equilibrium models are reviewed from the sequential approach to the multiclass combining trip generation, trip distribution, modal split, trip assignment and departure time model. A GIS-Based intelligent decision support system framework for urban transportation system analysis is suggested for implementation where the selection of optimized alternative solutions, single or packages, will be based on an intelligent agent rather than human being which would lead to reduction in time, cost and the elimination of the difficulty, by human being, for finding the best solution to the traffic congestion problem.

Design of a Neural Networks Classifier for Face Detection

Year: 2007 Volume: 1 Issue: 11 3473 - 3476 Pages

Abstract: Face detection and recognition has many applications in a variety of fields such as security system, videoconferencing and identification. Face classification is currently implemented in software. A hardware implementation allows real-time processing, but has higher cost and time to-market. The objective of this work is to implement a classifier based on neural networks MLP (Multi-layer Perceptron) for face detection. The MLP is used to classify face and non-face patterns. The systm is described using C language on a P4 (2.4 Ghz) to extract weight values. Then a Hardware implementation is achieved using VHDL based Methodology. We target Xilinx FPGA as the implementation support.

Design and Implementation of a Neural Network for Real-Time Object Tracking

Year: 2007 Volume: 1 Issue: 6 1631 - 1634 Pages

Abstract: Real-time object tracking is a problem which involves extraction of critical information from complex and uncertain imagedata. In this paper, we present a comprehensive methodology to design an artificial neural network (ANN) for a real-time object tracking application. The object, which is tracked for the purpose of demonstration, is a specific airplane. However, the proposed ANN can be trained to track any other object of interest. The ANN has been simulated and tested on the training and testing datasets, as well as on a real-time streaming video. The tracking error is analyzed with post-regression analysis tool, which finds the correlation among the calculated coordinates and the correct coordinates of the object in the image. The encouraging results from the computer simulation and analysis show that the proposed ANN architecture is a good candidate solution to a real-time object tracking problem.

A Scheme of Model Verification of the Concurrent Discrete Wavelet Transform (DWT) for Image Compression

Year: 2009 Volume: 3 Issue: 11 2594 - 2603 Pages

Abstract: The scientific community has invested a great deal of effort in the fields of discrete wavelet transform in the last few decades. Discrete wavelet transform (DWT) associated with the vector quantization has been proved to be a very useful tool for the compression of image. However, the DWT is very computationally intensive process requiring innovative and computationally efficient method to obtain the image compression. The concurrent transformation of the image can be an important solution to this problem. This paper proposes a model of concurrent DWT for image compression. Additionally, the formal verification of the model has also been performed. Here the Symbolic Model Verifier (SMV) has been used as the formal verification tool. The system has been modeled in SMV and some properties have been verified formally.