Showing papers in "Journal of Computers in 2008"

A Distributed AOA Based Localization Algorithm for Wireless Sensor Networks

Abstract: In this paper we propose a distributed algorithm for solving the positioning problem in ad-hoc wireless networks. The method is based on the capability of the nodes to measure the angle of arrival (AOA) of the signals they produce. The main features of the distributed algorithm are simplicity, asynchronous operations (i.e. no global coordination among nodes is required), ability to operate in disconnected networks. Moreover each node can join the computation at any time. Numerical results, obtained by simulating several scenarios, show that the algorithm can reach a good level of convergence even when the number of communications is limited.

Low-Power and High-Performance 1-Bit CMOS Full-Adder Cell

Abstract: In this paper a new low power and high performance adder cell using a new design style called “Bridge” is proposed. The bridge design style enjoys a high degree of regularity, higher density than conventional CMOS design style as well as lower power consumption, by using some transistors, named bridge transistors. Simulation results illustrate the superiority of the resulting proposed adder against conventional CMOS 1-bit full-adder in terms of power, delay and PDP. We have performed simulations using HSPICE in a 90 nanometer (nm) standard CMOS technology at room temperature; with supply voltage variation from 0.65v to 1.5v with 0.05v steps.

Neural Network-based Handwritten Signature Verification

Abstract: Handwritten signatures are considered as the most natural method of authenticating a person’s identity (compared to other biometric and cryptographic forms of authentication). The learning process inherent in Neural Networks (NN) can be applied to the process of verifying handwritten signatures that are electronically captured via a stylus. This paper presents a method for verifying handwritten signatures by using a NN architecture. Various static (e.g., height, slant, etc.) and dynamic (e.g., velocity, pen tip pressure, etc.) signature features are extracted and used to train the NN. Several Network topologies are tested and their accuracy is compared. The resulting system performs reasonably well with an overall error rate of 3:3% being reported for the best case.

Water Demand Prediction using Artificial Neural Networks and Support Vector Regression

Abstract: Computational Intelligence techniques have been proposed as an efficient tool for modeling and forecasting in recent years and in various applications. Water is a basic need and as a result, water supply entities have the responsibility to supply clean and safe water at the rate required by the consumer. It is therefore necessary to implement mechanisms and systems that can be employed to predict both short-term and long-term water demands. The increasingly growing field of computational intelligence techniques has been proposed as an efficient tool in the modeling of dynamic phenomena. The primary objective of this paper is to compare the efficiency of two computational intelligence techniques in water demand forecasting. The techniques under comparison are Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). In this study it was observed that ANNs perform significantly better than SVMs. This performance is measured against the generalization ability of the two techniques in water demand prediction.

Fine Classification & Recognition of Hand Written Devnagari Characters with Regular Expressions & Minimum Edit Distance Method

Abstract: Regular expressions are extremely useful, because they allow us to work with text in terms of patterns. They are considered the most sophisticated means of performing operations such as string searching, manipulation, validation, and formatting in all applications that deal with text data. Character recognition problem scenarios in sequence analysis that are ideally suited for the application of regular expression algorithms. This paper describes a use of regular expressions in this problem domain, and demonstrates how the effective use of regular expressions that can serve to facilitate more efficient and more effective character recognition.

Direct Anonymous Attestation for Next Generation TPM

Abstract: Trusted computing platforms have been proposed as a promising approach to enhance the security of general-purpose computing systems. Direct Anonymous Attestation(DAA) is a scheme that allows a Trusted Platform Module (TPM) which is the core component of the trusted computing platform to remotely convince a communication partner that it is indeed a Trusted Platform Module while preserving the user’s privacy. The first DAA scheme developed by Brickell which is relatively complex and time-consuming was adopted by the current TPM specification.As the ECC cryptosystem is more efficient compared to the RSA cryptosystem, more and more cryptographic device is based on the ECC cryptosystem, so it is anticipated that the TPM will be based on the ECC in near future. In this paper, we propose a new direct anonymous attestation which is suitable for the ECC-based TPM. This paper presents an efficient construction that implements all anonymous authentication features specified in DAA. The proposed scheme has the best computational performance of all the DAA schemes up to now. The new DAA scheme is provably secure in the random oracle model under the q- SDH and the decisional Diffie-Hellman assumption.

Hybrid Approach to Collaborative Context-Aware Service Platform for Pervasive Computing

Abstract: The behavior of pervasive applications depend not only on their internal state and user interactions but also on contexts sensed during their execution. In this work, we present a collaborative context-aware service platform based on hybrid context management model (enhanced CoCA). It performs reasoning and decisions based on context data, context semantics, and related rules and policies. Such data is organized into a hybrid context management model (HCoM). The platform also introduces a neighborhood collaboration mechanism to facilitate peer collaboration in order to share their resources. We have developed an initial prototype of the platform. Our preliminary test shows that the platform is a promising data independent development environment for pervasive context-aware applications and it possesses good standard of scalability.

DDSC : A Density Differentiated Spatial Clustering Technique

Abstract: Finding clusters with widely differing sizes, shapes and densities in presence of noise and outliers is a challenging job. The DBSCAN is a versatile clustering algorithm that can find clusters with differing sizes and shapes in databases containing noise and outliers. But it cannot find clusters based on difference in densities. We extend the DBSCAN algorithm so that it can also detect clusters that differ in densities. Local densities within a cluster are reasonably homogeneous. Adjacent regions are separated into different clusters if there is significant change in densities. Thus the algorithm attempts to find density based natural clusters that may not be separated by any sparse region. Computational complexity of the algorithm is O(n log n).

Neural Network-based Hwritten Signature Verification.

Abstract: Handwritten signatures are considered as the most natural method of authenticating a person’s identity (compared to other biometric and cryptographic forms of authentication). The learning process inherent in Neural Networks (NN) can be applied to the process of verifying handwritten signatures that are electronically captured via a stylus. This paper presents a method for verifying handwritten signatures by using a NN architecture. Various static (e.g., height, slant, etc.) and dynamic (e.g., velocity, pen tip pressure, etc.) signature features are extracted and used to train the NN. Several Network topologies are tested and their accuracy is compared. The resulting system performs reasonably well with an overall error rate of 3:3% being reported for the best case.

Profiling Tools for FPGA-Based Embedded Systems: Survey and Quantitative Comparison

Abstract: Profiling tools are computer-aided design (CAD) tools that help in determining the computationally intensive portions in software. Embedded systems consist of hardware and software components that execute concurrently and efficiently to execute a specific task or application. Profiling tools are used by embedded system designers to choose computationally intensive functions for hardware implementation and acceleration. In this paper we review and compare various existing profiling tools for FPGA-based embedded systems. We then describe Airwolf, an FPGAbased profiling tool. We present a quantitative comparison of Airwolf and a well known software-based profiling tool, GNU gprof. Four software benchmarks were used to obtain profiling results using Airwolf and gprof. We show that Airwolf provides up to 66.2% improvement in accuracy of profiled results and reduces the run time performance overhead, caused by software-based profiling tools, by up to 41.3%. The results show that Airwolf provides accurate profiling results with minimal overhead and it can help the designers of FPGA-based embedded systems in identifying the computationally intensive portions of software code for hardware implementation and acceleration.

Data Fusion for Traffic Incident Detector Using D-S Evidence Theory with Probabilistic SVMs

Abstract: Accurate Incident detection is one of the important components in Intelligent Transportation Systems. It identifies traffic abnormality based on input signals obtained from different type traffic flow sensors. To date, the development of Intelligent Transportation Systems has urged the researchers in incident detection area to explore new techniques with high adaptability to changing site traffic characteristics. From the viewpoint of evidence theory, information obtained from each sensor can be considered as a piece of evidence, and as such, multisensor based traffic incident detector can be viewed as a problem of evidence fusion. This paper proposes a new technique for traffic incident detection, which combines multiple multi-class probability support vector machines (MPSVM) using D-S evidence theory. We present a preliminary review of evidence theory and explain how the multi-sensor traffic incident detector problem can be framed in the context of this theory, in terms of incidents frame of discernment, mass functions is designed by mapping the outputs of standard support vector machines into a posterior probability using a learned sigmoid function. The experiment results suggest that MPSVM is a better adaptive classifier for incident detection problem with a changing site traffic environment.

Interacting Multiple Model Particle-type Filtering Approaches to Ground Target Tracking

Abstract: Ground maneuvering target tracking is a class of nonlinear and/or no-Gaussian filtering problem. A new interacting multiple model unscented particle filter (IMMUPF) is presented to deal with the problem. A bank of unscented particle filters is used in the interacting multiple model (IMM) framework for updating the state of moving target. To validate the algorithm, two groups of multiple model filters: IMM-type filters and particle-type multiple model filters, are compared for their capability in dealing with ground maneuvering target tracking problem. Simulation shows that particle-type filters outperform IMM-type filters in the estimate accuracy and the IMMUPF method relatively has much better performance than the IMMPF method.

A New Information Fusion Method for Bimodal Robotic Emotion Recognition

Abstract: Emotion recognition has become a popular area in human-robot interaction research. Through recognizing facial expressions, a robot can interact with a person in a more friendly manner. In this paper, we proposed a bimodal emotion recognition system by combining image and speech signals. A novel probabilistic strategy has been studied for a support vector machine (SVM)-based classification design to assign statistically information-fusion weights for two feature modalities. The fusion weights are determined by the distance between test data and the classification hyperplane and the standard deviation of training samples. In the latter bimodal SVM classification, the recognition result with higher weight is selected. The complete procedure has been implemented in a DSP-based embedded system to recognize five facial expressions on-line in real time. The experimental results show that an average recognition rate of 86.9% is achieved, a 5% improvement compared to using only image information.

Low Power SRAM with Boost Driver Generating Pulsed Word Line Voltage for Sub-1V Operation

Abstract: Instability of SRAM memory cells derived from the process variation and lowered supply voltage has recently been posing significant design challenges for low power SoCs. This paper presents a boosted word line voltage scheme, where an active bodybiasing controlled boost transistor generates a pulsed word line voltage by capacitive coupling only when accessed. Simulation results have shown that the proposed approach not only shortens the access time but mitigates the impact of Vth variation on performance even at ultra low supply voltage less than 0.5 V.

Implementation of CMOS Low-power Integer-N Frequency Synthesizer for SOC Design

Abstract: The paper reports the implementation of a frequency synthesizer for system-on-chip (SOC) design. The epi-digital CMOS process is used to provide SOC solution. This work focuses on low-power consumption to achieve longer life-time of batteries. A 2.4GHz frequency synthesizer has been fabricated in 0.18µm epi-digital CMOS technology for ZigBee applications, which consumed 7.95 mW from 1.8V supply. The synthesizer has achieved phase-noise of −81.55dBc/Hz and −108. 55dBc/Hz at 100kHz and 1MHz offset, respectively. The settling time measured is less than 25µs for an output frequency change of 75MHz from 2.4GHz. The chip core area is 0.75 × 0.65mm2.

Comparative Analysis of Resource Discovery Approaches in Grid Computing

Abstract: Grid technologies enable the sharing of a wide variety of distributed resources. To utilize these resources, effective Resource Management systems are needed. Resource Management system performs resource discovery to obtain information about the available resources. However, the complex and dynamic nature of grid resources make sharing and discovery, a challenging issue. Resource Discovery is initiated by a network application to find suitable resources with in the Grid. Resource Discovery process is critical for efficient resource allocation and management. For making the Resource Discovery more efficient and reliable large numbers of Approaches are there. This paper analyses some of existing Approaches for Resource Discovery, which can search for the preferred resources quickly and efficiently (return the correct results quickly and reduce network complexity) in Grid computing. Finally a qualitative comparison between these Approaches based on the factors that affect Grid Resource Discovery process, has been done and results are presented.

Life Cycle for Change Management in Business Processes using Semantic Technologies

Abstract: In a fast changing market environment the task of reducing the downtime for change management of business processes has high importance. Ensuring that IT reflects the updated business requirement is an important task related to change management in software development. We present a methodology and system for changing SOA-based business process implementation. We distinguish two layers: At the design layer processes are modeled in the ontology-based semantic markup language for web services OWL-S. For execution the processes are translated into BPEL. At the core of our system are a central change management component and a set of tools which perform various tasks in the overall lifecycle of change management. Finally, we demonstrate our approach with an example e-government.

K-Cosine Corner Detection

Abstract: This study presents a boundary-based corner detection method that achieves robust detection for digital objects containing wide angles and various curves using curvature. The boundary of an object is first represented into curvature measured by K-cosine. Then, by modifying the corner detection error, this study proposes a suitable K value and curvature threshold for robust corner detection. Furthermore, the proposed K-cosine corner detection (KCD) was verified with several commonly employed digital objects. The experimental results reveal that the proposed method is free from translation, rotation and scaling, and is superior to Tsai’s method [34] in computation speed in discriminating false targets. A simple case study is shown finally to demonstrate the feasibility and applicability for practical use of KCD.

Real-time System Identification of Unmanned Aerial Vehicles: A Multi-Network Approach

Abstract: In this paper, real-time system identification of an unmanned aerial vehicle (UAV) based on multiple neural networks is presented. The UAV is a multi-input multi-output (MIMO) nonlinear system. Models for such MIMO system are expected to be adaptive to dynamic behaviour and robust to environmental variations. This task of accurate modelling has been achieved with a multi-network architecture. The multi-network with dynamic selection technique allows a combination of online and offline neural network models to be used in the architecture where the most suitable outputs are selected based on a given criterion. The neural network models are based on the autoregressive technique. The online network uses a novel training scheme with memory retention. Flight test validation results for online and offline models are presented. The multi-network dynamic selection technique has been validated on real-time hardware in the loop (HIL) simulation and the results show the superiority in performance compared to the individual models.

Content-Based Computer Simulation of a Networking Course: An Assessment

Abstract: This paper presents a successful lab simulation experience to teach signal modulation and demodulation concepts in communication and computer networks to computer science and computer engineering students. Two sections of the same college course with a total of 80 subjects participated in this study. After receiving the same lecture at the same time, the subjects in each course were randomly split into two treatment groups. One group completed two laboratory experiments using the computerized simulation program, while the other completed the same two laboratory experiments using the traditional physical laboratory equipments. Upon the completion of the laboratory assignments, the performance instrument was individually administered to each student. The groups were compared on understanding the concepts, remembering the concepts, and displaying a positive attitude toward the treatment tools. Scores on a validated Concepts Test were collected once after the treatment and another time after three weeks in order to gain some insight on students’ knowledge retention. The validated Attitude Survey and qualitative study was administered at the completion of the treatment. The findings of this research indicate that conceptual simulation programs could be feasible substitute for hands-on exercises.

A Hypercube-based Scalable Interconnection Network for Massively Parallel Computing

Abstract: An important issues in the design of interconnection networks for massively parallel computers is scalability. A new scalable interconnection network topology, called Double-Loop Hypercube (DLH), is proposed. The DLH network combines the positive features of the hypercube topology, such as small diameter, high connectivity, symmetry and simple routing, and the scalability and constant node degree of a new double-loop topology. The DLH network can maintain a constant node degree regardless of the increase in the network size. The nodes of the DLH network adopt the hybrid coding combining Johnson code and Gray code. The hybrid coding scheme can make routing algorithms simple and efficient. Both unicasting and broadcasting routing algorithms are designed for the DLH network, and it is based on the hybrid coding scheme. A detailed analysis shows that the DLH network is a better interconnection network in the properties of topology and the performance of communication. Moreover, it also adopts a three-dimensional optical design methodology based on free-space optics. The optical implementation has totally space-invariant connection patterns at every node, which enables the DLH to be highly amenable to optical implementation using simple and efficient large space-bandwidth product space-invariant optical elements.

Visual Cryptography for General Access Structure Using Pixel-block Aware Encoding

Abstract: Multi-pixel encoding is an emerging method in visual cryptography for that it can encode more than one pixel for each encoding run. Nevertheless, in fact its encoding efficiency is still low because of that the encoding length is invariable and very small for each run. This paper presents a novel multi-pixel encoding called pixel-block aware encoding. It scans the secret image by zigzag and perceives a pixel block with as many pixels as possible to encode for each run. A pixel-block consists of consecutive pixels of same type during the scanning. The proposed scheme has advantage in encoding efficiency over singlepixel encoding and other known multi-pixel encoding methods. Furthermore, this scheme can work well for both threshold access structure and general access structure and well for both gray-scale and chromatic images without pixel expansion. The experimental results also show that it can achieve good quality for overlapped images.

A Bluetooth-based Sensor Node for Low-Power Ad Hoc Networks

Abstract: TCP/IP has recently taken promising steps toward being a viable communication architecture for networked sensor nodes. Furthermore, the use of Bluetooth can enable a wide range of new applications, and in this article, an overview of the performance and characteristics of a networked sensor node based on TCP/IP and Bluetooth is presented. The number of Bluetooth-enabled consumer devices on the market is increasing, which gives Bluetooth an advantage compared to other radio technologies from an interoperability point of view. However, this excellent ability to communicate introduces disadvantages since neither TCP/IP nor Bluetooth were designed with resource-constrained sensor nodes in mind. We, however, argue that the constraints imposed by general purpose protocols and technologies can be greatly reduced by exploiting characteristics of the communication scheme in use and efficient and extensive use of available low-power modes. Furthermore, we claim that a Bluetooth-enabled networked sensor node can achieve an operating lifetime in the range of years using a total volume of less than 10 cm3. The Mulle Embedded Internet System (EIS), along with its advanced power management architecture, is presented as a case-study to support the claims.

Performance Comparisons, Design, and Implementation of RC5 Symmetric Encryption Core using Reconfigurable Hardware

Abstract: With the wireless communications coming to homes and offices, the need to have secure data transmission is of utmost importance. Today, it is important that information is sent confidentially over the network without fear of hackers or unauthorized access to it. This makes security implementation in networks a crucial demand. Symmetric Encryption Cores provide data protection via the use of secret key only known to the encryption and decryption ends of the communication path. In this paper, first, an overview of two well known symmetric encryption cores is presented, namely the 3DES and RC5. Then a performance evaluation of their computer based implementation is compared to demonstrate the RC5 superior performance. The conventional hardware architecture of the RC5 core is presented and investigated. A hardware system design is proposed to improve its performance. The proposed architecture achieved with three stage pipeline technique an increased encryption throughput as compared to related work. By exploiting modern features in Field Programmable Gate Arrays (FPGA), which allow the modeling of a System-on- Programmable-Chip (SoPC), this paper proposes a model for symmetric encryption algorithms ( e.g., RC5). Structural System analysis of the proposed model shows that it offers extra security against single-site physical access attack that other implementations are vulnerable to. By evaluating the performance of this proposed SoPC model, one finds that it raises the encryption throughput to 300 Mbps. Hence, we report over 80% increase in the encryption throughput as compared to related work. Moreover, our work lowers the implementation cost due to the integration of all system parts into one chip.

A Neural Network Controller for Trajectory Control of Industrial Robot Manipulators

Abstract: This paper addresses the issue of trajectory tracking control based on a neural network controller for industrial manipulators. A new control scheme is proposed based on neural network technology and linear feedback approach for tracking a planned trajectory. In detail, the control system is designed with two parallel subsystems designed separately. One is a linear controller, and another one is neural network controller. The former is designed for trajectory tracking error regulation, the later for force/torque generation required by the designed dynamic trajectory. A leaning law for online weight updating of the neural network controller is derived based on simplified dynamic model of the robot. A Direct Drive (DD) SCARA type industrial robot arm AdeptOne is used as an application example for trajectory tracking control experiments. Simulations and experiments are carried out on AdeptOne robot. From the simulation and experimental results, the effectiveness and usefulness of the proposed control system are confirmed.

Shape Recognition by Clustering and Matching of Skeletons

Abstract: We perform the task of shape recognition using a skeleton based method. Skeleton of the shape is considered as a free tree and is represented by a connectivity graph. Geometric features of the shape are captured using Radius function along the skeletal curve segments. Matching of the connectivity graphs based on their topologies and geometric features gives a distance measure for determining similarity or dissimilarity of the shapes. Then the distance measure is used for clustering and classification of the shapes by employing hierarchical clustering methods. Moreover, for each class, a median skeleton is computed and is located as the indicator of its related class. The resulted hierarchy of the shapes classes and their indicators are used for the task of shape recognition. This is performed for any given shape by a top-down traversing of the resulted hierarchy and matching with the indicators. We evaluate the proposed method by different shapes of silhouette datasets and we show how the method efficiently recognizes and classifies shapes.

Research on Risk Evaluation in Supply Chain Based on Grey Relational Method

Abstract: Supply chain risk evaluation is a multi-criteria decision making problem under fuzzy environments. To tackle the problem, this paper firstly identifies and discusses some of the important and critical decision criteria and constructs the evaluation indicator framework. Then this paper presents a modified grey relational analysis method based on the concepts of ideal and anti-ideal points. In the method, the weight information is partially known and the vagueness and subjectivity are handled with linguistic terms parameterized by triangular fuzzy numbers. Besides, a single objective programming model is developed to determine the relation degree between every alternative and positive ideal point or negative ideal point. By solving the programming model, the weight vector of criteria is calculated. The alternatives are ranked by the relative relation degree. Finally, a case study is given to demonstrate the proposed method’s effectiveness.

A ZMP Feedback Control for Biped Balance and its Application to In-Place Lateral Stepping Motion

Abstract: Many biped robot control schemes adopt a zero moment point (ZMP) criterion, where the motion is initially planned as the positional trajectories such that ZMP stays within the support polygon, while the feedback control of each joint is later applied to follow the planned reference motion. Although this method is powerful, the ZMP is not always controlled in a feedback manner. Namely, when the environment such as the gradient of the ground varies, the planned motion may cause the tumble and so replanning or modification is sometimes required in order to avoid it. With respect to the environmental variations, the ZMP trajectory is invariant in the lateral plane of the biped robot, in which the ZMP moves from the one side to the other and vice versa. From this point of view, we propose a biped control method for the frontal plane motion based on the ZMP position feedback . It does not required the reference motion of the upper body and the motion replanning or modification of the reference motion are free against environmental variation. This method is applied in the in-place stepping motion and the stability of this method is examined analytically as well as by computer simulations. Finally, the effectiveness of this method is demonstrated by the robot experiment with some improvement points.

Fuzzy Logic Bandwidth Prediction and Policing in a DiffServ-Aware Network

Abstract: Differentiated Services (DiffServ)-aware network potentially can provide the next generation platform for multimedia support in the Internet. In this work we look at improving bandwidth allocation in such a network. We study how to implement bandwidth predicting and policing in a DiffServ aware network using fuzzy logic. A token bucket fuzzy logic bandwidth predictor for real time variable bit rate traffic class is proposed. Here, the AF traffic class is associated with real time variable bit rates traffic. The fuzzy logic bandwidth predictor facilitates bandwidth predicting and dynamic policing based on the class based packet aggregates. This improves the admission control of connections to the network. A simulation study was performed for the fuzzy logic predictor using Network Simulator-2. The simulation results show that the fuzzy logic predictor gave commendable bandwidth prediction value compared to a deterministic bandwidth allocation for the traffic class.

Hardware/Software Co-design Approach for an ADALINE Based Adaptive Control System

Abstract: In this paper, we report some results on hard- ware and software co-design of an adaptive linear neuron (ADALINE) based control system. A discrete-time Pro- portional-Integral-Derivative (PID) controller is designed based on the mathematical model of the plant. The pa- rameters of the plant model are identified on-line by an ADALINE neural network. In order to efficiently and economically implement the designed control system, a Field Programmable Gate Array (FPGA) chip is em- ployed to process the measured data and generate control signals. Moreover, a microprocessor is exploited to per- form the core computation of the ADALINE algorithm. Throughout the paper, we design and test the control system for a permanent magnetic DC motor. Our ex- periment results demonstrate the effectiveness of the pro- posed approach. It is worth noting that the experimental bed in the present paper can also be used as a low-cost general prototype to satisfactorily test adaptive control systems, owing to the benefit of software and hardware co-design.