Computer Sciences Commons^™

Fast Computation Of Determination Of The Prime Implicants By A Novel Near Minimum Minimization Method, Fati̇h Başçi̇ftçi̇, Şi̇rzat Kahramanli

Turkish Journal of Electrical Engineering and Computer Sciences

In this study proposed is an off-set-based direct-cover near-minimum minimization method for single-output Boolean functions represented in a sum-of-products form. To obtain the complete set of prime implicants including given on-cube (on-minterm), the proposed method uses off-cubes (off-minterms) expanded by this On-cube. The amount of temporary results produced by this method does not exceed the size of the off-set. To make fast computation, we used logic operations instead of standard operations. Expansion off-cubes, commutative absorption operations and intersection operations are realized by logic operations for fast computation. The proposed minimization method is tested on several different kinds of problems and …

A Second Order Approximation To Reduce The Complexity Of Ldpc Decoders Based On Gallager's Approach, Aykut Kalaycioğlu, Oktay Üreten, H. Gökhan İlk

Turkish Journal of Electrical Engineering and Computer Sciences

A piece-wise second order approximation to the f (x) = -log [tanh (x/2)] function is proposed to reduce the computational complexity of LDPC decoder's utilizing Log-Likelihood Ratio Belief Propagation (LLR-BP) algorithm based on Gallager's approach. Simulation results show that the proposed low complexity approximation doesn't cause BER performance degradation.

Optimal Feature Selection For 3d Facial Expression Recognition Using Coarse-To-Fine Classification, Hamit Soyel, Hasan Demirel

Turkish Journal of Electrical Engineering and Computer Sciences

Automatic facial expression recognition for novel individuals from 3D face data is a challenging task in pattern analysis. This paper describes a feature selection process for pose-invariant 3D facial expression recognition. The process provides a lower dimensional subspace representation, which is optimized to improve the classification accuracy, retrieved from geometrical localization of facial feature points to classify facial expressions. Fisher criterion-based approach is adopted to provide a basis for the optimal selection of features. Two-stage probabilistic neural network architecture is employed as a classifier to recognize the facial expressions. In the first stage, which can be regarded as the coarse …

Performance Analysis Of Swarm Optimization Approaches For The Generalized Assignment Problem In Multi-Target Tracking Applications, Ali̇ Önder Bozdoğan, Asim Egemen Yilmaz, Murat Efe

Turkish Journal of Electrical Engineering and Computer Sciences

The aim of this study is to investigate the suitability of selected swarm optimization algorithms to the generalized assignment problem as encountered in multi-target tracking applications. For this purpose, we have tested variants of particle swarm optimization and ant colony optimization algorithms to solve the 2D generalized assignment problem with simulated dense and sparse measurement/track matrices and compared their performance to that of the auction algorithm. We observed that, although with some modification swarm optimization algorithms provide improvement in terms of speed, they still fall behind the auction algorithm in finding the optimum solution to the problem. Among the investigated …

Fuzzy Adaptive Neural Network Approach To Path Loss Prediction In Urban Areas At Gsm-900 Band, Türkan Erbay Dalkiliç, Berna Yeşi̇m Hanci, Ayşen Apaydin

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents the results of the Adaptive-Network Based Fuzzy Inference System (ANFIS) for the prediction of path loss in a specific urban environment. A new algorithm based ANFIS for tuning the path loss model is introduced in this work. The performance of the path loss model which is obtained from proposed algorithm is compared to the Bertoni-Walfisch model, which is one of the best studied for propagation analysis involving buildings. This comparison is based on the mean square error between predicted and measured values. According to the indicated error criterion, the errors related to the predictions that are obtained …

Stpso: Strengthened Particle Swarm Optimization, Ai̇şe Zülal Şevkli̇, Fati̇h Erdoğan Sevi̇lgen

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we present a novel approach to strengthen Particle Swarm Optimization (PSO). PSO is a population-based metaheuristic that takes advantage of individual memory and social cooperation in a swarm. It has been applied to a variety of optimization problems because of its simplicity and fast convergence. However, straightforward application of PSO suffers from premature convergence and lack of intensification around the local best locations. To rectify these problems, we modify update procedure for the best particle in the swarm and propose a simple and random moving strategy. We perform a Reduced Variable Neighborhood Search (RVNS) based local search …

Behaviors Of Real-Time Schedulers Under Resource Modification And A Steady Scheme With Bounded Utilization, Refi̇k Samet, Orhan Fi̇kret Duman

Turkish Journal of Electrical Engineering and Computer Sciences

In this article we present an analysis for task models having random resource needs and different arrival patterns. In hard real-time environments like avionic systems or nuclear reactors, the inputs to the system are obtained from real world by using sensors. And it is highly possible for a task to have different resource needs for each period according to these changing conditions of real world. We made an analysis of schedulers for task models having random resources in each period. Since feasibility tests for usual task models are just limited to some specific schedulers and arrival patterns, we made our …

Prediction Of Brain Tumor Progression Using A Machine Learning Technique, Yuzhong Shen, Debrup Banerjee, Jiang Li, Adam Chandler, Yufei Shen, Frederic D. Mckenzie, Jihong Wang, Nico Karssemeijer (Ed.), Ronald M. Summers (Ed.)

Electrical & Computer Engineering Faculty Publications

A machine learning technique is presented for assessing brain tumor progression by exploring six patients' complete MRI records scanned during their visits in the past two years. There are ten MRI series, including diffusion tensor image (DTI), for each visit. After registering all series to the corresponding DTI scan at the first visit, annotated normal and tumor regions were overlaid. Intensity value of each pixel inside the annotated regions were then extracted across all of the ten MRI series to compose a 10 dimensional vector. Each feature vector falls into one of three categories:normal, tumor, and normal but progressed to …

A Collaborative Process Based Risk Analysis For Information Security Management Systems, Bilge Karabacak, Sevgi Ozkan

All Faculty and Staff Scholarship

Today, many organizations quote intent for ISO/IEC 27001:2005 certification. Also, some organizations are en route to certification or already certified. Certification process requires performing a risk analysis in the specified scope. Risk analysis is a challenging process especially when the topic is information security. Today, a number of methods and tools are available for information security risk analysis. The hard task is to use the best fit for the certification. In this work we have proposed a process based risk analysis method which is suitable for ISO/IEC 27001:2005 certifications. Our risk analysis method allows the participation of staff to the …

Improving Software Quality Using An Ontology-Based Approach, Yixin Luo

LSU Doctoral Dissertations

Ensuring quality in software development is a challenging process. The concepts of anti-pattern and bad code smells utilize the knowledge of reoccurring problems to improve the quality of current and future software development. Anti-patterns describe recurring bad design solutions while bad code smells describe source code that is error-free but difficult to understand and maintain. Code refactoring aims to remove bad code smells without changing a program’s functionality while improving program quality. There are metrics-based tools to detect a few bad code smells from source code; however, the knowledge and understanding of these indicators of low quality software are still …

Data Transfer Scheduling With Advance Reservation And Provisioning, Mehmet Balman

LSU Doctoral Dissertations

Over the years, scientific applications have become more complex and more data intensive. Although through the use of distributed resources the institutions and organizations gain access to the resources needed for their large-scale applications, complex middleware is required to orchestrate the use of these storage and network resources between collaborating parties, and to manage the end-to-end processing of data. We present a new data scheduling paradigm with advance reservation and provisioning. Our methodology provides a basis for provisioning end-to-end high performance data transfers which require integration between system, storage and network resources, and coordination between reservation managers and data transfer …

Universal Authentication Protocols For Anonymous Wireless Communications, Guomin Yang, Qiong Huang, Duncan S. Wong, Xiaotie Deng

Research Collection School Of Computing and Information Systems

A secure roaming protocol allows a roaming user U to visit a foreign server V and establish a session key in an authenticated way such that U authenticates V and at the same time convinces V that it is a legitimate subscriber of some server H, called the home server of U. The conventional approach requires the involvement of all the three parties. In this paper, we propose a new approach which requires only two parties, U and V, to get involved. We propose two protocols: one provides better efficiency and supports user anonymity to an extent comparable to that …

Anonymous Query Processing In Road Networks, Kyriakos Mouratidis, Man Lung Yiu

Research Collection School Of Computing and Information Systems

The increasing availability of location-aware mobile devices has given rise to a flurry of location-based services (LBSs). Due to the nature of spatial queries, an LBS needs the user position in order to process her requests. On the other hand, revealing exact user locations to a (potentially untrusted) LBS may pinpoint their identities and breach their privacy. To address this issue, spatial anonymity techniques obfuscate user locations, forwarding to the LBS a sufficiently large region instead. Existing methods explicitly target processing in the euclidean space and do not apply when proximity to the users is defined according to network distance …

On The Potential Of Limitation-Oriented Malware Detection And Prevention Techniques On Mobile Phones, Qiang Yan, Robert H. Deng, Yingjiu Li, Tieyan Li

Research Collection School Of Computing and Information Systems

The malware threat for mobile phones is expected to increase with the functionality enhancement of mobile phones. This threat is exacerbated with the surge in population of smart phones instilled with stable Internet access which provides attractive targets for malware developers. Prior research on malware protection has focused on avoiding the negative impact of the functionality limitations of mobile phones to keep the performance cost within the limitations of mobile phones. Being different, this paper investigates the positive impact of these limitations on suppressing the development of mobile malware. We study the state-of-the-art mobile malware, as well as the progress …

An Optical Machine Vision System For Applications In Cytopathology, Jonathan Blackledge, Dmitry Dubovitskiy

Articles

This paper discusses a new approach to the processes of object detection, recognition and classification in a digital image focusing on problem in Cytopathology. A unique self learning procedure is presented in order to incorporate expert knowledge. The classification method is based on the application of a set of features which includes fractal parameters such as the Lacunarity and Fourier dimension. Thus, the approach includes the characterisation of an object in terms of its fractal properties and texture characteristics. The principal issues associated with object recognition are presented which include the basic model and segmentation algorithms. The self-learning procedure for …

Encryption Using Deterministic Chaos, Jonathan Blackledge, Nikolai Ptitsyn

Articles

The concepts of randomness, unpredictability, complexity and entropy form the basis of modern cryptography and a cryptosystem can be interpreted as the design of a key-dependent bijective transformation that is unpredictable to an observer for a given computational resource. For any cryptosystem, including a Pseudo-Random Number Generator (PRNG), encryption algorithm or a key exchange scheme, for example, a cryptanalyst has access to the time series of a dynamic system and knows the PRNG function (the algorithm that is assumed to be based on some iterative process) which is taken to be in the public domain by virtue of the Kerchhoff-Shannon …

Book Review: Digital Forensic Evidence Examination, Gary C. Kessler

Publications

This document is Dr. Kessler's review of the second edition of Digital Forensic Evidence Examination by Fred Cohen. ASP Press, 2010. ISBN: 978-1-878109-45-3

Forensic Analysis Of A Playstation 3 Console, Scott Conrad, Greg Dorn, Philip Craiger

Publications

The Sony PlayStation 3 (PS3) is a powerful gaming console that supports Internet-related activities, local file storage and the playing of Blu-ray movies. The PS3 also allows users to partition and install a secondary operating system on the hard drive. This “desktop-like” functionality along with the encryption of the primary hard drive containing the gaming software raises significant issues related to the forensic analysis of PS3 systems. This paper discusses the PS3 architecture and behavior, and provides recommendations for conducting forensic investigations of PS3 systems.

Collaborative Risk Method For Information Security Management Practices: A Case Context Within Turkey, Bilge Karabacak, Sevgi Ozkan

All Faculty and Staff Scholarship

In this case study, a collaborative risk method for information security management has been analyzed considering the common problems encountered during the implementation of ISO standards in eight Turkish public organizations. This proposed risk method has been applied within different public organizations and it has been demonstrated to be effective and problem-free. The fundamental issue is that there is no legislation that regulates the information security liabilities of the public organizations in Turkey. The findings and lessons learned presented in this case provide useful insights for practitioners when implementing information security management projects in other international public sector organizations.

Evaluating Models Of Latent Document Semantics In The Presence Of Ocr Errors, Daniel D. Walker, William B. Lund, Eric K. Ringger

Faculty Publications

Models of latent document semantics such as the mixture of multinomials model and Latent Dirichlet Allocation have received substantial attention for their ability to discover topical semantics in large collections of text. In an effort to apply such models to noisy optical character recognition (OCR) text output, we endeavor to understand the effect that character-level noise can have on unsupervised topic modeling. We show the effects both with document-level topic analysis (document clustering) and with word-level topic analysis (LDA) on both synthetic and real-world OCR data. As expected, experimental results show that performance declines as word error rates increase. Common …

Photonic-Based Multi-Wavelength Sensor For Object Identification, Kavitha Venkataraayan, Sreten Askraba, Kamal Alameh, John Rowe

Research outputs pre 2011

A Photonic-based multi-wavelength sensor capable of discriminating objects is proposed and demonstrated for intruder detection and identification. The sensor uses a laser combination module for input wavelength signal multiplexing and beam overlapping, a custom-made curved optical cavity for multi-beam spot generation through internal beam reflection and transmission and a high-speed imager for scattered reflectance spectral measurements. Experimental results show that five different wavelengths, namely 473nm, 532nm, 635nm, 670nm and 785nm, are necessary for discriminating various intruding objects of interest through spectral reflectance and slope measurements. Objects selected for experiments were brick, cement sheet, cotton, leather and roof tile.

Structure Prediction For The Helical Skeletons Detected From The Low Resolution Protein Density Map, Kamal Al Nasr, Weitao Sun, Jing He

Computer Science Faculty Publications

Background: The current advances in electron cryo-microscopy technique have made it possible to obtain protein density maps at about 6-10 Å resolution. Although it is hard to derive the protein chain directly from such a low resolution map, the location of the secondary structures such as helices and strands can be computationally detected. It has been demonstrated that such low-resolution map can be used during the protein structure prediction process to enhance the structure prediction.

Results: We have developed an approach to predict the 3-dimensional structure for the helical skeletons that can be detected from the low resolution protein density …

Improving Predicted Protein Loop Structure Ranking Using A Pareto-Optimality Consensus Method, Yaohang Li, Ionel Rata, See-Wing Chiu, Erik Jakobsson

Computer Science Faculty Publications

Background

Accurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction.

Results

We have developed a Pareto Optimal Consensus (POC) method, which is a consensus model ranking approach to integrate multiple knowledge- or physics-based scoring functions. The procedure of identifying the models of best quality in a model set includes: 1) identifying the models at the Pareto optimal front with respect to a set of scoring functions, and 2) ranking them based on the fuzzy …

Fully Generalized Two-Dimensional Constrained Delaunay Mesh Refinement, Panagiotis A. Foteinos, Andrey N. Chernikov, Nikos P. Chrisochoides

Computer Science Faculty Publications

Traditional refinement algorithms insert a Steiner point from a few possible choices at each step. Our algorithm, on the contrary, defines regions from where a Steiner point can be selected and thus inserts a Steiner point among an infinite number of choices. Our algorithm significantly extends existing generalized algorithms by increasing the number and the size of these regions. The lower bound for newly created angles can be arbitrarily close to $30^{\circ}$. Both termination and good grading are guaranteed. It is the first Delaunay refinement algorithm with a $30^{\circ}$ angle bound and with grading guarantees. Experimental evaluation of our algorithm …