A Formal Approach for Proof Constructions in Cryptography

In this article we explore the application of a formal proof system to verification problems in cryptography. Cryptographic properties concerning correctness or security of some cryptographic algorithms are of great interest. Beside some basic lemmata, we explore an implementation of a complex function that is used in cryptography. More precisely, we describe formal properties of this implementation that we computer prove. We describe formalized probability distributions (σ-algebras, probability spaces and conditional probabilities). These are given in the formal language of the formal proof system Isabelle/HOL. Moreover, we computer prove Bayes- Formula. Besides, we describe an application of the presented formalized probability distributions to cryptography. Furthermore, this article shows that computer proofs of complex cryptographic functions are possible by presenting an implementation of the Miller- Rabin primality test that admits formal verification. Our achievements are a step towards computer verification of cryptographic primitives. They describe a basis for computer verification in cryptography. Computer verification can be applied to further problems in cryptographic research, if the corresponding basic mathematical knowledge is available in a database.

Design and Analysis of a Solar Refrigeration System with a Rotating Generator

A solar refrigeration system based on the adsorptiondesorption phenomena is designed and analyzed. An annular tubular generator filled with silica gel adsorbent and with a perforated inner cylinder is integrated within a flat solar collector. The working fluid in the refrigeration cycle is water. The thermodynamic analysis and because of the temperature level that could be attained with a flat solar collector it is required that the system operates under vacuum conditions. In order to enhance the performance of the system and to get uniform temperature in the silica gel and higher desorbed mass, an apparatus for rotation of the generator is incorporated in the system. Testing is carried out and measurements are taken on the designed installation. The effect of rotation is checked on the temperature distribution and on the performance of this machine and compared to the flat solar collector with fixed generator.

Fuzzy Logic Controlled Shunt Active Power Filter for Three-phase Four-wire Systems with Balanced and Unbalanced Loads

This paper presents a fuzzy logic controlled shunt active power filter used to compensate for harmonic distortion in three-phase four-wire systems. The shunt active filter employs a simple method for the calculation of the reference compensation current based of Fast Fourier Transform. This presented filter is able to operate in both balanced and unbalanced load conditions. A fuzzy logic based current controller strategy is used to regulate the filter current and hence ensure harmonic free supply current. The validity of the presented approach in harmonic mitigation is verified via simulation results of the proposed test system under different loading conditions.

Unsupervised Segmentation using Fuzzy Logicbased Texture Spectrum for MRI Brain Images

Textures are replications, symmetries and combinations of various basic patterns, usually with some random variation one of the gray-level statistics. This article proposes a new approach to Segment texture images. The proposed approach proceeds in 2 stages. First, in this method, local texture information of a pixel is obtained by fuzzy texture unit and global texture information of an image is obtained by fuzzy texture spectrum. The purpose of this paper is to demonstrate the usefulness of fuzzy texture spectrum for texture Segmentation. The 2nd Stage of the method is devoted to a decision process, applying a global analysis followed by a fine segmentation, which is only focused on ambiguous points. The above Proposed approach was applied to brain image to identify the components of brain in turn, used to locate the brain tumor and its Growth rate.

ANFIS Modeling of the Surface Roughness in Grinding Process

The objective of this study is to design an adaptive neuro-fuzzy inference system (ANFIS) for estimation of surface roughness in grinding process. The Used data have been generated from experimental observations when the wheel has been dressed using a rotary diamond disc dresser. The input parameters of model are dressing speed ratio, dressing depth and dresser cross-feed rate and output parameter is surface roughness. In the experimental procedure the grinding conditions are constant and only the dressing conditions are varied. The comparison of the predicted values and the experimental data indicates that the ANFIS model has a better performance with respect to back-propagation neural network (BPNN) model which has been presented by the authors in previous work for estimation of the surface roughness.

The Performance of Alternating Top-Bottom Strategy for Successive Over Relaxation Scheme on Two Dimensional Boundary Value Problem

This paper present the implementation of a new ordering strategy on Successive Overrelaxation scheme on two dimensional boundary value problems. The strategy involve two directions alternatingly; from top and bottom of the solution domain. The method shows to significantly reduce the iteration number to converge. Four numerical experiments were carried out to examine the performance of the new strategy.

Attacks Classification in Adaptive Intrusion Detection using Decision Tree

Recently, information security has become a key issue in information technology as the number of computer security breaches are exposed to an increasing number of security threats. A variety of intrusion detection systems (IDS) have been employed for protecting computers and networks from malicious network-based or host-based attacks by using traditional statistical methods to new data mining approaches in last decades. However, today's commercially available intrusion detection systems are signature-based that are not capable of detecting unknown attacks. In this paper, we present a new learning algorithm for anomaly based network intrusion detection system using decision tree algorithm that distinguishes attacks from normal behaviors and identifies different types of intrusions. Experimental results on the KDD99 benchmark network intrusion detection dataset demonstrate that the proposed learning algorithm achieved 98% detection rate (DR) in comparison with other existing methods.

Study on Environmental Statement for Home Appliances at Online Stores in Japan

This study aims to identify the current situation and problems of environmental statement for major four home appliances (refrigerators, washing machines, air conditioners and television receivers) sold at online stores in Japan, and then to suggest how to improve the situation, through a questionnaire survey conducted among businesses that operate online stores and online malls with multiple online stores. Results of the study boil down to: (1) It is found out that environmental statement for the home appliances at online stores have four problems; (i) less information on “three Rs" and “chemical substances" than the one on “energy conservation", (ii) cost for providing environmental statement, (iii) issues associated with a label and mark placement, and (iv) issues associated with energy conservation statement. (2) Improvements are suggested for each of the four problems listed above, and shown are (i) the effectiveness of, and need to promote, a label and mark placement, (ii) cost burden on buyers, and (iii) need of active efforts made by businesses and of dissemination of legal regulations to businesses.

An Algebra for Protein Structure Data

This paper presents an algebraic approach to optimize queries in domain-specific database management system for protein structure data. The approach involves the introduction of several protein structure specific algebraic operators to query the complex data stored in an object-oriented database system. The Protein Algebra provides an extensible set of high-level Genomic Data Types and Protein Data Types along with a comprehensive collection of appropriate genomic and protein functions. The paper also presents a query translator that converts high-level query specifications in algebra into low-level query specifications in Protein-QL, a query language designed to query protein structure data. The query transformation process uses a Protein Ontology that serves the purpose of a dictionary.

Large-Eddy Simulation of Hypersonic Configuration Aerodynamics

LES with mixed subgrid-scale model has been used to simulate aerodynamic performance of hypersonic configuration. The simulation was conducted to replicate conditions and geometry of a model which has been previously tested. LES Model has been successful in predict pressure coefficient with the max error 1.5% besides afterbody. But in the high Mach number condition, it is poor in predict ability and product 12.5% error. The calculation error are mainly conducted by the distribution swirling. The fact of poor ability in the high Mach number and afterbody region indicated that the mixed subgrid-scale model should be improved in large eddied especially in hypersonic separate region. In the condition of attach and sideslip flight, the calculation results have waves. LES are successful in the prediction the pressure wave in hypersonic flow.

Development of Thermal Model by Performance Verification of Heat Pipe Subsystem for Electronic Cooling under Space Environment

Heat pipes are used to control the thermal problem for electronic cooling. It is especially difficult to dissipate heat to a heat sink in an environment in space compared to earth. For solving this problem, in this study, the Poiseuille (Po) number, which is the main measure of the performance of a heat pipe, is studied by CFD; then, the heat pipe performance is verified with experimental results. A heat pipe is then fabricated for a spatial environment, and an in-house code is developed. Further, a heat pipe subsystem, which consists of a heat pipe, MLI (Multi Layer Insulator), SSM (Second Surface Mirror), and radiator, is tested and correlated with the TMM (Thermal Mathematical Model) through a commercial code. The correlation results satisfy the 3K requirement, and the generated thermal model is verified for application to a spatial environment.

Power Generation Potential of Dynamic Architecture

The main aim of this work is to establish the capabilities of new green buildings to ascertain off-grid electricity generation based on the integration of wind turbines in the conceptual model of a rotating tower [2] in Dubai. An in depth performance analysis of the WinWind 3.0MW [3] wind turbine is performed. Data based on the Dubai Meteorological Services is collected and analyzed in conjunction with the performance analysis of this wind turbine. The mathematical model is compared with Computational Fluid Dynamics (CFD) results based on a conceptual rotating tower design model. The comparison results are further validated and verified for accuracy by conducting experiments on a scaled prototype of the tower design. The study concluded that integrating wind turbines inside a rotating tower can generate enough electricity to meet the required power consumption of the building, which equates to a wind farm containing 9 horizontal axis wind turbines located at an approximate area of 3,237,485 m2 [14].

Real-Time Digital Oscilloscope Implementation in 90nm CMOS Technology FPGA

This paper describes the design of a real-time audiorange digital oscilloscope and its implementation in 90nm CMOS FPGA platform. The design consists of sample and hold circuits, A/D conversion, audio and video processing, on-chip RAM, clock generation and control logic. The design of internal blocks and modules in 90nm devices in an FPGA is elaborated. Also the key features and their implementation algorithms are presented. Finally, the timing waveforms and simulation results are put forward.

Reducing the False Rejection Rate of Iris Recognition Using Textural and Topological Features

This paper presents a novel iris recognition system using 1D log polar Gabor wavelet and Euler numbers. 1D log polar Gabor wavelet is used to extract the textural features, and Euler numbers are used to extract topological features of the iris. The proposed decision strategy uses these features to authenticate an individual-s identity while maintaining a low false rejection rate. The algorithm was tested on CASIA iris image database and found to perform better than existing approaches with an overall accuracy of 99.93%.

Designing a Football Team of Robots from Beginning to End

The Combination of path planning and path following is the main purpose of this paper. This paper describes the developed practical approach to motion control of the MRL small size robots. An intelligent controller is applied to control omni-directional robots motion in simulation and real environment respectively. The Brain Emotional Learning Based Intelligent Controller (BELBIC), based on LQR control is adopted for the omni-directional robots. The contribution of BELBIC in improving the control system performance is shown as application of the emotional learning in a real world problem. Optimizing of the control effort can be achieved in this method too. Next the implicit communication method is used to determine the high level strategies and coordination of the robots. Some simple rules besides using the environment as a memory to improve the coordination between agents make the robots' decision making system. With this simple algorithm our team manifests a desirable cooperation.

Fusion Classifier for Open-Set Face Recognition with Pose Variations

A fusion classifier composed of two modules, one made by a hidden Markov model (HMM) and the other by a support vector machine (SVM), is proposed to recognize faces with pose variations in open-set recognition settings. The HMM module captures the evolution of facial features across a subject-s face using the subject-s facial images only, without referencing to the faces of others. Because of the captured evolutionary process of facial features, the HMM module retains certain robustness against pose variations, yielding low false rejection rates (FRR) for recognizing faces across poses. This is, however, on the price of poor false acceptance rates (FAR) when recognizing other faces because it is built upon withinclass samples only. The SVM module in the proposed model is developed following a special design able to substantially diminish the FAR and further lower down the FRR. The proposed fusion classifier has been evaluated in performance using the CMU PIE database, and proven effective for open-set face recognition with pose variations. Experiments have also shown that it outperforms the face classifier made by HMM or SVM alone.

Remote-Sensing Sunspot Images to Obtain the Sunspot Roads

A combination of image fusion and quad tree decomposition method is used for detecting the sunspot trajectories in each month and computation of the latitudes of these trajectories in each solar hemisphere. Daily solar images taken with SOHO satellite are fused for each month and the result of fused image is decomposed with Quad Tree decomposition method in order to classifying the sunspot trajectories and then to achieve the precise information about latitudes of sunspot trajectories. Also with fusion we deduce some physical remarkable conclusions about sun magnetic fields behavior. Using quad tree decomposition we give information about the region on sun surface and the space angle that tremendous flares and hot plasma gases permeate interplanetary space and attack to satellites and human technical systems. Here sunspot images in June, July and August 2001 are used for studying and give a method to compute the latitude of sunspot trajectories in each month with sunspot images.

Application of Robot Formation Scheme for Screening Solar Energy in a Greenhouse

Many agricultural and especially greenhouse applications like plant inspection, data gathering, spraying and selective harvesting could be performed by robots. In this paper multiple nonholonomic robots are used in order to create a desired formation scheme for screening solar energy in a greenhouse through data gathering. The formation consists from a leader and a team member equipped with appropriate sensors. Each robot is dedicated to its mission in the greenhouse that is predefined by the requirements of the application. The feasibility of the proposed application includes experimental results with three unmanned ground vehicles (UGV).

FHOJ: A New Java Benchmark Framework

There are some existing Java benchmarks, application benchmarks as well as micro benchmarks or mixture both of them,such as: Java Grande, Spec98, CaffeMark, HBech, etc. But none of them deal with behaviors of multi tasks operating systems. As a result, the achieved outputs are not satisfied for performance evaluation engineers. Behaviors of multi tasks operating systems are based on a schedule management which is employed in these systems. Different processes can have different priority to share the same resources. The time is measured by estimating from applications started to it is finished does not reflect the real time value which the system need for running those programs. New approach to this problem should be done. Having said that, in this paper we present a new Java benchmark, named FHOJ benchmark, which directly deals with multi tasks behaviors of a system. Our study shows that in some cases, results from FHOJ benchmark are far more reliable in comparison with some existing Java benchmarks.

Neural Network based Texture Analysis of Liver Tumor from Computed Tomography Images

Advances in clinical medical imaging have brought about the routine production of vast numbers of medical images that need to be analyzed. As a result an enormous amount of computer vision research effort has been targeted at achieving automated medical image analysis. Computed Tomography (CT) is highly accurate for diagnosing liver tumors. This study aimed to evaluate the potential role of the wavelet and the neural network in the differential diagnosis of liver tumors in CT images. The tumors considered in this study are hepatocellular carcinoma, cholangio carcinoma, hemangeoma and hepatoadenoma. Each suspicious tumor region was automatically extracted from the CT abdominal images and the textural information obtained was used to train the Probabilistic Neural Network (PNN) to classify the tumors. Results obtained were evaluated with the help of radiologists. The system differentiates the tumor with relatively high accuracy and is therefore clinically useful.