Search results for: Elliptic Curve Digital Signature Algorithm

Authors: Olli Luoma, Johannes Tuikkala, Olli Nevalainen

Abstract:

In this paper, we propose a novel improvement for the generalized Lloyd Algorithm (GLA). Our algorithm makes use of an M-tree index built on the codebook which makes it possible to reduce the number of distance computations when the nearest code words are searched. Our method does not impose the use of any specific distance function, but works with any metric distance, making it more general than many other fast GLA variants. Finally, we present the positive results of our performance experiments.

Keywords: Clustering, GLA, M-Tree, Vector Quantization .

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Authors: Hassan Azarkish, Said Farahat, S.Masoud H. Sarvari

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In the present work, the performance of the particle swarm optimization and the genetic algorithm compared as a typical geometry design problem. The design maximizes the heat transfer rate from a given fin volume. The analysis presumes that a linear temperature distribution along the fin. The fin profile generated using the B-spline curves and controlled by the change of control point coordinates. An inverse method applied to find the appropriate fin geometry yield the linear temperature distribution along the fin corresponds to optimum design. The numbers of the populations, the count of iterations and time to convergence measure efficiency. Results show that the particle swarm optimization is most efficient for geometry optimization.

Keywords: Genetic Algorithm, Geometry Optimization, longitudinal Fin, Particle Swarm Optimization

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Authors: B. Mahmoud, M.H. Bedoui, R. Raychev, H. Essabbah

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We present in this paper an acquisition and treatment system designed for semi-analog Gamma-camera. It consists of a nuclear medical Image Acquisition, Treatment and Display chain(IATD) ensuring the acquisition, the treatment of the signals(resulting from the Gamma-camera detection head) and the scintigraphic image construction in real time. This chain is composed by an analog treatment board and a digital treatment board. We describe the designed systems and the digital treatment algorithms in which we have improved the performance and the flexibility. The digital treatment algorithms are implemented in a specific reprogrammable circuit FPGA (Field Programmable Gate Array).interface for semi-analog cameras of Sopha Medical Vision(SMVi) by taking as example SOPHY DS7. The developed system consists of an Image Acquisition, Treatment and Display (IATD) ensuring the acquisition and the treatment of the signals resulting from the DH. The developed chain is formed by a treatment analog board and a digital treatment board designed around a DSP [2]. In this paper we have presented the architecture of a new version of our chain IATD in which the integration of the treatment algorithms is executed on an FPGA (Field Programmable Gate Array)

Keywords: Nuclear medical image, scintigraphic image, digitaltreatment, linearity, spectrometry, FPGA.

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Authors: Z. Zainuddin, N. Mahat, Y. Abu Hassan

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Since the presentation of the backpropagation algorithm, a vast variety of improvements of the technique for training a feed forward neural networks have been proposed. This article focuses on two classes of acceleration techniques, one is known as Local Adaptive Techniques that are based on weightspecific only, such as the temporal behavior of the partial derivative of the current weight. The other, known as Dynamic Adaptation Methods, which dynamically adapts the momentum factors, α, and learning rate, η, with respect to the iteration number or gradient. Some of most popular learning algorithms are described. These techniques have been implemented and tested on several problems and measured in terms of gradient and error function evaluation, and percentage of success. Numerical evidence shows that these techniques improve the convergence of the Backpropagation algorithm.

Keywords: Backpropagation, Dynamic Adaptation Methods, Local Adaptive Techniques, Neural networks.

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Authors: Tatjana Eitrich, Bruno Lang

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This work deals with aspects of support vector machine learning for large-scale data mining tasks. Based on a decomposition algorithm for support vector machine training that can be run in serial as well as shared memory parallel mode we introduce a transformation of the training data that allows for the usage of an expensive generalized kernel without additional costs. We present experiments for the Gaussian kernel, but usage of other kernel functions is possible, too. In order to further speed up the decomposition algorithm we analyze the critical problem of working set selection for large training data sets. In addition, we analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our tests and conclusions led to several modifications of the algorithm and the improvement of overall support vector machine learning performance. Our method allows for using extensive parameter search methods to optimize classification accuracy.

Keywords: Support Vector Machine Training, Multi-ParameterKernels, Shared Memory Parallel Computing, Large Data

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Authors: P. N. Hrisheekesha, Jaydev Sharma

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In this paper, a method based on Non-Dominated Sorting Genetic Algorithm (NSGA) has been presented for the Volt / Var control in power distribution systems with dispersed generation (DG). Genetic algorithm approach is used due to its broad applicability, ease of use and high accuracy. The proposed method is better suited for volt/var control problems. A multi-objective optimization problem has been formulated for the volt/var control of the distribution system. The non-dominated sorting genetic algorithm based method proposed in this paper, alleviates the problem of tuning the weighting factors required in solving the multi-objective volt/var control optimization problems. Based on the simulation studies carried out on the distribution system, the proposed scheme has been found to be simple, accurate and easy to apply to solve the multiobjective volt/var control optimization problem of the distribution system with dispersed generation.

Keywords: Dispersed Generation, Distribution System, Non-Dominated Sorting Genetic Algorithm, Voltage / Reactive powercontrol.

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Authors: Delowar Hossain, Genci Capi

Abstract:

This paper concerns with the problem of deep learning parameters tuning using a genetic algorithm (GA) in order to improve the performance of deep learning (DL) method. We present a GA based DL method for robot object recognition and grasping. GA is used to optimize the DL parameters in learning procedure in term of the fitness function that is good enough. After finishing the evolution process, we receive the optimal number of DL parameters. To evaluate the performance of our method, we consider the object recognition and robot grasping tasks. Experimental results show that our method is efficient for robot object recognition and grasping.

Keywords: Deep learning, genetic algorithm, object recognition, robot grasping.

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Authors: Sheetal Kumar Parwar, J. Deb Barma, A. Majumder

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Submerged arc welding is a very complex process. It is a very efficient and high performance welding process. In this present study an attempt have been done to reduce the welding distortion by increased amount of oxide flux through TiO2 in submerged arc welding process. Care has been taken to avoid the excessiveness of the adding agent for attainment of significant results. Data Envelopment Analysis (DEA) based BAT algorithm is used for the parametric optimization purpose in which DEA is used to convert multi response parameters into a single response parameter. The present study also helps to know the effectiveness of the addition of TiO2 in active flux during submerged arc welding process.

Keywords: BAT algorithm, design of experiment, optimization, submerged arc welding.

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Authors: V Krishnaveni, S Jayaraman, A Gunasekaran, K Ramadoss

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The ElectroEncephaloGram (EEG) is useful for clinical diagnosis and biomedical research. EEG signals often contain strong ElectroOculoGram (EOG) artifacts produced by eye movements and eye blinks especially in EEG recorded from frontal channels. These artifacts obscure the underlying brain activity, making its visual or automated inspection difficult. The goal of ocular artifact removal is to remove ocular artifacts from the recorded EEG, leaving the underlying background signals due to brain activity. In recent times, Independent Component Analysis (ICA) algorithms have demonstrated superior potential in obtaining the least dependent source components. In this paper, the independent components are obtained by using the JADE algorithm (best separating algorithm) and are classified into either artifact component or neural component. Neural Network is used for the classification of the obtained independent components. Neural Network requires input features that exactly represent the true character of the input signals so that the neural network could classify the signals based on those key characters that differentiate between various signals. In this work, Auto Regressive (AR) coefficients are used as the input features for classification. Two neural network approaches are used to learn classification rules from EEG data. First, a Polynomial Neural Network (PNN) trained by GMDH (Group Method of Data Handling) algorithm is used and secondly, feed-forward neural network classifier trained by a standard back-propagation algorithm is used for classification and the results show that JADE-FNN performs better than JADEPNN.

Keywords: Auto Regressive (AR) Coefficients, Feed Forward Neural Network (FNN), Joint Approximation Diagonalisation of Eigen matrices (JADE) Algorithm, Polynomial Neural Network (PNN).

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Authors: Mahmoud Moh'd Mhashi

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A new algorithm called Character-Comparison to Character-Access (CCCA) is developed to test the effect of both: 1) converting character-comparison and number-comparison into character-access and 2) the starting point of checking on the performance of the checking operation in string searching. An experiment is performed using both English text and DNA text with different sizes. The results are compared with five algorithms, namely, Naive, BM, Inf_Suf_Pref, Raita, and Cycle. With the CCCA algorithm, the results suggest that the evaluation criteria of the average number of total comparisons are improved up to 35%. Furthermore, the results suggest that the clock time required by the other algorithms is improved in range from 22.13% to 42.33% by the new CCCA algorithm.

Keywords: Pattern matching, string searching, charactercomparison, character-access, text type, and checking

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Authors: Yasunori Sugita, Toshinori Yoshikawa

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The design problem of Infinite Impulse Response (IIR) digital filters is usually expressed as the minimization problem of the complex magnitude error that includes both the magnitude and phase information. However, the group delay of the filter obtained by solving such design problem may be far from the desired group delay. In this paper, we propose a design method of stable IIR digital filters with prespecified maximum group delay errors. In the proposed method, the approximation problems of the magnitude-phase and group delay are separately defined, and these two approximation problems are alternately solved using successive projections. As a result, the proposed method can design the IIR filters that satisfy the prespecified allowable errors for not only the complex magnitude but also the group delay by alternately executing the coefficient update for the magnitude-phase and the group delay approximation. The usefulness of the proposed method is verified through some examples.

Keywords: Filter design, Group delay approximation, Stable IIRfilters, Successive projection method.

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Authors: Ali Fellah Jahromi, Wen Fang Xie, Rama B. Bhat

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A semi-active control strategy for suspension systems of passenger cars is presented employing Magnetorheological (MR) dampers. The vehicle is modeled with seven DOFs including the, roll pitch and bounce of car body, and the vertical motion of the four tires. In order to design an optimal controller based on the actuator constraints, a Linear-Quadratic Regulator (LQR) is designed. The design procedure of the LQR consists of selecting two weighting matrices to minimize the energy of the control system. This paper presents a hybrid optimization procedure which is a combination of gradient-based and evolutionary algorithms to choose the weighting matrices with regards to the actuator constraint. The optimization algorithm is defined based on maximum comfort and actuator constraints. It is noted that utilizing the present control algorithm may significantly reduce the vibration response of the passenger car, thus, providing a comfortable ride.

Keywords: Full car model, Linear Quadratic Regulator, Sequential Quadratic Programming, Genetic Algorithm

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Authors: Ahmed Ashraf Khamis, Sherif A. Ibrahim, Mahmoud ElKhatieb, Mohamed A. Barakat

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Parametric design deemed to be one of icons of the modern architectural trends that facilitates taking complex design decisions counting on altering various design parameters. Double skin façades are one of the parametric applications that are used in parametric designs. This paper opts to enhance different daylight parameters of a selected case study office building in Cairo using a parametric double skin façade. First, the design and optimization process was executed utilizing Grasshopper parametric design software package, in which the daylighting performance of the base case building model was compared with the one used in the double façade showing an enhancement in task plane illuminance by 180%. Second, execution drawings are made for the optimized design using Revit software. Finally, computerized digital fabrication stages of the designed model with various scales are demonstrated to reach the final design decisions using Simplify 3D for mock-up digital fabrication.

Keywords: Parametric design, Double skin façades, Digital Fabrication, Grasshopper, Simplify 3D.

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Authors: Mabrouka Algherinai, Fatma Karkouri

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Today, Short Message Service (SMS) is an important means of communication. SMS is not only used in informal environment for communication and transaction, but it is also used in formal environments such as institutions, organizations, companies, and business world as a tool for communication and transactions. Therefore, there is a need to secure the information that is being transmitted through this medium to ensure security of information both in transit and at rest. But, encryption has been identified as a means to provide security to SMS messages in transit and at rest. Several past researches have proposed and developed several encryption algorithms for SMS and Information Security. This research aims at comparing the performance of common Asymmetric encryption algorithms on SMS security. The research employs the use of three algorithms, namely RSA, McEliece, and RABIN. Several experiments were performed on SMS of various sizes on android mobile device. The experimental results show that each of the three techniques has different key generation, encryption, and decryption times. The efficiency of an algorithm is determined by the time that it takes for encryption, decryption, and key generation. The best algorithm can be chosen based on the least time required for encryption. The obtained results show the least time when McEliece size 4096 is used. RABIN size 4096 gives most time for encryption and so it is the least effective algorithm when considering encryption. Also, the research shows that McEliece size 2048 has the least time for key generation, and hence, it is the best algorithm as relating to key generation. The result of the algorithms also shows that RSA size 1024 is the most preferable algorithm in terms of decryption as it gives the least time for decryption.

Keywords: SMS, RSA, McEliece, RABIN.

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

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This paper presents an approach for easy creation and classification of institutional risk profiles supporting endangerment analysis of file formats. The main contribution of this work is the employment of data mining techniques to support set up of the most important risk factors. Subsequently, risk profiles employ risk factors classifier and associated configurations to support digital preservation experts with a semi-automatic estimation of endangerment group for file format risk profiles. Our goal is to make use of an expert knowledge base, accuired through a digital preservation survey in order to detect preservation risks for a particular institution. Another contribution is support for visualisation of risk factors for a requried dimension for analysis. Using the naive Bayes method, the decision support system recommends to an expert the matching risk profile group for the previously selected institutional risk profile. The proposed methods improve the visibility of risk factor values and the quality of a digital preservation process. The presented approach is designed to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and values of file format risk profiles. To facilitate decision-making, the aggregated information about the risk factors is presented as a multidimensional vector. The goal is to visualise particular dimensions of this vector for analysis by an expert and to define its profile group. The sample risk profile calculation and the visualisation of some risk factor dimensions is presented in the evaluation section.

Keywords: linked open data, information integration, digital libraries, data mining.

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Authors: Hsian Ming Goo

Abstract:

The paper concerns a special approximate algorithm of the square root of the specific positive integer, which is built by the use of the property of positive integer solution of the Pell’s equation, together with using some elementary theorems of matrices, and then takes it to compare with general used the Newton’s method and give a practical numerical example and error analysis; it is unexpected to find its special property: the significant figure of the approximation value of the square root of positive integer will increase one digit by one. It is well useful in some occasions.

Keywords: Special approximate algorithm, square root, Pell’s equation, Newton’s method, error analysis.

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Authors: Khalid Elasnaoui, Brahim Aksasse, Mohammed Ouanan

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In this paper, we are interested in the problem of finding similar images in a large database. For this purpose we propose a new algorithm based on a combination of the 2-D histogram intersection in the HSV space and statistical moments. The proposed histogram is based on a 3x3 window and not only on the intensity of the pixel. This approach overcome the drawback of the conventional 1-D histogram which is ignoring the spatial distribution of pixels in the image, while the statistical moments are used to escape the effects of the discretisation of the color space which is intrinsic to the use of histograms. We compare the performance of our new algorithm to various methods of the state of the art and we show that it has several advantages. It is fast, consumes little memory and requires no learning. To validate our results, we apply this algorithm to search for similar images in different image databases.

Keywords: 2-D histogram, Statistical moments, Indexing, Similarity distance, Histograms intersection.

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Authors: Huaiyu Zhang

Abstract:

The method of modeling is the key technology for digital mockup (DMU). Based upon the developing for mechanical product DMU, the theory, method and approach for virtual environment (VE) and virtual object (VO) were studied. This paper has expounded the design goal and architecture of DMU system, analyzed the method of DMU application, and researched the general process of physics modeling and behavior modeling.

Keywords: DMU, VR, virtual environment, virtual object, physics modeling, behavior modeling

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Authors: J. Umamaheswari, G. Radhamani

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This paper describes about the process of recognition and classification of brain images such as normal and abnormal based on PSO-SVM. Image Classification is becoming more important for medical diagnosis process. In medical area especially for diagnosis the abnormality of the patient is classified, which plays a great role for the doctors to diagnosis the patient according to the severeness of the diseases. In case of DICOM images it is very tough for optimal recognition and early detection of diseases. Our work focuses on recognition and classification of DICOM image based on collective approach of digital image processing. For optimal recognition and classification Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Support Vector Machine (SVM) are used. The collective approach by using PSO-SVM gives high approximation capability and much faster convergence.

Keywords: Recognition, classification, Relaxed Median Filter, Adaptive thresholding, clustering and Neural Networks

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Authors: J. Hossen, A. Rahman, K. Samsudin, F. Rokhani, S. Sayeed, R. Hasan

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The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a novel Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data sets. A TSK type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the fuzzy c-means clustering and Apriori algorithm technique, respectively. The generated adaptive fuzzy inference engine is adjusted by the least-squares fit and a conjugate gradient descent algorithm towards better performance with a minimal set of rules. The proposed MAFIE is able to reduce the number of rules which increases exponentially when more input variables are involved. The performance of the proposed MAFIE is compared with other existing applications of pattern classification schemes using Fisher-s Iris and Wisconsin breast cancer data sets and shown to be very competitive.

Keywords: Apriori algorithm, Fuzzy C-means, MAFIE, TSK

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Authors: Jason Chien-Hsun Tseng, Nguyen Dong-Thai Dao, Chong-Ching Chang

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A novel PDE solver using the multidimensional wave digital filtering (MDWDF) technique to achieve the solution of a 2D seismic wave system is presented. In essence, the continuous physical system served by a linear Kirchhoff circuit is transformed to an equivalent discrete dynamic system implemented by a MD wave digital filtering (MDWDF) circuit. This amounts to numerically approximating the differential equations used to describe elements of a MD passive electronic circuit by a grid-based difference equations implemented by the so-called state quantities within the passive MDWDF circuit. So the digital model can track the wave field on a dense 3D grid of points. Details about how to transform the continuous system into a desired discrete passive system are addressed. In addition, initial and boundary conditions are properly embedded into the MDWDF circuit in terms of state quantities. Graphic results have clearly demonstrated some physical effects of seismic wave (P-wave and S–wave) propagation including radiation, reflection, and refraction from and across the hard boundaries. Comparison between the MDWDF technique and the finite difference time domain (FDTD) approach is also made in terms of the computational efficiency.

Keywords: Seismic Wave Propagation, Multi-dimension WaveDigital Filters, Partial Differential Equations.

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Authors: Farag Azzedin, Ali Rizvi

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In any trust model, the two information sources that a peer relies on to predict trustworthiness of another peer are direct experience as well as reputation. These two vital components evolve over time. Trust evolution is an important issue, where the objective is to observe a sequence of past values of a trust parameter and determine the future estimates. Unfortunately, trust evolution algorithms received little attention and the proposed algorithms in the literature do not comply with the conditions and the nature of trust. This paper contributes to this important problem in the following ways: (a) presents an algorithm that manages and models trust evolution in a P2P environment, (b) devises new mechanisms for effectively maintaining trust values based on the conditions that influence trust evolution , and (c) introduces a new methodology for incorporating trust-nurture incentives into the trust evolution algorithm. Simulation experiments are carried out to evaluate our trust evolution algorithm.

Keywords: P2P, Trust, Reputation, Incentives.

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Authors: Vinay Chandwani, Vinay Agrawal, Ravindra Nagar

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Artificial Neural Networks (ANN) trained using backpropagation (BP) algorithm are commonly used for modeling material behavior associated with non-linear, complex or unknown interactions among the material constituents. Despite multidisciplinary applications of back-propagation neural networks (BPNN), the BP algorithm possesses the inherent drawback of getting trapped in local minima and slowly converging to a global optimum. The paper present a hybrid artificial neural networks and genetic algorithm approach for modeling slump of ready mix concrete based on its design mix constituents. Genetic algorithms (GA) global search is employed for evolving the initial weights and biases for training of neural networks, which are further fine tuned using the BP algorithm. The study showed that, hybrid ANN-GA model provided consistent predictions in comparison to commonly used BPNN model. In comparison to BPNN model, the hybrid ANNGA model was able to reach the desired performance goal quickly. Apart from the modeling slump of ready mix concrete, the synaptic weights of neural networks were harnessed for analyzing the relative importance of concrete design mix constituents on the slump value. The sand and water constituents of the concrete design mix were found to exhibit maximum importance on the concrete slump value.

Keywords: Artificial neural networks, Genetic algorithms, Back-propagation algorithm, Ready Mix Concrete, Slump value.

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Authors: V. H. Mankar, T. S. Das, S. K. Sarkar

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In this paper, we have proposed a novel blind watermarking architecture towards its hardware implementation in VLSI. In order to facilitate this hardware realization, cellular automata (CA) concept is introduced. The CA has been already accepted as an attractive structure for VLSI implementation because of its modularity, parallelism, high performance and reliability. The hardware realizable multiresolution spread spectrum watermarking techniques are very few in numbers in spite of their best ever resiliency against signal impairments. This is because of the computational cost and complexity associated with their different filter banks and lifting techniques. The concept of cellular automata theory in order to form a new transform domain technique i.e. Cellular Automata Transform (CAT) have been incorporated. Since CA provides spreading sequences having very low cross-correlation properties, the CA based pseudorandom sequence generator is considered in the present work. Considering the watermarking technique as a digital communication process, an error control coding (ECC) must be incorporated in the data hiding schemes. Besides the hardware implementation of entire CA based data hiding technique, the individual blocks of the algorithm using CA provide the best result than that of some other methods irrespective of the hardware and software technique. The Cellular Automata Transform, CA based PN sequence generator, and CA ECC are the requisite blocks that are developed not only to meet the reliable hardware requirements but also for the basic spread spectrum watermarking features. The proposed algorithm shows statistical invisibility and resiliency against various common signal-processing operations. This algorithmic design utilizes the existing allocated bandwidth in the data transmission channel in a more efficient manner.

Keywords: Cellular automata, watermarking, error control coding, PN sequence, VLSI.

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Authors: Min-Shiang Hwang, Cheng-Chi Lee, Shiang-Feng Tzeng

Abstract:

A new secure knapsack cryptosystem based on the Merkle-Hellman public key cryptosystem will be proposed in this paper. Although it is common sense that when the density is low, the knapsack cryptosystem turns vulnerable to the low-density attack. The density d of a secure knapsack cryptosystem must be larger than 0.9408 to avoid low-density attack. In this paper, we investigate a new Permutation Combination Algorithm. By exploiting this algorithm, we shall propose a novel knapsack public-key cryptosystem. Our proposed scheme can enjoy a high density to avoid the low-density attack. The density d can also exceed 0.9408 to avoid the low-density attack.

Keywords: Public key, Knapsack problem, Knapsack cryptosystem, low-density attack.

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Authors: Ssu-Han Chen, Der-Baau Perng

Abstract:

Molding process in IC manufacturing secures chips against the harms done by hot, moisture or other external forces. While a chip was being molded,defects like cracks, dilapidation, or voids may be embedding on the molding surface. The molding surfaces the study poises to treat and the ones on the market, though, differ in the surface where texture similar to defects is everywhere. Manual inspection usually passes over low-contrast cracks or voids; hence an automatic optical inspection system for molding surface is necessary. The proposed system is consisted of a CCD, a coaxial light, a back light as well as a motion control unit. Based on the property of statistical textures of the molding surface, a series of digital image processing and classification procedure is carried out. After training of the parameter associated with above algorithm, result of the experiment suggests that the accuracy rate is up to 93.75%, contributing to the inspection quality of IC molding surface.

Keywords: Molding surface, machine vision, statistical texture, discrete Fourier transformation.

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Authors: Kerem Ok, Serafettin Senturk, Serdar Aktas, Cem Cevikbas

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Mobile technology is very popular nowadays and it provides a digital world where users can experience many value-added services. Service Providers are also eager to offer diverse value-added services to users such as digital identity, mobile financial services and so on. In this context, the security of data storage in smartphones and the security of communication between the smartphone and service provider are critical for the success of these services. In order to provide the required security functions, the SIM card is one acceptable alternative. Since SIM cards include a Secure Element, they are able to store sensitive data, create cryptographically secure keys, encrypt and decrypt data. In this paper, we design and implement a SIM and a smartphone framework that uses a SIM card for secure key generation, key storage, data encryption, data decryption and digital signing for mobile financial services. Our frameworks show that the SIM card can be used as a controlled Secure Element to provide required security functions for popular e-services such as mobile financial services.

Keywords: SIM Card, mobile financial services, cryptography, secure data storage.

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Authors: A. Badri

Abstract:

Restructured electricity markets may provide opportunities for producers to exercise market power maintaining prices in excess of competitive levels. In this paper an oligopolistic market is presented that all Generation Companies (GenCos) bid in a Cournot model. Genetic algorithm (GA) is applied to obtain generation scheduling of each GenCo as well as hourly market clearing prices (MCP). In order to consider network constraints a multiperiod framework is presented to simulate market clearing mechanism in which the behaviors of market participants are modelled through piecewise block curves. A mixed integer linear programming (MILP) is employed to solve the problem. Impacts of market clearing process on participants- characteristic and final market prices are presented. Consequently, a novel multi-objective model is addressed for security constrained optimal bidding strategy of GenCos. The capability of price-maker GenCos to alter MCP is evaluated through introducing an effective-supply curve. In addition, the impact of exercising market power on the variation of market characteristics as well as GenCos scheduling is studied.

Keywords: Optimal bidding strategy, Cournot equilibrium, market power, network constraints, market auction mechanism

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Authors: Lise P. Labéjof, Krisnayne S. Ribeiro, Jackson A. Santos, Nicolle P. dos Santos

Abstract:

An unrecorded experiment of use of the smartphone as a tool for practical classes of histology is presented in this paper. Behavior and learning of students of science courses at the University were analyzed and compared as well as the mode of teaching of this discipline and the appreciation of the students, using either digital photographs taken by phone or drawings for record microscopic observations, analyze and interpret histological sections of human or animal tissues.

Keywords: Cell phone, digital micrographs, learning of sciences, teaching practices.

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Authors: Jaimin Patel

Abstract:

Cloud computing is one of the most sharp and important movement in various computing technologies. It provides flexibility to users, cost effectiveness, location independence, easy maintenance, enables multitenancy, drastic performance improvements, and increased productivity. On the other hand, there are also major issues like security. Being a common server, security for a cloud is a major issue; it is important to provide security to protect user’s private data, and it is especially important in e-commerce and social networks. In this paper, encryption algorithms such as Advanced Encryption Standard algorithms, their vulnerabilities, risk of attacks, optimal time and complexity management and comparison with other algorithms based on software implementation is proposed. Encryption techniques to improve the performance of AES algorithms and to reduce risk management are given. Secure Hash Algorithms, their vulnerabilities, software implementations, risk of attacks and comparison with other hashing algorithms as well as the advantages and disadvantages between hashing techniques and encryption are given.

Keywords: Cloud computing, encryption algorithm, secure hashing algorithm, brute force attack, birthday attack, plaintext attack, man-in-the-middle attack.

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