Search results for: grey wolf optimization algorithm

Authors: Apidet Booranawong, Wiklom Teerapabkajorndet

Abstract:

An enhanced ad-hoc on-demand distance vector routing (E-AODV) protocol for control system applications in wireless sensor and actuator networks (WSANs) is proposed. Our routing algorithm is designed by considering both wireless network communication and the control system aspects. Control system error and network delay are the main selection criteria in our routing protocol. The control and communication performance is evaluated on multi-hop IEEE 802.15.4 networks for building-temperature control systems. The Gilbert-Elliott error model is employed to simulate packet loss in wireless networks. The simulation results demonstrate that the E-AODV routing approach can significantly improve the communication performance better than an original AODV routing under various packet loss rates. However, the control performance result by our approach is not much improved compared with the AODV routing solution.

Keywords: WSANs, building temperature control, AODV routing protocol, control system error, settling time, delay, delivery ratio.

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Authors: Vineet K. Srivastava, Mukesh K. Awasthi, Mohammad Tamsir

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A fully implicit finite-difference method has been proposed for the numerical solutions of one dimensional coupled nonlinear Burgers’ equations on the uniform mesh points. The method forms a system of nonlinear difference equations which is to be solved at each iteration. Newton’s iterative method has been implemented to solve this nonlinear assembled system of equations. The linear system has been solved by Gauss elimination method with partial pivoting algorithm at each iteration of Newton’s method. Three test examples have been carried out to illustrate the accuracy of the method. Computed solutions obtained by proposed scheme have been compared with analytical solutions and those already available in the literature by finding L2 and L∞ errors.

Keywords: Burgers’ equation, Implicit Finite-difference method, Newton’s method, Gauss elimination with partial pivoting.

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Authors: Vishal Awasthi, Krishna Raj

Abstract:

Unwanted side effects because of spectral aliasing and spectral imaging during signal processing would be the major concern over the sampling rate alteration. Multirate-multistage implementation of digital filter could come about a large computational saving than single rate filter suitable for sample rate conversion. This implementation can further improve through high-level architectural transformation in circuit level. Reallocating registers and  relocating flip-flops across logic gates through retiming certainly a prominent sequential transformation technology, that optimize hardware circuits to achieve faster clocking speed without affecting the functionality. In this paper, we proposed an efficient compensated cascade Integrator comb (CIC) decimation filter structure that analyze the consequence of filter order variation which has a retimed FIR filter being compensator while using the cutset retiming technique and achieved an improvement in the passband droop by 14% to 39%, in computation time by 38.04%, 25.78%, 12.21%, 6.69% and 4.44% and reduction in path delay by 62.27%, 72%, 86.63%, 91.56% and 94.42% of 3, 6, 8, 12 and 24 order filter respectively than the non-retimed CIC compensation filter.

Keywords: Multirate Filtering, CIC decimation filter, Compensation theory, Retiming, Retiming algorithm, Filter order, Synchronous dataflow graph.

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Authors: Amit Kumar, Himanshu Singal, Arnav Bhavsar

Abstract:

In this modern era of automation, most of the academic exams and competitive exams are Multiple Choice Questions (MCQ). The responses of these MCQ based exams are recorded in the Optical Mark Reader (OMR) sheet. Evaluation of the OMR sheet requires separate specialized machines for scanning and marking. The sheets used by these machines are special and costs more than a normal sheet. Available process is non-economical and dependent on paper thickness, scanning quality, paper orientation, special hardware and customized software. This study tries to tackle the problem of evaluating the OMR sheet without any special hardware and making the whole process economical. We propose an image processing based algorithm which can be used to read and evaluate the scanned OMR sheets with no special hardware required. It will eliminate the use of special OMR sheet. Responses recorded in normal sheet is enough for evaluation. The proposed system takes care of color, brightness, rotation, little imperfections in the OMR sheet images.

Keywords: OMR, image processing, hough circle transform, interpolation, detection, Binary Thresholding.

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Authors: Mohamed Abdallah, Mostafa Warith, Roberto Narbaitz, Emil Petriu, Kevin Kennedy

Abstract:

Heterogeneity of solid waste characteristics as well as the complex processes taking place within the landfill ecosystem motivated the implementation of soft computing methodologies such as artificial neural networks (ANN), fuzzy logic (FL), and their combination. The present work uses a hybrid ANN-FL model that employs knowledge-based FL to describe the process qualitatively and implements the learning algorithm of ANN to optimize model parameters. The model was developed to simulate and predict the landfill gas production at a given time based on operational parameters. The experimental data used were compiled from lab-scale experiment that involved various operating scenarios. The developed model was validated and statistically analyzed using F-test, linear regression between actual and predicted data, and mean squared error measures. Overall, the simulated landfill gas production rates demonstrated reasonable agreement with actual data. The discussion focused on the effect of the size of training datasets and number of training epochs.

Keywords: Adaptive neural fuzzy inference system (ANFIS), gas production, landfill

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Authors: R. Lokeshkumar, P. Sengottuvelan

Abstract:

Web mining is to discover and extract useful Information. Different users may have different search goals when they search by giving queries and submitting it to a search engine. The inference and analysis of user search goals can be very useful for providing an experience result for a user search query. In this project, we propose a novel approach to infer user search goals by analyzing search web logs. First, we propose a novel approach to infer user search goals by analyzing search engine query logs, the feedback sessions are constructed from user click-through logs and it efficiently reflect the information needed for users. Second we propose a preprocessing technique to clean the unnecessary data’s from web log file (feedback session). Third we propose a technique to generate pseudo-documents to representation of feedback sessions for clustering. Finally we implement k-medoids clustering algorithm to discover different user search goals and to provide a more optimal result for a search query based on feedback sessions for the user.

Keywords: Data Preprocessing, Session Identification, Web log mining, Web Personalization.

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Authors: Iqra Noor, Ihtzaz Qamar

Abstract:

Pitot-tube Jet pump, single-stage pump with low flow rate and high head, consists of a radial impeller that feeds water to rotating cavity. Water then enters stationary pitot-tube collector (diffuser), which discharges to the outside. By means of ANSYS Fluent 15.0, the internal flow characteristics for Pitot-tube Jet pump with standard pitot and curved pitot are studied. Under design condition, realizable k-e turbulence model and SIMPLEC algorithm are used to calculate 3D flow field inside both pumps. The simulation results reveal that energy is imparted to the flow by impeller and inside the rotor, forced vortex type flow is observed. Total pressure decreases inside pitot-tube whereas static pressure increases. Changing pitot-tube from standard to curved shape results in minimum flow circulation inside pitot-tube and leads to a higher pump performance.

Keywords: CFD, flow circulation, high pressure pump, impeller, internal flow, pickup tube pump, rectangle channels, rotating casing, turbulence.

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Authors: V. Masilamani, C. Vanniarajan, Kamala Krithivasan

Abstract:

As the Computed Tomography(CT) requires normally hundreds of projections to reconstruct the image, patients are exposed to more X-ray energy, which may cause side effects such as cancer. Even when the variability of the particles in the object is very less, Computed Tomography requires many projections for good quality reconstruction. In this paper, less variability of the particles in an object has been exploited to obtain good quality reconstruction. Though the reconstructed image and the original image have same projections, in general, they need not be the same. In addition to projections, if a priori information about the image is known, it is possible to obtain good quality reconstructed image. In this paper, it has been shown by experimental results why conventional algorithms fail to reconstruct from a few projections, and an efficient polynomial time algorithm has been given to reconstruct a bi-level image from its projections along row and column, and a known sub image of unknown image with smoothness constraints by reducing the reconstruction problem to integral max flow problem. This paper also discusses the necessary and sufficient conditions for uniqueness and extension of 2D-bi-level image reconstruction to 3D-bi-level image reconstruction.

Keywords: Discrete Tomography, Image Reconstruction, Projection, Computed Tomography, Integral Max Flow Problem, Smooth Binary Image.

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Authors: Omid A. Zargar

Abstract:

The simulated mass and spring method evaluation for subway or railways construction and installation systems have a wide application in rail industries. This kind of design should be optimizing all related parameters to reduce the amount of vibration in cities, homelands, historical zones and other critical locations. Finite element method could help us a lot to analysis such applications with an excellent accuracy but always developing some simple, fast and user friendly evaluation method required in subway industrial applications. In addition, process parameter optimization extremely required in railway industries to achieve some optimal design of railways with maximum safety, reliability and performance. Furthermore, it is important to reduce vibrations and further related maintenance costs as well as possible. In this paper a simple but useful simulated mass and spring evaluation system developed for Esfahan subway construction. Besides, some of related recent patent and innovations in rail world industries like Suspension mass tuned vibration reducer, short sleeper vibration attenuation fastener and Airtight track vibration-noise reducing fastener discussed in details.

Keywords: Subway construction engineering, natural frequency, operation frequency, vibration analysis, polyurethane layer.

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Authors: Stephen Karungaru, Minoru Fukumi, Norio Akamatsu

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In this paper, we propose a morphing method by which face color images can be freely transformed. The main focus of this work is the transformation of one face image to another. This method is fully automatic in that it can morph two face images by automatically detecting all the control points necessary to perform the morph. A face detection neural network, edge detection and medium filters are employed to detect the face position and features. Five control points, for both the source and target images, are then extracted based on the facial features. Triangulation method is then used to match and warp the source image to the target image using the control points. Finally color interpolation is done using a color Gaussian model that calculates the color for each particular frame depending on the number of frames used. A real coded Genetic algorithm is used in both the image warping and color blending steps to assist in step size decisions and speed up the morphing. This method results in ''very smooth'' morphs and is fast to process.

Keywords: color transition, genetic algorithms morphing, warping

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Authors: Leila Jafari, Viliam Makis, Akram Khaleghei G.B.

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In this paper, we present a maintenance model of a two-unit series system with economic dependence. Unit#1 which is considered to be more expensive and more important, is subject to condition monitoring (CM) at equidistant, discrete time epochs and unit#2, which is not subject to CM has a general lifetime distribution. The multivariate observation vectors obtained through condition monitoring carry partial information about the hidden state of unit#1, which can be in a healthy or a warning state while operating. Only the failure state is assumed to be observable for both units. The objective is to find an optimal opportunistic maintenance policy minimizing the long-run expected average cost per unit time. The problem is formulated and solved in the partially observable semi-Markov decision process framework. An effective computational algorithm for finding the optimal policy and the minimum average cost is developed, illustrated by a numerical example.

Keywords: Condition-Based Maintenance, Semi-Markov Decision Process, Multivariate Bayesian Control Chart, Partially Observable System, Two-unit System.

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Authors: Mauricio Terceros, Jann-Eike Saathoff, Martin Achmus

Abstract:

In non-cohesive soil, onshore wind turbines are often found on shallow foundations with a circular or octagonal shape. For the current generation of wind turbines, shallow foundations with very large breadths are required. The foundation support costs thus represent a considerable portion of the total construction costs. Therefore, an economic optimization of the type of foundation is highly desirable. A conceivable alternative foundation type would be a pier foundation, which combines the load transfer over the foundation area at the pier base with the transfer of horizontal loads over the shaft surface of the pier. The present study aims to evaluate the load-bearing behavior of a pier foundation based on comprehensive parametric studies. Thereby, three-dimensional numerical simulations of both pier and shallow foundations are developed. The evaluation of the results focuses on the rotational stiffnesses of the proposed soil-foundation systems. In the design, the initial rotational stiffness is decisive for consideration of natural frequencies, whereas the rotational secant stiffness for a maximum load is decisive for serviceability considerations. A systematic analysis of the results at different load levels shows that the application of the typical pier foundation is presumably limited to relatively small onshore wind turbines.

Keywords: Onshore wind foundation, pier foundation, rotational stiffness of soil-foundation system, shallow foundation.

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Authors: K. Nagaraju, S. Sivanagaraju, T. Ramana, V. Ganesh

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This paper presents a simple method for estimation of additional load as a factor of the existing load that may be drawn before reaching the point of line maximum loadability of radial distribution system (RDS) with different realistic load models at different substation voltages. The proposed method involves a simple line loadability index (LLI) that gives a measure of the proximity of the present state of a line in the distribution system. The LLI can use to assess voltage instability and the line loading margin. The proposed method also compares with the existing method of maximum loadability index [10]. The simulation results show that the LLI can identify not only the weakest line/branch causing system instability but also the system voltage collapse point when it is near one. This feature enables us to set an index threshold to monitor and predict system stability on-line so that a proper action can be taken to prevent the system from collapse. To demonstrate the validity of the proposed algorithm, computer simulations are carried out on two bus and 69 bus RDS.

Keywords: line loadability index, line loading margin, maximum line loadability, system stability, radial distribution system

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Authors: Mohamed K. Watfa

Abstract:

Like any sentient organism, a smart environment relies first and foremost on sensory data captured from the real world. The sensory data come from sensor nodes of different modalities deployed on different locations forming a Wireless Sensor Network (WSN). Embedding smart sensors in humans has been a research challenge due to the limitations imposed by these sensors from computational capabilities to limited power. In this paper, we first propose a practical WSN application that will enable blind people to see what their neighboring partners can see. The challenge is that the actual mapping between the input images to brain pattern is too complex and not well understood. We also study the connectivity problem in 3D/2D wireless sensor networks and propose distributed efficient algorithms to accomplish the required connectivity of the system. We provide a new connectivity algorithm CDCA to connect disconnected parts of a network using cooperative diversity. Through simulations, we analyze the connectivity gains and energy savings provided by this novel form of cooperative diversity in WSNs.

Keywords: Wireless Sensor Networks, Pervasive Computing, Eye Vision Application, 3D Connectivity, Clusters, Energy Efficient, Cooperative diversity.

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Authors: C. Adam, G. Henri, T. Levent, J-B Mauro, A-L Mayet

Abstract:

Energy Efficiency Management is the heart of a worldwide problem. The capability of a multi-agent system as a technology to manage the micro-grid operation has already been proved. This paper deals with the implementation of a decisional pattern applied to a multi-agent system which provides intelligence to a distributed local energy network considered at local consumer level. Development of multi-agent application involves agent specifications, analysis, design, and realization. Furthermore, it can be implemented by following several decisional patterns. The purpose of present article is to suggest a new approach for a decisional pattern involving a multi-agent system to control a distributed local energy network in a decentralized competitive system. The proposed solution is the result of a dichotomous approach based on environment observation. It uses an iterative process to solve automatic learning problems and converges monotonically very fast to system attracting operation point.

Keywords: Energy Efficiency Management, Distributed Smart- Grid, Multi-Agent System, Decisional Decentralized Competitive System.

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Authors: A. Gherbi, L. Dahmani, A. Boudjemia

Abstract:

This paper presents the Finite Element Method (FEM) for analyzing the failure pattern of rectangular slab with various edge conditions. Non-Linear static analysis is carried out using ANSYS 15 Software. Using SOLID65 solid elements, the compressive crushing of concrete is facilitated using plasticity algorithm, while the concrete cracking in tension zone is accommodated by the nonlinear material model. Smeared reinforcement is used and introduced as a percentage of steel embedded in concrete slab. The behavior of the analyzed concrete slab has been observed in terms of the crack pattern and displacement for various loading and boundary conditions. The finite element results are also compared with the experimental data. One of the other objectives of the present study is to show how similar the crack path found by ANSYS program to those observed for the yield line analysis. The smeared reinforcement method is found to be more practical especially for the layered elements like concrete slabs. The value of this method is that it does not require explicit modeling of the rebar, and thus a much coarser mesh can be defined.

Keywords: ANSYS, cracking pattern, displacements, RC Slab, smeared reinforcement.

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Authors: Azamossadat Nourbakhsh, Moharram Habibnezhad Korayem

Abstract:

In the visual servoing systems, the data obtained by Visionary is used for controlling robots. In this project, at first the simulator which was proposed for simulating the performance of a 6R robot before, was examined in terms of software and test, and in the proposed simulator, existing defects were obviated. In the first version of simulation, the robot was directed toward the target object only in a Position-based method using two cameras in the environment. In the new version of the software, three cameras were used simultaneously. The camera which is installed as eye-inhand on the end-effector of the robot is used for visual servoing in a Feature-based method. The target object is recognized according to its characteristics and the robot is directed toward the object in compliance with an algorithm similar to the function of human-s eyes. Then, the function and accuracy of the operation of the robot are examined through Position-based visual servoing method using two cameras installed as eye-to-hand in the environment. Finally, the obtained results are tested under ANSI-RIA R15.05-2 standard.

Keywords: 6R Robot , camera, visual servoing, Feature-based visual servoing, Position-based visual servoing, Performance tests.

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Authors: A. Taheri

Abstract:

Inspired by topology of humpback whale flippers, a meta-model is designed for wing planform design. The net is trained based on experimental data using cascade-forward artificial neural network (ANN) to investigate effects of the amplitude and wavelength of sinusoidal leading edge configurations on the wing performance. Afterwards, the trained ANN is coupled with a genetic algorithm method towards an optimum design strategy. Finally, flow physics of the problem for an optimized rectangular planform and also a real flipper geometry planform is simulated using Lam-Bremhorst low Reynolds number turbulence model with damping wall-functions resolving to the wall. Lift and drag coefficients and also details of flow are presented along with comparisons to available experimental data. Results show that the proposed strategy can be adopted with success as a fast-estimation tool for performance prediction of wing planforms with wavy leading edge at preliminary design phase.  

Keywords: Humpback whale flipper, cascade-forward ANN, GA, CFD, Bionics.

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Authors: M. Govindarajan, R. M.Chandrasekaran

Abstract:

Text data mining is a process of exploratory data analysis. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. This paper describes proposed radial basis function Classifier that performs comparative crossvalidation for existing radial basis function Classifier. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: direct Marketing. Direct marketing has become an important application field of data mining. Comparative Cross-validation involves estimation of accuracy by either stratified k-fold cross-validation or equivalent repeated random subsampling. While the proposed method may have high bias; its performance (accuracy estimation in our case) may be poor due to high variance. Thus the accuracy with proposed radial basis function Classifier was less than with the existing radial basis function Classifier. However there is smaller the improvement in runtime and larger improvement in precision and recall. In the proposed method Classification accuracy and prediction accuracy are determined where the prediction accuracy is comparatively high.

Keywords: Text Data Mining, Comparative Cross-validation, Radial Basis Function, runtime, accuracy.

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Authors: Min Kyung An

Abstract:

In this paper, we study the data collection problem in Wireless Sensor Networks (WSNs) adopting the two interference models: The graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR). The main issue of the problem is to compute schedules with the minimum number of timeslots, that is, to compute the minimum latency schedules, such that data from every node can be collected without any collision or interference to a sink node. While existing works studied the problem with unit-sized and unbounded-sized message models, we investigate the problem with the bounded-sized message model, and introduce a constant factor approximation algorithm. To the best known of our knowledge, our result is the first result of the data collection problem with bounded-sized model in both interference models.

Keywords: Data collection, collision-free, interference-free, physical interference model, SINR, approximation, bounded-sized message model, wireless sensor networks, WSN.

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Authors: Cinna Soltanpur, Mohammad Ghamari, Behzad Momahed Heravi, Fatemeh Zare

Abstract:

Low-density parity-check (LDPC) codes have been shown to deliver capacity approaching performance; however, problematic graphical structures (e.g. trapping sets) in the Tanner graph of some LDPC codes can cause high error floors in bit-error-ratio (BER) performance under conventional sum-product algorithm (SPA). This paper presents a serial concatenation scheme to avoid the trapping sets and to lower the error floors of LDPC code. The outer code in the proposed concatenation is the LDPC, and the inner code is a high rate array code. This approach applies an interactive hybrid process between the BCJR decoding for the array code and the SPA for the LDPC code together with bit-pinning and bit-flipping techniques. Margulis code of size (2640, 1320) has been used for the simulation and it has been shown that the proposed concatenation and decoding scheme can considerably improve the error floor performance with minimal rate loss.

Keywords: Concatenated coding, low–density parity–check codes, array code, error floors.

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Authors: Elnaz Lashgari, Emel Demircan

Abstract:

Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: Electrocardiogram, manifold learning, Laplacian Eigenmaps, running pattern.

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Authors: Saleem Z. Ramadan

Abstract:

This paper discusses the effects of using progressive Type-I right censoring on the design of the Simple Step Accelerated Life testing using Bayesian approach for Weibull life products under the assumption of cumulative exposure model. The optimization criterion used in this paper is to minimize the expected pre-posterior variance of the P^th percentile time of failures. The model variables are the stress changing time and the stress value for the first step. A comparison between the conventional and the progressive Type-I right censoring is provided. The results have shown that the progressive Type-I right censoring reduces the cost of testing on the expense of the test precision when the sample size is small. Moreover, the results have shown that using strong priors or large sample size reduces the sensitivity of the test precision to the censoring proportion. Hence, the progressive Type-I right censoring is recommended in these cases as progressive Type-I right censoring reduces the cost of the test and doesn't affect the precision of the test a lot. Moreover, the results have shown that using direct or indirect priors affects the precision of the test.

Keywords: Reliability, Accelerated life testing, Cumulative exposure model, Bayesian estimation, Progressive Type-I censoring, Weibull distribution.

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Authors: Jaipal Bisht, Aditya Goel

Abstract:

Optical Burst Switching (OBS) is a promising technology for the future generation Internet. Control architecture and Contention resolution are the main issues faced by the Optical Burst Switching networks. In this paper we are only taking care of the Contention problem and to overcome this issue we propose Pre-Deflection Routing with Control Packet Signal Scheme for Contention Resolution in Optical Burst Switch Networks. In this paper Pre-deflection routing approach has been proposed in which routing is carried out in two ways, Shortest Path First (SPF) and Least Hop First (LHF) Routing to forward the clusters and canoes respectively. Hereafter Burst Offset Time Control Algorithm has been proposed where a forward control packet (FCP) collects the congestion price and contention price along its paths. Thereafter a reverse-direction control packet (RCP) sent by destination node which delivers the information of FCP to the source node, and source node uses this information to revise its offset time and burst length.

Keywords: Contention Resolution, FCP, OBS, Offset Time, PST, RCP.

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Authors: M. Bouguerra, I. Habi

Abstract:

The continuity in the electric supply of the electric installations is becoming one of the main requirements of the electric supply network (generation, transmission, and distribution of the electric energy). The achievement of this requirement depends from one side on the structure of the electric network and on the other side on the avaibility of the reserve source provided to maintain the supply in case of failure of the principal one. The avaibility of supply does not only depends on the reliability parameters of the both sources (principal and reserve) but it also depends on the reliability of the circuit breaker which plays the role of interlocking the reserve source in case of failure of the principal one. In addition, the principal source being under operation, its control can be ideal and sure, however, for the reserve source being in stop, a preventive maintenances which proceed on time intervals (periodicity) and for well defined lengths of time are envisaged, so that this source will always available in case of the principal source failure. The choice of the periodicity of preventive maintenance of the source of reserve influences directly the reliability of the electric feeder system In this work and on the basis of the semi- markovian's processes, the influence of the time of interlocking the reserve source upon the reliability of an industrial electric network is studied and is given the optimal time of interlocking the reserve source in case of failure the principal one, also the influence of the periodicity of the preventive maintenance of the source of reserve is studied and is given the optimal periodicity.

Keywords: Semi-Markovians processes, reliability, optimization, industrial electric network.

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Authors: Jae Chul Cha, Sandeep K. Gupta

Abstract:

The demand for higher performance graphics continues to grow because of the incessant desire towards realism. And, rapid advances in fabrication technology have enabled us to build several processor cores on a single die. Hence, it is important to develop single chip parallel architectures for such data-intensive applications. In this paper, we propose an efficient PIM architectures tailored for computer graphics which requires a large number of memory accesses. We then address the two important tasks necessary for maximally exploiting the parallelism provided by the architecture, namely, partitioning and placement of graphic data, which affect respectively load balances and communication costs. Under the constraints of uniform partitioning, we develop approaches for optimal partitioning and placement, which significantly reduce search space. We also present heuristics for identifying near-optimal placement, since the search space for placement is impractically large despite our optimization. We then demonstrate the effectiveness of our partitioning and placement approaches via analysis of example scenes; simulation results show considerable search space reductions, and our heuristics for placement performs close to optimal – the average ratio of communication overheads between our heuristics and the optimal was 1.05. Our uniform partitioning showed average load-balance ratio of 1.47 for geometry processing and 1.44 for rasterization, which is reasonable.

Keywords: Data Partitioning and Placement, Graphics, PIM, Search Space Reduction.

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Authors: Desmond B. Keenan

Abstract:

The clinical usefulness of heart rate variability is limited to the range of Holter monitoring software available. These software algorithms require a normal sinus rhythm to accurately acquire heart rate variability (HRV) measures in the frequency domain. Premature ventricular contractions (PVC) or more commonly referred to as ectopic beats, frequent in heart failure, hinder this analysis and introduce ambiguity. This investigation demonstrates an algorithm to automatically detect ectopic beats by analyzing discrete wavelet transform coefficients. Two techniques for filtering and replacing the ectopic beats from the RR signal are compared. One technique applies wavelet hard thresholding techniques and another applies linear interpolation to replace ectopic cycles. The results demonstrate through simulation, and signals acquired from a 24hr ambulatory recorder, that these techniques can accurately detect PVC-s and remove the noise and leakage effects produced by ectopic cycles retaining smooth spectra with the minimum of error.

Keywords: Heart rate variability, vagal tone, sympathetic, parasympathetic, wavelets, ectopic beats, spectral analysis.

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Authors: A. Faro, D. Giordano, F. Maiorana

Abstract:

Data mining uses a variety of techniques each of which is useful for some particular task. It is important to have a deep understanding of each technique and be able to perform sophisticated analysis. In this article we describe a tool built to simulate a variation of the Kohonen network to perform unsupervised clustering and support the entire data mining process up to results visualization. A graphical representation helps the user to find out a strategy to optimize classification by adding, moving or delete a neuron in order to change the number of classes. The tool is able to automatically suggest a strategy to optimize the number of classes optimization, but also support both tree classifications and semi-lattice organizations of the classes to give to the users the possibility of passing from one class to the ones with which it has some aspects in common. Examples of using tree and semi-lattice classifications are given to illustrate advantages and problems. The tool is applied to classify macroeconomic data that report the most developed countries- import and export. It is possible to classify the countries based on their economic behaviour and use the tool to characterize the commercial behaviour of a country in a selected class from the analysis of positive and negative features that contribute to classes formation. Possible interrelationships between the classes and their meaning are also discussed.

Keywords: Unsupervised classification, Kohonen networks, macroeconomics, Visual data mining, Cluster interpretation.

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Authors: Rameswar Debnath, Haruhisa Takahashi

Abstract:

An evolutionary method whose selection and recombination operations are based on generalization error-bounds of support vector machine (SVM) can select a subset of potentially informative genes for SVM classifier very efficiently [7]. In this paper, we will use the derivative of error-bound (first-order criteria) to select and recombine gene features in the evolutionary process, and compare the performance of the derivative of error-bound with the error-bound itself (zero-order) in the evolutionary process. We also investigate several error-bounds and their derivatives to compare the performance, and find the best criteria for gene selection and classification. We use 7 cancer-related human gene expression datasets to evaluate the performance of the zero-order and first-order criteria of error-bounds. Though both criteria have the same strategy in theoretically, experimental results demonstrate the best criterion for microarray gene expression data.

Keywords: support vector machine, generalization error-bound, feature selection, evolutionary algorithm, microarray data

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Authors: F.Alilat, S.Loumi, H.Merrad, B.Sansal

Abstract:

In this article a modification of the algorithm of the fuzzy ART network, aiming at returning it supervised is carried out. It consists of the search for the comparison, training and vigilance parameters giving the minimum quadratic distances between the output of the training base and those obtained by the network. The same process is applied for the determination of the parameters of the fuzzy ARTMAP giving the most powerful network. The modification consist in making learn the fuzzy ARTMAP a base of examples not only once as it is of use, but as many time as its architecture is in evolution or than the objective error is not reached . In this way, we don-t worry about the values to impose on the eight (08) parameters of the network. To evaluate each one of these three networks modified, a comparison of their performances is carried out. As application we carried out a classification of the image of Algiers-s bay taken by SPOT XS. We use as criterion of evaluation the training duration, the mean square error (MSE) in step control and the rate of good classification per class. The results of this study presented as curves, tables and images show that modified fuzzy ARTMAP presents the best compromise quality/computing time.

Keywords: Neural Networks, fuzzy ART, fuzzy ARTMAP, Remote sensing, multispectral Classification.

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