Search results for: Genetic Algorithm

Authors: Gayadhar Panda, P. K. Rautraya

Abstract:

Damping of inter-area electromechanical oscillations is one of the major challenges to the electric power system operators. This paper presents Gravitational Search Algorithm (GSA) for tuning Static Synchronous Series Compensator (SSSC) based damping controller to improve power system oscillation stability. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The effectiveness of the scheme in damping power system oscillations during system faults at different loading conditions is demonstrated through time-domain simulation.

Keywords: FACTS, Damping controller design, Gravitational search algorithm (GSA), Power system oscillations, Single-machine infinite Bus power system, SSSC.

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Authors: Linyu Wang, Furui Huo, Jianhong Xiang

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The Doppler shift generated by high-speed movement and multipath effects in the channel are the main reasons for the generation of a time-frequency doubly-selective (DS) channel. There is severe inter-carrier interference (ICI) in the DS channel. Channel estimation for an orthogonal frequency division multiplexing (OFDM) system over a DS channel is very difficult. The simultaneous orthogonal matching pursuit (SOMP) algorithm under distributed compressive sensing theory (DCS-SOMP) has been used in channel estimation for OFDM systems over DS channels. However, the reconstruction accuracy of the DCS-SOMP algorithm is not high enough in the low Signal-to-Noise Ratio (SNR) stage. To solve this problem, in this paper, we propose an improved DCS-SOMP algorithm based on the inner product difference comparison operation (DCS-DCSOMP). The reconstruction accuracy is improved by increasing the number of candidate indexes and designing the comparison conditions of inner product difference. We combine the DCS-DCSOMP algorithm with the basis expansion model (BEM) to reduce the complexity of channel estimation. Simulation results show the effectiveness of the proposed algorithm and its advantages over other algorithms.

Keywords: OFDM, doubly selective, channel estimation, compressed sensing

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Authors: Farhad Namdari, Reza Sedaghati

Abstract:

Economic dispatch (ED) has been considered to be one of the key functions in electric power system operation which can help to build up effective generating management plans. The practical ED problem has non-smooth cost function with nonlinear constraints which make it difficult to be effectively solved. This paper presents a novel heuristic and efficient optimization approach based on the new Bat algorithm (BA) to solve the practical non-smooth economic dispatch problem. The proposed algorithm easily takes care of different constraints. In addition, two newly introduced modifications method is developed to improve the variety of the bat population when increasing the convergence speed simultaneously. The simulation results obtained by the proposed algorithms are compared with the results obtained using other recently develop methods available in the literature.

Keywords: Non-smooth, economic dispatch, bat-inspired, nonlinear practical constraints, modified bat algorithm.

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Authors: Ahmed Rekik, Mourad Zribi, Ahmed Ben Hamida, Mohamed Benjelloun

Abstract:

This paper presents an optimal and unsupervised satellite image segmentation approach based on Pearson system and k-Means Clustering Algorithm Initialization. Such method could be considered as original by the fact that it utilised K-Means clustering algorithm for an optimal initialisation of image class number on one hand and it exploited Pearson system for an optimal statistical distributions- affectation of each considered class on the other hand. Satellite image exploitation requires the use of different approaches, especially those founded on the unsupervised statistical segmentation principle. Such approaches necessitate definition of several parameters like image class number, class variables- estimation and generalised mixture distributions. Use of statistical images- attributes assured convincing and promoting results under the condition of having an optimal initialisation step with appropriated statistical distributions- affectation. Pearson system associated with a k-means clustering algorithm and Stochastic Expectation-Maximization 'SEM' algorithm could be adapted to such problem. For each image-s class, Pearson system attributes one distribution type according to different parameters and especially the Skewness 'β1' and the kurtosis 'β2'. The different adapted algorithms, K-Means clustering algorithm, SEM algorithm and Pearson system algorithm, are then applied to satellite image segmentation problem. Efficiency of those combined algorithms was firstly validated with the Mean Quadratic Error 'MQE' evaluation, and secondly with visual inspection along several comparisons of these unsupervised images- segmentation.

Keywords: Unsupervised classification, Pearson system, Satellite image, Segmentation.

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Authors: P. Keerthika, P. Suresh

Abstract:

Grid is an environment with millions of resources which are dynamic and heterogeneous in nature. A computational grid is one in which the resources are computing nodes and is meant for applications that involves larger computations. A scheduling algorithm is said to be efficient if and only if it performs better resource allocation even in case of resource failure. Resource allocation is a tedious issue since it has to consider several requirements such as system load, processing cost and time, user’s deadline and resource failure. This work attempts in designing a resource allocation algorithm which is cost-effective and also targets at load balancing, fault tolerance and user satisfaction by considering the above requirements. The proposed Budget Constrained Load Balancing Fault Tolerant algorithm with user satisfaction (BLBFT) reduces the schedule makespan, schedule cost and task failure rate and improves resource utilization. Evaluation of the proposed BLBFT algorithm is done using Gridsim toolkit and the results are compared with the algorithms which separately concentrates on all these factors. The comparison results ensure that the proposed algorithm works better than its counterparts.

Keywords: Grid Scheduling, Load Balancing, fault tolerance, makespan, cost, resource utilization.

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Authors: Mohammed Boutalline, Imad Badi, Belaid Bouikhalene, Said Safi

Abstract:

In this paper we describe the Levenvberg-Marquardt (LM) algorithm for identification and equalization of CDMA signals received by an antenna array in communication channels. The synthesis explains the digital separation and equalization of signals after propagation through multipath generating intersymbol interference (ISI). Exploiting discrete data transmitted and three diversities induced at the reception, the problem can be composed by the Block Component Decomposition (BCD) of a tensor of order 3 which is a new tensor decomposition generalizing the PARAFAC decomposition. We optimize the BCD decomposition by Levenvberg-Marquardt method gives encouraging results compared to classical alternating least squares algorithm (ALS). In the equalization part, we use the Minimum Mean Square Error (MMSE) to perform the presented method. The simulation results using the LM algorithm are important.

Keywords: Identification and equalization, communication channel, Levenvberg-Marquardt, tensor decomposition

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Authors: S. Manzoor, A. Nadeem, M. Javed, ME. Babar

Abstract:

A major goal in animal genetics is to understand the role of common genetic variants in diseases susceptibility and production traits. Sahiwal cattle can be considered as a global animal genetic resource due to its relatively high milk producing ability, resistance against tropical diseases and heat tolerant. CYP11B1 gene provides instructions for making a mitochondrial enzyme called steroid 11-beta-hydroxylase. It catalyzes the 11deoxy-cortisol to cortisol and 11deoxycorticosterone to corticosterone in cattle. The bovine CYP11B1 gene is positioned on BTA14q12 comprises of eight introns and nine exons and protein is associated with mitochondrial epithelium. The present study was aimed to identify the single-nucleotide polymorphisms in CYP11B1 gene in Sahiwal cattle breed of Pakistan. Four polymorphic sites were identified in exon one of CYP11B1 gene through sequencing approach. Significant finding was the incidence of the C→T polymorphism in 5'-UTR, causing amino acid substitution from alanine to valine (A30V) in Sahiwal cattle breed. That Ala/Val polymorphism may serve as a powerful genetic tool for the development of DNA markers that can be used for the particular traits for different local cattle breeds.

Keywords: CYP11B1, single nucleotide polymorphism, sahiwal cattle, Pakistan.

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Authors: Ayyaz Mahmood

Abstract:

A new approach for the improvement of coding gain in channel coding using Advanced Encryption Standard (AES) and Maximum A Posteriori (MAP) algorithm is proposed. This new approach uses the avalanche effect of block cipher algorithm AES and soft output values of MAP decoding algorithm. The performance of proposed approach is evaluated in the presence of Additive White Gaussian Noise (AWGN). For the verification of proposed approach, computer simulation results are included.

Keywords: Advanced Encryption Standard (AES), Avalanche Effect, Maximum A Posteriori (MAP), Soft Input Decryption (SID).

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Authors: S. Heydarzadeh, A. Kadivarian, P. Torkzadeh

Abstract:

Implemented 5-bit 125-MS/s successive approximation register (SAR) analog to digital converter (ADC) on FPGA is presented in this paper.The design and modeling of a high performance SAR analog to digital converter are based on monotonic capacitor switching procedure algorithm .Spartan 3 FPGA is chosen for implementing SAR analog to digital converter algorithm. SAR VHDL program writes in Xilinx and modelsim uses for showing results.

Keywords: Analog to digital converter, Successive approximation, Capacitor switching algorithm, FPGA

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Authors: Fateme Ashari-Ghomi, Najme Khorasani, Abbas Nowzari-Dalini

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In this paper, we present two new ranking and unranking algorithms for k-ary trees represented by x-sequences in Gray code order. These algorithms are based on a gray code generation algorithm developed by Ahrabian et al.. In mentioned paper, a recursive backtracking generation algorithm for x-sequences corresponding to k-ary trees in Gray code was presented. This generation algorithm is based on Vajnovszki-s algorithm for generating binary trees in Gray code ordering. Up to our knowledge no ranking and unranking algorithms were given for x-sequences in this ordering. we present ranking and unranking algorithms with O(kn2) time complexity for x-sequences in this Gray code ordering

Keywords: k-ary Tree Generation, Ranking, Unranking, Gray Code.

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Authors: H.R. Balouchi

Abstract:

To evaluate genetic variation of wheat (Triticum aestivum) affected by heat and drought stress on eight Australian wheat genotypes that are parents of Doubled Haploid (HD) mapping populations at the vegetative stage, the water stress experiment was conducted at 65% field capacity in growth room. Heat stress experiment was conducted in the research field under irrigation over summer. Result show that water stress decreased dry shoot weight and RWC but increased osmolarity and means of Fv/Fm values in all varieties except for Krichauff. Krichauff and Kukri had the maximum RWC under drought stress. Trident variety was shown maximum WUE, osmolarity (610 mM/Kg), dry mater, quantum yield and Fv/Fm 0.815 under water stress condition. However, the recovery of quantum yield was apparent between 4 to 7 days after stress in all varieties. Nevertheless, increase in water stress after that lead to strong decrease in quantum yield. There was a genetic variation for leaf pigments content among varieties under heat stress. Heat stress decreased significantly the total chlorophyll content that measured by SPAD. Krichauff had maximum value of Anthocyanin content (2.978 A/g FW), chlorophyll a+b (2.001 mg/g FW) and chlorophyll a (1.502 mg/g FW). Maximum value of chlorophyll b (0.515 mg/g FW) and Carotenoids (0.234 mg/g FW) content belonged to Kukri. The quantum yield of all varieties decreased significantly, when the weather temperature increased from 28 ÔùªC to 36 ÔùªC during the 6 days. However, the recovery of quantum yield was apparent after 8th day in all varieties. The maximum decrease and recovery in quantum yield was observed in Krichauff. Drought and heat tolerant and moderately tolerant wheat genotypes were included Trident, Krichauff, Kukri and RAC875. Molineux, Berkut and Excalibur were clustered into most sensitive and moderately sensitive genotypes. Finally, the results show that there was a significantly genetic variation among the eight varieties that were studied under heat and water stress.

Keywords: Abiotic stress, genetic variation, fluorescence, wheat genotypes.

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Authors: Kesari Verma, Aniruddha S. Thoke, Pritam Singh

Abstract:

Automatic face detection is a complex problem in image processing. Many methods exist to solve this problem such as template matching, Fisher Linear Discriminate, Neural Networks, SVM, and MRC. Success has been achieved with each method to varying degrees and complexities. In proposed algorithm we used upright, frontal faces for single gray scale images with decent resolution and under good lighting condition. In the field of face recognition technique the single face is matched with single face from the training dataset. The author proposed a neural network based face detection algorithm from the photographs as well as if any test data appears it check from the online scanned training dataset. Experimental result shows that the algorithm detected up to 95% accuracy for any image.

Keywords: Face Detection, Face Recognition, NN Approach, PCA Algorithm.

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Authors: Minghui Wang

Abstract:

Based on the classical algorithm LSQR for solving (unconstrained) LS problem, an iterative method is proposed for the least-squares like-minimum-norm symmetric solution of AXB+CYD=E. As the application of this algorithm, an iterative method for the least-squares like-minimum-norm biymmetric solution of AXB=E is also obtained. Numerical results are reported that show the efficiency of the proposed methods.

Keywords: Matrix equation, bisymmetric matrix, least squares problem, like-minimum norm, iterative algorithm.

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

Abstract:

Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: Local nonlinear estimation, LWPR algorithm, Online training method.

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Authors: Sh. Jamali, A. Eftekhari

Abstract:

Transmission control protocol (TCP) Vegas detects network congestion in the early stage and successfully prevents periodic packet loss that usually occurs in TCP Reno. It has been demonstrated that TCP Vegas outperforms TCP Reno in many aspects. However, TCP Vegas suffers several problems that affect its congestion avoidance mechanism. One of the most important weaknesses in TCP Vegas is that alpha and beta depend on a good expected throughput estimate, which as we have seen, depends on a good minimum RTT estimate. In order to make the system more robust alpha and beta must be made responsive to network conditions (they are currently chosen statically). This paper proposes a modified Vegas algorithm, which can be adjusted to present good performance compared to other transmission control protocols (TCPs). In order to do this, we use PSO algorithm to tune alpha and beta. The simulation results validate the advantages of the proposed algorithm in term of performance.

Keywords: Self-managing, Congestion control, TCP.

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Authors: C. Rajan, N. Shanthi, C. Rasi Priya, K. Geetha

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Routing in MANET is extremely challenging because of MANETs dynamic features, its limited bandwidth, frequent topology changes caused by node mobility and power energy consumption. In order to efficiently transmit data to destinations, the applicable routing algorithms must be implemented in mobile ad-hoc networks. Thus we can increase the efficiency of the routing by satisfying the Quality of Service (QoS) parameters by developing routing algorithms for MANETs. The algorithms that are inspired by the principles of natural biological evolution and distributed collective behavior of social colonies have shown excellence in dealing with complex optimization problems and are becoming more popular. This paper presents a survey on few meta-heuristic algorithms and naturally-inspired algorithms.

Keywords: Ant colony optimization, genetic algorithm, Naturally-inspired algorithms and particle swarm optimization.

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Authors: Nazif Çalış, Murat Erişoğlu, Hamza Erol, Tayfun Servi

Abstract:

In the recent works related with mixture discriminant analysis (MDA), expectation and maximization (EM) algorithm is used to estimate parameters of Gaussian mixtures. But, initial values of EM algorithm affect the final parameters- estimates. Also, when EM algorithm is applied two times, for the same data set, it can be give different results for the estimate of parameters and this affect the classification accuracy of MDA. Forthcoming this problem, we use Self Organizing Mixture Network (SOMN) algorithm to estimate parameters of Gaussians mixtures in MDA that SOMN is more robust when random the initial values of the parameters are used [5]. We show effectiveness of this method on popular simulated waveform datasets and real glass data set.

Keywords: Self Organizing Mixture Network, MixtureDiscriminant Analysis, Waveform Datasets, Glass Identification, Mixture of Multivariate Normal Distributions

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Authors: R. Khorshidi , T. Niknam, M. Nayeripour

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Recently, distributed generation technologies have received much attention for the potential energy savings and reliability assurances that might be achieved as a result of their widespread adoption. Fueling the attention have been the possibilities of international agreements to reduce greenhouse gas emissions, electricity sector restructuring, high power reliability requirements for certain activities, and concern about easing transmission and distribution capacity bottlenecks and congestion. So it is necessary that impact of these kinds of generators on distribution feeder reconfiguration would be investigated. This paper presents an approach for distribution reconfiguration considering Distributed Generators (DGs). The objective function is summation of electrical power losses A Tabu search optimization is used to solve the optimal operation problem. The approach is tested on a real distribution feeder.

Keywords: Distributed Generator, Daily Optimal Operation, Genetic Algorithm.

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Authors: M. H. Abedi, A. Jalilvand

Abstract:

The economic development benefits of wind energy may be the most tangible basis for the local and state officials’ interests. In addition to the direct salaries associated with building and operating wind projects, the wind energy industry provides indirect jobs and benefits. The optimal planning of a wind farm is one most important topic in renewable energy technology. Many methods have been implemented to optimize the cost and output benefit of wind farms, but the contribution of this paper is mentioning different types of service providers and also time of installation of wind turbines during planning horizon years. Genetic algorithm (GA) is used to optimize the problem. It is observed that an appropriate layout of wind farm can cause to minimize the different types of cost.

Keywords: Renewable energy, wind farm, optimization, planning.

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Authors: Liping Jing, Michael K. Ng, Xinhua Yang, Joshua Zhexue Huang

Abstract:

This paper presents a text clustering system developed based on a k-means type subspace clustering algorithm to cluster large, high dimensional and sparse text data. In this algorithm, a new step is added in the k-means clustering process to automatically calculate the weights of keywords in each cluster so that the important words of a cluster can be identified by the weight values. For understanding and interpretation of clustering results, a few keywords that can best represent the semantic topic are extracted from each cluster. Two methods are used to extract the representative words. The candidate words are first selected according to their weights calculated by our new algorithm. Then, the candidates are fed to the WordNet to identify the set of noun words and consolidate the synonymy and hyponymy words. Experimental results have shown that the clustering algorithm is superior to the other subspace clustering algorithms, such as PROCLUS and HARP and kmeans type algorithm, e.g., Bisecting-KMeans. Furthermore, the word extraction method is effective in selection of the words to represent the topics of the clusters.

Keywords: Subspace Clustering, Text Mining, Feature Weighting, Cluster Interpretation, Ontology

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Authors: Anton Stadler, Thorsten Ike

Abstract:

In this paper, we present a video based smoke detection algorithm based on TVL1 optical flow estimation. The main part of the algorithm is an accumulating system for motion angles and upward motion speed of the flow field. We optimized the usage of TVL1 flow estimation for the detection of smoke with very low smoke density. Therefore, we use adapted flow parameters and estimate the flow field on difference images. We show in theory and in evaluation that this improves the performance of smoke detection significantly. We evaluate the smoke algorithm using videos with different smoke densities and different backgrounds. We show that smoke detection is very reliable in varying scenarios. Further we verify that our algorithm is very robust towards crowded scenes disturbance videos.

Keywords: Low density, optical flow, upward smoke motion, video based smoke detection.

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Authors: Parvinder S. Sandhu, Sandeep Khimta, Kiranpreet Kaur

Abstract:

The prediction of Software quality during development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. “Whether a module is faulty or not" approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.

Keywords: Growing Neural Gas, data clustering, fault prediction.

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Authors: A. Ghaffari, A. S. Mostafavi

Abstract:

Complex assemblies of interacting proteins carry out most of the interesting jobs in a cell, such as metabolism, DNA synthesis, mitosis and cell division. These physiological properties play out as a subtle molecular dance, choreographed by underlying regulatory networks that control the activities of cyclin-dependent kinases (CDK). The network can be modeled by a set of nonlinear differential equations and its behavior predicted by numerical simulation. In this paper, an innovative approach has been proposed that uses genetic algorithms to mine a set of behavior data output by a biological system in order to determine the kinetic parameters of the system. In our approach, the machine learning method is integrated with the framework of existent biological information in a wiring diagram so that its findings are expressed in a form of system dynamic behavior. By numerical simulations it has been illustrated that the model is consistent with experiments and successfully shown that such application of genetic algorithms will highly improve the performance of mathematical model of the cell division cycle to simulate such a complicated bio-system.

Keywords: Cell cycle, Cyclin-dependent kinase, Fission yeast, Genetic algorithms, Mathematical modeling, Wiring diagram

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Authors: Yanwen Li, Shuguo Xie

Abstract:

In electromagnetic imaging, because of the diffraction limited system, the pixel values could change slowly near the edge of the image targets and they also change with the location in the same target. Using traditional digital image segmentation methods to segment electromagnetic gradient images could result in lots of errors because of this change in pixel values. To address this issue, this paper proposes a novel image segmentation and extraction algorithm based on Mean-Shift and dictionary learning. Firstly, the preliminary segmentation results from adaptive bandwidth Mean-Shift algorithm are expanded, merged and extracted. Then the overlap rate of the extracted image block is detected before determining a segmentation region with a single complete target. Last, the gradient edge of the extracted targets is recovered and reconstructed by using a dictionary-learning algorithm, while the final segmentation results are obtained which are very close to the gradient target in the original image. Both the experimental results and the simulated results show that the segmentation results are very accurate. The Dice coefficients are improved by 70% to 80% compared with the Mean-Shift only method.

Keywords: Gradient image, segmentation and extract, mean-shift algorithm, dictionary learning.

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Authors: Said Elkassimi, Said Safi, B. Manaut

Abstract:

This paper presented a study of three algorithms, the equalization algorithm to equalize the transmission channel with ZF and MMSE criteria, application of channel Bran A, and adaptive filtering algorithms LMS and RLS to estimate the parameters of the equalizer filter, i.e. move to the channel estimation and therefore reflect the temporal variations of the channel, and reduce the error in the transmitted signal. So far the performance of the algorithm equalizer with ZF and MMSE criteria both in the case without noise, a comparison of performance of the LMS and RLS algorithm.

Keywords: Adaptive filtering second equalizer, LMS, RLS Bran A, Proakis (B) MMSE, ZF.

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Authors: Konstanca Nikolajevic, Nicolas Belanger, David Duvivier, Rabie Ben Atitallah, Abdelhakim Artiba

Abstract:

Finding the optimal 3D path of an aerial vehicle under flight mechanics constraints is a major challenge, especially when the algorithm has to produce real time results in flight. Kinematics models and Pythagorian Hodograph curves have been widely used in mobile robotics to solve this problematic. The level of difficulty is mainly driven by the number of constraints to be saturated at the same time while minimizing the total length of the path. In this paper, we suggest a pragmatic algorithm capable of saturating at the same time most of dimensioning helicopter 3D trajectories’ constraints like: curvature, curvature derivative, torsion, torsion derivative, climb angle, climb angle derivative, positions. The trajectories generation algorithm is able to generate versatile complex 3D motion primitives feasible by a helicopter with parameterization of the curvature and the climb angle. An upper ”motion primitives’ concatenation” algorithm is presented based. In this article we introduce a new way of designing three-dimensional trajectories based on what we call the ”Dubins gliding symmetry conjecture”. This extremely performing algorithm will be soon integrated to a real-time decisional system dealing with inflight safety issues.

Keywords: Aerial robots, Motion primitives, Robotics.

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Authors: Nikolaos Kroupis, Minas Dasygenis, Dimitrios Soudris, Antonios Thanailakis

Abstract:

One of the most growing areas in the embedded community is multimedia devices. Multimedia devices incorporate a number of complicated functions for their operation, like motion estimation. A multitude of different implementations have been proposed to reduce motion estimation complexity, such as spiral search. We have studied the implementations of spiral search and identified areas of improvement. We propose a modified spiral search algorithm, with lower computational complexity compared to the original spiral search. We have implemented our algorithm on an embedded ARM based architecture, with custom memory hierarchy. The resulting system yields energy consumption reduction up to 64% and performance increase up to 77%, with a small penalty of 2.3 dB, in average, of video quality compared with the original spiral search algorithm.

Keywords: Spiral Search, Motion Estimation, Embedded Systems, Low Power

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Authors: Bing Yu , Xingshi He

Abstract:

In this paper, Differential Evolution (DE) algorithm, a new promising evolutionary algorithm, is proposed to train Radial Basis Function (RBF) network related to automatic configuration of network architecture. Classification tasks on data sets: Iris, Wine, New-thyroid, and Glass are conducted to measure the performance of neural networks. Compared with a standard RBF training algorithm in Matlab neural network toolbox, DE achieves more rational architecture for RBF networks. The resulting networks hence obtain strong generalization abilities.

Keywords: differential evolution, neural network, Rbf function

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Authors: J. S. Nah, A. Y. Jeon, J. H. Ro, G. R. Jeon

Abstract:

ECG analysis method was developed using ROC analysis of PVC detecting algorithm. ECG signal of MIT-BIH arrhythmia database was analyzed by MATLAB. First of all, the baseline was removed by median filter to preprocess the ECG signal. R peaks were detected for ECG analysis method, and normal VCG was extracted for VCG analysis method. Four PVC detecting algorithm was analyzed by ROC curve, which parameters are maximum amplitude of QRS complex, width of QRS complex, r-r interval and geometric mean of VCG. To set cut-off value of parameters, ROC curve was estimated by true-positive rate (sensitivity) and false-positive rate. sensitivity and false negative rate (specificity) of ROC curve calculated, and ECG was analyzed using cut-off value which was estimated from ROC curve. As a result, PVC detecting algorithm of VCG geometric mean have high availability, and PVC could be detected more accurately with amplitude and width of QRS complex.

Keywords: Vectorcardiogram (VCG), Premature Ventricular contraction (PVC), ROC (receiver operating characteristic) curve, ECG

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Authors: P. Asadee

Abstract:

Multiplication algorithms have considerable effect on processors performance. A new high-speed, low-power multiplication algorithm has been presented using modified Dadda tree structure. Three important modifications have been implemented in inner product generation step, inner product reduction step and final addition step. Optimized algorithms have to be used into basic computation components, such as multiplication algorithms. In this paper, we proposed a new algorithm to reduce power, delay, and transistor count of a multiplication algorithm implemented using low power modified counter. This work presents a novel design for Dadda multiplication algorithms. The proposed multiplication algorithm includes structured parts, which have important effect on inner product reduction tree. In this paper, a 1.3V, 64-bit carry hybrid adder is presented for fast, low voltage applications. The new 64-bit adder uses a new circuit to implement the proposed carry hybrid adder. The new adder using 80 nm CMOS technology has been implemented on 700 MHz clock frequency. The proposed multiplication algorithm has achieved 14 percent improvement in transistor count, 13 percent reduction in delay and 12 percent modification in power consumption in compared with conventional designs.

Keywords: adder, CMOS, counter, Dadda tree, encoder.

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