Search results for: variable step-size adaptive algorithms

Authors: Young-Seok Choi

Abstract:

This paper presents a normalized subband adaptive filtering (NSAF) algorithm to cope with the sparsity condition of an underlying system in the context of compressive sensing. By regularizing a weighted l1-norm of the filter taps estimate onto the cost function of the NSAF and utilizing a subgradient analysis, the update recursion of the l1-norm constraint NSAF is derived. Considering two distinct weighted l1-norm regularization cases, two versions of the l1-norm constraint NSAF are presented. Simulation results clearly indicate the superior performance of the proposed l1-norm constraint NSAFs comparing with the classical NSAF.

Keywords: Subband adaptive filtering, sparsity constraint, weighted l1-norm.

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Authors: Marine Segui, Simon Bezin, Ruxandra Mihaela Botez

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As part of a ‘Morphing-Wing’ idea, this study consists of measuring how a winglet, which is able to change its shape during the flight, is efficient. Conventionally, winglets are fixed-vertical platforms at the wingtips, optimized for a cruise condition that the airplane should use most of the time. However, during a cruise, an airplane flies through a lot of cruise conditions corresponding to altitudes variations from 30,000 to 45,000 ft. The fixed winglets are not optimized for these variations, and consequently, they are supposed to generate some drag, and thus to deteriorate aircraft fuel consumption. This research assumes that it exists a winglet position that reduces the fuel consumption for each cruise condition. In this way, the methodology aims to find these optimal winglet positions, and to further simulate, and thus estimate the fuel consumption of an aircraft wearing this type of adaptive winglet during several cruise conditions. The adaptive winglet is assumed to have degrees of freedom given by the various changes of following surfaces: the tip chord, the sweep and the dihedral angles. Finally, results obtained during cruise simulations are presented in this paper. These results show that an adaptive winglet can reduce, thus improve up to 2.12% the fuel consumption of an aircraft during a cruise.

Keywords: Aerodynamics, Cessna Citation X, optimization, winglet, adaptive, morphing, wing, aircraft.

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Authors: Saeed Mian Qaisar, Laurent Fesquet, Marc Renaudin

Abstract:

Mostly the real life signals are time varying in nature. For proper characterization of such signals, time-frequency representation is required. The STFT (short-time Fourier transform) is a classical tool used for this purpose. The limitation of the STFT is its fixed time-frequency resolution. Thus, an enhanced version of the STFT, which is based on the cross-level sampling, is devised. It can adapt the sampling frequency and the window function length by following the input signal local variations. Therefore, it provides an adaptive resolution time-frequency representation of the input. The computational complexity of the proposed STFT is deduced and compared to the classical one. The results show a significant gain of the computational efficiency and hence of the processing power. The processing error of the proposed technique is also discussed.

Keywords: Level Crossing Sampling, Activity Selection, Adaptive Resolution Analysis, Computational Complexity

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Authors: Andrey V. Timofeev

Abstract:

An adaptive nonparametric method is proposed for stable real-time detection of seismoacoustic sources in multichannel C-OTDR systems with a significant number of channels. This method guarantees given upper boundaries for probabilities of Type I and Type II errors. Properties of the proposed method are rigorously proved. The results of practical applications of the proposed method in a real C-OTDR-system are presented in this report.

Keywords: Adaptive detection, change point, interval estimation, guaranteed detection, multichannel monitoring systems.

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Authors: N. Praveen Kumar, Abhijit Mitra, C. Ardil

Abstract:

Decision feedback equalizers are commonly employed to reduce the error caused by intersymbol interference. Here, an adaptive decision feedback equalizer is presented with a new adaptation algorithm. The algorithm follows a block-based approach of normalized least mean square (NLMS) algorithm with set-membership filtering and achieves a significantly less computational complexity over its conventional NLMS counterpart with set-membership filtering. It is shown in the results that the proposed algorithm yields similar type of bit error rate performance over a reasonable signal to noise ratio in comparison with the latter one.

Keywords: Decision feedback equalizer, Adaptive algorithm, Block based computation, Set membership filtering.

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Authors: Seung-Won Jung, Hye-Soo Kim, Le Thanh Ha, Seung-Jin Baek, Sung-Jea Ko

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In this paper, a novel deinterlacing algorithm is proposed. The proposed algorithm approximates the distribution of the luminance into a polynomial function. Instead of using one polynomial function for all pixels, different polynomial functions are used for the uniform, texture, and directional edge regions. The function coefficients for each region are computed by matrix multiplications. Experimental results demonstrate that the proposed method performs better than the conventional algorithms.

Keywords: Deinterlacing, polynomial interpolation.

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Authors: Anamika Jain, A. S. Thoke, R. N. Patel

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This paper addresses the problems encountered by conventional distance relays when protecting double-circuit transmission lines. The problems arise principally as a result of the mutual coupling between the two circuits under different fault conditions; this mutual coupling is highly nonlinear in nature. An adaptive protection scheme is proposed for such lines based on application of artificial neural network (ANN). ANN has the ability to classify the nonlinear relationship between measured signals by identifying different patterns of the associated signals. One of the key points of the present work is that only current signals measured at local end have been used to detect and classify the faults in the double circuit transmission line with double end infeed. The adaptive protection scheme is tested under a specific fault type, but varying fault location, fault resistance, fault inception angle and with remote end infeed. An improved performance is experienced once the neural network is trained adequately, which performs precisely when faced with different system parameters and conditions. The entire test results clearly show that the fault is detected and classified within a quarter cycle; thus the proposed adaptive protection technique is well suited for double circuit transmission line fault detection & classification. Results of performance studies show that the proposed neural network-based module can improve the performance of conventional fault selection algorithms.

Keywords: Double circuit transmission line, Fault detection and classification, High impedance fault and Artificial Neural Network.

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Authors: C. Rajan, K. Geetha, C. Rasi Priya, R. Sasikala

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Nature is a great source of inspiration for solving complex problems in networks. It helps to find the optimal solution. Metaheuristic algorithm is one of the nature-inspired algorithm which helps in solving routing problem in networks. The dynamic features, changing of topology frequently and limited bandwidth make the routing, challenging in MANET. Implementation of appropriate routing algorithms leads to the efficient transmission of data in mobile ad hoc networks. The algorithms that are inspired by the principles of naturally-distributed/collective behavior of social colonies have shown excellence in dealing with complex optimization problems. Thus some of the bio-inspired metaheuristic algorithms help to increase the efficiency of routing in ad hoc networks. This survey work presents the overview of bio-inspired metaheuristic algorithms which support the efficiency of routing in mobile ad hoc networks.

Keywords: Ant colony optimization algorithm, Genetic algorithm, naturally inspired algorithms and particle swarm optimization algorithm.

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Authors: M. Soryani, N. Rafat

Abstract:

Dealing with hundreds of features in character recognition systems is not unusual. This large number of features leads to the increase of computational workload of recognition process. There have been many methods which try to remove unnecessary or redundant features and reduce feature dimensionality. Besides because of the characteristics of Farsi scripts, it-s not possible to apply other languages algorithms to Farsi directly. In this paper some methods for feature subset selection using genetic algorithms are applied on a Farsi optical character recognition (OCR) system. Experimental results show that application of genetic algorithms (GA) to feature subset selection in a Farsi OCR results in lower computational complexity and enhanced recognition rate.

Keywords: Feature Subset Selection, Genetic Algorithms, Optical Character Recognition.

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Authors: S. Balaji, E. Golden Julie, Y. Harold Robinson

Abstract:

The context aware adaptive routing protocol is presented for unicast communication in intermittently connected mobile ad hoc networks (MANETs). The selection of the node is done by the Kalman filter prediction theory and it also makes use of utility functions. The context aware adaptive routing is defined by spray and wait technique, but the time consumption in delivering the message is too high and also the resource wastage is more. In this paper, we describe the spray and focus routing scheme for avoiding the existing problems.

Keywords: Context aware adaptive routing, Kalman filter prediction, spray and wait, spray and focus, intermittent networks.

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Authors: Roelien Goede

Abstract:

Data Structures and Algorithms is a module in most Computer Science or Information Technology curricula. It is one of the modules most students identify as being difficult. This paper demonstrates how programming a solution for Sudoku can make abstract concepts more concrete. The paper relates concepts of a typical Data Structures and Algorithms module to a step by step solution for Sudoku in a human type as opposed to a computer oriented solution.

Keywords: Data Structures, Algorithms, Sudoku, ObjectOriented Programming, Programming Teaching, Education.

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Authors: D. Elleuch, T. Damak

Abstract:

Performance control law is studied for an interconnected fractional nonlinear system. Applying a backstepping algorithm, a backstepping sliding mode controller (BSMC) is developed for fractional nonlinear system. To improve control law performance, BSMC is coupled to an adaptive sliding mode observer have a filtered error as a sliding surface. The both architecture performance is studied throughout the inverted pendulum mounted on a cart. Simulation result show that the BSMC coupled to an adaptive sliding mode observer have stable control law and eligible control amplitude than the BSMC.

Keywords: Backstepping sliding mode controller, interconnected fractional nonlinear system, adaptive sliding mode observer.

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Authors: S. Prabha, K. Duraiswamy, J. Indhumathi

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Search engine plays an important role in internet, to retrieve the relevant documents among the huge number of web pages. However, it retrieves more number of documents, which are all relevant to your search topics. To retrieve the most meaningful documents related to search topics, ranking algorithm is used in information retrieval technique. One of the issues in data miming is ranking the retrieved document. In information retrieval the ranking is one of the practical problems. This paper includes various Page Ranking algorithms, page segmentation algorithms and compares those algorithms used for Information Retrieval. Diverse Page Rank based algorithms like Page Rank (PR), Weighted Page Rank (WPR), Weight Page Content Rank (WPCR), Hyperlink Induced Topic Selection (HITS), Distance Rank, Eigen Rumor, Distance Rank Time Rank, Tag Rank, Relational Based Page Rank and Query Dependent Ranking algorithms are discussed and compared.

Keywords: Information Retrieval, Web Page Ranking, search engine, web mining, page segmentations.

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Authors: Jianwei Wu

Abstract:

Based on the one-bit-matching principle and by turning the de-mixing matrix into an orthogonal matrix via certain normalization, Ma et al proposed a one-bit-matching learning algorithm on the Stiefel manifold for independent component analysis [8]. But this algorithm is not adaptive. In this paper, an algorithm which can extract kurtosis and its sign of each independent source component directly from observation data is firstly introduced.With the algorithm , the one-bit-matching learning algorithm is revised, so that it can make the blind separation on the Stiefel manifold implemented completely in the adaptive mode in the framework of natural gradient.

Keywords: Independent component analysis, kurtosis, Stiefel manifold, super-gaussians or sub-gaussians.

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Authors: Seung-Jun Yu, Hwan-Jun Choi, Hyoung-Kyu Song

Abstract:

Among spatial diversity scheme, orthogonal space-time block code (OSTBC) and cyclic delay diversity (CDD) have been widely studied for the cooperative wireless relaying system. However, conventional OSTBC and CDD cannot cope with change in the number of relays owing to low throughput or error performance. In this paper, we propose a cooperative cyclic delay diversity (CDD) scheme that use hierarchical modulation at the source and adaptive modulation based on cyclic redundancy check (CRC) code at the relays.

Keywords: Adaptive modulation, Cooperative communication, CDD, OSTBC.

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Authors: Najmeh Abedzadeh, Matthew Jacobs

Abstract:

An intrusion detection system (IDS) is a software application that monitors malicious activities and generates alerts if any are detected. However, most network activities in IDS datasets are normal, and the relatively few numbers of attacks make the available data imbalanced. Consequently, cyber-attacks can hide inside a large number of normal activities, and machine learning algorithms have difficulty learning and classifying the data correctly. In this paper, a comprehensive literature review is conducted on different types of algorithms for both implementing the IDS and methods in correcting the imbalanced IDS dataset. The most famous algorithms are machine learning (ML), deep learning (DL), synthetic minority over-sampling technique (SMOTE), and reinforcement learning (RL). Most of the research use the CSE-CIC-IDS2017, CSE-CIC-IDS2018, and NSL-KDD datasets for evaluating their algorithms.

Keywords: IDS, intrusion detection system, imbalanced datasets, sampling algorithms, big data.

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Authors: Mhand Hifi, Hedi Mhalla, Mustapha Michaphy

Abstract:

In this paper, we study the multi-scenario knapsack problem, a variant of the well-known NP-Hard single knapsack problem. We investigate the use of an adaptive algorithm for solving heuristically the problem. The used method combines two complementary phases: a size reduction phase and a dynamic 2- opt procedure one. First, the reduction phase applies a polynomial reduction strategy; that is used for reducing the size problem. Second, the adaptive search procedure is applied in order to attain a feasible solution Finally, the performances of two versions of the proposed algorithm are evaluated on a set of randomly generated instances.

Keywords: combinatorial optimization, max-min optimization, knapsack, heuristics, problem reduction

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Authors: Nazleeni Samiha Haron, Anang Hudaya Muhamad Amin, Mohd Hilmi Hasan, Izzatdin Abdul Aziz, Wirdhayu Mohd Wahid

Abstract:

In any distributed systems, process scheduling plays a vital role in determining the efficiency of the system. Process scheduling algorithms are used to ensure that the components of the system would be able to maximize its utilization and able to complete all the processes assigned in a specified period of time. This paper focuses on the development of comparative simulator for distributed process scheduling algorithms. The objectives of the works that have been carried out include the development of the comparative simulator, as well as to implement a comparative study between three distributed process scheduling algorithms; senderinitiated, receiver-initiated and hybrid sender-receiver-initiated algorithms. The comparative study was done based on the Average Waiting Time (AWT) and Average Turnaround Time (ATT) of the processes involved. The simulation results show that the performance of the algorithms depends on the number of nodes in the system.

Keywords: Distributed Systems, Load Sharing, Process Scheduling, AWT and ATT

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Authors: Fituri H Belgassem, Abdulbasit Nigrat, Seddeeq Ghrari

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In this paper, the construction of fast algorithms for the computation of Periodic Walsh Piecewise-Linear PWL transform and the Periodic Haar Piecewise-Linear PHL transform will be presented. Algorithms for the computation of the inverse transforms are also proposed. The matrix equation of the PWL and PHL transforms are introduced. Comparison of the computational requirements for the periodic piecewise-linear transforms and other orthogonal transforms shows that the periodic piecewise-linear transforms require less number of operations than some orthogonal transforms such as the Fourier, Walsh and the Discrete Cosine transforms.

Keywords: Piece wise linear transforms, Fast transforms, Fast algorithms.

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Authors: Hamed.Vezvaei, Sepideh.Ordibeheshti, Mehdi.Ardjmand

Abstract:

Gasoline Octane Number is the standard measure of the anti-knock properties of a motor in platforming processes, that is one of the important unit operations for oil refineries and can be determined with online measurement or use CFR (Cooperative Fuel Research) engines. Online measurements of the Octane number can be done using direct octane number analyzers, that it is too expensive, so we have to find feasible analyzer, like ANFIS estimators. ANFIS is the systems that neural network incorporated in fuzzy systems, using data automatically by learning algorithms of NNs. ANFIS constructs an input-output mapping based both on human knowledge and on generated input-output data pairs. In this research, 31 industrial data sets are used (21 data for training and the rest of the data used for generalization). Results show that, according to this simulation, hybrid method training algorithm in ANFIS has good agreements between industrial data and simulated results.

Keywords: Adaptive Neuro-Fuzzy Inference Systems, GasolineOctane Number, Soft-sensor, Catalytic Naphtha Reforming

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Authors: M. Hadi Mashinchi, M. Reza Mashinchi, Siti Mariyam H. J. Shamsuddin

Abstract:

In this paper a genetic algorithms approach for solving the linear and quadratic fuzzy equations Ãx̃=B̃ and Ãx̃² + B̃x̃=C̃ , where Ã, B̃, C̃ and x̃ are fuzzy numbers is proposed by genetic algorithms. Our genetic based method initially starts with a set of random fuzzy solutions. Then in each generation of genetic algorithms, the solution candidates converge more to better fuzzy solution x̃_b . In this proposed method the final reached x̃_b is not only restricted to fuzzy triangular and it can be fuzzy number.

Keywords: Fuzzy coefficient, fuzzy equation, genetic algorithms.

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Authors: Cuiran Li, Jianli Xie, Chengshu Li

Abstract:

Multicarrier code-division multiple-access is one of the effective techniques to gain its multiple access capability, robustness against fading, and to mitigate the ISI. In this paper, we propose an improved mulcarrier CDMA system with adaptive subchannel allocation. We analyzed the performance of our proposed system in frequency selective fading environment with narrowband interference existing and compared it with that of parallel transmission over many subchannels (namely, conventional MC-CDMA scheme) and DS-CDMA system. Simulation results show that adaptive subchannel allocation scheme, when used in conventional multicarrier CDMA system, the performance will be greatly improved.

Keywords: MC-CDMA, Rayleigh fading, Narrowbandinterference, Channel estimation.

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Authors: Lu Yanhui, Zhang Lizhi, Yin Changchuan, Yue Guangxin

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In this paper, an adaptive radio resource allocation (RRA) algorithm applying to multiple traffic OFDMA system is proposed, which distributes sub-carrier and loading bits among users according to their different QoS requirements and traffic class. By classifying and prioritizing the users based on their traffic characteristic and ensuring resource for higher priority users, the scheme decreases tremendously the outage probability of the users requiring a real time transmission without impact on the spectrum efficiency of system, as well as the outage probability of data users is not increased compared with the RRA methods published.

Keywords: OFDMA, adaptive radio resource allocation, QoS.

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Authors: M. D. Kokate, T. R. Sontakke, P. W. Wani

Abstract:

This paper deals with an adaptive multiuser detector for direct sequence code division multiple-access (DS-CDMA) systems. A modified receiver, precombinig LMMSE is considered under time varying channel environment. Detector updating is performed with two criterions, mean square estimation (MSE) and MOE optimization technique. The adaptive implementation issues of these two schemes are quite different. MSE criterion updates the filter weights by minimizing error between data vector and adaptive vector. MOE criterion together with canonical representation of the detector results in a constrained optimization problem. Even though the canonical representation is very complicated under time varying channels, it is analyzed with assumption of average power profile of multipath replicas of user of interest. The performance of both schemes is studied for practical SNR conditions. Results show that for poor SNR, MSE precombining LMMSE is better than the blind precombining LMMSE but for greater SNR, MOE scheme outperforms with better result.

Keywords: LMMSE, MOE, MUD.

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Authors: Da-kuan Wei, Xian-zhong Zhou, Dong-jun Xin, Zhi-wei Chen

Abstract:

The information systems with incomplete attribute values and fuzzy decisions commonly exist in practical problems. On the base of the notion of variable precision rough set model for incomplete information system and the rough set model for incomplete and fuzzy decision information system, the variable rough set model for incomplete and fuzzy decision information system is constructed, which is the generalization of the variable precision rough set model for incomplete information system and that of rough set model for incomplete and fuzzy decision information system. The knowledge reduction and heuristic algorithm, built on the method and theory of precision reduction, are proposed.

Keywords: Rough set, Incomplete and fuzzy decision information system, Limited valued tolerance relation, Knowledge reduction, Variable rough set model

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Authors: Sheena Mathew, K. Poulose Jacob

Abstract:

Extensive use of the Internet coupled with the marvelous growth in e-commerce and m-commerce has created a huge demand for information security. The Secure Socket Layer (SSL) protocol is the most widely used security protocol in the Internet which meets this demand. It provides protection against eaves droppings, tampering and forgery. The cryptographic algorithms RC4 and HMAC have been in use for achieving security services like confidentiality and authentication in the SSL. But recent attacks against RC4 and HMAC have raised questions in the confidence on these algorithms. Hence two novel cryptographic algorithms MAJE4 and MACJER-320 have been proposed as substitutes for them. The focus of this work is to demonstrate the performance of these new algorithms and suggest them as dependable alternatives to satisfy the need of security services in SSL. The performance evaluation has been done by using practical implementation method.

Keywords: Confidentiality, HMAC, Integrity, MACJER-320, MAJE4, RC4, Secure Socket Layer

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Authors: J. K. Alhassan, B. Attah, S. Misra

Abstract:

Human beings have the ability to make logical decisions. Although human decision - making is often optimal, it is insufficient when huge amount of data is to be classified. Medical dataset is a vital ingredient used in predicting patient’s health condition. In other to have the best prediction, there calls for most suitable machine learning algorithms. This work compared the performance of Artificial Neural Network (ANN) and Decision Tree Algorithms (DTA) as regards to some performance metrics using diabetes data. WEKA software was used for the implementation of the algorithms. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were the two algorithms used for ANN, while RegTree and LADTree algorithms were the DTA models used. From the results obtained, DTA performed better than ANN. The Root Mean Squared Error (RMSE) of MLP is 0.3913 that of RBF is 0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206 respectively.

Keywords: Artificial neural network, classification, decision tree, diabetes mellitus.

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Authors: Harish Chander, Balwinder Singh, Ravinder Khanna

Abstract:

In this paper, we present the comparative subjective analysis of Improved Minima Controlled Recursive Averaging (IMCRA) Algorithm, the Kalman filter and the cascading of IMCRA and Kalman filter algorithms. Performance of speech enhancement algorithms can be predicted in two different ways. One is the objective method of evaluation in which the speech quality parameters are predicted computationally. The second is a subjective listening test in which the processed speech signal is subjected to the listeners who judge the quality of speech on certain parameters. The comparative objective evaluation of these algorithms was analyzed in terms of Global SNR, Segmental SNR and Perceptual Evaluation of Speech Quality (PESQ) by the authors and it was reported that with cascaded algorithms there is a substantial increase in objective parameters. Since subjective evaluation is the real test to judge the quality of speech enhancement algorithms, the authenticity of superiority of cascaded algorithms over individual IMCRA and Kalman algorithms is tested through subjective analysis in this paper. The results of subjective listening tests have confirmed that the cascaded algorithms perform better under all types of noise conditions.

Keywords: Speech enhancement, spectral domain, time domain, PESQ, subjective analysis, objective analysis.

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Authors: A. Habibizad Navin, M. Naghian Fesharaki, M. Mirnia, M. Kargar

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In this paper we introduce Digital Probability Hyper Digraph for modeling random variable as the hierarchical data-oriented model.

Keywords: Data-Oriented Models, Data Structure, DigitalProbability Hyper Digraph, Random Variable, Statistic andProbability.

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Authors: Bothinah Altaf, Gary Montague, Elaine B. Martin

Abstract:

This study involves the modeling and monitoring of an ammonia synthesis fixed-bed reactor using partial least squares (PLS) and its variants. The process exhibits complex dynamic behavior due to the presence of heat recycling and feed quench. One limitation of static PLS model in this situation is that it does not take account of the process dynamics and hence dynamic PLS was used. Although it showed, superior performance to static PLS in terms of prediction, the monitoring scheme was inappropriate hence adaptive PLS was considered. A limitation of adaptive PLS is that non-conforming observations also contribute to the model, therefore, a new adaptive approach was developed, robust adaptive dynamic PLS. This approach updates a dynamic PLS model and is robust to non-representative data. The developed methodology showed a clear improvement over existing approaches in terms of the modeling of the reactor and the detection of faults.

Keywords: Ammonia synthesis fixed-bed reactor, dynamic partial least squares modeling, recursive partial least squares, robust modeling.

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