Search results for: Recursive least squares(RLS)

Authors: N.B. Adams

Abstract:

This article discusses the concept of student ownership of knowledge and seeks to determine how to move students from knowledge acquisition to knowledge application and ultimately to knowledge generation in a virtual setting. Instructional strategies for fostering student engagement in a virtual environment are critical to the learner-s strategic ownership of the knowledge. A number of relevant theories that focus on learning, affect, needs and adult concerns are presented to provide a basis for exploring the transfer of knowledge from teacher to learner. A model under development is presented that combines the dimensions of knowledge approach, the teacher-student relationship with regards to knowledge authority and teaching approach to demonstrate the recursive and scaffolded design for creation of virtual learning environments.

Keywords: Virtual learning environments, learning theory, teaching model, online learning.

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Authors: S. Elnobi, Iman El-Zahaby, Amr M. Mahros

Abstract:

In mobile communication systems, performance and capacity are affected by multi-path fading, delay spread and Co-Channel Interference (CCI). For this reason Orthogonal Frequency Division Multiplexing (OFDM) and adaptive antenna array are used is required. The goal of the OFDM is to improve the system performance against Inter-Symbol Interference (ISI). An array of adaptive antennas has been employed to suppress CCI by spatial technique. To suppress CCI in OFDM systems two main schemes the pre-FFT and the post-FFT have been proposed. In this paper, through a system level simulation, the behavior of the pre-FFT and post-FFT beamformers for OFDM system has been investigated based on two algorithms namely, Least Mean Squares (LMS) and Recursive Least Squares (RLS). The performance of the system is also discussed in multipath fading channel system specified by 3GPP Long Term Evolution (LTE).

Keywords: OFDM, Beamforming, Adaptive Antennas Array.

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Authors: Kaan T. Oner, Ertugrul Cetinsoy, Efe Sirimoglu, Cevdet Hancer, Taylan Ayken, Mustafa Unel

Abstract:

We present our ongoing work on the development of a new quadrotor aerial vehicle which has a tilt-wing mechanism. The vehicle is capable of take-off/landing in vertical flight mode (VTOL) and flying over long distances in horizontal flight mode. Full dynamic model of the vehicle is derived using Newton-Euler formulation. Linear and nonlinear controllers for the stabilization of attitude of the vehicle and control of its altitude have been designed and implemented via simulations. In particular, an LQR controller has been shown to be quite effective in the vertical flight mode for all possible yaw angles. A sliding mode controller (SMC) with recursive nature has also been proposed to stabilize the vehicle-s attitude and altitude. Simulation results show that proposed controllers provide satisfactory performance in achieving desired maneuvers.

Keywords: UAV, VTOL, dynamic model, stabilization, LQR, SMC

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Authors: O.M. Mohamed vall, M. Radhi

Abstract:

This work presents a recursive identification algorithm. This algorithm relates to the identification of closed loop system with Variable Structure Controller. The approach suggested includes two stages. In the first stage a genetic algorithm is used to obtain the parameters of switching function which gives a control signal rich in commutations (i.e. a control signal whose spectral characteristics are closest possible to those of a white noise signal). The second stage consists in the identification of the system parameters by the instrumental variable method and using the optimal switching function parameters obtained with the genetic algorithm. In order to test the validity of this algorithm a simulation example is presented.

Keywords: Closed loop identification, variable structure controller, pseud-random binary sequence, genetic algorithms.

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Authors: Tomohiro Hachino, Kengo Nagatomo, Hitoshi Takata

Abstract:

This paper deals with an on-line identification method of continuous-time Hammerstein systems by using the radial basis function (RBF) networks and immune algorithm (IA). An unknown nonlinear static part to be estimated is approximately represented by the RBF network. The IA is efficiently combined with the recursive least-squares (RLS) method. The objective function for the identification is regarded as the antigen. The candidates of the RBF parameters such as the centers and widths are coded into binary bit strings as the antibodies and searched by the IA. On the other hand, the candidates of both the weighting parameters of the RBF network and the system parameters of the linear dynamic part are updated by the RLS method. Simulation results are shown to illustrate the proposed method.

Keywords: Continuous-time System, Hammerstein System, OnlineIdentification, Immune Algorithm, RBF network.

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

Abstract:

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: Karandeep Singh, Baibing Li

Abstract:

Traffic density, an indicator of traffic conditions, is one of the most critical characteristics to Intelligent Transport Systems (ITS). This paper investigates recursive traffic density estimation using the information provided from inductive loop detectors. On the basis of the phenomenological relationship between speed and density, the existing studies incorporate a state space model and update the density estimate using vehicular speed observations via the extended Kalman filter, where an approximation is made because of the linearization of the nonlinear observation equation. In practice, this may lead to substantial estimation errors. This paper incorporates a suitable transformation to deal with the nonlinear observation equation so that the approximation is avoided when using Kalman filter to estimate the traffic density. A numerical study is conducted. It is shown that the developed method outperforms the existing methods for traffic density estimation.

Keywords: Density estimation, Kalman filter, speed-densityrelationship, Traffic surveillance.

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Authors: Mohammad Ghavami, Reza S. Dilmaghani

Abstract:

This paper presents an adaptive framework for modelling financial markets using equity risk premiums, risk free rates and volatilities. The recorded economic factors are initially used to train four adaptive filters for a certain limited period of time in the past. Once the systems are trained, the adjusted coefficients are used for modelling and prediction of an important financial market index. Two different approaches based on least mean squares (LMS) and recursive least squares (RLS) algorithms are investigated. Performance analysis of each method in terms of the mean squared error (MSE) is presented and the results are discussed. Computer simulations carried out using recorded data show MSEs of 4% and 3.4% for the next month prediction using LMS and RLS adaptive algorithms, respectively. In terms of twelve months prediction, RLS method shows a better tendency estimation compared to the LMS algorithm.

Keywords: Prediction of financial markets, Adaptive methods, MSE, LSE.

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Authors: Jac W. Andrews, J. B. Hale

Abstract:

Effective practicing psychologists require ongoing skill development that is constructivist and recursive in nature, with mentor, colleague, co-worker, and patient feedback critical to successful acquisition and maintenance of professional competencies. This paper will provide an overview of the nature and scope of psychologist skill development through multisource feedback (MSF) or 360 degree evaluation, present a rationale for its use for assessing practicing psychologist performance, and advocate its use in psychology given the demonstrated model utility in other health professions. The paper will conclude that an international research design is needed to assess the feasibility, reliability, and validity of MSF system ratings intended to solicit feedback from mentors, colleagues, coworkers, and patients about psychologist competencies. If adopted, the MSF model could lead to enhanced skill development that fosters patient satisfaction within and across countries.

 

Keywords: Psychologist, multisource feedback, psychologist competency, professionalism.

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Authors: Abder-Rahman Ali, Adélaïde Albouy-Kissi, Manuel Grand-Brochier, Viviane Ladan-Marcus, Christine Hoeffl, Claude Marcus, Antoine Vacavant, Jean-Yves Boire

Abstract:

In this paper, we present a new segmentation approach for focal liver lesions in contrast enhanced ultrasound imaging. This approach, based on a two-cluster Fuzzy C-Means methodology, considers type-II fuzzy sets to handle uncertainty due to the image modality (presence of speckle noise, low contrast, etc.), and to calculate the optimum inter-cluster threshold. Fine boundaries are detected by a local recursive merging of ambiguous pixels. The method has been tested on a representative database. Compared to both Otsu and type-I Fuzzy C-Means techniques, the proposed method significantly reduces the segmentation errors.

Keywords: Defuzzification, fuzzy clustering, image segmentation, type-II fuzzy sets.

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Authors: Khalid M. Aamir, Mohammad A. Maud

Abstract:

Power Spectral Density (PSD) of quasi-stationary processes can be efficiently estimated using the short time Fourier series (STFT). In this paper, an algorithm has been proposed that computes the PSD of quasi-stationary process efficiently using offline autoregressive model order estimation algorithm, recursive parameter estimation technique and modified sliding window discrete Fourier Transform algorithm. The main difference in this algorithm and STFT is that the sliding window (SW) and window for spectral estimation (WSA) are separately defined. WSA is updated and its PSD is computed only when change in statistics is detected in the SW. The computational complexity of the proposed algorithm is found to be lesser than that for standard STFT technique.

Keywords: Power Spectral Density (PSD), quasi-stationarytime series, short time Fourier Transform, Sliding window DFT.

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Authors: Ali Shatnawi

Abstract:

Signal processing applications which are iterative in nature are best represented by data flow graphs (DFG). In these applications, the maximum sampling frequency is dependent on the topology of the DFG, the cyclic dependencies in particular. The determination of the iteration bound, which is the reciprocal of the maximum sampling frequency, is critical in the process of hardware implementation of signal processing applications. In this paper, a novel technique to compute the iteration bound is proposed. This technique is different from all previously proposed techniques, in the sense that it is based on the natural flow of tokens into the DFG rather than the topology of the graph. The proposed algorithm has lower run-time complexity than all known algorithms. The performance of the proposed algorithm is illustrated through analytical analysis of the time complexity, as well as through simulation of some benchmark problems.

Keywords: Data flow graph, Iteration period bound, Rateoptimalscheduling, Recursive DSP algorithms.

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Authors: Ming-Hui Lee, Tsung-Chien Chen, Yuh-Shiou Tai

Abstract:

The intelligent fuzzy input estimator is used to estimate the input force of the rigid bar structural system in this study. The fuzzy Kalman filter without the input term and the fuzzy weighting recursive least square estimator are two main portions of this method. The practicability and accuracy of the proposed method were verified with numerical simulations from which the input forces of a rigid bar structural system were estimated from the output responses. In order to examine the accuracy of the proposed method, a rigid bar structural system is subjected to periodic sinusoidal dynamic loading. The excellent performance of this estimator is demonstrated by comparing it with the use of difference weighting function and improper the initial process noise covariance. The estimated results have a good agreement with the true values in all cases tested.

Keywords: Fuzzy Input Estimator, Kalman Filter, RecursiveLeast Square Estimator.

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Authors: Esra E. Aleisa, Li Lin

Abstract:

Complex engineering design problems consist of numerous factors of varying criticalities. Considering fundamental features of design and inferior details alike will result in an extensive waste of time and effort. Design parameters should be introduced gradually as appropriate based on their significance relevant to the problem context. This motivates the representation of design parameters at multiple levels of an abstraction hierarchy. However, developing abstraction hierarchies is an area that is not well understood. Our research proposes a novel hierarchical abstraction methodology to plan effective engineering designs and processes. It provides a theoretically sound foundation to represent, abstract and stratify engineering design parameters and tasks according to causality and criticality. The methodology creates abstraction hierarchies in a recursive and bottom-up approach that guarantees no backtracking across any of the abstraction levels. The methodology consists of three main phases, representation, abstraction, and layering to multiple hierarchical levels. The effectiveness of the developed methodology is demonstrated by a design problem.

Keywords: Hierarchies, Abstraction, Loop-free, Engineering Design

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Authors: Hamza Nejib, Okba Taouali

Abstract:

This paper presents two of the most knowing kernel adaptive filtering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing filters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to figure the performance of the approaches presented and finally to result which of them is the adapted one.

Keywords: KLMS, online prediction, KAF, signal processing, RKHS, Kernel methods, KRLS, KLMS.

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Authors: Xinjian Kou, Linlin Li, Yongju Zhou, Jimian Song

Abstract:

We introduce the redundancy matrix that expresses clearly the geometrical/topological configuration of the structure. With the matrix, the redundancy of the structure is resolved into redundant components and assigned to each member or rigid joint. The values of the diagonal elements in the matrix indicates the importance of the corresponding members or rigid joints, and the geometrically correlations can be shown with the non-diagonal elements. If a member or rigid joint failures, reassignment of the redundant components can be calculated with the recursive method given in the paper. By combining the indexes of reliability and redundancy components, we define an index concerning the structural robustness. To further explain the properties of the redundancy matrix, we cited several examples of statically indeterminate structures, including two trusses and a rigid frame. With the examples, some simple results and the properties of the matrix are discussed. The examples also illustrate that the redundancy matrix and the relevant concepts are valuable in structural safety analysis.

Keywords: Structural robustness, structural reliability, redundancy component, redundancy matrix.

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Authors: S. Kavitha, Nirmala P. Ratchagar

Abstract:

This paper presents the novel deterministic dynamic programming approach for solving optimization problem with quadratic objective function with linear equality and inequality constraints. The proposed method employs backward recursion in which computations proceeds from last stage to first stage in a multi-stage decision problem. A generalized recursive equation which gives the exact solution of an optimization problem is derived in this paper. The method is purely analytical and avoids the usage of initial solution. The feasibility of the proposed method is demonstrated with a practical example. The numerical results show that the proposed method provides global optimum solution with negligible computation time.

Keywords: Backward recursion, Dynamic programming, Multi-stage decision problem, Quadratic objective function.

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Authors: T Panigrahi, G Panda, Mulgrew

Abstract:

This paper introduces a new approach for the performance analysis of adaptive filter with error saturation nonlinearity in the presence of impulsive noise. The performance analysis of adaptive filters includes both transient analysis which shows that how fast a filter learns and the steady-state analysis gives how well a filter learns. The recursive expressions for mean-square deviation(MSD) and excess mean-square error(EMSE) are derived based on weighted energy conservation arguments which provide the transient behavior of the adaptive algorithm. The steady-state analysis for co-related input regressor data is analyzed, so this approach leads to a new performance results without restricting the input regression data to be white.

Keywords: Error saturation nonlinearity, transient analysis, impulsive noise.

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Authors: Taweechai Nuntawisuttiwong, Natasha Dejdumrong

Abstract:

Geometric modeling plays an important role in the constructions and manufacturing of curve, surface and solid modeling. Their algorithms are critically important not only in the automobile, ship and aircraft manufacturing business, but are also absolutely necessary in a wide variety of modern applications, e.g., robotics, optimization, computer vision, data analytics and visualization. The calculation and display of geometric objects can be accomplished by these six techniques: Polynomial basis, Recursive, Iterative, Coefficient matrix, Polar form approach and Pyramidal algorithms. In this research, the coefficient matrix (simply called monomial form approach) will be used to model polynomial rectangular patches, i.e., Said-Ball, Wang-Ball, DP, Dejdumrong and NB1 surfaces. Some examples of the monomial forms for these surface modeling are illustrated in many aspects, e.g., construction, derivatives, model transformation, degree elevation and degress reduction.

Keywords: Monomial form, rectangular surfaces, CAGD curves, monomial matrix applications.

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Authors: Lorena Urricelqui, Armando Malanda, Arantxa Villanueva

Abstract:

Purpose: To develop a method for automatic segmentation of adipose and muscular tissue in thighs from magnetic resonance images. Materials and methods: Thirty obese women were scanned on a Siemens Impact Expert 1T resonance machine. 1500 images were finally used in the tests. The developed segmentation method is a recursive and multilevel process that makes use of several concepts such as shaped histograms, adaptative thresholding and connectivity. The segmentation process was implemented in Matlab and operates without the need of any user interaction. The whole set of images were segmented with the developed method. An expert radiologist segmented the same set of images following a manual procedure with the aid of the SliceOmatic software (Tomovision). These constituted our 'goal standard'. Results: The number of coincidental pixels of the automatic and manual segmentation procedures was measured. The average results were above 90 % of success in most of the images. Conclusions: The proposed approach allows effective automatic segmentation of MRIs from thighs, comparable to expert manual performance.

Keywords: Segmentation, thigh, magnetic resonance image, fat, muscle.

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Authors: Xiaofei Hu, Zhiyu Cai, Weian Yao

Abstract:

V-notch problem under dynamic loading condition is considered in this paper. In the time domain, the precise time domain expanding algorithm is employed, in which a self-adaptive technique is carried out to improve computing accuracy. By expanding variables in each time interval, the recursive finite element formulas are derived. In the space domain, a Symplectic Analytical Singular Element (SASE) for V-notch problem is constructed addressing the stress singularity of the notch tip. Combining with the conventional finite elements, the proposed SASE can be used to solve the dynamic stress intensity factors (DSIFs) in a simple way. Numerical results show that the proposed SASE for V-notch problem subjected to dynamic loading condition is effective and efficient.

Keywords: V-notch, dynamic stress intensity factor, finite element method, precise time domain expanding algorithm.

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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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Authors: Pashayev A., Askerov D., C. Ardil, Sadiqov R., Abdullayev P.

Abstract:

Groundlessness of application probability-statistic methods are especially shown at an early stage of the aviation GTE technical condition diagnosing, when the volume of the information has property of the fuzzy, limitations, uncertainty and efficiency of application of new technology Soft computing at these diagnosing stages by using the fuzzy logic and neural networks methods. It is made training with high accuracy of multiple linear and nonlinear models (the regression equations) received on the statistical fuzzy data basis. At the information sufficiency it is offered to use recurrent algorithm of aviation GTE technical condition identification on measurements of input and output parameters of the multiple linear and nonlinear generalized models at presence of noise measured (the new recursive least squares method (LSM)). As application of the given technique the estimation of the new operating aviation engine D30KU-154 technical condition at height H=10600 m was made.

Keywords: Identification of a technical condition, aviation gasturbine engine, fuzzy logic and neural networks.

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Authors: P. C. Tewari, Parveen Kumar

Abstract:

The paper describes the availability analysis of milling system of a rice milling plant using probabilistic approach. The subsystems under study are special purpose machines. The availability analysis of the system is carried out to determine the effect of failure and repair rates of each subsystem on overall performance (i.e. steady state availability) of system concerned. Further, on the basis of effect of repair rates on the system availability, maintenance repair priorities have been suggested. The problem is formulated using Markov Birth-Death process taking exponential distribution for probable failures and repair rates. The first order differential equations associated with transition diagram are developed by using mnemonic rule. These equations are solved using normalizing conditions and recursive method to drive out the steady state availability expression of the system. The findings of the paper are presented and discussed with the plant personnel to adopt a suitable maintenance policy to increase the productivity of the rice milling plant.

Keywords: Markov process, milling system, availability modeling, rice milling plant.

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Authors: Pashayev A., Askerov D., C. Ardil, Sadiqov R., Abdullayev P.

Abstract:

Groundlessness of application probability-statistic methods are especially shown at an early stage of the aviation GTE technical condition diagnosing, when the volume of the information has property of the fuzzy, limitations, uncertainty and efficiency of application of new technology Soft computing at these diagnosing stages by using the fuzzy logic and neural networks methods. It is made training with high accuracy of multiple linear and nonlinear models (the regression equations) received on the statistical fuzzy data basis. At the information sufficiency it is offered to use recurrent algorithm of aviation GTE technical condition identification on measurements of input and output parameters of the multiple linear and nonlinear generalized models at presence of noise measured (the new recursive least squares method (LSM)). As application of the given technique the estimation of the new operating aviation engine D30KU-154 technical condition at height H=10600 m was made.

Keywords: Identification of a technical condition, aviation gasturbine engine, fuzzy logic and neural networks.

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Authors: Mohammad Shams Esfand Abadi, John Hakon Husøy

Abstract:

Employing a recently introduced unified adaptive filter theory, we show how the performance of a large number of important adaptive filter algorithms can be predicted within a general framework in nonstationary environment. This approach is based on energy conservation arguments and does not need to assume a Gaussian or white distribution for the regressors. This general performance analysis can be used to evaluate the mean square performance of the Least Mean Square (LMS) algorithm, its normalized version (NLMS), the family of Affine Projection Algorithms (APA), the Recursive Least Squares (RLS), the Data-Reusing LMS (DR-LMS), its normalized version (NDR-LMS), the Block Least Mean Squares (BLMS), the Block Normalized LMS (BNLMS), the Transform Domain Adaptive Filters (TDAF) and the Subband Adaptive Filters (SAF) in nonstationary environment. Also, we establish the general expressions for the steady-state excess mean square in this environment for all these adaptive algorithms. Finally, we demonstrate through simulations that these results are useful in predicting the adaptive filter performance.

Keywords: Adaptive filter, general framework, energy conservation, mean-square performance, nonstationary environment.

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Authors: Monika Garg, Lillie Dewan

Abstract:

Three novel and significant contributions are made in this paper Firstly, non-recursive formulation of Haar connection coefficients, pioneered by the present authors is presented, which can be computed very efficiently and avoid stack and memory overflows. Secondly, the generalized approach for state analysis of singular bilinear time-invariant (TI) and time-varying (TV) systems is presented; vis-˜a-vis diversified and complex works reported by different authors. Thirdly, a generalized approach for parameter estimation of bilinear TI and TV systems is also proposed. The unified framework of the proposed method is very significant in that the digital hardware once-designed can be used to perform the complex tasks of state analysis and parameter estimation of different types of bilinear systems single-handedly. The simplicity, effectiveness and generalized nature of the proposed method is established by applying it to different types of bilinear systems for the two tasks.

Keywords: Bilinear Systems, Haar Wavelet, Haar ConnectionCoefficients, Parameter Estimation, Singular Bilinear Systems, StateAnalysis.

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Authors: V. Prapulla, A. Mitra, R. Bhattacharjee, S. Nandi

Abstract:

We present a simplified equalization technique for a π/4 differential quadrature phase shift keying ( π/4 -DQPSK) modulated signal in a multipath fading environment. The proposed equalizer is realized as a fractionally spaced adaptive decision feedback equalizer (FS-ADFE), employing exponential step-size least mean square (LMS) algorithm as the adaptation technique. The main advantage of the scheme stems from the usage of exponential step-size LMS algorithm in the equalizer, which achieves similar convergence behavior as that of a recursive least squares (RLS) algorithm with significantly reduced computational complexity. To investigate the finite-precision performance of the proposed equalizer along with the π/4 -DQPSK modem, the entire system is evaluated on a 16-bit fixed point digital signal processor (DSP) environment. The proposed scheme is found to be attractive even for those cases where equalization is to be performed within a restricted number of training samples.

Keywords: Adaptive decision feedback equalizer, Fractionally spaced equalizer, π/4 DQPSK signal, Digital signal processor.

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Authors: Vickneswaran Jeyabalan, Andrews Samraj, Loo Chu Kiong

Abstract:

The noteworthy point in the advancement of Brain Machine Interface (BMI) research is the ability to accurately extract features of the brain signals and to classify them into targeted control action with the easiest procedures since the expected beneficiaries are of disabled. In this paper, a new feature extraction method using the combination of adaptive band pass filters and adaptive autoregressive (AAR) modelling is proposed and applied to the classification of right and left motor imagery signals extracted from the brain. The introduction of the adaptive bandpass filter improves the characterization process of the autocorrelation functions of the AAR models, as it enhances and strengthens the EEG signal, which is noisy and stochastic in nature. The experimental results on the Graz BCI data set have shown that by implementing the proposed feature extraction method, a LDA and SVM classifier outperforms other AAR approaches of the BCI 2003 competition in terms of the mutual information, the competition criterion, or misclassification rate.

Keywords: Adaptive autoregressive, adaptive bandpass filter, brain machine Interface, EEG, motor imaginary.

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Authors: M.Machhout, M.Zeghid, W.El hadj youssef, B.Bouallegue, A.Baganne, R.Tourki

Abstract:

Long number multiplications (n ≥ 128-bit) are a primitive in most cryptosystems. They can be performed better by using Karatsuba-Ofman technique. This algorithm is easy to parallelize on workstation network and on distributed memory, and it-s known as the practical method of choice. Multiplying long numbers using Karatsuba-Ofman algorithm is fast but is highly recursive. In this paper, we propose different designs of implementing Karatsuba-Ofman multiplier. A mixture of sequential and combinational system design techniques involving pipelining is applied to our proposed designs. Multiplying large numbers can be adapted flexibly to time, area and power criteria. Computationally and occupation constrained in embedded systems such as: smart cards, mobile phones..., multiplication of finite field elements can be achieved more efficiently. The proposed designs are compared to other existing techniques. Mathematical models (Area (n), Delay (n)) of our proposed designs are also elaborated and evaluated on different FPGAs devices.

Keywords: finite field, Karatsuba-Ofman, long numbers, multiplication, mathematical model, recursivity.

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