647 research outputs found

    Development of Fuzzy Receiver for GSM Application

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    A channel equalizer is one of the most important subsystems in any cellular communication receiver. It is also the subsystem that consumes maximum computation time in the receiver. Traditionally maximum-likelihood sequence estimation (MLSE) was the most popular form of equalizer. Owing to non-stationary characteristics of the communication channel MLSE receivers perform poorly. Under these circumstances maximum-aposteriori probability (MAP) receivers also called Bayesian receivers perform better. This thesis proposes a fuzzy receiver that implements MAP equalizer and provides a performance close to the optimal Bayesian receiver. Here Bit Error Rate (BER) has been used as the performance index. This thesis presents an investigation on design of fuzzy based receivers for GSM application. Extensive simulation studies which shows that the performance of the proposed receiver is close to optimal receiver for variety of channel conditions in different receiver speeds where channel suffers from Rayleigh fading. The proposed receiver also provides near optimal performance when channel suffers from nonlinearities

    Time series prediction and channel equalizer using artificial neural networks with VLSI implementation

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    The architecture and training procedure of a novel recurrent neural network (RNN), referred to as the multifeedbacklayer neural network (MFLNN), is described in this paper. The main difference of the proposed network compared to the available RNNs is that the temporal relations are provided by means of neurons arranged in three feedback layers, not by simple feedback elements, in order to enrich the representation capabilities of the recurrent networks. The feedback layers provide local and global recurrences via nonlinear processing elements. In these feedback layers, weighted sums of the delayed outputs of the hidden and of the output layers are passed through certain activation functions and applied to the feedforward neurons via adjustable weights. Both online and offline training procedures based on the backpropagation through time (BPTT) algorithm are developed. The adjoint model of the MFLNN is built to compute the derivatives with respect to the MFLNN weights which are then used in the training procedures. The Levenberg–Marquardt (LM) method with a trust region approach is used to update the MFLNN weights. The performance of the MFLNN is demonstrated by applying to several illustrative temporal problems including chaotic time series prediction and nonlinear dynamic system identification, and it performed better than several networks available in the literature

    A study on different linear and non-linear filtering techniques of speech and speech recognition

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    In any signal noise is an undesired quantity, however most of thetime every signal get mixed with noise at different levels of theirprocessing and application, due to which the information containedby the signal gets distorted and makes the whole signal redundant.A speech signal is very prominent with acoustical noises like bubblenoise, car noise, street noise etc. So for removing the noises researchershave developed various techniques which are called filtering. Basicallyall the filtering techniques are not suitable for every application,hence based on the type of application some techniques are betterthan the others. Broadly, the filtering techniques can be classifiedinto two categories i.e. linear filtering and non-linear filtering.In this paper a study is presented on some of the filtering techniqueswhich are based on linear and nonlinear approaches. These techniquesincludes different adaptive filtering based on algorithm like LMS,NLMS and RLS etc., Kalman filter, ARMA and NARMA time series applicationfor filtering, neural networks combine with fuzzy i.e. ANFIS. Thispaper also includes the application of various features i.e. MFCC,LPC, PLP and gamma for filtering and recognition

    Efficient Adaptive Filter Algorithms Using Variable Tap-length Scheme

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    Today the usage of digital signal processors has increased, where adaptive filter algorithms are now routinely employed in mostly all contemporary devices such as mobile phones, camcorders, digital cameras, and medical monitoring equipment, to name few. The filter tap-length, or the number of taps, is a significant structural parameter of adaptive filters that can influences both the complexity and steady-state performance characteristics of the filter. Traditional implementation of adaptive filtering algorithms presume some fixed filter-length and focus on estimating variable filter\u27s tap-weights parameters according to some pre-determined cost function. Although this approach can be adequate in some applications, it is not the case in more complicated ones as it does not answer the question of filter size (tap-length). This problem can be more apparent when the application involves a change in impulse response, making it hard for the adaptive filter algorithm to achieve best potential performance. A cost-effective approach is to come up with variable tap-length filtering scheme that can search for the optimal length while the filter is adapting its coefficients. In direct form structure filtering, commonly known as a transversal adaptive filter, several schemes were used to estimate the optimum tap-length. Among existing algorithms, pseudo fractional tap-length (FT) algorithm, is of particular interest because of its fast convergence rate and small steady-state error. Lattice structured adaptive filters, on the other hand, have attracted attention recently due to a number of desirable properties. The aim of this research is to develop efficient adaptive filter algorithms that fill the gap where optimal filter structures were not proposed by incorporating the concept of pseudo fractional tap-length (FT) in adaptive filtering algorithms. The contribution of this research include the development of variable length adaptive filter scheme and hence optimal filter structure for the following applications: (1) lattice prediction; (2) Least-Mean-Squares (LMS) lattice system identification; (3) Recursive Least-Squares (RLS) lattice system identification; (4) Constant Modulus Algorithm (CMA) blind equalization. To demonstrate the capability of proposed algorithms, simulations examples are implemented in different experimental conditions, where the results showed noticeable improvement in the context of mean square Error (MSE), as well as in the context of convergence rate of the proposed algorithms with their counterparts adaptive filter algorithms. Simulation results have also proven that with affordable extra computational complexity, an optimization for both of the adaptive filter coefficients and the filter tap-length can be attained

    Intelligent Adaptive Filtering For Noise Cancellation

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    Abstract: In current scenario of modern technology, we are facing a necessity of noise removal in signal processing. Various approaches are used for the same. This paper describes an intelligent adaptive filtering for noise cancellation. Here ANFIS method is being used for removal of noise from audio speech signals. An audio signal contaminated with noise is taken and inspected with eight types of membership functions: bell MF, triangle MF, Gaussian MF, two-sided MF, pi-shaped MF, product of two sigmoid MF, difference of two sigmoid MF and trapezoidal MF. Finally using ANFIS, the original audio speech signal is restored. The major advantage of this system is its ease of implementation and faster convergence rate

    Digital Signal Processing

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    Contains table of contents for Part III, table of contents for Section 1, an introduction and reports on seventeen research projects.National Science Foundation FellowshipNational Science Foundation (Grant ECS 84-07285)National Science Foundation (Grant MIP 87-14969)U.S. Navy - Office of Naval Research (Contract N00014-81-K-0742)Scholarship from the Federative Republic of BrazilU.S. Air Force - Electronic Systems Division (Contract F19628-85-K-0028)AT&T Bell Laboratories Doctoral Support ProgramCanada, Bell Northern Research ScholarshipCanada, Fonds pour la Formation de Chercheurs et I'Aide a la Recherche Postgraduate FellowshipSanders Associates, Inc.OKI Semiconductor, Inc.Tel Aviv University, Department of Electronic SystemsU.S. Navy - Office of Naval Research (Contract N00014-85-K-0272)Natural Sciences and Engineering Research Council of Canada, Science and Engineering Scholarshi
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