Radio frequency identification (RFID) is one of the most attractive and futuristic technologies that can identify an object or person wirelessly, using electromagnetic radio waves. In a multiple reader RFID system, th...
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Radio frequency identification (RFID) is one of the most attractive and futuristic technologies that can identify an object or person wirelessly, using electromagnetic radio waves. In a multiple reader RFID system, the reader interference problem is a very common phenomenon. Efforts are required to minimize this problem as performance, speed and reliability of the entire system highly depends on our ability to solve this problem efficiently. A simple adaptive beam-forming technique is proposed for solving the reader interference problem in a multiple reader RFID system. This new approach is able to effectively generate deep nulls at the direction of interference and respond to the direction of our desired signal. Simulations are carried out to evaluate the performance of our adaptive beam-forming approach and the results confirmed that, the proposed method can adaptively generate a high gain beam to the desired signal direction to acquire and track the signal completely, and it can generate deep nulls at the direction of other readers effectively to avoid interference. Moreover, this method has been found to be very efficient in terms of Bit Error Rate and energy consumption.
In this paper, an artificial neural network is proposed for feature extraction of hand written characters. The learning algorithm is developed based on a proposed modified Sammon's stress for our feedforward neura...
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In this paper, an artificial neural network is proposed for feature extraction of hand written characters. The learning algorithm is developed based on a proposed modified Sammon's stress for our feedforward neural networks, which can not only minimize intra class pattern distances but also preserve interclass distances in the output feature space. The proposed feature extraction method tries to calculate rough classes using a Competitive Learning neural network, which is an unsupervised neural network. Then the proposed neural network was used with modified Sammon's stress to perform feature extraction using information obtained by means of a Competitive Learning Network. The features thus obtained were compared with a standard PCA neural network and a neural network using Sammon's stress in terms of their classification accuracy. Two numerical criteria were used for performance evaluation of the features - the normalized classification error rate and modified Sammon's stress. It is found that proposed modified Sammon's stress provides features that are more efficient based on these two numerical criteria.
This paper studies the second moment behavior of the adaptive line enhancer (ALE)/adaptive line canceler (ALC) for a cyclostationary input consisting of a fixed amplitude random phase sine wave plus a white Gaussian p...
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This paper studies the second moment behavior of the adaptive line enhancer (ALE)/adaptive line canceler (ALC) for a cyclostationary input consisting of a fixed amplitude random phase sine wave plus a white Gaussian process with periodic power variations. Both transient and steady-state results are shown to be in good-to-excellent agreement with Monte Carlo simulations. The main conclusion is that periodic input power variations cause periodic processing gain variations of the ALE/ALC with the same period as the input power. However, these variations do not cause a large degradation in the ALE/ALC performance.
Fractionally-spaced equalizers (FSE) are designed using the adaptive lattice algorithm. The design is compared with the lattice structure synchronous equalizer (SE). The effect of the sampler phase on the mean square ...
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Fractionally-spaced equalizers (FSE) are designed using the adaptive lattice algorithm. The design is compared with the lattice structure synchronous equalizer (SE). The effect of the sampler phase on the mean square error (MSE) is investigated. It is shown that the FSE is not affected by the sampler phase. Improved performance of the FSE over the SE design as applied to severe amplitude distortion channel is obtained when its time span is made equal to that of the synchronous equalizer.
This paper extends a recently proposed approach to pipeline an adaptive filter to the case of least mean square (lms)-based adaptive decision feedback equalizer. It is shown that by certain manipulation of the equaliz...
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This paper extends a recently proposed approach to pipeline an adaptive filter to the case of least mean square (lms)-based adaptive decision feedback equalizer. It is shown that by certain manipulation of the equalizer weight update equation, a pipelined architecture can be realized which generates the output at the first stage itself and thus is free from any latency. The proposed method overcomes certain limitations of the conventional approaches in that due to the absence of any latency, there is no compulsion to employ delayed lms algorithm for coefficient adaptation and thus no compromise with convergence rate becomes necessary. (C) 2003 Elsevier B.V. All rights reserved.
A regularized estimator is proposed for regression models in the case where the covariances may be singular. Conditions guaranteeing proximity of a regularized estimator to the optimal estimator are obtained by approp...
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A regularized estimator is proposed for regression models in the case where the covariances may be singular. Conditions guaranteeing proximity of a regularized estimator to the optimal estimator are obtained by appropriate choice of regularization parameters by allowing a prescribed level of uncertainty. A simple Monte-Carlo simulation study is reported to highlight some aspects and performance of the proposed approach.
The active control of tractor noise requires the ability to track and control a signal that changes in frequency as the speed of the engine, in revolutions per minute (rpm), changes during operation. The most common c...
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The active control of tractor noise requires the ability to track and control a signal that changes in frequency as the speed of the engine, in revolutions per minute (rpm), changes during operation. The most common control approach is typically based on some version of the filtered-x algorithm. For this algorithm, the convergence and tracking speed are functions of the frequency dependent eigenvalues of the filtered-x autocorrelation matrix. To maintain stability, the system must be implemented based on the slowest converging frequency that will be encountered. This often leads to significant degradation in the overall performance of the control system. This paper will present an approach which largely overcomes this frequency dependent performance, maintains a relatively simple control implementation, and improves the overall performance of the control system. The control approach is called the eigenvalue equalization filtered-x least mean squares (EE-FXlms) algorithm and its effectiveness will be demonstrated through an application to tractor noise in a mock cab. Experimental results will be presented which show that the EE-FXlms algorithm has faster convergence times and provides on average a 1 dB increase in attenuation. A 3.5 dB increase in attenuation was seen in some of the cases presented. (c) 2008 Institute of Noise Control Engineering.
A new class of algorithms based on the fractional lower order statistics is proposed for finite-impulse response adaptive filtering in the presence of alpha-stable processes, It is shown that the normalized least mean...
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A new class of algorithms based on the fractional lower order statistics is proposed for finite-impulse response adaptive filtering in the presence of alpha-stable processes, It is shown that the normalized least mean p-norm (NLMP) and Douglas' family of normalized least mean square algorithms are special cases of the proposed class of algorithms. A convergence proof for the new algorithm is given by showing that it performs a descent-type update of the NLMP cost function. Simulation studies indicate that the proposed algorithms provide superior performance in impulsive noise environments compared to the existing approaches.
A new weighted parallel interference cancellation system is proposed in which the weights are adjusted user by user and adaptively to changing environments. Based on computer simulation results, it is observed that th...
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A new weighted parallel interference cancellation system is proposed in which the weights are adjusted user by user and adaptively to changing environments. Based on computer simulation results, it is observed that the proposed system outperforms conventional parallel interference cancellation systems.
The design of adaptive finite impulse response filters is a linear optimization problem and the design of adaptive infinite impulse response (IIR) filters in the presence of observation noise is a nonlinear optimizati...
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The design of adaptive finite impulse response filters is a linear optimization problem and the design of adaptive infinite impulse response (IIR) filters in the presence of observation noise is a nonlinear optimization problem. This paper considers the parameter estimation issues of an infinite impulse response (IIR) filter with colored noise which is treated as an autoregressive process. The key is to investigate novel estimation methods of an IIR filter with an autoregressive disturbance noise from the viewpoint of the observation data filtering. Firstly, we simply give the least mean square (lms) algorithm for an IIR filter with autoregressive noise and derive a multi-innovation lms (MI-lms) algorithm for improving the parameter estimation accuracy. Secondly, we present a data filtering based lms algorithm and a data filtering based MI-lms algorithm for further improving the parameter estimation accuracy. The theoretical analyses show that the proposed algorithms are convergent and the simulation results indicate that the MI-lms algorithm and the data filtering based MI-lms algorithm are superior to the lms algorithm and the data filtering based lms algorithm in accuracy, respectively. The proposed methods in this paper have been extended to an IIR filter with autoregressive moving average noise. Finally, two simulation examples test the performances of the proposed algorithms. (C) 2017 Elsevier Inc. All rights reserved.
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