7,046 research outputs found

    Anti-aliasing Filter in Hybrid Filter Banks

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    International audienceHybrid Filter Banks allow wide-band, high frequency conversion. All existing design methods suppose that the input signal is band-limited and that each sub-band signal is sampled at 1/M times the effective Nyquist frequency of the input signal 1/T . To avoid aliasing in the sampling process, an analog anti-aliasing filter should be used in order to eliminate noise in frequency bands in which there is no signal (or a few signal) . In this paper, it is shown that this pre-filtering operation is critical and has to be done taking into account the respective power spectral densities of signal and noise due to the spectral aliasing with the sampling rate compressor. Results will be demonstrated for the design of a realistic 8 channel Hybrid Filter Bank

    Synthesis of Complex Subband Hybrid Filter Banks A/D Converters using Adaptive Filters

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    Hybrid Filter Banks (HFB) A/D converters (ADC) may be an attractive solution for future cognitive radio application. In this paper, using complex adaptive finite impulse response (FIR) filter whose coefficients are initialized with zero, their optimal values are achieved by iterative adjustments. This synthesis method is validated for a complex subband HFB by simulation in the time-domain

    Subband Architecture for Hybrid Filter Bank A/D Converters

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    Hybrid Filter Bank A/D conversion systems applied to future telecommunication scenarios

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    Hybrid Filter Banks (HFB) A/D converters (ADC) are attractive to software-defined radio applications. Starting from a given sampling rate, they enlarge the conversion band-width. Also, it is possible to adapt the conversion characteristics (e.g. bandwidth and resolution) by software control. HFB have been studied in the context of the ANR VersaNum project. This work proposes optimal HFBs and a calibration technique to compensate the mismatch between the analog part and the digital part. Results are given for future telecommunication scenarios

    Effectively Finding the Optimal Wavelet for Hybrid Wavelet - Large Margin Signal Classification

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    For hybrid wavelet - large margin classifiers, adapting the wavelet may significantly improve the classification performance. We propose to select the wavelet with respect to a large margin classifier and data to improve class separability and minimise the generalisation error. In this paper, we show that this wavelet adaptation problem can be formulated as an optimisation problem with polynomial objective function and investigate some techniques to solve it. In particular, we propose an adaptive grid search algorithm that efficiently solves the problem compared with standard optimisation techniques

    Efficient Multiband Algorithms for Blind Source Separation

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    The problem of blind separation refers to recovering original signals, called source signals, from the mixed signals, called observation signals, in a reverberant environment. The mixture is a function of a sequence of original speech signals mixed in a reverberant room. The objective is to separate mixed signals to obtain the original signals without degradation and without prior information of the features of the sources. The strategy used to achieve this objective is to use multiple bands that work at a lower rate, have less computational cost and a quicker convergence than the conventional scheme. Our motivation is the competitive results of unequal-passbands scheme applications, in terms of the convergence speed. The objective of this research is to improve unequal-passbands schemes by improving the speed of convergence and reducing the computational cost. The first proposed work is a novel maximally decimated unequal-passbands scheme.This scheme uses multiple bands that make it work at a reduced sampling rate, and low computational cost. An adaptation approach is derived with an adaptation step that improved the convergence speed. The performance of the proposed scheme was measured in different ways. First, the mean square errors of various bands are measured and the results are compared to a maximally decimated equal-passbands scheme, which is currently the best performing method. The results show that the proposed scheme has a faster convergence rate than the maximally decimated equal-passbands scheme. Second, when the scheme is tested for white and coloured inputs using a low number of bands, it does not yield good results; but when the number of bands is increased, the speed of convergence is enhanced. Third, the scheme is tested for quick changes. It is shown that the performance of the proposed scheme is similar to that of the equal-passbands scheme. Fourth, the scheme is also tested in a stationary state. The experimental results confirm the theoretical work. For more challenging scenarios, an unequal-passbands scheme with over-sampled decimation is proposed; the greater number of bands, the more efficient the separation. The results are compared to the currently best performing method. Second, an experimental comparison is made between the proposed multiband scheme and the conventional scheme. The results show that the convergence speed and the signal-to-interference ratio of the proposed scheme are higher than that of the conventional scheme, and the computation cost is lower than that of the conventional scheme

    Wavelet Theory

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    The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior
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