This correspondence presents a statistical performance analysis of subspace-based directions-of-arrival (DOA) estimation algorithms in the presence of correlated observation noise with unknown covariance. Our analysis...
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This correspondence presents a statistical performance analysis of subspace-based directions-of-arrival (DOA) estimation algorithms in the presence of correlated observation noise with unknown covariance. Our analysis of five different estimation algorithms is unified by a single expression for the mean-squared DOA estimation error which is derived using a subspace perturbation expansion. The analysis assumes that only a finite amount of array data is available.
We derive a fast algorithm for calculating the Capon maximum likelihood method (MLM) power spectrum estimate when given uniformly spaced samples of the correlation function. This algorithm computes a weighted correlat...
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We derive a fast algorithm for calculating the Capon maximum likelihood method (MLM) power spectrum estimate when given uniformly spaced samples of the correlation function. This algorithm computes a weighted correlation of the predictor coefficients found by running Levinson recursion. The Fourier transform of the result gives the MLM spectrum. This approach also suggests an interesting new comparison between MLM and MEM.
The present paper describes initial aspects of a framework for realization of n-D signalprocessing tasks under rigorous budgetary demands, as usually found in industrial applications. Besides design on the basis of a...
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The present paper describes initial aspects of a framework for realization of n-D signalprocessing tasks under rigorous budgetary demands, as usually found in industrial applications. Besides design on the basis of adequate guidelines, development of algorithms with respect to a dedicated hardware platform is an important means of optimization. An exemplary realization of a hand detection device is presented, which incorporates several efficient algorithms that are limited in accuracy but in combination lead to a robust hand detection algorithm. These basic processing blocks are mainly based on FPGA realizations of well known FIR filter structures in combination with convolution-like operations based on Boolean logic instead of complex arithmetic. One example for this class of algorithms is binary morphology, which can be realized in low-cost FPGAs for high data throughput of several Gpixel per second and with large operator masks, e.g. with 25/spl times/25 pixels in size.
A universal baseband signalprocessing system for I/Q modem based on DSP and ASSP (application specific standard product) is proposed in this paper. By choosing an universal digital radio baseband processor (ASSP) as ...
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A universal baseband signalprocessing system for I/Q modem based on DSP and ASSP (application specific standard product) is proposed in this paper. By choosing an universal digital radio baseband processor (ASSP) as the co-processor of DSP, the computation burden on DSP has been released significantly. Compared with some traditional solutions to the issue, the technique discussed in this paper is more promising and attractive. It is extremely compact and power-efficient that is often required by the portable devices in mobile communication. While in the illustration of the system, special emphases are laid on the architecture and operation principle of the system and the algorithms used in the baseband signalprocessing.
This paper describes a highly practical blind signal separation (BSS) scheme operating on subband domain data to blindly segregate convolutive mixtures of speech. The proposed method relies on spatiotemporal separatio...
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This paper describes a highly practical blind signal separation (BSS) scheme operating on subband domain data to blindly segregate convolutive mixtures of speech. The proposed method relies on spatiotemporal separation carried out in the time domain by using a multichannel blind deconvolution (MBD) algorithm that enforces separation by entropy maximization through the popular natural gradient algorithm (NGA). Numerical experiments with binaural impulse responses affirm the validity and illustrate the practical appeal of the presented technique even for difficult speech separation setups.
This paper proposes a new low-complexity decision feedback nonlinear equalization architecture. It applies the Least Square (LS) algorithm to the coefficient updating structure of decision feedback blind equalization ...
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ISBN:
(数字)9781728167763
ISBN:
(纸本)9781728167770
This paper proposes a new low-complexity decision feedback nonlinear equalization architecture. It applies the Least Square (LS) algorithm to the coefficient updating structure of decision feedback blind equalization polynomials and momentum coefficient updating algorithm. It uses a low-complexity memory polynomial model to compensate for the nonlinearity and memory effects caused by amplifiers and channels. Besides, the new structure uses an improved iterative LS algorithm when updating the coefficients of the memory polynomial model, and it uses the decision feedback signal as a standard point to calculate the cost function. The new processing method can handle sizeable nonlinear distortion and memory effects. In the model coefficient updating, the symbol point data block is used to update the memory polynomial coefficients by the improved LS algorithm. Compared with other traditional Least Mean Square (LMS) algorithms or Recursive Least Square (RLS) algorithms, the new method has higher stability. The new algorithm can use shorter data blocks to meet the Low-Density Parity Check Code (LDPC) decoding performance requirements and save a lot of computing and storage resources for satellite receivers. It has excellent reference significance for practical engineering applications.
A novel method of signal reconstruction from a modified auditory representation is presented. This consists of three parts: (1) an algorithm to reconstruct a signal from its modified wavelet transform with a general w...
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A novel method of signal reconstruction from a modified auditory representation is presented. This consists of three parts: (1) an algorithm to reconstruct a signal from its modified wavelet transform with a general wavelet; (2) obtaining an auditory representation using an auditory wavelet transform whose analyzing wavelet is the impulse response of an auditory peripheral model; and (3) estimating the reconstruction algorithm both with and without data reduction. An example of its application to the time-scale modification of speech is presented. High-quality speech successfully generated by time-scale modification shows that the reconstruction method is suitable for various applications as well as making experimental auditory stimuli.< >
In this paper, we introduce a data driven iterative low pass filtering technique, the Empirical Iterative Algorithm (EIA) for Galvanic Skin Response (GSR) signal preprocessing. This algorithm is inspired on Empirical ...
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In this paper, we introduce a data driven iterative low pass filtering technique, the Empirical Iterative Algorithm (EIA) for Galvanic Skin Response (GSR) signal preprocessing. This algorithm is inspired on Empirical Mode Decomposition (EMD), with performance enhancements provided by applying Midpoint-based Empirical Decomposition (MED), and removing the sifting process in order to make it computational inexpensive while maintaining effectiveness towards removal of high frequency artefacts. Based on GSR signals recorded at the wrist we present an algorithm benchmark, with results from EIA being compared with a smoothing technique based on moving average filter - commonly used to pre-process GSR signals. The comparison is established on data from 20 subjects, collected while performing 33 different randomized activities with right hand, left hand and both hands, respectively. In average, the proposed algorithm enhances the signal quality by 51%, while the traditional moving average filter reaches 16% enhancement. Also, it performs 136 times faster than the EMD in terms of average computational time. As a show case, using the GSR signal from one subject, we inspect the impact of applying our algorithm on GSR features with psychophysiological relevance. Comparison with no preprocessing and moving average filtering shows the ability of our algorithm to retain relevant low frequency information.
The authors describe a special type of dynamic neural network called the recursive neural network (RNN). The RNN is a single-input single-output nonlinear dynamical system with a nonrecursive subnet and two recursive ...
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The authors describe a special type of dynamic neural network called the recursive neural network (RNN). The RNN is a single-input single-output nonlinear dynamical system with a nonrecursive subnet and two recursive subnets arranged in the configuration shown. The authors describe the architecture of the RNN, present a learning algorithm for the network, and provide some examples of its use.< >
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