Many blind deconvolution algorithms have been designed to extract digital communications signals corrupted by inter-symbol interference. Such algorithms generally fail when applied to signals with impulsive characteri...
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Many blind deconvolution algorithms have been designed to extract digital communications signals corrupted by inter-symbol interference. Such algorithms generally fail when applied to signals with impulsive characteristics, such as acoustic signals. In this paper, we provide a theoretical analysis and explanation as to why Bussgang-type algorithms are generally unsuitable for deconvolving impulsive signals. We then propose a novel modification of one such algorithm, the Sato algorithm, to enable it to deconvolve such signals. Sufficient conditions on the source signal to guarantee local stability of the modified Sato algorithm about a deconvolving solution are derived. computer simulations show the efficiency of the proposed approach as compared to the Shalvi-Weinstein algorithm for deconvolving impulsive signals.
In this paper, we propose an efficient compression algorithm for 3D triangular mesh, which is composed of connectivity data and geometry data. First, the vertex degree warping method is proposed to compress the connec...
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In this paper, we propose an efficient compression algorithm for 3D triangular mesh, which is composed of connectivity data and geometry data. First, the vertex degree warping method is proposed to compress the connectivity data losslessly. While the connectivity data and the geometry data are encoded independently in most conventional algorithms, the geometrical information is exploited to efficiently compress the connectivity data in the proposed approach. Second, the dual parallelogram prediction technique is proposed as an effective geometry prediction scheme. By adopting the triangle fan structure, the proposed algorithm provides smaller prediction error than the conventional algorithm. Simulation results for various 3D mesh models demonstrate that the proposed algorithm yields higher compression ratio than the conventional algorithm.
In this work warped linear prediction (WLP) is applied to a model-based method to detect impulsive disturbances in audio signals. According to simulations performed on artificially corrupted audio signals the adoption...
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We propose a text scanner, which detects wide text strings in a sequence of scene images. For scene text detection, we use a multiple-CAMShift algorithm on a text probability image produced by a multi-layer perceptron...
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We propose a text scanner, which detects wide text strings in a sequence of scene images. For scene text detection, we use a multiple-CAMShift algorithm on a text probability image produced by a multi-layer perceptron...
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Analysis and comparing with three existing and popularly used forcasting customer lifetime value (CLV) methods, which are the Dwyer method, customer event\|method and fitting method, and using performances of the exis...
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Analysis and comparing with three existing and popularly used forcasting customer lifetime value (CLV) methods, which are the Dwyer method, customer event\|method and fitting method, and using performances of the existent artificial neural network, we apply the Radial basis Function(RBF) network to forecast the CLV, the RBF network can approach the objective function partially. To every input/output pairs, it only needs adjust the weight a little and learn quickly which is very important to the forecast precision. Simulation and experimental results on the customers’ data of a company in Shaanxi Province reveal the effectiveness and feasibility of the RBF network.\;
We describe a method for the registration of functional brain data acquired with transcranial magnetic stimulation (TMS) on MRI brain images. TMS is a non-invasive method largely used in the study of brain functions. ...
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We describe a method for the registration of functional brain data acquired with transcranial magnetic stimulation (TMS) on MRI brain images. TMS is a non-invasive method largely used in the study of brain functions. For the registration process we acquire 150 points on the patient's scalp with a magnetic-field digitizer. Then, we minimize the mean square distance between those points and the segmented scalp surface drawn from the MR image. The distance to the scalp surface is computed with the help of a 3D Euclidean distance transformation. For each stimulation, the position of the TMS device is acquired with the digitizer. The registration transformation is applied to the TMS coordinate in order to map TMS data and anatomical information. The results show that the method is precise (4 mm) and reproducible (1 mm).
A new type of nonlinear analog transistor networks has recently been proposed for "turbo" decoding of error correcting codes. However, the influence of various nonidealities on the performance of such analog...
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A new type of nonlinear analog transistor networks has recently been proposed for "turbo" decoding of error correcting codes. However, the influence of various nonidealities on the performance of such analog decoders is not yet well understood. The paper addresses the performance degradation due to transistor mismatch. Some analytical results are derived that allow to compare the accuracy of analog decoders with that of digital decoders. Moreover, these results enable to incorporate transistor mismatch into fast high-level simulations.
A lumped physical model of the glottal source is presented. Vocal folds are described as single masses, but vertical phase differences between upper and lower margins of the folds are taken into account by appropriate...
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ISBN:
(纸本)8790834100
A lumped physical model of the glottal source is presented. Vocal folds are described as single masses, but vertical phase differences between upper and lower margins of the folds are taken into account by appropriately describing the non-linear interaction of the mechanical model with aerodynamics. This results in a modified one-mass model, or a "one-delayed-mass model". Analysis on numerical simulations shows that the system behaves qualitatively as higher-dimensional models (such as the two-mass model by Ishizaka and Flanagan);in particular, control over flow skewness is guaranteed, allowing for synthesis of realistic glottal flow waveforms. As only one degree of freedom (one mass) is needed in the model, structure and number of parameters are drastically reduced, thus making it suitable for real-Time synthesis applications.
Automated systems are needed to assist the tedious visual analysis of polygraphic recordings. Most systems need detection of different electroencephalogram (EEG) waveforms. The problem in automated detection of alpha ...
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Automated systems are needed to assist the tedious visual analysis of polygraphic recordings. Most systems need detection of different electroencephalogram (EEG) waveforms. The problem in automated detection of alpha activity is the large inter-individual variability of its amplitude and duration. In this work, a fuzzy reasoning based method for the detection of alpha activity was designed and tested. The ranges of the fuzzy rules were determined based on feature statistics. The advantage of the presented detector is that no alpha amplitude threshold needs to be selected. The performance of the alpha detector with four modifications was assessed with ROC curves. When the true positive rate was 85%, the false positive rate was 13%, which is sufficient for sleep EEG analysis.
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