Abstmct-Cyclic redundancy check (CRC) is one of the most important error-detection schemes used in digital communications. In this paper, a new parallel algorithm for CRC generation and its software as well as hardware implementation is described. For the software implementation, this paper has focussed on the 32-bit CRC used in Ethernet, computed on a general purpose PowerPC microprocessor with the new AltiVec technology. A speedup by a factor of 4.57 over the standard table-lookup algorithm was obtained. A hardware implementation of the algorithm is then discussed, which yields an unlimited speed-up potential over the bit-wise serial algorithm.
The parameters in a nuclear magnetic resonance (NMR) free induction decay (FID) signal contain information that is useful in biological and biomedical applications and research. A real time-sampled FID signal is well modeled as a finite mixture of modulated exponential sequences plus noise. We propose to use the generalized Gabor expansion for noise reduction, where the generalized Gabor expansion represents a signal in terms of a collection of time-shifted and frequency-modulated versions of a single sequence (prototype sequence). For FID signal-fitting, we choose the exponential sequence as the prototype function. Using the generalized Gabor expansion and exponential prototype sequences for FID model-fitting, an NMR FID signal can be well represented by the Gabor coefficients distributed in the joint time-frequency domain (JTFD). The Gabor coefficients reflect the weights of modulated exponential sequences in a signal. One of the important features is that the nonzero Gabor coefficients of a modulated exponential sequence will span a very small area in the JTFD, whereas the Gabor coefficients of the noise will not. If the exponent constant of the prototype sequence in the generalized Gabor expansion matches that of a modulated exponential sequence in the signal, then only one of the Gabor coefficients is nonzero in the JTFD. This is a very important property since it can be exploited to separate a signal from noise and to estimate modulated exponential sequence parameters.
Abstract-Pattern classification is an important task in speech recognition and speaker verification. Given the feature vectors of an input the goal is to capture the characteristics of these features unique to each class. This paper deals with exploring Auto Associative Neural Network (AANN) models for the task of speaker verification and speech recognition. We show that AANN models produce comparable performance with that of GMM based speaker verification and speech recognition.
A new adaptive single pixel based lossless intra coding technique employs pixel based DPCM(Differential Pulse Code Modulation) is presented as an enhancement of H.264/MPEG-4 AVC(Advanced Video Coding) standard.. In this paper, we have addressed a technique to trace a single significant pixel in the source block adaptively based on perceptual considerations, applied pixel wise DPCM for spatial prediction for residual transform coding. However, the block style of H.264/AVC is not troubled for the transform encoding and decoding process. From the experiments, it follows that the new adaptive single pixel based lossless intra coding technique offers a better image quality and good compression ratio as compared with the current standard. If the visual significance of source block is considered for spatial prediction, it offers better results. In this paper, we highlight the exclusion of excess visual data present in both Luma and Chromo components of a video frame for lossless Intra coding of H.264/AVC
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