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Input Sequences (input + sequence)

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Engineering	83%

Selected Abstracts

Performance of multi level-turbo coding with neural network-based channel estimation over WSSUS MIMO channels

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 3 2009
Ersin Gose
Abstract This paper presents the performance of the transmit diversity-multi level turbo codes (TD-MLTC) over the multiple-input,multiple-output (MIMO) channels based on the wide sense stationary uncorrelated scattering (WSSUS). The multi level-turbo code (ML-TC) system contains more than one turbo encoder/decoder block in its structure. At the transmitter side, the ML-TC uses the group partitioning technique that partitions a signal set into several levels and encodes each level separately by a proper component of the encoder to improve error performance. The binary input sequence is passed through the MLTC encoder and mapped to 4-PSK and then fed into the transmit diversity scheme for high data transmission over wireless fading channels. At the receiver side, distorted multi-path signals are received by multiple receiver antennae. WSSUS MIMO channel parameters are estimated by using an artificial neural network and an iterative combiner. Input sequence of the first level of the MLTC encoder is estimated at the first level of MLTC decoder. Subsequently, the other input sequences are computed by using the estimated input bit streams of the previous levels. 4-PSK two-level turbo codes are simulated for 2Tx,1Rx and 2Tx,2Rx antenna configurations over WSSUS MIMO channels. Here, TD-MLTC and its efficient implementations are discussed and simulation results are given. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Strong robustness in multi-phase adaptive control: the basic scheme

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2004
Maria Cadic
Abstract The general structure of adaptive control systems based on strong robustness is introduced. This approach splits into two phases. In the first phase, emphasis is put on identification until enough information is obtained in order to design a controller that stabilizes the actual system, and even under adaptation. This is achieved if the input sequence is computed in such a way that the uncertainty on the parameters of the system to be controlled becomes sufficiently small. Then, in the second phase, effort is shifted to control via a traditional certainty equivalence type of strategy. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Fast and Efficient Skinning of Animated Meshes

COMPUTER GRAPHICS FORUM, Issue 2 2010
L. Kavan
Abstract Skinning is a simple yet popular deformation technique combining compact storage with efficient hardware accelerated rendering. While skinned meshes (such as virtual characters) are traditionally created by artists, previous work proposes algorithms to construct skinning automatically from a given vertex animation. However, these methods typically perform well only for a certain class of input sequences and often require long pre-processing times. We present an algorithm based on iterative coordinate descent optimization which handles arbitrary animations and produces more accurate approximations than previous techniques, while using only standard linear skinning without any modifications or extensions. To overcome the computational complexity associated with the iterative optimization, we work in a suitable linear subspace (obtained by quick approximate dimensionality reduction) and take advantage of the typically very sparse vertex weights. As a result, our method requires about one or two orders of magnitude less pre-processing time than previous methods. [source]

Adaptive least mean squares block Volterra filters

INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, Issue 4 2001
Tarek I. Haweel
Abstract Adaptive filtering has found many applications in situations where the underlying signals are changing or unknown. While linear filters are simple from implementation and conceptual points of view, many signals are non-linear in nature. Non-linear filters based on truncated Volterra expansions can effectively model a large number of systems. Unfortunately, the resulting input auto-moment matrix is ill conditioned, which results in a slow convergence rate. This paper proposes a class of block adaptive Volterra filters in which the input sequences are Hadamard transformed to improve the condition number of the input auto-moment matrix and consequently improve the convergence rate. This is achieved by the decorrelation effect produced by the orthogonality of the transform. Since Hadamard transformation employs only ±1's, the additional required computational and implementation burdens are few. The effect of additive white Gaussian noise is introduced. Simulation experiments are given to illustrate the improved performance of the proposed method over the conventional Volterra LMS method. Copyright © 2001 John Wiley & Sons, Ltd. [source]

Performance of multi level-turbo coding with neural network-based channel estimation over WSSUS MIMO channels

Performance of multilevel-turbo codes with blind/non-blind equalization over WSSUS multipath channels

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 3 2006
Osman N. Ucan
Abstract In this paper, in order to improve error performance, we introduce a new type of turbo codes, called ,multilevel-turbo codes (ML-TC)' and we evaluate their performance over wide-sense stationary uncorrelated scattering (WSSUS) multipath channels. The basic idea of ML-TC scheme is to partition a signal set into several levels and to encode each level separately by a proper component of the turbo encoder. In the considered structure, the parallel input data sequences are encoded by our multilevel scheme and mapped to any modulation type such as MPSK, MQAM, etc. Since WSSUS channels are very severe fading environments, it is needed to pass the received noisy signals through non-blind or blind equalizers before turbo decoders. In ML-TC schemes, noisy WSSUS corrupted signal sequence is first processed in equalizer block, then fed into the first level of turbo decoder and the first sequence is estimated from this first Turbo decoder. Subsequently, the other following input sequences of the frame are computed by using the estimated input bit streams of previous levels. Here, as a ML-TC example, 4PSK 2 level-turbo codes (2L-TC) is chosen and its error performance is evaluated in WSSUS channel modelled by COST 207 (Cooperation in the field of Science & Technology, Project #207). It is shown that 2L-TC signals with equalizer blocks exhibit considerable performance gains even at lower SNR values compared to 8PSK-turbo trellis coded modulation (TTCM). The simulation results of the proposed scheme have up to 5.5 dB coding gain compared to 8PSK-TTCM for all cases. It is interesting that after a constant SNR value, 2L-TC with blind equalizer has better error performance than non-blind filtered schemes. We conclude that our proposed scheme has promising results compared to classical schemes for all SNR values in WSSUS channels. Copyright © 2005 John Wiley & Sons, Ltd. [source]