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International Conference on Automatic Control and Artificial Intelligence

作者： L. Wu Y. Liu Department of Computer Science Guangdong University of Education College of Automation Science and Engineering South China University of Technology

ISBN: (纸本)9781627481205

The problem of distributed estimation of an unknown noise-corrupted parameter in wireless sensor networks (WSNs) is considered in this paper. Because of the impact of noise channels, convolutional coding is used to protect the transmission of sensor observations here. To minimize the total power consumption, a novel kinds of power scheduling is derived. The formulas of the proposed power scheduling suggest local sensors with poor observation qualities should decrease their quantization levels and the levels should be also related to coding methods, channel qualities, given mean squared error (MSE) and local signal-to-noise ratio (SNR). Finally, simulation results show that not only is the scheme energy-efficient but also it achieves comparable performance of a given MSE.

关键词： Wireless sensor networks Distributed estimation convolutional coding

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Superposition Modulation with Irregular convolutional coding

Superposition Modulation with Irregular Convolutional Coding

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IEEE Global Communications Conference

作者： Meelis Noemm Alaa Mourad Peter Adam Hoeher Faculty of Engineering University of Kiel

ISBN: (纸本)9781467309202

Bijective mapping techniques with uniformly distributed constellation diagrams, like PSK and QAM, are standard in today's communications systems. Yet, it is well known that without additional effort in form of active signal shaping, bijective mapping techniques are not capacity achieving. In recent years, an alternative technique called superposition modulation (SM) has been investigated, which theoretically capacity is achieving on the Gaussian channel. However, non-bijective mapping (which can be interpreted as lossy source coding) is challenging with respect to the design of proper channel codes. In this paper, the suitability of irregular convolutional coding in conjunction with superposition modulation is investigated. With proper code design, this combination is capable of achieving 6 bits/symbol at just 0.7 dB away from the Shannon capacity limit given a memory-4 mother code.

关键词： convolutional coding lossy source coding Quadrature amplitude modulation PS-K Phase shift keying Bijective Constellation diagram MAPPING(MATHEMATICAL)

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Novel Scheme of Orthogonal convolutional coding and Non-iterative Decoding for Mobile Satellite Communication Systems

Novel Scheme of Orthogonal Convolutional Coding and Non-iter...

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IEEE Vehicular Technology Conference

作者： Xiangming Meng Sheng Wu Linling Kuang Zuyao Ni Jianhua Lu Department of Electronic Engineering Tsinghua University Beijing China Tsinghua Space Center Tsinghua University Beijing China

ISBN: (纸本)9781479980925

Iterative decoding of orthogonal convolutional code is widely used for its excellent performance. However, the iterations lead to high complexity and long decoding delay, which is unsuitable for low-rate voice service in mobile satellite communications with on-board processing. In this paper, a novel scheme of orthogonal convolutional coding as well as an associated non-iterative joint decoding algorithm based on factor graph are proposed. Due to its non-iterative nature, this novel scheme has low decoding complexity and short latency, which indicates its potential on-board use in the low-rate satellite voice service, or other kinds of services with a high bit error rate (BER) tolerance and a tight delay requirement. Simulation results demonstrate the efficacy of the proposed novel coding scheme and non-iterative decoding algorithm in both AWGN and Rician channels.

关键词： Orthogonal convolutional code mobile satellite communication joint decoding factor graph mobile satellite communication Factor Graph Satellite communication systems convolutional coding DEcoding voice service Orthogonal Iterative decoding

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Gaussian Image Denoiser Based on Deep convolutional Sparse coding with Attention Mechanism

Gaussian Image Denoiser Based on Deep Convolutional Sparse C...

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IEEE International Conference on Green Computing and Communications

作者： Yu Shi Yige Xu Zhenya Wang Yingying Hua Haoran Gao Benchang Zheng Be Honors Schoo Nnjing Universiy of Poss nd TeecounicionsBe Honors Schoo Nnjing Universiy of Poss nd TeecounicionsChin Acdey of Lunch Vehice TechnoogyCoege of Teecounicion nd Inforion Engineering Nnjing Universiy of Poss nd TeecounicionsSchoo of Inforion Engineering Wuhn Universiy of TechnoogyChin Acdey of Lunch Vehice Technoogy

ISBN: (数字)9781728176475

ISBN: (纸本)9781728176482

Image denoising is the foundation of computer vision and a classic research project of computer vision. Currently, image denoising is widely used in various fields. In order to perform image denoising more effectively, we propose a deep convolutional sparse coding based on the attention mechanism (DCSCA) denoising model. In order to identify noise and remove noise more effectively, we sparsely encode the feature map instead of encoding the original image. Our model introduces attention blocks in the network structure, which allows our model to mine the noise hidden behind complex images. Our model has the advantages of a compact network structure and does not involve complex optimization models. We did not use the traditional mean square error (MSE) as our loss function, and chose to combine the loss function L1 with ssim. Compared with the most advanced methods, our model can produce better denoising results than some of the latest methods.

关键词： Noise reduction Convolution Image denoising Computational modeling convolutional codes Image coding Task analysis image denoising Noise reduction task analysis convolutional coding image coding Computational modeling images Convolution NETWORK STRUCTURE Mean square error computer vision Sparse coding feature map

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convolutional Sparse and Low-Rank coding-Based Image Decomposition

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2018年第5期27卷 2121-2133页

作者： Zhang, He Patel, Vishal M. Rutgers Univ New Brunswick Dept Elect & Comp Engn New Brunswick NJ 08901 USA

We propose novel convolutional sparse and low-rank coding-based methods for cartoon and texture decomposition. In our method, we first learn a set of generic filters that can efficiently represent cartoon-and texture-type images. Then, using these learned filters, we propose two optimization frameworks to decompose a given image into cartoon and texture components: convolutional sparse coding-based image decomposition;and convolutional low-rank coding-based image decomposition. By working directly on the whole image, the proposed image separation algorithms do not need to divide the image into overlapping patches for leaning local dictionaries. The shift-invariance property is directly modeled into the objective function for learning filters. Extensive experiments show that the proposed methods perform favorably compared with state-of-theart image separation methods.

关键词： Image decomposition convolutional coding low-rank coding sparse coding

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Non-binary convolutional codes for turbo coding

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ELECTRONICS LETTERS 1999年第1期35卷 39-40页

作者： Berrou, C Jézéquel, M ENST Bretagne F-29285 Brest France

The authors consider the use of non-binary convolutional codes in turbo coding. It is shown that quaternary codes can be advantageous, both from performance and complexity standpoints, but that higher-order codes may ... 详细信息

关键词： Turbo Codes Worm gears turbo codes convolutional coding

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On combined ring convolutional coded quantisation and CPM for joint source/channel coding

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EUROPEAN TRANSACTIONS ON TELECOMMUNICATIONS 2008年第4期19卷 443-453页

作者： Lin, Zihuai Aulin, Tor Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Chalmers Univ Technol Dept Comp Engn Telecommun Theory Grp SE-41296 Gothenburg Sweden

A new trellis-based coded quantisation scheme using ring convolutional codes is presented. When it is combined with trellis-coded continuous phase modulation (TCCPM) with convolutional codes over rings, a powerful joint source and channel coding (JSCC) scheme can be formed. We investigate the additive white Gaussian noise (AWGN) channel and the Rayleigh fading channel. Optimal soft decoding for the proposed JSCC scheme is studied. The soft decoder is based on the a posteriori probability (APP) algorithm for TCCPM with ring convolutional codes. It is shown that these systems with soft decoding outperform the same systems with hard decoding especially when the systems operate at low to medium signal-to-noise ratio (SNR). It is demonstrated that the new trellis quantisation schemes based on ring convolutional codes is superior to conventional trellis-coded quantisation (TCQ) of the same complexity. The novelty of this work is in the development of a trellis-based quantisation method using ring convolutional codes and the use of a soft decoder and the APP algorithm for the combined systems. Copyright (C) 2008 John Wiley & Sons, Ltd.

关键词： Critical path method Quantization signal to noise ratios Annulus Rayleigh fading convolutional coding Ring device Trellis codes rings group system Combined source-channel coding

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MAXIMUM LIKELIHOOD DE coding OF convolutional CODES USING VITERBI ALGORITHM WITH IMPROVED ERROR CORRECTION CAPABILITY

MAXIMUM LIKELIHOOD DE CODING OF CONVOLUTIONAL CODES USING VI...

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IEEE Conference on Information and Communication Technologies (ICT)

作者： Abubeker, K. M. Bushara, A. R. Backer, Sabana Amal Jyothi Coll Engg Dept ECE Kottayam Kerala India KMEA Coll Engn Cochin Kerala India

ISBN: (纸本)9781467357586;9781467357593

convolutional codes are applied in applications that require good performance with low implementation cost. It is a finite state machine, processing information bits in a series mariner. Viterbi algorithm [1] can be applied to a host of problems encountered in digital communication systems. The Viterbi algorithm cannot detect any error but can sometimes correct it, while calculating one survivor path with minimum metric value. The maximum likelihood decoding of convolutional encoder with Viterbi algorithm is a good forward error correction [3] method suitable for single and double bit error correction by means of finding the code branch in the code trellis that was most likely to transmit. The modified decoding process proposed in this paper, we shall use a different approach to derive the exact bit, double bit, burst error and a symbol error correction process. It will detect and correct the errors by means of connecting and comparing the metric values at the present, previous and next states of the Viterbi decoding. also it is offering 30-36% better than the Viterbi and 99.9% of error correction, but the computational complexity is decreases and time are increases about 20-40%

关键词： convolutional coding Constraint length Path metric value Additive white Gaussian noise (AWGN) Tree diagram Trellis diagram Hamming distance Survivor path Code rate EEC finite state machine

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Turbo modulation and coding: Design and evaluation of iterative bit-interleaved coded modulation methods for wireless systems beyond 3G

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EUROPEAN TRANSACTIONS ON TELECOMMUNICATIONS 2007年第8期18卷 845-850页

作者： Bauch, Gerhard Schreckenbach, Frank Hausl, Christoph Abel, Tetsushi DoCoMo Euro Labs D-80687 Munich Germany Tech Univ Munich Inst Commun Engn D-80290 Munich Germany

We give an overview of iterative techniques for bit-interleaved coded modulation. We start with simple methods where a convolutional code is concatenated with a standard QAM modulation scheme. The mapping is carefully chosen different from Gray mapping such that significant gains can be obtained by an iterative (turbo) receiver. The idea can also be applied to transmit antenna diversity as well as spatial multiplexing (MIMO). We then consider various powerful iterative coding schemes which are under discussion in standardisation for wireless systems beyond 3G such as serial or parallel concatenated convolutional codes, repeat accumulate codes and LDPC codes. We give a comparison in terms of complexity and performance. The main intention is to provide an illustrative overview over relevant applications of the turbo principle in demodulation and decoding. It is a summary of results which have been obtained in collaboration between the Institute of Communications Engineering, TUM and DoCoMo Euro-Labs. Copyright (C) 2007 John Wiley & Sons, Ltd.

关键词： third generation Turbo coding Bit interleaved coded modulation Worm gears Iterative modulation mode low density parity check codes CARRIER SYSTEM convolutional coding Demodulation

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convolutional GROUP-SPARSE coding AND SOURCE LOCALIZATION

CONVOLUTIONAL GROUP-SPARSE CODING AND SOURCE LOCALIZATION

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Pol del Aguila Pla Joakim Jaldén School of Electrical Engineering and Computer Science Department of Information Science and Engineering KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781538646595

In this paper, we present a new interpretation of non-negatively constrained convolutional coding problems as blind deconvolution problems with spatially variant point spread function. In this light, we propose an optimization framework that generalizes our previous work on non-negative group sparsity for convolutional models. We then link these concepts to source localization problems that arise in scientific imaging, and provide a visual example on an image derived from data captured by the Hubble telescope.

关键词： Sparse representation Source localization Non-negative group sparsity Position measurement sparse representation Scientific imaging convolutional coding

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