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Punctured recursive convolutional encoders and their applications in turbo codes

IEEE TRANSACTIONS ON INFORMATION THEORY 2001年第6期47卷 2300-2320页

作者： Shen, BZ Patapoutian, A McEwen, PA Quantum Corp Shrewsbury MA 01545 USA Broadcom Corp Irvine CA 92619 USA Maxtor Corp Shrewsbury MA 01545 USA Maxtor Corp Milpitas CA 95035 USA

Puncturing is the predominant strategy to construct high code rate convolutional encoders, and infinite impulse response (IIR) convolutional encoders are an essential building block in turbo codes. In this paper, various properties of convolutional encoders with these characteristics are developed. In particular, the closed-form representation of a punctured convolutional encoder and its generator matrix is constructed, necessary and sufficient conditions are given such that the punctured encoders retain the IIR property, and various lower bounds on distance properties, such as effective free distance, are developed. Finally, necessary and sufficient conditions are given on the inverse puncturing problem: representing a known convolutional encoder as a punctured encoder.

关键词： convolutional encoder effective free distance generator matrix infinite impulse response (IIR) iterative decoding polyphase composition polyphase decomposition punctured convolutional encoder systematic convolutional encoder turbo codes

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Hayman-like techniques for computing input-output weight distribution of convolutional encoders

Hayman-like techniques for computing input-output weight dis...

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2010 IEEE International Symposium on Information Theory

作者： Ravazzi, Chiara Fagnani, Fabio MIT 77 Massachusetts Ave Cambridge MA 02139 USA Politecnico Torino DIMAT Turin Italy

ISBN: (纸本)9781424469604

In this paper we derive exact formulae of the input-output weight enumerators for truncated convolutional encoders. Although explicit analytic expressions can be computed for relatively small code lengths, they become prohibitively complex to calculate as the truncation length increases. By applying Hayman-like techniques, we present an accurate and easy to compute approximation of the weight enumerators. One of our main results is the proof that the sequence of their exponential growths converges uniformly to the asymptotic growth rate. Finally, we estimate the speed of this convergence.

关键词： Asymptotic spectral function convolutional encoder input-output weight distribution maximum likelihood decoding multiple concatenated coding scheme turbo-like codes

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A Lightning Classification Method Based on convolutional Encoding Features

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REMOTE SENSING 2024年第6期16卷 965-965页

作者： Zhu, Shunxing Zhang, Yang Fan, Yanfeng Sun, Xiubin Zheng, Dong Zhang, Yijun Lyu, Weitao Zhang, Huiyi Wang, Jingxuan Chinese Acad Meteorol Sci State Key Lab Severe Weather Beijing 100081 Peoples R China Chengdu Univ Informat Technol Coll Elect Engn Chengdu 610200 Peoples R China Fudan Univ Inst Atmospher Sci Dept Atmospher & Ocean Sci Shanghai 200433 Peoples R China

At present, for business lightning positioning systems, the classification of lightning discharge types is mostly based on lightning pulse signal features, and there is still a lot of room for improvement. We propose a lightning discharge classification method based on convolutional encoding features. This method utilizes convolutional neural networks to extract encoding features, and uses random forests to classify the extracted encoding features, achieving high accuracy discrimination for various lightning discharge events. Compared with traditional multi-parameter-based methods, the new method proposed in this paper has the ability to identify multiple lightning discharge events and does not require precise detailed feature engineering to extract individual pulse parameters. The accuracy of this method for identifying lightning discharge types in intra-cloud flash (IC), cloud-to-ground flash (CG), and narrow bipolar events (NBEs) is 97%, which is higher than that of multi-parameter methods. Moreover, our method can complete the classification task of lightning signals at a faster speed. Under the same conditions, the new method only requires 28.2 mu s to identify one pulse, while deep learning-based methods require 300 mu s. This method has faster recognition speed and higher accuracy in identifying multiple discharge types, which can better meet the needs of real-time business positioning.

关键词： lightning classification convolutional encoder deep learning encoding features

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An asynchronous low-power high-performance sequential decoder implemented with QDI templates

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2006年第9期14卷 975-985页

作者： Ozdag, Recep O. Beerel, Peter A. Univ So Calif Dept Elect Engn Syst Div Los Angeles CA 90089 USA

This paper presents the design of a channel-based asynchronous sequential decoder implemented with quasi-delay-insensitive templates. The Powermill simulation results in TSMC 0.25-CMOS technology show that the circuit runs at 430 MHz and consumes 32 mW. Techniques to effectively partition and implement the top level design, the implementation of fast shift registers, memories, and various other structures are discussed. Compared to a previously designed synchronous Fano decoder, the asynchronous version consumes 1/3 the power and runs at 2.15 times the speed assuming standard process normalization. The design also highlights the introduction of a standard-cell library and back-end design flow for asynchronous designs based on precharged half buffer (PCHB) templates.

关键词： asynchronous asynchronous library design convolutional encoder fano algorithm QDI precharged half buffer (PCHB)

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Incorporating convolutions into transformers for textile fiber identification from fabric images

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TEXTILE RESEARCH JOURNAL 2023年第23-24期93卷 5380-5390页

作者： Xu, Luoli Li, Fenying Chang, Shan Donghua Univ Sch Comp Sci & Technol Shanghai 201620 Peoples R China ZEKU Technol Silicon Engineer Grp Shanghai Peoples R China

Simple, fast and effective fiber identification can help consumers purchase their desired apparel and help the industry conduct large-scale textile testing. This paper presents a transformer architecture incorporating convolutions to recognize fibers in textile surface images, which meets the above requirements. Firstly, a convolution operation is performed on textile images to pick up overlapping patches as tokens and the linear projections in transformer encoders are replaced by depth-wise separable convolutions to extract the fiber representations. Secondly, the multi-head cross-attention module enables each label embedding to be compared with features at each spatial location to locate and pool the corresponding fiber characteristics. Finally, a simplified asymmetric loss is introduced to further purify the extracted fiber features. Experiments demonstrate that the proposed approach provides a significant improvement in fiber identification accuracy over both state-of-the-art multi-label classification frameworks and fiber identification architectures.

关键词： Fiber identification non-destructive fiber recognition pattern recognition multi-label classification convolutional encoder multi-head cross-attention

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Algebraic decoder of multidimensional convolutional code: constructive algorithms for determining syndrome decoder and decoder matrix based on Grobner basis

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2011年第1-3期22卷 67-81页

作者： Jangisarakul, P. Charoenlarpnopparut, C. Thammasat Univ Sch Informat Comp & Commun Technol Sirindhorn Int Inst Technol Klongluang 12121 Pathum Thani Thailand

A representation of an multidimensional (m-D) convolutional encoder is analogous to the representation of a transfer function for a MIMO m-D FIR system. The encoder matrix is usually not square and thus finding its inverse (decoder matrix) typically employs the Moore-Penrose generalized inverse. However, the result may not be FIR (polynomial matrix) even if the generator matrix is a polynomial matrix. In this paper a constructive algorithm for computing the FIR pseudo inverse, based on the usage of Grobner basis is presented along with detailed examples. The result obtained can be parameterized to cover the class of all possible FIR inverses. In addition, by using the computation method of syzygy with the Grobner basis module, the syndrome matrix for a given m-D convolutional encoder is shown. Furthermore, the theory of Grobner basis is applied to solve the algebraic syndrome decoder problems using the maximum likelihood (nearest neighborhood) criteria and the procedure for 2-D convolutional code error correction is proposed. Despite the complication of the decoding process, the proposed method is the only error correcting decoder for multidimensional convolutional code available to date.

关键词： convolutional encoder Grobner basis Syndrome matrix Algebraic decoder Maximum likelihood decoder

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An attentive convolutional transformer-based network for road safety

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JOURNAL OF SUPERCOMPUTING 2023年第14期79卷 16351-16377页

作者： Jayanthan, K. S. Domnic, S. Natl Inst Technol Dept Comp Applicat Tiruchirappalli 620005 Tamil Nadu India

The sharp surge in the number of vehicles on the road leads to numerous traffic violations (Road Transport and Highways in Ministry of road transport and highways, 2021. ). The detection of traffic violations in a dynamic environment is a complex task. This paper focuses on detecting a particular traffic violation, such as riding a motorcycle without a helmet. We propose to address the problem as an object detection task. In this paper, we propose a novel convolutional encoder-transformer decoder architecture (CETD) for the object detection task. The proposed architecture comprises two primary modules: a convolutional neural network (CNN)-based convolution encoder that extracts high-level features from input images and a transformer-based decoder that utilizes attention mechanisms to identify relevant components, such as helmets or missing helmets, in the image. This architecture is designed to achieve accurate object detection and localization in images by combining advanced feature extraction techniques with state-of-the-art attention mechanisms. Layer normalization module of the proposed architecture acts as an intermediate bias stabilizer for the encoder-decoder network. The design also includes a standard backbone feature extractor and fused backbone feature extractor. The model gives strong confidence in detecting occluded objects compared to other state-of-the-art models. The detector works in an end-to-end fashion with fewer handcrafted features. We have studied the applicability of the model with a miniature version of the COCO dataset. The model gives a competitive performance with models like Faster region-based convolutional neural network (Faster R-CNN) and Mask region-based convolutional neural network on this dataset. The proposed model is also fine-tuned on the traffic data with occlusion for helmet detection. The model's performance on helmet detection from traffic data is comparable with the state-of-the-art real-time detectors such as EspiNet, a m

关键词： Object detection convolutional encoder Layer normalization Transformer decoder Helmet detection Traffic data

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On Optimal TCM encoders

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IEEE TRANSACTIONS ON COMMUNICATIONS 2013年第6期61卷 2178-2189页

作者： Alvarado, Alex Graell i Amat, Alexandre Brannstrom, Fredrik Agrell, Erik Univ Cambridge Dept Engn Cambridge CB2 1TN England Chalmers Dept Signals & Syst S-41296 Gothenburg Sweden

An asymptotically optimal trellis-coded modulation (TCM) encoder requires the joint design of the encoder and the binary labeling of the constellation. Since analytical approaches are unknown, the only available solution is to perform an exhaustive search over the encoder and the labeling. For large constellation sizes and/or many encoder states, however, an exhaustive search is unfeasible. Traditional TCM designs overcome this problem by using a labeling that follows the set-partitioning principle and by performing an exhaustive search over the encoders. In this paper we study binary labelings for TCM and show how they can be grouped into classes, which considerably reduces the search space in a joint design. For 8-ary constellations, the number of different binary labelings that must be tested is reduced from 8! = 40320 to 240. For the particular case of an 8-ary pulse amplitude modulation constellation, this number is further reduced to 120 and for 8-ary phase shift keying to only 30. An algorithm to generate one labeling in each class is also introduced. Asymptotically optimal TCM encoders are tabulated which are up to 0.3 dB better than the previously best known encoders.

关键词： Binary reflected Gray code bit-interleaved coded modulation coded modulation convolutional encoder performance bounds set-partitioning trellis-coded modulation Viterbi decoding

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A Closed-Form Expression for the Exact Bit Error Probability for Viterbi Decoding of convolutional Codes

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IEEE TRANSACTIONS ON INFORMATION THEORY 2012年第7期58卷 4635-4644页

作者： Bocharova, Irina E. Hug, Florian Johannesson, Rolf Kudryashov, Boris D. St Petersburg State Univ Informat Technol Mech & Dept Informat Syst St Petersburg 197101 Russia Lund Univ Dept Elect & Informat Technol SE-22100 Lund Sweden

In 1995, Best et al. published a formula for the exact bit error probability for Viterbi decoding of the rate R = 1/2, memory m = 1 (two-state) convolutional encoder with generator matrix G(D) = (1 1 +D) when used to communicate over the binary symmetric channel. Their formula was later extended to the rate R = 1/2 memory m = 2 (four-state) convolutional encoder with generator matrix by G(D) = (1 + D-2 1 + D + D-2) by Lentmaier et al. In this paper, a different approach to derive the exact bit error probability is described. A general recurrent matrix equation, connecting the average information weight at the current and previous states of a trellis section of the Viterbi decoder, is derived and solved. The general solution of this matrix equation yields a closed-form expression for the exact bit error probability. As special cases, the expressions obtained by Best et al. for the two-state encoder and by Lentmaier et al. for a four-state encoder are used. The closed-form expression derived in this paper is evaluated for various realizations of encoders, including rate R = 1/2 and R = 2/3 encoders, of as many as 16 states. Moreover, it is shown that it is straightforward to extend the approach to communication over the quantized additive white Gaussian noise channel.

关键词： Additive white Gaussian noise (AWGN) channel binary symmetric channel (BSC) bit error probability convolutional code convolutional encoder exact bit error probability Viterbi decoding

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Robust hybrid watermarking approach for 3D multiresolution meshes based on spherical harmonics and wavelet transform

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第26期82卷 39841-39866页

作者： Sayahi, Ikbel Jallouli, Malika Ben Mabrouk, Anouar Mahjoub, Mohamed Ali Ben Amar, Chokri Univ Sfax Res Grp Intelligent Machines Lab ENIS Sfax Tunisia Univ Sousse LATIS Lab Adv Technol & Intelligent Syst Ecole Natl Ingn Sousse Sousse 4023 Tunisia Univ Kairouan Higher Inst Appl Math & Comp Sci Dept Math Kairouan 3100 Tunisia Univ Monastir Lab Algebra Number Theory & Nonlinear Anal LR18ES15 Dept MathFac Sci Monastir 5019 Tunisia Univ Tabuk Fac Sci Dept Math Computat & Analyt Math & Applicat Res Unit Tabuk 71491 Saudi Arabia Taif Univ Coll Comp & Informat Technol Taif Saudi Arabia

Since the 3D mesh security problems imposed themselves, attempts to design watermarking algorithms targeting this data type have continued to grow up to secure data shared by remote users. The originality of the present approach is to insert a whole grayscale image in hybrid domain by using wavelet transform and spherical harmonics. Our algorithm includes then two rounds of insertion. The first is operating multiresolution domain by applying wavelet transform. The watermark, which is an image already coded using a convolutional encoder, is embedded into wavelet coefficients (using Least Significant Bit method) after a transformation to spherical coordinate system and a modulation step. Finally watermarked mesh is reconstructed using inverse wavelet transform. This mesh undergoes a second round on watermarking using spherical harmonics. In this case the same steps are executed to embed data into SHs harmonic coefficients before reconstructing the final version of the watermarked mesh. The experimentation of our approach has shown a very high insertion rate due to the use of hybrid insertion domain, while maintaining the mesh quality. Watermarked mesh and extracted data are obtained in real time. Our approach is also robust against the most popular attacks. Our results show that the present approach improves the existing works.

关键词： Butterfly wavelet convolutional encoder LSB method Multiresolution 3D meshes Spherical harmonics

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