Convolutional Codes Research Articles

Discriminative Convolutional Sparse Coding of ECG Signals for Automated Recognition of Cardiac Arrhythmias

Electrocardiogram (ECG) is a common and powerful tool for studying heart function and diagnosing several abnormal arrhythmias. In this paper, we present a novel classification model that combines the discriminative convolutional sparse coding (DCSC) framework with the linear support vector machine (LSVM) classification strategy. In the training phase, most existing convolutional sparse coding frameworks are unsupervised in the sense that label information is ignored in the convolutional filter training stage. In this work, we explicitly incorporate a label consistency constraint called “discriminative sparse-code error” into the objective function to learn discriminative dictionary filters for sparse coding. The learned dictionary filters encourage signals from the same class to have similar sparse codes, and signals from different classes to have dissimilar sparse codes. To reduce the computational complexity, we propose to perform a max-pooling operation on the sparse coefficients. Using LSVM as a classifier, we examine the performance of the proposed classification system on the MIT-BIH arrhythmia database in accordance with the AAMI EC57 standard. The experimental results show that the proposed DCSC + LSVM algorithm can obtain 99.32% classification accuracy for cardiac arrhythmia recognition.

Convolutional Codes With a Maximum Distance Profile Based on Skew Polynomials

We construct a family of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(n,k)$ </tex-math></inline-formula> convolutional codes with degree <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta \in \{k,n-k\}$ </tex-math></inline-formula> that have a maximum distance profile. The field size required for our construction is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Theta (n^{2\delta })$ </tex-math></inline-formula> , which improves upon the known constructions of convolutional codes with a maximum distance profile. Our construction is based on the theory of skew polynomials.

Finite field construction for quasi-cyclic LDPC convolutional codes with cyclic 2-D MDS codes

Finite field construction for quasi-cyclic LDPC convolutional codes with cyclic 2-D MDS codes

Phase-Based Palmprint Identification With Convolutional Sparse Coding

Phase-based image matching has shown high recognition accuracy in palmprint verification. The algorithm compares a pair of palmprint images by extracting local phase features from the images and computing local correlation functions between them. A major drawback of this algorithm is its high computational cost associated with the evaluation of local correlation functions. This needs to be addressed, especially in the case of one-to-many comparisons required for palmprint identification. The problem becomes increasingly severe as the number of enrolled images increases. In this paper, we propose a novel palmprint identification algorithm with low computational complexity, which employs a sparse representation of enrolled phase features (i.e., phase templates) to evaluate local correlation functions. For this purpose, we also develop an efficient Convolutional Sparse Coding (CSC) algorithm that can derive a compact representation of phase templates. The proposed method reduces the computational cost of phase-based palmprint identification without significant degradation of recognition performance. Our experiments using public databases clearly demonstrate the advantage of the proposed method over conventional methods.

Ultrasonic defect characterization using time-domain scattering matrices and convolutional sparse coding

In this paper, the use of the time-domain scattering matrix for ultrasonic characterization of defects is explored. An approach based on convolutional sparse coding is proposed for extraction of the shift-invariant features of the time-domain scattering matrix. The results of a simulation show that the proposed approach can be reliably used for classification of the rough cracks. In addition, the mean sizing error and its standard deviation are also reduced compared with the use of the frequency domain scattering matrix. In experiments, a 1.5 mm slot and 1.5 mm elliptical defect were sized to within 0.25 mm using a 2.5 MHz, 64 element linear array with an element pitch of 0.5 mm. Excellent sizing results were obtained for inclined slots with a large orientation angle (i.e. 60°).

Proximal PanNet: A Model-Based Deep Network for Pansharpening

Recently, deep learning techniques have been extensively studied for pansharpening, which aims to generate a high resolution multispectral (HRMS) image by fusing a low resolution multispectral (LRMS) image with a high resolution panchromatic (PAN) image. However, existing deep learning-based pansharpening methods directly learn the mapping from LRMS and PAN to HRMS. These network architectures always lack sufficient interpretability, which limits further performance improvements. To alleviate this issue, we propose a novel deep network for pansharpening by combining the model-based methodology with the deep learning method. Firstly, we build an observation model for pansharpening using the convolutional sparse coding (CSC) technique and design a proximal gradient algorithm to solve this model. Secondly, we unfold the iterative algorithm into a deep network, dubbed as Proximal PanNet, by learning the proximal operators using convolutional neural networks. Finally, all the learnable modules can be automatically learned in an end-to-end manner. Experimental results on some benchmark datasets show that our network performs better than other advanced methods both quantitatively and qualitatively.

Design of Neural Network Model for Cross-Media Audio and Video Score Recognition Based on Convolutional Neural Network Model.

In this paper, the residual convolutional neural network is used to extract the note features in the music score image to solve the problem of model degradation; then, multiscale feature fusion is used to fuse the feature information of different levels in the same feature map to enhance the feature representation ability of the model. A network composed of a bidirectional simple loop unit and a chained time series classification function is used to identify notes, parallelizing a large number of calculations, thereby speeding up the convergence speed of training, which also makes the data in the dataset no longer need to be strict with labels. Alignment also reduces the requirements on the dataset. Aiming at the problem that the existing cross-modal retrieval methods based on common subspace are insufficient for mining local consistency within modalities, a cross-modal retrieval method fused with graph convolution is proposed. The K-nearest neighbor algorithm is used to construct modal graphs for samples of different modalities, and the original features of samples from different modalities are encoded through a symmetric graph convolutional coding network and a symmetric multilayer fully connected coding network, and the encoded features are fused and input. We jointly optimize the intramodal semantic constraints and intermodal modality-invariant constraints in the common subspace to learn highly locally consistent and semantically consistent common representations for samples from different modalities. The error value of the experimental results is used to illustrate the effect of parameters such as the number of iterations and the number of neurons on the network. In order to more accurately illustrate that the generated music sequence is very similar to the original music sequence, the generated music sequence is also framed, and finally the music sequence spectrogram and spectrogram are generated. The accuracy of the experiment is illustrated by comparing the spectrogram and the spectrogram, and genre classification predictions are also performed on the generated music to show that the network can generate music of different genres.

Study of Different Types of Error Detection and Correction Code in Wireless Communication

The integrity of received data is a critical consideration in the design of digital communications and storage systems. The technique involved in attaining data reliability while transmission over a wireless channel is to use Channel Coding. These coding methods involve the use of Error Control Codes and there are two basic ways of controlling errors. They are Automatic Repeat Request and Forward Error Correction. This thesis concentrates on Forward error correction that deals with error detection and error correction. There are two types of error control codes: Block Codes and Convolutional Codes. The extent to which the errors are detected is a measure of the success of the code. The main trade-off in the error correction/detection technique lies in the key parameters involved in evaluating a coding system. Various block codes are analyzed using the performance metrics, namely, Improvement ratio and Error Resilience. It is observed that Cyclic Redundancy Check (CRC) codes showed better performance, and hence, they are chosen for further study.

Damage localization with Lamb waves using dense convolutional sparse coding network

Composite materials are progressively employed in many safety-critical structural applications due to their superior properties. Structural health monitoring techniques based on Lamb waves have been utilized to assess the damages of composite structures. Recently, deep learning algorithms are adopted for damage detection and localization. Identifying valid damage-related features through neural networks is a crucial step in the analysis process. However, most implemented deep learning architectures are still lacking physical interpretability to some extent. In this paper, a dense convolutional sparse coding network (DCSCNet) is presented for Lamb wave-based damage localization in composite structures, providing a possibility to interpret current networks. In DCSCNet, narrowband Hanning windowed toneburst signals are utilized as kernels of the first convolutional layer to learn more meaningful features. Dense connection is theoretically demonstrated in the scope of DCSCNet, which gathers multiple feature maps directly to develop the potential of the network through feature reuse. The multi-layer iterative soft thresholding algorithm with the dense connection is then employed for solving the multi-layer convolutional sparse coding model. Moreover, effective Squeeze-Excitation is introduced as the channel attention module to boost the representational capability of the network. The experimental results demonstrate the high-performance and interpretable characteristics of the proposed DCSCNet, verifying its feasibility and effectiveness in Lamb wave-based damage localization of composite structures.

Using a Deep Quantum Neural Network to Enhance the Fidelity of Quantum Convolutional Codes

The fidelity of quantum states is an important concept in quantum information. Improving quantum fidelity is very important for both quantum communication and quantum computation. In this paper, we use a quantum neural network (QNN) to enhance the fidelity of [6, 2, 2] quantum convolutional codes. Towards the circuit of quantum convolutional codes, the target quantum state |0⟩ or |1⟩ is turned into entangled quantum states, which can defend against quantum noise more effectively. As the quantum neural network works better for quantum states with low dimension, we divide the quantum circuits into two parts. Then we apply the quantum neural network to each part of the circuit. The results of the simulation show that the network performs well in enhancing the fidelity of the quantum states. Through the quantum neural network, the fidelity of the first part is enhanced from 95.2% to 99.99%, and the fidelity of the second part is enhanced from 93.88% to 94.57%.

CRC-Aided List Decoding of Convolutional Codes in the Short Blocklength Regime

We consider the concatenation of a convolutional code (CC) with an optimized cyclic redundancy check (CRC) code as a promising paradigm for good short blocklength codes. The resulting CRC-aided convolutional code naturally permits the use of serial list Viterbi decoding (SLVD) to achieve maximum-likelihood decoding. The convolutional encoder of interest is of rate-$1/\omega$ and the convolutional code is either zero-terminated (ZT) or tail-biting (TB). The resulting CRC-aided convolutional code is called a CRC-ZTCC or a CRC-TBCC. To design a good CRC-aided convolutional code, we propose the distance-spectrum optimal (DSO) CRC polynomial. A DSO CRC search algorithm for the TBCC is provided. Our analysis reveals that the complexity of SLVD is governed by the expected list rank which converges to $1$ at high SNR. This allows a good performance to be achieved with a small increase in complexity. In this paper, we focus on transmitting $64$ information bits with a rate-$1/2$ convolutional encoder. For a target error probability $10^{-4}$, simulations show that the best CRC-ZTCC approaches the random-coding union (RCU) bound within $0.4$ dB. Several CRC-TBCCs outperform the RCU bound at moderate SNR values.

Quantum convolutional codes concatenated with the GKP code for correcting continuous errors

Quantum convolutional codes concatenated with the GKP code for correcting continuous errors

MITA interleaver for OFDM-IDMA and SCFDMA-IDMA techniques using QPSK modulation over PLC

The paper aims to examine the performance of multiplicative interleaving with tree algorithm (MITA) interleaver for grouped IDMA systems i.e., SCFDMA-IDMA and OFDM-IDMA, using QPSK modulation over powerline channel. The prime objective is to evaluate the MITA algorithm for its complexity and throughput over multiple communication systems so that it fulfills the requirements of 5G technology. Higher throughput and low complexity are achieved by the structure of MITA interleaver, in which more users are allotted interleavers per clock cycle as compared to existing interleavers primarily, random, tree, and FLRITI. The analysis is carried out in MATLAB environment for varying parameter values such as data length and user count and is plotted in terms of bit error rate (BER). Next, the effect of Convolutional coding is observed on grouped IDMA systems and lastly comparison in terms complexity is carried out. The simulation results over grouped IDMA systems show that MITA interleaver has better BER performance than FLRITI for large user count and the response further improves with greater number of users. The comparison of interleavers in terms of complexity reveals that MITA has lower complexity than FLRITI. Thus, MITA interleaver can be preferred over FLRITI and is a better option to be implemented in 5G technologies.

Design and Analysis of Joint Source-Channel Code System with Fixed-Length Code

As the demands of multimedia and data services increase, efficient communication systems are being investigated to meet the high data rate requirements. Joint source-channel coding (JSCC) schemes were proposed for improving overall system performance. However, existing JSCC systems may suffer a symbol error rate (SER) performance loss when residual source redundancy is not fully exploited. This paper presents a novel, low-complexity JSCC system, which consists of a fixed-length source block code and an irregular convolutional channel code. A simple approach is proposed to design source codes that minimize the SER of source detection and guarantee the convergence of iterative source-channel decoding (ISCD). To improve the waterfall performance of ISCD, the channel code is optimized by using the extrinsic information transfer (EXIT) chart and the concept of irregular code. The channel code is constituted by recursive non-systematic convolutional (RNSC) subcodes. The weights of subcodes are optimized to make the EXIT curves of the channel decoder and the source decoder well-matched, and therefore, a near-capacity performance is achieved. Simulation results show that the proposed system achieves more than 1 dB gains and 0.3 dB gains compared to the separate source-channel code system and the other optimal JSCC systems, respectively. Additionally, the performance of the proposed system is within 1 dB deviation from the Shannon limit capacity.

Joint self-supervised and reference-guided learning for depth inpainting

Depth information can benefit various computer vision tasks on both images and videos. However, depth maps may suffer from invalid values in many pixels, and also large holes. To improve such data, we propose a joint self-supervised and reference-guided learning approach for depth inpainting. For the self-supervised learning strategy, we introduce an improved spatial convolutional sparse coding module in which total variation regularization is employed to enhance the structural information while preserving edge information. This module alternately learns a convolutional dictionary and sparse coding from a corrupted depth map. Then, both the learned convolutional dictionary and sparse coding are convolved to yield an initial depth map, which is effectively smoothed using local contextual information. The reference-guided learning part is inspired by the fact that adjacent pixels with close colors in the RGB image tend to have similar depth values. We thus construct a hierarchical joint bilateral filter module using the corresponding color image to fill in large holes. In summary, our approach integrates a convolutional sparse coding module to preserve local contextual information and a hierarchical joint bilateral filter module for filling using specific adjacent information. Experimental results show that the proposed approach works well for both invalid value restoration and large hole inpainting.

PAPR and BER analysis of coded FBMC-OQAM system with non-linear companding techniques

Filter Bank Multicarrier with offset QAM (OQAM) is a suitable multicarrier waveform technique for 5 G and future wireless networks and standards. Being a multicarrier technique, it suffers from high peak-to-average-power ratio (PAPR) problem. In this paper we have used some non-linear companding transforms such as μ-law, A-law, rooting, tangent rooting, and logarithmic rooting to overcome the PAPR problem. Since non-linear companding techniques degrade bit-error-rate (BER) performance of a system. Hence, some channel coding techniques such as convolutional codes and turbo codes are employed to enhance the BER performance of non-linearly companded FBMC-OQAM system. It was observed that the combination of convolutional coding with logarithmic rooting companding provides PAPR reduction gain of about 4.896 dB and coding gain of about 2 dB as compared to conventional FBMC-OQAM system and is an appropriate choice for applications requiring reduced system complexity. It was observed that the combination of turbo coding with logarithmic rooting companding provides PAPR reduction gain of about 4.922 dB and a sufficiently high coding gain of about 7 dB as compared to conventional FBMC-OQAM system and is suitable for applications requiring improved data reliability at the expense of increased system complexity and latency.

Down-step statistics in generalized Dyck paths

The number of down-steps between pairs of up-steps in $k_t$-Dyck paths, a generalization of Dyck paths consisting of steps $\{(1, k), (1, -1)\}$ such that the path stays (weakly) above the line $y=-t$, is studied. Results are proved bijectively and by means of generating functions, and lead to several interesting identities as well as links to other combinatorial structures. In particular, there is a connection between $k_t$-Dyck paths and perforation patterns for punctured convolutional codes (binary matrices) used in coding theory. Surprisingly, upon restriction to usual Dyck paths this yields a new combinatorial interpretation of Catalan numbers.

PUNCTURED NCC CODES FOR INFORMATION PROTECTION IN INFORMATION AND MEASUREMENT SYSTEMS

During development and design of information-measuring systems, enabling to carry out collection, processing and transmission of information, one of the main problems is the choice of effective methods of information protection against defects in noisy communication channels. Effective use of frequency-time resources of information communication channels, as the most valuable part of the information transmission system, is the key to provide reliable delivery of transmitted messages. One of effective directions of reliability increase and information transfer reliability in information-measuring communication networks is implementation of methods and algorithms of noise-resistant coding, providing for detection and coping with errors, arising due to interferences in the communication channel. In this case, the choice in favor of one or another coding method depends on the information characteristics of the data channel. Parameters of the noise coder must be coordinated with the source of the message, the communication channel, as well as the requirements for the reliability of bringing information to the recipient. The problem of obtaining a wide range of codec parameters with simultaneous preservation of the unified macrostructure of the codec in communication systems causes the need for research on the development of adaptive algorithms for error information protection. In the article the research results of the characteristics of variable rate slot convolutional codes for adaptive coding/decoding in information-measuring systems of information transmission are proposed. Consequently, when creating communication networks, there is no need to use a large number of different codecs, even with completely different requirements to the code rate, channel rate and gain due to coding. In addition, there is a real opportunity to create terminal equipment, working on unified algorithms of protection against errors and access.

Intelligent Classification Method of Archive Data Based on Multigranular Semantics.

With the rapid development of information technology, the amount of data in various digital archives has exploded. How to reasonably mine and analyze archive data and improve the effect of intelligent management of newly included archives has become an urgent problem to be solved. The existing archival data classification method is manual classification oriented to management needs. This manual classification method is inefficient and ignores the inherent content information of the archives. In addition, for the discovery and utilization of archive information, it is necessary to further explore and analyze the correlation between the contents of the archive data. Facing the needs of intelligent archive management, from the perspective of the text content of archive data, further analysis of manually classified archives is carried out. Therefore, this paper proposes an intelligent classification method for archive data based on multigranular semantics. First, it constructs a semantic-label multigranular attention model; that is, the output of the stacked expanded convolutional coding module and the label graph attention module are jointly connected to the multigranular attention Mechanism network, the weighted label output by the multigranularity attention mechanism network is used as the input of the fully connected layer, and the output value of the fully connected layer used to map the predicted label is input into a Sigmoid layer to obtain the predicted probability of each label; then, the model for training: use the multilabel data set to train the constructed semantic-label multigranularity attention model, adjust the parameters until the semantic-label multigranularity attention model converges, and obtain the trained semantic-label multigranularity attention model. Taking the multilabel data set to be classified as input, the semantic-label multigranularity attention model after training outputs the classification result.

MRCON-Net: Multiscale reweighted convolutional coding neural network for low-dose CT imaging

Background and objectiveLow-dose computed tomography (LDCT) has become increasingly important for alleviating X-ray radiation damage. However, reducing the administered radiation dose may lead to degraded CT images with amplified mottle noise and nonstationary streak artifacts. Previous studies have confirmed that deep learning (DL) is promising for improving LDCT imaging. However, most DL-based frameworks are built intuitively, lack interpretability, and suffer from image detail information loss, which has become a general challenging issue. MethodsA multiscale reweighted convolutional coding neural network (MRCON-Net) is developed to address the above problems. MRCON-Net is compact and more explainable than other networks. First, inspired by the learning-based reweighted iterative soft thresholding algorithm (ISTA), we extend traditional convolutional sparse coding (CSC) to its reweighted convolutional learning form. Second, we use dilated convolution to extract multiscale image features, allowing our single model to capture the correlations between features of different scales. Finally, to automatically adjust the elements in the feature code to correct the obtained solution, a channel attention (CA) mechanism is utilized to learn appropriate weights. ResultsThe visual results obtained based on the American Association of Physicians in Medicine (AAPM) Challenge and United Image Healthcare (UIH) clinical datasets confirm that the proposed model significantly reduces serious artifact noise while retaining the desired structures. Quantitative results show that the average structural similarity index measurement (SSIM) and peak signal-to-noise ratio (PSNR) achieved on the AAPM Challenge dataset are 0.9491 and 40.66, respectively, and the SSIM and PSNR achieved on the UIH clinical dataset are 0.915 and 42.44, respectively; these are promising quantitative results. ConclusionCompared with recent state-of-the-art methods, the proposed model achieves subtle structure-enhanced LDCT imaging. In addition, through ablation studies, the components of the proposed model are validated to achieve performance improvements.