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A collection of sets displays a proximity gap with respect to some property if for every set in the collection, either (i) all members are δ-close to the property in relative Hamming distance or (ii) only a tiny fraction of members are δ-close to the property. In particular, no set in the collection has roughly half of its members δ-close to the property and the others δ-far from it. We show that the collection of affine spaces displays a proximity gap with respect to Reed--Solomon (RS) codes, even over small fields, of size polynomial in the dimension of the code, and the gap applies to any δ smaller than the Johnson/Guruswami--Sudan list-decoding bound of the RS code.We also show near-optimal gap results, over fields of (at least) linear size in the RS code dimension, for δ smaller than the unique decoding radius. Concretely, if δ is smaller than half the minimal distance of an RS code V ⊂ Fnq, then every affine space is either entirely δ-close to the code or, alternatively, at most an (n/q)-fraction of it is δ-close to the code. Finally, we discuss several applications of our proximity gap results to distributed storage, multi-party cryptographic protocols, and concretely efficient proof systems. We prove the proximity gap results by analyzing the execution of classical algebraic decoding algorithms for Reed--Solomon codes (due to Berlekamp--Welch and Guruswami--Sudan) on a formal element of an affine space. This involves working with Reed--Solomon codes whose base field is an (infinite) rational function field. Our proofs are obtained by developing an extension (to function fields) of a strategy of Arora and Sudan for analyzing low-degree tests. [ABSTRACT FROM AUTHOR]

To enhance the decoding efficiency of Globally Coupled (GC) LDPC codes, we incorporated Early Termination (ET) and Forced Convergence (FC) into the local/global two-phase decoding algorithm to expedite the decoding process. The two-phase decoding scheme integrates the ET technique to halt unnecessary iterations in the local decoding phase while employing the FC technique to accelerate convergence in the global phase decoding. The application of ET technology in the local decoding of GC-LDPC codes will not cause performance loss as in traditional block codes and will cause considerable complexity gains. For a longer code length and larger convergence differences between nodes' global codes, the FC technique operates more efficiently in global code than local code. Two variants are proposed for the ET scheme in the local decoding, namely ET-1 and ET-2. The initial variant, ET-1, predicts whether local decoding can be successful according to data characteristics and stop the local decoding iteration that is not expected to be successful in time. In the case of ET-2, the saved local iterations are transformed to global decoding equally. The results show that ET-1 saves considerable decoding time complexity and ET-2 improves the performance of the GC-LDPC code with the same decoding time complexity. The combined approach of ET-1 with FC reduces the decoding time complexity up to 42% at a low Signal Noise Rate region while maintaining its performance; ET-2-FC two-phase decoding saves approximately 25% decoding time complexity while improving the BER by about 0.18 dB and FER by about 0.23 dB. [ABSTRACT FROM AUTHOR]

Recently, Arıkan proposed a polarization‐adjusted convolutional (PAC) codes, demonstrating their superior error correction performance over polar codes at short block lengths. It was confirmed that PAC codes approached the optimal performance achievable with limited code length. This paper proposes a novel low‐complexity list decoding algorithm for PAC codes, incorporating path splitting and pruning strategies based on a set of highly reliable information bits. Simulation results reveal that the proposed algorithm significantly reduces sorting complexity and average list size, all while incurring negligible performance loss. Unlike previous pruning algorithms designed for polar codes, the proposed strategy eliminates the need to individually assess the reliability of decoding paths in each decoding process. Instead, the algorithm minimizes redundant decoding paths through a high‐reliability information bit set, constructed using Monte Carlo experiments. [ABSTRACT FROM AUTHOR]

In this paper, we first examine the properties of the matrices of the generalized m -step Jacobsthal sequence, and we develop a public key cryptosystem using key exchange based on the relationship between inner product and orthogonality. Then we create encryption and decryption algorithms using this key exchange, self-adjoint operator, and generalized m -step Jacobsthal sequence, and we provide a detailed example of how encryption and decryption algorithms work. Finally, a security strength and complexity analysis of these algorithms will be made. [ABSTRACT FROM AUTHOR]

Neural decoding is a tool for understanding how activities from a population of neurons inside the brain relate to the outside world and for engineering applications such as brain-machine interfaces. However, neural decoding studies mainly focused on different decoding algorithms rather than different neuron types which could use different coding strategies. In this study, we used two-photon calcium imaging to assess three auditory spatial decoders (space map, opponent channel, and population pattern) in excitatory and inhibitory neurons in the dorsal inferior colliculus of male and female mice. Our findings revealed a clustering of excitatory neurons that prefer similar interaural level difference (ILD), the primary spatial cues in mice, while inhibitory neurons showed random local ILD organization. We found that inhibitory neurons displayed lower decoding variability under the opponent channel decoder, while excitatory neurons achieved higher decoding accuracy under the space map and population pattern decoders. Further analysis revealed that the inhibitory neurons' preference for ILD off the midline and the excitatory neurons' heterogeneous ILD tuning account for their decoding differences. Additionally, we discovered a sharper ILD tuning in the inhibitory neurons. Our computational model, linking this to increased presynaptic inhibitory inputs, was corroborated using monaural and binaural stimuli. Overall, this study provides experimental and computational insight into how excitatory and inhibitory neurons uniquely contribute to the coding of sound locations. [ABSTRACT FROM AUTHOR]

This paper presents an efficient decoding method (namely SORTED decoding) for the de facto encoding in size optimization of trusses with automatic member grouping. The efficiency of the proposed method is evaluated through numerical experiments driven by two metaheuristic algorithms of different search mechanisms: Colliding Bodies Optimization and Jaya Algorithm. The first group of experiments show that the SORTED decoding method significantly outperforms the de facto and the two other decoding methods introduced in this study (ACCUMULATED and SORTED-ACCUMULATED) in terms of solution quality; moreover, the authors proved that the superiority of the SORTED decoding is not algorithm-dependent. Considerably better member-grouping configurations that provide up to 15% material economy are discovered for some of the well-known pre-grouped benchmark problems in the second group of experiments; surprisingly, the worst member-grouping configuration discovered by the SORTED decoding for one of the problems is better than that of the pre-grouped version. [ABSTRACT FROM AUTHOR]

Conventional baseband processing techniques for communication must be adapted to the specificities of terahertz channels. Currently, there is limited research on channel coding applicable to terahertz bands, particularly in terms of performing real-time tests that differentiate them from offline processing. This paper presents a test platform for a 300 GHz wireless communication system based on the PXIe platform. The experiment utilizes a high-performance, low-complexity serial scheduling approach with the Minimum Sum-Log Likelihood Ratio Belief Propagation (MS-LLR-BP) soft decision decoding algorithm to test point-to-point real-time line-of-sight communication for low-density parity-check (LDPC) code communication systems. The results indicate that the implementation of LDPC coding in QPSK terahertz wireless transmission systems results in a performance enhancement of approximately 25–55% in bit error rate (BER), demonstrating the excellent performance of LDPC codes against terahertz channel fading. This highlights their potential in future ultra-high-speed communication systems. [ABSTRACT FROM AUTHOR]

In this paper?, we propose a low-density parity check (LDPC) code design scheme that improves the performance of the existing genetic algorithm-based LDPC scheme. In particular, we enhance the performance of the LDPC code by removing the girth-4 property of the parity check matrix and utilizing the min-sum decoding algorithm instead of the belief propagation decoding algorithm. In addition, we consider various short block-length scenarios, specifically focusing on 64-bit and 128-bit lengths, which are well-suited for IoT systems. Then, we evaluate the block error rate (BLER) of the LDPC code over the binary input additive white Gaussian noise (BI-AWGN) channel. Finally, extensive simulation results indicate that our proposed approach achieves more than 11% gain in terms of BLER compared with the benchmarked schemes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Structured illumination microscopy (SIM) can achieve optical sectioning with high resolution, and have aroused extensive research interest. In SIM, a set of high-contrast illumination patterns are projected onto the sample to modulate the surface height information, and then, a decoding algorithm is applied to the modulated pattern images for high-quality optical sectioning. Applied to samples with large dynamic range of reflectivity, however, SIM may fail to achieve high quality sectioning for accurate surface reconstruction. Herein, an active digital micromirror device (DMD) based illumination method using per-pixel coded strategy is proposed in SIM to realize high-quality measurement for surface with complex reflection characteristics. In this method, the mapping relationship between DMD and the camera is established pixels by pixels, which enables the illumination intensity on the sample surface can be flexibly modulated by DMD pixel-level modulation corresponding to reflectivity distribution of the surface, and allows the camera pixels always to have reasonable exposure intensity for high precision measurement. More importantly, we put forward an adaptive light intensity control algorithm to improves the signal-to-noise ratio of acquired images without compromising modulation depth of pattern and measurement efficiency. Extensive comparative experiments were conducted and demonstrated that the proposed method can retrieve the surface morphology information of micro-scale complex reflectivity surfaces with high accuracy. [ABSTRACT FROM AUTHOR]

In this article we will approach the soft-decision decoding for the linear block codes, is a kind of decoding algorithms used to decode data to form better original estimated received message, it is considered as a NP-hard problem. In this article we present a new decoder using memetic algorithm such metaheuristic technic operates on the dual code rather than the code itself that aims to find the error caused when sending a codeword calculated from a message of k bits of information, the resulting codeword contains n bits, including the redundancy bits, the efficiency of an error-correcting code is equivalent to the ratio k/n, the rate is belong the interval [0,1]. Hence a good code is the one that ensures a certain error correcting capability at minimum ratio. The results proved that this approach using a combination of genetic algorithm and local search algorithm provides a sufficiently good solution to an optimization problem; the new decoder is applied on linear codes where the structure is given by a parity check matrix. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we present a new encoding and decoding method based on the recently introduced Minesweeper Model. Here, the main idea is to use Fibonacci–Lucas tree diagrams, and this proposed method uses a public key and a private key. To test this method, a new algorithm is constructed to enable a faster and more accurate control. [ABSTRACT FROM AUTHOR]

Sparse coding provides a versatile framework for efficiently capturing and representing crucial data (information) concisely, which plays an essential role in various computer science fields, including data compression, feature extraction, and general signal processing. Here, we propose symmetric quantum neural networks for realizing sparse coding and decoding algorithms. Our networks consist of multi-layer, two-level unitary transformations that are naturally suited for optical circuits. Specifically, the two networks we propose can be efficiently trained together or separately via a quantum natural gradient descent algorithm. Utilizing the trained model, we achieve coding and decoding of sparse data including sparse classical data of binary and grayscale images, as well as sparse quantum data that are quantum states in a certain smaller subspace. The results demonstrate an accuracy of 98.77% for image reconstruction and a fidelity of 97.68% for quantum state revivification. Our quantum sparse coding and decoding model offers improved generalization and robustness compared to the classical model, giving insights to further research on quantum advantages in artificial neural networks. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are a key class of endogenous non-coding RNAs that play a pivotal role in regulating diseases. Accurately predicting the intricate relationships between miRNAs and diseases carries profound implications for disease diagnosis, treatment, and prevention. However, these prediction tasks are highly challenging due to the complexity of the underlying relationships. While numerous effective prediction models exist for validating these associations, they often encounter information distortion due to limitations in efficiently retaining information during the encoding-decoding process. Inspired by Multi-layer Heterogeneous Graph Transformer and Machine Learning XGboost classifier algorithm, this study introduces a novel computational approach based on multi-layer heterogeneous encoder—machine learning decoder structure for miRNA-disease association prediction (MHXGMDA). First, we employ the multi-view similarity matrices as the input coding for MHXGMDA. Subsequently, we utilize the multi-layer heterogeneous encoder to capture the embeddings of miRNAs and diseases, aiming to capture the maximum amount of relevant features. Finally, the information from all layers is concatenated to serve as input to the machine learning classifier, ensuring maximal preservation of encoding details. We conducted a comprehensive comparison of seven different classifier models and ultimately selected the XGBoost algorithm as the decoder. This algorithm leverages miRNA embedding features and disease embedding features to decode and predict the association scores between miRNAs and diseases. We applied MHXGMDA to predict human miRNA-disease associations on two benchmark datasets. Experimental findings demonstrate that our approach surpasses several leading methods in terms of both the area under the receiver operating characteristic curve and the area under the precision-recall curve. [ABSTRACT FROM AUTHOR]

Low-density parity check (LDPC) is a channel coding technique widely utilized in the 5G New Radio standard, it is of utmost importance in facilitating proficient and secure communication in noisy environments by effectively minimizing errors during data transmission. It is primarily used in the 5G New Radio (NR) standard for encoding user information on the physical downlink shared channel (PDSCH). The necessity to satisfy the increasing expectations for throughput, latency, and dependability led to the decision to deploy LDPC codes for user data, especially in the enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) scenarios of 5G networks. The present system proposes the use of NR-LDPC codes to transmit data across a lognormal multipath fading channel model in the presence of AWGN. Wireless communication channels often use a lognormal multipath fading channel model, where the received signal experiences both multipath fading and lognormal shadowing. The research investigates the effectiveness of NR-LDPC coding in improving QAM-OFDM system performance by analyzing two rate-compatible base graphs and comparing their effectiveness with an uncoded system. This analysis is crucial for optimizing communication network design, especially in scenarios where the integrity of data is of utmost importance. We introduce a new method to improve the 5G NR LDPC code capability under lognormal fading conditions. This approach develops a layered min-sum (LMS) algorithm to provide enhanced error-correcting capabilities. The developed and implemented decoding algorithm represents a significant advancement over traditional detection methods. The outcomes of the simulation provide evidence of the effectiveness of the proposed NR-LDPC coding techniques in terms of their error correction and identification capabilities. In addition, the developed LMS decoding algorithm was shown to significantly decrease the BER of the system. [ABSTRACT FROM AUTHOR]

As high-speed big-data communications impose new requirements on storage latency, low-density parity-check (LDPC) codes have become a widely used technology in flash-memory channels. However, the iterative LDPC decoding algorithm faces a high decoding latency problem due to its mechanism based on iterative message transmission. Motivated by the unbalanced bit reliability of codeword, this paper proposes two technologies, i.e., serial entropy feature-based layered normalized min-sum (S-EFB-LNMS) decoding and parallel entropy feature-based layered normalized min-sum (P-EFB-LNMS) decoding. First, we construct an entropy feature vector that reflects the real-time bit reliability of the codeword. Then, the reliability of the output information of the layered processing unit (LPU) is evaluated by analyzing the similarity between the check matrix and the entropy feature vector. Based on this evaluation, we can dynamically allocate and schedule LPUs during the decoding iteration process, thereby optimizing the entire decoding process. Experimental results show that these techniques can significantly reduce decoding latency. [ABSTRACT FROM AUTHOR]

With the exponential growth of digital data, there is a pressing need for innovative storage media and techniques. DNA molecules, due to their stability, storage capacity, and density, offer a promising solution for information storage. However, DNA storage also faces numerous challenges, such as complex biochemical constraints and encoding efficiency. This paper presents Explorer , a high-efficiency DNA coding algorithm based on the De Bruijn graph, which leverages its capability to characterize local sequences. Explorer enables coding under various biochemical constraints, such as homopolymers, GC content, and undesired motifs. This paper also introduces Codeformer , a fast decoding algorithm based on the transformer architecture, to further enhance decoding efficiency. Numerical experiments indicate that, compared with other advanced algorithms, Explorer not only achieves stable encoding and decoding under various biochemical constraints but also increases the encoding efficiency and bit rate by ¿10%. Additionally, Codeformer demonstrates the ability to efficiently decode large quantities of DNA sequences. Under different parameter settings, its decoding efficiency exceeds that of traditional algorithms by more than two-fold. When Codeformer is combined with Reed–Solomon code, its decoding accuracy exceeds 99%, making it a good choice for high-speed decoding applications. These advancements are expected to contribute to the development of DNA-based data storage systems and the broader exploration of DNA as a novel information storage medium. [ABSTRACT FROM AUTHOR]

Erasure coding has been widely used in commodity datacenter to tolerate faults, due to its ability to simultaneously provide high storage space utilization and data reliability. However, when data loss occurs, the extra data decoding and traffic overhead makes it difficult to improve their data repair efficiency, limiting their further application in the hot data storage systems. In this paper, we proposed an FPGA-based Data Repair accelerating scheme (FPGA-ECDR) for erasure-coded storage system, which employs the Cauchy Reed-Solomon(CRS) code, to overcome the aforementioned limitation. In FPGA-ECDR, multiple modules are designed to work collaboratively, enhancing the efficiency of data flow and ensuring the reliability of the data repair process. Then, a CRS decoding algorithm based on check matrix is used to reduce the complexity of matrix inversion in the decoding process, and hardware acceleration of the algorithm is realized by FPGA. Moreover, we proposed a Hierarchical Parallel Decoding Structure (HPDS) to optimize cache data reading timing and XOR logic operations in the decoding process. HPDS can effectively reduce the impact of Column Address Strobe (CAS) latency and improve repair efficiency. Finally, we conducted FPGA board-level verification of the proposed scheme, testing on CRS codes with different data sizes and parameters. The experimental results show that compared with the schemes of the current mainstream open-source erasure coding library Jerasure and Intel ISA-L acceleration library, as well as the Xilinx RS code decoding acceleration scheme based on the same FPGA platform, our proposed scheme has lower decoding latency and can improve the data decoding rate by 3.2 to 148.5 times and enhances the repair throughput by up to 21.4 times in fault-tolerant storage system. [ABSTRACT FROM AUTHOR]

Inter-Domain Path Computation under Node-Defined Domain Uniqueness Constraint (IDPC-NDU) is one of the routing cost optimization problems in multi-domain networks that has been proposed and received much attention by researchers. Because the IDPC-NDU is NP-Hard, the approaches using approximation algorithms are frequently used. These approximation algorithms often construct solutions based on connecting edges between adjacent domains, with little consideration of edges between non-adjacent domains. Furthermore, this study investigates how to combine an algorithm with the capability of exploration with another algorithm with the capability of exploiting in order to balance the ability to explore and exploit the search space. As a result, based on new encoding and decoding solution methods, this study proposes combinations of the algorithms Multi-start Method and Variable Neighbourhood Search Method. By considering the inter-domain edges connecting non-adjacent domains, the new encoding and decoding methods help to examine more possible paths between the source node and the destination node. The proposed algorithm is evaluated by comparing it to the most recent algorithms that were proposed to solve the IDPC-NDU. Analysis of experimental results on various instances shows that the proposed algorithm exceeds other algorithms in most of the experimental cases. In particular, one-fifth of the test cases on which the proposed algorithm finds the optimal solution. Besides, the study also analyses the influence of the attributes of the input data on the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Composite DNA letters, by merging all four DNA nucleotides in specified ratios, offer a pathway to substantially increase the logical density of DNA digital storage (DDS) systems. However, these letters are susceptible to nucleotide errors and sampling bias, leading to a high letter error rate, which complicates precise data retrieval and augments reading expenses. To address this, Derrick‐cp is introduced as an innovative soft‐decision decoding algorithm tailored for DDS utilizing composite letters. Derrick‐cp capitalizes on the distinctive error sensitivities among letters to accurately predict and rectify letter errors, thus enhancing the error‐correcting performance of Reed‐Solomon codes beyond traditional hard‐decision decoding limits. Through comparative analyses in the existing dataset and simulated experiments, Derrick‐cp's superiority is validated, notably halving the sequencing depth requirement and slashing costs by up to 22% against conventional hard‐decision strategies. This advancement signals Derrick‐cp's significant role in elevating both the precision and cost‐efficiency of composite letter‐based DDS. [ABSTRACT FROM AUTHOR]

There is a growing demand for indoor positioning systems (IPSs) in a wide range of applications. However, traditional solutions such as GPS face many technical challenges. In recent years, a promising alternative has been emerging, the visible light communication (VLC)-based IPS, which offers a combination of high accuracy, low cost, and energy efficiency. This article presents a comprehensive review of VLC-based IPSs, providing a tutorial-like overview of the system. It begins by comparing various positioning systems and providing background information on their inherent limitations. Experimental results have demonstrated that VLC-based systems can achieve localization accuracy to within 10 cm in controlled environments. The mechanisms of VLC-based IPSs are then discussed, including a comprehensive examination of their performance metrics and underlying assumptions. The complexity, operating range, and efficiency of VLC-based IPSs are examined by analyzing factors such as channel modeling, signal processing, and localization algorithms. To optimize VLC-based IPSs, various strategies are explored, including the design of efficient modulation schemes, the development of advanced encoding and decoding algorithms, the implementation of adaptive power control, and the application of state-of-the-art localization algorithms. In addition, system parameters are carefully examined. These include LED placement, receiver sensitivity, and transmit power. Their impact on energy efficiency and localization accuracy is highlighted. Altogether, this paper serves as a comprehensive guide to VLC IPSs, providing in-depth insights into their vast potential and the challenges that they present. [ABSTRACT FROM AUTHOR]

High-density electromyography (HD-EMG) can provide a natural interface to enhance human–computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with 97.3 ± 0.3 % accuracy calculated on a 100 ms bin-by-bin basis. In a separate mixed dataset consisting of 19 movements randomly interspersed, decoding performance achieved an average bin-wise accuracy of 92.8 ± 0.8 % . When evaluating decoders for use in real-time scenarios, we found that decoders can reliably decode both movements and movement transitions, achieving an average accuracy of 93.3 ± 0.9 % on the sequential set and 88.5 ± 0.9 % on the mixed set. Furthermore, we estimated continuous joint angles from the EMG sleeve data, achieving a R 2 of 0.884 ± 0.003 in the sequential set and 0.750 ± 0.008 in the mixed set. Median absolute error (MAE) was kept below 10° across all joints, with a grand average MAE of 1.8 ± 0. 04 ∘ and 3.4 ± 0. 07 ∘ for the sequential and mixed datasets, respectively. We also assessed two algorithm modifications to address specific challenges for EMG-driven HCI applications. To minimize decoder latency, we used a method that accounts for reaction time by dynamically shifting cue labels in time. To reduce training requirements, we show that pretraining models with historical data provided an increase in decoding performance compared with models that were not pretrained when reducing the in-session training data to only one attempt of each movement. The HD-EMG sleeve, combined with sophisticated machine learning algorithms, can be a powerful tool for hand gesture recognition and joint angle estimation. This technology holds significant promise for applications in HCI, such as prosthetics, assistive technology, rehabilitation, and human–robot collaboration. [ABSTRACT FROM AUTHOR]

Let λ = λ0 + uλ1 + ⋯ + uk−1λ k−1 be a Type 1 unit in ℜk = 픽pm + u픽pm + ⋯ + uk−1픽 pm(uk = 0), where p is an odd prime, m is a positive integer and λ0,λ1,…,λk−1 ∈ 픽pm,λ0≠0,λ1≠0. In this paper, we give the complete structure of all Type 1 λ-constacyclic codes and their duals of length 8ps over the finite commutative chain ring ℜk in terms of their generator polynomials. Using this structure, we determine the Hamming distance and the Rosenbloom–Tsfasman (RT) distance of all Type 1 λ-constacyclic codes. For pm ≡ 1(mod 4) and a unit λ ∈ ℜ2, we determine the b -symbol distances of all λ-constacyclic codes of length 8ps over ℜ2, where b ≤ 8. As illustrations, we provide several λ-constacyclic codes with new parameters with respect to Hamming, RT and b-symbol metrics. MDS codes are widely recognized for their optimal error-correction capability, and MDS b-symbol codes are generalization of MDS codes. We found some MDS b-symbol constacyclic codes of length 8ps over ℜ2. Additionally, for pm ≡ 1(mod 4), we provide a decoding algorithm for Type 1 constacyclic codes of length 8ps over ℜk with respect to the Hamming, RT and b-symbol metrics. [ABSTRACT FROM AUTHOR]

In our paper, we propose a generalized version of the Alternating Projections Digital Hard Successive Interference Cancellation (AP-HSIC) algorithm that is capable of decoding any order of constellation M in an M-Quadrature Amplitude Modulation (QAM) system. Our approach applies to Rayleigh deep-fading Multiple-Input Multiple-Output (MIMO) channels with high-level Additive White Gaussian Noise (AWGN). It can handle various destructive phenomena without restricting the number of antenna arrays in the transmitter/receiver. Importantly, it does not rely on closed-loop MIMO feedback or the need for Channel-State Information Transmission (CSIT). We have demonstrated the effectiveness of our approach and provided a Bit Error Rate (BER) analysis for 16-, 32-, and 64-QAM modulation systems. Real-time simulations showcase the differences and advantages of our proposed algorithm compared to the Multi-Group Space-Time Coding (MGSTC) decoding algorithm and the Lagrange Multipliers Hard Successive Interference Cancellation (LM-HSIC) algorithm, which we have also developed here. Additionally, our paper includes a mathematical analysis of the LM-HSIC algorithm. The AP-HSIC algorithm is not only effective and fast in decoding, including interference cancellation computational feedback, but it can also be integrated with any Linear Processing Complex Orthogonal Design (LPCOD) technique, including Complex Orthogonal Design (COD) schemes such as high-order Orthogonal Space–Time Block Code (OSTBC) with high-order QAM symbols. [ABSTRACT FROM AUTHOR]

A lossless data compression code based on a binary-coded ternary number representation is investigated. The code is complete, universal, synchronizable, and supports a direct search in a compressed file. A simple monotonous encoding and very fast decoding algorithms are constructed owing to code properties. Experiments show that in natural language text compression, the new code outperforms byte-aligned codes SCDC and RPBC either in compression ratio or in decoding speed. [ABSTRACT FROM AUTHOR]

In this paper, we define expanded LRPC codes from LRPC codes with a decoding algorithm using the one of the underlying LRPC codes. Next, we propose to use these codes for cryptography by deriving two cryptosystems in a McEliece setting. in order to reduce the key sizes, we use a generator matrix in systematic form for the first scheme, and an m − order quasi-cyclic LRPC code that we define for the second scheme. The obtained code has a very poor structure and is more likely to be a random linear code. Next, we give some security parameters and compare the key sizes of our public key with the key sizes of some cryptosystems. [ABSTRACT FROM AUTHOR]

Dynamic optical structural color is always desired in various display applications and usually involves active materials. Full-color generation, especially bi-directional full-color generation in both reflective and transmissive modes, without any active materials included, has rarely been investigated. Herein, we demonstrate a scheme of bi-directional full-color generation based on a plasmonic metasurface modulated by the rotation of the polarization angle of the incident light without varying the geometry and the optical properties of the materials and the environment where the metasurface resides. The metasurface unit cell consists of plasmonic modules aligning in three directions and is patterned in a square array. The metasurface structural color device is numerically confirmed to generate full colors in both reflection and transmission. Based on the proposed polarization-dependent structural color, the information encoding process is demonstrated for three multiplexed animal images and quick-responsive (QR) codes to verify the efficient information encoding and decoding of the proposed scheme. In the simulation, the animals can be seen under different polarization incidences, and the QR codes can be successfully decoded by the polarization rotation in transmission. The proposed bi-directional full-color generation metasurface has great potential in applications such as kaleidoscope generation, anti-counterfeiting, dynamic color display, and optical information encoding. [ABSTRACT FROM AUTHOR]

In this paper, we consider a resource overload problem and add a tardiness penalty to the objective function when a prescribed project makespan is exceeded, which enables a trade-off between a balanced resource utilization and a project delay. For the tardiness penalty, we distinguish between a constant and variable delay cost variant. Based on the structural properties of the resource overload problem, we show that the search space of the resource overload problem with tardiness penalty can also be reduced utilizing quasistable schedules. In addition, we discuss the application of these findings to further problems, which include objectives composed of a locally concave and a concave function or a reward structure for an early project completion instead of a tardiness penalty. As solution approaches, we present mixed-integer linear model formulations as well as a novel genetic algorithm with a decoding procedure, which exploits the devised structural properties. The performance of the genetic algorithm is improved by implementing learning methods and utilizing lower bounds. Finally, we present results from experiments on small to medium sized problem instances. [ABSTRACT FROM AUTHOR]

This article presents a novel system, LLDPC,1 which brings Low-Density Parity-Check (LDPC) codes into Long Range (LoRa) networks to improve Forward Error Correction, a task currently managed by less efficient Hamming codes. Three challenges in achieving this are addressed: First, Chirp Spread Spectrum (CSS) modulation used by LoRa produces only hard demodulation outcomes, whereas LDPC decoding requires Log-Likelihood Ratios (LLR) for each bit. We solve this by developing a CSS-specific LLR extractor. Second, we improve LDPC decoding efficiency by using symbol-level information to fine-tune LLRs of error-prone bits. Finally, to minimize the decoding latency caused by the computationally heavy Soft Belief Propagation (SBP) algorithm typically used in LDPC decoding, we apply graph neural networks to accelerate the process. Our results show that LLDPC extends default LoRa's lifetime by 86.7% and reduces SBP algorithm decoding latency by 58.09×. [ABSTRACT FROM AUTHOR]

BCH codes are an interesting class of cyclic codes with good error-correcting capability and have wide applications in communication and storage systems due to their efficient encoding and decoding algorithms. Let F q be the finite field of size q and n = q m - 1 , where m is a positive integer. Let C (q , m , δ) be the primitive narrow-sense BCH codes of length n over F q with designed distance δ . Denote s = m - t , r = m mod s and λ = ⌊ t / s ⌋ . In this paper, we mainly investigate the dimensions and Bose distances of the codes C (q , m , δ) with designed distance of the following two types: δ = q t + h , ⌈ m 2 ⌉ ≤ t < m , 0 ≤ h < q s + ∑ i = 1 λ - 1 q r + i s ; δ = q t - h , ⌈ m 2 ⌉ < t < m , 0 ≤ h < (q - 1) ∑ i = 1 s q i . This extensively extends the results on Bose distance in Ding et al (IEEE Trans Inf Theory 61(5):2351–2356, 2015). Moreover, the parameters of the hulls of the BCH code C (q , m , q t) are studied in some cases. [ABSTRACT FROM AUTHOR]

Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system that reads out mental planning activity and issues commands in a proactive manner. To demonstrate this, we recorded brain activity from freely-moving monkeys performing an instructed task and decoded it with an energy-efficient, small and mobile field-programmable gate array hardware decoder triggering real-time action execution on smart devices. Core of this is an adaptive decoding algorithm that can compensate for the day-by-day neuronal signal fluctuations with minimal re-calibration effort. We show that open-loop planning-ahead control is possible using signals from primary and pre-motor areas leading to significant time-gain in the execution of action sequences. This novel approach provides, thus, a stepping stone towards improved and more humane control of different smart environments with mobile brain machine interfaces. [ABSTRACT FROM AUTHOR]

Based on the IEEE 802.16e standard's (672,336) LDPC code and the normalized Min-Sum decoding algorithm, this paper designs and implements an LDPC decoder that optimizes the channel information. The correction factor for check nodes is converted into a correction factor for the initial channel information, replacing the optimization of check node information with that of initial channel information. This achieves decoding performance equivalent to the traditional normalized Min-Sum decoding algorithm. Different correction factor values vary in complexity during FPGA implementation, as they involve different amounts of shift-add operations. For NMS decoding requiring a high number of shift-add operations to achieve optimal correction values, this can be converted into an LDPC decoding algorithm optimized for channel information, reducing computational overhead without sacrificing performance. A partially parallel improved decoder was designed and implemented on an FPGA, and its feasibility was verified using the Vivado simulation platform. [ABSTRACT FROM AUTHOR]

Realistic co-speech gestures are important to anthropomorphize ECAs, as nonverbal behavior improves expressiveness of their speech greatly. However, the existing approaches to generating co-speech gestures with sufficient details (including fingers, etc.) in 3D scenarios are indeed rare. Additionally, they hardly address the problem of abnormal gestures, temporal–spatial coherence and diversity of gesture sequences comprehensively. To handle abnormal gesture issues, we put forward an angle conversion method to remove body part length from the original in-the-wild video dataset via transferring coordinates of human upper body key points into relative deflection angles and pitch angles. We also propose a neural network called HARP with encoder–decoder architecture to transfer MFCC featured speech audio into aforementioned angles on the basis of CNN and LSTM. The angles then can be rendered as corresponding co-speech gestures. Compared with the other latest approaches, the co-speech gestures generated by HARP are proved to be almost as good as the real person, i.e., they have strong temporal–spatial coherence, diversity, persuasiveness and credibility. Our approach puts finer control on co-speech gestures than most of the existing works by handling all key points of the human upper body. It is more feasible for industrial application, since HARP can be adaptive to any human upper body model. All related code and evidence videos of HARP can be accessed at https://github.com/drrobincroft/HARP. [ABSTRACT FROM AUTHOR]

In this paper, we define a window code to be the portion of a Spatially-coupled low-density parity check (SC-LDPC) code seen by a single iteration of a windowed decoder. We consider the design of SC-LDPC codes for windowed decoding via optimization of the window code. In particular, because iterative decoding is optimal on codes with cycle-free graph representations, we ask fundamental questions about the construction and parameters of cycle-free window codes. We show that it is possible to have an SC-LDPC code with cycles and with cycle-free window codes. We consider the relationship between the distance of the window code and the distance of the SC-LDPC code. Further, we show that SC-LDPC codes with MDS window codes exist, and all such codes are asymptotically bad. This work gives insight into the tradeoffs between window code parameters and performance of the SC-LDPC code. [ABSTRACT FROM AUTHOR]

In digital baseband processing, the forward error correction (FEC) unit belongs to the most demanding components in terms of computational complexity and power consumption. Hence, efficient implementation of FEC decoders is crucial for next-generation mobile broadband standards and an ongoing research topic. Quantization has a significant impact on the decoder area, power consumption and throughput. Thus, lower bit widths are preferred for efficient implementations but degrade the error correction capability. To address this issue, a non-uniform quantization based on the Information Bottleneck (IB) method is proposed that enables a low bit width while maintaining the essential information. Many investigations on the use of the IB method for Low-density parity-check code) LDPC decoders exist and have shown its advantages from an implementation perspective. However, for polar code decoder implementations, there exists only one publication that is not based on the state-of-the-art Fast Simplified Successive-Cancellation (Fast-SSC) decoding algorithm, and only synthesis implementation results without energy estimation are shown. In contrast, our paper presents several optimized Fast-SSC polar code decoder implementations using IB-based quantization with placement and routing results using advanced 12 nm FinFET technology. Gains of up to 16% in area and 13% in energy efficiency are achieved with IB-based quantization at a Frame Error Rate (FER) of 10 − 7 and a polar code of N = 1024 , R = 0.5 compared to state-of-the-art decoders. [ABSTRACT FROM AUTHOR]

Low-density parity check (LDPC) codes, are a family of error-correcting codes, their performances close to the Shannon limit make them very attractive solutions for digital communication systems. There are several algorithms for decoding LDPC codes that show great diversity in terms of performance related to error correction. Also, very recently, many research papers involved the genetic algorithm (GA) in coding theory, in particular, in the decoding linear block codes case, which has heavily contributed to reducing the bit error rate (BER). In this paper, an efficient method based on the GA is proposed and it is used to improve the power of correction in terms of BER and the frame error rate (FER) of LDPC codes. Subsequently, the proposed algorithm can independently decide the most suitable moment to stop the decoding process, moreover, it does not require channel information (CSI) making it adaptable for all types of channels with different noise or intensity. The simulations show that the proposed algorithm is more efficient in terms of BER compared to other LDPC code decoders. [ABSTRACT FROM AUTHOR]

Let GRSk(α, υ) be a k-dimensional generalized Reed-Solomon (GRS) code over Fq associated with α = (α1,. . ., αn) and υ = (υ1, . . ., υn). In this paper, we determined the dimension of the Euclidean hull GRSk(α, υ) ∩ GRSk(α, υ)⊥, which addresses an open problem posed in [Chen et al., IEEE-TIT, 2023]. We also presentd a new approach to generating all self-dual RS codes. [ABSTRACT FROM AUTHOR]

The job shop scheduling problem (JSSP) is to find the optimal jobs sequence to optimise one or more performance indicators and makespan is the most common optimisation target. In solving NP-hard problems such as JSSPs by genetic algorithm (GA), trapping in local extremum, low search efficiency and instability are often encountered, especially unable to find the optimisation direction. To restrain this condition, a new neighbourhood structure with adaptive GA was put forward. The crossover probability (Pc) and mutation probability (Pm) can be adjusted in nonlinear and adaptive based on the dispersion of the fitness of population in the evolution. The idle time before critical operations can be made full use of through the multi-operations combination and adjustment. To research the performance of the proposed method in solving JSSPs, a detailed application scheme was given out for the process of it. In the solving scheme, the chromosome active decoding algorithm with the objective function of maximum makespan was proposed. From the results of testing of 28 JSSP benchmark instances in 3 adaptive strategies and 3 neighbourhood strategies, the new neighbourhood structure with adaptive GA has been significant improvement in solution accuracy and convergence efficiency. [ABSTRACT FROM AUTHOR]

In the resource levelling problem (RLP) under uncertainty, existing studies focus on obtaining an open-loop activity list that is not updated during project execution. In project management practice, it is also necessary to address more situations, such as activity overlaps and resource breakdowns. In this paper, we extend the uncertain RLP by proposing a resource levelling problem with multiple uncertainties (RLP-MU) that simultaneously considers uncertainties in activity durations, activity overlaps and resource availabilities. We formulate the RLP-MU as a Markov decision process model. Aimed at levelling resource usage by dynamically scheduling activities at each decision point based on the observed information, we develop a hybrid open–closed-loop approximate dynamic programming algorithm (HOC-ADP). In the HOC-ADP, we devise a closed-loop rollout policy to approximate the cost-to-go function and use the concept of the average project to avoid time-consuming simulation. A greedy-decoding-based estimation of distributed algorithm is also devised to construct an open-loop policy that is embedded in the HOC-ADP to further improve it. We additionally develop a simulation algorithm to evaluate the resource levelling performance of the HOC-ADP. Computational experiments on a benchmark dataset consisting of 540 problem instances are conducted to analyze the performance of the HOC-ADP, and the impact of various factors on resource levelling are investigated. The comparison experimental results indicate that our HOC-ADP outperforms the state-of-the-art meta-heuristics. [ABSTRACT FROM AUTHOR]

An adaptively hybrid method was proposed to improve the performance of fractal coding methods. First, it is found that the range blocks with large variances (RBLVs) play a crucial role in degrading decoded images, and the effect of the remaining range blocks with small variances (RBSVs) can be ignored. Then, an adaptive method was proposed to divide the range blocks into the above two categories: RBLVs and RBSVs. Second, RBLVs were designed to be encoded in an extended domain block pool (EDBP). Then, better block‐matching effect can be obtained, which will result in better decoded image quality. Further, the no‐search fractal encoding method is adopted for RBSVs to achieve faster encoding speed and fewer bits per pixel (bpp). Finally, four fractal coding methods were adopted to assess the performance of the proposed method. Experimental results show that compared with the previous methods, the PSNR quality of decoded images in the proposed method can be improved by about 0.15–0.4 dB, about 20%–35% of the total computations in encoding process can be saved, and about 0.2 bpp can be saved. Moreover, under the same decoding time, the proposed method can achieve comparable or smaller deviations regarding the decoded image. [ABSTRACT FROM AUTHOR]

In this paper, we consider a downlink non-orthogonal multiple access (NOMA) system over Nakagami-m channels. The single-antenna base station serves two single-antenna NOMA users based on statistical channel state information (CSI). We derive the closed-form expression of the exact outage probability under a given decoding order, and we also deduce the asymptotic outage probability and diversity order in a high-SNR regime. Then, we analyze all the possible power allocation ranges and theoretically prove the optimal power allocation range under the corresponding decoding order. The demarcation points of the optimal power allocation ranges are affected by target data rates and total power, without an effect from the CSI. In particular, the values of the demarcation points are proportional to the total power. Furthermore, we formulate a joint decoding order and power allocation optimization problem to maximize the sum throughput, which is solved by efficiently searching in our obtained optimal power allocation ranges. Finally, Monte Carlo simulations are conducted to confirm the accuracy of our derived exact outage probability. Numerical results show the accuracy of our deduced demarcation points of the optimal power allocation ranges. And the optimal decoding order is not constant at different total transmit power levels. [ABSTRACT FROM AUTHOR]

The soft cancellation list (SCANL) decoding algorithm for polar codes runs L soft cancellation (SCAN) decoders with different decoding factor graphs. Although it can achieve better decoding performance than SCAN algorithm, it has high latency. In this paper, a fast simplified SCANL (Fast-SSCANL) algorithm that runs L independent Fast-SSCAN decoders is proposed. In Fast-SSCANL decoder, special nodes in each factor graph is identified, and corresponding low-latency decoding approaches for each special node is propose first. Then, syndrome check aided Fast-SSCANL (SC-Fast-SSCANL) algorithm is further put forward. The ordinary nodes satisfied the syndrome check will execute hard decision directly without traversing the factor graph, thereby reducing the decoding latency further. Simulation results show that Fast-SSCANL and SC-Fast-SSCANL algorithms can achieve the same BER performance as the SCANL algorithm with lower latency. Fast-SSCANL algorithm can reduce latency by more than 83% compared with SCANL, and SCFast-SSCANL algorithm can reduce more than 85% latency compared with SCANL regardless of code length and code rate. [ABSTRACT FROM AUTHOR]

For the hard guessing random additive noise decoding Markov order (GRAND-MO) algorithm, it is crucial to develop an efficient noise error patterns (NEPs) generator to facilitate its application in bursty channels. This paper proposes a practical hardware realization by generating the NEPs in a sequential manner. Based on classification of the four types of NEPs, we propose to iteratively calculate the "1" and the "0" permutations in the same time. Then, the novel "0" permutation regularization and bit flipping techniques are employed, through which the generation of the four types of NEPs is uniformed at the same way. Moreover, the proposed NEPs generator can generate all NEPs by using the "1" burst parameters, and is suitable for the guessing decoding of any linear block codes. Built on field programmable gate array (FPGA) implementation and comparison with existing benchmark, we show the proposed NEPs generator is a power-efficient architecture for realization. This work presents a new solution for the hardware implementation of the NEPs generator in GRAND-MO. [ABSTRACT FROM AUTHOR]

Moving microphones allow for the fast acquisition of sound-field data that encode acoustic impulse responses in time-invariant environments. Corresponding decoding algorithms use the knowledge of instantaneous microphone positions for relating the dynamic samples to a positional context and solving the involved spatio-temporal channel estimation problem subject to the particular parameterization model. Usually, the resulting parameter estimates are supposed to remain widely unaffected by the Doppler effect despite the continuously moving sensor. However, this assumption raises issues from the physical point of view. So far, mathematical investigations into the actual meaning of the Doppler effect for such dynamic sampling procedures have been barely provided. Therefore, in this paper, we propose a new generic concept for the dynamic sampling model, introducing a channel representation that is explicitly based on the instantaneous Doppler shift according to the microphone trajectory. Within this model, it can be clearly seen that exact trajectory tracking implies exact Doppler-shift rendering and, thus, enables unbiased parameter recovery. Further, we investigate the impact of non-perfect trajectory data and the resulting Doppler-shift mismatches. Also, we derive a general analysis scheme that decomposes the microphone signal along with the encoded parameters into particular subbands of Doppler-shifted frequency components. Finally, for periodic excitation, we exactly characterize the Doppler-shift influences in the sampled signal by convolution operations in the frequency domain with trajectory-dependent filters. [ABSTRACT FROM AUTHOR]

LiDAR semantic segmentation is an essential task in understanding 3D semantic information. Currently, the most efficient approach to LiDAR data segmentation is to project the point cloud into the 2D plane and process it using 2D convolution. The results of this approach are encouraging. However, the elevation angle of LiDAR is larger than the azimuth angle, resulting in the range map being vertically elongated in the 3D space captured per unit pixel area. If a square convolution kernel is used, the extracted features will be distorted. To address these limitations, we propose the flexible asymmetric convolutional attention network (FACANet), built from flexible asymmetric convolution and lightweight decoding modules. In this encoder structure, a meta-kernel accounts for the geometric information in 3D space, which helps encode the input range image features effectively. Moreover, a flexible asymmetric convolutional attention block (FACAB) is proposed to capture elongated features in the range image. To facilitate lightweight decoding, the channel uniform interpolation block (CUIB) uses 1 × 1 convolutions to reduce channels and bilinear interpolation to upsample features at each resolution. Furthermore, the continuous multiscale feature fusion block (CMFB) is proposed to fuse features at different resolutions. Finally, a convolutional spatial propagation network (CSPN)-based segmentation head is introduced to improve the accuracy of the segmentation results. Quantitative and qualitative experiments are conducted on the public datasets SemanticKITTI and SemanticPOSS, and our approach achieves better accuracy than advanced models. [ABSTRACT FROM AUTHOR]

This study investigates receiver design solutions for distributed Massive Multiple Input Multiple Output (D-m MIMO) systems, taking into account parameters such as number of access points as well as concerns related to channel estimates that use single-carrier frequency-domain equalization (SC-FDE). A significant contribution of this research is the integration of Low-Density Parity-Check (LDPC) codes to simplify coding complexity and enhance communication efficiency. The research examines different receiver designs, such as spatial antenna correlation and sophisticated channel estimation methods. The authors propose integrating LDPC codes into the receiver architecture to simplify computations and enhance error correction and decoding. Moreover, the paper examines performance evaluation measures and approaches, highlighting the trade-offs among complexity, spectral efficiency, and error performance. The comparative analysis indicates the benefits, in terms of performance, of incorporating LDPC codes and improving system throughput and dependability. We examine four distinct receiver algorithms: zero-forcing (ZF), minimum mean square error (MMSE), maximum ratio combining (MRC), and equal gain combining (EGC). The study shows that MRC and EGC receivers work well in D-m MIMO because they make the receiver system less computationally demanding. [ABSTRACT FROM AUTHOR]