Low-density parity-check (LDPC) codes have been widely used in practice due to their excellent error correction capabilities. However, the error correction performance of traditional decoding algorithms for LDPC codes is limited under short block lengths. To address this problem, this paper proposes an effective scheme that contains two stages for decoding short LDPC codes. In the first stage, the traditional belief propagation (BP) iterative algorithm is used to decode an LDPC code. The decoding process terminates if a codeword is obtained in this stage. Otherwise, the second stage decoding is invoked, in which the ordered reliability bits guessing random additive noise decoding (ORBGRAND) algorithm is applied. The accumulated log-likelihood ratios in the first stage are used to order the test error patterns in the ORBGRAND algorithm. Simulation results indicate that the proposed BP-ORBGRAND scheme can outperform the traditional BP iterative algorithm with relatively low additional complexity. Compared with the decoding scheme that concatenates the BP algorithm and the order statistics decoding (OSD) algorithm, the proposed decoding scheme has similar performance and the operations involved can be efficiently implemented in the hardware platform. All these indicate that the proposed scheme is desirable for practical purposes.

In modern communication systems, the concatenation of a low-density parity-check (LDPC) code with a cyclic redundancy check (CRC) code is commonly used for error correction. In this paper, we propose a low-complexity two-stage scheme for decoding these codes using their concatenation structures. In the first stage, the traditional belief propagation (BP)-based iterative algorithm with a relative small maximum number of iterations is performed for decoding the LDPC code. If an LDPC codeword is obtained in this stage, the decoding process terminates. Otherwise, the second stage of the decoding process is performed, in which the guessing random additive noise decoding (GRAND) algorithm is applied to the CRC code. A list of information sequences satisfying the CRC check is obtained, each of which is then encoded to an LDPC codeword. The most likely codeword among them is the output of the decoding approach. The simulation results indicate that the proposed two-stage decoding approach can outperform the traditional BP-based iterative algorithm with a large maximum number of iterations. Moreover, the average complexity of the proposed approach is relatively low.

The electrochemical reduction of nitrate to ammonia can serve as an effective complement to the traditional Haber-Bosch process. Currently, rapid and continuous ammonia production is challenging because of the multistep hydrogenation process and the constant alkalinization of the electrolyte. Herein, Ru atoms are incorporated into the octahedral sites of Co3O4 to achieve an ammonia yield rate of 24.6 mg h-1 cm-2. Electrochemical in situ spectroscopic analyses and theoretical calculations reveal that Ru sites improve water molecule coverage and facilitate the production of active hydrogen atoms, leading to stable and orderly ammonia production. Furthermore, a peak power density of 32.28 mW cm-2, a high ammonia Faradaic efficiency of 98.2%, and excellent durability (91 h) are achieved in a Ru-Co3O4-based Zn-nitrate battery, indicating its practical applicability. This work may provide a method for efficient nitrate reduction to ammonia or other hydrogenation reactions via the synergistic modulation of active sites.

The performance of classical channel decoding algorithms in resistive random access memory (ReRAM) channels is severely degraded due to the sneak path interference (SPI) caused by the crossbar array structure. In this letter, we first propose a sneak path-aware reliability-based iterative majority-logic decoding (SPR-IMLGD) algorithm for low-density parity-check (LDPC) codes by integrating the sneak path information into reliability metrics. In particular, we introduce the sneak path distance, derived from the associated sneak path probability, which provides a desirable characterization of the presence and extent of sneak paths within a given memory cell. Moreover, we present an enhanced version of the SPR-IMLGD algorithm (ESPR-IMLGD) that further improves the error correction performance. We also optimize the calculation of sneak path distance, ensuring that the SPR-IMLGD and ESPR-IMLGD algorithms have the same level of computational complexity. Simulation results demonstrate that the SPR-IMLGD and ESPR-IMLGD algorithms can achieve better performance with lower computational cost compared with the existing methods.

Electrochemical ammonia synthesis is a promising alternative to the Haber-Bosch process, offering significant potential for sustainable agricultural production and the development of portable, carbon-free energy carriers. The development of electrocatalytic systems is currently dependent on the exploration of electrocatalysts with high activity, selectivity, and stability. Metal single-atom catalysts (SACs) have become a new attractive frontier for ammonia electrosynthesis, owing to their maximized atom utilization, unsaturated atom coordination, and tunable electronic structure. In this review, we focused on different metal sites inside the single-atom catalysts and summarized recent advances in SACs for ammonia electrosynthesis. The properties of small nitrogenous substances (including N2, NO, NO2-, and NO3-) are summarized. In addition, the SACs for different catalytic systems are reviewed, with a particular focus on the special and common grounds of metal atom sites. Finally, the perspectives and challenges of SACs for ammonia electrosynthesis are comprehensively discussed, aspiring to provide insights into the development of electrochemical ammonia synthesis.

Accurate modeling and estimation of the threshold voltages of the flash memory can facilitate the efficient design of channel codes and detectors. However, most flash memory channel models are based on Gaussian distributions, which fail to capture certain key properties of the threshold voltages, such as their heavy-tails. To enhance the model accuracy, we first propose a piecewise student's t-distribution mixture model (PSTMM), which features degrees of freedom to control the left and right tails of the voltage distributions. We further propose an PSTMM based expectation maximization (PSTMM-EM) algorithm to estimate model parameters for flash memories by alternately computing the expected values of the missing data and maximizing the likelihood function with respect to the model parameters. Simulation results demonstrate that our proposed algorithm exhibits superior stability and can effectively extend the flash memory lifespan by 1700 program/erase (PE) cycles compared with the existing parameter estimation algorithms.

Electrochemical C–N coupling of CO 2 and NO 3 − is a promising green approach for synthesizing urea. However, achieving efficient C–N coupling remains challenging because of the kinetic mismatch between CO 2 and NO 3 − reduction. Stabilizing key N‐containing intermediates to facilitate their coupling with C‐containing intermediates is crucial for achieving high Faradaic efficiency. In this study, defect‐rich CuBi clusters (D‐CuBi) were synthesized, and the effects of the enhanced functionalities of the defects of D‐CuBi on the performance of urea synthesis at different critical processes were systematically investigated. Density functional theory calculations and in situ electrochemical spectroscopic analysis revealed that the defects in D‐CuBi facilitated the adsorption of CO 2 and prevented the desorption of the key *NO 2 intermediate as NO 2 − . Moreover, these sites facilitated the adsorption of active water molecules, thereby accelerating the reaction kinetics for urea production. As a result, the D‐CuBi catalyst achieved a remarkable urea Faradaic efficiency of 53.1% and a yield rate of 2.57 µmol h −1 cm −2 at −1.0 V vs. the reversible hydrogen electrode, representing an approximately tenfold enhancement over the intact CuBi. This work presents insight into urea electrosynthesis from CO 2 and NO 3 − through defect engineering.

While polar codes are capacity-achieving and adopted in the 5G standard, most practical decoders rely on soft decisions. Few works have considered efficient hard-decision decoding. In this work, we propose a majority-logic-based hard-decision decoder for polar codes by exploiting their nested subcode relations with Reed-Muller (RM) codes. The proposed decoder can achieve the error correction capability up to the theoretical bound determined by the minimum distance of the code. Moreover, we utilize the positions of non-information bits to design a complexity reduction mechanism and introduce a verification criterion to identify successful decoding outcomes. We further extend the hard-decision decoding to a soft-decision approximate maximum likelihood (ML) decoder based on the approach proposed by Kaneko et al. To improve the decoding efficiency, a termination strategy is employed, which stops the decoding process once a predefined number of candidate codewords is found. Simulation results demonstrate that the proposed decoder achieves near-ML performance while reducing computational complexity compared to existing soft-decision algorithms for polar codes.

Atomic metal-nitrogen-codoped carbon (M-N-C) catalysts are highly efficient for various electrocatalytic reactions because of their high atomic utilization efficiency. However, the high surface energy of M-N-C catalysts often results in stability issues in electrochemical reactions. Therefore, understanding the stability and dynamic evolution of M-N-C catalysts is crucial for elucidating the active centers and the composition/structure-activity relationship. This review summarizes the factors affecting the durability of atomic catalysts in electrochemical reactions and discusses possible changes in catalysts during these electrochemical processes. Finally, advanced characterization techniques are described, with a focus on tracking the dynamic evolution of M-N-C catalysts during electrocatalysis. This review offers insights into the rational optimization of M-N-C electrocatalysts and provides a framework for linking their composition and structure with their catalytic activity in future research.

This paper presents a coding scheme based on bilayer low-density parity-check (LDPC) codes for multi-level cell (MLC) NAND flash memory. The main feature of the proposed scheme is that it exploits the asymmetric properties of an MLC flash channel and stores the extra parity-check bits in the lower page, which are activated only after the decoding failure of the upper page. To further improve the performance of the error correction, a perturbation process based on the genetic algorithm (GA) is incorporated into the decoding process of the proposed coding scheme, which can convert uncorrectable read sequences into error-correctable regions of the corresponding decoding space by introducing GA-trained noises. The perturbation decoding process is particularly efficient at low program-and-erase (P/E) cycle regions. The simulation results suggest that the proposed bilayer LDPC coding scheme can extend the lifetime of MLC NAND flash memory up to 10,000 P/E cycles. The proposed scheme can achieve a better balance between performance and complexity than traditional single LDPC coding schemes. All of these findings indicate that the proposed coding scheme is suitable for practical purposes in MLC NAND flash memory.

As the technology scales down, two-dimensional (2D) NAND flash memory has reached its bottleneck. Three-dimensional (3D) NAND flash memory was proposed to further increase the storage capacity by vertically stacking multiple layers. However, the new architecture of 3D flash memory leads to new sources of errors, which severely affects the reliability of the system. In this paper, for the first time, we derive the channel probability density function of 3D NAND flash memory by taking major sources of errors. Based on the derived channel probability density function, the mutual information (MI) for 3D flash memory with multiple layers is derived and used as a metric to design the quantization. Specifically, we propose a dynamic programming algorithm to jointly optimize read-voltage thresholds for all layers by maximizing the MI (MMI). To further reduce the complexity, we develop an MI derivative (MID)-based method to obtain read-voltage thresholds for hard-decision decoding (HDD) of error correction codes (ECCs). Simulation results show that the performance with jointly optimized read-voltage thresholds can closely approach that with read-voltage thresholds optimized for each layer, with much less read latency. Moreover, the MID-based MMI quantizer almost achieves the optimal performance for HDD of ECCs.

Rechargeable aqueous zinc-ion batteries (ZIBs) are highly promising energy storage devices due to their advantages of high energy density, low cost, environmental friendliness, and excellent safety. Investigation of advanced cathode materials featuring high capacity is desired for their applications in high-capacity ZIBs. In this study, a porous N-doped carbon-coated manganese oxide/zinc manganate (MZM@N-C) composite was successfully prepared as an advanced cathode material for aqueous ZIBs. The MZM@N-C cathode demonstrated a superior specific capacity of 772.8 mA h g-1 at 50 mA g-1 and maintained a high specific capacity of 205 mA h g-1 after 300 cycles at a high current density of 500 mA g-1. As compared to the unmodified MnOx cathode, MZM@N-C has a higher reversible capacity and cycling stability which could be assigned to the robust one-dimensional (1D) structure and the synergistic effect of MZM@N-C, providing instructive insight into the design of high-capacity manganese-based cathodes for rechargeable aqueous ZIBs. Furthermore, a soft-pack battery was assembled using the MZM@N-C cathode, demonstrating its potential applications in various devices.

为了解决自然场景包裹破损检测中由于目标形态与尺度多样、模型耗时过长造成的检测难题,设计了一种基于通道注意力机制与快速空间金字塔池化(Space Pyramid Pool-Fast,SPPF)的轻量级包裹破损检测算法.在YOLOv5s的基础上,使用改进的 ShuffleNetV2 网络模型对其主干结构进行轻量级优化,降低模型计算量,提高检测速度;在模型的主干网络部分引入通道注意力机制——Squeeze Excitation(SE),减少了卷积神经网络对图像相关特征的重复提取,提高信息的表征能力;利用 SPPF有效避免了对图像区域裁剪、缩放操作导致的图像失真,有效减少误检与漏检.在数据集上的测试结果表明,该方法对包裹图像的检测速度达到了 68.5 帧/秒,模型计算量仅为 2.5 GFLOPs,与YOLOv5s相比,检测速度提升了 105.7%,模型计算量下降了 84.2%,利于边缘计算设备部署.

This letter is concerned with incorrigible sets of binary linear codes. For a given binary linear code C, we represent the numbers of incorrigible sets of size up to [ 3/2 d-1] using the weight enumerator of C, where d is the minimum distance of C. In addition, we determine the incorrigible set enumerators of binary Golay codes G(23) and G(24) through combinatorial methods.

In this paper, we investigate, for the first time, the design and optimization of the polarization-adjusted convolutional (PAC) code for the spin-torque transfer magnetic random access memory (STT-MRAM) channel. A crucial problem for the application of PAC codes to the STT-MRAM channel is to optimize the index set of the non-frozen bits of the PAC codes. Hence, a rate-profile optimization method based on the genetic algorithm assisted bit-swapping is proposed. Simulation results show that the PAC code with the optimized rate-profile outperforms both the polar code and the PAC code with existing generic rate-profiles.

Spin-torque transfer magnetic random access memory (STT-MRAM) is one potential candidate to replace the dynamic random access memory (DRAM) due to its superior features of nonvolatility, fast read/write speed, and high scalability. The error correction coding methods are applied to improve the reliability of STT-MRAM which is affected by the process variation and thermal fluctuation. In this paper, we investigate, for the first time, the design and optimization of the polarization-adjusted convolutional (PAC) code for the STT-MRAM channel. A crucial problem for the application of PAC codes to the STT-MRAM channel is the optimization of the index set of the non-frozen bits of the PAC codes. Hence, a rate-profile optimization method based on the genetic-algorithm-assisted bit-swapping is proposed. Simulation results show that the PAC code with the optimized rate-profile outperforms both the polar code and the PAC code with existing generic rate-profiles.

The error-correcting performance of multi-level-cell (MLC) NAND flash memory is closely related to the block length of error-correcting codes (ECCs) and log-likelihood-ratios of the read-voltage thresholds. Driven by this issue, this paper optimizes the read-voltage thresholds for MLC flash memory to improve the decoding performance of ECCs with finite block length. First, through the analysis of channel coding rate and decoding error probability under finite block length, the optimization problem of read-voltage thresholds to minimize the maximum decoding error probability is formulated. Second, a cross-iterative search algorithm to optimize read-voltage thresholds under the perfect knowledge of flash memory channel is developed. However, it is challenging to analytically characterize the voltage distribution under the effect of data retention noise. To address this problem, a deep neural network (DNN)-aided optimization strategy to optimize the read-voltage thresholds is developed, where a multi-layer perception network is employed to learn the relationship between voltage distribution and read-voltage thresholds. Simulation results show that, compared with the existing schemes, the proposed DNN-aided read-voltage threshold optimization strategy with a well-designed Low Density Parity Check (LDPC) code can not only improve the program-and-erase endurance but also reduce the read latency.

Automatic ultrasound image segmentation plays an important role in early diagnosis of human diseases. This paper introduces a novel and efficient encoder–decoder network, called Lightweight Attention Encoder–Decoder Network (LAEDNet), for automatic ultrasound image segmentation. In contrast to previous encoder–decoder networks that involve complicated architecture with numerous parameters, our LAEDNet adopts lightweight version of EfficientNet as encoder. On the other hand, a Lightweight Residual Squeeze-and-Excitation (LRSE) block is employed in decoder. To achieve trade-off between segmentation accuracy and implementing efficiency, we also present a family of models, from light to heavy (denoted as LAEDNet-S, LAEDNet-M, and LAEDNet-L, respectively), with varying lightweight version of EfficientNet backbones. To evaluate LAEDNet, we have conducted extensive experiments on Brachial Plexus Dataset (BP), Breast Ultrasound Images Dataset (BUSI), and Head Circumference Ultrasound Images Dataset (HCUS), where ultrasound images are suffered from high noise, blurred borders and low contrast. The experiments show that, compared with U-Net and its variants, e.g., M-Net, U-Net++ and TransUNet, our LAEDNet achieves better results in terms of Dice Coefficient (DSC) and running speed. Particularly, LAEDNet-M only has 10.75M model parameters with 40.7 FPS, yet obtaining 73.0%, 73.8% and 91.3% DSC on BP, BUSI and HCUS datasets, respectively.

大数据及云存储的发展对传统的存储技术提出了更高的要求.为了进一步提升多级存储单元的存储效率,提出了一种双层低密度奇偶校验(Low-Density Parity-Check,LDPC)码的优化设计方法.该方法针对多层单元(Multi-level Cell,MLC)信道的非对称性特性,对存储单元的单页添加额外校验比特以提高存储单元闪存信道的译码性能,并对单层LDPC码和本算法的性能进行了分析.仿真结果表明,在多级存储单元信道中使用BP译码算法进行译码,误码率为10-5时,基于双层LDPC码构造的纠错码算法比单层LDPC码有约额外4000次的擦除次数的提升,且译码复杂度也相应降低.