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Polar codes have emerged as a focal point in the field of error-correcting codes, owing to their remarkable capacity-achieving characteristics and their relevance in various modern communication systems. The basic successive cancellation (SC) approach is not optimal to use in terms of the trade-off between performance and decoding complexity. SC-Creeper algorithm performs better with about the same low complexity as the SC version of the algorithm. However, the SC-Creeper algorithm did not have the ability to use the candidate list as a measure to improve performance and refine the search for the true codeword. To compare with successive cancellation list (SCL) approach and the ability to use more computing memory, the SCL-Creeper method was developed, using two additional lists. This method can also be used as a development of Fano algorithms for polar codes (mainly, Fano decoding in polar decoding does not use lists). This paper addresses the challenge of computational complexity in polar code decoding by integrating a list structure with the SC-Creeper algorithm. Building on prior research that introduced the concept of SC-Creeper, the study focuses on enhancing error correction performance while mitigating computational burden. The first chapters describe the polar encoding process and basic decoding technologies, then discuss the basic Creeper algorithm. In the following chapters, the authors describe a modified version of the two-list Creeper approach (that is, the SCL-Creeper version of the algorithm). Extensive simulations and numerical analysis presented in the paper underscore the tangible advantages of this novel decoding strategy. Leveraging the basic list algorithm, renowned for its superior error correction capabilities, the research explores the integration of Creeper to systematically prune unnecessary decoding paths. The resulting SCL-Creeper hybrid approach aims to strike a balance between error correction efficiency and computational complexity. Finally, the optimal selection of parameters for the SCL-Creeper approach and future directions in the research of the list version of the fast Creeper algorithm are discussed.

The 3-D TLC NAND flash memory significantly increases the storage capacity but makes data more prone to errors. To address the reliability problem, Polar Code is one coding method that can reach the channel capacity (Arikan 2009). However, the 3-D NAND flash memory channel is uncertain due to the variation of retention time and program/erase(P/E) cycle. So it is difficult to construct a Polar Code in the NAND flash memory. This letter proposes a new Polar Code construction method called dynamic merge Monte Carlo (DMMC). DMMC uses the Monte Carlo method to obtain the optimal Polar Code construction and then reduces the space overhead by combining similar construction so that it can be used in flash memory systems. Compared with the conventional method, the uncorrectable bit error rate (UBER) of the proposed method can reduce by up to 90.5%, and the read latency can reduce by more than 50%. [ABSTRACT FROM AUTHOR]

In this article, we address the problem of 4D facial expressions generation. This is usually addressed by animating a neutral 3D face to reach an expression peak, and then get back to the neutral state. In the real world though, people show more complex expressions, and switch from one expression to another. We thus propose a new model that generates transitions between different expressions, and synthesizes long and composed 4D expressions. This involves three sub-problems: (1) modeling the temporal dynamics of expressions, (2) learning transitions between them, and (3) deforming a generic mesh. We propose to encode the temporal evolution of expressions using the motion of a set of 3D landmarks, that we learn to generate by training a manifold-valued GAN (Motion3DGAN). To allow the generation of composed expressions, this model accepts two labels encoding the starting and the ending expressions. The final sequence of meshes is generated by a Sparse2Dense mesh Decoder (S2D-Dec) that maps the landmark displacements to a dense, per-vertex displacement of a known mesh topology. By explicitly working with motion trajectories, the model is totally independent from the identity. Extensive experiments on five public datasets show that our proposed approach brings significant improvements with respect to previous solutions, while retaining good generalization to unseen data. [ABSTRACT FROM AUTHOR]

Deep learning techniques for change detection have undergone rapid development in the past few years. However, it is still a challenge how to reduce massive network parameters and sufficiently fuse bitemporal image features to improve detection accuracy. Therefore, this work proposes a novel and lightweight network based on feature interleaved fusion and bistage decoding (FFBDNet) for change detection. In the encoding stage, considering the application problems caused by a large number of network parameters, we use the more efficient EfficientNet as the backbone to extract the bitemporal image features based on Siamese architecture. To fuse the bitemporal image features and reduce interference from surrounding objects, we propose a feature interleaved fusion module, which can interleave the shared feature information and the difference variance feature information. During the decoding stage, the fused features are split into two groups, and a novel bistage decoding framework is proposed to generate the accuracy change map gradually. Extensive experiments and ablation studies are validated on three public change detection datasets: WHU-CD, LEVIR-CD, and SYSU-CD datasets. Compared to state-of-the-art methods, the experimental results demonstrate that the proposed FFBDNet produces a better balance between performance and model parameters. Specifically, the F1 values obtained for these three datasets are 93.27%, 91.11%, and 80.10%, respectively, and the model parameters of the network are just 2.85 M. [ABSTRACT FROM AUTHOR]

This article addresses the problem of building segmentation for rural areas with high-resolution remote sensing images. Due to the irregular spatial distribution of rural buildings, it is often challenging to perform pixel-wise dense prediction to entire areas like the usual segmentation task to extract buildings. Specifically, we present a multilevel decoding network model that classifies the input image on the patch and image levels according to the distribution of buildings. A scene head module is used to identify scenes defined as patches that contain buildings. Depending on the scene classification results, a decode gate is taken to determine the level of prediction. This hierarchical extraction strategy reduces the amount of inference time. Experiments on our constructed rural building dataset with large-scale images validate the high efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Computing-in-memory (CIM) is a promising candidate for highly energy-efficient neural networks, alleviating the well-known bottleneck in Von Neumann architecture. MRAM has garnered significant attention in the CIM field, providing advantages in terms of non-volatility, high speed, and endurance. However, most existing MRAM-CIM primarily support low-precision operations, which poses a challenge in fulfilling the requirements of complex neural network models for high inference accuracy. To resolve this dilemma, an in-memory Booth Multiplier is proposed with the aim of enhancing the energy efficiency of neural networks performing multi-bit multiply-and-accumulate (MAC) operations. The MRAM array stores the multiplicand, while the multiplier is encoded by a Booth encoder into corresponding control signals, which perform negation and shift operations, reducing half of the partial products and accelerating the overall processing. Simulation results demonstrate at least a 17.3% improvement in energy efficiency compared to the previous in-SRAM counterpart in 8-bit multiplication. [ABSTRACT FROM AUTHOR]

Quantum computers have the potential to provide exponential speedups over their classical counterparts. Quantum principles are being applied to fields such as communications, information processing, and artificial intelligence to achieve quantum advantage. However, quantum bits are extremely noisy and prone to decoherence. Thus, keeping the qubits error free is extremely important toward reliable quantum computing. Quantum error correcting codes have been studied for several decades and methods have been proposed to import classical error correcting codes to the quantum domain. Along with the exploration into novel and more efficient quantum error correction codes, it is also essential to design circuits for practical realization of these codes. This article serves as a tutorial on designing and simulating quantum encoder and decoder circuits for stabilizer codes. We first describe Shor’s 9-qubit code which was the first quantum error correcting code. We discuss the stabilizer formalism along with the design of encoding and decoding circuits for stabilizer codes such as the five-qubit code and Steane code. We also design nearest neighbor compliant circuits for the above codes. The circuits were simulated and verified using IBM Qiskit. [ABSTRACT FROM AUTHOR]