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Reduced-search guessing random additive noise decoding of polar codes

Science China(Information Sciences) 2023年第2期66卷 283-284页

作者： Kefan WANG Yuejun WEI Zhenyuan CHEN Huarui YIN Liping LI Wenyi ZHANG Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education of China Anhui University Huawei Technologies Department of Electronic Engineering and Information Science University of Science and Technology of China

Guessing random additive noise decoding(GRAND) has attracted researchers since its introduction in [1]. For an additive noise channel with hard decision output, the algorithm checks all error patterns, and it schedules its search in descending likelihood order. Ordered reliability bit GRAND(ORBGRAND) [2] is a variant of *** soft channel output is available,

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Optimizing Antenna Design through AI Integration: A Concise Review

Optimizing Antenna Design through AI Integration: A Concise ...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Xiaotian Liu Kwok L. Chung Wenbiao Li Ruihong Li Shuwei Huang Yingsong Li School of Computer Science and Engineering Huizhou University Huizhou China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

The integration of artificial intelligence with antenna design and optimization represents a promising research frontier. This paper provides a concise overview of current research, emphasizing the computational cost challenges associated with implementing machine learning in antenna design and optimization. It explores the advantages and limitations of utilizing the Kriging model and the Surrogate-assisted Differential Evolution Algorithm (SADEA) series to tackle these cost constraints.

关键词： Microwave antennas Technological innovation Analytical models Reviews Computational modeling Machine learning Transforms

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Kernel Adaptive Filtering with Weibull M-transform Maximum Versoria Criterion

Kernel Adaptive Filtering with Weibull M-transform Maximum V...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Chenchong Bi Yingsong Li Liping Li Nikola Zlatanov Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Innopolis Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

A kernel Weibull M-transform maximum-versoria-criterion (KWMMVC) algorithm is constructed, which is to use for nonlinear system identification. The KWMMVC algorithm ingeniously integrates the Weibull M-transform scheme into the maximum-versoria-criterion to formulate a novel cost function. The ability of KWMMVC algorithm to identify nonlinear channels under various impulse noise environments is tested in the kernel adaptive filters, and the results proved its superior performance.

关键词： Adaptive systems Noise Adaptive filters Filtering algorithms Information filters Cost function Information and communication technology

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SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection

arXiv

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arXiv 2022年

作者： Liu, Zhengyi Tan, Yacheng He, Qian Xiao, Yun Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei China

Convolutional neural networks (CNNs) are good at extracting contexture features within certain receptive fields, while transformers can model the global long-range dependency features. By absorbing the advantage of transformer and the merit of CNN, Swin Transformer shows strong feature representation ability. Based on it, we propose a cross-modality fusion model, SwinNet, for RGB-D and RGB-T salient object detection. It is driven by Swin Transformer to extract the hierarchical features, boosted by attention mechanism to bridge the gap between two modalities, and guided by edge information to sharp the contour of salient object. To be specific, two-stream Swin Transformer encoder first extracts multi-modality features, and then spatial alignment and channel re-calibration module is presented to optimize intra-level cross-modality features. To clarify the fuzzy boundary, edge-guided decoder achieves inter-level cross-modality fusion under the guidance of edge features. The proposed model outperforms the state-of-the-art models on RGB-D and RGB-T datasets, showing that it provides more insight into the cross-modality complementarity ***://***/liuzywen/SwinNet © 2022, CC0.

关键词： Convolutional neural networks

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Robust DOA Estimation Against Outliers Based on Bias-compensated Adaptive Filtering

Robust DOA Estimation Against Outliers Based on Bias-compens...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Wudang Xiao Canping Yu Yingsong Li Liping Li Dingcheng Zou Nikola Zlatanov Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Innopolis Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

The direction-of-arrival (DOA) estimation approach utilizing the adaptive nulling array framework has gained widespread attention due to its low complexity. However, in impulsive noise environments, traditional least mean squares (LMS) algorithm may experience a sharp decline in performance or even fail to work properly. To address this challenge, we propose a DOA estimation approach designed for impulsive noise situations based on a robust nonlinear function. In addition, the algorithm can mitigate bias caused by auxiliary array noise and uses the variable step-size strategy to enhance its performance. Furthermore, the mean stability of the proposed approach is also assessed. Simulation results demonstrate its superior performance in impulsive noise environments.

关键词： Resistance Direction-of-arrival estimation Simulation Noise Estimation Adaptive arrays Information filters

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A Novel Transformed Nested Array with Enhanced DOFs Based on Sum-Difference Coarray

A Novel Transformed Nested Array with Enhanced DOFs Based on...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Wei Xin Yingsong Li Harbin Engineering University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

Sum-difference driven coarray (SDCA) was paid great attention in array-signal-processing (ASP). By considering SDCA, the degrees of freedom (DOFs) for sparse arrays can be further improved. Here, a new transformed nested-array (TNA) is constructed, which reduces the element redundancy via rearranging the density of sub-arrays of the constructed new TNA. Compared to former TNAs, the constructed TNA gets higher DOFs and maximization signals, numerical simulation are given for getting its superior behaviors.

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Wideband Bonding Wire Compensation Structure Design and Electric-thermal Coupling Analysis for Chip Package

Wideband Bonding Wire Compensation Structure Design and Elec...

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Asia-Pacific Conference on Microwave

作者： Pei Ge Hao-Ran Zhu Yu Fa Sun School of Electronic Science and Engineering Anhui University An Hui China Key Laboratory of Intelligent Computing and Signal Processing of Heifei Anhui University An Hui China

ISBN: (数字)9798350363548

ISBN: (纸本)9798350363555

In this paper, a novel wire-bonding and BGA package is applied to a highly integrated MMIC. The novel compensation component of the bonding wires is composed of a capacitive strip-line and an inductive via below the transmission line. In addition, A large amount of heat is generated by high-intensity chip, leading to a decrease in transmission performance. Multilayered system in package is designed to improve the heat dissipation capability of chip. Multi physics field simulation is introduced to verify the effectiveness of the design. The proposed multilayered system in package is made using LTCC process and loaded with low noise amplifier chip. The return loss of the is better than 10dB and the gain is greater than 10dB in the frequency range of DC-20GHz.

关键词： Low-noise amplifiers System-in-package Power transmission lines Wires Noise Routing Microwave amplifiers Bonding Physics Wideband

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LE-CAM++: A Lighter and More Efficient CAM++ for Speaker Verification 14

LE-CAM++: A Lighter and More Efficient CAM++ for Speaker Ver...

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14th International Symposium on Chinese Spoken Language processing, ISCSLP 2024

作者： Liu, Shuanghong Song, Zhida Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi China School of Intelligence Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9798331516826

Due to its superior performance and fewer parameters, CAM++ has become the state-of-the-art model for speaker verification tasks. This model uses 2D convolutional blocks to extract front-end features, which are then fed into a densely connected time-delay neural network backbone to extract deep features. However, the simple stacking of 2D convolutions may lead to the generation of a significant amount of redundant features, which is detrimental to efficient feature extraction. Furthermore, although CAM++ already has a relatively small number of parameters, there is still room for further optimization. To address these issues, this paper first employs depthwise separable convolutions to replace the dilated convolutions in the backend network of CAM++, making the model more lightweight. Next, we introduce spatial and channel reconstruction convolution (SCConv) in the ResBlock module of CAM++ to reduce redundant features and optimize the feature extraction process. Finally, after SCConv, we apply squeeze and excitation attention mechanism to model the interdependencies between channels and recalibrate each channel, further enhancing the model’s representational capacity. We name the resulting model LE-CAM++. Our proposed model achieves an EER of 0.686 and a minDCF of 0.084 on the VoxCeleb1-O dataset. Compared to the baseline model CAM++, the EER is reduced by 11%, and the minDCF is reduced by 28%. Additionally, the model parameters are reduced by 8%. ©2024 IEEE.

关键词： Deep neural networks

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Edge-Preserving Multi-Exposure Fusion with Guided Filtering for Structural Patch Decomposition 6

Edge-Preserving Multi-Exposure Fusion with Guided Filtering ...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Zhen, Yang Qi, Junwei Li, Yingsong College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China College of Computer Science and Technology Harbin Engineering University Harbin150001 China

ISBN: (纸本)9798350399059

Multi-exposure image fusion (MEF) is one of the effective ways to obtain high dynamic range (HDR) images. Multi-scale structural patch decomposition based MEF(MSPD) and edge-preserving structural patch decomposition (MESPD) have achieved state-of-the-art fusion quality. MESPD incorporates edge-preserving factors into MSPD to preserve details in a single-scale setting. However, we find that the edge-preserving factors put too much emphasis on the edge, resulting in artifacts on some edges and loss of details. To tackle this issue, guided filter is introduced into MSPD, which is directly used as an edge-preserving filter instead of mean filter to implicitly decompose images. In addition, we have designed a flexible adaptive weight, which can further maintain details of the light and dark areas. Likewise, we inherit MESPD and MSPD to extend our method to multi-scale framework. Extensive experimental results demonstrate our method can produce satisfactory results in both static and dynamic scenes with fewer artifacts and low computational cost. © 2023 IEEE.

关键词： Image fusion

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Variational Clustering and Denoising of Spatial Transcriptomics

Variational Clustering and Denoising of Spatial Transcriptom...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Cuiyuan Zhang, Fa Hu, Kai Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Beijing Institute of Technology School of Medical Technology Beijing China Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan China Xiangnan University Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Chenzhou China

ISBN: (纸本)9798350337488

Spatial transcriptomics data provides a unique opportunity to investigate both gene expression and spatial structure in tissues at the same time. However, incorporating spatial information to accurately identify spatial domains is difficult due to factors such as high-dimensionality, sparsity, noise, and dropout events. To address these issues, we introduce vGraphST, a novel graph-based deep learning approach tailored for spatial transcriptomics data. Our method combines auto-encoder and contrastive learning techniques to process high-dimensional data and generate meaningful low-dimensional embeddings. Additionally, we use continuous distributions instead of discrete values in both the latent space and the denoised gene expression space. Specifically, Gaussian distributions are used to model the latent space, while zero-inflated Poisson distributions are used to model the denoised gene expression space. Experimental results demonstrate the effectiveness of vGraphST in accurately representing and analyzing spatial transcriptomics data. When compared to other methods using the DLPFC dataset, vGraphST achieves an average Adjusted Rand Index (ARI) of 0.58, demonstrating its superiority in segmenting spatial domains and recognizing biologically relevant spatiotemporal patterns. © 2023 IEEE.

关键词： clustering contrastive learning denoising spatial transcriptomics variational auto-encoder

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