Low complexity uplink multiuser MIMO detecting algorithm by HEI Yong-qiang1, LI Xiao-hui1, YI Ke-chu1, YANG Hong2

“…Aiming at the uplink multiuser MIMO system,a new space-time detecting algorithm was proposed,in which a linear recombination strategy was imposed on the received signal first,and then the matched filter vectors orthogonal to each users’ interference sets were used to eliminate the interference among users.Finally the signal of each user was line-arly space-time decoded by means of the orthogonal equivalent channel.The design of the algorithm under imperfect channel scenario was also provided.Theory analysis and simulation results reveal that the proposed algorithm has the advantages of low complexity and good robustness,and is superior to other detecting algorithms.…”
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Low-Complexity Detection Algorithms for Spatial Modulation MIMO Systems by Xinhe Zhang, Yuehua Zhang, Chang Liu, Hanzhong Jia

“…In this paper, the authors propose three low-complexity detection schemes for spatial modulation (SM) systems based on the modified beam search (MBS) detection. …”
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Low-Complexity Saliency Detection Algorithm for Fast Perceptual Video Coding by Pengyu Liu, Kebin Jia

“…A low-complexity saliency detection algorithm for perceptual video coding is proposed; low-level encoding information is adopted as the characteristics of visual perception analysis. …”
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Low-complexity signal detection algorithm for the power splitting receiver scheme by WANG Yanyan, LI Qidi, TANG Xiaohu

“…To address the issue of high signal detection complexity in mobile communication systems based on the splitting receiver architecture, a low-complexity signal detection algorithm was proposed. …”
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Contrastive Learning Algorithm for Low-Resource Cryptographic Attack Event Detection by Peng Luo, Rangjia Cai, Yuanbo Guo

“…Thus, we propose a method CLAD: Contrastive Learning Algorithm for Detecting Low-resource Cryptographic Attack Event. …”
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3L-YOLO: A Lightweight Low-Light Object Detection Algorithm by Zhenqi Han, Zhen Yue, Lizhuang Liu

“…In comparison to the LOL-YOLO low-light object detection algorithm, 3L-YOLO requires 16.9 GFLOPs, representing a reduction of 4 GFLOPs.…”
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A Low-Latency Dynamic Object Detection Algorithm Fusing Depth and Events by Duowen Chen, Liqi Zhou, Chi Guo

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Minimising latency of pitch detection algorithms for live vocals on low-cost hardware

“…However, very little information is available concerning the latency of pitch detection algorithms and methods with which this can be minimised. …”
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Low scaling factor Seam Carving tamper detection algorithm with hybrid attention by ZHAO Jie, CHANG Haochan, WU Bin

“…The existing seam carving tamper detection algorithms have the problems of low detection accuracy and weak robustness for the case of low scaling factor. …”
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Space alternating generalized maximum-likelihood based low-complexity list detection algorithm by Chang-le JING, Xin WANG, Ji-bo WEI, Bin CHEN, Shi-qi WU

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Low complexity hybrid iterative algorithm based signal detection in massive MIMO system by Shufeng ZHAO, Bin SHEN, Furong YANG

“…Among the uplink signal detection algorithms for massive MIMO systems,the minimum mean square error (MMSE) algorithm can achieve the near-optimal linear detection performance.However,conventional MMSE usually involves high complexity due to the required matrix inversion of large-size matrix,which makes it hard to implement in realistic applications.Based on joint steepest descent (SD) algorithm and Gauss-Seidel iteration,a low complexity hybrid iterative detection algorithm was proposed.The SD algorithm was employed to obtain an efficient searching direction for the following Gauss-Seidel to speed up convergence.Meanwhile,an approximated method was also proposed to compute the bit log-likelihood ratio (LLR) for soft channel decoding.Simulation results verify that the proposed algorithm can converge rapidly and achieve its performance quite close to that of the MMSE algorithm with only a small number of iterations.Meanwhile,the complexity is reduced by an order of magnitude,which is kept consistently of O(K ²).…”
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Characteristics and Detection Algorithm of Strong and Weak Arc in Low Voltage DC Distribution Network by Guoqiang LIU, Guochun LI, Yanan HAO, Guihai LI, Zhongjie WU

“…Combined with the neural network model, the arc detection algorithm suitable for 750 V system is designed, and the detection accuracy of the algorithm is 99.12%.…”
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LI-YOLO: An Object Detection Algorithm for UAV Aerial Images in Low-Illumination Scenes by Songwen Liu, Hao He, Zhichao Zhang, Yatong Zhou

“…Aiming at the problem of low brightness, high noise, and obscure details of low-illumination images, an object detection algorithm, LI-YOLO (Low-Illumination You Only Look Once), for UAV aerial images in low-illumination scenes is proposed. …”
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Adaptive Robust Low-Beam Feature Point Detection Algorithm Based on Convex Hull by Qiqi Shen, Jie Wu

“…The existing algorithms for detecting low-beam feature points are neither universally applicable nor effective. …”
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Small target drone algorithm in low-altitude complex urban scenarios based on ESMS-YOLOv7 by Yuntao Wei, Xiujia Wang, Chunjuan Bo, Zhan Shi

“…Notably, there is a deficit in anti- UAV technologies for civilian use, particularly in complex urban environments at low altitudes. This paper proposes the ESMS-YOLOv7 algorithm, which is specifically engineered to detect small target UAVs in such challenging urban landscapes. …”
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A low complexity detection algorithm for large scale multiuser MIMO based on message passing by Qiong WANG, Wei YE, Mingming JI

“…According to the problem of high complexity of base station detection in large scale multiuser multiple input multiple output (MIMO) system,a low complexity multiuser variable node full information Gaussian message passing iterative detection algorithm based on forced convergence (VFI-GMPID-FC) was proposed.Firstly,the traditional Gaussian message passing iterative detection (GMPID) algorithm was improved to obtain VFI-GMPID algorithm,the detection performance of the VFI-GMPID algorithm approximates the minimum mean square error detection (MMSE) algorithm,but the complexity was considerably less than the MMSE algorithm.Then,the VFI-GMPID-FC algorithm was proposed to reduce the complexity of the algorithm and improve the detection efficiency.Finally,the simulation results show that the proposed algorithm can effectively reduce the algorithm complexity while ensuring the detection performance.…”
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Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms by Ye Liu, Shixin Li, Liming Zhou, Haichen Liu, Zhiyu Li

“…To address these challenges, this paper proposes a low-light object detection algorithm named Dark-YOLO, which dynamically extracts features. …”
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A low-complexity AMP detection algorithm with deep neural network for massive mimo systems by Zufan Zhang, Yang Li, Xiaoqin Yan, Zonghua Ouyang

Subjects: “…Approximate message passing (AMP) detection algorithm…”
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Low Voltage AC Series Arc Fault Detection Method Based on Approximate Entropy for Low Computational Performance Device by Kyoung-Tak Kim, Min-Ho Yoon, Chan-Muk Park, Joung-Hu Park, Sung-Hun Lim

“…This paper proposes an algorithm for detecting series arc faults in low-voltage AC residential environments. …”
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On-board Multi-User Detection Algorithm Based on Conditional Neural Process by Yilun LIU, Liang JIN, Jiali LI, Lidong ZHU

“…With the characteristics of all-terrain, all-weather and seamless coverage, satellite communications have become a potentially important part of 6G.An important prerequisite for achieving satellite intelligence is that the satellite have on-board processing capabilities.Multi-user detection (MUD) is a classic method of suppressing multiple access interference (MAI) in wireless communication, such as MMSE, Gaussian processregression (GPR) and other algorithms.Due to the inverse matrix required in the detection process, the algorithm complexity is usually cubic, and it is diff cult to directly apply to satellite platforms because of its limited processing capabilities.The conditional neural process combined the characteristics of the low complexity of the neural network and the data-eff cient of the Gaussian process.The neural network was used to parameterized the Gaussian process to avoided the inversion of the matrix, thereby reduced the computational complexity.The application of conditional neural process in MUD was studied.The simulation results showed that, while reduced complexity, conditional neural process also greatly improved the performance of bit error rate (BER).…”
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