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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Sparse Adaptive Optimization Based on Low Rank Decomposition for Image Defect Detection by Daihong Jiang, Zhixiang Chen, Sanyou Zhang, Yunfei Li, Lu Zhao

Subjects: “…Low rank decomposition…”
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Improved flow direction algorithm for WSN coverage optimization by CHEN Wei, YANG Panlong

“…Addressing issues of local optima and low convergence accuracy existed in the standard flow direction algorithm , we propose an improved flow direction algorithm by incorporating Levy flight and weed invasion strategy. …”
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Identification of Low‐Value Defects in Infrared Images of Porcelain Insulators Based on STCE‐YOLO Algorithm by Shaotong Pei, Weiqi Wang, Chenlong Hu, Keyu Li, Haichao Sun, Mianxiao Wu, Bo Lan

“…To solve the above problems, this paper optimizes the small target and complex environment problems in the low‐value defect recognition of insulator infrared images, and proposes the STCE‐YOLO algorithm: based on YOLOv8, the deformable large kernel attention is used to improve the detection ability of small targets; then the cross‐modal contextual feature module is applied to Integrate the features of different scales to reduce the computation of the model. …”
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The "Low Slow and Small" UAV target detection and tracking algorithm based on improved YOLOv7 and DeepSort by JIAN Yuhong, YANG Huiyue, WANG Xinggang, RONG Yisheng, ZHU Yukun

“…To improve the accuracy of Low altitude unmanned aerial vehicle(UAV) target detection and tracking, an improved UAV detection algorithm based on YOLOv7 and DeepSort framework is proposed. …”
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LLD-YOLO: A Low-Light Object Detection Algorithm Based on Dynamic Weighted Fusion of Shallow and Deep Features by Wenhao Cai, Yajun Chen, Xiaoyang Qiu, Meiqi Niu, Jianying Li

“…Object detection in low-light scenarios has a wide range of applications, but existing algorithms often struggle to preserve the scarce low-level features in dark environments and exhibit limitations in localization accuracy for blurred edges and occluded objects, leading to suboptimal performance. …”
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Design and Implementation of Low-Complexity Multiple Symbol Detection Algorithm Using Hybrid Stochastic Computing in Aircraft Wireless Communications by Yukai Liu, Rongke Liu, Kairui Tian, Zheng Lu, Ling Zhao

“…The Multiple Symbol Detection (MSD) algorithm can effectively lower the demodulation threshold in Frequency Modulation (FM) technology, which is widely used in aircraft wireless communications due to its insensitivity to large Doppler shifts. …”
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Detection of low-altitude infrared small targets for UAVs using a density-based artificial bee colony algorithm by Haixia Wang, Hailong Wang, Fen Han

“…Abstract The objective of this paper is to address the issue of the inadequate detection accuracy of UAVs operating at low-altitudes in conditions of weak thermal signals. …”
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A Low Complexity Near-Optimal Detector Based on Teaching-Learning Algorithm for Massive MIMO by Hamid Amiriara, Mohammadreza Zahabi

“…In this paper, a low-complexity receiver is proposed using a Teaching-Learning based optimization (TLBO) heuristic algorithm for a large-scale system. …”
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Genetic Algorithm-Enhanced Direct Method in Protein Crystallography by Ruijiang Fu, Wu-Pei Su, Hongxing He

“…Direct methods based on iterative projection algorithms can determine protein crystal structures directly from X-ray diffraction data without prior structural information. …”
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An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study by Inge A. H. van den Berk, Colin Jacobs, Maadrika M. N. P. Kanglie, Onno M. Mets, Miranda Snoeren, Alexander D. Montauban van Swijndregt, Elisabeth M. Taal, Tjitske S. R. van Engelen, Jan M. Prins, Shandra Bipat, Patrick M. M. Bossuyt, Jaap Stoker, The OPTIMACT study group

“…Abstract Background To retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose computed tomography (ULDCT) performed at the emergency department (ED). …”
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Application of CycleGAN-based low-light image enhancement algorithm in foreign object detection on belt conveyors in underground mines by Anxin Zhao, Qiuhong Zheng, Liang Li

“…To address this issue, an improved CycleGAN-based low-illumination image enhancement algorithm is proposed, which employs a cycle generative adversarial network for unsupervised learning. …”
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Para-YOLO: An Efficient High-Parameter Low-Computation Algorithm Based on YOLO11n for Remote Sensing Object Detection by Hang Chen, Qi Cao, Yongqiang Wang, Shang Wang, Haisheng Fu, Zhenjiao Chen, Feng Liang

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A belief propagation algorithm based on track‐before‐detect for tracking low‐observable and manoeuvering targets using multiple sensors by Chenghu Cao, Haisheng Huang, Xin Li, Yongbo Zhao

“…Abstract It is notoriously challenging work to track an unknown number of low‐observable manoeuvering targets. In this paper, a sequential Bayesian inference method based on the multiple‐model dynamic model and track‐before‐detect measurement (TBD) model is proposed for tracking low‐observable manoeuvering targets using multiple sensors. …”
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Detection, Parameter Estimation and Direction Finding of Periodic Pulse Signals by V. B. Manelis, I. S. Faustov, V. A. Kozmin

“…To develop algorithms for detecting, estimating parameters, and direction finding of periodic pulse signals in the presence of low signal-to-noise ratios and the absence of a priori information about the parameters of a periodic pulse signal.Materials and methods. …”
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A low complexity pilot assignment algorithm based on user polar coordinates in CF-mMIMO systems by Shao GUO, Peng PAN, Yaozong FAN

“…Absrtact: In order to reduce the pilot contamination in the cell-free massive multi-input multi-output (MIMO) system, a low complexity pilot assignment algorithm based on user polar coordinates was proposed.Firstly, a Gaussian weighted density algorithm was proposed to determine a centroid as the polar coordinates center point in the system coverage area, then pre-assigned the pilot in order according to the angular coordinates, so that users who reused the same pilot had a greater probability of having a longer distance, and henced reduce the pilot contamination.A low complexity distance detection algorithm was then proposed to ensure that the user spacing between any two users multiplexing the same pilot was greater than the threshold.The simulation results show that the proposed pilot assignment algorithm effectively reduce pilot contamination, improve the uplink throughput of 95% users of the system, and achieve a good compromise between performance and complexity.…”
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Image forgery detection algorithm based on U-shaped detection network by Zhuzhu WANG

“…Aiming at the defects of traditional image tampering detection algorithm relying on single image attribute,low applicability and current high time-complexity detection algorithm based on deep learning,an U-shaped detection network image forgery detection algorithm was proposed.Firstly,the multi-stage feature information in the image by using the continuous convolution layers and the max-pooling layers was extracted by U-shaped detection network,and then the obtained feature information to the resolution of the input image through the upsampling operation was restored.At the same time,in order to ensure higher detection accuracy while extracting high-level semantic information of the image,the output features of each stage in U-shaped detection network would be merged with the corresponding output features through the upsampling layer.Further the hidden feature information between tampered and un-tampered regions in the image upon the characteristics of the general network was explored by U-shaped detection network,which could be realized quickly by using its end-to-end network structure and extracting the attributes of strong correlation information among image contexts that could ensure high-precision detection results.Finally,the conditional random field was used to optimize the output of the U-shaped detection network to obtain a more exact detection results.The experimental results show that the proposed algorithm outperforms those traditional forgery detection algorithms based on single image attribute and the current deep learning-based detection algorithm,and has good robustness.…”
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Image forgery detection algorithm based on U-shaped detection network by Zhuzhu WANG

“…Aiming at the defects of traditional image tampering detection algorithm relying on single image attribute,low applicability and current high time-complexity detection algorithm based on deep learning,an U-shaped detection network image forgery detection algorithm was proposed.Firstly,the multi-stage feature information in the image by using the continuous convolution layers and the max-pooling layers was extracted by U-shaped detection network,and then the obtained feature information to the resolution of the input image through the upsampling operation was restored.At the same time,in order to ensure higher detection accuracy while extracting high-level semantic information of the image,the output features of each stage in U-shaped detection network would be merged with the corresponding output features through the upsampling layer.Further the hidden feature information between tampered and un-tampered regions in the image upon the characteristics of the general network was explored by U-shaped detection network,which could be realized quickly by using its end-to-end network structure and extracting the attributes of strong correlation information among image contexts that could ensure high-precision detection results.Finally,the conditional random field was used to optimize the output of the U-shaped detection network to obtain a more exact detection results.The experimental results show that the proposed algorithm outperforms those traditional forgery detection algorithms based on single image attribute and the current deep learning-based detection algorithm,and has good robustness.…”
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Impulsive Sound Detection Directly in Sigma-Delta Domain by Igor Dantas dos Santos MIRANDA, Antonio C. de C. LIMA

“…This work proposes an algorithm based on Discrete Cosine Transform for impulsive signal detection to be applied directly on modulated Sigma-delta bitstream, targeting to reduce computational cost in acoustic event detection applications such as gunshot recognition systems. …”
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