Low-complexity optimal MPSK detection for spatial modulation by Hong-zhi MEN, Ming-lu JIN

“…The low-complexity optimal SM detection algorithm for MQAM signal detection had been proposed,but no similar method was found for MPSK signal.The problem of low-complexity detection for MPSK signal was considered in SM systems.Utilizing 2-D vector quantization of ML demodulation and the property of MPSK constellation,a low-complexity optimal detection which is independent to modulation order was developed.Since the approach avoids the exhaustive searching on signal constellation space,the computational complexity can be significantly reduced.The proposed detection algorithm can provide the identical performance with ML-optimum detector and has lower computational complexity.Therefore,it has both theoretical and practical significance.The proposed algorithm is of great significance in the large antenna and green communication technology.…”
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Deterministic Aided STAP for Target Detection in Heterogeneous Situations by J.-F. Degurse, L. Savy, S. Marcos, J.-Ph. Molinié

“…Indeed, in this case, representative target free data are no longer available. Single dataset algorithms, such as the MLED algorithm, have proved their efficiency in overcoming this problem by only working on primary data. …”
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Time synchronization attack detection for industrial wireless network by Sichao ZHANG, Wei LIANG, Xudong YUAN, Yinlong ZHANG, Meng ZHENG

“…High-precision time synchronization is the basis for ensuring the secure and reliable transmission of industrial wireless network (IWN).Delay attacks, as a class of time synchronization attacks which cannot be solved by cryptographic techniques, seriously threaten the secure operation of IWN.Firstly, based on the in-depth analysis on the time synchronization mechanisms of IWN, three-time synchronization attack models were proposed, including the one-way full life cycle delay attack, two-way full life cycle delay attack, and one-way non-full-life cycle delay attack.Stealthier delay attacks could be realized by the attack models under the premise that target nodes were not captured.Secondly, considering the problem that existing detection algorithms are difficult to detect stealthier delay attacks without obvious changes in time features, an attack detection algorithm based on a Bayesian model was proposed that extracts four representative features, including transmission rate, transmission delay, transmission success rate and time synchronization interval.In addition, in order to ensure the accuracy of the attack detection and classification in the presence of noise interference, the noise model of wireless channel was introduced to the Bayesian feature information matrix.Experimental results show that the proposed algorithm can effectively detect three kinds of attacks in the presence of noise.…”
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Time synchronization attack detection for industrial wireless network by Sichao ZHANG, Wei LIANG, Xudong YUAN, Yinlong ZHANG, Meng ZHENG

“…High-precision time synchronization is the basis for ensuring the secure and reliable transmission of industrial wireless network (IWN).Delay attacks, as a class of time synchronization attacks which cannot be solved by cryptographic techniques, seriously threaten the secure operation of IWN.Firstly, based on the in-depth analysis on the time synchronization mechanisms of IWN, three-time synchronization attack models were proposed, including the one-way full life cycle delay attack, two-way full life cycle delay attack, and one-way non-full-life cycle delay attack.Stealthier delay attacks could be realized by the attack models under the premise that target nodes were not captured.Secondly, considering the problem that existing detection algorithms are difficult to detect stealthier delay attacks without obvious changes in time features, an attack detection algorithm based on a Bayesian model was proposed that extracts four representative features, including transmission rate, transmission delay, transmission success rate and time synchronization interval.In addition, in order to ensure the accuracy of the attack detection and classification in the presence of noise interference, the noise model of wireless channel was introduced to the Bayesian feature information matrix.Experimental results show that the proposed algorithm can effectively detect three kinds of attacks in the presence of noise.…”
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Detection of Montage in Lossy Compressed Digital Audio Recordings by Rafał KORYCKI

“…This paper addresses the problem of tampering detection and discusses methods used for authenticity analysis of digital audio recordings. …”
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Visual detection of screen defects in occlusion and missing scenes by YIN Dongfu, DU Mingchen, HU Tianhao, LI Youming, ZHANG Xiaohong, YU Fei Richard

“…To improve the intelligent level and solve the difficult problem of defect detection in complex scenarios, a visual detection system for screen defects in occluded and missing scenes is constructed based on object detection and matching, as well as image difference technology. …”
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Automatic Fall Risk Detection Based on Imbalanced Data by Yen-Hung Liu, Patrick C. K. Hung, Farkhund Iqbal, Benjamin C. M. Fung

“…In this paper, we propose a pose estimation-based fall detection algorithm to detect fall risks. We use body ratio, acceleration and deflection as key features instead of using the body keypoints coordinates. …”
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Improved printed circuit board defect detection scheme by Lufeng Bai, Wen Hao Xu

“…Abstract In this paper, an improved printed circuit board(PCB)defect detection scheme named PD-YOLOv8 is proposed, which is specialized in the common and challenging problem of small target recognition in PCB inspection. …”
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Semi-supervised anchorless single-engine grip detection by Yun Shi, Gang Zhang, Min Kong, Jie Fang

“…The extensive application of artificial intelligence in wireless communication, 3D reconstruction, and target location has successfully solved the modeling problem. Grasping specific objects in a stacked scene is a difficult task to achieve robot grasping. …”
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Domain Adaptation for Pedestrian Detection Based on Prediction Consistency by Yu Li-ping, Tang Huan-ling, An Zhi-yong

“…Pedestrian detection is an active area of research in computer vision. …”
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Detection and Optimization of Traffic Networks Based on Voronoi Diagram by Rui Tao, Jian Liu, Yuqing Song, Rui Peng, Dali Zhang, Jiangang Qiao

“…As compared with the existing algorithms, our approach works directly on noisy data and detects salient peaks without a smoothing prestep and thus avoids the dilemma in choosing an appropriate smoothing scale and prevents the occurrence of removing/degrading real peaks during smoothing step. …”
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Statistical bayesian algorithm for processing thermographic images of the cow udder for diagnosing mastitis by I. I. Hirutsky, A. G. Senkov, Y. A. Rakevich

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Determined the Edges Using the ant Colony Algorithm and Apply them to Medical Images by Maha Rahman Hasso, Aseel Ali

“…The results show high accuracy in edge detection of different biomedical images with different neighbors, the proposed algorithm is implemented in C Sharp 2008 language which provides high-efficiency software visible language and speed. …”
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Abnormal Event Detection Method in Multimedia Sensor Networks by Qi Li, Xiaoming Liu, Xinyu Yang, Ting Li

“…Audio sensors also have price advantage. Detecting abnormal audio events in complicated background environment is a very difficult problem; only few previous researches could offer good solution. …”
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Machine vision model for drip leakage detection of pipeline. by Cong Xiao, Yiyuan Zhou, Yanwen Wu, Guodong Zhang

“…The droplet boundary extraction algorithm is proposed, and the three-dimensional model of the target droplet is established, so the volume calculation problem of the droplet is solved, which provides a way of thinking for drip leakage detection of the pipeline.…”
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Spectrum sensing algorithm based on stochastic resonance and non-central F-distribution by Yuanmeng FAN, Shunlan LIU

“…To solve the problem that the detection probability of the spectrum sensing algorithm is low and the number of samples required for detection is large at low signal-to-noise ratio (SNR), a spectrum sensing algorithm based on stochastic resonance and non-central F-distribution (SRNF) was proposed.By introducing direct-current stochastic resonance noise, the system model of SRNF was established, and the expression of test statistic, false alarm probability and detection probability, and the expression of decision threshold obeying non-central F-distribution were deduced, and the optimal stochastic resonance noise parameter was solved by numerical method.The simulation results show that the detection performance of the proposed SRNF algorithm is better than that of energy detection (ED) algorithm and blind spectrum sensing based on F-distribution (BSF) algorithm at a low SNR.When the false alarm probability is 5%, the SNR is -12 dB, and the number of samples is 200, the detection probability of the proposed algorithm is 95%, which is 34% and 67% higher than BSF algorithm and ED algorithm, respectively.When the SNR is -12 dB, and the detection probability reaches 95%, the number of samples required by the proposed algorithm is 210, which saves 340 samples compared to the BSF algorithm.Furthermore, the proposed algorithm is less affected by noise uncertainty than ED algorithm.…”
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Use of deep learning in early software bug detection by Syed Mibran Hassan Zaidi, Maria Khan, Mustafa Latif, Ali Akhtar, Sallar Khan

“…To increase software efficiency, dependability, and quality early software problem identification is important. Therefore, an efficient software bug detection model is a critically significant challenging operation, and that has been developed in this work by designing the efficient model while using the promises dataset as input for bug identification. …”
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Domain Adaptation for Satellite-Borne Multispectral Cloud Detection by Andrew Du, Anh-Dzung Doan, Yee Wei Law, Tat-Jun Chin

“…In this paper, we address the domain gap problem in the context of onboard multispectral cloud detection. …”
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Fire and Smoke Detection Based on Improved YOLOV11 by Zhipeng Xue, Lingyun Kong, Haiyang Wu, Jiale Chen

“…In this paper, the core DCN2 (Deformable Convolutional Networks2) of the YOLOV11 Head is replaced with the DCN3 module to form a new detection head. In addition, the loss function CIOU in YOLOV11 is replaced with IOU to consider the irregular shape of fire and smoke and the problem of multi-scale targets. …”
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Analysis of Internet Marketing Forecast Model Based on Parallel K-Means Algorithm by Xiaolei Chen, Sikun Ge

“…Secondly, the weights in the K-means algorithm are mostly only applicable to target detection tasks. …”
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