Deep learning-based object detection and robotic arm grasping by ZHANG Lei, ZHANG Senhui, YAN Song, YUAN Yuan

“…For the grasping task, a single-stage grasping pose detection algorithm was designed. Firstly, considering the interference present in unstructured environments, RGB-D images were selected as the input data for the grasping network, and GG-CNN was chosen as the backbone network. …”
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An Iterative Information-Theoretic Approach to the Detection of Structures in Complex Systems by Marco Villani, Laura Sani, Riccardo Pecori, Michele Amoretti, Andrea Roli, Monica Mordonini, Roberto Serra, Stefano Cagnoni

“…The method iterates two basic steps: detection of relevant variable sets based on the computation of the Relevance Index, and application of a sieving algorithm, which refines the results. …”
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Automated detection of hospital outbreaks: A systematic review of methods. by Brice Leclère, David L Buckeridge, Pierre-Yves Boëlle, Pascal Astagneau, Didier Lepelletier

“…However, the usefulness of these methods to detect nosocomial outbreaks remains unclear. The goal of this review was to describe outbreak detection algorithms that have been tested within hospitals, consider how they were evaluated, and synthesize their results.…”
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Transformer Online Monitoring Data Abnormal Value Detection and Cleaning by QIAN Yucheng, ZHEN Chao, JI Kun, ZHAO Changwei, FU Longming, ZHANG Yajing

“…Finally, the time series forecasting method is studied, the trend forecast is completed and the missing values and noise values are filled to maintain data integrity The algorithm is verified by the online monitoring data of a substation The results show that the method can complete abnormal detection and cleaning in time The accuracy rate after cleaning is 93.9%, and the completion rate can reach 98.6%, which has high use value…”
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Hate Speech Detection Using Machine Learning: A Survey by Seble, H., Muluken, S., Edemealem Kingawa, Kafte, T., Terefe, F., Mekashaw, G., Abiyot, B., Senait, T.

“…Furthermore, limitations, along with algorithm selection and use challenges, data collection, and cleaning challenges, and future research directions are discussed in detail.…”
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Detection, Parameter Estimation and Direction Finding of Periodic Pulse Signals by V. B. Manelis, I. S. Faustov, V. A. Kozmin

“…Simple-to-implement algorithms for detecting periodic pulse signals, evaluating their parameters, and direction finding of the source have been developed. …”
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Deep Learning Methods and UAV Technologies for Crop Disease Detection by S. G. Mudarisov, I. R. Miftakhov

“…The findings demonstrate that deep learning algorithms offer more accurate and earlier detection of diseases, highlighting their potential for application in plant growing. …”
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Deepfake detection method based on patch-wise lighting inconsistency by Wenxuan WU, Wenbo ZHOU, Weiming ZHANG, Nenghai YU

“…The rapid development and widespread dissemination of deepfake techniques has caused increased concern.The malicious application of deepfake techniques also poses a potential threat to the society.Therefore, how to detect deepfake content has become a popular research topic.Most of the previous deepfake detection algorithms focused on capturing subtle forgery traces at pixel level and have achieved some results.However, most of the deepfake algorithms ignore the lighting information before and after generation, resulting in some lighting inconsistency between the original face and the forged face, which provided the possibility of using lighting inconsistency to detect deepfake.A corresponding algorithm was designed from two perspectives: introducing lighting inconsistency information and designing a network structure module for a specific task.For the introduction of lighting task, a new network structure was derived by designing the corresponding channel fusion method to provide more lighting inconsistency information to the network feature extraction layer.In order to ensure the portability of the network structure, the process of feature channel fusion was placed before the network extraction information, so that the proposed method can be fully planted to common deepfake detection networks.For the design of the network structure, a deepfake detection method was proposed for lighting inconsistency based on patch-similarity from two perspectives: network structure and loss function design.For the network structure, based on the characteristic of inconsistency between the forged image tampering region and the background region, the extracted features were chunked in the network feature layer and the feature layer similarity matrix was obtained by comparing the patch-wise cosine similarity to make the network focus more on the lighting inconsistency.On this basis, based on the feature layer similarity matching scheme, an independent ground truth and loss function was designed for this task in a targeted manner by comparing the input image with the untampered image of this image for patch-wise authenticity.It is demonstrated experimentally that the accuracy of the proposed method is significantly improved for deepfake detection compared with the baseline method.…”
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Fault detection method for distribution network based on edge computing by YANG Hao, ZHAO Huan, XUE Rong, YU Yubin, WEI Enwei, HUANG Bing

“…In order to accurately and quickly detect the faults of distribution network and ensure the stable power supply, a distribution network fault detection method based on edge computing is proposed. …”
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Coffee-Leaf Diseases and Pests Detection Based on YOLO Models by Jonatan Fragoso, Clécio Silva, Thuanne Paixão, Ana Beatriz Alvarez, Olacir Castro Júnior, Ruben Florez, Facundo Palomino-Quispe, Lucas Graciolli Savian, Paulo André Trazzi

“…This paper proposes an approach for detecting diseases and pests on coffee leaves using an efficient single-shot object-detection algorithm. …”
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An explainable EEG epilepsy detection model using friend pattern by Turker Tuncer, Sengul Dogan

“…EEG signals have commonly been used to detect epilepsy. Therefore, the main objective of this research is to demonstrate the epilepsy detection capability of the presented new-generation relation-centric feature extraction function. …”
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River floating object detection with transformer model in real time by Chong Zhang, Jie Yue, Jianglong Fu, Shouluan Wu

“…Our experimental findings are compelling: LR-DETR achieves a 5% increase in mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5, a 25.8% reduction in parameter count, and a 22.8% decrease in GFLOPs, compared to the RT-DETR algorithm. These improvements are particularly pronounced in the real-time detection of river floating objects, showcasing LR-DETR’s potential in specific environmental monitoring scenarios. …”
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Broiler Behavior Detection and Tracking Method Based on Lightweight Transformer by Haixia Qi, Zihong Chen, Guangsheng Liang, Riyao Chen, Jinzhuo Jiang, Xiwen Luo

“…The experimental results show that the proposed model is superior to the traditional network in the speed, accuracy and generalization ability of broiler behavior detection. (1) The detection speed is improved from 49.5 frames per second to 68.5 frames per second, which is 22.6 frames and 10.9 frames higher than Yolov7 and Yolov8, respectively. (2) mAP0.5 reaches 99.4%, and MAP0.5:0.95 increases from 84.9 to 88.4%. (3) Combined with the multi-target tracking algorithm, the chicken flock counting, behavior recognition, and individual tracking tasks are successfully realized.…”
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Kneeliverse: A universal knee-detection library for performance curves by Mário Antunes, Tyler Estro, Pranav Bhandari, Anshul Gandhi, Geoff Kuenning, Yifei Liu, Carl Waldspurger, Avani Wildani, Erez Zadok

“…Kneeliverse further includes Z-Method, a recently developed algorithm specifically designed for multi-knee detection.…”
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Detection of DRFM Deception Jamming Based on Diagonal Integral Bispectrum by Dianxing Sun, Ao Li, Hao Ding, Jifeng Wei

“…Simulations and experimental results show that the correct detection rate reaches 92% at a jamming-to-signal ratio (JSR) and SNR of 0 dB, validating the effectiveness of the algorithm.…”
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Non-cooperative signal modulation recognition algorithm based on joint feature parameter extraction by Haifei SI, Xingliu HU, Zhen SHI, Shiyong LI

“…Aiming at the problem that in the current electromagnetic environment,the modulation method is complicated,the frequency-consuming equipment increases,the spectrum is congested,and the electromagnetic environment interference increases,the algorithm of OFDM signal detection and subcarrier identification in the background of non-cooperative communication were deeply studied.Using the different distribution states of OFDM signals and single carrier signals in the time domain,a joint characteristic parameter was proposed to solve the existence problem of OFDM in the received signal.For the phase shift and frequency offset problems caused by the channel transmission to the signal,by using the periodic stability the blind parameter estimation was performed to obtain the signal prior information.On the basis of the obtained signal prior information,a multi-level classification and recognition method for non-cooperative OFDM signal sub-carrier signals was proposed.Therefore,a model based on non-cooperative communication system OFDM signal detection and subcarrier modulation identification was designed,and finally modulation identification of unknown signals was completed.Simulation experiments show that in non-cooperative communication systems,OFDM signals and single-carrier signals can be accurately identified,and ideal modulation recognition effects can be achieved on empty subcarriers,QPSK,and 16QAM in the receiver OFDM signal subcarriers,overcoming the channel transmission band The problems of phase shift and frequency offset have improved the accuracy of modulation mode identification.…”
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Wearable damaged clothing fabric image recognition system based on image restoration algorithm by Xu Li, Longlin Zhang

“…These data demonstrate the significant performance advantages of WFR2S in image recovery and damage detection. The newly developed image recovery algorithm improves the accuracy, availability and stability of damage recognition in garment fabrics. …”
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Independently Identifying Noise Clusters in 2D LiDAR Scanning with Clustering Algorithms by Chiew Wei Wen, Chuan Hsian Pu

“…LiDAR environmental mapping technology is often highly praised for its precise mapping information with intricate features for various detection or tracking based applications. The research proposes a novel method for independently identifying and filtering noise clusters in 2-Dimensional (2D) LiDAR scans based on 2 distinct clustering algorithms of K-Means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). …”
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High-accuracy parameter estimation algorithm for spread spectrum signal in LEO satellite communication by JIN Lei, ZENG Fuhua, GU Liyan, WANG Yuan

“…Firstly, the comparative detection with correlation integral peak and adaptive detection threshold was employed to acquire correlation integral peak/side-peak and its pseudo-code phase/carrier frequency index. …”
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Research on Optimized Algorithm for Deep Learning Based Recognition of Sediment Particles in Turbulent Flow by WANG Hao, YANG Feiqi, ZHANG Lei, WU Wei, XIE Haonan, ZHAO Lin

“…The YOLOv5 algorithm adopted in this study excels at detecting small targets and provides multi-scale detection, strong versatility, fast training, inference speeds, and adaptable fine-tuning capabilities. …”
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