RP-DETR: end-to-end rice pests detection using a transformer by Jinsheng Wang, Tao Wang, Qin Xu, Lu Gao, Guosong Gu, Liangquan Jia, Chong Yao

“…Owing to its high efficiency, deep learning is now the favored approach for detecting plant pests. In this regard, the paper introduces an effective rice pest detection framework utilizing the Transformer architecture, designed to capture long-range features. …”
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FD-YOLO: A YOLO Network Optimized for Fall Detection by Hoseong Hwang, Donghyun Kim, Hochul Kim

“…First, a global attention module (GAM) based on the Convolutional Block Attention Module (CBAM) was employed to improve detection performance. …”
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CBAM-SwinT-BL: Small Rail Surface Defect Detection Method Based on Swin Transformer With Block Level CBAM Enhancement by Jiayi Zhao, Alison Wun-Lam Yeung, Muhammad Ali, Songjiang Lai, Vincent To-Yee Ng

Subjects: “…Object detection…”
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Development of an H&E on-block staining technique for collagen detection in cryo-fluorescence tomography imaging of frozen breast tissue samples by Erin P. Snoddy, Tien T. Tang, Natalie Fowlkes, Thomas Huynh, Kari J. Brewer Savannah, Alejandro Contreras, Gregory Reece, Kristy K. Brock

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Content-based image retrieval using block truncation coding based on edge quantization by Yan-Hong Chen, Ching-Chun Chang, Cheng-Yi Hsu

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Research on distributed network intrusion detection system for IoT based on honeyfarm by Hao WU, Jiajia HAO, Yunlong LU

“…To solve the problems that the network intrusion detection system in the Internet of things couldn’t identify new attacks and has limited flexibility, a network intrusion detection system based on honeyfarm was proposed, which could effectively identify abnormal traffic and have continuous learning ability.Firstly, considering the characteristics of the convolutional block attention module, an abnormal traffic detection model was developed, focusing on both channel and spatial dimensions, to enhance the model’s recognition abilities.Secondly, a model training scheme utilizing federated learning was employed to enhance the model’s generalization capabilities.Finally, the abnormal traffic detection model at the edge nodes was continuously updated and iterated based on the honeyfarm, so as to improve the system’s accuracy in recognizing new attack traffic.The experimental results demonstrate that the proposed system not only effectively detects abnormal behavior in network traffic, but also continually enhances performance in detecting abnormal traffic.…”
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YOLO-Extreme: Obstacle Detection for Visually Impaired Navigation Under Foggy Weather by Wei Wang, Bin Jing, Xiaoru Yu, Wei Zhang, Shengyu Wang, Ziqi Tang, Liping Yang

“…Comprehensive experiments conducted on the Real-world Task-driven Traffic Scene (RTTS) foggy dataset demonstrate that YOLO-Extreme achieves state-of-the-art detection accuracy and maintains high inference speed, outperforming existing dehazing-and-detect and mainstream object detection methods. …”
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Boosting Detection Accuracy: An Enhanced YOLOv8 for Small Target Detection in Remote Sensing by Boyuan Chen, Zheng Ma, Xiang Li

Subjects: “…Remote sensing object detection…”
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Development of an H&E on-block staining technique for collagen detection in cryo-fluorescence tomography imaging of frozen breast tissue samples. by Erin P Snoddy, Tien T Tang, Natalie Fowlkes, Thomas Huynh, Kari J Brewer Savannah, Alejandro Contreras, Gregory Reece, Kristy K Brock

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Optimized ReXNet variants with spatial pyramid pooling, CoordAttention, and convolutional block attention module for money plant disease detection by Parul Nasra, Jaibir Singh, Suman Rani, Gunjan Shandilya, Salil Bharany, Sandeep Sood, Ateeq Ur Rehman, Seada Hussen

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BlockDroid: detection of Android malware from images using lightweight convolutional neural network models with ensemble learning and blockchain for mobile devices by Emre Şafak, İbrahim Alper Doğru, Necaattin Barışçı, İsmail Atacak

“…This article presents BlockDroid, an approach that combines convolutional neural network (CNN) models, ensemble learning, and blockchain technology to increase the accuracy and efficiency of malware detection for mobile devices. …”
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Urban change detection of remote sensing images via deep-feature extraction by Haiying Wang, Mingzhong Wu

Subjects: “…Change detection…”
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LMD_YOLO: A Lightweight and Efficient Model for Pavement Defects Detection by Shuai He, Ye Yuan, Bingyang Yin

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Performance of multiple amplitudes differential space-time block coding scheme concatenated with channel coding by YU Xiang-bin1, ZHANG Sheng-nan1, BI Guang-guo2

Subjects: “…space-time block coding…”
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Diversity Gain through Antenna Blocking by V. Dehghanian, J. Nielsen, G. Lachapelle

“…As part of the typical usage mode, interaction between a handheld receiver antenna and the operator's RF absorbing body and nearby objects is known to generate variability in antenna radiation characteristics through blocking and pattern changes. It is counterintuitive that random variations in blocking can result in diversity gain of practical applicability. …”
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Steel Defect Detection Based on YOLO-SAFD by Feihong Yu, Jinshan Zhang, Dingdiao Mu

Subjects: “…Diverse branch block (DBB)…”
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Impact of early detection and steroid treatment on fetal ventricular heart rate and pacemaker implantation in anti‐Ro/SSA positive congenital heart block by Sven‐Erik Sonesson, Aurelie Ambrosi, Felicia Nordenstam, Håkan Eliasson, Marie Wahren‐Herlenius

“…Abstract Introduction We investigated the effects of timing of detection and transplacental fluorinated steroid treatment on ventricular heart rate (HR) and age at pacemaker implantation in fetal third‐degree atrioventricular block (AVB). …”
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Detecting Left Ventricular Systolic Dysfunction in Left Bundle Branch Block Patients Using Electrocardiogram: A Deep Learning Approach with Limited Data by Chanjin Kwon, Hye Bin Gwag, Jongwon Seok

“…This pilot study was designed to develop an AI model for LVSD detection in the LBBB population using a limited dataset. …”
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Perfectly blind image watermarking scheme with multi-purpose based on region segment for sub-block and self-embedding technology by Tian-yu YE

“…Finally, a watermarked image was obtained after conducting inverse discrete cosine transformation on each block. The proposed algorithm achieved perfectly blind detection and multi-purpose by combining self-embedding feature watermark from re-gion 1 into region 2 and blindly extracting authentication watermark n region 2. …”
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Detecting memberships in multiplex networks via nonnegative matrix factorization and tensor decomposition by Fengqin Tang, Xiaozong Wang, Xuejing Zhao, Chunning Wang

Subjects: “…community detection…”
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