ResCalib: Joint LiDAR and Camera Calibration Based on Geometrically Supervised Deep Neural Networks by Jiahui CHAI, Minglei LI, Min LI, Dazhou WEI, Guangyong CHEN

“…This paper presents a ResCalib deep neural network model, which can be used to solve the problem of the online joint calibration of LiDAR and a camera. …”
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Turbofan Performance Estimation Using Neural Network Component Maps and Genetic Algorithm-Least Squares Solvers by Giuseppe Lombardo, Pierantonio Lo Greco, Ivano Benedetti

“…Generalization of rotational component maps by feedforward neural networks leads to an average interpolation error up to around <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1</mn><mo>%</mo></mrow></semantics></math></inline-formula>, for all variables. …”
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DEDSWIN-Net: Dual Encoder Dilated Convolution and Swin Transformer Network for the Classification of Liver CT Images by Jyoshna Allenki, Hemant Kumar Soni

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Implementation of Large Field-of-View Detection for UWOC Systems Based on a Diffractive Deep Neural Network by Jianmin Xiong, Jingxuan Cheng, Huan Deng, Yan Hua, Yufan Zhang, Zihao Du, Lyufang Zhao, Ning Deng, Wenqiang Li, Zejun Zhang, Jing Xu

“…The link alignment in underwater wireless optical communication (UWOC) systems is a knotty problem. The diffractive deep neural network (D²NN) has shown great potential in accomplishing tasks all optically these years. …”
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Remaining Useful Life Prediction for Rolling Bearings Based on TCN–Transformer Networks Using Vibration Signals by Xiaochao Jin, Yaping Ji, Shiteng Li, Kailang Lv, Jianzheng Xu, Haonan Jiang, Shengnan Fu

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Prediction method of TBM excavation axis deviation for small turning tunnels based on LSTM neural network by Lijie DU, Hongda HAO, Qingwei LI, Yalei YANG, Weidong ZHANG, Jiayi LIU, Hongchao FENG

“…During the construction of full face tunnel boring machine (TBM), due to the influence of geological environment, personnel operation, equipment itself and other factors, it is easy to cause large deviation between TBM boring route and design axis, especially in small turning tunnels, TBM heading orientation and attitude control are more difficult. To solve this problem, a prediction method of TBM tunnelling axis deviation based on Long Short-Term Memory (LSTM) neural network is proposed. …”
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A Novel Dual-Branch Global and Local Feature Extraction Network for SAR and Optical Image Registration by Xuanran Zhao, Yan Wu, Xin Hu, Zhikang Li, Ming Li

“…However, the inherent differences between the two modalities pose a challenge to the existing deep-learning algorithms that only depend on local features. To address this problem, we propose a global and local feature extraction network (GLFE-Net) for SAR and optical image registration. …”
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State Diagnosis of Elevator Control Transformer over Vibration Signal Based on MEA-BP Neural Network by QingHui Song, QingJun Song, Linjing Xiao, HaiYan Jiang, LiNa Li

“…Finally, a fault diagnosis model composed of MEA and BP neural network is developed, which avoids the problems of premature convergence and poor diagnosis effect. …”
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High-throughput and low-overhead probabilistic routing based on multi-player bargaining game for opportunistic networks by Zhi REN, Jian-wei SUO, Wen-peng LIU, Hong-jiang LEI, Qian-bin CHEN

“…To address the problems existing in the present probabilistic routing based on bargaining games,including unidirectional transmission of messages degrades the success ratio,depending on the virtual money decreases the purchasing power of nodes,and redundancy exists in the interaction process of messages,an routing algorithm based on multi-player bargaining game for opportunistic networks,HLPR-MG,was proposed.Through extending the two-player game to a multi-player bargaining game,introducing the barter trade to enhance purchasing power of nodes,and improving the existing interaction mechanism to reduce the times of game,proposed algorithm achieves the effect of increasing network throughput and decreasing control overhead.Theoretical analysis verifies the effectiveness of HLPR-MG,and simulation results show that HLPR-MG improves the network throughput and success ratio at least 3.63%,and reduces the control overhead and average end-to-end delay by more than 17.76% and 4.03%,respectively,as compared to the classical GSCP and BG algorithms.…”
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Proposing Lane and Obstacle Detection Algorithm Using YOLO to Control Self-Driving Cars on Advanced Networks by Phat Nguyen Huu, Quyen Pham Thi, Phuong Tong Thi Quynh

“…Developing self-driving cars is an important foundation for the development of intelligent transportation systems with advanced telecommunications network infrastructure such as 6G networks. The paper mentions two main problems, namely, lane detection and obstacle detection (road signs, traffic lights, vehicles ahead, etc.) through image processing algorithms. …”
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Impact of Load Factor on Distinct Feeders of 132/11 kV Grid Station in Distribution Network by Mohsin Ali Koondhar

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Calcium carbonate sediment corrosion and formation investigation in drinking water distribution network in Sough City, Iran by Abdol Hosein Salehi Sarvak, Seyed Ali Almodaresi, Abolghasem Mirhosseini Deh-abadi, Masoud Reza Shishehbore, Abdol Hosein Kangazian, Ali Akbar Jamali

“…Abstract One of the major problems facing the water industry is corrosion and sedimentation, which causes problems such as reduced water quality and the useful life of water supply network equipment. …”
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A Blockchain-Based Cross-Domain Authentication Scheme for Unmanned Aerial Vehicle-Assisted Vehicular Networks by Wenming Wang, Shumin Zhang, Guijiang Liu, Yue Zhao

“…However, these networks also raise privacy and security concerns. …”
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Traffic-driven ions motion optimization-based clustering routing protocol for cognitive radio sensor networks. by Jihong Wang, Hao Ni, Yiyang Ge, Shuo Li

“…In cognitive radio sensor networks, single clustering protocol cannot simultaneously satisfy the various requirements of time-triggered and event-driven traffic, as a result, different kinds of clustering protocols are designed to serve them separately. …”
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Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals by Siddhant Arora, Sridharan Naveen Venkatesh, Vaithiyanathan Sugumaran, Anoop Prabhakaranpillai Sreelatha, Vetri Selvi Mahamuni

“…This approach yields a variety of features, such as autoregressive moving average (ARMA), statistical and histogram features. …”
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Improved YOLOv8 Network of Aircraft Target Recognition Based on Synthetic Aperture Radar Imaging Feature by Xing Wang, Wen Hong, Yunqing Liu, Guanyu Yan, Dongmei Hu, Qi Jing

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LPRLC: Linear predictive run length coding to improve energy consumption of wireless body area networks by Mahdieh Hajiloo Vakil, Zahra Shirmohammadi

“…A Wireless Body Area Network (WBAN) is a collection of sensors that play a significant role in healthcare. …”
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Fluorescence Spectroscopy and a Convolutional Neural Network for High-Accuracy Japanese Green Tea Origin Identification by Rikuto Akiyama, Kana Suzuki, Yvan Llave, Takashi Matsumoto

“…This study aims to develop a system combining fluorescence spectroscopy and machine learning through a convolutional neural network (CNN) to identify the origins of various Japanese green teas (Sayama tea, Kakegawa tea, Yame tea, and Chiran tea). …”
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An Integrated Process-Network Load Balancing in Edge-Assisted Autonomous Vehicles Using Multimodal Applications With Shared Workloads by Sinuk Choi, Pyeongjun Choi, Donghyeon Kim, Jeongho Kwak, Ji-Woong Choi

“…To solve this problem, we leverage Lyapunov optimization to transform the long-term average problem into a slot-by-slot problem. …”
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FedWT: Federated Learning with Minimum Spanning Tree-based Weighted Tree Aggregation for UAV networks by Geonhui Kim, Jiha Kim, Yongho Kim, Hwan Kim, Hyunhee Park

“…In this paper, the Federated Learning with Minimum Spanning Tree (MST)-based Weighted Tree Aggregation (FedWT) is proposed to address transmission failure, delayed model updates, and single point of failure problems. FedWT uses MST to minimize model exchange during local aggregation, and addresses data heterogeneity through dynamic weighted averaging. …”
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