Dual-stage feature specialization network for robust visual object detection in autonomous vehicles by Ze Liu, Junhua Wu, Yingfeng Cai, Hai Wang, Long Chen, Qingchao Liu

“…Existing two-stage object detection methods often suffer from feature interference between candidate region generation and classification regression tasks, leading to suboptimal performance in complex scenes. …”
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Efficient collision-avoidance navigation strategy for autonomous surface vehicles in unstructured and constricted marine environments by Wenlong Meng, Rongdian Ku, Yanbo Pu, Xiaoxiao Shi, Ya Gong

“…In this study, we tackle the aforementioned complexities by incorporating progressive sampling and point cloud clustering, which jointly expedite the detection of constrained waterways in unstructured marine environments. …”
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Tunable Energy-Efficient Approximate Circuits for Self-Powered AI and Autonomous Edge Computing Systems by Shubham Garg, Kanika Monga, Nitin Chaturvedi, S. Gurunarayanan

“…Moreover, this problem becomes more complex while deploying computationally intensive heavy machine learning (ML) models on energy-constrained edge devices. …”
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Collaborative Optimization of Model Pruning and Knowledge Distillation for Efficient and Lightweight Multi-Behavior Recognition in Piglets by Yizhi Luo, Kai Lin, Zixuan Xiao, Yuankai Chen, Chen Yang, Deqin Xiao

“…In modern intensive pig farming, accurately monitoring piglet behavior is crucial for health management and improving production efficiency. However, the complexity of existing models demands high computational resources, limiting the application of piglet behavior recognition in farming environments. …”
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Recent Efficient Iterative Algorithms on Cognitive Radio Cooperative Spectrum Sensing to Improve Reliability and Performance by Mohsen Akbari, Ahmed Wasif Reza, Kamarul Ariffin Noordin, Kaharudin Dimyati, Mohsen Riahi Manesh, Mohammad Nour Hindia

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Efficient Gearbox Fault Diagnosis Based on Improved Multi-Scale CNN with Lightweight Convolutional Attention by Bin Yuan, Yaoqi Li, Suifan Chen

“…The framework extracts the multi-band features of vibration signals through the improved multi-scale convolutional neural network, which significantly enhances adaptability to complex working conditions (variable rotational speed, strong noise); at the same time, the lightweight convolutional attention mechanism is used to replace the multi-attention of the traditional Transformer, which greatly reduces computational complexity while guaranteeing accuracy and realizes highly efficient, lightweight local–global feature modeling. …”
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Image-Based Object Identification for Efficient Event-Driven Sensing in Wireless Multimedia Sensor Networks by Mohsin S. Alhilal, Adel Soudani, Abdullah Al-Dhelaan

“…This paper presents a contribution to the design of low complexity scheme based on object identification for efficient sensing of multimedia information in wireless multimedia sensor networks. …”
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A spoonful of L‐fucose—an efficient therapy for GFUS‐CDG, a new glycosylation disorder by René G Feichtinger, Andreas Hüllen, Andreas Koller, Dieter Kotzot, Valerian Grote, Erdmann Rapp, Peter Hofbauer, Karin Brugger, Christian Thiel, Johannes A Mayr, Saskia B Wortmann

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A high-efficiency and stable transfection approach by using calcium phosphate in HEK293T cells by Kuang Ye, Fang Yourong, Liu Li, Wang Rong, Yan Ziqin, Li Hailong, Meng Fanguo, Sheng Qing, Ou Wenbin

“…Higher transfection efficiency was also detected when the vortex time was 10 s. …”
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Efficient Deep Learning-Based Device-Free Indoor Localization Using Passive Infrared Sensors by Sira Yongchareon, Jian Yu, Jing Ma

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Innovative Ghost Channel Spatial Attention Network with Adaptive Activation for Efficient Rice Disease Identification by Yang Zhou, Yang Yang, Dongze Wang, Yuting Zhai, Haoxu Li, Yanlei Xu

“…To address the computational complexity and deployment challenges of traditional convolutional neural networks in rice disease identification, this paper proposes an efficient and lightweight model: Ghost Channel Spatial Attention ShuffleNet with Mish-ReLU Adaptive Activation Function (GCA-MiRaNet). …”
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Creating and Validating Hybrid Large-Scale, Multi-Modal Traffic Simulations for Efficient Transport Planning by Fabian Schuhmann, Ngoc An Nguyen, Jörg Schweizer, Wei-Chieh Huang, Markus Lienkamp

“…Mobility digital twins (MDTs), which utilize multi-modal microscopic (micro) traffic simulations and an activity-based demand generation, are envisioned as flexible and reliable planning tools for addressing today’s increasingly complex and diverse transport scenarios. Hybrid models may become a resource-efficient solution for building MDTs by creating large-scale, mesoscopic (meso) traffic simulations, using simplified, queue-based network-link models, in combination with more detailed local micro-traffic simulations focused on areas of interest. …”
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Efficient Method of Road Outlier Recognition Using Deep Learning Coupled with Data Augmentation Approach by Sarfaraz Natha, Fareed Ahmed Jokhio, Muhammad Shafique, Naeem Ahmed, Danish Munir Arain

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Genome-wide analyses reveal intricate genetic mechanisms underlying egg production efficiency in chickens by Lizhi Tan, Xinyu Cai, Yuan Kong, Zexuan Liu, Zilong Wen, Lina Bu, Yuzhan Wang, Xiaojun Liu, Zhiwu Zhang, Jianlin Han, Dandan Wang, Yiqiang Zhao

“…Furthermore, our results identified the CNNM2 gene, known for its role in magnesium homeostasis, plays a dual role in egg production variance, promoting variability during the up-stage while reducing it during the sustained-stage to optimize egg production efficiency. Conclusions Collectively, our multiple genome analyses reveal a complex genetic mechanism underlying more efficient and stable egg production, and establish chicken genetics as a model for studying reproductive efficiency across species.…”
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Advancing multi-categorization and segmentation in brain tumors using novel efficient deep learning approaches by Nadenlla RajamohanReddy, G. Muneeswari

“…Brain tumors are most commonly detected by magnetic resonance imaging (MRI) scans. …”
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Defects Localization and Classification Method of Power Transmission Line Insulators Aerial Images Based on YOLOv5 EfficientNet and SVM by Lin Li, Qiaoling Yin, Xiaofeng Wang, Hang Wang

“…The framework specifically targets overcoming the challenges associated with low signal-to-noise ratios in defect detection. The proposed approach divides the task into two primary modules: 1) YOLOv5-based object detection for accurate defect localization, and 2) defect classification using EfficientNet and SVM. …”
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VRU-YOLO: A Small Object Detection Algorithm for Vulnerable Road Users in Complex Scenes by Yunxiang Liu, Yuqing Shi

“…Additionally, a lightweight Optimized Shared Detection Head (OSDH-Head) is introduced, reducing computational complexity while improving detection efficiency. …”
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Balancing Complexity and Performance of Machine Learning Models for Avian Pests Sound Detection in Agricultural Environments by Micheline Kazeneza, Anna Sergeevna Bosman, Destiny Kwabla Amenyedzi, Damien Hanyurwimfura, Emmanuel Ndashimye, Anthony Vodacek

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GSBYOLO: A lightweight Multi-Scale fusion network for road crack detection in complex environments by Yuhao Wang, Heran Zhu, Yirong Wang, Jianping Liu, Jun Xie, Bi Zhao, Siyue Zhao

“…However, existing detection methods face challenges such as varying target scales, large model parameters, and poor adaptability to complex backgrounds. …”
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Lightweight deep neural network for contour detection and extraction of wheat spikes in complex field environments by Xin Xu, Haiyang Zhang, Jiangchuan Lu, Ziyi Guo, Juanjuan Zhang, Jibo Yue, Hongbo Qiao, Xinming Ma

“…Method Building on two-year multi-angle wheat spike imagery, we propose an enhanced YOLOv9-LDS multi-scale object detection framework. The algorithm innovatively constructs a lightweight depthwise separable network (LDSNet) as backbone, balancing computational efficiency and accuracy through channel re-parameterization strategy; incorporates an Efficient Local Attention (ELA) module to build feature enhancement networks, and employs dual-path feature fusion mechanisms to strengthen edge texture responses, significantly improving discrimination of overlapping spikes and complex backgrounds. …”
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