LiteTom-RTDETR: A Lightweight Real-Time Tomato Detection System for Plant Factories by Wenshuai Liu, Qingzheng Liu, Wenyong Quan, Junli Wang, Xiaomin Yao, Qiang Liu, Yuxiang Tian

“…The proposed LiteTom-RTDETR model was shown to balance high tomato identification accuracy (88.2%) with an excellent real-time inference speed (52.2 frames per second) and computational efficiency (36.3 GFLOPs). Notably, the average detection accuracy of LiteTom-RTDETR was 0.6% higher, its detection speed was 15.5% faster, and its computational load and model size were 36.2% and 31.6% smaller, respectively, than those of the original RTDETR model. …”
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Wavelet-Analysis of Cardiosignals Using Matlab by B. F. Badalyan, H. A. Gomtsyan, S. G. Gomtsyan

“…This article is devoted to the problem of accurate detection of cardiosignal QRS-complexes for early diagnosis of various diseases of human cardiovascular system. …”
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RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification by Yutong Wang, Ziming Kou, Cong Han, Yuchen Qin

“…Coal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. …”
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Tear replacement by cationic emulsion in complex therapy of superficial punctate keratitis in hypotensively medicated glaucoma patients by S. V. Trufanov, S. A. Malozhen, D. A. Krakhmaleva, E. A. Pivin

“…The testing procedures and efficiency monitoring included biomicroscopy, Schirmer test, Norn test and evaluation of tear film osmolarity. …”
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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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Enhanced pulmonary nodule detection using a transformer framework with dual fusion and gated mechanism by Kelei Sun, Yihang Wang, Huaping Zhou

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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 novel criterion for crack detection in beam structures by frequency response functions by Nguyen Tien Khiem, Tran Thanh Hai, Le Khanh Toan, Nguyen Thi Lan, Ho Quang Quyet

“…However, the complex feature of FRF has not been thoroughly employed for structural damage detection. …”
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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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Research on herd sheep facial recognition based on multi-dimensional feature information fusion technology in complex environment by Fu Zhang, Fu Zhang, Xiaopeng Zhao, Shunqing Wang, Yubo Qiu, Sanling Fu, Yakun Zhang

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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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A parallel algorithm for network traffic anomaly detection based on Isolation Forest by Xiaoling Tao, Yang Peng, Feng Zhao, Peichao Zhao, Yong Wang

“…With the rapid development of large-scale complex networks and proliferation of various social network applications, the amount of network traffic data generated is increasing tremendously, and efficient anomaly detection on those massive network traffic data is crucial to many network applications, such as malware detection, load balancing, network intrusion detection. …”
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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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