Lightweight deep learning method for end-to-end point cloud registration by Linjun Jiang, Yue Liu, Zhiyuan Dong, Yinghao Li, Yusong Lin

“…Specifically, our approach utilizes pruning and weight-sharing quantization techniques to reduce model size and simplify the network structure. …”
Get full text

CNN-Based Optimization for Fish Species Classification: Tackling Environmental Variability, Class Imbalance, and Real-Time Constraints by Amirhosein Mohammadisabet, Raza Hasan, Vishal Dattana, Salman Mahmood, Saqib Hussain

“…Optimization techniques, including pruning and quantization, reduced model size by 73.7%, enabling real-time deployment on resource-constrained devices. …”
Get full text

A novel method for power transformer fault diagnosis considering imbalanced data samples by Jun Chen, Yong Wang, Lingming Kong, Yilong Chen, Mianzhi Chen, Qian Cai, Gehao Sheng

“…We derive a sample parameter correlation quantization matrix from oil chromatography fault data using association rules, which serves as the initial value for the NCA algorithm’s training metric matrix. …”
Get full text

Ensemble Transformer–Based Detection of Fake and AI–Generated News by Md. Ishraquzzaman, Mohammed Ashraful Islam Chowdhury, Shahreen Rahman, Riasat Khan

“…The proposed ensemble model is optimized by applying model pruning (reducing parameters from 265M to 210M, improving training time by 25%) and dynamic quantization (reducing model size by 50%, maintaining 95.68% accuracy), enhancing scalability and efficiency while minimizing computational overhead. …”
Get full text

Automated Arrhythmia Classification System: Proof-of-Concept With Lightweight Model on an Ultra-Edge Device by Namho Kim, Seongjae Lee, Seungmin Kim, Sung-Min Park

“…Model compression methods including knowledge distillation, pruning, and quantization were employed to enhance arrhythmia classification performance while reducing computational complexity. …”
Get full text

Slim-sugarcane: a lightweight and high-precision method for sugarcane node detection and edge deployment in natural environments by Lijiao Wei, Lijiao Wei, Shuo Wang, Xinwei Liang, Dongjie Du, Xinyi Huang, Ming Li, Yuangang Hua, Weihua Huang, Zhenhui Zheng, Zhenhui Zheng

“…The proposed framework is optimized with TensorRT and deployed using FP16 quantization on the NVIDIA Jetson Orin NX platform to ensure real-time performance under limited hardware conditions. …”
Get full text

Chaotic moving video quality enhancement based on deep in-loop filtering by Tong Tang, Yi Yang, Dapeng Wu, Ruyan Wang, Zhidu Li

“…The in-loop filter in VVC inherits the De-Blocking Filter (DBF) and Sample Adaptive Offset (SAO) of High Efficiency Video Coding (HEVC, H.265), and adds the Adaptive Loop Filter (ALF) to minimize the error between the original sample and the decoded sample. …”
Get full text

A fast monocular 6D pose estimation method for textureless objects based on perceptual hashing and template matching by Jose Moises Araya-Martinez, Jose Moises Araya-Martinez, Vinicius Soares Matthiesen, Vinicius Soares Matthiesen, Simon Bøgh, Jens Lambrecht, Rui Pimentel de Figueiredo

“…Additionally, our algorithm efficiently utilizes all CPU cores and includes adjustable parameters for balancing computation time and accuracy, making it suitable for a wide range of applications where hardware cost and power efficiency are critical. …”
Get full text

FloodNet-Lite: A Lightweight Deep Learning for Flood Mapping Using Remote Sensing Data With Optimized UNet and Edge Deployment Approach in 6G by Puviyarasi Thirugnanasammandamoorthi, Debabrata Ghosh, Ram Kishan Dewangan, Mohammad Kamrul Hasan, Khairul Akram Zainol Ariffin, Huda Saleh Abbas, Hashim Elshafie, Rashid A. Saeed, Ala Eldin Awouda

“…The system integrates an optimized UNet architecture with MobileNetV3 as a backbone, enhanced by depthwise separable convolutions, attention mechanisms, and advanced model compression techniques, such as quantization-aware training and structured pruning. Knowledge distillation transfers learning from a high-capacity teacher model to a compact student model, while self-supervised learning further reduces dependency on labeled data to boost efficiency and robustness. …”
Get full text

Reinforcement learning energy management control strategy of electric tractor based on condition identification by Liqiao Li, Jiangchun Chen, Jing Nie, Zongyu Gao

“…However, low traction efficiency, short battery life, and high energy consumption are the main reasons hindering the industrialization of ET. …”
Get full text

Category semantic and global relation distillation for object detection by Yanpeng LIANG, Zhonggui MA, Zongjie WANG, Zhuo LI

“…Compared with other baseline methods, the proposed approach achieved competitive improvements in mean average precision without considerably increasing the number of parameters and FLOPS during distillation training, thereby striking a better balance between accuracy and efficiency.…”
Get full text

Text-Guided Synthesis in Medical Multimedia Retrieval: A Framework for Enhanced Colonoscopy Image Classification and Segmentation by Ojonugwa Oluwafemi Ejiga Peter, Opeyemi Taiwo Adeniran, Adetokunbo MacGregor John-Otumu, Fahmi Khalifa, Md Mahmudur Rahman

“…The validation accuracy of various classification models Big Transfer (BiT), Fixed Resolution Residual Next Generation Network (FixResNeXt), and Efficient Neural Network (EfficientNet) were 92%, 91%, and 86%, respectively. …”
Get full text

An Optimised CNN Hardware Accelerator Applicable to IoT End Nodes for Disruptive Healthcare by Arfan Ghani, Akinyemi Aina, Chan Hwang See

“…Addressing the challenges posed by constrained dataset sizes, compute-intensive AI algorithms, and hardware limitations, the approach presented in this paper leverages efficient image augmentation and pre-processing techniques to enhance both prediction accuracy and the training efficiency. …”
Get full text

Video matching method based on "bag of words" by LI Yuan-ning1, LIU Ting1, JIANG Shu-qiang1, HUANG Qing-ming1

“…A "bag of words" was presented based method for video representation and matching.First,all local features of all video frames were quantized into a dictionary of visual words.Then each sub-shot of the video was represented by a set of visual words.Finally,a revered index of visual words was created to speed the matching process of video clips.This method not only takes local appearance and spatial information into account,but also compresses the representation of video content.Highly competitive experimental results show that our proposed method is more effective and efficient than former methods for video matching in large video dataset.…”
Get full text

Embedded Sensor Data Fusion and TinyML for Real-Time Remaining Useful Life Estimation of UAV Li Polymer Batteries by Jutarut Chaoraingern, Arjin Numsomran

“…This study proposes an end-to-end TinyML-based framework that integrates embedded sensor data fusion with an optimized feedforward neural network (FFNN) model for efficient RUL estimation under strict hardware limitations. …”
Get full text

Voice-activated home automation system for IoT edge devices using TinyML by Timothy Malche, Sandeep Budhani, Pramod Kumar Soni, Govind Murari Upadhyay

“…The results show that our keyword spotting model is both highly accurate and efficient and uses minimum computational resources. …”
Get full text

Embedded Artificial Intelligence for IoT Applications Using the MAX78000 by Martina Balbi, Lance Doherty, Thomas Watteyne

“…We give practical guidance on creating, training, and quantizing AI models, detailing essential tools, frameworks, and the deployment process. …”
Get full text

Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors by N. Sriraam

“…The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. …”
Get full text

Leveraging Large Language Models for Departmental Classification of Medical Records by Baha Ihnaini, Xintong Zeng, Handi Yan, Feige Fang, Abdur Rashid Sangi

“…The models utilize medical records as a dataset for fine-tuning and use clinical knowledge bases to enhance accuracy and efficiency in identifying appropriate departments. This study explores the integration of advanced large language models (LLMs) with quantized low-rank adaptation (QLoRA) for efficient training. …”
Get full text

Transformers—Messages in disguise by Joshua H. Tyler, Donald R. Reising, Mohamed M.K. Fadul

“…However, NN-based encryption faces challenges, including communication overhead due to encoding for bit errors, quantizing the NN’s continuous-valued output, and enabling One-Time Pad encryption. …”
Get full text