Engineering a multi model fallback system for edge devices by Gaurav Kadve, Abishi Chowdhury, Vishal Krishna Singh, Amrit Pal

“…The growing deployment of machine learning applications in edge environments, such as IoT devices and embedded systems, has highlighted the need for efficient, resource-aware systems for edge environments. …”
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FPG-AI RNN: A Technology-Agnostic Framework for the Automatic Acceleration of LSTM/GRU-Based Models on FPGAs by Tommaso Pacini, Pietro Nannipieri, Silvia Moranti, Luca Fanucci

“…FPGAs have emerged as an efficient technology for accelerating these algorithms, especially in resource- and power-constrained platforms such as edge devices. …”
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High-Performance and Lightweight AI Model with Integrated Self-Attention Layers for Soybean Pod Number Estimation by Qian Huang

“…<b>Methods:</b> To address these challenges, this study presents a set of lightweight, efficient AI models designed to overcome these limitations. …”
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A Digital Simulation Model of Broadband Phased Array RF System and Its Application by Jia Ding, Huaizong Shao, Jianxing Lv, Fake Ding

“…The model research and integrated simulation software construction of the broadband phased array RF system provide an efficient and accurate simulation tool for system design and optimization.…”
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Edge AI for Real-Time Anomaly Detection in Smart Homes by Manuel J. C. S. Reis, Carlos Serôdio

“…Optimization strategies such as quantization reduced LSTM-AE inference time by 76% and power consumption by 35%. …”
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Real-Time Acoustic Scene Recognition for Elderly Daily Routines Using Edge-Based Deep Learning by Hongyu Yang, Rou Dong, Rong Guo, Yonglin Che, Xiaolong Xie, Jianke Yang, Jiajin Zhang

“…This architecture provides an efficient, reliable, and user-friendly solution for real-time acoustic scene monitoring in elderly care.…”
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Optimizing encrypted search in the cloud using autoencoder-based query approximation by Mahmoud Mohamed, Khaled Alosman

“…However, encryption reduces search efficiency due to inability to directly compute on ciphertexts. …”
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A Comparative Study and Optimization of Camera-Based BEV Segmentation for Real-Time Autonomous Driving by Woomin Jun, Sungjin Lee

“…Notably, the lift–splat–shoot view transformation model with the InternImage-T encoder and EfficientNet-B0 decoder demonstrated performance of 53.1 mIoU while achieving high efficiency (51.7 ms and 159.5 MB, respectively). …”
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Optimizing Semantic-Aware Video Compression Using Particle Swarm Optimization Technique for Automotive Applications by Vadivel Shanmugam, B. Uma Maheswari

“…This paper proposes a framework for semantic-aware video compression with reduced functional complexity and a proper flow for quantization parameter optimization. The optimized quantization factors used for encoding the region of interest and the region out of interest of the input video frame. …”
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Fast Backpropagation Neural Network for VQ-Image Compression by Basil Mahmood, Omaima AL-Allaf

“…<br />In this work, a three layered backpropagation neural network (BPNN) is designed to compress images using vector quantization technique (VQ).The results coming out from the hidden layer represent the codebook used in vector quantization, therefore this is a new method to generate VQ-codebook. …”
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SVPDSA: Selective View Perception Data Synthesis With Annotations Using Lightweight Diffusion Network by S. Raghavendra, Vijayalakshmi, Vainidhi, S. K. Abhilash, Venu Madhav Nookala, P. V. Arun Kumar, Ramyashree

“…The proposed model efficiently generates diverse synthetic images with high-quality perception annotations. …”
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Empowering Healthcare: TinyML for Precise Lung Disease Classification by Youssef Abadade, Nabil Benamar, Miloud Bagaa, Habiba Chaoui

“…Our data preprocessing included bandpass filtering and feature extraction through Mel-Frequency Cepstral Coefficients (MFCCs). We applied quantization techniques to ensure model efficiency. The custom CNN model achieved the highest performance, with 96% accuracy and 97% precision, recall, and F1-scores, while maintaining moderate resource usage. …”
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A DSP–FPGA Heterogeneous Accelerator for On-Board Pose Estimation of Non-Cooperative Targets by Qiuyu Song, Kai Liu, Shangrong Li, Mengyuan Wang, Junyi Wang

“…Optimization techniques, including batch normalization fusion, ReLU integration, and linear quantization, are applied to improve inference efficiency. …”
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QP-Adaptive Dual-Path Residual Integrated Frequency Transformer for Data-Driven In-Loop Filter in VVC by Cheng-Hsuan Yeh, Chi-Ting Ni, Kuan-Yu Huang, Zheng-Wei Wu, Cheng-Pin Peng, Pei-Yin Chen

“…As AI-enabled embedded systems such as smart TVs and edge devices demand efficient video processing, Versatile Video Coding (VVC/H.266) becomes essential for bandwidth-constrained Multimedia Internet of Things (M-IoT) applications. …”
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YOLO-RWY: A Novel Runway Detection Model for Vision-Based Autonomous Landing of Fixed-Wing Unmanned Aerial Vehicles by Ye Li, Yu Xia, Guangji Zheng, Xiaoyang Guo, Qingfeng Li

“…Third, the neck structure is reorganized as a bidirectional feature pyramid network (BiFPN) module with skip connections and attention allocation, enabling efficient multi-scale and across-stage feature fusion. …”
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BinaryViT: Binary Vision Transformer for Hyperspectral Image Classification by Xiang Hu, Taolin Liu, Zhe Guo, Yuxiang Tang, Yuanxi Peng, Tong Zhou

“…However, binary quantization in transformers faces challenges such as degradation of feature representation capability after binarizing self-attention mechanisms and decline in fusion efficiency of multiscale spectral&#x2013;spatial information, leading to relatively lagging progress in this field. …”
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A Channel-Adaptive Range-Doppler Domain Filtering Serial BAQ Algorithm and Comparative Analysis by Tao Jiang, Fubo Zhang, Yi Xie, Chengwei Zhang, Longyong Chen, Yihao Xu, Haibo Tang

“…To address these issues, this paper introduces the Channel-Adaptive Range-Doppler domain filtering Serial Block Adaptive Quantization algorithm (CARDS-BAQ). By incorporating a point-frequency RD domain filtering approach and leveraging serial data matrix splicing and rollback combined with point-frequency ABAQ, CARDS-BAQ enables efficient data compression for arbitrary channel counts. …”
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Anatomy of Deep Learning Image Classification and Object Detection on Commercial Edge Devices: A Case Study on Face Mask Detection by Dimitrios Kolosov, Vasilios Kelefouras, Pandelis Kourtessis, Iosif Mporas

“…To leverage the computational power of the edge devices, the models have been optimized, first, by using the SOTA optimization frameworks (TensorFlow Lite, OpenVINO, TensorRT, eIQ) and, second, by evaluating/comparing different optimization options, e.g., different levels of quantization. Note that the five edge devices are evaluated and compared too, in terms of inference time, value and efficiency. …”
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Learned distributed image compression with decoder side information by Yankai Yin, Zhe Sun, Peiying Ruan, Ruidong Li, Feng Duan

“…Multi-view image compression aims to improve compression efficiency by leveraging correlations between images. …”
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Trends and technologies of video coding by Jiantong ZHOU, Haitao YANG, Dong LIU, Xiang MA, Tian WANG

“…The current video coding uses block based hybrid architecture,which uses predictive,transform,quantization and entropy coding techniques to efficiently compress video signals.Further optimizations on current architectures provide more flexible processing and coding for local characteristics of video image signals.Video coding based on machine learning was expected to change the existing hybrid coding framework partially or comprehensively,and bring new research ideas to video coding.In addition to existing 2D video signal,the future of video also needs to spherical video coding and volumetric video coding for AR/VR applications,the new video source data format of the video encoding technology has brought new opportunities and challenges.…”
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