Edge AI for Real-Time Anomaly Detection in Smart Homes by Manuel J. C. S. Reis, Carlos Serôdio

“…The increasing adoption of smart home technologies has intensified the demand for real-time anomaly detection to improve security, energy efficiency, and device reliability. Traditional cloud-based approaches introduce latency, privacy concerns, and network dependency, making Edge AI a compelling alternative for low-latency, on-device processing. …”
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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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Benchmarking In-Sensor Machine Learning Computing: An Extension to the MLCommons-Tiny Suite by Fabrizio Maria Aymone, Danilo Pietro Pau

“…With the exponential growth of edge devices, efficient local processing is essential to mitigate economic costs, latency, and privacy concerns associated with the centralized cloud processing. …”
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A seed-epidermis-feature-recognition-based lightweight peanut seed selection method for embedded systems by Dehao Li, Jinlong Huang, Xincheng Li, Zhaolei Yang, Xueke An, Pengfei Xu, Yuliang Yun

“…The model was based on deep learning and model quantization techniques. The transfer learning method was used to use four pre-trained models, EfficientNet_b0, EfficientNetv2-b0, MobileNet_v2_35_224, and NasNet_Mobile, as feature extraction layers, the input layer was added before the feature extraction layer, and the dropout and dense layers were added after the feature extraction layer to construct a classifier. …”
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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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A Configurable Accelerator for CNN-Based Remote Sensing Object Detection on FPGAs by Yingzhao Shao, Jincheng Shang, Yunsong Li, Yueli Ding, Mingming Zhang, Ke Ren, Yang Liu

“…This paper proposes a new generation of high flexibility and intelligent CNNs hardware accelerator for satellite remote sensing in order to make its computing carrier more lightweight and efficient. A data quantization scheme for INT16 or INT8 is designed based on the idea of dynamic fixed point numbers and is applied to different scenarios. …”
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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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RDM-YOLO: A Lightweight Multi-Scale Model for Real-Time Behavior Recognition of Fourth Instar Silkworms in Sericulture by Jinye Gao, Jun Sun, Xiaohong Wu, Chunxia Dai

“…This study presents RDM-YOLO, a computationally efficient deep learning framework derived from YOLOv5s architecture, specifically designed for the automated detection of three essential behaviors (resting, wriggling, and eating) in fourth instar silkworms. …”
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ConflLlama: Domain-specific adaptation of large language models for conflict event classification by Shreyas Meher, Patrick T. Brandt

“…We demonstrate how to adapt open-source language models to specialized political science tasks, using conflict event classification as our proof of concept. Through quantization and efficient fine-tuning techniques, we show state-of-the-art performance while minimizing computational requirements. …”
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Retracted: Computer Medical Image Segmentation Based on Neural Network by Xiaopeng Wang, Lei Gu, Zhongyi Wang

“…Finally, we propose an FPGA-based multilevel optimization architecture for energy-efficient cellular neural networks. The optimization scheme includes three levels: system level, module level, and design space. …”
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Lightweight Deep Learning Model for Fire Classification in Tunnels by Shakhnoza Muksimova, Sabina Umirzakova, Jushkin Baltayev, Young-Im Cho

“…Deployment optimizations, such as quantization and layer fusion, ensure computational efficiency, achieving an average inference time of 12ms/frame, making it suitable for resource-constrained environments like IoT and edge devices. …”
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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–spatial information, leading to relatively lagging progress in this field. …”
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VVC coded distortion prediction model based on frame-level transform coefficient modeling of generalized Gaussian distribution by Yiyin GU, Hongkui WANG, Haibin YIN

“…In versatile video coding (VVC), a variety of advanced coding tools work together to achieve excellent coding performance.Compared with high efficient video coding (HEVC), the transform coefficient distribution (TCD) of VVC has sharper peaks.In order to solve this phenomenon, the probability density function (PDF) of frame-level TCD was modeled, and a frame-level coding distortion prediction model based on statistical modeling was proposed, which modeled frame-level distortion as a function of TCD distribution parameters and quantization parameters.The experimental results show that compared with the Laplace distribution and Cauchy distribution, the generalized Gaussian distribution has the best performance in TCD probability density fitting.The prediction results based on the generalized Gaussian distribution distortion prediction model are closest to the actual coding distortion.…”
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Long-context inference optimization for large language models: a survey by TAO Wei, WANG Jianzong, ZHANG Xulong, QU Xiaoyang

“…However, long-text inference faces challenges such as high memory consumption and latency. To improve the efficiency of LLMs in long-text inference, a comprehensive review and analysis of existing optimization techniques were conducted. …”
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Optimizing Semantic-Aware Video Compression Using Particle Swarm Optimization Technique for Automotive Applications by Vadivel Shanmugam, B. Uma Maheswari

“…Our experimental results demonstrated that our proposed framework with the AV1 codec achieved the highest efficiency (compression ratio of 12:1). HEVC produced the highest quality results, with a notable improvement in both SSIM (0.9981) and PSNR (53.5656). …”
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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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Analysis of space labeling through binary fingerprinting by Marouan Mizmizi, Luca Reggiani

“…In the context of fingerprinting applications, this article presents the performance analysis of a type of space labeling based on the binary quantization of the received signal strength indicator. …”
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Long-context inference optimization for large language models: a survey by TAO Wei, WANG Jianzong, ZHANG Xulong, QU Xiaoyang

“…However, long-text inference faces challenges such as high memory consumption and latency. To improve the efficiency of LLMs in long-text inference, a comprehensive review and analysis of existing optimization techniques were conducted. …”
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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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