Improved sub-band JND model with textural decomposition and its application in perceptual image coding by Ming-kui ZHENG, Kai-xiong SU, Wei-xing WANG, Cheng-dong LAN, Xiu-zhi YANG

“…Considering the compatibility and the auxiliary information which would affect the encoding efficiency, the JND model was adjusted to the quantization process and re-moved more visual redundancy. …”
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Categorical-Parallel Adversarial Defense for Perception Models on Single-Board Embedded Unmanned Vehicles by Yilan Li, Xing Fan, Shiqi Sun, Yantao Lu, Ning Liu

“…Notably, Ca-PAT introduces an innovative categorical-parallel adversarial training mechanism for efficient defense in large-scale models, coupled with an alternate-direction optimization framework to minimize the adverse impacts of weight quantization. …”
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A Survey on Video Compression Optimization Techniques for Accuracy Enhancement in Video Analytics Applications (VAPs) by Kholidiyah Masykuroh, Hendrawan, Eueung Mulyana, Farhan Krishna

“…This paper presents a comprehensive survey of video compression optimization techniques, focusing on Quantization Parameter (QP), Frames per Second (FPS), Entropy Coding, and Motion Estimation for video analytics tasks. …”
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A lightweight intelligent compression method for fast Sea Level Anomaly data transmission. by Xiaodong Ma, Xiang Wan, Lei Zhang, Dong Wang, Zeyuan Dai

“…Furthermore, parametric pruning and adaptive quantization strategies are embedded to optimize memory efficiency for shipborne hardware constraints. …”
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AI-Based Anomaly Detection and Optimization Framework for Blockchain Smart Contracts by Hassen Louati, Ali Louati, Elham Kariri, Abdulla Almekhlafi

“…In this study, we propose an AI-based optimization framework that enhances the efficiency and security of blockchain smart contracts. …”
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Temporal dependent rate-distortion optimization based on distortion backward propagation by Hongwei GUO, Ce ZHU, Xu YANG, Lei LUO

“…Rate-distortion optimization (RDO) is a crucial technique in block based hybrid video encoders.However, the widely used independent RDO is far from obtaining optimal coding performance.To improve the rate-distortion (R-D) performance of high efficiency video coding (HEVC), a temporal dependent RDO algorithm was proposed.Firstly, the formula to calculate temporal distortion propagation factor was derived by using an exponential R-D function.Then, the coding distortion and motion compensation predicted error were obtained by pre-encoding, and the temporal distortion propagation factor was estimated by using distortion backward propagation.Finally, the Lagrange multiplier and quantization parameter of coding tree unit were adaptively adjusted to optimize bit resources allocation.Experimental results show that compared with the original RDO method in HEVC under the low-delay configuration, the proposed algorithm achieves an average 4.4% bit rate reduction for all test sequences, and up to 13.0% bit rate reduction for test sequence BasketballDrill, at the same reconstructed video quality.…”
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Topology Property Based on Network Tomography for Wireless Mobile Multihop Communication Network by Danyang Qin, Lin Ma, Qun Ding

“…The random movement of nodes makes the dynamic topology structure be one of the most important characteristics in wireless multihop communication network, which makes the description and quantization of the dynamic property the very foundation of the design, simulation, and measurement for this kind of network. …”
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Temporal dependent rate-distortion optimization based on distortion backward propagation by Hongwei GUO, Ce ZHU, Xu YANG, Lei LUO

“…Rate-distortion optimization (RDO) is a crucial technique in block based hybrid video encoders.However, the widely used independent RDO is far from obtaining optimal coding performance.To improve the rate-distortion (R-D) performance of high efficiency video coding (HEVC), a temporal dependent RDO algorithm was proposed.Firstly, the formula to calculate temporal distortion propagation factor was derived by using an exponential R-D function.Then, the coding distortion and motion compensation predicted error were obtained by pre-encoding, and the temporal distortion propagation factor was estimated by using distortion backward propagation.Finally, the Lagrange multiplier and quantization parameter of coding tree unit were adaptively adjusted to optimize bit resources allocation.Experimental results show that compared with the original RDO method in HEVC under the low-delay configuration, the proposed algorithm achieves an average 4.4% bit rate reduction for all test sequences, and up to 13.0% bit rate reduction for test sequence BasketballDrill, at the same reconstructed video quality.…”
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Two-Step Single Slope/SAR ADC with Error Correction for CMOS Image Sensor by Fang Tang, Amine Bermak, Abbes Amira, Mohieddine Amor Benammar, Debiao He, Xiaojin Zhao

“…The measurement results show that the energy efficiency figure-of-merit (FOM) of the proposed ADC core is only 125 pJ/sample under 1.4 V power supply and the chip area efficiency is 84 k μm2·cycles/sample.…”
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Surface State Treatment of Carbon Dots Using Sulphur Dioxide Isotherm by L. Natrayan, Dhinakaran Veeman, Pravin P. Patil, V. Swamy Nadh, P. Balamurugan, Muse Degefe Chewaka

“…The proposed method shows 8.5% efficiency in relative fluorescence intensity and 8% efficiency in terms of photoluminescence intensity.…”
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Hardware Implementation of Block Floating-Point FFT Based on Approximate Computation and Conflict-Free Access by Xiaohui Zhang, Zenghui Yu, Shushan Qiao, Yi Zhan

“…Operating at a frequency of 671.14 MHz, it delivers a throughput of 1073.82 MS/s for 4096-point FFT computations, with a power consumption of only 110.85 mW and a Signal–to–Quantization Noise Ratio of 58.51 dB. Compared to state-of-the-art designs, this architecture achieves breakthrough improvements in throughput performance while maintaining competitive energy efficiency.…”
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Federated Collaborative Learning with Sparse Gradients for Heterogeneous Data on Resource-Constrained Devices by Mengmeng Li, Xin He, Jinhua Chen

“…Extensive experiments demonstrate that the proposed algorithm achieves high classification efficiency, effectively addressing the challenges posed by data heterogeneity.…”
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Open-Loop Wide-Bandwidth Phase Modulation Techniques by Nitin Nidhi, Pin-En Su, Sudhakar Pamarti

“…A detailed analysis on the impact of timing and quantization of the cancellation signal is presented. …”
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Consistent Quality in Video Coding: A Perceptual RDO and Neural Network-Based QP Prediction Approach by Tien Vu Huu, Huy Bui Quoc, Minh Do Ngoc, Xiem HoangVan

“…Second, we propose a novel VMAF-QP (quantization parameter) neural network that learns how QP can be adjusted to achieve a certain VMAF grade. …”
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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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Performance Analysis of Superposition M-ary QAM Modulation in Coded Protograph LDPC MIMO Communication Systems With Low-Resolution ADCs by Duc A. Hoang, Hieu T. Nguyen, Thang N. Le

“…Overall, this work offers a unified perspective on superposition modulation, low-resolution ADCs, and advanced LDPC decoding, opening new avenues for designing energy-efficient and spectrally efficient communication systems suitable for 5G-and-beyond networks.…”
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Effects of Hybridizing the U-Net Neural Network in Traffic Lane Detection Process by Aron Csato, Florin Mariasiu

“…Future research should investigate model compression techniques, such as knowledge distillation, pruning, or quantization, to improve efficiency without sacrificing accuracy.…”
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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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Leveraging Edge Intelligence for Solar Energy Management in Smart Grids by Trong-Minh Hoang, Tuan-Anh Pham, van-Nhan Nguyen, Duc-Thang Doan, Nhu-Ngoc Dao

“…The proposed model leveraged TCN’s capability to capture long-range temporal dependencies and GRU’s efficiency in sequential data processing. To optimize performance for edge computing environments, we employed Neural Architecture Search, model pruning, and quantization techniques, reducing both model size and complexity. …”
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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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