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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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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Design Optimization of Feedforward Equalization for Mobile Fronthaul Based on Delta-Sigma Modulation With High-Order QAM Signals by Jianghao Li, Yangsheng Yuan, Yangjian Cai

“…Delta-sigma modulation can be used as a high spectral efficiency interface in place of conventional common public radio interface (CPRI) in mobile fronthaul (MFH) networks. …”
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Enhanced Vehicle Tracking With Discretization Error Control for OFDM-Based Radar System by Seong-Hwan Hyun, Jiho Song, Keunwoo Kim, Jong-Ho Lee, Seong-Cheol Kim

“…However, the discrete sample acquisition process inherent in OFDM radar introduces quantization errors in the measured distance and relative velocity. …”
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Hybrid CNN–BiLSTM–DNN Approach for Detecting Cybersecurity Threats in IoT Networks by Bright Agbor Agbor, Bliss Utibe-Abasi Stephen, Philip Asuquo, Uduak Onofiok Luke, Victor Anaga

“…Advanced optimization techniques, including model pruning and quantization, are applied to enhance deployment efficiency in resource-constrained IoT environments. …”
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TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing by Amna Khatoon, Weixing Wang, Mengfei Wang, Limin Li, Asad Ullah

“…Our framework addresses critical gaps in existing methodologies, such as high computational demands and limited real-time processing capabilities, by leveraging model compression, quantization, and pruning techniques. These enhancements facilitate efficient real-time analysis directly on edge devices. …”
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Binary-Weighted Neural Networks Using FeRAM Array for Low-Power AI Computing by Seung-Myeong Cho, Jaesung Lee, Hyejin Jo, Dai Yun, Jihwan Moon, Kyeong-Sik Min

“…Furthermore, the combination of binary weight quantization and in-memory computing enables energy-efficient inference without significant loss in recognition accuracy, as validated using MNIST datasets. …”
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A Deep Learning-Driven Solution to Limited-Feedback MIMO Relaying Systems by Kwadwo Boateng Ofori-Amanfo, Bridget Durowaa Antwi-Boasiako, Prince Anokye, Suho Shin, Kyoung-Jae Lee

“…By harnessing binary feedback information from the implemented DNNs together with efficient beamforming vectors, a novel approach to the resulting problem is presented. …”
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Microcontroller-Based EdgeML: Health Monitoring for Stress and Sleep via HRV by Priyanshu Srivastava, Namita Shah, Kavita Jaiswal

“…However, traditional ML systems often face challenges in real-time processing and resource efficiency, limiting their application in life-critical scenarios. …”
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Fast jet tagging with MLP-Mixers on FPGAs by Chang Sun, Jennifer Ngadiuba, Maurizio Pierini, Maria Spiropulu

“…By using advanced optimization techniques such as High-Granularity Quantization and Distributed Arithmetic, we achieve unprecedented efficiency. …”
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Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers by S. B. G. Tilak Babu, Ch Srinivasa Rao

“…The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.…”
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Cloud–Edge Collaborative Learning and Model Compression Framework for Communication-Assisted Human Action Recognition by Bo Gao, Quan Zhou

“…The growing demand for efficient human action recognition has led to significant advancements in applications such as surveillance, healthcare, and human–computer interaction. …”
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TANS: A Tolerance-Aware Neighborhood Search Method for Workflow Scheduling with Uncertainties in Cloud Manufacturing by Haiyan Xu, Fanhao Ma, Long Chen

“…Workflow tasks in cloud manufacturing often involve uncertain execution and logistics times due to large-scale and geographically distributed resources, creating significant challenges for efficient and reliable scheduling. To address these challenges, we propose the Tolerance-aware Neighborhood Search (TANS) algorithm, which integrates fuzzy time quantization with heuristic neighborhood search techniques. …”
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DPU empowered intelligent congestion control mechanism for the intelligent computing center network by CHEN Jinqian, GUO Shaoyong, LIU Chang, QI Feng, QIU Xuesong

“…By integrating pruning and quantization, the model was lightweighted. Moreover, the model was transformed into the efficient gradient-boosted decision tree through knowledge distillation method, allowing for precise matching of control actions with real-time network conditions. …”
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Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects by Ma Yuhan

“…Techniques like knowledge distillation, model quantization, and pruning are proposed to enhance computational efficiency and minimize communication costs. …”
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A multi-chroma format cascaded coding method for full-chroma image in AVS2 by Liping ZHAO, Tao LIN, Kailun ZHOU, Keli HU, Chunmei LIN

“…In the second generation of audio video coding standard (AVS2),a multi-chroma format cascaded coding method (MCFCC) for full-chroma (4:4:4 sampling format) images coding was proposed.The MCFCC algorithm firstly converts the 4:4:4 sampling format image into 4:2:0 sampling format image,then the 4:2:0 sampling format image was processed by 4:2:0 sampling format intra prediction,transform,quantization,inverse quantization,inverse transform,entropy coding and a weighted 4:4:4 sampling format distortion calculation method in the rate-distortion optimization process,finally 4:4:4 sampling format in-loop filtering and offset algorithms were applied to the 4:4:4 sampling format image after up sampling.The experimental results show that,for full-chroma natural images,the MCFCC algorithm achieves higher coding efficiency at very low additional encoding complexity and very low additional design and implementation cost.…”
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Dual-Branch Neural Network-Based In-Loop Filter for VVC Intra Coding Using Spatial-Frequency Feature Fusion by Zhen Feng, Xu Liu, Cheolkon Jung

“…In company with the reconstructed frame, the quantization parameter (QP) map, the predicted frame, and the partition frame are popularly used as auxiliary input for the neural network-based in-loop filter (NNLF). …”
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High-speed railway track components inspection framework based on YOLOv8 with high-performance model deployment by Youzhi Tang, Yu Qian

“…Our research introduces an efficient model inference pipeline based on a producer-consumer model, effectively utilizing parallel processing and concurrent computing to enhance performance. …”
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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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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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