Acceleration of Urdu Optical Character Recognition on Zynq UltraScale+ MPSoC Using Deep Convolutional Neural Network by Fauzia Yasir, Majida Kazmi

“…Benchmarking against CPU and GPU platforms confirmed substantial improvements in speed and energy efficiency. This work establishes a high-performance, scalable, and energy-efficient FPGA-based OCR framework for Urdu and sets the foundation for extending such solutions to other cursive, low-resource languages like Arabic, Pashto, and Persian.…”
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Energy-aware federated learning for secure edge computing in 5G-enabled IoT networks by Milad Rahmati

“…To address these challenges, we propose an energy-aware federated learning (EAFL) framework, integrating adaptive client selection, quantization-aware model updates, and blockchain-enhanced security mechanisms to improve both energy efficiency and resistance to model poisoning attacks and adversarial gradient manipulations. …”
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Continual Learning With Neuromorphic Computing: Foundations, Methods, and Emerging Applications by Mishal Fatima Minhas, Rachmad Vidya Wicaksana Putra, Falah Awwad, Osman Hasan, Muhammad Shafique

“…The challenging deployment of compute- and memory-intensive methods from Deep Neural Network (DNN)-based Continual Learning (CL), underscores the critical need for a paradigm shift towards more efficient approaches. Neuromorphic Continual Learning (NCL) appears as an emerging solution, by leveraging the principles of Spiking Neural Networks (SNNs) and their inherent advantages (e.g., sparse spike-driven operations and bio-plausible learning rules) for improving energy efficiency and performance, thereby enabling efficient CL algorithms (e.g., unsupervised learning approach) executed in dynamically-changed environments with resource-constrained computing systems. …”
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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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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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Nuclei segmentation and classification from histopathology images using federated learning for end-edge platform. by Anjir Ahmed Chowdhury, S M Hasan Mahmud, Md Palash Uddin, Seifedine Kadry, Jung-Yeon Kim, Yunyoung Nam

“…To protect data privacy, the framework employs a FedAvg-based federated learning scheme, enabling decentralized training without exposing sensitive data. For efficient deployment on edge devices, full integer quantization is applied to reduce computational overhead while maintaining accuracy. …”
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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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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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GHMSA-Net: Gated Hierarchical Multi-Scale Self-Attention for Perceptually-Guided AV1 Post-Processing by Bopu Zhao, Woowoen Gwun, Kiho Choi

“…The AOMedia Video 1 (AV1) codec achieves excellent compression efficiency but often introduces visually distracting artifacts at high quantization parameters (QPs), impairing perceptual quality. …”
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Retracted: Computer Medical Image Segmentation Based on Neural Network by Xiaopeng Wang, Lei Gu, Zhongyi Wang

“…This method introduces a non-linear template and data quantization on the basis of a basic network model, which greatly reduces the computational complexity while maintaining the accuracy of image segmentation. …”
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Speed up integer-arithmetic-only inference via bit-shifting by Mingjun Song, Yiming Zhou, Mengmeng Song, Sujie Liu, Shungen Xiao, Youshun Zheng

“…Integer-arithmetic-only quantization is an important approach in quantization and holds great significance for hardware deployment with limited resources. …”
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Lightweight deep learning for real-time road distress detection on mobile devices by Yuanyuan Hu, Ning Chen, Yue Hou, Xingshi Lin, Baohong Jing, Pengfei Liu

“…Utilizing a diverse dataset collected from Europe and Asia, MobiLiteNet incorporates Efficient Channel Attention to boost model performance, followed by structural refinement, sparse knowledge distillation, structured pruning, and quantization to significantly increase the computational efficiency while preserving high detection accuracy. …”
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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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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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