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IEEE Symposium on High-Performance computer Architecture

作者： Jiangwei Zhang Chong Wang Zhenhua Zhu Donald Kline Alex K. Jones Huazhong Yang Yu Wang Department of Electronic Engineering Tsinghua University Intel Corporation Department of Electrical and Computer Engineering University of Pittsburgh

Phase-change memory (PCM) and resistive memory (RRAM) are promising alternatives to traditional memory technologies. However, both PCM and RRAM suffer from limited write endurance. Wear-leveling (WL) techniques are essential to extend the lifetime of these memories before experiencing endurance faults. Beyond the additional usage afforded by WL, row-sparing and focused error correction can extend the lifetime further after wear faults appear. Unfortunately, the need for extended WL techniques continues to become more pressing as scaling exacerbates process variation. Similarly, scaling causes challenges such as more severe noise and crosstalk to traditional *** this paper, we propose novel fault-aware WL schemes to allocate write frequencies according to the strength of the rows and handle the imbalance of writes in columns. We use runtime detection schemes to identify weak rows and protect them prior to wear out. In particular, row-level WL, aka RETROFIT, leverages the spare rows provided for redundancy to be used strategically to guard against early cell wear out. RETROFIT is compatible with error correction schemes that guarantee to mitigate hard faults and error-correcting codes (ECC). Rather than discard retired rows, when any spare row completely replaces a retired row, we retarget the retired row to assist with column sparing. It becomes a group of Page Protecting Pointers (PPPs), which utilizes otherwise discarded error correction potential to further enhance the leveling ability of RETROFIT. To relieve column-level imbalance, we apply idle error correction bits before they are used to reduce average bit flips. The evaluation demonstrates that RETROFIT and enhanced RETROFIT with the PPPs improve lifetime by as much as 0.64× and 5.4× in the average case, respectively, over state-of-the-art row-level method while also reducing area overhead. In the worst-case scenario, these improvements further increase to 2.6× and 16.0×. Combined with the proposed column

关键词： Phase change materials Runtime Microprocessors Redundancy Crosstalk Random access memory computer architecture

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Power Optimization for Low Transmission Delay in Software Defined Data Center Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5716-5730页

作者： Zhao, Yong Cao, Jiannong Wang, Xingwei Li, Fuliang He, Qiang Liu, Xiaojie University of Electronic Science and Technology of China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Laboratory of Intelligent Collaborative Computing Chengdu611731 China Hong Kong Polytechnic University Department of Computing Hung Hom Hong Kong Northeastern University College of Computer Science and Engineering Shenyang110819 China Northeastern University College of Medicine and Biological Information Engineering Shenyang110819 China Pengcheng Laboratory Shenzhen518066 China

Software-Defined Data Center Networks (SDDCNs) utilizes Software Defined Networking (SDN) as a network architecture to achieve highly flexible, programmable, and automated management of Data Center Networks (DCNs). The high energy consumption of DCNs remains a persistent and significant challenge. Thus, the energy saving is crucial and imperative for DCNs. Current energy-efficient solutions primarily rely on flow consolidation and dynamic device sleeping techniques to reduce energy consumption. However, these approaches often yield long Flow Completion Time (FCT), potentially resulting in violations of service-level agreements, particularly for delay-sensitive applications. In this paper, we formulate the problem of minimizing the power consumption in SDDCNs as key objective while ensuring timely FCT for delay-sensitive applications. To solve this problem, we first introduce the Active Network Generation (ANG) approach, which generates a minimal active subnet with the least number of active devices while meeting the current traffic demand. Subsequently, we propose two algorithms based on the type of applications: the Delay-Tolerant Flow Route (DTFR) algorithm for delay-tolerant applications and the Delay-Sensitive Flow Route (DSFR) algorithm for delay-sensitive applications. Simulation results demonstrate that our propose solution achieves an energy-saving rate of up to 67.77% and significantly reduces FCT compared to benchmark solutions. © 2002-2012 IEEE.

关键词： Energy efficiency

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An Open-Source Framework for Developing Human Behavior in Humanoid Robots

An Open-Source Framework for Developing Human Behavior in Hu...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Mohammad Tarek Aziz Nayeem Uddin Ahmed Khan Md. Maskat Sharif Md. Shahriar Abdullah Md. Riduan Tanjim Mahmud Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Department of Electrical and Electronic Engineering University of Chittagong Chittagong Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Department of Civil and Environmental Engineering Lamar University Texas United States Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

We need someone to rise from childhood to adulthood and then to help us work the graveyard. Humans are mortal, and we engage in many activities that carry a higher risk of death, such as those performed by the military, firefighters, astronauts, and divers, among other professions. To rescue people, this includes ourselves if we replace people from those kinds of riskier sectors. So, in the interest of mankind, we aim to create a robot that looks like a man from head to toe and mimics our movements. The goal of this research is to develop a robot that can act like a human, be fully controlled by an electronic device or voice, converse with humans, walk but not run, connect to the internet and gather data from there, and express emotion using holographic eyes and faces so that humans can believe the robot is alive even though it is incapable of making decisions on its own. This paper proposes an open-source and Arduino-based scientific and technological machine to develop a humanoid robot. This robot is designed internally and externally with a circuit designed in the back-end system. The behavior of the proposed robot is most similar to human behavior. To find out the performance of the proposed robotics system, some software libraries, such as VoiceHexData for voice command, and some hardware instruments, such as circuit diagrams, OLED displays, and Arduino IDEs for software modules, were used.

关键词： Legged locomotion Software libraries Instruments MIMICs Humanoid robots Organic light emitting diodes Software Magnetic heads Hardware Behavioral sciences

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Detection of Dead Victims at Volcanic Disaster Location based on Drone and LoRa

Detection of Dead Victims at Volcanic Disaster Location base...

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International Conference on computer engineering, Network and Intelligent Multimedia (CENIM)

作者： Moch. Zen Samsono Hadi Achmad Abie Dafa Prima Kristalina Department of Informatics and Computer Engineering Electronic Engineering Polythecnic Institute of Surabaya (EEPIS) Surabaya East Java Indonesia

ISBN: (纸本)9781665476515

When a natural disaster occurs, the process of evacuating victims after a disaster must be carried out immediately to reduce the risk due to the late evacuation process. In this evacuation process, the Search and Rescue (SAR) team played a big role in addition to focusing on the safety of victims and also paying attention to their own safety. Meanwhile, due to the large area of the disaster the search took quite a long time. In this paper, research was carried out to facilitate the evacuation process at the location of volcanic disasters by using drones carrying electronic devices to find the whereabouts of victims on the surface. This process of detecting and classifying is based on the Convolutional Neural Network (CNN) method with the MobileNetV2 model. We train the data set of disaster victims using batch sizes 16 and 32 with epochs of 40, 80 and 100. The resulting model has the greatest accuracy of 0.81 and f1-score of 0.86.

关键词： Computational modeling Simulation Neural networks Training data Focusing Safety Convolutional neural networks

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Joint Task Coding and Transfer Optimization for Edge Computing Power Networks

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IEEE Transactions on Network Science and engineering 2025年

作者： Liu, Jiajia Lu, Yunlong Wu, Hao Ai, Bo Jamalipour, Abbas Zhang, Yan Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China The University of Sydney School of Electrical and Information Engineering SydneyNSW2006 Australia University of Oslo Department of Informatics Norway

Driven by the exponential growth of the Internet of Everything (IoE) and the substantial advancements in artificial intelligence, services based on deep learning have seen a significant increase in demand for computing resources. The existing edge computing paradigms struggle to handle the explosive growth in computing demands. They also face challenges in jointly optimizing the high transmission overhead and privacy concerns of task collaboration while failing to utilize computing resources efficiently in complex and dynamic computing power networks. In this paper, we investigate an edge computing power network framework that integrates heterogeneous computing resources from both horizontal and vertical dimensions. We formulate a collaborative task transfer problem to minimize the total execution time of multiple tasks by joint optimization task coding, computing-task association, and collaborative transfer computing strategies among nodes. To solve the formulated problem, we conduct in-depth theoretical analyses and design a two-layer multi-agent optimization algorithm. Specifically, the task coding problem is reformulated in the inner layer into a solvable form, and a closed-form expression for the task coding ratio is derived. Subsequently, we design an adaptive hybrid reward-based multi-agent deep reinforcement learning algorithm to address the sparsity challenges of single-layer rewards while ensuring efficient and stable training convergence. Numerical results show the superiority of our proposed algorithm. © 2013 IEEE.

关键词： computing power networks Edge intelligence multi-agent deep reinforcement learning task coding

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The Efficiency of ICA-based Representation Analysis: Application to Speech Feature Extraction*

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Chinese Journal of electronics 2023年第2期20卷 287-292页

作者： Jun Du Xin Zou Jie Hao Ju Liu School of Information Science and Engineering Shandong University Jinan China Department of Electronic Electrical and Computer Engineering University of Birmingham Birmingham UK

This paper is focused on the theoretical analysis of the efficiency of Independent component analysis (ICA) based non-Gaussian signal representations corrupted by different types of noises. We first present a mathematical derivation demonstrating that ICA-based signal representations, in which the ICA transformation matrix was derived on clean non-Gaussian signals (denoted by $\text{ICA}_{c}$ ) are efficient representations than DCT for signals when being both clean and corrupted by Gaussian noise, while it may not have better performance for the non-Gaussian noises corrupted signals. The analysis also demonstrates that to obtain efficient representations of non-Gaussian corrupted signals, the ICA transformation matrix should be obtained from noise corrupted signal (denoted by $\text{ICA}_{n}$ ). The $\text{ICA}_{n}$ -based features can provide significant recognition accuracy improvements to non-Gaussian corrupted signals over both the $\text{ICA}_{c}$ -based features and MFCC features. Our findings are experimentally demonstrated by employing the ICA for speech feature extraction; specifically, the ICA is used to transform the logarithm filter-bank-energies (instead of the DCT which provides MFCC features). The evaluation is presented for a GMM-based speaker identification task on the TIMIT database for clean speech and speech corrupted by Gaussian noises and non-Gaussian noises.

关键词： Databases Gaussian noise Transforms White noise Feature extraction Discrete cosine transforms Signal representation

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A Multi-Class Classification Approach for Anemia Level Prediction with Machine Learning Models

A Multi-Class Classification Approach for Anemia Level Predi...

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South Eastern European Design Automation, computer engineering, computer Networks and Social Media Conference (SEEDA-CECNSM)

作者： Maria Trigka Elias Dritsas Phivos Mylonas Department of Informatics and Computer Engineering University of West Attica Egaleo Athens Greece Department of Electrical and Computer Engineering University of Patras Patras Greece

It is a common belief that Artificial Intelligence (AI) and Machine Learning (ML) provide researchers and medical experts with concepts, tools, and techniques to build intelligent systems able to analyze, process and detect hidden patterns, in order to turn data into actionable knowledge for personalized medicine and decision-making (e.g., disease prevention via associated risk prediction, treatment, etc.). The present study seeks an ML solution that will facilitate the recognition of anemia level by investigating the performance of two well-established strategies for multi-class classification tasks; the One-Vs-All (OVA) and One-Vs-One (OVO). Under the specific strategies, two well-known ML models were assumed, namely, Logistic Regression (LR) and Support Vector Machines (SVM). The validation of both strategies was carried out in a publicly available dataset by measuring the weighted average performance in all involved classes that capture the anemia levels; accuracy, precision, recall and Area Under the ROC curve (AUC) were captured and compared for the identification of the dominant model and strategy. After the experimental evaluation, the OVO strategy with the LR model is the main suggestion of the current study achieving an accuracy of 95.05 %, precision and recall equal to 0.951 and an AUC of 0.990.

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KNN-Based Collaborative Filtering for Fine-Grained Intelligent Grad-School Recommendation System 19th

KNN-Based Collaborative Filtering for Fine-Grained Intellig...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Xu, Jinfeng Liu, Jiyi Ma, Zixiao Wang, Yuyang Wang, Wei Ngai, Edith Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong School of Software Engineering Sun Yat-Sen University Guangdong Zhuhai528406 China Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Computer Science Cornell University IthacaNY14853 United States Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BITUniversity Guangdong Shenzhen518172 China

ISBN: (纸本)9783031651250

The development of the Internet has led to information overload, and how to filter and sift information is a rigorous requirement in all fields. In response to this challenge, recommendation systems have emerged as a valuable tool, offering personalized content and services by efficiently searching and processing dynamically generated information. For students applying to grad schools, finding relevant information can be time-consuming and unreliable from official websites or forums. In light of these challenges, we present a novel solution in the form of an application recommendation platform. Our proposed platform leverages specific open-source datasets and real-time information from platform users using KNN (K-Nearest Neighbor) and CF (Collaborative Filtering) techniques to provide recommendations based on users’ individual backgrounds, we aim to reduce the complexity inherent in information retrieval while simultaneously enhancing the relevance of the recommendations delivered to users. Specifically, we first collect user behavior data, then we will construct the data model and perform some preprocessing on it. Calculate the user similarity, and find out the K-nearest neighbors and rate based on K-nearest neighbors, finally, the recommendation engine is used to calculate the highest-rated items to be recommended to the users. © ICST Institute for computer Sciences, Social informatics and Telecommunications engineering 2024.

关键词： Collaborative filtering

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Machine Learning-Based Channel Estimation Techniques for ATSC 3.0

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Information (Switzerland) 2024年第6期15卷 350页

作者： Liu, Yu-Sun You, Shingchern D. Lai, Yu-Chun Department of Electronic Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei10608 Taiwan

Channel estimation accuracy significantly affects the performance of orthogonal frequency-division multiplexing (OFDM) systems. In the literature, there are quite a few channel estimation methods. However, the performances of these methods deteriorate considerably when the wireless channels suffer from nonlinear distortions and interferences. Machine learning (ML) shows great potential for solving nonparametric problems. This paper proposes ML-based channel estimation methods for systems with comb-type pilot patterns and random pilot symbols, such as ATSC 3.0. We compare their performances with conventional channel estimations in ATSC 3.0 systems for linear and nonlinear channel models. We also evaluate the robustness of the ML-based methods against channel model mismatch and signal-to-noise ratio (SNR) mismatch. The results show that the ML-based channel estimations achieve good mean squared error (MSE) performance for linear and nonlinear channels if the channel statistics used for the training stage match those of the deployment stage. Otherwise, the ML estimation models may overfit the training channel, leading to poor deployment performance. Furthermore, the deep neural network (DNN)-based method does not outperform the linear channel estimation methods in nonlinear channels. © 2024 by the authors.

关键词： Signal to noise ratio

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Analysis of Banking and Financial Sector Services Using Artificial Intelligence (AI) 1

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Isavnin, Alexey Kosorukova, Irina Madaminov, Inomjon Laxmi Lydia, E. Satapathy, Suresh Department of Business-Informatics and Mathematical Methods in Economics Naberezhnye Chelny Institute KFU Kazan Federal University Naberezhnye Chelny Russia Department of Corporate Finance and Corporate Governance Financial University under the Government of the Russian Federation Moscow Russia Department of Accounting and Auditing Urgench State University Urgench Uzbekistan Department of Computer Science and Engineering Vignan’s Institute of Information Technology Visakhapatnam530049 India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India

ISBN: (数字)9789819748921

ISBN: (纸本)9789819748914

This research proposes the assessment of banking and financial services using AI to monitor how banks apply AI approaches and the feedback they receive from customers. In order to better track, anticipate, and react to customer behavior, the financial information generated by the growing utilization of digital pay and banking can be mined by banks and other financial institutions. Based on research, secondary sources are reviewed to ascertain the data used in banking and financial services. A methodical survey is designed to gather the main information clients have regarding AI applications. The study is that private banks and other financial institutions use a variety of artificial intelligence (AI) services to better serve their clients and ensure that they are happy with their financial services, as some clients are not happy with banking services. Additionally, the results indicated that clients would be more committed to banking and financial services representatives by providing creative preparation for the advancement of AI practices in the workplace. © The Author(s).

关键词： Decentralized finance

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