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Design Automation Conference

作者： S. S. Teja Nibhanupudi Sirish Oruganti Rahul Mathur Nishant Gupta Meizhi Wang Jaydeep P. Kulkarni Chandra Department of Electrical and Computer Engineering Circuit Research Lab The University of Texas at Austin Austin TX USA

Buried power rails and back-side power grids are promising technology-scaling boosters for advanced CMOS technology nodes. System-level evaluation of these technologies shows tremendous promise from power-performance-area (PPA), IR drop, and dynamic voltage droop perspective. However, several process, device, and architectural challenges must be addressed to realize the full potential of this technology. This article reviews the advancements and challenges in successfully adopting buried power rail and back-side power grid technology.

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Purification Of Contaminated Convolutional Neural Networks Via Robust Recovery: An Approach with Theoretical Guarantee in One-Hidden-Layer Case

arXiv

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arXiv 2024年

作者： Lu, Hanxiao Huang, Zeyu Wang, Ren The Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States The Trustworthy and Intelligent Machine Learning Research Lab The Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

Convolutional neural networks (CNNs), one of the key architectures of deep learning models, have achieved superior performance on many machine learning tasks such as image classification, video recognition, and power systems. Despite their success, CNNs can be easily contaminated by natural noises and artificially injected noises such as backdoor attacks. In this paper, we propose a robust recovery method to remove the noise from the potentially contaminated CNNs and provide an exact recovery guarantee on one-hidden-layer non-overlapping CNNs with the rectified linear unit (ReLU) activation function. Our theoretical results show that both CNNs' weights and biases can be exactly recovered under the overparameterization setting with some mild assumptions. The experimental results demonstrate the correctness of the proofs and the effectiveness of the method in both the synthetic environment and the practical neural network setting. Our results also indicate that the proposed method can be extended to multiple-layer CNNs and potentially serve as a defense strategy against backdoor attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Recovery

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Methodology for Assessment of Mobile Network Saturation based on Traffic and Resource Utilization 13

Methodology for Assessment of Mobile Network Saturation base...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Lee, Yeeun Cheon, Kyung-Yul Kwon, Hyeyeon Kim, Ki-Il ETRI Chungnam National University Radio Resource Research Lab. Daejeon Korea Republic of ETRI Radio Resource Research Lab. Daejeon Korea Republic of Chungnam National University Department of Computer Science and Engineering Daejeon Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

As the importance of high-capacity, low-latency services increase in the 5G mobile communication era, the spectrum as a means of transmission must be utilized more efficiently. As one of the methods for efficiently using the spectrum, it is necessary to check the margin of the 4G spectrum. This is because unused resources of 4G spectrum can be reused as resources for 5G. For this, it is necessary to analyze and evaluate4G network saturation and spectrum availability. In this paper, we present a methodology to analyze and evaluate the saturation and spectrum availability of a 4G LTE network using spectrum utilization and traffic volumes with simultaneously measured data during congestion at a co-site in Gangnam, Seoul, a traffic dense area. The proposed methodology is expected to be applied to analyzing and evaluating 5G network saturation and spectrum availability in the future. © 2022 IEEE.

关键词： Wireless networks

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Exploiting the Brain’s Network Structure for Automatic Identification of ADHD Subjects

arXiv

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arXiv 2023年

作者： Dey, Soumyabrata Rao, A. Ravishankar Shah, Mubarak Computer Vision lab EECS Department University of Central Florida OrlandoFL United States IBM T.J. Watson Research Center Yorktown Heights NY United States

Attention Deficit Hyperactive Disorder (ADHD) is a common behavioral problem affecting children. In this work, we investigate the automatic classification of ADHD subjects using the resting state Functional Magnetic Resonance Imaging (fMRI) sequences of the brain. We show that the brain can be modeled as a functional network, and certain properties of the networks differ in ADHD subjects from control subjects. We compute the pairwise correlation of brain voxels’ activity over the time frame of the experimental protocol which helps to model the function of a brain as a network. Different network features are computed for each of the voxels constructing the network. The concatenation of the network features of all the voxels in a brain serves as the feature vector. Feature vectors from a set of subjects are then used to train a PCA-LDA (principal component analysis-linear discriminant analysis) based classifier. We hypothesized that ADHD-related differences lie in some specific regions of the brain and using features only from those regions is sufficient to discriminate ADHD and control subjects. We propose a method to create a brain mask that includes the useful regions only and demonstrate that using the feature from the masked regions improves classification accuracy on the test data set. We train our classifier with 776 subjects and test on 171 subjects provided by The Neuro Bureau for the ADHD-200 challenge. We demonstrate the utility of graph-motif features, specifically the maps that represent the frequency of participation of voxels in network cycles of length 3. The best classification performance (69.59%) is achieved using 3-cycle map features with masking. Our proposed approach holds promise in being able to diagnose and understand the disorder. © 2023, CC BY-NC-SA.

关键词： Brain

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Prediction of Noise Pollution in Omu-Aran Township Using Artificial Neural Network

Prediction of Noise Pollution in Omu-Aran Township Using Art...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： O. O. Elemile O. S. Olajide M.O. Arowolo O. P. Ejigboye E. M. Ibitogbe E. O. Ajayi A.O. Akinola O.A. Ogunrinde Civil. Engineering SDG 11 (Sustainable Cities and Communities Research Group) Landmark University Omu-Aran Nigeria Departmant of Electrical Engineering and Computer Sciences (EECS) University of Missouri Columbia USA Civil. Engineering SDG 11 (Sustainable Cities and Communities Research Group) Landmark University Landmark University Omu Aran Nigeia Department of Civil. Engineering Elizade University Ilara-Mokin Nigeia Department of Physics Astrophysics Research Lab Ariel University Ari'el Isreal

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Human health is unprotected upon constant exposure to impermissible noise. In Nigeria, several studies have suggested different models to validate noise pollution, but there is none carried out on noise levels in Omu-Aran, therefore this study, was aimed at validating noise levels in Omu-Aran Township using Artificial Neural Network. Data was collected from 21 selected locations in the morning, afternoon, and evening during the school and holiday periods for 3-weeks with a sound level meter model SL4010. ANN architecture was designed to incorporate 19 noise-determinant factors as the input, which were collected using the descriptive field investigation method. A total of 1134 data was loaded to the model, by random sampling, 70% of the data was used to train the network, and 30% being used for testing. Importation of algorithms and selection of epoch value is based on two hidden layers chosen. ArcGIS was adopted for mapping the noise level of all locations. The average mean noise levels in the mornings, afternoons and evenings were 67.82± 2.1, 68.7± 1.87, and 69.53± 2.24 dB respectively. There was a significant difference in noise levels during the working hours at the most concentrated locations, which were all above the permissible noise level given by OSHA and WHO. The ANN validation gives one of the best results with an increase in input data when compared to other publications with a prediction accuracy of 97.84%, and the root means a square error were given to be (RMSE =0.1096). Population and human activities are not the only determinant factors of noise pollution, but also biological factors have impact on the noise levels at the different locations in Omu-Aran Township. Hence, there is a need for proper urban planning and law enforcement at the local level to regulate noise pollution to permissible limits.

关键词： Pollution Noise Urban planning Artificial neural networks Data models Sustainable development Statistics

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Double-Blockchain Assisted Secure and Anonymous Data Aggregation for Fog-Enabled Smart Grid

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engineering 2022年第1期8卷 159-169页

作者： Siguang Chen Li Yang Chuanxin Zhao Vijayakumar Varadarajan Kun Wang Jiangsu Key Lab of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal UniversityWuhu 241000China School of Computer Science and Engineering Vellore Institute of TechnologyChennai 600127India Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

As a future energy system,the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable ***,this efficient and reliable service relies on collecting and analyzing users’electricity consumption data frequently,which induces various security and privacy *** address these challenges,we propose a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named ***,we design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain,which provides strong support for achieving efficient and secure data collection in smart ***,we develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption,batch aggregation signature and anonymous *** particular,the system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data ***,the superiority of the proposed scheme is illustrated by a series of security and computation cost analyses.

关键词： Blockchain Fog computing Homomorphic encryption Smart grid Anonymity

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Smart Happy Device with IoT : For those who suffer anxiety, stress, and depression 12

Smart Happy Device with IoT : For those who suffer anxiety, ...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Yakub, Fitri Azizan, Azizul Dhamanti, Inge Khudzari, Ahmad Zahran Md. Govindan, Niranjana Muljono, Muljono UTM Malaysia-Japan International Institute of Technology Electronic System Engineering Department Kuala Lumpur Malaysia Universiti Teknologi Malaysia Cyberphysical Research Group Embedded AI & ML Science Lab Faculty of Artificial Intelligence Kuala Lumpur Malaysia Universitas Airlangga Faculty of Public Health Department of Health Policy and Administration Surabaya Indonesia UTM Faculty of Engineering Department of Biosciences & Medical Engineering Johor Bahru Malaysia SRMIST Faculty of Engineering & Technology Department of Computing Technologies Chennai India Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350340204

Due to the rising cases of Covid-19 in the world especially in Malaysia, the impact had been hit hard for many people. Due to this all people must be isolated in their home. It has affected the well-being and working life of millions of people across the world. Due to this disease, the mental health among people has increased significantly for the past 2 years. According to MoH, out of 983 Malaysian students, about 20.4%, 6.6%, and 2.8% are experienced minimal to moderate, marked to severe, and most extreme levels of anxiety, respectively. The main stressors are the remote online teaching, and uncertainty about the future regarding study and career which can affect mental health. Thus, the main objective of this project is to develop a product that may provide different medication methods to its users by helping people who suffer anxiety, stress, and depression. We used aromatherapy, positive quotes, and music therapy in order to release stress. The user can also check his or her pulse rate by only touching the fingerprint through the sensor. The microcontrollers used in this project are Arduino Uno and Arduino Mega to build the project circuit. The experimental results can improve body health, mind and spirit which enhances both physical and emotional health. © 2024 IEEE.

关键词： COVID-19

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Excavation pits—progress estimation and cause of delay identification

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Construction Robotics 2023年第1期7卷 53-63页

作者： Vierling, Axel Groll, Tobias Meckel, Dennis Heim, Kristina Walter, Daniel Körkemeyer, Dr. Karsten Berns, Dr. Karsten Department of Computer Science Robotics Research Lab University of Kaiserslautern-Landau Kaiserslautern Germany Department of Civil Engineering Chair in Construction Management University of Kaiserslautern-Landau Kaiserslautern Germany

This work presents a method for automated excavation speed and progress estimation. First, a measure for the progress speed of an excavation pit is taken from the literature and evaluated regarding the possibility of automation. For each possible parameter, an automated extraction algorithm is presented. The used system is an autonomous excavator arm of a backhoe loader where the used hardware and software system is described. An experimental evaluation of the presented approach has been done with the autonomous system for a small trench, including multiple digging cycles. The resulting measurements seem to include some systematic errors which could be identified and suitable sanity checks could be implemented, removing the erroneous measurements. The remaining measurements were used to determine the excavation speed of the autonomous excavator arm and compared to the values of experienced and amateur operators. The resulting speed and the relevant parameters are then sent to a web application called “DiBa” to notice a potential delay. If a measurement is smaller or larger than expected a fault identification is done in the DiBa.

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HyperScore: A unified measure to model hypertension progression using multi-modality measurements and semi-supervised learning

HyperScore: A unified measure to model hypertension progress...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Alkhodari, Mohanad Lapidaire, Winok Xiong, Zhaohan Kart, Turkay Iturria-Medina, Yasser Hadjileontiadis, Leontios Khandoker, Ahsan Lewandowski, Adam J. Banerjee, Abhirup Leeson, Paul University of Oxford Cardiovascular Clinical Research Facility Radcliffe Department of Medicine Oxford United Kingdom Khalifa University Healthcare Engineering Innovation Center Department of Biomedical Engineering Abu Dhabi United Arab Emirates Imperial College London Biomedical Image Analysis Group Department of Computing London United Kingdom McGill University Neuroinformatics for Personalized Medicine Lab Montreal Canada Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece University of Oxford Institute of Biomedical Engineering Department of Engineering Science Oxford United Kingdom

ISBN: (纸本)9798350337488

Hypertension is a serious medical condition that affects over a billion people worldwide. The proper management of disease progression requires an extended knowledge of the overall functional and structural changes in the whole body in response to the hypertension. Here, we propose HyperScore, an integrative and unified measure of hypertension progression relative to multi-organ and multi-modality clinical measurements and based on a semi-supervised machine learning (ML) approach. We developed the measure based on a large participating cohort from the UK Biobank database (n=27,099) with over 500 imaging and clinical variables from multiple modalities. The semi-supervised approach was developed based on the contrastive trajectory inference mechanism to provide a score that reflects the proximity of a participant to the disease state (range: 0-1). Modelling revealed that majority of hypertensive participants had scores above 0.25, whereas normotensives had scores below this threshold. The sensitivity and specificity were above 89%, with an area under the receiver operating characteristics of 96.4%. The modelling showed a stable performance when evaluating hidden testing sets on a 10-fold cross-validation scheme with nearly 0.1 error. There was a strong association (r2>0.6) between HyperScore and organs' phenotypic patterns, especially for variables such as white matter hyperintensity and body mass index. This study is the first to potentiate ML-based modelling of hypertension progression from a multi-organ perspective, which could significantly aid in clinical decision making to save lives. © 2023 IEEE.

关键词： blood pressure contrastive PCA Hypertension machine learning semi-supervised learning

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Conflict Resolution Strategy in Handover Management for 4G and 5G Networks

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computers, Materials & Continua 2022年第9期72卷 5215-5232页

作者： Abdulraqeb Alhammadi Wan Haslina Hassan Ayman A.El-Saleh Ibraheem Shayea Hafizal Mohamad Yousef Ibrahim Daradkeh Communication Systems and Networks Research Lab Malaysia-Japan International Institute of TechnologyUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia College of Engineering A’sharqiyah University(ASU)Ibra400Oman Department of Electronics and Communication Engineering Faculty of Electrical and Electronics EngineeringIstanbul Technical University(ITU)Istanbul34467Turke Faculty of Engineering and Built Environment Universiti Sains Islam MalaysiaBandar Baru Nilai71800Malaysia Department of Computer Engineering and Networks College of Engineering at Wadi AddawasirPrince Sattam Bin Abdulaziz UniversityAl Kharj11991KSA

Fifth-generation(5G)cellular networks offer high transmission rates in dense urban ***,a massive deployment of small cells will be required to provide wide-area coverage,which leads to an increase in the number of handovers(HOs).Mobility management is an important issue that requires considerable attention in heterogeneous networks,where 5G ultra-dense small cells coexist with current fourth-generation(4G)*** mobility robustness optimization(MRO)and load balancing optimization(LBO)functions have been introduced in the 3GPP standard to address HO problems,non-robust and nonoptimal algorithms for selecting appropriate HO control parameters(HCPs)still exist,and an optimal solution is subjected to compromise between LBO and MRO ***,HO decision algorithms become *** paper proposes a conflict resolution technique to address the contradiction between MRO and LBO *** proposed technique exploits received signal reference power(RSRP),cell load and user speed to adapt HO margin(HM)and time to trigger(TTT).Estimated HM and TTT depend on a weighting function and HO type which is represented by user status during *** proposed technique is validated with other existing algorithms from the *** results demonstrate that the proposed technique outperforms existing algorithms overall performance *** proposed technique reduces the overall average HO ping-pong probability,HO failure rate and interruption time by more than 90%,46%and 58%,respectively,compared with the other schemes overall speed scenarios and simulation time.

关键词： Mobility management handover 5G heterogeneous networks

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