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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： R. Mohandas M Elavarasi RVS Praveen Hemanand Chittapragada C. Nithiya S. Prabagar Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of BCA - IT Vels Institute of Science Technology & Advanced Studies Pallavaram India Digital Engineering and Assurance LTIMindtree Limited Hyderabad India Department of Computer Science and Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Puduvoyal India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The rapid growth of Internet of Things (IoT) devices has increased security weaknesses, rendering networks vulnerable to advanced cyber-attacks. Traditional detection systems frequently encounter difficulties in real-time anomaly detection and resource limitations, leaving IoT systems exposed. This study presents a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction with Gradient Boosting (GB) to improve classification accuracy. Driven by the increasing incidence and complexity of cyber threats, the study seeks to rectify significant deficiencies in IoT security by providing a scalable, efficient, and high-performance detection system. Experimental findings confirm the model's efficacy, attaining 98.5% accuracy, an F1-score of 0.97, and a recall of 0.97, markedly surpassing current methodologies. The study addresses the issues of protecting IoT networks, suggests a comprehensive detection methodology, and offers a comparative analysis of performance indicators, laying the groundwork for refined, versatile approaches to counter new threats in IoT ecosystems.

关键词： Wireless communication Accuracy Computational modeling Scalability Boosting Rendering (computer graphics) Complexity theory Internet of Things Reliability Cyberattack

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Wireless,multimodal sensors for continuous measurement of pressure,temperature,and hydration of patients in wheelchair

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npj Flexible Electronics 2023年第1期7卷 479-493页

作者： Seokjoo Cho Hyeonseok Han Hyunwoo Park Sung-Uk Lee Jae-Hwan Kim Sung Woo Jeon Mengqiu Wang Raudel Avila Zhaoqian Xi Kabseok Ko Minsu Park Jungyup Lee Myungwoo Choi Je-Sang Lee Weon Gi Min Byeong-Ju Lee Soyeong Lee Jungrak Choi Jimin Gu Jaeho Park Min Seong Kim Junseong Ahn Osman Gul Chankyu Han Gihun Lee Seunghwan Kim Kyuyoung Kim Jeonghyun Kim Chang-Mo Kang Jahyun Koo Sung Soo Kwak Sungbong Kim Dong Yun Choi Seokwoo Jeon Hyung Jin Sung Yong Bae Park Minkyu Je Young Tae Cho Yong Suk Oh Inkyu Park Department of Mechanical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Advanced 3D Printing Technology Development Division Korea Atomic Energy Research InstituteDaejeon 34057Republic of Korea Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsDalian University of TechnologyDalian 116023P.R.China Department of Mechanical Engineering Northwestern UniversityEvanstonIL 60208USA DUT-BSU Joint Institute Dalian University of TechnologyDalian 116023China Ningbo Institute of Dalian University of Technology Ningbo 315016China Department of Electronics Engineering Kangwon National UniversityChuncheon 24341Republic of Korea Querrey-Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Sibel Health Inc NilesIL 60714USA Department of Materials Science and Engineering KAIST Institute for The Nanocentury(KINC)Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Rehabilitation Medicine Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of planning and development Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of rehabilitation medicine Pusan national university hospitalBusan 49241Republic of Korea Department of AI Convergence Network Ajou UniversitySuwon 16499Republic of Korea Department of Electronic Convergence Engineering Kwangwoon UniversitySeoul 01897Republic of Korea Korea Photonics Technology Institute Gwangju 61007Republic of Korea School of Biomedical Engineering Korea UniversitySeoul 02841Republic of Korea Interdisciplinary Program in Precision Public Health Korea UniversitySeoul 02841Republic of Korea Center

Individuals who are unable to walk independently spend most of the day in a *** population is at high risk for developing pressure injuries caused by ***,early diagnosis and prevention of these injuries still remain ***,we introduce battery-free,wireless,multimodal sensors and a movable system for continuous measurement of pressure,temperature,and hydration at skin *** device design includes a crack-activated pressure sensor with nanoscale encapsulations for enhanced sensitivity,a temperature sensor for measuring skin temperature,and a galvanic skin response sensor for measuring skin hydration *** movable system enables power harvesting,and data communication to multiple wireless devices mounted at skin-cushion interfaces of wheelchair users over full body *** evaluations and numerical simulations of the devices,together with clinical trials for wheelchair patients,demonstrate the feasibility and stability of the sensor system for preventing pressure injuries caused by sitting.

关键词： injuries modal mounted

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Automatic Root Length Estimation from Images Acquired In Situ without Segmentation

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Plant Phenomics 2024年第1期6卷 79-93页

作者： Faina Khoroshevsky Kaining Zhou Sharon Chemweno Yael Edan Aharon Bar-Hillel Ofer Hadar Boris Rewald Pavel Baykalov Jhonathan EEphrath Naftali Lazarovitch Department of Industrial Engineering and Management Ben-Gurion University of the NegevBeer ShevaIsrael The Jacob Blaustein Center for Scientific Cooperation The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevSede BoqerIsrael The Albert Katz International School for Desert Studies The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevSede BoqerIsrael Department of Communication Systems Engineering School of Electrical and Computer EngineeringBen-Gurion University of the NegevBeer ShevaIsrael Institute of Forest Ecology Department of Forest and Soil SciencesUniversity of Natural Resources and Life SciencesVienna(BOKU)ViennaAustria Faculty of Forestry and Wood Technology Mendel University in BrnoBrnoCzech Republic Vienna Scientific Instruments GmbH AllandAustria French Associates Institute for Agriculture and Biotechnology of Drylands The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevSede BoqerIsrael

Image-based root phenotyping technologies,including the minirhizotron(MR),have expanded our understanding of the in situ root responses to changing environmental *** conventional manual methods used to analyze MR images are time-consuming,limiting their *** study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total(cumulative)root length(TRL)per MR image without requiring *** data were derived from manual annotations in Rootfly,commonly used software for MR image *** compared TRL estimation with 2 models,a regression-based model and a detection-based model that detects the annotated points along the ***,the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR *** models were trained and tested with 4,015 images acquired using 2 MR system types(manual and automated)and from 4 crop species(corn,pepper,melon,and tomato)grown under various abiotic *** datasets are made publicly available as part of this *** coefficients of determination(R2),between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets,demonstrating that this tool is accurate and *** analyses were conducted to examine the effects of(a)the data acquisition system and thus the image quality on the models'performance,(b)automated differentiation between images with and without roots,and(c)the use of the transfer learning *** approaches can support precision agriculture by providing real-time root growth information.

关键词： Image Root image

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Boosted Neural Decoders: Achieving Extreme Reliability of LDPC Codes for 6G Networks

arXiv

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arXiv 2024年

作者： Kwak, Hee-Youl Yun, Dae-Young Kim, Yongjune Kim, Sang-Hyo No, Jong-Seon The Department of Electrical Electronic and Computer Engineering University of Ulsan Ulsan44610 Korea Republic of Samsung Electronics Co. Ltd. Hwasung18448 Korea Republic of The Department of Electrical Engineering Pohang University of Science and Technology Pohang37673 Korea Republic of The Institute for Convergence Research and Education in Advanced Technology Yonsei University Seoul03722 Korea Republic of The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering INMC Seoul National University Seoul08826 Korea Republic of

Ensuring extremely high reliability in channel coding is essential for 6G networks. The next-generation of ultra-reliable and low-latency communications (xURLLC) scenario within 6G networks requires frame error rate (FER) below 10−9. However, low-density parity-check (LDPC) codes, the standard in 5G new radio (NR), encounter a challenge known as the error floor phenomenon, which hinders to achieve such low rates. To tackle this problem, we introduce an innovative solution: boosted neural min-sum (NMS) decoder. This decoder operates identically to conventional NMS decoders, but is trained by novel training methods including: i) boosting learning with uncorrected vectors, ii) block-wise training schedule to address the vanishing gradient issue, iii) dynamic weight sharing to minimize the number of trainable weights, iv) transfer learning to reduce the required sample count, and v) data augmentation to expedite the sampling process. Leveraging these training strategies, the boosted NMS decoder achieves the state-of-the art performance in reducing the error floor as well as superior waterfall performance. Remarkably, we fulfill the 6G xURLLC requirement for 5G LDPC codes without a severe error floor. Additionally, the boosted NMS decoder, once its weights are trained, can perform decoding without additional modules, making it highly practical for immediate application. The source code is available at https://***/ghy1228/LDPC_Error_Floor. © 2024, CC BY.

关键词： Decoding

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Engineered common environmental effects on multitransistor systems

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Physical Review B 2023年第7期107卷 075440-075440页

作者： Uthpala N. Ekanayake Sarath D. Gunapala Malin Premaratne Advanced Computing and Simulation Laboratory (AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton Victoria 3800 Australia Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 USA

In this paper, we analyze the impact of physically large baths used in terminals of thermal multitransistor systems formed using two-level systems (TLSs). In particular, we simulate the effects of a two-transistor system introduced as a thermal Darlington pair (DP). The size and proximity of the baths can cause multiple interactions with the transistor terminals represented by the TLS, not just the TLS directly connected to them. Such interactions can ultimately suppress the heat flows or impair the transistor action. However, we demonstrate that the DP model can achieve more than a 50% increase in heat flows. Using the engineered interactions leading to the correlated TLS-thermal bath interactions, we establish an incoherent (no quantum coherence in the density matrix at the steady state) yet correlated (joint excitation of two TLSs due to bath interaction) heat transfer model to a two-transistor arrangement in a substrate. This model helps us to interpret the environmental effects on the device by treating the common environment as separate thermal baths and all the transitions in the system as independent. We also show that this model can be mapped to contain dark-states. These dark-states can provide an external channel for transistor switching. We use this knowledge to design thermal counterparts of electronic AND and OR gates, and to study their switching time and operation, paving the way to realizing thermal logic gates.

关键词： Open quantum systems Quantum gates Thermal properties Nanotechnology Transistors

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High Temperature Robustness of Enhancement-Mode p-GaN-Gated AlGaN/GaN HEMT Technology 9

High Temperature Robustness of Enhancement-Mode p-GaN-Gated ...

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9th IEEE Workshop on Wide Bandgap Power Devices and Applications, WiPDA 2022

作者： Yuan, Mengyang Xie, Qingyun Niroula, John Isamotu, Mohamed Fadil Rajput, Nitul S. Chowdhury, Nadim Palacios, Tomas Massachusetts Institute of Technology Microsystems Technology Laboratories CambridgeMA02139 United States Morgan State University Department of Electrical and Computer Engineering BaltimoreMD21251 United States Advanced Materials Research Center Technology Innovation Institute United Arab Emirates Bangladesh University of Engineering and Technology Department of Electrical and Electronic Engineering Dhaka1205 Bangladesh

ISBN: (数字)9781665489003

ISBN: (纸本)9781665489003

This paper reports on the high temperature (HT) robustness of enhancement-mode (E-mode) p-GaN-gated Al-GaN/GaN high electron mobility transistors (HEMTs), with an emphasis on the key transistor-level parameters for digital and analog mixed-signal applications. In-situ measurements from room temperature (RT) to 500 °C show that trends in Vth, RON, ID,max and IG,max are largely as expected based on first-order changes in the semiconductor properties. The fabricated transistors exhibited stable performance over 20 days at 500 °C. To the best of the authors' knowledge, this work is the first systematic study on the HT performance of E-mode p-GaN-gated AlGaN/GaN HEMTs, and sheds light on their use in mixed-signal and low-voltage power circuits. © 2022 IEEE.

关键词： III-V semiconductors

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Enhanced Software Defect Prediction using Quantum Hamiltonian Generative Adversarial Network for Improved Software Performance Reliability

Enhanced Software Defect Prediction using Quantum Hamiltonia...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Jyoti Chaudhary Vijaya Kumar A V Manikannan K Amol Sapatnekar Amit Barve Ramya Maranan Department of Computer Engineering The Technological Institute of Textile and Sciences Bhiwani Haryana India Department of Computer Science & Engineering Presidency University Bangalore Karnataka India Department of Computer Science and Engineering R.M.K Engineering College Thiruvalluvar Tamil Nadu India Faculty of Law Symbiosis Centre for Advanced Legal Studies and Research Symbiosis Law School Symbiosis International (Deemed University) Pune India Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Gujarat India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The development of software systems with high quality and decreased maintenance costs depends on Software Defect Prediction (SDF). The current dataset qualities combined with feature redundancies make confident fault detection difficult through conventional methods. Current defect prediction techniques display multiple deficits through inaccurate results along with many false alarms and dataset bias. A high-performance quantum-inspired method must be researched in working to create an accurate precision model. The project will create the Enhanced Software Defect Prediction using Quantum Hamiltonian Generative Adversarial Network for Improved Software Performance Reliability (DBO-QH-GAN) model. Several software metrics need to be retrieved from the PROMISE datasets before this process can be initiated. The second step includes Fuzzy K- Top Matcher (FK- TM) functionality that removes undesirable noise factors such as redundant features and missing values as well as outliers. The Botox Optimization (BotO) algorithm selects five essential metrics that encompass cyclomatic complexity as well as Halstead measures and coupling and code churn and historical defect density. These preprocessed set of characteristics are fed to a Quantum Hamiltonian Generative Adversarial Network (QH-GAN) which identifies the defects from probabilistic thresholding and thereafter optimized using Duck Bevy Optimization (DBO) to achieve the optimal possible performance. Accuracy of the model was 99.2%, precision was 99.4%, and recall was 99.1 %. The DBO-QH-GAN model sets new standards for defect prediction by providing an improved enhancement of reliability along with decreased maintenance cost.

关键词： Quantum computing Costs Accuracy Software performance Predictive models Generative adversarial networks Software reliability Maintenance Optimization Standards

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Qi Standard Metasurface for Free-Positioning and Multi-Device Supportive Wireless Power Transfer

arXiv

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arXiv 2023年

作者： Wang, Hanwei Yu, Joshua Ye, Xiaodong Chen, Yun-Sheng Zhao, Yang Nick Holonyak Micro and Nanotechnology Laboratory The Electrical and Computer Engineering Department University of Illinois at Urbana-Champaign Urbana61801 United States The Electrical and Computer Engineering Department University of Illinois at Urbana-Champaign Urbana61801 United States The Beckman Institute for Advanced Science and Technology The Electrical and Computer Engineering Department University of Illinois at Urbana-Champaign Urbana61801 United States

Free-positioning and multi-user supportive wireless power transfer systems represent the next-generation technology for wireless charging under the Qi standard. Traditional approaches employ multiple transmitting coils and multi-channel driving circuits with active control algorithms to achieve these goals. However, these traditional approaches are significantly limited by cost, weight, and heating due to their relatively low efficiency. Here, we demonstrate an innovative approach by using a metasurface to achieve free-positioning and multi-user compatibility. The metasurface works as a passive device to reform the magnetic field and enables high-efficiency free-positioning wireless power transfer with only a single transmitting coil. It shows up to 4.6 times improvement in efficiency. The metasurface also increases the coverage area from around 5 cm by 5 cm with over 40% efficiency to around 10 cm by 10 cm with over 70% efficiency. We further show that the system can support multiple receivers. Besides increasing the overall efficiency, we demonstrate tuning the power division between the multiple receivers, enabling compensation of receivers of different sizes to achieve their desired power. © 2023, CC BY.

关键词： Efficiency

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer 24

Predicting Microsatellite Instability from Histology Images ...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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Transforming Hematological Data Interpretation: A Deep Learning and NLP Framework for Blood Cancer Prognostics

Transforming Hematological Data Interpretation: A Deep Learn...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Erupaka Nitya Vinay Kumar Nassa Anupam Singh Priyanka K. Maithili Vartika Agarwal Department of CSIT CVR College of Engineering Hyderabad India Department of Information Communication Technology(ICT} Tecnia Institute of Advanced Studies (Delhi) Delhi India Department of Computer Science and Engineering Graphic Era Hill University Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India University Institute of Computing Chandigarh University Gharuan Gharuan India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

In this research work, we have investigated the transformative potential of a deep learning and natural language processing framework in enriching the capabilities to prognosticate blood cancer. Specifically, we used a dataset that covers a complete range of a clinical, high-resolution blood smear, bone marrow biopsy and imaging such as CT, MRI and PET scans. To achieve our objectives, we also designed and implemented hybrid models of CNNs and LSTM networks, specifically VGG 19 + LSTM, VGG 16 + LSTM and ResNet 50 + LSTM. The results of the study showed that the VGG 19 + LSTM model achieved the highest accuracy than the VGG 16 + LSTM and ResNet 50 + LSTM models. The confusion matrix, precision, recall and F1 score metrics gave a clear indication of the fact that VGG 19 + LSTM has the highest capacity predictive capabilities. During the calculation of the confusion matrix, this model showed the minimum number of false positives and false negatives. We also noticed that all models converge during the loss and accuracy in every epoch, which suggests that the model has the potential to learn from the complex data. Through the extensive utilisation of clinical data with imaging, we designed reliable diagnostic tools that have more chances to prognosticate accurately. Moreover, it has made a significant contribution to the validation of deep learning in interpreting medical data. Most importantly, it will also give a kind of approach to future work to further extend these methods for other cancers.

关键词： Deep learning Measurement Accuracy Data models Natural language processing Positron emission tomography Long short term memory Blood Medical diagnostic imaging Cancer

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