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IEEE Open Journal of Nanotechnology 2023年 4卷 229-238页

作者： Kola, Sekhar Reddy Li, Yiming National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Biomedical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

Characteristic variability induced by process variation effect (PVE) is one of technological challenges in semiconductor industry. In this work, we computationally study electrical characteristic and power fluctuations induced by six factors of PVE of the gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field-effect-transistors (CFETs) which are formed by vertically stacking n-FET on top of p-FET. Among the six factors, NS thickness (TNS), NS width (WNS), and gate length (LG) are identified as crucial factors contributing to large variations in device characteristics. The p-FET exhibits substantial off-state current fluctuation (about 151%) due to the bottom parasitic channel leakages. Compared with the magnitudes of dynamic and short circuit powers, the static power is marginal, but it possesses the largest fluctuation (up to 148%). If we assume that each factor of PVE has the same probability distribution as the others and all are mutually independent, the statistical sum of their power fluctuations will exhibit more than 50% overestimations, compared with the results when all factors are considered simultaneously. © 2020 IEEE.

关键词： III-V semiconductors

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Diffusion models applied to skin and oral cancer classification

arXiv

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arXiv 2025年

作者： Uliana, José J.M. Krohling, Renato A. Labcin - Nature-inspired Computing Lab Federal University of Espírito Santo Vitória Brazil Labcin - Nature-inspired Computing Lab Graduate Program in Computer Science PPGI UFES Federal University of Espírito Santo Vitória Brazil

This study investigates the application of diffusion models in medical image classification (DiffMIC), focusing on skin and oral lesions. Utilizing the datasets PAD-UFES-20 for skin cancer and P-NDB-UFES for oral cancer, the diffusion model demonstrated competitive performance compared to state-of-the-art deep learning models like Convolutional Neural Networks (CNNs) and Transformers. Specifically, for the PAD-UFES-20 dataset, the model achieved a balanced accuracy of 0.6457 for six-class classification and 0.8357 for binary classification (cancer vs. non-cancer). For the P-NDB-UFES dataset, it attained a balanced accuracy of 0.9050. These results suggest that diffusion models are viable models for classifying medical images of skin and oral lesions. In addition, we investigate the robustness of the model trained on PAD-UFES-20 for skin cancer but tested on the clinical images of the HIBA dataset. © 2025, CC BY.

关键词： Lung cancer

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An Enhanced TSP-Based Approach for Active Debris Removal Mission Planning 10th

An Enhanced TSP-Based Approach for Active Debris Removal Mis...

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10th Brazilian Conference on Intelligent Systems, BRACIS 2021

作者： Rodrigues Neto, João Batista de Oliveira Ramos, Gabriel Graduate Program in Applied Computing Universidade do Vale do Rio dos Sinos RS São Leopoldo Brazil

ISBN: (纸本)9783030917012

The extensive exploration of the Low Earth Orbit (LEO) has created a dangerous spacial environment, where space debris has threatened the feasibility of future operations. In this sense, Active Debris Removal (ADR) missions are required to clean up the space, deorbiting the debris with a spacecraft. ADR mission planning has been investigated in the literature by means of metaheuristic approaches, focused on maximizing the amount of removed debris given the constraints of the spacecraft. The state-of-the-art approach uses an inver-over and maximal open walk algorithms to solve this problem. However, that approach fails to deal with large instances and duration constraints. This work extends the state of the art, increasing its performance and modeling all the constraints. Experimental results evidence the improvements over the original approach, including the ability to run for scenarios with thousands of debris. © 2021, Springer Nature Switzerland AG.

关键词： Space debris

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Multilabel Machine Learning System for Predicting Gender and Age Group from Human Gait 2

Multilabel Machine Learning System for Predicting Gender and...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Qomariyah, Nunung Nurul Nurul Fajar, Ahmad Yono, Vincent Kazakov, Dimitar Bina Nusantara University School of Computing and Creative Arts Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Information System Management Information Systems Management Department Jakarta11480 Indonesia University of York Computer Science Department YO10 5GH United Kingdom

ISBN: (纸本)9798350360523

This paper explores the development of a multilabel machine learning system for predicting both gender and age from human gait patterns. Gait analysis, a non-intrusive method of identifying subtle nuances in human movement, has proven to be a rich source of information related to demographic characteristics. The research extends beyond traditional single-label classification approaches, adopting a multilabel framework to simultaneously predict gender and age *** study evaluates various multilabel machine learning algorithms, with the Random k-labeLsets (RAKEL) algorithm demonstrating superior performance in predicting gender and age labels from human gait datasets. The accuracy of the algorithm can reach up to 87%. We also compared the multilabel approach with several multiclass algorithms such as Decision Tree, Random Forest, Gradient Boosting, K-Neighbors and XGBoost. However, when we also considering the training time, Classifier Chain algorithm showed the best trade off with the accuracy of 86% and the training time is twice faster than the RAKEL algorithm. © 2024 IEEE.

关键词： Gait analysis

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Evaluation of an agent-based reinforcement learning framework for model-based testing

Evaluation of an agent-based reinforcement learning framewor...

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Inteligencia Ambiental, Ingeniería de Software y Salud Electrónica y Móvil (AmITIC), V Congreso Internacional en

作者： Jose Ramírez-Méndez Christian Quesada-López Graduate Program in Computing and Informatics University of Costa Rica San Pedro Costa Rica Department of Computer Science and Informatics University of Costa Rica San Pedro Costa Rica

ISBN: (数字)9798350364538

ISBN: (纸本)9798350364545

This study evaluates an agent-based reinforcement learning framework for model-based testing (MBT). The framework’s performance was assessed on three key metrics: effectiveness and efficiency in achieving model coverage objectives, the quantity and uniqueness of generated test cases, and code coverage. The results show improved metrics for the framework compared to traditional testers in model coverage evaluation and test case metrics. Specifically, the framework achieved higher effectiveness and efficiency, generating a higher average number of test cases with a substantial proportion of unique cases, indicating more diverse and thorough testing. Additionally, the framework, along with random and greedy testers, achieved over 70% coverage across branch, method, and line code metrics. The framework showed slightly higher values in method and line coverage compared to other testers. The evaluation highlights the use of agent-based reinforcement learning to support model-based testing in planning test case generation, exploring models using multiple strategies, and learning guided by testing metrics. Future work will focus on further refining the framework learning model, including error coverage evaluation, and testing its applicability across different software systems.

关键词： Measurement Software testing Codes Calculators Refining Reinforcement learning Software systems Planning Testing

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Lightweight SPIFFE Verifiable Identity Document (LSVID): A Nested Token Approach for Enhanced Security and Flexibility in SPIFFE

Lightweight SPIFFE Verifiable Identity Document (LSVID): A N...

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IEEE International Conference on Cloud computing Technology and Science (CloudCom)

作者： Henrique Z. Cochak Charles C. Miers Pedro H.B. Correia Marco A. Marques Marcos A. Simplicio Graduate Program in Applied Computing (PPGCAP) Santa Catarina State University (UDESC) Brazil Laboratory of Computer Networks and Architecture (LARC) Universidade de Sao Paulo (USP) Brazil

ISBN: (数字)9798331507589

ISBN: (纸本)9798331507596

Service identities are crucial for authentication and access control, ensuring that only authorized services access specific resources. The SPIFFE framework addresses workload identity management and authentication effectively but needs support for solutions (e.g., extensible tokens) that fine-granular authorization mechanisms in distributed scenarios can use. In this context, we present the Lightweight SVID (LSVID), an identity document in JSON format that can be extended and used as a token. As an extensible token, Lightweight SVID (LSVID) enables features such as delegation, attenuation, and traceability, enhancing its flexibility and applicability. Our approach provides efficient handling of token extensions and validations, demonstrated through a proof-of-concept implemented in Go. Baseline results indicate that LSVID critical operations are efficient, with processing times in the microsecond range, offering significant functional advantages over the traditional JWT-SVIDs, one of two key security documents from SPIFFE.

关键词： Computers Authorization Cloud computing Authentication Attenuation

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Architectural Exploration for Energy-Efficient LMS and NLMS Adaptive Filters VLSI Design

Architectural Exploration for Energy-Efficient LMS and NLMS ...

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Pedro T. L. Pereira Guilherme Paim Eduardo Costa Paulo Flores Sergio Bampi Graduate Program on Microelectronics (PGMicro) Federal University of Rio Grande do Sul (UFRGS) Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Brazil INESC-ID Instituto Superior Técnico ULisboa Portugal

Adaptive filtering plays an essential role in digital signal processing (DSP), being present in many application fields such as noise reduction, telecommunication, and audio signal processing. Notably, the least mean square (LMS) and its normalized version (NLMS) are the most used algorithms due to their simplicity and robustness, which turn them attractive to hardware solutions. This work proposes architectural solutions for LMS and NLMS algorithms targeting an energy-efficient VLSI design. We explore fully sequential, fully parallel, and semi-parallel architectures reusing the hardware as much as possible for both algorithms. It allows for investigating the number of steps necessary for each architecture, demonstrating the energy per operation, circuit area, and throughput savings. The synthesis results highlight the semi-parallel architectures LMS-2 steps and NLMS-3 steps have the best trade-off energy-speed. In addition, we proved the architecture’s energy efficiency in a harmonic canceling case study, achieving more than 60% in energy savings using both filters.

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A Design Framework for Neural Network Architecture Exploration

A Design Framework for Neural Network Architecture Explorati...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Luis Antonio Spader Simon Lucas Soares Brunno Abreu Mateus Grellert Embedded Computing Laboratory (ECL) Federal University of Santa Catarina (UFSC) Brazil Graduate Program in Microelectronics (PGMicro) Federal University of Rio Grande do SuI (UFRGS) Brazil

The use of neural networks (NNs) has significantly increased due to their ability to solve complex problems in different domains. NNs are increasingly being employed in embedded applications, so energy-efficient solutions are critical to enabling the wide adoption of such technologies. However, the flexibility of NNs makes the design of accelerators quite challenging, as there are many architectural parameters that must be optimized for each case, such as the number of inputs, input width, number of pipeline stages, activation functions, etc. This work proposes a framework that provides an automatic generation of RTL descriptions of NN architectures to expedite design space exploration. To show the usefulness of our solution, we compare several NN models in terms of important design parameters such as area and power, along with a model accuracy performance. Special attention should be given to carefully managing the bit width. Additionally, when prioritizing model accuracy, careful consideration of the bit width and the number of hidden layers is essential, as hidden layers may contribute to propagated errors.

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Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data

Multi-task Learning Graph Neural Networks for Cancer Prognos...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Tsung-Wei Lin Sofia Ormazabal Arriagada Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taiwan International Graduate Program AIoT NTU Research Center for Information Technology Innovation Academia Sinica Department of Electrical Engineering NTU Center for Advanced Computing and Imaging in Biomedicine NTU Smart Medicine and Health Informatics Program NTU

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a novel approach that combines multi-task learning (MTL) and graph neural networks (GNNs) to address this issue. By representing gene-gene interactions as a graph network, our approach leverages multi-task learning to effectively capture the relationships of genes relevant to the oncogenesis and progression of breast, lung, and colon cancer. We demonstrate that our approach improves the cancer prognosis prediction for cancers with fewer samples, such as colon adenocarcinoma, by leveraging the shared gene-gene interactions across different cancer types, obtaining increases in the area under the precision-recall curve (AUPRC) of 24%. Our work contributes to the field of smart healthcare by demonstrating the potential of MTL and GNNs for enhancing cancer prognosis prediction, even with limited data samples.

关键词： Lungs Genomics Medical services Colorectal cancer Breast Multitasking Oncology Graph neural networks Prognostics and health management Cancer

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AxASRE: A Novel Approach to Approximate Adder Synthesis Results Estimation.

AxASRE: A Novel Approach to Approximate Adder Synthesis Resu...

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International Conference on Dependable Systems and Networks Workshops (DSN-W)

作者： Pedro T. L. Pereira Guilherme Paim Paulo Flores Eduardo Costa Sergio Bampi Graduate Program on Microelectronics (PGMicro) Federal University of Rio Grande do Sul (UFRGS) Brazil INESC-ID Instituto Superior Técnico ULisboa Portugal Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Brazil

Thanks to the high energy efficiency achieved by approximate circuits, the exploration of approximate solutions for a wide of applications is the focus of academic and industrial research. However, defining the most appropriate approximation considering the power consumption and accuracy trade-off can be exhaustive. This paper proposes a novel approach to estimate the synthesis results for approximate adders (AxA) with different bit-width and approximation levels based on a database and simple mathematical operations to reduce the synthesis process complexity during an approximate circuit development. We implement the proposed approach to estimate the synthesis results for the Copy, Error-Tolerant Adder I (ETA-I), Lower-part-or (LOA), and Truncation (Trunc) AxAs. Comparing our estimation with real synthesis results obtained by an industrial synthesis tool, we prove the approach’s efficiency by presenting an average error on the estimated parameters (area, power and delay) less than 10% in the worst case. In addition, our approach reduces by more than 25 times the number of synthesis runs when estimating the synthesis results for all circuits in the bit width range of 2 to 64 with all possible approximation levels.

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