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检索条件"任意字段=International Conference on Embedded Computer Systems -Architectures, Modeling and Simulation"

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Towards a Dynamic Composability Approach for using Heterogeneous systems in Remote Sensing 18

Towards a Dynamic Composability Approach for using Heterogen...

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IEEE 18th international conference on E-Science (E-Science)

作者： Altintas, Ilkay Perez, Ismael Mishin, Dmitry Trouillaud, Adrien Irving, Christopher Graham, John Tatineni, Mahidhar DeFanti, Thomas Strande, Shawn Smarr, Larry Norman, Michael L. Univ Calif San Diego La Jolla CA 92093 USA Admiralty Seattle WA USA

ISBN: (数字)9781665461245

ISBN: (纸本)9781665461245

Influenced by the advances in data and computing, the scientific practice increasingly involves machine learning and artificial intelligence driven methods which requires specialized capabilities at the system-, science- and service-level in addition to the conventional large-capacity supercomputing approaches. The latest distributed architectures built around the composability of data-centric applications led to the emergence of a new ecosystem for container coordination and integration. However, there is still a divide between the application development pipelines of existing supercomputing environments, and these new dynamic environments that disaggregate fluid resource pools through accessible, portable and re-programmable interfaces. New approaches for dynamic composability of heterogeneous systems are needed to further advance the data-driven scientific practice for the purpose of more efficient computing and usable tools for specific scientific domains. In this paper, we present a novel approach for using composable systems in the intersection between scientific computing, artificial intelligence (AI), and remote sensing domain. We describe the architecture of a first working example of a composable infrastructure that federates Expanse, an NSF-funded supercomputer, with Nautilus, a Kubernetes-based GPU geo-distributed cluster. We also summarize a case study in wildfire modeling, that demonstrates the application of this new infrastructure in scientific workflows: a composed system that bridges the insights from edge sensing, AI and computing capabilities with a physics-driven simulation.

关键词： Composable systems Kubernetes Multi-Cluster Federation Artificial Intelligence Remote Sensing

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Deep Learning Techniques for Enhanced Classification of Gastrointestinal Images

Deep Learning Techniques for Enhanced Classification of Gast...

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embedded systems and Artificial Intelligence (ESAI), international conference on

作者： Zakariae El-Jabbari Jaouad Boumhidi Department of Computer Sciences LISAC Laboratory Faculty of Sciences Dhar El MahrazSidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

Deep learning has revolutionized medical image analysis, particularly in the classification of gastrointestinal abnormalities. This paper proposes a classification model based on an ensemble learning approach, combining three Convolutional Neural Network (CNN) architectures: VGG16, DenseNet121, and InceptionV3. We first trained each model independently, then reused them with pre-trained weights from ImageNet to enhance performance. The outputs of the three models are concatenated to create a unified feature vector, which is then used for final classification through fully connected layers. This model was tested on the HyperKvasir dataset, a benchmark in gastrointestinal imaging. Our results demonstrate that using pretrained weights significantly improves accuracy compared to training from scratch, highlighting the effectiveness of the ensemble approach and pre-training in medical image classification.

关键词： Deep learning Training Adaptation models Accuracy Computational modeling Feature extraction Gastrointestinal tract Ensemble learning Convolutional neural networks Biomedical imaging

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Investigating synchronization phenomena in chaotic ring oscillators coupled through memristive devices

Investigating synchronization phenomena in chaotic ring osci...

引用

international conference on Synthesis, modeling, Analysis and simulation Methods and Applications to Circuit Design (SMACD)

作者： Rafailia-Eleni Karamani Iosif-Angelos Fyrigos Georgios Ch. Sirakoulis Dept. of Electrical & Computer Engineering (EECE) Democritus University of Thrace (DUTh) Xanthi Greece Dept. of EECE DUTh Xanthi Greece

This paper aims to present the synchronization phenomena that emerge through the interaction of chaotic oscillators forming a network, coupled utilizing the promising characteristics of novel nanoelectronic circuit elements, namely memristors. This architecture exploits the reprogrammability feature of memristors to offer the capability of controlling the coupling radius of the oscillatory network without the need to modify its structure. Memristive devices can be manufactured in crossbar architectures, allowing for dense, reprogrammable structures. Chaotic CMOS ring oscillators (CMOS ROs) have been chosen as they provide a low-size and low-power consumption alternative that is easy to implement in ICs. Chaotic oscillators can be integrated with other hardware components, such as memristors, to create hybrid neuromorphic systems that combine the advantages of these technologies for the introduction of versatile, reconfigurable architectures.

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A Network Attack Evaluation Model Based on Variable Weight Theory

A Network Attack Evaluation Model Based on Variable Weight T...

引用

2021 international conference on Network Communication and Information Security, ICNCIS 2021

作者： Li, Zhigeng Wu, Bin Beijing University of Posts and Telecommunications China

ISBN: (数字)9781510653283

ISBN: (纸本)9781510653276

With the development of the network, people pay more and more attention to network security. Network attack assessment is a very important item in network security assessment. Starting from three aspects: network attack evaluation attribute index selection, index weight determination, and network attack evaluation model establishment, based on the shortcomings of existing evaluation methods, this paper proposes a variable weight evaluation algorithm and a network attack evaluation model based on TOPSIS. Spatio-temporal correlation attributes are introduced into the attack effect evaluation index system, and a method of fusion of constant weight and variable weight is proposed to determine the weight of the index system. While ensuring the rationality of the indicator system, the compensation mechanism between indicators is overcome. Finally, the algorithm was verified through simulation experiments, and the experimental results show that the algorithm improves the accuracy and effectiveness of the evaluation. © 2022 SPIE

关键词： Networks Network architectures Statistical analysis Algorithms computer simulations Defense systems Network security Data modeling Factor analysis Telecommunications

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Autism Disease Prediction Using Deep Learning & Transfer Learning

Autism Disease Prediction Using Deep Learning & Transfer Lea...

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international conference on Networking, embedded and Wireless systems (ICNEWS)

作者： Abhishek Das Towhidul Alam Md. Zihad Hossain Habiba Rashid Lamiya Department of Electrical and Computer Engineering North South University Dhaka Bangladesh

ISBN: (数字)9798350367461

ISBN: (纸本)9798350367478

The neurological disease known as autism spectrum disorder (ASD) is characterized by impaired social interaction, communication issues, and constrained and repetitive behavior patterns. For the benefit of early interventions and support for afflicted persons, timely and accurate ASD prognosis is essential. Deep learning methods have become effective tools for predictive modeling across a range of industries, including healthcare. This study examines the use of deep learning and transfer learning to forecast ASD using a large dataset of clinical and behavioral variables. In this study, the effectiveness of three well-known deep learning architectures VGG16, DenseNet121, and MobileNetv2 in predicting ASDs is compared. A sizable dataset with a variety of ASD-related variables, such as demographic data, medical histories, and behavioral assessments, is used to train the models. To take use of pre-learned weights from models trained on extensive generic image recognition tasks, transfer learning is used. With accuracy rates of 97% apiece, the experimental results show remarkable prediction performance for VGG16 and DenseNet121. These models have significant generalization abilities that make it possible to make reliable predictions for identifying those who are at risk for ASD. In contrast to the other architectures, MobileNetv2 only obtains an accuracy of 73%. The results show that deeper architectures like VGG16 and DenseNet121 capture the rich patterns and fine details of the input data, resulting in more precise predictions. Additionally, thorough investigations are carried out to look into the models' learned representations and pinpoint the primary features that influence ASD prediction. These revelations aid in a better comprehension of the underlying causes and potential biomarkers of ASD. The information gleaned from these studies can direct ongoing research projects and support the creation of individualized interventions and therapies. Overall, the study empha

关键词： Deep learning Wireless communication Autism Accuracy Biological system modeling Transfer learning Predictive models Biomarkers Reliability Prognostics and health management

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Systematic Synthesis of Energy-Aware Timing Models in Automotive Software systems 8th

Systematic Synthesis of Energy-Aware Timing Models in Automo...

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8th international conference on Model-Driven Engineering and Software Development (MODELSWARD)

作者： Iyenghar, Padma Univ Osnabrueck Software Engn Res Grp Osnabruck Germany

ISBN: (纸本)9783030674441;9783030674458

In automotive embedded software, functions have several performance requirements such as timing, energy, safety and reliability. For such complex software architectures, an early evaluation and decision on the best set of performance configuration (e.g. timing vs energy trade-offs) could save costly corrections of potential errors in the design. For example, appropriate performance analysis workflows and frameworks if employed already during early design stages, allow us to understand the performance aspects and behavior of the system depending on software and hardware characteristics. The main input required for such analysis is the performance-analysis model based on the underlying design model. In this context, this chapter presents a workflow for synthesis of energy-aware timing analysis models for AUTOSAR-based embedded software systems developed using the Unified modeling Language (UML)/systems modeling Language (SysML) domains. A prototype of the model transformations for the synthesis of the energy-aware timing models and its evaluation in an automotive use case is presented.

关键词： Energy-aware timing model AUTOSAR Unified modeling language (UML) Synthesis Meta-model Model transformation

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Memristor-based High Speed and Area Efficient Comparators in IMPLY Logic

Memristor-based High Speed and Area Efficient Comparators in...

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international conference on VLSI Design

作者： Nandit Kaushik B. Srinivasu School Of Computing & Electrical Engineering Indian Institute of Technology Mandi Mandi India

Because of its In-Memory-Computation (IMC) capacity and low area footprint, memristive technology is a rapidly growing alternative to traditional computer architectures. Using IMPLY logic, this research proposes an efficient multi-bit comparator architecture in memristor technology. This article proposes three one-bit comparator designs, two of which conduct serial computations and the other performs parallel computations. An area efficient comparator design takes only four memristors to make the comparison in thirteen computational steps by reusing the input memristors. When the number of memristors that are reused decreases, the design speed increases. This is seen in the second serial comparator design, which computes the result in only eight steps using six memristors. A comparator design with seven memristors that performs the parallel computations takes only six steps to compute the output. The proposed method can be extended to multi-bit designs. A two-bit high speed comparator is also proposed, which takes twenty-two steps to perform the magnitude comparison. The proposed comparator designs were simulated in the Cadence Virtuoso using the VTEAM model.

关键词： embedded systems Computational modeling Memristors computer architecture Very large scale integration Computational efficiency

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Towards Web Platform for Cloud-Based Virtual Commissioning of IEC 61499 Distributed Automation systems

Towards Web Platform for Cloud-Based Virtual Commissioning o...

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international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Tuojian Lyu Tung Nguyen Roman Rumiantsev Mikhail Kolesnikov Hansani Perera Udayanto Dwi Atmojo Valeriy Vyatkin Department of Electrical Engineering and Automation Aalto University Espoo Finland Department of Computer Science Computer and Space Engineering Luleå Tekniska Universitet Sweden

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

In this paper, we propose a cloud-based virtual commissioning Web platform for IEC 61499 compliant distributed automation systems that support Create-Read-Update-Delete operations for SoftPLC containers from various versions and provide a remote engineering workstation with an embedded web Integrated Development Environment (IDE), Human Machine Interface (HMI), and 3D simulation models. A water tank use case demonstrates the platform's flexibility, enabling control software testing entirely via a Web browser without local installations.

关键词： Water Software testing Cloud computing Solid modeling Three-dimensional displays Human-machine systems Storage management Workstations IEC Standards Manufacturing automation

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Stability Analysis for Frequency Tunable Bandpass Delta-Sigma ADC architectures

Stability Analysis for Frequency Tunable Bandpass Delta-Sigm...

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international conference on Synthesis, modeling, Analysis and simulation Methods and Applications to Circuit Design (SMACD)

作者： Jesko Flemming Bernhard Wicht Pascal Witte Department of Integrated Circuits and Embedded Systems University of Applied Sciences and Arts Hanover Germany Institute of Microelectronic Systems Leibniz Universitat Hannover Hanover Germany Laboratory of Nano and Quantum Engineering Leibniz Universitat Hannover Hanover Germany

This paper presents bandpass delta-sigma modulator (BP-Δ∑M) structures, which are specifically suited for frequency tuning. The structures are identified through stability analyses and comparisons between chain of resonators feedforward (CRFF) and chain of resonators feedback (CRFB) delta-sigma modulator (Δ∑M) configurations. Frequency tuning is predominantly achieved through altering the resonator coefficients. Therefore, the paper guides designers though the analysis process for identifying suitable tunable BP-Δ∑M structures. When moderately changing the resonators the structures perform almost identical to a non-tunable modulator optimized for the same frequency. For large frequency shifts, the out-of-band gain (OOBG) of the signal transfer function (STF) and noise transfer function (NTF) in the CRFF structure, drastically increases and the control loop becomes unstable. It is shown how utilizing CRFB structures is advantageous for frequency tuning and that the OOBG in CRFF structures can be compensated through a unity STF (USTF) Δ∑M structure.

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WE-HML: hybrid WCET estimation using machine learning for architectures with caches 27

WE-HML: hybrid WCET estimation using machine learning for ar...

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27th IEEE international conference on embedded and Real-Time Computing systems and Applications (RTCSA)

作者： Amalou, Abderaouf N. Puaut, Isabelle Muller, Gilles Univ Rennes Rennes France Inria Paris Rennes France

ISBN: (纸本)9781665441889

Modern processors raise a challenge for WCET estimation, since detailed knowledge of the processor micro-architecture is not available. This paper proposes a novel hybrid WCET estimation technique, WE-HML, in which the longest path is estimated using static techniques, whereas machine learning (ML) is used to determine the WCET of basic blocks. In contrast to existing literature using ML techniques for WCET estimation, WE-HML (i) operates on binary code for improved precision of learning, as compared to the related techniques operating at source code or intermediate code level;(ii) trains the ML algorithms on a large set of automatically generated programs for improved quality of learning;(iii) proposes a technique to take into account data caches. Experiments on an ARM Cortex-A53 processor show that for all benchmarks, WCET estimates obtained by WE-HML are larger than all possible execution times. Moreover, the cache modeling technique of WE-HML allows an improvement of 65 percent on average of WCET estimates compared to its cache-agnostic equivalent.

关键词： Maximum likelihood estimation Program processors Pollution Machine learning computer architecture Benchmark testing Prediction algorithms

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