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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications X"

共 16651 条记录，以下是1041-1050 订阅

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A Synthesis Methodology for Intelligent Memory Interfaces in Accelerator systems 25

A Synthesis Methodology for Intelligent Memory Interfaces in...

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30th Asia and South Pacific Design Automation conference, ASP-DAC 2025

作者： Limaye, Ankur Bohm Agostini, Nicolas Barone, Claudio Castellana, Vito Giovanni Fiorito, Michele Ferrandi, Fabrizio Marquez, Andres Tumeo, Antonino Pacific Northwest National Laboratory RichlandWA United States Politecnico di Milano Milano Milano Italy

ISBN: (纸本)9798400706356

Domain-specific systems improve the performance of specific applications compared to general-purpose processing systems by deploying custom hardware accelerators. These hardware accelerators are generated using high-level synthesis (HLS) tools. The HLS tools enable a comprehensive design space exploration, optimizing the accelerators' compute performance. However, they often ignore the challenges of implementing the accelerators in a system-on-chip, particularly how they access memory. Our work introduces a buffering system design that improves accelerators' memory accesses by intelligently employing burst transactions to prefetch useful data from external memory to on-chip local buffers. Our design is dynamic, parametric, and transparent to the accelerators generated by HLS tools. We derive the buffering system parameters using appropriate compiler-based analysis passes and memory channel latency constraints. The proposed buffering system design results in, on average, 8.8× performance improvements while lowering memory channel utilization by 53.2% for a set of PolyBench kernels. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： System-on-chip

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DDDAS for systems Analytics in Applied Mechanics 4th

DDDAS for Systems Analytics in Applied Mechanics

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4th International conference on Dynamic Data Driven applications systems, DDDAS 2022

作者： Korobenko, A. Niu, S. Deng, x. Zhang, E. Srivastava, V. Bazilevs, Y. School of Engineering Brown University Providence United States Department of Mechanical and Manufacturing Engineering University of Calgary Calgary Canada Department of Civil Engineering University of Hong Kong Hong Kong

ISBN: (纸本)9783031526695

This contribution is comprised of two parts. In the first part we provide an overview of the Dynamically Data-Driven applications systems (DDDAS) concept, with particular emphasis on the analytics of systems coming from the field of Applied Mechanics and focusing on the applications to aerospace structures. Aerospace composite materials and structures exhibit a strong multiscale behavior, which necessitates the development of a multiscale DDDAS framework wherein measurements and models interact at all the relevant spatial scales of the system of interest to maximize the resulting predictive power. We present a large-scale structural system example where the combination of dynamic data and advanced models are needed to be truly predictive. In the second part we examine the Neural Network (NN)-based data-driven approaches for systems analytics in applied mechanics, in particular, the Physics-Informed Neural Networks (PINNs) framework. The main idea of PINNs is to compensate for the lack of sufficient volume of measured data by forcing the system to obey the laws of physics expressed in the form of boundary-value problems (BVPs) based on partial differential equations (PDEs). A distinguishing feature of PINNs is that the discretization of a BVP does not make use of traditional methods, but rather NNs themselves. We focus on the ability of the approaches, incorporating NNs (as a tool) into DDDAS, to model large-deformation elastoplastic behavior of solids and structures so that they can be seamlessly integrated into structural systems analytics and beyond. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Boundary value problems

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A Review Paper on Machine Learning Techniques and Its applications in Health Care Sector 1

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2nd International conference on Ubiquitous Computing and Intelligent Information systems (ICUIS)

作者： Gautam, Priya Dehraj, Pooja Noida Inst Engn & Technol Greater Noida India

ISBN: (数字)9789811925412

ISBN: (纸本)9789811925412;9789811925405

Healthcare data analysis has seen a significant rise in recent years and thus becoming a prominent research field. Healthcare data types include sensor data, Clinical data, and Omics data. Clinical data are generally collected whereas a patient is being treated whereas multiple wearable and wireless sensor devices are used for collecting the sensor data. Omics data includes transcriptome, genome, and proteome data types, all of which are complicated and high-dimensional. Since the data type varies and manually dealing with the raw data becomes a challenging task. This is where Machine Learning emerges as a significant tool for analyzing the data. To precisely forecast the healthcare data results, Machine Learning utilizes varied statistical techniques and advanced algorithms. For analysis ML utilizes different algorithms such as Reinforcement Learning, Supervised Learning, and Unsupervised Learning. This paper discusses the Machine learning algorithms of many forms that can be used to analyse and survey enormous volumes of healthcare data.

关键词： Classification algorithm Disease diagnostics Healthcare data Machine learning

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RSM-PA:Research on Route Selection Method of Inter-domain Routing System Based on Path Availability 23

RSM-PA:Research on Route Selection Method of Inter-domain Ro...

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23rd IEEE International conference on High Performance Computing and Communications, 7th IEEE International conference on Data Science and systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhu, Huihu Qiu, Han Zhu, Junhu Wang, Qingxian State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665494571

The inter-domain routing system is the key infrastructure of the Internet. However, BGP does not consider the link state in the routing selection process. As a result, when the inter-domain routing system faces security incidents such as disasters and attacks, it is easy to cause the continuous propagation of link failures, resulting in a large-scale network disconnection and posing a considerable threat to the security of the Internet. The previous research mainly aims to enhance the protocol's safety, cannot change the original failure propagation mechanism, and cannot fundamentally solve cascading failure propagation. In response to this problem, this paper introduces SDN and proposes RSM-PA. This method perceives the available path topology by sharing the link state information in the region, provides a verification mechanism for the availability of the route in the routing process to avoid the link failure in the region due to overload. The comparison of routing results before and after the link state changes under the same experimental conditions shows that RSM-PA can effectively adjust the routing strategy according to the path availability, and lay a solid foundation for suppressing the propagation of cascading failures. © 2021 IEEE.

关键词： Network topology Heuristic algorithms Power system protection Routing Real-time systems Routing protocols Internet

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Low-frequency spring-like MEMS for photoacoustic trace gas detection 24

Low-frequency spring-like MEMS for photoacoustic trace gas d...

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2025 conference on MOEMS and Miniaturized systems

作者： Dello Russo, Stefano Pelini, Jacopo Garcia, Inaki Lopez Galli, Iacopo Pastor, Pablo Cancio Canino, Maria Concetta Roncaglia, Alberto De Natale, Paolo Borri, Simone de Cumis, Mario Siciliani ASI Agenzia Spaziale Italiana Ctr Geodesia Spaziale I-75100 Matera Italy CNR INO Ist Nazl Ott Largo E Fermi 6 I-50125 Florence Italy LENS Via N Carrara 1 I-50019 Sesto Fiorentino FI Italy Univ Naples Federico II Corso Umberto I 40 I-80138 Naples Italy Consiglio Nazl Ric CNR Ist Microelettron & Microsistemi IMM Zona Ind Ottava Str 5 I-95121 Catania Italy Consiglio Nazl Ric CNR Ist Microelettron & Microsistemi IMM Via P Gobetti 101 I-40129 Bologna Italy

ISBN: (数字)9781510685130

ISBN: (纸本)9781510685123;9781510685130

Studies on gas trace spectroscopy have intensified considerably in recent years due to the great demand for reliable sensors in the quantification of gas concentrations in various fields, including environmental monitoring, medical sensors, early fire detection, and security control. Among various approaches, photoacoustic spectroscopy (PAS) is promising due to wavelength independence, zero background, and small investigating volumes. In this context, we report the realization and implementation of a new low-frequency, spring-like Micro Electro Mechanical System (MEMS) acting as a microphone in a PAS setup. The MEMS behaviors were studied experimentally both in amplitude and frequency modulation, analyzing the figures of merit (FOM) of each approach. Finally, a long-term stability analysis is also reported to exploit the ultimate detection limits achievable with each approach.

关键词： Cantilever-enhanced photo-acoustic spectroscopy (CEPAS) MID-IR sensing Spectrophones Trace-gas photo-acoustic sensor

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Airspace Geofencing Volume Sizing with an advanced Air Mobility Vehicle Performance Model 41

Airspace Geofencing Volume Sizing with an Advanced Air Mobil...

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41st IEEE/AIAA Digital Avionics systems conference, DASC 2022

作者： Kim, Joseph Liberko, Nicholas Atkins, Ella University of Michigan Robotics Institute United States Collins Aerospace Mission Systems University of Michigan Aiaa Fellow Aerospace Engineering Department and Robotics Department United States

ISBN: (数字)9781665486071

ISBN: (纸本)9781665486071

Airspace geofencing is a key enabler for versatile and safe low-altitude Unmanned Aircraft System (UAS) Traffic Management (UTM). Geofenced airspace volume sizing analysis is necessary to assure efficiency and safety for a broad suite of emerging UAS applications. Airspace safety buffer sizes can be determined and verified by modeling vehicle dynamics as well as quantifying guidance, navigation and control uncertainties. This paper proposes a methodology to design safety buffer sizes given specific vehicle and sensor properties and wind. These parameters are then translated to geofence safety buffer dimensions for straight and turning flight to statistically guarantee an aircraft will stay inside its flight trajectory based keep-in geofence. Vehicle, sensor, and wind parameters are tabulated and analyzed over a prescribed flight trajectory using a Computational Fluid Dynamics (CFD) analysis. Case studies using a small UAS model and a full-size advanced air mobility (AAM) aircraft model provide comparison of geofence volume sizings. © 2022 IEEE.

关键词： Air mobility

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Convolutional Neural Network Classification of Vulnerable Road Users based on Micro-Doppler Signatures using an Automotive Radar 25

Convolutional Neural Network Classification of Vulnerable Ro...

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IEEE 25th International conference on Intelligent Transportation systems (ITSC)

作者： Wachtel, Diogo Edler, Johannes Schroder, Sabine Queiroz, Samuel Huber, Werner TH Ingolstadt Ctr Automot Res Integrated Safety Syst & Measurem Inst Automated Driving CARISSMA IAD Ingolstadt Germany Friedrich Alexander Univ Erlangen Nurnberg FAU Erlangen Germany Inst Tecnol Aeronaut Sao Jose Dos Campos Brazil

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

advanced driver assistance systems (ADAS) are growing in quantity and complexity, requiring even more sensor effectiveness and computational resources. Even though cameras and LiDAR sensors can provide a steady solution in ideal conditions, they are unreliable for adverse weather conditions, such as rain and dense fog. Therefore, radar technology is used for those circumstances is an option to be used for those circumstances. However, its classification accuracy must be enhanced to compete with the other sensor types. This work presents a solution to enhance radar classification quality, adapting a Convolutional Neural Network (CNN) structure to the input of micro-Doppler images of vulnerable road users (VRUs), such as pedestrians, cyclists, and e-scooter drivers. Moreover, two CNNs were trained, each for a particular region of interest (ROIs), one in the near field of the radar until 15m, and the second one up to 25m. The proposed solution presents a promising overall accuracy for its first trial. Nevertheless, it must be further extended to more complex scenes, including more participants, obstacles, and weather conditions.

关键词： radar applications convolutional neural networks object classification

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13th International conference on Data Science, Technology and applications, DATA 2024

13th International Conference on Data Science, Technology an...

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13th International conference on Data Science, Technology and applications, DATA 2024

ISBN: (纸本)9789897587078

The proceedings contain 64 papers. The special focus in this conference is on Data Science, Technology and applications. The topics include: Reputation, Sentiment, Time Series and Prediction;a Cascade of Consequences: Improving an Accident Analysis Method by Learning from a Real Life Telecommunications Accident;is Positive Sentiment Missing in Corporate Reputation?;A Deep Dive into GPT-4’s Data Mining Capabilities for Free-Text Spine Radiology Reports;privacy-Preserving Big Hierarchical Data Analytics via Co-Occurrence Analysis;risk-Stratified Multi-Objective Resource Allocation for Optimal Aviation Security;cross-Lingual Low-Resources Speech Emotion Recognition with Domain Adaptive Transfer Learning;HTC-GEN: A Generative LLM-Based Approach to Handle Data Scarcity in Hierarchical Text Classification;course Recommendation System for Company Job Placement Using Collaborative Filtering and Hybrid Model;intelligent Sampling System for Connected Vehicle Big Data;an Exploratory Analysis of Malaria and Climatic Factors in India;intelligent Transportation systems: A Survey on Data Engineering;expert Agent Guided Learning with Transformers and Knowledge Graphs;a Max Flow Min Cut View of Social Media Posts;brain Stroke Prediction Using Visual Geometry Group Model;stochastic Simulation Agent for Unknown Inventory Demands in Healthcare Supply Management;dynamic Price Prediction for Revenue Management System in Hospitality Sector;an Effective Prediction of Events in Social Networks Using Influence Score of Communities;logical Rule Set to Data Acquisition and Database Semantics;a Data-Driven Approach for Predictive Maintenance of Impellers in Flexible Impeller Pumps Using Prophet;objective Evaluation of Sleep Disturbances in Older Adults with Cognitive Impairment Using a Bed sensor System and Self-Organizing Map Analysis.

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Mm-wave near-field focused antenna array for chipless RFID detection

Mm-wave near-field focused antenna array for chipless RFID d...

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International conference on Electromagnetics in advanced applications (ICEAA)

作者： S. Rodini S. Genovesi G. Manara F. Costa Dipartimento di Ingegneria dell’Informazione Università di Pisa Pisa

ISBN: (数字)9798350360974

ISBN: (纸本)9798350360981

This work reports a focused antenna array that could allow for an accurate interrogation of passive sensors. In recent years, some passive sensors have been proposed [1, 2], however there are often problems reading these sensors due to interference with the surrounding environment. The proposed idea is therefore to focus the beam exclusively on the sensor. The reading system is formed by two cross-polarized near-field arrays which are able to concentrate the radiated fields in the area occupied by the sensor under test thus avoiding scattering from nearby objects with a consequent reduction of clutter. The proposed NF focused array is composed of 16x16 elements and it works around 20 GHz (λ equal to 15 mm). The array has a layered structure. There are two dielectric substrates (Itera MT40 material), the lower substrate has a thickness of 0.254 mm and the upper one 0.762 mm. The feeding network, which represents the bottom of the structure, is created on the first substrate. On the top of this substrate there is a perforated metal surface. Metallic rectangular patches are made on the top of the second substrate. Vias cross the entire structure to connect the feeding network with the patches.

关键词： Dielectric substrates Scattering Metals sensor systems sensors Noise measurement Electromagnetics Substrates sensor arrays Antenna arrays

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4th International conference on Emerging Trends and Technologies on Intelligent systems, ETTIS 2024

4th International Conference on Emerging Trends and Technolo...

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4th International conference on Emerging Trends and Technologies on Intelligent systems, ETTIS 2024

ISBN: (纸本)9789819757022

The proceedings contain 55 papers. The special focus in this conference is on Emerging Trends and Technologies on Intelligent systems. The topics include: A Machine Learning-Based Cross-Layer DoS Attack Detection Technique for IoT;advanced Neural Network MRI Evaluation for Automated Knee Trauma Detection;enhancing Cybersecurity Defenses: A Comprehensive Exploration of Applied Artificial Intelligence Strategies;A Comprehensive Analysis of Artificial Intelligence Methods to Detect COVID-19 from Chest x-rays and CT Scans;a Blockchain-Based Land Registration System in the Indian Context Incorporating Mortgage and Legal Aspects;From Sparsity to Accuracy: Enhancing Recommendation systems with Item-Based Collaborative Filtering and SVD Matrix Factorization;a Comparative Analysis of Sorting Algorithms for Large-Scale Data: Performance Metrics and Language Efficiency;genetic Algorithm-Based Probability Distribution Fitting for Improved Classification of Echocardiogram Regions;Investigate the Properties of FTA Through Empirical Experimentation;BDSLL: Biomedical Document Recommendation Using Knowledge-Empowered Semantic Intercepted Large Language Models;a Review of Digital Twin and the Metaverse from the Standpoint of Smart Factories;A Comprehensive Patient Triage Algorithm Incorporating ChatGPT API for Symptom-Based Healthcare Decision-Making;enhancing Segmentation Accuracy of Diabetic Retinal Images Using Transform Techniques and deepLabV3+ with Ensemble Classification;predicting Ocean Currents: A Deep Learning Approach;Geotagging in Livestock Farming: AI-Based Technologies, applications, and Challenges;a Simulation-Based Approach to Evaluate the Effects of Cyberattacks Targeting Transducers in Control systems;urban Greens—An Initiative Towards Clean Air;Sign Languages and AI: A Survey of The Impact of Artificial Intelligence on Deafhood;cosine Similarity Preserving Curse of Dimensionality Reduction for Managing Computational Complexity;comprehensive Mathematical Analysis an

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