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检索条件"任意字段=Conference on Photonic Devices and Algorithms for Computing II"

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Hardware-in-the-loop simulations of remote sensing disaster monitoring systems with real-time on-board computation 1

Hardware-in-the-loop simulations of remote sensing disaster ...

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IEEE International conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (IEEE MetroXRAINE)

作者： Spiller, Dario Carbone, Andrea Latorre, Francesco Curti, Fabio Sapienza Univ Rome Sch Aerosp Engn Rome Italy

ISBN: (数字)9781665485746

ISBN: (纸本)9781665485746

Real-time monitoring systems in remote sensing could enable a variety of new applications concerning emergency and disaster management. Coupling remote sensing imaging devices with hardware accelerators for artificial intelligence (AI) post-processing algorithms can pave the way for future constellations of small satellites offering services such as detection of wildfires, volcanoes eruptions, landslides, floods, etc. This paper presents MONSTER, a robotic facility hosted in the ARCA laboratory (ARCAlab) at the School of Aerospace Engineering in Rome. The facility was born as a simulator of lunar landing operations, but is currently being updated to perform hardware-in-the-loop simulation campaigns of many different scenarios including wildfires and volcanic eruption observation from space or airborne platforms. Having the possibility to perform translation and rotation maneuvers of the target observation platform, properly simulating the terrain, and performing the AI-aided computation on hardware accelerators such as graphics processing units (GPUs), visual processing units (VPUs), and field programmable gate arrays (FPGAs), the MONSTER facility could be used to efficiently simulate new remote sensing paradigms for disaster monitoring through on-the-edge artificial intelligence.

关键词： MONSTER disaster monitoring artificial intelligence real-time system edge computing

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A case study in clock synchronization for distributed camera systems

A case study in clock synchronization for distributed camera...

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conference on Embedded Processors for Multimedia and Communications ii

作者： Lin, CH Wolf, WH Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

ISBN: (纸本)081945656X

We studied several well-known timing synchronization algorithms in this paper, and applied them to our distributed real-time camera system. We proposed a peer-to-peer framework for distributed real-time gesture recognition, and this study serves as the keystone of the system. Cameras and microprocessors are now cheap enough that many smart camera nodes can be used in a single system. A smart camera node contains not only video capturing devices, but also processing elements to perform both capture and process in the same package. Sometimes, using multiple relatively inexpensive cameras with lower resolution might even provide better performance and be more cost efficient than using single high-end camera. In order to perform real-time video processing, distributed multiple cameras require distributed processing power, and the distributed nodes have to remain synchronized to ensure the correctness of distributed video processing. Though clock synchronization has already been well studied in decades. most previous work uses domain knowledge to achieve good synchronization result on a certain network structure. Precision in microseconds could be achieved in several previous approaches. However, for our distributed real-time video processing system, such precision is not required;only frame-precision is needed for our system, which is around tens of milliseconds. In our distributed camera system, we assume no deterministic network structure for the camera nodes. Since there is no single timing synchronization algorithm would suit every network structure, we would like to investigate the possibility to perform clock synchronization in application layer, where domain knowledge of underlying structure is not needed. We choose algorithms that achieve synchronization by exchanging messages between camera nodes to achieve synchronization on heterogeneous network structures. We investigate three well-known message-exchanging timing synchronization methods: Lamport's, Lundelius

关键词： distributed computing synchronization real-time video processing

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Edge-Fog-Cloud Data Analysis for IoT: A Survey 1

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2nd international conference Artificial Intelligence and Industrial Applications, A2IA 2023

作者： Zaoui, Chaimae Benabbou, Faouzia Ettaoufik, Abdelaziz University Hassan II of Casablanca Casablanca Morocco

ISBN: (数字)9783031435249

ISBN: (纸本)9783031435232

In recent years, Internet of Things (IoT) devices have begun to take an important place in our daily lives, allowing the collect of massive data in various domains. IoT combined with other technologies like Cloud, Fog, and Edge computing has made possible the development of complex systems where artificial intelligence plays an essential role. The domains of applications are diverse, from security, transportation, home, healthcare, to the more global domain such as the city. Usually, the data collected is stored and analyzed in the Cloud, but latency and security issues have led researchers to see in Edge and Fog nodes a more interesting location to store and analyze data especially for real-time applications. In this paper, we present an analytical and comparative study of different proposals that integrate Edge and Fog computing for an easy, fast and secure sensor data processing with Machine Learning (ML) and Deep Learning (DL) algorithms. We aim to identify challenges faced and the solutions proposed to execute ML models in Edge computing environment. A comparative study is proposed based on the different sources and datasets used as well as the different ML and DL techniques applied and their performance in the Edge or Fog nodes. Lastly, to compare efficiently the performance of baseline ML, an experiment has been carried on the same dataset Human Activity Recognition (HAR), to improve the result obtained on the survey. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Edge computing

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Using GPGPU Accelerated Interpolation algorithms for Marine Bathymetry Processing with On-Premises and Cloud Based Computational Resources 12th

Using GPGPU Accelerated Interpolation Algorithms for Marine ...

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12th International conference on Parallel Processing and Applied Mathematics (PPAM)

作者： Marcellino, Livia Montella, Raffaele Kosta, Sokol Galletti, Ardelio Di Luccio, Diana Santopietro, Vincenzo Ruggieri, Mario Lapegna, Marco D'Amore, Luisa Laccetti, Giuliano Univ Napoli Parthenope Dept Sci & Technol Naples Italy Univ Napoli Federico II Dept Math & Applicat Naples Italy Aalborg Univ CMI Copenhagen Denmark

ISBN: (纸本)9783319780542;9783319780535

Data crowdsourcing is one of most remarkable results of pervasive and internet connected low-power devices making diverse and different "things" as a world wide distributed system. This paper is focused on a vertical application of GPGPU virtualization software exploitation targeted on high performance geographical data interpolation. We present an innovative implementation of the Inverse Distance Weight (IDW) interpolation algorithm leveraging on CUDA GPGPUs. We perform tests in both physical and virtualized environments in order to demonstrate the potential scalability in production. We present an use case related to high resolution bathymetry interpolation in a crowdsource data context.

关键词： GPGPU Virtualization High performance computing Geographic data Interpolation

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Fish tracking using acoustical and optical data fusion in underwater environment 2017

Fish tracking using acoustical and optical data fusion in un...

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2017 International conference on Watermarking and Image Processing, ICWIP 2017

作者： Boudhane, Mohcine Nsiri, Benayad GITIL Laboratory Hassan II University Faculty of Sciences AinChock Morocoo Km 8 Route d'El Jadida B.P 5366 Maarif 20100 Casablanca20000 Morocco

ISBN: (纸本)9781450353076

Fish tracking is an important topic in computer vision. The growth of high-powered computers, the evolution of high-quality video cameras with low cost and the growing need for automated video analysis have caused more interest in the development of object tracking algorithms. In the sea things are completely different, the spread of light and sound are not uniform. Therefore, visibility becomes increasingly di cult due to the physical properties of the water. Two devices can observe underwater: camera and sonar. Each of these sensors has certain advantages and limitations. Merging features from both sensors can offer new information that cannot be provided before. In this paper, we monitor fish species to ensure their traceability by combining optical and acoustical characteristics. This process can be extended to many areas, as well as, fish recognition and monitoring. Experimental results on a suite of representative video sequences of realistic underwater scenarios demonstrate the performance of the proposed sensors fusion in terms of ac-curacy and robustness. © 2017 Association for computing Machinery.

关键词： Fish

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Assessing the Impact of Emerging Technologies on Cybersecurity with a Special Emphasis on Artificial Intelligence, the Internet of Things, and Blockchain Innovations

Assessing the Impact of Emerging Technologies on Cybersecuri...

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2024 OPJU International Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Mankhi, Eliph Mazher Rajitha, Akula Revathi, V. Thethi, H. Pal Yadav, Dinesh Kumar Kanthi, K. Surya National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Applied Sciences Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India St. Martin's Engineering College Dept. of It Telangana Secunderabad India

ISBN: (纸本)9798350373783

This article proposes a safety strategy that addresses the complex concerns raised by blockchain, AI, and the Internet of Things. Use Threat Intelligence Integration (Tii), Dynamic Risk Assessment (DRA), Blockchain Integrity Verification (BIV), AI Adversarial Robustness Assessment (AARA), and IoT Security Compliance Assessment (ISCA). Each program is part of a larger, more linked defense system for sophisticated cyberthreats. The program uses Threat Intelligence Integration. Combining historical data with realtime hazard sources predicts assaults and their outcomes. The Tii enabled new algorithms like DRA. The algorithms discover assets, assess weaknesses, and prioritize threats. Blockchain Integrity Verification (BIV) checks for issues and performs complicated hash and weight computations to secure the blockchain. AI Adversarial Robustness Assessment (AARA) evaluates AI models in BIV tests and other adversarial tasks. The ISCA ensures IoT devices fulfill AARA safety and security criteria. Each algorithm prioritizes tracking, updating, and assessing to keep up with the ever-changing risk situation. The recommended solution is more accurate, finds more threats, reduces false positives, is scalable, and is simpler to set up than current defense solutions. © 2024 IEEE.

关键词： Adversarial machine learning

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A Configurable Pruning Gaussian Image Filter for Energy-Efficient Edge Detection 26

A Configurable Pruning Gaussian Image Filter for Energy-Effi...

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26th IEEE International conference on Electronics, Circuits and Systems (ICECS)

作者： Soares, Leonardo Bandeira Cesar da Costa, Eduardo Antonio Bampi, Sergio Fed Univ Rio Grande Sul UFRGS Grad Program Microelect Porto Alegre RS Brazil Catholic Univ Pelotas UCPel Grad Program Elect Engn & Comp Pelotas RS Brazil Fed Inst Technol Rio Grande Sul IFRS Rio Grande Brazil

ISBN: (纸本)9781728109961

This paper presents a new configurable pruning Gaussian image filter CMOS architecture to address energy efficiency requirements regarding edge detection applications. Low-energy consumption is key for Internet of Things (IoT) devices. Many emerging IoT applications rely on cameras to extract video or image features by running power-hungry computer vision algorithms. The Gaussian image filter is one of the most compute intensive tasks for pre-processing edge detection techniques which are widely adopted in the computer vision domain. Therefore, our proposed 2D Gaussian filter architecture enables: i) a low power and low area overhead run-time configuration scheme based on clock gating technique to prune the Gaussian filter (GF) window size, and ii) run-time capability to balance the tradeoff between edge detection quality and energy efficiency. Our proposed configurable architecture is synthesized and mapped onto 45 nm technology for an ASIC implementation. Results show that for 6 different run-time profiles our proposed configurable architecture provides power dissipation reduction of up to 64% with multiple levels of edge detection quality, which is assessed by considering the performance conformance metric.

关键词： Gaussian Image filter Edge detection Energy Efficiency Approximate computing Architecture

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EQC: Ensembled Quantum computing for Variational Quantum algorithms 22

EQC: Ensembled Quantum Computing for Variational Quantum Alg...

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49th IEEE/ACM Annual International Symposium on Computer Architecture (ISCA)

作者： Stein, Samuel Wiebe, Nathan Ding, Yufei Bo, Peng Kowalski, Karol Baker, Nathan Ang, James Li, Ang Pacific Northwest Natl Lab Richland WA 99354 USA Univ Calif Santa Barbara Santa Barbara CA USA Univ Toronto Toronto ON Canada

ISBN: (纸本)9781450386104

Variational quantum algorithm (VQA), which is comprised of a classical optimizer and a parameterized quantum circuit, emerges as one of the most promising approaches for harvesting the power of quantum computers in the noisy intermediate scale quantum (NISQ) era. However, the deployment of VQAs on contemporary NISQ devices often faces considerable system and time-dependant noise and prohibitively slow training speeds. On the other hand, the expensive supporting resources and infrastructure make quantum computers extremely keen on high utilization. In this paper, we propose a virtualized way of building up a quantum backend for variational quantum algorithms: rather than relying on a single physical device which tends to introduce ever-changing device-specific noise with less reliable performance as time-since-calibration grows, we propose to constitute a quantum ensemble, which dynamically distributes quantum tasks asynchronously across a set of physical devices, and adjusts the ensemble configuration with respect to machine status. In addition to reduced machine-dependant noise, the ensemble can provide significant speedups for VQA training. With this idea, we build a novel VQA training framework called EQC - a distributed gradient-based processor-performance-aware optimization system - that comprises: (i) a system architecture for asynchronous parallel VQA cooperative training;(ii) an analytical model for assessing the quality of a circuit output concerning its architecture, transpilation, and runtime conditions;(iii) a weighting mechanism to adjust the quantum ensemble's computational contribution according to the systems' current performance. Evaluations comprising 500K times' circuit evaluations across 10 IBMQ NISQ devices using a VQE and a QAOA applications demonstrate that EQC can attain error rates very close to the most performant device of the ensemble, while boosting the training speed by 10.5x on average (up to 86x and at least 5.2x). EQC is available at

关键词： Quantum computing Variational Quantum algorithms Distributed computing

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User Recruitment with Budget Redistribution in Edge-Aided Mobile Crowdsensing 20th

User Recruitment with Budget Redistribution in Edge-Aided Mo...

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20th International conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Zhang, Yanlin Li, Peng Zhang, Tao Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan Hubei Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan Hubei Peoples R China New York Inst Technol Dept Comp Sci New York NY USA

ISBN: (纸本)9783030602390;9783030602383

Mobile crowdsensing is an efficient data collection method using various mobile smart devices to collect data. Most of the existing mobile crowdsensing frameworks adopt a two-layer architecture model with only a cloud platform to recruit users. However new challenges arise: one is how to reduce the pressure and transmission cost of the cloud platform. Another is how to improve the coverage rate of some areas with low completion rates to ensure data quality. In this paper, we propose an original three-layer mobile crowdsensing framework composed of the cloud platform, edge nodes, and users. It transfers user recruitment and data processing to edge nodes, which offload the data of the cloud platform. Moreover, we propose the offline and online mechanisms based on users' reputations to solve user recruitment in the edge node. Furthermore, a budget redistribution (BRD) algorithm is proposed. It dynamically redistributes the budget according to the task completion rate of different edge nodes. Finally, we show the proposed mechanism was truthful, individual rationality, calculation efficiency, and budget feasibility. Extensive simulations on real data sets show the reliability and effectiveness of our proposed framework and algorithms.

关键词： Mobile Crowdsensing Edge computing User recruitment Budget redistribution

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A Cloud Based Augmented Reality Framework - Enabling User-Centered Interactive Systems Development 2nd

A Cloud Based Augmented Reality Framework - Enabling User-Ce...

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2nd International conference on Human Systems Engineering and Design (IHSED) - Future Trends and Applications

作者： Abdelrazeq, Anas Kohlschein, Christian Hees, Frank Rhein Westfal TH Aachen Cybernet Lab Aachen Dennewartst 27 D-52068 Aachen Germany

ISBN: (纸本)9783030279288;9783030279271

Concepts for user interfaces are developed in order to enable humans exchanging information and having a better understanding of different processes. With the rise of technology, concepts of enriching our environments with computer-generated graphics appeared in the 1960s. The expansion of visual perception of the surrounding is the core of Augmented Reality (AR). Many AR applications have been appearing in different areas, opening the door for revolutionizing and changing interactivity models and the way people perform tasks. Even though AR systems' structures are well defined, applications are not yet widespread. One of the main reasons for this is that AR system developers face difficulties with developing their applications and adapting them to different hardware. Each hardware set (i.e. AR device) requires different software development kits and different technical requirements. That leaves AR concepts hard to be scaled and implemented in real life scenarios. One more additional challenge facing the implementing of AR is that AR mostly relies on complex vision-based algorithms. Based on that, this paper explores a lightweight, modular, and cross-platform solution. It presents a novel framework based on redesigning the AR pipeline, introducing a prototype of a solution where AR can operate within the cloud, converting all AR devices into thin clients with high performance capability. This also contributes directly to the Industry 4.0 aim of preparing the way for digitalized and optimized processes in a connected and integrated network.

关键词： Augmented Reality Cloud-computing Industry 4.0 Intelligent interfaces Mixed reality

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