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Adaptive real-time Imaging Synthesis Telescopes

INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2012年第4期26卷 358-366页

作者： Wright, Melvyn Univ Calif Berkeley Radio Astron Lab Berkeley CA 94720 USA

The digital revolution is transforming astronomy from a data-starved to a data-submerged science. Instruments such as the Atacama Large Millimeter Array (ALMA), the Large Synoptic Survey Telescope (LSST), and the Square Kilometre Array (SKA) will measure their accumulated data in petabytes. The capacity to produce enormous volumes of data must be matched with the computing power to process that data and produce meaningful results. In addition to handling huge data rates, we need adaptive calibration and beamforming to handle atmospheric fluctuations and radio frequency interference, and to provide a user environment which makes the full power of large telescope arrays accessible to both expert and non-expert users. Delayed calibration and analysis limit the science which can be done. To make the best use of both telescope and human resources we must reduce the burden of data reduction. We propose to build a heterogeneous computing platform for real-time processing of radio telescope array data. Our instrumentation comprises a flexible correlator, beam former, and imager that is based on state-of-the-art digital signal processing closely coupled with a computing cluster. This instrumentation will be highly accessible to scientists, engineers, and students for research and development of real-time processing algorithms, and will tap into the pool of talented and innovative students and visiting scientists from engineering, computing, and astronomy backgrounds. The instrument can be deployed on several telescopes to get feedback from dealing with real sky data on working telescopes. Adaptive real-time imaging will transform radio astronomy by providing real-time feedback to observers. Calibration of the data is made in close to real time using a model of the sky brightness distribution. The derived calibration parameters are fed back into the imagers and beam formers. The regions imaged are used to update and improve the a priori model, which becomes the final calibrat

关键词： high performance heterogeneous computing real-time data processing adaptive calibration and image formation synthesis telescopes

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Integrating workload balancing and fault tolerance in distributed stream processing system

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2019年第6期22卷 2471-2496页

作者： Fang, Junhua Chao, Pingfu Zhang, Rong Zhou, Xiaofang Soochow Univ Sch Comp Sci & Technol Inst Artificial Intelligence Suzhou 215006 Peoples R China Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia East China Normal Univ Sch Data Sci & Engn Shanghai 200062 Peoples R China Neusoft Corp Shenyang 110179 Liaoning Peoples R China

Distributed Stream processing Engine (DSPE) is designed for processing continuous streams so as to achieve the real-time performance with low latency guaranteed. To satisfy such requirement, the availability and efficiency are the main concern of the DSPE system, which can be achieved by a proper design of the fault tolerance module and the workload balancing module, respectively. However, the inherent characteristics of data streams, including persistence, dynamic and unpredictability, pose great challenges in satisfying both properties. As far as we know, most of the state-of-the-art DSPE systems take either fault tolerance or workload balancing as its single optimization goal, which in turn receives a higher resource overhead or longer recovery time. In this paper, we combine the fault tolerance and workload balancing mechanisms in the DSPE to reduce the overall resource consumption while keeping the system interactive, high-throughput, scalable and highly available. Based on our data-level replication strategy, our method can handle the dynamic data skewness and node failure scenario: during the distribution fluctuation of the incoming stream, we rebalance the workload by selectively inactivate the data in high-load nodes and activate their replicas on low-load nodes to minimize the migration overhead within the stateful operator;when a fault occurs in the process, the system activates the replicas of the data affected to ensure the correctness while keeping the workload balanced. Extensive experiments on various join workloads on both benchmark data and real data show our superior performance compared with baseline systems.

关键词： real-time data processing Distributed systems Load Balancing High availability computing

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Following linguistic footprints: Automatic deception detection in online communication

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COMMUNICATIONS OF THE ACM 2008年第9期51卷 119-122页

作者： Zhou, Lina Zhang, Dongsong Univ Maryland Dept Informat Syst Baltimore MD 21201 USA

The article discusses deception that occurs within online communication, examining models that can be used to automatically detect such deceptive communication practices. The authors indicate that certain linguistic patterns exist which underlay deception. Topics include people who want to avoid detection, the differences between linguistic cues and content-based cues, and underlying clues to a deceiver's state of mind. Also discussed are language processing techniques, deterring online deception, and automated computational models.

关键词： LINGUISTICS RESEARCH INTERNET fraud LANGUAGE & languages DECEPTION real-time data processing COMPUTER crimes

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Joule: A real-time Framework for Decentralized Sensor Networks

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IEEE INTERNET OF THINGS JOURNAL 2018年第5期5卷 3615-3623页

作者： Donnal, John US Naval Acad Weap & Syst Engn Annapolis MD 21402 USA

Traditional Internet of Things (IoT) sensor nodes transmit raw data to centralized servers for analysis and storage. At higher data rates this architecture is no longer feasible. Instead data must be processed at the lowest level possible to include the sensor node itself. The Joule framework enables decentralized data processing by distributing computation into modules which are dynamically linked at run time by a process supervisor. Modules may be collocated on a single machine or distributed across a network. data is stored where it is processed creating a fully decentralized architecture. Processed data can be queried by centralized servers or delivered directly to the end user. This allows high bandwidth sensors to be incorporated seamlessly into existing IoT deployments without depleting network bandwidth or server resources. This paper presents the Joule framework as well as performance benchmarks and deployment case studies.

关键词： Edge computing embedded software Internet of Things (IoT) real-time data processing sensor systems

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Evaluation of a real-time operating system in self-contained data recorders

Evaluation of a real-time operating system in self-contained...

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作者： Berry, Savinay Michigan State University

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GreatBlue: a 55-Pound Vertical-Takeoff-and-Landing Fixed-Wing sUAS for Science;Systems, Communication, Simulation, data processing, Payloads, Package Delivery, and Mission Flight Performance

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2024年第3期110卷 98-98页

作者： Coopmans, Calvin Slack, Stockton Schwemmer, Nathan Vance, Chase Beckwith, A. J. Robinson, Daniel J. Utah State Univ Elect & Comp Engn Dept 4120 Old Main Hill Logan UT 84322 USA Utah State Univ Utah Water Res Lab 4200 Old Main Hill Logan UT 84322 USA

As small, uncrewed systems (sUAS) grow in popularity and in number, larger and larger drone aircraft will become more common-up to the FAA limit of 55 pound gross take-off weight (GTOW) and beyond. Due to their larger payload capabilities, longer flight time, and better safety systems, autonomous systems that maximize CFR 14 Part 107 flight drone operations regulations will become more common, especially for operations such as imagery or other data collection which scale well with longer flight times and larger flight areas. In this new paper, a unique all-electric 55-pound VTOL transition fixed-wing sUAS specifically engineered for scientific data collection named "GreatBlue" is presented, along with systems, communications, scientific payload, data collection and processing, package delivery payload, ground control station, and mission simulation system. Able to fly for up to 2.5 hours while collecting multispectral remotely-sensed imagery, the unique GreatBlue system is shown, along with a package delivery flight example, flight data from two scientific data collection flights over California almond fields and a Utah Reservoir are shown including flight plan vs. as-flown.

关键词： Uncrewed aerial vehicle systems Remote sensing real-time data processing High-altitude drones Multispectral sensors Communication radios Drone operations Aircraft systems

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ELECTROENCEPHALOGRAM (EEG) BASED EMOTIONAL LIGHTING DESIGN USING DEEP-LEARNING FOR A USER-CENTRIC APPROACH 29th

ELECTROENCEPHALOGRAM (EEG) BASED EMOTIONAL LIGHTING DESIGN U...

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29th International Conference of the Association-for-Computer-Aided-Architectural-Design-Research-in-Asia (CAADRIA)

作者： Kang, Se Yeon Cho, Ju Eun Jun, Han Jong Hanyang Univ Seoul South Korea

ISBN: (纸本)9789887891833

This study proposes a methodology for using artificial intelligence (AI) and biometrics in spatial design. The research mainly applies a gated recurrent unit (GRU) model, a recurrent neural network (RNN), to analyze electroencephalogram (EEG) data and dynamically adjust lighting according to the user's emotional state. This study suggests an illumination adjustment system that modifies lighting according to the user's emotional state using the proposed method. Integration of EEG data can overcome the limitations of lighting systems. It can effectively target individual emotional responses. The GRU model represents a significant improvement in lighting design by addressing both cognitive and emotional user needs. The model's effectiveness in processing real-time data and adapting through incremental learning was evaluated. The model has shown a significant impact on emotional architecture and spatial design, with a focus on individual experience.

关键词： Gated Recurrent Unit EEG EEG data Analysis User-Centric Design Emotional Lighting real-time data processing Affective Computing BCI BMI

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Building Financial Enterprise Risk Assessment System Based on Big data and Integrated Learning Technology

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Procedia Computer Science 2024年 247卷 900-907页

作者： Yaling Peng Beibei Wen School of Information and Business Management Chengdu Neusoft University Chengdu China

This article aims to explore the use of big data and integrated learning technologies to build a more efficient and accurate financial enterprise risk assessment system. By analyzing the limitations of current financial risk assessment methods, this paper proposes a comprehensive framework based on random forests. The performance of a financial enterprise risk assessment system based on random forest was evaluated through four experiments in the experimental stage. Through systematic comparative experiments, it was found that the random forest method showed better performance compared to the Support Vector Machine (SVM) algorithm, with an accuracy of 92%, SVM at 85%, recall index at 90%, better than SVM at 82%, and F1 score of 91%, higher than SVM at 83%. In the multi scenario risk assessment experiment, the accuracy of random forest in conventional market scenarios is 85%, the recall rate is 80%, and the F1 score is 82%. In real-time data evaluation experiments, the accuracy of the model gradually increased from the initial 85% to 95% at 10 different time points. The above experimental data results collectively confirm that systems based on random forests have strong adaptability and efficient risk assessment capabilities in diverse financial environments.

关键词： Financial risk assessment random forest algorithm big data technology support vector machine algorithm real-time data processing

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Intelligent and real time data Acquisition and Evaluation to Determine Critical Events in Intensive Medicine

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Procedia Technology 2012年 5卷 716-724页

作者： Filipe Portela Pedro Gago Manuel Filipe Santos José Machado António Abelha Álvaro Silva Fernando Rua César Quintas Filipe Pinto Centro Algoritmi Universidade do Minho Guimarães Portugal Instituto Politécnico de Leiria Leiria Portugal Centro de Ciências e Tecnologias de Computação Universidade do Minho Braga Portugal Serviço de Cuidados Intensivos Centro Hospitalar do Porto Hospital Santo António Porto Portugal

Using the information regarding critical events to support decision making in Intensive Care Units would be useful. However it is seldom used in real settings as the information regarding those critical events is difficult to gather and make available in real time. The most usual procedures record only those events that are related to errors. This paper presents a solution to obtain critical events from clinical data. From data collected using an automatic and real-time data acquisition system it is possible to calculate the critical events regarding five variables that are usually monitored in an ICU. These results are presented to the medical and nursing staff in a friendly and intuitive mode. Using a color code our system provides visual warnings related to the evolution of the monitored variables values. Actually, a quick glance allows doctors to get a high level overview of the patient's condition

关键词： Critical Events Adverse Events ICU INTCare real-time data processing

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Dynamic Noise Estimation Approach for X-Ray Detectors on FPGAs

Dynamic Noise Estimation Approach for X-Ray Detectors on FPG...

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16th Euromicro Conference on Digital System Design (DSD)

作者： Aschauer, Florian Stechele, Walter Treis, Johannes Max Planck Inst Extraterr Phys Giessenbachstr D-85741 Garching Germany Tech Univ Munich Lehrstuhl Integrierte Syst D-80333 Munich Germany PNDetector GmbH D-81739 Munich Germany

ISBN: (纸本)9780769550749

The proposed algorithm enables real-time noise estimation directly on the raw data stream of an X-ray detection system. This allows for better detector monitoring and increases the measurement accuracy, especially in long term measurements and under changing environment conditions. The noise value is used as an event extraction threshold and is therefore the most critical parameter for the performance in X-ray data processing. Inaccurately estimated noise values can result into significantly poorer energy resolutions. The new algorithm is designed for low memory bandwidth, precise estimation results and a low processing complexity. This allows real-time data processing even for large and fast detector systems with an output rate of hundreds of MPixels/s. Compared to the calculation method used up to now the memory bandwidth can be reduced down to a factor of a hundred with the new algorithm. The new developed real-time Dynamic Noise Estimation (DNE) algorithm uses an iterative approach for the estimation. For the calculation is only an increment/decrement operation and an integer comparison required. This simple approach allows a resource efficient hardware implementation on FPGAs. Simulation results confirm the excellent estimation accuracy and stability with real-time performance. Measurements with Fano-limited energy resolution, the theoretically best achievable energy resolution with semiconductor detectors, confirm this outstanding performance on real detector systems. The reliability and stability of the system was demonstrated during a long term irradiation campaign for Mercury Imaging X-ray Spectrometer (MIXS) [5], [3]. MIXS is an instrument onboard ESA's corner stone mission BepiColombo [1] and will be launched 2015 into an orbit around Mercury.

关键词： FPGA Noise estimation X-ray real-time data processing MIXS

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