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Intelligent Cloud Resource Management with Deep Reinforcement Learning

IEEE CLOUD COMPUTING 2017年第6期4卷 60-69页

作者： Zhang, Yu Yao, Jianguo Guan, Haibing Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

The cloud provides low-cost and flexible IT resources (hardware and software) across the Internet. As more cloud providers seek to drive greater business outcomes and the environments of the cloud become more complicated, it is evident that the era of the intelligent cloud has arrived. The intelligent cloud faces several challenges, including optimizing the economic cloud service configuration and adaptively allocating resources. In particular, there is a growing trend toward using machine learning to improve the intelligence of cloud management. This article discusses an architecture of intelligent cloud resource management with deep reinforcement learning. The deep reinforcement learning makes clouds automatically and efficiently negotiate the most appropriate configuration, directly from complicated cloud environments. Finally, we give an example to evaluate and conclude the remarkable ability of the intelligent cloud with deep reinforcement learning.

关键词： Cloud Computing Learning Artificial Intelligence Resource Allocation Adaptively Allocated Resource Machine Learning Economic Cloud Service Configuration Cloud Providers Intelligent Cloud Resource Management Deep Reinforcement Learning Cloud Computing Resource Management Machine Learning Learning Artificial Intelligence Computer Architecture Monitoring algorithm design and analysis Intelligent Cloud Resource Management Deep Reinforcement Learning

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Automatic Target Recognition in Missing Data Cases

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IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE 2017年第7期32卷 40-49页

作者： Lim, Deoksu Gianelli, Chris D. Li, Jian Univ Florida Gainesville FL USA

Automatic target recognition (ATR) is the process where computer algorithms are used to detect and classify objects or regions of interest in sensor data [1]. ATR algorithms have been developed for a broad range of sensors, including electro-optic (EO), infrared (IR), and microwave sensors (radar). A key advantage of microwave radar is its utility for all weather and time of day compared with other types of sensors. One limitation of many radar systems’ ATR performance is the poor spatial resolution in the range and cross-range dimensions. While the range resolution of the microwave radar system can be straightforwardly improved by increasing the system’s radio frequency bandwidth, superior cross-range (or azimuth) resolution requires an increase in antenna size. In order to achieve resolution similar to EO or IR systems, an enormous antenna must be used for transmission and reception, hindering microwave radar system’s general applicability to ATR problems. However, by operating a synthetic aperture radar (SAR), the large antenna requirement can be overcome, and very fine cross-range resolution can be obtained with a modest \"real\" aperture antenna. Indeed, SAR has been used to generate high-resolution 2-D or 3-D object images at a variety of different microwave frequencies. In particular, wideangle SAR images can contain important features of an object from a diverse set of observation angles collected by a radar system orbiting around a target or scene. These feature-rich wide-angle SAR images are useful for ATR due to their high resolution and comprehensive coverage of the target. Interference, jamming, or data dropouts, however, are commonplace in practical SAR environments, and result in an incomplete data set. These missing and corrupted data cause substantial degradation in the generated SAR imagery, hampering their utility for subsequent ATR processing especially when data-independent image formation algorithms are used. A prime example of these difficulti

关键词： Synthetic aperture radar Radar antennas Apertures Radar polarimetry Spatial resolution Microwave antennas Target tracking algorithm design and analysis

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Reservoir Computing in Embedded Systems Three variants of the reservoir algorithm

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IEEE CONSUMER ELECTRONICS MAGAZINE 2017年第3期6卷 67-73页

作者： Soures, Nicholas Merkel, Cory Kudithipudi, Dhireesha Thiem, Clare McDonald, Nathan Rochester Inst Technol Engn Rochester NY 14623 USA Rochester Inst Technol Comp Engn Rochester NY 14623 USA Air Force Res Lab Informat Directorate Wright Patterson AFB OH USA Rochester Inst Technol Dept Comp Engn Rochester NY 14623 USA NanoComp Res Lab Rochester NY USA Eta Kappa Nu New York NY USA Air Force Res Labs Informat Directorates High Performance Syst Branch Wright Patterson AFB OH USA

The rich computational dynamics coupled with simple training in reservoir computing (RC) algorithms makes them a natural choice for spatiotemporal learning systems in embedded platforms. In this article, we study three variants of the reservoir algorithm: the echo-state network (ESN), the liquid-state machine (LSM), and the time-delay reservoirs (TDRs). A digital reservoir architecture is used as a baseline test bed to benchmark for epileptic seizure detection. An average accuracy of 85% is observed across the three variants of the algorithm and the effectiveness of the reservoir is assessed with different metrics.

关键词： algorithm design and analysis Neurons Topology Embedded systems Consumer electronics

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Supervised Learning algorithms for Spacecraft Attitude Determination and Control System Health Monitoring

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IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE 2017年第4期32卷 26-39页

作者： Nassar, Bassem Hussein, Wessam Medhat, Mohamed Egyptian Armed Forces Cairo Egypt NARSS Cairo Egypt MTC Mechatron Cairo 11646 Egypt

The main concern of any space mission operation is to ensure the health and safety of spacecrafts. The worst case in this circumstance is probably the loss of a mission but the more common interruption of spacecraft functionality can result in compromised mission objectives. Spacecraft telemetry holds information related to state-of-health (SOH) of its subsystems. Each parameter has information that represents a time-variant property (i.e., a status or a measurement) to be checked. Moreover, the tremendous increase in telemetry data volume and its complexity directs the need for more efficient and scalable data processing systems. As a consequence, there is a continuous improvement of telemetry monitoring applications in order to reduce the time required to respond to changes in a spacecraft's SOH. So, a fast conception of the current state of the spacecraft is thus very important in order to respond to failures. Furthermore, the progressive growth in spacecrafts leads to increase of the level of standardization in spacecraft operations.

关键词： Monitoring Detection algorithms Space vehicles algorithm design and analysis Data models Mathematical model Supervised learning

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Offloading Collective Operations to Programmable Logic

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IEEE MICRO 2017年第5期37卷 52-60页

作者： Arap, Omer Brasilino, Lucas R. B. Kissel, Ezra Shroyer, Alexander Swany, Martin Indiana Univ Bloomington IN 47405 USA Indiana Univ Intelligent Syst Engn Dept Bloomington IN 47405 USA Indiana Univ Experience Microcontroller Data Acquisit Devices Bloomington IN 47405 USA Indiana Univ Intelligent Syst Engn Dept Sch Informat & Comp Bloomington IN 47405 USA

In this article, the authors present a framework for offloading collective operations to programmable logic for use in applications using the Message Passing Interface (MPI). They evaluate their approach on the Xilinx Zynq system on a chip and the NetFPGA, a network interface card based on a field-programmable gate array. Results are presented from microbenchmarks and a benchmark scientific application.

关键词： algorithm design and analysis collective operations Field programmable gate arrays field-programmable gate array FPGA message passing interface MPI Network interfaces Ports (Computers) Programming Random access memory SoC system on a chip Zynq

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Distributed Coordination Control and Industrial Applications

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2017年第6期64卷 4967-4971页

作者： Shi, Yang Qin, Jiahu Ahn, Hyo-Sung Univ Victoria Dept Mech Engn Victoria BC V8N 3P6 Canada Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Gwangju Inst Sci & Technol Sch Mech Engn Gwangju 500712 South Korea

The papers in this special section focus on new developments in the area of distributed coordination control and industrial applications. [ABSTRACT FROM AUTHOR]

关键词： algorithm design and analysis Clustering algorithms Control systems Cooperative systems Distributed processing Power systems Special issues and sections

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Bioinformatics of DNA

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PROCEEDINGS OF THE IEEE 2017年第3期105卷 419-421页

作者： Heath, Lenwood S. Bravo, Hector Corrada Caccamo, Mario Schatz, Michael Virginia Tech Dept Comp Sci Blacksburg VA USA Univ Maryland College Pk MD USA Univ Maryland Ctr Bioinformati & Computat Biol College Pk MD USA Univ Maryland Inst Adv Comp Studies UMIACS College Pk MD USA Natl Inst Agr Bot NIAB Crop Bioinformat Cambridge England Univ East Anglia Norwich Norfolk England Johns Hopkins Univ Baltimore MD USA Cold Spring Harbor Lab Cold Spring Harbor NY USA

This special issue introduces current developments in bioinformatics, the use of computational techniques in the service of the life sciences, in the specific context of the analysis and interpretation of DNA molecules, where DNA is the primary informational molecule of all life. To fully appreciate the papers in this issue, some background knowledge in biology is required. The DNA in a cell is organized into a genome consisting of one or more chromosomes, each of which is a DNA molecule, which can be thought of as a long string over the alphabet of nucleotides, A, C, G, and T. Within a DNA string or sequence are substrings that are genes that normally encode the recipe for the functional proteins in the cell. The human genome is organized into 23 pairs of chromosomes, one maternal and one paternal chromosome set, containing a total of approximately six billion nucleotides. Amazingly, all ~1 trillion cells in one’s body, of diverse form and function, contain the same “molecular blueprint” with a near perfect copy of their genome.

关键词： Special issues and sections Bioinformatics Genomes Data analysis Genetics DNA Biodiversity algorithm design and analysis

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Student Research Highlight: Gyro-Aided Visual Tracking Using Iterative Earth Mover's Distance

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IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE 2017年第10期32卷 52-55页

作者： Yao, Gang Dani, Ashwin Univ Connecticut Elect & Comp Engn 347 Fairfield Way Storrs CT 06269 USA

Visual tracking is an important problem in the field of robotics, especially for the cameras mounted on agile flying robots. The information generated from these images by the tracking algorithm can be used by autonomous vehicle navigation [1], or human-robot interaction [2], [3]. Image-based tracking algorithms are categorized as point tracking, kernel tracking, or silhouette tracking [4]. First, distinguishing features, such as color, shape, and region are selected to identify objects for visual tracking. Then, the tracked object is modeled, on the basis of the selected features. The correspondence or similarity measurement between the target and the candidate across frames is constructed, such as the sum of squared differences of the pixel intensity values [5], the mutual information (MI) [6], [7], the normalized cross correlation, and the Bhattacharyya coefficient [8]. Supervised or unsupervised online learning algorithms are also used for visual tracking.

关键词： Target tracking Visualization Cameras Autonomous vehicles algorithm design and analysis Robot vision systems

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Guest Editorial: Large-Scale Multimedia Data Retrieval, Classification, and Understanding

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IEEE TRANSACTIONS ON MULTIMEDIA 2017年第9期19卷 1965-1967页

作者： Song, J. Jegou, H. Snoek, C. Tian, Q. Sebe, N. Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China Facebook Paris France Facebook Menlo Pk CA USA Univ Amsterdam NL-94323 Amsterdam Netherlands Univ Texas San Antonio San Antonio TX 78249 USA Univ Trento I-38123 Trento Italy

The papers in this special section focus on multimedia data retrieval and classification via large-scale systems. Today, large collections of multimedia data are explosively created in different fields and have attracted increasing interest in the multimedia research area. Large-scale multimedia data provide great unprecedented opportunities to address many challenging research problems, e.g., enabling generic visual classification to bridge the well-known semantic gap by exploring large-scale data, offering a promising possibility for in-depth multimedia understanding, as well as discerning patterns and making better decisions by analyzing the large pool of data. Therefore, the techniques for large-scale multimedia retrieval, classification, and understanding are highly desired. Simultaneously, the explosion of multimedia data puts urgent needs for more sophisticated and robust models and algorithms to retrieve, classify, and understand these data. Another interesting challenge is, how can the traditional machine learning algorithms be scaled up to millions and even billions of items with thousands of dimensionalities? This motivated the community to design parallel and distributed machine learning platforms, exploiting GPUs as well as developing practical algorithms. Besides, it is also important to exploit the commonalities and differences between different tasks, e.g., image retrieval and classification have much in common while different indexing methods evolve in a mutually supporting way.

关键词： Special issues and sections Multimedia communication Information retrieval Machine learning Classification algorithms algorithm design and analysis Large-scale systems Visualization

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Meta-algorithms in Cognitive Computing

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IEEE INTELLIGENT SYSTEMS 2017年第4期32卷 35-39页

作者： Sellmann, Meinolf GE Global Res Niskayuna NY 12309 USA

A brief overview of algorithms that improve other algorithms and their role in cognitive computing.

关键词： Predictive models algorithm design and analysis Prediction algorithms Training Cognitive systems Runtime Artificial intelligence

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