Data Mining Practical Machine Learning Tools and Techniques

25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Multiple Imputation for Nonresponse in Surveys

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.

Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

System Identification I

International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

Among the panoply of applications enabled by the Internet of Things (IoT), smart and connected health care is a particularly important one. Networked sensors, either worn on the body or embedded in our living environments, make possible the gathering of rich information indicative of our physical and mental health. Captured on a continual basis, aggregated, and effectively mined, such information can bring about a positive transformative change in the health care landscape. In particular, the availability of data at hitherto unimagined scales and temporal longitudes coupled with a new generation of intelligent processing algorithms can: (a) facilitate an evolution in the practice of medicine, from the current post facto diagnose-and-treat reactive paradigm, to a proactive framework for prognosis of diseases at an incipient stage, coupled with prevention, cure, and overall management of health instead of disease, (b) enable personalization of treatment and management options targeted particularly to the specific circumstances and needs of the individual, and (c) help reduce the cost of health care while simultaneously improving outcomes. In this paper, we highlight the opportunities and challenges for IoT in realizing this vision of the future of health care.

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Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges

Face recognition applications for airport security and surveillance can benefit from the collaborative coupling of mobile and cloud computing as they become widely available today. This paper discusses our work with the design and implementation of face recognition applications using our mobile-cloudlet-cloud architecture named MOCHA and its initial performance results. The challenge lies with how to perform task partitioning from mobile devices to cloud and distribute compute load among cloud servers (cloudlet) to minimize the response time given diverse communication latencies and server compute powers. Our preliminary simulation results show that optimal task partitioning algorithms significantly affect response time with heterogeneous latencies and compute powers. Motivated by these results, we design, implement, and validate the basic functionalities of MOCHA as a proof-of-concept, and develop algorithms that minimize the overall response time for face recognition. Our experimental results demonstrate that high-powered cloudlets are technically feasible and indeed help reduce overall processing time when face recognition applications run on mobile devices using the cloud as the backend servers.

http://www.tolgasoyata.com/mocha/pdfs/soyata.iscc12.pdf

Cloud-Vision: Real-time face recognition using a mobile-cloudlet-cloud acceleration architecture

In combination with current sociological trends, the maturing development of Internet of Things devices is projected to revolutionize healthcare. A network of body-worn sensors, each with a unique ID, can collect health data, that is, orders-of-magnitude richer than what is available today from sporadic observations in clinical/hospital environments. When databased, analyzed, and compared against information from other individuals using data analytics, Healthcare Internet of Things data enables the personalization and modernization of care with radical improvements in outcomes and reductions in cost. In this article, we survey the existing and emerging technologies that can enable this vision for the future of healthcare, particularly, in the clinical practice of healthcare. Three main technology areas underlie the development of this field: 1) sensing, where there is an increased drive for miniaturization and power efficiency; 2) communications, where the enabling factors are ubiquitous connectivity, standardized protocols, and the wide availability of cloud infrastructure; and 3) data analytics and inference, where the availability of large amounts of data and computational resources is revolutionizing algorithms for individualizing inference and actions in health management. Throughout this article, we use a case study to concretely illustrate the impact of these trends. We conclude this article with a discussion of the emerging directions, open issues, and challenges.

A Survey of Healthcare Internet of Things (HIoT): A Clinical Perspective

As CMOS scales beyond the 45nm technology node, leakage concerns are starting to limit microprocessor performance growth. To keep dynamic power constant across process generations, traditional MOSFET scaling theory prescribes reducing supply and threshold voltages in proportion to device dimensions, a practice that induces an exponential increase in subthreshold leakage. As a result, leakage power has become comparable to dynamic power in current-generation processes, and will soon exceed it in magnitude if voltages are scaled down any further. Beyond this inflection point, multicore processors will not be able to afford keeping more than a small fraction of all cores active at any given moment. Multicore scaling will soon hit a power wall. This paper presents resistive computation, a new technique that aims at avoiding the power wall by migrating most of the functionality of a modern microprocessor from CMOS to spin-torque transfer magnetoresistive RAM (STT-MRAM)---a CMOS-compatible, leakage-resistant, non-volatile resistive memory technology. By implementing much of the on-chip storage and combinational logic using leakage-resistant, scalable RAM blocks and lookup tables, and by carefully re-architecting the pipeline, an STT-MRAM based implementation of an eight-core Sun Niagara-like CMT processor reduces chip-wide power dissipation by 1.7× and leakage power by 2.1× at the 32nm technology node, while maintaining 93% of the system throughput of a CMOS-based design.

http://www.tolgasoyata.com/file/guo.isca10.pdf

Resistive computation: avoiding the power wall with low-leakage, STT-MRAM based computing

This paper presents an overview of passive Radio Frequency (RF) energy reception and power harvesting circuits for isolated communications and computing systems lacking access to primary power sources. A unified understanding of the energy harvesting alternatives is provided, followed by an elaborate study of RF energy harvesting within the context of embedded systems. A detailed discussion of RF technologies ranging from the directed communications signal reception to dispersed ambient power harvesting is provided. A comparative focus on design tradeoffs and process alterations is provided to represent the diversity in the applications requiring wireless RF harvesting units. Also included is an analysis of system combinations, and how wake up units, active storage, and duty cycling play roles in the consumption and harvesting of RF energy.

Tolga Soyata

Papers

Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges

Cloud-Vision: Real-time face recognition using a mobile-cloudlet-cloud acceleration architecture

A Survey of Healthcare Internet of Things (HIoT): A Clinical Perspective

Resistive computation: avoiding the power wall with low-leakage, STT-MRAM based computing

RF Energy Harvesting for Embedded Systems: A Survey of Tradeoffs and Methodology