今年MWC最吸引消费者眼球的就是各家旗舰智能机、穿戴式装置、4GLTE、智能家庭以及移动支付。

This technical report is duplicative documentation of the approved doctorial thesis of one of the co-authors. The research was funded in part by the U.S. Army Aviation and Missile Research, Development, and Engineering Center (AMRDEC) for the development of micromechanical sensors under the Army Technology Objective (ATO) of Sensor, Warhead, and Fuzing Technology Integrated for Combined Effects (SWFTICE). Particular technical progress at AMRDEC within this report includes resonant array processing (Chapter 3), electrets integration with Microelectromechanical Systems (MEMS) with localized heater fabrication for wafer bonding and microcharging grids for in-situ charging using microcoronas (Chapter 4), and processing of MEMS transducers (Chapter 5).

Obtaining accurate non-cooperative geolocation is vital for persistent surveillance of a hostile emitter. Current research for developing a small, cheap and energy efficient sensor network for non-cooperative geolocation measurements via received signal strength (RSS) is limited. Most existing work focuses on simulating a non-cooperative network (NN) and in doing so, simulated models often ignore localization errors caused from the hardware processing raw RSS data and often model environment-dependent errors as random. By comparing real-time measured non-cooperative geolocation data to a simulated system a more accurate model can be developed. This thesis discusses the development and performance of a small, low cost, low complexity, and energy efficient sensor network that can locate a NN via RSS. The main focus of this research effort is designing a Poor Man's Spectrum Analyzer (PMSA) to locate a wireless device in a non-cooperative network (NN) that is transmitting in the Industrial, Scientific and Medical (ISM) radio band of 2.403 GHz to 2.48 GHz by measuring the emitter's received signal strength (RSS).

Emerging applications of wireless sensor networks (WSNs) require real-time quality of service (QoS) guarantees to be provided by the network. Traditional analysis work only focuses on the first-order statistics, such as the mean and the variance of the QoS performance. However, due to unique characteristics of WSNs, a cross-layer probabilistic analysis of QoS performance is essential. In this dissertation, a comprehensive cross-layer probabilistic analysis framework is developed to investigate the probabilistic evaluation and optimization of QoS performance provided by WSNs. In this framework, the distributions of QoS performance metrics are derived, which are natural tools to discover the probabilities to achieve given QoS requirements. Compared to first-order statistics, the distribution of these metrics reveals the relationship between the performance of QoS-based operations and the probability to achieve the performance.

This report documents the study of baseline sensor technology for enabling condition based maintenance plus in Army ground vehicles. The sensor study was driven from Failure Mode Effects Analysis (FMEA) conducted on four high cost driver components in Army ground vehicles by Tank Automotive Research, Development and Engineering Center (TARDEC). The four high cost driver components in Army ground vehicles as identified by TARDEC are engines, transmissions, batteries, and alternators. This report provides an assessment of current ground vehicle sensor systems and new baseline sensor technologies that may be used to support prognostic/diagnostic fault mode coverage including structural and component health monitoring for enabling condition based maintenance plus (CBM +) strategies to increase the operational availability of Army ground vehicles.

This report documents the idea or concept of operating an existing Silicon-on-Insulator (SOI) Micro-ElectroMechanical Systems (MEMS) gyroscopic sensor previously developed in the early-2000s under MEMS-Based Angular Rate Sensor (MBARS) and MicroControlled Array Sensors (mCAS). This report serves as documented evidence for future use of this open-sourced idea of the measurement of two-axis of linear acceleration from a previous sensor originally designed to operate as a single-axis rotation sensor.

Designing a high frequency (HF) magnetic field direction finding (DF) array for use onboard a military aircraft is the challenge that drives the effort of the research presented. The frequency range of interest, 2-32 MHz, has a maximum wavelength (150 meters) that exceeds the maximum length of any platform in the USAF inventory. The large wavelengths in the HF range make it difficult to accurately estimate from which direction a magnetic field is emitting. Accurate DF estimates are necessary for search and rescue operations and geolocating RF emitters of interest. The primary goal of this research is to characterize the performance of the MGL-S8A (Multi-Gap loop) B-Dot sensor. Although the sensors are designed to operate at frequencies above 5 GHz, their small size and potential to accurately detect magnetic fields in the 2-32 MHz range make them likely to be one type of an ensemble of sensors in the design of a HF DF array. The sensors are characterized in the azimuthal angles of 0, 45, -45, 90, and -90 degrees. Each sensor is characterized using two different types of magnetic field generators: a transverse electromagnetic (TEM) cell and a Helmholtz coil. The TEM cell generates a consistent magnetic field that acts as the input to the B-Dot sensor. The second type of magnetic field generator used, which is the secondary objective of this research, is a Helmholtz coil. An ideally designed Helmholtz coil is intended to be an inexpensive alternative to help in the characterization of B-Dot sensors in the HF range. The sensors can accurately measure electromagnetic (EM) fields in the HF range. Although the detection capability of the sensors is good, small differences between the 0 and 45 degree measurements may make it difficult for the sensors to be used in a DF array.

This chapter presents a reliability model to quantitatively study the reliability of energy-efficient parallel disk systems equipped with the MAID technique. Note that MAID is a well-known effective energy saving schemes for parallel disk systems. It aims to skew I/O load toward a few disks so that the other disks can be transitioned to low power states to conserve energy. I/O load skewing techniques such as MAID inherently affect reliability of parallel disks because disks storing popular data tend to have high failure rates than disks storing cold data. To address the reliability issue in MAID, we developed single disk swapping strategies to improve disk reliability by alternating disks storing hot data with disks holding cold data. In addition, we introduced multiple disk swapping scheme to further improve reliability of MAID. Then, we quantitatively evaluated the impacts of the disk swapping strategies on reliability of MAID-based disk systems.

Results obtained with the grant fall into several groupings: (a) Combinatorial conditions on the graphical representation of a two dimensional sensor network that will guarantee localizability of the network in the event of loss of any p sensors and/or q links in the network, for nonnegative integers p and q; (b) analysis of the effects of measurement error on the quality of localization of sensor positions in a sensor network, or more generally a target being localized; (c) the derivation of a measure, including algorithms for computing it, of the quality of connectivity of a network modeled by a graph with nodes and links, and in which the individual links are operative with defined a priori probabilities, and the probability that any one link is operative is independent of the probability that any other link is operative; (d) connectivity and capacity of networks with randomly positioned nodes and probabilistic channel models; (e) Doppler localization problems and miscellaneous multi-agent problems.

Owing to their size and heterogeneity, large-scale distributed systems require scalable, robust, fault-tolerant, and energy-efficient resource management infrastructures. This chapter presented ERIDIS: an Energy-efficient Reservation Infrastructure for large-scale Distributed Systems. ERIDIS is empowered to optimize the energy consumption of the computing and networking resources and to have a flexible and adaptive reservation management that satisfies user requirements through strict reservation policies.

Perchlorate is a pervasive water contaminant that has drawn national attention as a public health concern. Although perchlorate contamination has both natural and anthropogenic origins, its recurrent use in military munitions makes perchlorate the highest-priority military pollutant. Currently, perchlorate detection at the critical parts-per-billion level requires large, sophisticated instrumentation in a centralized laboratory. This report describes a fieldable, microchip capillary electrophoresis (MCE) device that is selective for perchlorate and exhibits reduced analysis times and reagent consumption. The device employs contact conductivity detection and zwitterionic surfactant chemistry to selectively resolve perchlorate from abundant environmental species such as chloride, nitrate, and sulfate. The prototype MCE system is capable of detection limits of 3.4 + or - 1.8 ppb in standards and 5.6 + or - 1.7 ppb in drinking water. Additional work modified the microchip geometry and separation chemistry, to account for higher ionic strength sample matrices such as surface and ground water, which cause interferences with perchlorate detection. A novel extraction method, incorporating the fundamentals of electrostatic ion chromatography (EIC), is presented as a way to overcome this challenge. Two extraction formats, employing either a packed bed or a monolith, were also investigated and presented in this work.

New geolocation methods are needed that will allow accurate location of LPI emitters despite low signal-to-noise ratio (SNR) and multipath propagation. To address this recent work has provided a single-stage localization method called Direct Position Determination (DPD). However, all its computation is concentrated at one computing node. We developed several methods to remedy this problem. To provide even better performance in these challenging real-world scenarios we developed a one-stage TDOA/FDOA localization method based on spatial sparsity of emitters. The proposed sparsity-based method has better performance (especially in multi-path and multi-emitter cases) compared to direct position determination (DPD) and two- stage Classic localization methods.

In today's military environment, emphasis has been placed on bandwidth efficiency and total use of the available spectrum. Current communication standards divide the spectrum into several different frequency bands, all of which are assigned to one or multiple primary users. Cognitive Radio utilizes potential white spaces that exist between currently defined channels or in time. One under-explored dimension of white space exploration is spatial. If a frequency band is being used in one region, it may be underutilized, or not occupied in another. Using an active localization method can allow for the discovery of spatial white; trying to spatially map all of the frequencies in a large area would become very computationally intensive, and may even be impractical using modern centralized methods. Applying a distributed method and the concepts discussed in Wireless Distributed Computing to the problem can be scaled onto many small wireless sensors and could improve the measuring system's effectiveness. For a bandwidth contested environment that must be spectrally mapped, three metrics stand out: Accuracy, Power Consumption, and Latency. All of these metrics must be explored and measured to determine which method could be most effectively applied to the spectral mapping of a spatial environment.

In this chapter, we demonstrated how we can embrace the memory wall to address the power wall for scientific computing. We proposed a software approach that provides the active-state power management of the CPU by means of DVFS and takes advantage of sublinear performance scaling in non-CPU activities. In detail, we presented a DVFS algorithm called the β-adaptation algorithm. This PMU-assisted, interval-based algorithm uses a compute-boundedness metric called β to capture the effect of sublinear performance scaling. By design, the algorithm minimizes its dependence on the PMU, which is essential for the portability of the algorithm.

No abstract available.

本刊以七大战略性新兴产业——节能环保、新一代信息技术、生物、高端装备制造、新能源、新材料、新能源汽车为研究重点，关注国家高层和各部委的动态，剖析国家和地方的新兴产业政策。本刊设有政策导读、领导讲话、七大战略性新兴产业本周国内外行业动态和重点企业新闻、投资专题四大板块，能够实时监测新兴产业和重点企业动态，把握新兴产业发展方向，研究发展重点，寻求发展机遇。