Pseudo‐3D RSSI‐based WSN localization algorithm using linear regression

Receiver Strength Signal Indication based Wireless Sensor Networks offer a cheap solution for location‐aware applications. For a final breakthrough these systems need fast deployment and easy auto‐configuration. In this study, we use the real‐life iMinds test bed to expand a two‐dimensional localization algorithm to the pseudo third dimension with very low additional computational time. Our experiments show that this fast three‐dimensional algorithm has no outliers and avoids manual calibration. Our algorithm has lower position errors than a maximum likelihood algorithm with a mean square error cost function. Furthermore, with non‐parametric statistical tests, we show that our previously designed two‐dimensional preprocessing performs equally well in pseudo‐three dimensions: the preprocessing reduces the position error in a statistically significant way.Copyright © 2013 John Wiley & Sons, Ltd.     

Mobile information services enabled by mobile publishing (MIS-MP)

Mobile users and devices want to discover and share a growing range of information as the processing and storage capabilities of mobile devices grow. For example, users want to discover nearby networks, and location-based or time-sensitive user information contents. A mobile device may want to discover neighboring networks and the parameters required to access these networks so that it can intelligently decide which networks to use next, and use its existing network connection to authenticate with selected neighboring networks before it moves into the coverage areas of the selected networks. This can significantly reduce handoff delays. Existing service discovery frameworks are not effective for such neighboring network discovery or for discovering dynamic, location- or time-sensitive user information contents. This paper describes and evaluates a new approach—Mobile Information Services enabled by Mobile Publishing (MIS-MP)—for real time collection, discovery, and sharing of network and user information. With MIS-MP, mobiles take full advantage of the wealth of information they can accumulate during their routine mobility and use of networks to help each other to discover the information they want when and where they want it. This is accomplished by mobiles publishing the information they collect about the networks they visited, and the user information contents they learned or used, to make the information available to other mobiles. This paper presents analytical models and simulation results to evaluate the feasibility and performance of MIS-MP. It also describes a testbed implementation of MIS-MP and some of the lessons we learned. Tao Zhang is Director of Mobile Networking Research Group at Telcordia Technologies, Piscataway, New Jersey, USA. He develops and directs research and advanced development programs in mobile networking and applications, including mobility and applications across heterogeneous radio networks, mobile information services, vehicular networking, mobile peer-to-peer applications, sensor networking and applications, and collaborative networking .Dr. Zhang’s work has led to several new commercial products. He co-authored the book “IP-Based Next Generation Wireless Networks” published by John Wiley & Sons in 2004. He initiated the International Conference on Collaborative Computing: Networking, Applications, and Worksharing (CollaborateCom), and has been serving in various roles for it. Dr. Zhang holds 4 U.S. patents, with over 25 more pending. He received the 2000 Telcordia CEO Award (for most exceptional teams and individuals who have achieved a significant business success) and 2002 SAIC’s Executive Science and Technology Council Publication Prize. Eric van den Berg received his Ph.D. in Applied Mathematics from Cornell University in 1999. After obtaining his degree, he joined Telcordia Technologies, where he is a Research Scientist in Applied Research Department. His research interests include traffic modeling and performance analysis of IP- and wireless networks. He received the 2000 Telcordia CEO Award (for most exceptional individuals and teams who have achieved a significant business success) and the 2002 SAIC Executive Science and Technology Council Publication Prize. Sunil Madhani is a Distinguished MTS with Motorola where he manages the IP Realization team in Mobile Device Technology Office. He aims at working on unconventional and disruptive IP technologies. His current research focus is on convergent networks, dynamic mobility management and fast handoff in secured/seamless wireless LAN/WAN roaming. His past research includes registration/configuration protocols in wireless environment, application layer mobility management, secured Mobile IP, managed DOS attack sensor and TCP/IP boosters. Sunil Madhani holds MS (2002) in Engineering Management & System from Columbia University and MS (1997) in Computer Science from State University of New York.     

正确计算DAC功耗数据

随着便携式多媒体系统设计师将电池寿命推向极限,他们正把前所未有的时间花在研究不同硅供应商提供的功耗数据上.以牙还牙式的比较通常是困难的,因为变量实在是太多了,而且竞争器件之间的关键差异常常远不是那么明显.     

Deterministic regression methods for unbiased estimation of time-varying autoregressive parameters from noisy observations

A great deal of interest has been paid to autoregressive parameter estimation in the noise-free case or when the observation data are disturbed by random noise. Tracking time-varying autoregressive (TVAR) parameters has been also discussed, but few papers deal with this issue when there is an additive zero-mean white Gaussian measurement noise. In this paper, one considers deterministic regression methods (or evolutive methods) where the TVAR parameters are assumed to be weighted combinations of basis functions. However, the additive white measurement noise leads to a weight-estimation bias when standard least squares methods are used. Therefore, we propose two alternative blind off-line methods that allow both the variance of the additive noise and the weights to be estimated. The first one is based on the errors-in-variable issue whereas the second consists in viewing the estimation issue as a generalized eigenvalue problem. A comparative study with other existing methods confirms the effectiveness of the proposed methods.     

Single-Cell Microgels for Diagnostics and Therapeutics

Cell encapsulation within hydrogel droplets is transforming what is feasible in multiple fields of biomedical science such as tissue engineering and regenerative medicine, in vitro modeling, and cell-based therapies. Recent advances have allowed researchers to miniaturize material encapsulation complexes down to single-cell scales, where each complex, termed a single-cell microgel, contains only one cell surrounded by a hydrogel matrix while remaining <100 μm in size. With this achievement, studies requiring single-cell resolution are now possible, similar to those done using liquid droplet encapsulation. Of particular note, applications involving long-term in vitro cultures, modular bioinks, high-throughput screenings, and formation of 3D cellular microenvironments can be tuned independently to suit the needs of individual cells and experimental goals. In this progress report, an overview of established materials and techniques used to fabricate single-cell microgels, as well as insight into potential alternatives is provided. This focused review is concluded by discussing applications that have already benefited from single-cell microgel technologies, as well as prospective applications on the cusp of achieving important new capabilities.     

Exploration of Electrochemical Reactions at Organic–Inorganic Halide Perovskite Interfaces via Machine Learning in In Situ Time‐of‐Flight Secondary Ion Mass Spectrometry

The instability of hybrid organic–inorganic perovskite (HOIP) devices is one of the significant challenges preventing commercialization. Exploring these phenomena is severely limited by the complexity of the intrinsic electrochemistry of HOIPs, the presence of multiple volatile and mobile ionic species, and the possible role of environmentally induced reactions at surfaces and triple‐phase junctions. Here, in situ studies of the electrochemistry of methylammonium lead bromide perovskite with the Au electrode interface are reported via light‐ and voltage‐dependent time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) imaging of lateral perovskite heterostructures. While ToF‐SIMS allows for the visualization of the chemical composition along the surface and its evolution with light and electrical bias, the interpretation of the multidimensional data obtained is often limited due to strong correlations between chemical signatures and the need to track multiple peaks at once. Here, a machine learning workflow combining the Hough transform and non‐negative matrix factorization and non‐negative tensor decomposition is developed to avoid this limitation and extract salient features of associated chemical changes and to separate the light‐ and voltage‐dependent dynamics. Combining these in situ characterizations and the machine learning workflow provides comprehensive information on the chemical nature of moving species, ion accumulation, and interfacial electrochemical reactions in HOIP devices.     

Microwave radiometry for cement kiln temperature measurements.

The maximum temperature inside a cement kiln is a critical operating parameter, but is often difficult or impossible to measure. We present here the first data that show a correlation between cement kiln temperature measured using a microwave radiometer and product chemistry over an eight-hour period. The microwave radiometer senses radiation in the 12-13 GHz range and has been described previously [Stephan and Pearce (2002), JMPEE 37: 112-124].     

Partial differential equation-based approach for empirical mode decomposition: application on image analysis

The major problem with the empirical mode decomposition (EMD) algorithm is its lack of a theoretical framework. So, it is difficult to characterize and evaluate this approach. In this paper, we propose, in the 2-D case, the use of an alternative implementation to the algorithmic definition of the so-called "sifting process" used in the original Huang's EMD method. This approach, especially based on partial differential equations (PDEs), was presented by Niang in previous works, in 2005 and 2007, and relies on a nonlinear diffusion-based filtering process to solve the mean envelope estimation problem. In the 1-D case, the efficiency of the PDE-based method, compared to the original EMD algorithmic version, was also illustrated in a recent paper. Recently, several 2-D extensions of the EMD method have been proposed. Despite some effort, 2-D versions for EMD appear poorly performing and are very time consuming. So in this paper, an extension to the 2-D space of the PDE-based approach is extensively described. This approach has been applied in cases of both signal and image decomposition. The obtained results confirm the usefulness of the new PDE-based sifting process for the decomposition of various kinds of data. Some results have been provided in the case of image decomposition. The effectiveness of the approach encourages its use in a number of signal and image applications such as denoising, detrending, or texture analysis.     

Transformation champ proche — champ lointain bidimensionnelle et monodimensionnelle appliquée à des mesures de SER

In high frequency thercs measurement of large target must be done at important distances, so the distance of measurement (Fraunhofer’s distance) R is given by R ≥ 2d² /λ where d is the tranversal length of the target and λ the radiation wavelength. In this paper we present a method for target that are large relative to the curvature of the spherical wavefront and the curvature of the cylindrical wavefront. A two-dimensional analytical algorithm transforms the spherical wave (near-field) measurement into the desired plane wave (far-field) and a one-dimensionnal analytical algorithm transforms the cylindrical wave (near-field) measurement into the desired plane wave (far-field). We present some results on simulations and measurements. A comparison of one-dimensional and two-dimensional methods demonstrates the efficiency of the two dimensional methods.     

Multichannel AR parameter estimation from noisy observations as an errors-in-variables issue

In various applications from radar processing to mobile communication systems based on CDMA or OFDM, M-AR multichannel processes are often considered and may be combined with Kalman filtering. However, the estimations of the M-AR parameter matrices and the autocorrelation matrices of the additive noise and the driving process from noisy observations are key problems to be addressed. In this paper, we suggest solving them as an errors-in-variables issue. In that case, the noisy-observation autocorrelation matrix compensated by a specific diagonal block matrix and whose kernel is defined by the M-AR parameter matrices must be positive semi-definite. Hence, the parameter estimation consists in searching every diagonal block matrix that satisfies this property, in reiterating this search for a higher model order and then in extracting the solution that belongs to both sets. A comparative study is then carried out with existing methods including those based on the Extended Kalman Filter (EKF) and the Sigma-Point Kalman Filters (SPKF). It illustrates the relevance and advantages of the proposed approaches.