Order selection statistical test for nonstationary AR models

A new statistical test for selecting the order of a nonstationary AR modelyk is presented based on the predictive least-squares principle. This test is of the same order as the accumulated cost function _n = _k=1 ⁿ ( _k ^* – _k )²;i.e., ^* wherey _k ^* is the predictive least-square estimate. It is constructed to show how many times the integrated AR processy _k is differenced in order to obtain a stationary AR process given that the exact order of the process is unknown.     

Using Stochastic Differential Equation for Verification of Noise in Analog/RF Circuits

Today’s analog/RF design and verification face significant challenges due to circuit complexity, process variations and short market windows. In particular, the influence of technology parameters on circuits, and the issues related to noise modeling and verification still remain a priority for many applications. Noise could be due to unwanted interaction between the circuit elements or it could be inherited from the circuit elements. In addition, manufacturing disparity influence the characteristic behavior of the manufactured circuits. In this paper, we propose a methodology for modeling and verification of analog/RF designs in the presence of noise and process variations. Our approach is based on modeling the designs using stochastic differential equations (SDE) that will allow us to incorporate the statistical nature of noise. We also integrate the device variation due to 0.18μ m fabrication process in an SDE based simulation framework for monitoring properties of interest in order to quickly detect errors. Our approach is illustrated on nonlinear Tunnel-Diode and a Colpitts oscillator circuits.     

Formamide driven synthesis of well-aligned ZnO nanorod arrays on glass substrate

A cost effective, low-temperature approach has been developed for the large-area deposition of ZnO nanorod/nanotube arrays on a ZnO coated glass substrate by the natural oxidation of zinc metal in formamide/water mixtures. The two-step seed deposition and wet-chemical approach exhibited well-controlled growth of highly oriented and densely packed ZnO nanorod/nanotube arrays with large-area homogeneity and uniform morphologies. In order to investigate the quality and alignment of ZnO nanorod arrays grown on the ZnO seed layer coated substrate, three different methods of ZnO coating have been deposited by ultrahigh vacuum evaporation system, DC sputtering and RF sputtering, respectively. Our results showed that the ZnO seed layer grown by RF sputtering resulted in high quality ZnO nanorod arrays.     

A Self-powered Wearable Wireless Sensor System Powered by a Hybrid Energy Harvester for Healthcare Applications

Wireless Personal Communications - In this paper, a wearable medical sensor system is designed for long-term healthcare applications. This system is used for monitoring temperature, heartbeat,...     

An optimal energy‐efficient clustering method in wireless sensor networks using multi‐objective genetic algorithm

In this study, an optimal method of clustering homogeneous wireless sensor networks using a multi‐objective two‐nested genetic algorithm is presented. The top level algorithm is a multi‐objective genetic algorithm (GA) whose goal is to obtain clustering schemes in which the network lifetime is optimized for different delay values. The low level GA is used in each cluster in order to get the most efficient topology for data transmission from sensor nodes to the cluster head. The presented clustering method is not restrictive, whereas existing intelligent clustering methods impose certain conditions such as performing two‐tiered clustering. A random deployed model is used to demonstrate the efficiency of the proposed algorithm. In addition, a comparison is made between the presented algorithm other GA‐based clustering methods and the Low Energy Adaptive Clustering Hierarchy protocol. The results obtained indicate that using the proposed method, the network's lifetime would be extended much more than it would be when using the other methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Collaborative and Cognitive Network Platforms: Vision and Research Challenges

In this paper, we present a visionary concept referred to as Collaborative and Cognitive Network Platforms (CCNPs) as a future-proof solution for creating a dependable, self-organizing and self-managing communication substrate for effective ICT solutions to societal problems. CCNP creates a cooperative communication platform to support critical services across a range of business sectors. CCNP is based on the personal network (PN) technology which is an inherently cooperative environment prototyped in the Dutch Freeband PNP2008 and the European Union IST MAGNET projects. In CCNP, the cognitive control plane strives to exploit the resources to better satisfy the requirements of networked applications. CCNP facilitates collaboration inherently. Through cognition in the cognitive control plane, CCNP becomes a self-managed substrate. The self-managed substrate, in this paper, is defined as cognitive and collaborative middleware on which future applications run without user intervention. Endemic sensor networks may be incorporated into the CCNP concept to feed its cognitive control plane. In this paper, we present the CCNP concept and discuss the research challenges related to collaboration and cognition.     

Ice‐Assisted Synthesis of Black Phosphorus Nanosheets as a Metal‐Free Photocatalyst: 2D/2D Heterostructure for Broadband H2 Evolution

A 2D/2D heterojunction of black phosphorous (BP)/graphitic carbon nitride (g‐C₃N₄) is designed and synthesized for photocatalytic H₂ evolution. The ice‐assisted exfoliation method developed herein for preparing BP nanosheets from bulk BP, leads to high yield of few‐layer BP nanosheets (≈6 layers on average) with large lateral size at reduced duration and power for liquid exfoliation. The combination of BP with g‐C₃N₄ protects BP from oxidation and contributes to enhanced activity both under λ > 420 nm and λ > 475 nm light irradiation and to long‐term stability. The H₂ production rate of BP/g‐C₃N₄ (384.17 µmol g^?1 h^?1) is comparable to, and even surpasses that of the previously reported, precious metal‐loaded photocatalyst under λ > 420 nm light. The efficient charge transfer between BP and g‐C₃N₄ (likely due to formed N? P bonds) and broadened photon absorption (supported both experimentally and theoretically) contribute to the excellent photocatalytic performance. The possible mechanisms of H₂ evolution under various forms of light irradiation is unveiled. This work presents a novel, facile method to prepare 2D nanomaterials and provides a successful paradigm for the design of metal‐free photocatalysts with improved charge‐carrier dynamics for renewable energy conversion.     

Target Multiplicity Performance Analysis of Radar CFAR Detection Techniques for Partially Correlated Chi-Square Targets

Most radar targets are complex objects and produce a wide variety of reflections. An important class of targets is represented by the so-called moderately fluctuating Rayleigh targets, which, when illuminated by a coherent pulse train, return a train of correlated pulses with a correlation coefficient in the range 0<ρ<1 (intermediate between SWII and SWI models). The detection of this type of fluctuating targets is therefore of great interest. On the other hand, the CFAR detection is one of the desirable features for radar receivers. Because of the simplicity of cell-averaging (CA detectors in their implementation, they are commonly used in modern radar systems to automatically adapt the detection threshold to the local background noise or clutter power in an attempt to maintain an approximately constant rate of false alarm. In this paper, we analyze the performance of these detectors for the case where the radar receiver postdetection integrates M square-law detected pulses and the signal fluctuation obeys chi-square statistics with two degrees of freedom. These detectors include the mean-level ML), the greatest-of (GO) and the smallest-of (SO) schemes. In these processors, the estimation of the noise power levels from the leading and the trailing reference windows is based on the CA technique. Exact formulas for the detection probabilities are derived, in the absence as well as in the presence of spurious targets. The primary and the secondary interfering targets are assumed to be fluctuating in accordance with the chi-square fluctuation model with two degrees of freedom. Swerling's well known fluctuation models I and II represent the cases where the signal is completely correlated and completely decorrelated, respectively, from pulse to pulse. Probability of detection curves are presented for the chi-square family of fluctuations, including the Swerling cases I and II. The ML detector has the best homogeneous performance, the SO processor has the best multiple-target performance, while the GO scheme does not offer any merits neither in the absence nor in the presence of outlying targets.     

ZF and MMSE Detectors Performances of a Massive MIMO System Combined with OFDM and M-QAM Modulation

Wireless Personal Communications - This paper presents a least squares channel estimation (LSCE) in the UpLink transmission for a Massive MIMO systems in 5G wireless communications, combined with...