Minimizing broadcast latency and redundancy in asynchronous wireless sensor networks

Asynchronous duty cycle Medium Access Control (MAC) protocols do not require global synchronization because nodes determine their wake-up schedule independently. As a result, these MACs have superior performance to those that employ synchronous duty-cycles in terms of energy expenditure, and advantageously, they are simple to implement. A key limitation is that they do not support efficient broadcast. A node needs to transmit a broadcast packet multiple times via unicast because only a subset of its neighbors may be awake at any given point in time. To address this problem, this paper proposes a centralized and distributed asynchronous broadcast algorithm that achieves minimal broadcast latency and redundancy. In addition, it uses a novel asynchronous MAC protocol that ensures all neighbors of a broadcasting node are awake to receive a broadcast. The performance of our algorithms is evaluated under different network configurations. We show via extensive simulation studies that our algorithms have near optimal network performance in terms of broadcast latency. In particular, compared to OTAB, the best broadcast scheduling algorithm to date, the broadcast latency and transmission times achieved by our designs are 1/5 and 1/2 that of OTAB, respectively.     

Selection of network and learning parameters for an adaptive neural robotic control scheme

This paper presents the results of a study in the design of a neural network based adaptive robotic control scheme. The neural network used here is a two hidden layer feedforward network and the learning scheme is the well-known backpropagation algorithm. The neural network essentially provides the inverse of the plant and acts in conjunction with a standard PD controller in the feedback loop. The objective of the controller is to accurately control the end position of a single link manipulator in the presence of large payload variations, variations in the link length and also variations in the damping constant. Based on results of this study, guidelines are presented in selecting the number of neurons in the hidden layers and also the parameters for the learning scheme used for training the network. Results also indicate that increasing the number of neurons in the hidden layer will improve the convergence speed of learning scheme up to a certain limit beyond which the addition of neurons will cause oscillations and instability. Guidelines for selecting the proper learning rate, momentum and fast backpropagation constant that ensure stability and convergence are presented. Also, a relationship between the r.m.s. error and the number of iterations used in training the neural network is established.     

Hall and drift mobilities in molecular beam epitaxial grown GaAs

A series of nominally undoped and Si-doped GaAs samples have been grown by molecular beam epitaxy (MBE) with Hall concentrations ranging from 10¹⁵ to 10¹⁹ cm⁻³ and mobilities measured at 77 and 300K by Hall-van der Pauw methods. Drift mobilities were calculated using the variational principle method and Hall scattering factors obtained from a relaxation-time approximation to permit cross-correlation of experimental data with drift or Hall mobilities and actual or Hall electron concentrations. At 77K, both high purity and heavily doped samples are well represented by either drift or Hall values since piezoelectric acoustic phonon scattering and strongly screened ionized impurity scattering hold the Hall factor close to unity in the respective regimes. Between n≊10¹⁵ and 10¹⁷ cm⁻³, where lightly screened ionized impority scattering predominates, Hall mobility overestimates drift mobility by up to 50 percent and Hall concentration similarly underestimates n. At 300K, polar optical phonons limit mobility and a Hall factor up to 1.4 is found in the lowest doped material, falling close to unity above about 10¹⁶ cm⁻³. Our calculation also agrees remarkably well with the Hall mobility of the highest purity MBE grown sample reported to date.     

Variable VCC design techniques forbattery-operated DRAMs

Wide-voltage-range DRAMs with extended data retention are desirable for battery-operated or portable computers and consumer devices. The techniques required to obtain wide operation, functionality, and performance of standard DRAMs from 1.8 V (two NiCd or alkaline batteries) to 3.6 V (upper end of LVTTL standard) are described. Specific techniques shown are: (1) a low-power and low-voltage reference generator for detecting V_CC level; (2) compensation of DC generators, V_BB and V_PP, for obtaining high speed at reduced voltages; (3) a static word-line driver and latch-isolation sense amplifier for reducing operating current; and (4) a programmable V_CC variable self-refresh scheme for obtaining maximum data retention time over a full operating range. A sub-50-ns access time is obtained for a 16 M DRAM (2 M×8) by simulation     

Temperature-compensation circuit techniques for high-density CMOSDRAMs

Temperature-compensation circuit techniques are presented for the CMOS DRAM internal voltage converter, the RC-delay circuit, and the back-bias generator, which do not need any additional process steps. The above-mentioned circuits have been designed and evaluated through a 16-Mb CMOS DRAM process. These circuits have shown an internal voltage converter (IVC) with an internal voltage temperature coefficient of 185 ppm/°C, and an RC-delay circuit with a delay time temperature coefficient of 0.03%/°C. As a result, a 6.5-ns faster RAS access time and improved latchup immunity have been achieved, compared with conventional circuit techniques     

An adaptive algorithm for separating and tracking multipledirectional sources in linear arrays

An algorithm for spatially filtering out, enhancing, and tracking individual directional sources in an adaptive array is proposed and investigated. In this algorithm, the sources are separated by using an adaptive beamformer whose outputs are processed by using the LMS algorithm to track distinct sources individually. From the LMS weights used, the source locations can be estimated. Whenever significant changes in these are detected, the beamformer is updated so that its outputs will be due to different sources in the steady state. With this algorithm, the problems of look-direction errors in look-direction constrained arrays and of large signal power in power inversion arrays are eliminated, and the enhancement of multiple moving sources becomes a natural process. Furthermore, because the sources are individually tracked and the beamformer is only updated occasionally, the algorithm possesses fast tracking behavior, and its implementation complexity is comparable to that of beamformer-based adaptive arrays using the LMS algorithm     

Interaction of relativistic electrons with ultrashort laser pulses:generation of femtosecond X-rays and microprobing of electron beams

The interaction of relativistic electrons with terawatt femtosecond laser pulses is reviewed with emphasis on how the scattering process can be used as a source of ultrashort X-ray pulses and as a time-resolved diagnostic for measuring transverse and longitudinal phase space distributions of an electron beam with microns spatial and subpicosecond temporal resolution. Recent experimental results are presented in which 90° Thomson scattering was utilized to generate 300 fs duration pulses of X-rays with peak energy of 30 keV. It is shown that, through analysis of the scattered X-ray beam properties, transverse and longitudinal distributions of the electron beam can be obtained     

A Bi‐Lineage Conducive Scaffold for Osteochondral Defect Regeneration

Because cartilage and bone tissues have different lineage‐specific biological properties, it is challenging to fabricate a single type of scaffold that can biologically fulfill the requirements for regeneration of these two lineages simultaneously within osteochondral defects. To overcome this challenge, a lithium‐containing mesoporous bioglass (Li‐MBG) scaffold is developed. The efficacy and mechanism of Li‐MBG for regeneration of osteochondral defects are systematically investigated. Histological and micro‐CT results show that Li‐MBG scaffolds significantly enhance the regeneration of subchondral bone and hyaline cartilage‐like tissues as compared to pure MBG scaffolds, upon implantation in rabbit osteochondral defects for 8 and 16 weeks. Further investigation demonstrates that the released Li+ ions from the Li‐MBG scaffolds may play a key role in stimulating the regeneration of osteochondral defects. The corresponding mechanistic pathways involve Li+ ions enhancing the proliferation and osteogenic differentiation of bone mesenchymal stem cells (BMSCs) through activation of the Wnt signalling pathway, as well as Li+ ions protecting chondrocytes and cartilage tissues from the inflammatory osteoarthritis (OA) environment through activation of autophagy. These findings suggest that the incorporation of Li+ ions into bioactive MBG scaffolds is a viable strategy for fabricating bi‐lineage conducive scaffolds that enhance regeneration of osteochondral defects.     

A Unified Framework for Transmitter Power Control in Cellular Radio Systems

In earlier works on transmitter power control in cellular radio systems, the problem of whether a signal‐to‐interference ratio (SIR) threshold is achievable is determined by apparently different rules for homogeneous and heterogeneous SIR systems. In this paper, we present a unified and more universal framework for both cases. We also highlight the conditions under which a given SIR threshold vector for the heterogeneous SIR system is achievable, although so far there is no general solution to this problem.     

Automated analysis of nerve-cell images using active contour models

The number of nerve fibers (axons) in a nerve, the axon size, and shape can all be important neuroanatomical features in understanding different aspects of nerves in the brain. However, the number of axons in a nerve is typically in the order of tens of thousands and a study of a particular aspect of the nerve often involves many nerves. Potentially meaningful studies are often prohibited by the huge number involved when manual measurements have to be employed. A method that automates the analysis of axons from electron-micrographic images is presented. It begins with a rough identification of all the axon centers by use of an elliptical Hough transform procedure. Boundaries of each axons are then extracted based on active contour model, or snakes, approach where physical properties of the axons and the given image data are used in an optimization scheme to guide the snakes to converge to axon boundaries for accurate sheath measurement. However, false axon detection is still common due to poor image quality and the presence of other irrelevant cell features, thus a conflict resolution scheme is developed to eliminate false axons to further improve the performance of detection. The developed method has been tested on a number of nerve images and its results are presented.