Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi-hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show the LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional outing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.

/pdf/energy-efficient-communication-protocol-for-wireless-4vhuagusz2.pdf

Energy-efficient communication protocol for wireless microsensor networks

Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have signicant impact on the overall energy dissipation of these networks. Based on our ndings that the conventional protocols of direct transmission, minimum-transmission-energy, multihop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster base stations (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show that LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional routing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.

Energy-efficient communication protocols for wireless microsensor networks

Determining optical flow

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.10062

Fast robust automated brain extraction

Multiple description coding has been shown to provide improved error resiliency for video streaming over error-prone channels. In a typical multiple description codec, the decoder simply reproduces the signal using the received description(s). The optimal reconstruction is obtained from the available description in the sense that the mean squared error is minimized. However, this reconstruction operation does not consider the unique spatial and temporal properties underlying a video signal. In this paper, we propose a joint multiple description transform coding and statistical error concealment algorithm that adopts a maximum a posteriori estimation approach to compensate for the loss of descriptions. Our method considers not only the correlation between descriptions but also the spatial/temporal redundancy in video signals. Simulation results with several video sequences show that the proposed algorithm results in significant visual improvements over a conventional multiple description transform decoder.

An integrated application of multiple description transform coding and error concealment for error-resilient video streaming

Burg's maximum entropy method has been used with success in spectral estimation. This paper is an attempt to generalize the maximum entropy method to the deconvolution of positive signals from a finite set of linear measurements. In this paper, we also investigate the existence of a solution to the deconvolution problem using a geometric approach. By formulating the deconvolution problem as an unconstrained optimization problem, our procedure will provide the maximum entropy deconvolved positive signal when it exists. Otherwise, the algorithm diverges.

Maximum entropy deconvolution

Two-dimensional signals are normally sampled and processed as rectangular arrays. For signals which are bandlimited with a circular region of support in the Fourier plane, however, it has been known for some time that a savings in the number of samples required for an exact reconstruction can be realized by sampling the signal on a hexagonal sampling raster. In this presentation we show that a substantial savings in computation can result as well. Included are methods for signal representation, linear system implementation, Fourier transform computation and FIR filter design. Some comparisons between systems defined over rectangular and hexagonal sampling rasters will also be given.

Two-dimensional signal processing from hexagonal rasters

A novel model for one-dimensional signal analysis, called a weighted multiresolution process (WiMP), is introduced. The model combines the scale and time-frequency localization properties of the wavelet representation with the self-affine characteristics of signals to be modeled. A WiMP can be viewed as a Markov chain of weighted translations between levels of the wavelet decomposition. Both aperiodic and periodic signals are investigated, and the corresponding decomposition/reconstruction algorithms are presented. >

Wavelet representations and coding of self-affine signals

In this paper, we present a data association algorithm for people tracking in a 3D world using multiple cameras. Our approach expands an independent partitioned particle filter with a data association vector. For the association parameter, we propose a proposal function using likelihood functions based on color and distance. This proposed algorithm solves the data association problem without dramatically increasing the computational complexity even in the case of trajectories that cross.

Russell M. Mersereau

Papers

An integrated application of multiple description transform coding and error concealment for error-resilient video streaming

Maximum entropy deconvolution

Two-dimensional signal processing from hexagonal rasters

Wavelet representations and coding of self-affine signals

Data association for people tracking using multiple cameras