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

Hidden Markov modeling has proven extremely useful for statistical analysis of speech signals. There are, however, inherent problems in two dimensional extensions to HMMs, one of which is the exponential complexity associated with fully 2-D HMMs. We propose a new 2-D HMM-like structure obtained by embedding states within regions of a deformable template structure. With this state-embedded deformable template (SEDT), each region of a deformable template has an underlying observation probability distribution. This structure allows for computation of the P[image/template]. The template that maximizes this probability provides an optimal segmentation of the image. This segmentation capability is demonstrated in facial analysis applications.

On merging hidden Markov models with deformable templates

Face images that are captured by surveillance cameras usually have a very low resolution, which significantly limits the performance of face recognition systems. In the past, super-resolution techniques have been proposed that attempt to increase the resolution by combining information from multiple images. These techniques use super-resolution as a preprocessing system to obtain a high resolution image that can later be passed to a face recognition system. Considering that most state-of-the-art face recognition systems use an initial dimensionality reduction method, we propose embedding the super-resolution algorithm into the face recognition system so that super-resolution is not performed in the pixel domain, but is instead performed in a reduced dimensional domain. The advantage of such an approach is a significant decrease in the computational complexity of the super-resolution algorithm because the algorithm no longer tries to construct a visually improved high quality image, but instead constructs the information required by the recognition algorithm directly in the lower dimensional domain without any unnecessary overhead.

Eigenface-based super-resolution for face recognition

An effective way of providing error resilience with a relatively small reduction in compression ratio is multiple description coding (MDC). All multiple description codes to date assume the existence of multiple independent "on-off" channels between the transmitter and receiver. Each link is either broken in which case the symbols or packets are completely lost, or it functions properly, in which case symbols are received free of errors. This channel model is appropriate for wireline networks, such as the Internet, but it may not be applicable to other commonly used channels such as wireless channels. We replace the parallel independent on-off channel model with wireless channel models such as a Rayleigh fading model. We also communicate using multiple transmit and receive antennas over the channel. Furthermore, we use digital bandpass modulation to transmit the source symbols.

Multiple description coding with multiple transmit and receive antennas for wireless channels: the case of digital modulation

Target Recognition Based on Directional Filter Banks and Higher-Order Neural Networks

A major drawback of block-based still-image or video-compression methods at low rates is the visible block boundaries that are also known as blocking artifacts. Several methods have been proposed in the literature to reduce these artifacts. Most are single image methods, which do not distinguish between video and still images. However, video has a temporal dimension that can lead to better reconstruction if utilized effectively. We show how to combine information from multiple frames to reduce blocking artifacts. We derive constraint sets using motion between neighboring frames and quantization information that is available in the video bit stream. These multiframe constraint sets can be used to reduce blocking artifacts in an alternating-projections scheme. They can also be included in existing set-theoretic algorithms to improve their performance by narrowing down the feasibility set. Experimental results show the effectiveness of using these multiframe constraint sets.

Russell M. Mersereau

Papers

On merging hidden Markov models with deformable templates

Eigenface-based super-resolution for face recognition

Multiple description coding with multiple transmit and receive antennas for wireless channels: the case of digital modulation

Target Recognition Based on Directional Filter Banks and Higher-Order Neural Networks

Multiframe blocking-artifact reduction for transform-coded video