خط مشی دسترسیدرباره ماپشتیبانی آنلاین
ثبت نامثبت نام
راهنماراهنما
فارسی
ورودورود
صفحه اصلیصفحه اصلی
جستجوی مدارک
تمام متن
منابع دیجیتالی
رکورد قبلیرکورد بعدی
Document Type:Latin Dissertation
Language of Document:English
Record Number:53960
Doc. No:TL23914
Call number:‭3220149‬
Main Entry:Mohammad H. Rahimi
Title & Author:Bioscope: Actuated sensor network for biological scienceMohammad H. Rahimi
College:University of Southern California
Date:2005
Degree:Ph.D.
student score:2005
Page No:114
Abstract:Characterizing and understanding three-dimensional natural phenomena (e.g. the energy budget of a forest canopy) requires massive parallel sensing and data processing. Static wireless sensor networks enable sampling of the environment at a finer granularity than ever before to increase our knowledge of biological phenomena. However, static networks are spatially constrained to a limited number of sampling points. A combination of actuated and static sensor network, on the other hand, can reveal much greater detail about the phenomena. This is because a mobile node can achieve a high degree of spatial sampling and the static nodes can achieve a high degree of temporal sampling. This dissertation presents Bioscope: a set of algorithms and techniques that enable a scientist to use an actuated wireless sensor network to systematically study biological phenomena. It consists of two major components: (1) Space-filling component, an exploration method that samples a phenomenon such that the sample distribution is spread in space with maximum inter-sample distances. The primary goal of the space-filling component is a high degree of robustness in understanding the fundamentals of the phenomenon. (2) Adaptive component, which generates a model of the phenomenon through runtime adaptation and orchestrates sample collection such that the performance of regenerated model is maximized. The combination of these two approaches enables the scientist to efficiently observe the underlying phenomenon. This thesis presents statistical techniques that form the skeleton of a data collection experiment using an actuated sensor network and discusses the necessary modifications and design choices to adapt such schemes to the constraints of our problem. It describes our approach toward the problem and provides experimental evidences that demonstrate the applicability of our approach.
Subject:Applied sciences; Bioscope; Field estimation; Robotics; Sensor network; Computer science; Biomedical research; Artificial intelligence; 0984:Computer science; 0541:Biomedical research; 0800:Artificial intelligence
Added Entry:G. Sukhatme
Added Entry:University of Southern California