S. Cian O'Mathuna

Development of field programmable modular wireless sensor network nodes for ambient systems

The goal of this work is to fabricate robust, miniature, wireless sensor modules. These provide an enabling technology platform to conduct research in creating ambient systems, through implementing wireless sensor network applications. The approach taken is to partition the wireless sensor module into a series of layers with area 25mmx25mm. This modular approach has resulted in the specification of a series of layers, including a field programmable gate array layer for digital signal processing type operations, forming the initial elements of the 25mm sensor node toolkit that can be programmed for use with different sensors depending on application. This paper highlights the development of the sensor, processing, communication and power layers, and the connection approach used to form a robust modular system. Comparisons are made with other wireless sensor nodes and application examples are given.

Design considerations of sub-mW indoor light energy harvesting for wireless sensor systems

For most wireless sensor networks, one common and major bottleneck is the limited battery lifetime. The frequent maintenance efforts associated with battery replacement significantly increase the system operational and logistics cost. Unnoticed power failures on nodes will degrade the system reliability and may lead to system failure. In building management applications, to solve this problem, small energy sources such as indoor light energy are promising to provide long-term power to these distributed wireless sensor nodes. This article provides comprehensive design considerations for an indoor light energy harvesting system for building management applications. Photovoltaic cells characteristics, energy storage units, power management circuit design, and power consumption pattern of the target mote are presented. Maximum power point tracking circuits are proposed which significantly increase the power obtained from the solar cells. The novel fast charge circuit reduces the charging time. A prototype was then successfully built and tested in various indoor light conditions to discover the practical issues of the design. The evaluation results show that the proposed prototype increases the power harvested from the PV cells by 30% and also accelerates the charging rate by 34% in a typical indoor lighting condition. By entirely eliminating the rechargeable battery as energy storage, the proposed system would expect an operational lifetime 10--20 years instead of the current less than 6 months battery lifetime.

Thermoelectric Energy Harvesting for Building Energy Management Wireless Sensor Networks

A thermoelectric energy harvester powered wireless sensor networks (WSNs) module designed for building energy management (BEM) applications is built and tested in this work. An analytic thermoelectric generator (TEG) electrical model is built and verified based on parameters given in manufacturer data sheets of Bismuth Telluride TEGs. A charge pump/switching regulator two-stage ultra-low voltage step-up DC/DC converter design is presented in this work to boost the <0.5 V output voltage of TEG to usable voltage level for WSN (3.3 V). The design concept, device simulation, circuits schematic, and the measurement results are presented in detail. The prototype device test results show 25% end-to-end conversion efficiency in a wide range of input temperatures/voltages. Further tests demonstrate that the proposed thermoelectric generator design can effectively power WSN module which operates with a 1.7% duty cycle (5.8 seconds measurement time interval) when the prototype is placed on a typical wall-mount heater (60°C surface temperature). The thermoelectric energy harvesting powered WSN demonstrates duty cycles significantly higher than the required duty cycle for BEM WSN applications.

Visualizing uncertainty in multi-resolution volumetric data using marching cubes

Data sets acquired from complex scientific simulation, high precision engineering experiment and high-speed computer network have been exponentially increased, and visualization and analysis of such large-scale of data sets have been identified as a significant challenge to the visualization community. Over the past years many scientists have made attempt to address this problem by proposing various data reduction techniques. Consequently the size of data can be reduced and issues associated to the visualization can be improved (e.g. real-time interaction and visual overload). However, during the process of data reduction, the information of original data sets was approximated and potential errors were introduced. It leads to a new problem with regard to the integrity of the data and might mislead users for incorrect decision making. Therefore in this paper we aim to solve the problem by introducing three novel uncertainty visualization methods, which depict both the multi-resolution (MR) approximations of the original data set and the errors associated with each of its low resolution representations. As a result we faithfully represent the MR data sets and allow users to make suitable decisions from the visual output. We applied our techniques on a data set from medical domain to demonstrate their effectiveness and usability.

Effects of environmental colour on mood: a wearable LifeColour capture device

Colour is everywhere in our daily lives and impacts things like our mood, yet we rarely take notice of it. One method of capturing and analysing the predominant colours that we encounter is through visual lifelogging devices such as the SenseCam. However an issue related to these devices is the privacy concerns of capturing image level detail. Therefore in this work we demonstrate a hardware prototype wearable camera that captures only one pixel - of the dominant colour prevelant in front of the user, thus circumnavigating the privacy concerns raised in relation to lifelogging. To simulate whether the capture of dominant colour would be sufficient we report on a simulation carried out on 1.2 million SenseCam images captured by a group of 20 individuals. We compare the dominant colours that different groups of people are exposed to and show that useful inferences can be made from this data. We believe our prototype may be valuable in future experiments to capture colour correlated associated with an individuals mood.

A Multi-technology Approach to Identifying the Reasons for Lateral Drift in Professional and Recreational Darts

This work performs an extensive charterisation of precision targeted throwing in professional and recreational darts. The goal is to identify the contributing factors for lateral drift or throwing inaccuracy in the horizontal plane. A multi technology approach is adopted whereby a custom built body area network of wireless inertial measurement devices monitor tilt, force and timing, an optical 3D motion capture system provides a complete kinematic model of the subject, electromyography sensors monitor muscle activation patterns and a force plate and pressure mat capture tactile pressure and force measurements. The study introduces the concept of constant throwing rhythm and highlights how landing errors in the horizontal plane can be attributable to a number of variations in arm force and speed, centre of gravity and the movements of some of the bodies non throw related extremities.

An Antiwindup Approach to Power Controller Switching in an Ambient Healthcare Network

This paper proposes a methodology for improved power controller switching in mobile Body Area Networks operating within the ambient healthcare environment. The work extends Anti-windup and Bumpless transfer results to provide a solution to the ambulatory networking problem that ensures sufficient biometric data can always be regenerated at the base station. The solution thereby guarantees satisfactory quality of service for healthcare providers. Compensation is provided for the nonlinear hardware constraints that are a typical feature of the type of network under consideration and graceful performance degradation in the face of hardware output power saturation is demonstrated, thus conserving network energy in an optimal fashion.

Analyzing and Visualizing Multivariate Volumetric Scalar Data and Their Uncertainties

Data sets from the real world and most scientific simulations are known to be imperfect, often incorporating uncertainty information. Exploration and analysis of such variable data can lead to inaccurate or even incorrect results and inferences. As a powerful tool to communicate the data with users, an effective visualization system should present and inform users of the uncertainty information existing in the data. While some research has been conducted on visualizing uncertainty in spatio-temporal data and univariate data, little work has been reported on multivariate data. In addition, there are two main disadvantages in the existing uncertainty visualization methods for volumetric data. First, they rely heavily on the human perceptual system to recognize the uncertainty information, lacking the capability to depict them quantitatively. Second, they often present large amounts of diverse information in a single display, which may result in visual clutter and occlusion. In this paper, we present our hybrid framework that combines both information visualization techniques and scientific visualization techniques together to allow users to interactively specify features of interest, quantitatively explore and analyze the multivariate volumetric data and their uncertainties as well as localize features in the 3D object space. In comparison with those existing methods, we argue that our method not only allows users to quantitatively visualize the uncertainties within multivariate volumetric data, but also yields a clearer data presentation and facilitates a greater level of visual data analysis. We demonstrate the effectiveness of our framework by reporting a case study from the OpenGGCM (Open Geospace General Circulation Model) simulation in space science application domain.

The Evaluation of Direct Volume Rendering-Based Uncertainty Visualization Techniques for 3D Scalar Data

Many techniques have been proposed to represent uncertainty in data visualization. However, little research has been reported on the evaluation of their effectiveness. Moreover, no studies have been conducted to evaluate direct volume rendering (DVR)-based uncertainty visualization techniques. In this paper, we present a novel method that evaluates the perceptual effectiveness of four existing and one proposed DVR-based uncertainty visualization techniques. Four types of searching tasks that include identifying the maximum uncertainty data, identifying the minimum uncertainty data, identifying the maximum scalar data and identifying the minimum scalar data have been involved in this study, and a total of twenty-eight participants have contributed to the final main user study. Our analysis suggested that the proposed linked views and interactive specification (LVIS) technique appears to be the most accurate among all techniques, although it takes the longest task completion time. For the four existing techniques, the overlays technique appears to be the most advantageous, and it takes similar task completion time as the others. We believe that these findings can provide useful guidance for future uncertainty visualization design.

Model and Visualise the Relationship between Energy Consumption and Temperature Distribution in Cold Rooms

Science Foundation Ireland (CSET - Centre for Science, Engineering and Technology, grant 07/CE/I1147)