Design of coreless PCB transformers for power and signal isolation in a modular ADC system for power quality data acquisition

Available industrial energy meters offer high accuracy and reliability, but are typically expensive and low-bandwidth, making them poorly suited to multi-sensor data acquisition schemes and power quality analysis. An alternative measurement system is proposed in this paper that is highly modular, extensible and compact. To minimise cost, the device makes use of planar coreless PCB transformers to provide galvanic isolation for both power and data. Samples from multiple acquisition devices may be concentrated by a central processor before integration with existing host control systems. This paper focusses on the practical design and implementation of planar coreless PCB transformers to facilitate the module's isolated power, clock and data signal transfer. Calculations necessary to design coreless PCB transformers, and circuits designed for the transformer's practical application in the measurement module are presented. The designed transformer and each application circuit have been experimentally verified, with test data and conclusions made applicable to coreless PCB transformers in general.

Non-parametric consistency test for multiple-sensing-modality data fusion

Fusing data from multiple sensing modalities, e.g. laser and radar, is a promising approach to achieve resilient perception in challenging environmental conditions. However, this may lead to \emph{catastrophic fusion} in the presence of inconsistent data, i.e. when the sensors do not detect the same target due to distinct attenuation properties. It is often difficult to discriminate consistent from inconsistent data across sensing modalities using local spatial information alone. In this paper we present a novel consistency test based on the log marginal likelihood of a Gaussian process model that evaluates data from range sensors in a relative manner. A new data point is deemed to be consistent if the model statistically improves as a result of its fusion. This approach avoids the need for absolute spatial distance threshold parameters as required by previous work. We report results from object reconstruction with both synthetic and experimental data that demonstrate an improvement in reconstruction quality, particularly in cases where data points are inconsistent yet spatially proximal.

Data driven modeling for power transformer lifespan evaluation

Large sized power transformers are important parts of the power supply chain. These very critical networks of engineering assets are an essential base of a nation’s energy resource infrastructure. This research identifies the key factors influencing transformer normal operating conditions and predicts the asset management lifespan. Engineering asset research has developed few lifespan forecasting methods combining real-time monitoring solutions for transformer maintenance and replacement. Utilizing the rich data source from a remote terminal unit (RTU) system for sensor-data driven analysis, this research develops an innovative real-time lifespan forecasting approach applying logistic regression based on the Weibull distribution. The methodology and the implementation prototype are verified using a data series from 161 kV transformers to evaluate the efficiency and accuracy for energy sector applications. The asset stakeholders and suppliers significantly benefit from the real-time power transformer lifespan evaluation for maintenance and replacement decision support.

Understanding data flow and security requirements in wireless Body Area Networks for healthcare

The Body Area Network (BAN) is an emerging technology that focuses on monitoring physiological data in, on and around the human body. BAN technology permits wearable and implanted sensors to collect vital data about the human body and transmit it to other nodes via low-energy communication. In this paper, we investigate interactions in terms of data flows between parties involved in BANs under four different scenarios targeting outdoor and indoor medical environments: hospital, home, emergency and open areas. Based on these scenarios, we identify data flow requirements between BAN elements such as sensors and control units (CUs) and parties involved in BANs such as the patient, doctors, nurses and relatives. Identified requirements are used to generate BAN data flow models. Petri Nets (PNs) are used as the formal modelling language. We check the validity of the models and compare them with the existing related work. Finally, using the models, we identify communication and security requirements based on the most common active and passive attack scenarios.

Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.

A Novel Approach to Haptic Tele-operation of Aerial Robot Vehicles

We present a novel, simple and effective approach for tele-operation of aerial robotic vehicles with haptic feedback. Such feedback provides the remote pilot with an intuitive feel of the robot’s state and perceived local environment that will ensure simple and safe operation in cluttered 3D environments common in inspection and surveillance tasks. Our approach is based on energetic considerations and uses the concepts of network theory and port-Hamiltonian systems. We provide a general framework for addressing problems such as mapping the limited stroke of a ‘master’ joystick to the infinite stroke of a ‘slave’ vehicle, while preserving passivity of the closed-loop system in the face of potential time delays in communications links and limited sensor data

IDOL

Idol is a collaborative performance work for vocal performer and dancers. The work explores movement and sound relative to a vocal interface called the eMic (Extended Microphone Interface Controller). The eMic is a gestural controller designed by the composer for live vocal performance an real-time processing. The process for generating the work involves the choreographer being provided an opportunity to experiment with gestures ad movement relative to the eMic interface. The choreographer explored the interface as an object,a prop, an instrument and as an extension of the body. the movement was then videoed and the data coming from the sensors simultaneously recorded. The data and the video were then used as part of the compositional process, allowing the composer to see what the performance looks like and to experiment with mapping strategies using the captured sensor data. This approach represents a new compositional direction for working with the eMic, in that previously the compositional process commenced at the computer, building processing patches and assigning parameters to eMic sensors. In order to play the composition, the body needed to adapt to 'playing' the instrument. This approach treats the eMic like a traditional instrument that requires the human body to develop a command over the instrument. Working with the movement as a starting point inverts the process using choreographic gestures as the basis for musical structures.

Sequential nonlinear manifold learning

The computation of compact and meaningful representations of high dimensional sensor data has recently been addressed through the development of Nonlinear Dimensional Reduction (NLDR) algorithms. The numerical implementation of spectral NLDR techniques typically leads to a symmetric eigenvalue problem that is solved by traditional batch eigensolution algorithms. The application of such algorithms in real-time systems necessitates the development of sequential algorithms that perform feature extraction online. This paper presents an efficient online NLDR scheme, Sequential-Isomap, based on incremental singular value decomposition (SVD) and the Isomap method. Example simulations demonstrate the validity and significant potential of this technique in real-time applications such as autonomous systems.

Sampling environmental acoustic recordings to determine species richness : I

Acoustic sensors provide an effective means of monitoring biodiversity at large spatial and temporal scales. They can continuously and passively record large volumes of data over extended periods, however these data must be analysed to detect the presence of vocal species. Automated analysis of acoustic data for large numbers of species is complex and can be subject to high levels of false positive and false negative results. Manual analysis by experienced users can produce accurate results, however the time and effort required to process even small volumes of data can make manual analysis prohibitive. Our research examined the use of sampling methods to reduce the cost of analysing large volumes of acoustic sensor data, while retaining high levels of species detection accuracy. Utilising five days of manually analysed acoustic sensor data from four sites, we examined a range of sampling rates and methods including random, stratified and biologically informed. Our findings indicate that randomly selecting 120, one-minute samples from the three hours immediately following dawn provided the most effective sampling method. This method detected, on average 62% of total species after 120 one-minute samples were analysed, compared to 34% of total species from traditional point counts. Our results demonstrate that targeted sampling methods can provide an effective means for analysing large volumes of acoustic sensor data efficiently and accurately.

Assessment of daily light and ultraviolet exposure in young adults

Purpose: There are some limited reports, based on questionnaire data, which suggest that outdoor activity decreases the risk of myopia in children and may offset the myopia risk associated with prolonged near work. The aim of this study was to explore the relationship between near work, indoor illumination, daily sunlight and ultraviolet (UV) exposure in emmetropic and myopic University students, given that University students perform significant amounts of near work and as a group have a high prevalence of myopia. Methods: Participants were 35 students, aged 17 to 25 years who were classified as being emmetropic (n=13), or having stable (n=12) or progressing myopia (n=10). During waking hours on three separate days participants wore a light sensor data logger (HOBO) and a polysulphone UV dosimeter; these devices measured daily illuminance and accumulative UV exposure respectively; participants also completed a daily activity log. Results: No significant between group differences were observed for average daily illuminance (p=0.732), number of hours per day spent in sunlight (p=0.266), outdoor shade (p=0.726), bright indoor/dim outdoor light (p=0.574) or dim room illumination (p=0.484). Daily UV exposure was significantly different across the groups (p=0.003); with stable myopes experiencing the greatest UV exposure (versus emmetropes p=0.002; versus progressing myopes p=0.004). Conclusions: The current literature suggests there is a link between myopia protection and spending time outdoors in children. Our data provides some evidence of this relationship in young adults and highlights the need for larger studies to further investigate this relationship longitudinally.

Context-aware device self-configuration using self-organizing maps

Modern mobile computing devices are versatile, but bring the burden of constant settings adjustment according to the current conditions of the environment. While until today, this task has to be accomplished by the human user, the variety of sensors usually deployed in such a handset provides enough data for autonomous self-configuration by a learning, adaptive system. However, this data is not fully available at certain points in time, or can contain false values. Handling potentially incomplete sensor data to detect context changes without a semantic layer represents a scientific challenge which we address with our approach. A novel machine learning technique is presented - the Missing-Values-SOM - which solves this problem by predicting setting adjustments based on context information. Our method is centered around a self-organizing map, extending it to provide a means of handling missing values. We demonstrate the performance of our approach on mobile context snapshots, as well as on classical machine learning datasets.

Street Computing : Urban Informatics and City Interfaces

This book develops tools and techniques that will help urban residents gain access to urban computing. Metaphorically speaking, it is taking computing to the street by giving the general public – rather than just researchers and professionals – the power to leverage available city infrastructure and create solutions tailored to their individual needs. It brings together five chapters that are based on presentations given at the Street Computing Workshop held on 24 November 2009 in Melbourne in conjunction with the Australian Computer-Human Interaction Conference (OZCHI 2009). This book focuses on applying urban informatics, urban and community sensing and open application programming interfaces (APIs) to the public space through the delivery of online services, on demand and in real time. It then offers a case study of how the city of Singapore has harnessed the potential of an online infrastructure so that residents and visitors can access services electronically. This book was published as a special issue of the Journal of Urban Technology, 19(2), 2012.

A context-fusion framework for enhancing context-awareness on mobile application

Mobile devices and smartphones have become a significant communication channel for everyday life. The sensing capabilities of mobile devices are expanding rapidly, and sensors embedded in these devices are cheaper and more powerful than before. It is evident that mobile devices have become the most suitable candidates to sense contextual information without needing extra tools. However, current research shows only a limited number of sensors are being explored and investigated. As a result, it still needs to be clarified what forms of contextual information extracted from mo- bile sensors are useful. Therefore, this research investigates the context sensing using current mobile sensors, the study follows experimental methods and sensor data is evaluated and synthesised, in order to deduce the value of various sensors and combinations of sensor for the use in context-aware mobile applications. This study aims to develop a context fusion framework that will enhance the context-awareness on mobile applications, as well as exploring innovative techniques for context sensing on smartphone devices.

Sampling environmental acoustic recordings to determine bird species richness

Acoustic sensors can be used to estimate species richness for vocal species such as birds. They can continuously and passively record large volumes of data over extended periods. These data must subsequently be analyzed to detect the presence of vocal species. Automated analysis of acoustic data for large numbers of species is complex and can be subject to high levels of false positive and false negative results. Manual analysis by experienced surveyors can produce accurate results; however the time and effort required to process even small volumes of data can make manual analysis prohibitive. This study examined the use of sampling methods to reduce the cost of analyzing large volumes of acoustic sensor data, while retaining high levels of species detection accuracy. Utilizing five days of manually analyzed acoustic sensor data from four sites, we examined a range of sampling frequencies and methods including random, stratified, and biologically informed. We found that randomly selecting 120 one-minute samples from the three hours immediately following dawn over five days of recordings, detected the highest number of species. On average, this method detected 62% of total species from 120 one-minute samples, compared to 34% of total species detected from traditional area search methods. Our results demonstrate that targeted sampling methods can provide an effective means for analyzing large volumes of acoustic sensor data efficiently and accurately. Development of automated and semi-automated techniques is required to assist in analyzing large volumes of acoustic sensor data. Read More: http://www.esajournals.org/doi/abs/10.1890/12-2088.1

Perception Quality Evaluation with Visual and Infrared Cameras in Challenging Environmental Conditions

This work aims to contribute to the reliability and integrity of perceptual systems of unmanned ground vehicles (UGV). A method is proposed to evaluate the quality of sensor data prior to its use in a perception system by utilising a quality metric applied to heterogeneous sensor data such as visual and infrared camera images. The concept is illustrated specifically with sensor data that is evaluated prior to the use of the data in a standard SIFT feature extraction and matching technique. The method is then evaluated using various experimental data sets that were collected from a UGV in challenging environmental conditions, represented by the presence of airborne dust and smoke. In the first series of experiments, a motionless vehicle is observing a ’reference’ scene, then the method is extended to the case of a moving vehicle by compensating for its motion. This paper shows that it is possible to anticipate degradation of a perception algorithm by evaluating the input data prior to any actual execution of the algorithm.