Hybrid polymer/metal nanoparticles with surface enhanced raman tracking for potential use in nanomedicine

This project was a preliminary step towards the development of novel methods for early stage cancer diagnosis and treatment. Diagnostic imaging agents with high Raman signal enhancement were developed based on tailored assemblies of gold nanoparticles, which demonstrated potential for non-invasive detection from deep under the skin surface. Specifically designed polymers were employed to assemble gold nanoparticles into controlled morphologies including dimers, nanochains, nanoplates, globular and core-satellite nanostructures. Our findings suggest that the Raman enhancement is strongly dependent on assembly morphology and can be tuned to adapt to the requirements of the diagnostic agent.

A Bit-Stream based space vector modulator

Bit-Stream based control, which uses one bit wide signals to control power electronics applications, is a new approach for controller design in power electronic systems. Bit-Stream signals are inherently high frequency in nature, and as such some form of down sampling or modulating is essential to avoid excessive switching losses. This paper presents a novel three-phase space vector modulator, which is based on the Bit-Stream technique and suitable for standard three-phase inverter systems. The proposed modulator simultaneously converts a two phase reference to the three-phase domain and reduces switching frequencies to reasonable levels. The modulator consumes relatively few logic elements and does not require sector detectors, carrier oscillators or trigonometric functions. The performance of the modulator was evaluated using ModelSim. Results indicate that, subject to limits on the modulation index, the proposed modulator delivers a spread-spectrum output with total harmonic distortion comparable to standard space vector pulse width modulation techniques.

Stock prices of clean energy firms, oil and carbon markets : a vector autoregressive analysis

Recent discussions of energy security and climate change have attracted significant attention to clean energy. We hypothesize that rising prices of conventional energy and/or placement of a price on carbon emissions would encourage investments in clean energy firms. The data from three clean energy indices show that oil prices and technology stock prices separately affect the stock prices of clean energy firms. However, the data fail to demonstrate a significant relationship between carbon prices and the stock prices of the firms.

Dual inverter based battery energy storage system for grid connected photovoltaic systems

This paper explores a new breed of energy storage system interfacing for grid connected photovoltaic (PV) systems. The proposed system uses the popular dual inverter topology in which one inverter is supplied by a PV cell array and the other by a Battery Energy Storage System (BESS). The resulting conversion structure is controlled in a way that both demand matching and maximum power point tracking of the PV cell array are performed simultaneously. This dual inverter topology can produces 2, 3, 4 and 5 level inverter voltage waveforms at the dc-link voltage ratios of 0:1, 1:1, 2:1 and 3:2 respectively. Since the output voltage of the PV cell array and the battery are uncorrelated and dynamically change, the resulting dc-link voltage ratio can take non-integer values as well. These noninteger dc-link voltage ratios produce unevenly distributed space vectors. Therefore, the main issue with the proposed system is the generation of undistorted current even in the presence of unevenly distributed and dynamically changing space vectors. A modified space vector modulation method is proposed in this paper to address this issue and its efficacy is proved by simulation results. The ability of the proposed system to act as an active power source is also verified.

Topological localization using optical flow descriptors

We propose a topological localization method based on optical flow information. We analyse the statistical characteristics of the optical flow signal and demonstrate that the flow vectors can be used to identify and describe key locations in the environment. The key locations (nodes) correspond to significant scene changes and depth discontinuities. Since optical flow vectors contain position, magnitude and angle information, for each node, we extract low and high order statistical moments of the vectors and use them as descriptors for that node. Once a database of nodes and their corresponding optical flow features is created, the robot can perform topological localization by using the Mahalanobis distance between the current frame and the database. This is supported by field trials, which illustrate the repeatability of the proposed method for detecting and describing key locations in indoor and outdoor environments in challenging and diverse lighting conditions.

Data fusion and tracking with multiple UAVs

This chapter describes decentralized data fusion algorithms for a team of multiple autonomous platforms. Decentralized data fusion (DDF) provides a useful basis with which to build upon for cooperative information gathering tasks for robotic teams operating in outdoor environments. Through the DDF algorithms, each platform can maintain a consistent global solution from which decisions may then be made. Comparisons will be made between the implementation of DDF using two probabilistic representations. The first, Gaussian estimates and the second Gaussian mixtures are compared using a common data set. The overall system design is detailed, providing insight into the overall complexity of implementing a robust DDF system for use in information gathering tasks in outdoor UAV applications.

Lazy days of simply going with the flow

A travel article about a river cruise from Amsterdam to Basel. When Captain Plamen Veselinov invites me to join him on the bridge, I can at last put a question that’s been running through my mind for days. It’s about the locks. How does he manage to line up the vessel as it approaches? Is the ship guided in electronically? He returns my questions with a boyish smile that does a good deal to veil his many years on the river. Crunching his way through a heavy Bulgarian accent, he says, “No, it’s all in the eyes and the hands. It’s magic. Don’t tell David Copperfield. He would get very jealous...

Speaker verification using I-vector features

This PhD research has provided novel solutions to three major challenges which have prevented the wide spread deployment of speaker recognition technology: (1) combating enrolment/ verification mismatch, (2) reducing the large amount of development and training data that is required and (3) reducing the duration of speech required to verify a speaker. A range of applications of speaker recognition technology from forensics in criminal investigations to secure access in banking will benefit from the research outcomes.

Three-dimensional flow-through protein platform

We have developed a new protein microarray (Immuno-Flow Protein Platform, IFPP) that utilizes a porous nitrocellulose (NC) membrane with printed spots of capture probes. The sample is pumped actively through the NC membrane, to enhance binding efficiency and introduce stringency. Compared to protein microarrays assayed with the conventional incubation-shaking method the rate of binding is enhanced on the IFPP by at least a factor of 10, so that the total assay time can be reduced drastically without compromising sensitivity. Similarly, the sensitivity can be improved. We demonstrate the detection of 1 pM of C-reactive protein (CRP) in 70 mu L of plasma within a total assay time of 7 min. The small sample and reagent volumes, combined with the speed of the assay, make our IFPP also well-suited for a point-of-care/near-patient setting. The potential clinical application of the IFPP is demonstrated by validating CRP detection both in human plasma and serum samples against standard clinical laboratory methods.

Large-scale analysis of soccer matches using spatiotemporal tracking data

Although the collection of player and ball tracking data is fast becoming the norm in professional sports, large-scale mining of such spatiotemporal data has yet to surface. In this paper, given an entire season's worth of player and ball tracking data from a professional soccer league (approx 400,000,000 data points), we present a method which can conduct both individual player and team analysis. Due to the dynamic, continuous and multi-player nature of team sports like soccer, a major issue is aligning player positions over time. We present a "role-based" representation that dynamically updates each player's relative role at each frame and demonstrate how this captures the short-term context to enable both individual player and team analysis. We discover role directly from data by utilizing a minimum entropy data partitioning method and show how this can be used to accurately detect and visualize formations, as well as analyze individual player behavior.