Bandwidth considerations for multilevel converters

Multilevel converters can achieve an overall effective switch frequency multiplication and consequent ripple reduction through the cancellation of the lowest order switch frequency terms. This paper investigates the harmonic content and the frequency response of these multimodulator converters. It is shown that the transfer function of uniformly sampled modulators is a bessel function associated with the inherent sampling process. Naturally sampled modulators have a flat transfer function, but multiple switchings per switch cycle will occur unless the input is slew-rate limited. Lower sideband harmonics of the effective carrier frequency and, in uniform converters, harmonics of the input signal also limit the useful bandwidth. Observations about the effect of the number of converters, their type (naturally or uniformly sampled), and the ratio of modulating frequency and switch frequency are made

High efficiency auxiliary power converters

The auxiliary load DC-DC converters of the Sunshark solar car have never been examined. An analysis of the current design reveals it is complicated, and inefficient. Some simple measures to greatly improve the efficiency are present which will achieve an overall worthwhile power saving. Two switch-mode power supply DC-DC converter designs are presented. One is a constant current supply for the LED brake and turn indicators, which allows them to be powered directly from the main DC bus, and switched only as necessary. The second is a low power flyback converter, which employs synchronous rectification among other techniques to achieve good efficiency and regulation over a large range of output powers. Practical results from both converters, and an indication of the overall improvement in system efficiency will be offered.

Taming the terabytes : a human-centered approach to surviving the information deluge

A fear of imminent information overload predates the World Wide Web by decades. Yet, that fear has never abated. Worse, as the World Wide Web today takes the lion’s share of the information we deal with, both in amount and in time spent gathering it, the situation has only become more precarious. This chapter analyses new issues in information overload that have emerged with the advent of the Web, which emphasizes written communication, defined in this context as the exchange of ideas expressed informally, often casually, as in verbal language. The chapter focuses on three ways to mitigate these issues. First, it helps us, the users, to be more specific in what we ask for. Second, it helps us amend our request when we don't get what we think we asked for. And third, since only we, the human users, can judge whether the information received is what we want, it makes retrieval techniques more effective by basing them on how humans structure information. This chapter reports on extensive experiments we conducted in all three areas. First, to let users be more specific in describing an information need, they were allowed to express themselves in an unrestricted conversational style. This way, they could convey their information need as if they were talking to a fellow human instead of using the two or three words typically supplied to a search engine. Second, users were provided with effective ways to zoom in on the desired information once potentially relevant information became available. Third, a variety of experiments focused on the search engine itself as the mediator between request and delivery of information. All examples that are explained in detail have actually been implemented. The results of our experiments demonstrate how a human-centered approach can reduce information overload in an area that grows in importance with each day that passes. By actually having built these applications, I present an operational, not just aspirational approach.

A people-to-people matching system using graph mining techniques

A people-to-people matching system (or a match-making system) refers to a system in which users join with the objective of meeting other users with the common need. Some real-world examples of these systems are employer-employee (in job search networks), mentor-student (in university social networks), consume-to-consumer (in marketplaces) and male-female (in an online dating network). The network underlying in these systems consists of two groups of users, and the relationships between users need to be captured for developing an efficient match-making system. Most of the existing studies utilize information either about each of the users in isolation or their interaction separately, and develop recommender systems using the one form of information only. It is imperative to understand the linkages among the users in the network and use them in developing a match-making system. This study utilizes several social network analysis methods such as graph theory, small world phenomenon, centrality analysis, density analysis to gain insight into the entities and their relationships present in this network. This paper also proposes a new type of graph called “attributed bipartite graph”. By using these analyses and the proposed type of graph, an efficient hybrid recommender system is developed which generates recommendation for new users as well as shows improvement in accuracy over the baseline methods.

Scaffolding techniques : a teacher training for cooperative learning in Thailand primary education

Using cooperative learning in classrooms promotes academic achievement, communication skills, problem-solving, social skills and student motivation. Yet it is reported that cooperative learning as a Western educational concept may be ineffective in Asian cultural contexts. The study aims to investigate the utilisation of scaffolding techniques for cooperative learning in Thailand primary mathematics classes. A teacher training program was designed to foster Thai primary school teachers’ cooperative learning implementation. Two teachers participated in this experimental program for one and a half weeks and then implemented cooperative learning strategies in their mathematics classes for six weeks. The data collected from teacher interviews and classroom observations indicates that the difficulty or failure of implementing cooperative learning in Thailand education may not be directly derived from cultural differences. Instead, it does indicate that Thai culture can be constructively merged with cooperative learning through a teacher training program and practices of scaffolding techniques.

Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the a mission should be aborted due to mechanical or other failure. On-board cameras provide information that can be used in the determination of potential landing sites, which are continually updated and ranked to prevent injury and minimize damage. Pulse Coupled Neural Networks have been used for the detection of features in images that assist in the classification of vegetation and can be used to minimize damage to the aerial vehicle. However, a significant drawback in the use of PCNNs is that they are computationally expensive and have been more suited to off-line applications on conventional computing architectures. As heterogeneous computing architectures are becoming more common, an OpenCL implementation of a PCNN feature generator is presented and its performance is compared across OpenCL kernels designed for CPU, GPU and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images obtained during unmanned aerial vehicle trials to determine the plausibility for real-time feature detection.

Frontier Techniques for Measuring and Estimating Airport Efficiency : An Empirical Review with Practical Guidelines for Analysis and Future Research

Airport efficiency is important because it has a direct impact on customer safety and satisfaction and therefore the financial performance and sustainability of airports, airlines, and affiliated service providers. This is especially so in a world characterized by an increasing volume of both domestic and international air travel, price and other forms of competition between rival airports, airport hubs and airlines, and rapid and sometimes unexpected changes in airline routes and carriers. It also reflects expansion in the number of airports handling regional, national, and international traffic and the growth of complementary airport facilities including industrial, commercial, and retail premises. This has fostered a steadily increasing volume of research aimed at modeling and providing best-practice measures and estimates of airport efficiency using mathematical and econometric frontiers. The purpose of this chapter is to review these various methods as they apply to airports throughout the world. Apart from discussing the strengths and weaknesses of the different approaches and their key findings, the paper also examines the steps faced by researchers as they move through the modeling process in defining airport inputs and outputs and the purported efficiency drivers. Accordingly, the chapter provides guidance to those conducting empirical research on airport efficiency and serves as an aid for aviation regulators and airport operators among others interpreting airport efficiency research outcomes.

Response of a buried tunnel to surface blast using different numerical techniques

This paper presents a comparative study on the response of a buried tunnel to surface blast using the arbitrary Lagrangian-Eulerian (ALE) and smooth particle hydrodynamics (SPH) techniques. Since explosive tests with real physical models are extremely risky and expensive, the results of a centrifuge test were used to validate the numerical techniques. The numerical study shows that the ALE predictions were faster and closer to the experimental results than those from the SPH simulations which over predicted the strains. The findings of this research demonstrate the superiority of the ALE modelling techniques for the present study. They also provide a comprehensive understanding of the preferred ALE modelling techniques which can be used to investigate the surface blast response of underground tunnels.

Integrating time forgetting mechanisms into topic-based user interest profiling

The rapid development of the World Wide Web has created massive information leading to the information overload problem. Under this circumstance, personalization techniques have been brought out to help users in finding content which meet their personalized interests or needs out of massively increasing information. User profiling techniques have performed the core role in this research. Traditionally, most user profiling techniques create user representations in a static way. However, changes of user interests may occur with time in real world applications. In this research we develop algorithms for mining user interests by integrating time decay mechanisms into topic-based user interest profiling. Time forgetting functions will be integrated into the calculation of topic interest measurements on in-depth level. The experimental study shows that, considering temporal effects of user interests by integrating time forgetting mechanisms shows better performance of recommendation.

Rough set based approach to text classification

Textual document set has become an important and rapidly growing information source in the web. Text classification is one of the crucial technologies for information organisation and management. Text classification has become more and more important and attracted wide attention of researchers from different research fields. In this paper, many feature selection methods, the implement algorithms and applications of text classification are introduced firstly. However, because there are much noise in the knowledge extracted by current data-mining techniques for text classification, it leads to much uncertainty in the process of text classification which is produced from both the knowledge extraction and knowledge usage, therefore, more innovative techniques and methods are needed to improve the performance of text classification. It has been a critical step with great challenge to further improve the process of knowledge extraction and effectively utilization of the extracted knowledge. Rough Set decision making approach is proposed to use Rough Set decision techniques to more precisely classify the textual documents which are difficult to separate by the classic text classification methods. The purpose of this paper is to give an overview of existing text classification technologies, to demonstrate the Rough Set concepts and the decision making approach based on Rough Set theory for building more reliable and effective text classification framework with higher precision, to set up an innovative evaluation metric named CEI which is very effective for the performance assessment of the similar research, and to propose a promising research direction for addressing the challenging problems in text classification, text mining and other relative fields.

Improving short utterance i-vector speaker verification using utterance variance modelling and compensation techniques

This paper proposes techniques to improve the performance of i-vector based speaker verification systems when only short utterances are available. Short-length utterance i-vectors vary with speaker, session variations, and the phonetic content of the utterance. Well established methods such as linear discriminant analysis (LDA), source-normalized LDA (SN-LDA) and within-class covariance normalisation (WCCN) exist for compensating the session variation but we have identified the variability introduced by phonetic content due to utterance variation as an additional source of degradation when short-duration utterances are used. To compensate for utterance variations in short i-vector speaker verification systems using cosine similarity scoring (CSS), we have introduced a short utterance variance normalization (SUVN) technique and a short utterance variance (SUV) modelling approach at the i-vector feature level. A combination of SUVN with LDA and SN-LDA is proposed to compensate the session and utterance variations and is shown to provide improvement in performance over the traditional approach of using LDA and/or SN-LDA followed by WCCN. An alternative approach is also introduced using probabilistic linear discriminant analysis (PLDA) approach to directly model the SUV. The combination of SUVN, LDA and SN-LDA followed by SUV PLDA modelling provides an improvement over the baseline PLDA approach. We also show that for this combination of techniques, the utterance variation information needs to be artificially added to full-length i-vectors for PLDA modelling.

Improving PLDA speaker verification with limited development data

This paper analyses the probabilistic linear discriminant analysis (PLDA) speaker verification approach with limited development data. This paper investigates the use of the median as the central tendency of a speaker’s i-vector representation, and the effectiveness of weighted discriminative techniques on the performance of state-of-the-art length-normalised Gaussian PLDA (GPLDA) speaker verification systems. The analysis within shows that the median (using a median fisher discriminator (MFD)) provides a better representation of a speaker when the number of representative i-vectors available during development is reduced, and that further, usage of the pair-wise weighting approach in weighted LDA and weighted MFD provides further improvement in limited development conditions. Best performance is obtained using a weighted MFD approach, which shows over 10% improvement in EER over the baseline GPLDA system on mismatched and interview-interview conditions.

Blast response of reinforced concrete pile using fully coupled computer simulation techniques

Pile foundations transfer loads from superstructures to stronger sub soil. Their strength and stability can hence affect structural safety. This paper treats the response of reinforced concrete pile in saturated sand to a buried explosion. Fully coupled computer simulation techniques are used together with five different material models. Influence of reinforcement on pile response is investigated and important safety parameters of horizontal deformations and tensile stresses in the pile are evaluated. Results indicate that adequate longitudinal reinforcement and proper detailing of transverse reinforcement can reduce pile damage. Present findings can serve as a benchmark reference for future analysis and design.

Transforming morning to afternoon using linear regression techniques

Visual localization in outdoor environments is often hampered by the natural variation in appearance caused by such things as weather phenomena, diurnal fluctuations in lighting, and seasonal changes. Such changes are global across an environment and, in the case of global light changes and seasonal variation, the change in appearance occurs in a regular, cyclic manner. Visual localization could be greatly improved if it were possible to predict the appearance of a particular location at a particular time, based on the appearance of the location in the past and knowledge of the nature of appearance change over time. In this paper, we investigate whether global appearance changes in an environment can be learned sufficiently to improve visual localization performance. We use time of day as a test case, and generate transformations between morning and afternoon using sample images from a training set. We demonstrate the learned transformation can be generalized from training data and show the resulting visual localization on a test set is improved relative to raw image comparison. The improvement in localization remains when the area is revisited several weeks later.

Photodissociation of TEMPO-modified peptides: New approaches to radical-directed dissociation of biomolecules

Radical-directed dissociation of gas phase ions is emerging as a powerful and complementary alternative to traditional tandem mass spectrometric techniques for biomolecular structural analysis. Previous studies have identified that coupling of 2-[(2,2,6,6-tetramethylpiperidin-1-oxyl)methyl] benzoic acid (TEMPO-Bz) to the N-terminus of a peptide introduces a labile oxygen-carbon bond that can be selectively activated upon collisional activation to produce a radical ion. Here we demonstrate that structurally-defined peptide radical ions can also be generated upon UV laser photodissociation of the same TEMPO-Bz derivatives in a linear ion-trap mass spectrometer. When subjected to further mass spectrometric analyses, the radical ions formed by a single laser pulse undergo identical dissociations as those formed by collisional activation of the same precursor ion, and can thus be used to derive molecular structure. Mapping the initial radical formation process as a function of photon energy by photodissociation action spectroscopy reveals that photoproduct formation is selective but occurs only in modest yield across the wavelength range (300-220 nm), with the photoproduct yield maximised between 235 and 225 nm. Based on the analysis of a set of model compounds, structural modifications to the TEMPO-Bz derivative are suggested to optimise radical photoproduct yield. Future development of such probes offers the advantage of increased sensitivity and selectivity for radical-directed dissociation. © 2014 the Owner Societies.