Scale Space Smoothing, Image Feature Extraction and Bessel Filters

The Green function of Mumford-Shah functional in the absence of discontinuities is known to be a modified Bessel function of the second kind and zero degree. Such a Bessel function is regularized here and used as a filter for feature extraction. It is demonstrated in this paper that a Bessel filter does not follow the scale space smoothing property of bounded linear filters such as Gaussian filters. The features extracted by the Bessel filter are therefore scale invariant. Edges, blobs, and junctions are features considered here to show that the extracted features remain unchanged by varying the scale of a Bessel filter. The scale invariance property of Bessel filters for edges is analytically proved here. We conjecture that Bessel filters also enjoy this scale invariance property for other kinds of features. The experimental results presente

Research-teaching linkages: enhancing graduate attributes. Arts, humanities and social sciences

This publication represents one output of the Quality Enhancement Theme of Research-Teaching Linkages: enhancing graduate attributes. Sections 2-5 relate primarily to the project outcomes of use to educational developers and arts, humanities and social sciences academics looking for approaches to enhance their practice. Section 5 comprises in-depth case studies. Section 6 is an introductory discussion of the evidence from the interviews undertaken by the team. Section 7 explores project conclusions and recommendations for the future

Customizing kernel functions for SVM-based hyperspectral image classification

Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms. However, few efforts have been made to extend SVMs to cover the specific requirements of hyperspectral image classification, for example, by building tailor-made kernels. Observation of real-life spectral imagery from the AVIRIS hyperspectral sensor shows that the useful information for classification is not equally distributed across bands, which provides potential to enhance the SVM's performance through exploring different kernel functions. Spectrally weighted kernels are, therefore, proposed, and a set of particular weights is chosen by either optimizing an estimate of generalization error or evaluating each band's utility level. To assess the effectiveness of the proposed method, experiments are carried out on the publicly available 92AV3C dataset collected from the 220-dimensional AVIRIS hyperspectral sensor. Results indicate that the method is generally effective in improving performance: spectral weighting based on learning weights by gradient descent is found to be slightly better than an alternative method based on estimating ";relevance"; between band information and ground trut

Does science need computer science?

IBM Hursley Talks Series 3An afternoon of talks, to be held on Wednesday March 10 from 2:30pm in Bldg 35 Lecture Room A, arranged by the School of Chemistry in conjunction with IBM Hursley and the Combechem e-Science Project.The talks are aimed at science students (undergraduate and post-graduate) from across the faculty. This is the third series of talks we have organized, but the first time we have put them together in an afternoon. The talks are general in nature and knowledge of computer science is certainly not necessary. After the talks there will be an opportunity for a discussion with the lecturers from IBM.Does Science Need Computer Science?Chair and Moderator - Jeremy Frey, School of Chemistry.- 14:00 "Computer games for fun and profit" (*) - Andrew Reynolds - 14:45 "Anyone for tennis? The science behind WIBMledon" (*) - Matt Roberts - 15:30 Tea (Chemistry Foyer, Bldg 29 opposite bldg 35) - 15:45 "Disk Drive physics from grandmothers to gigabytes" (*) - Steve Legg - 16:35 "What could happen to your data?" (*) - Nick Jones - 17:20 Panel Session, comprising the four IBM speakers and May Glover-Gunn (IBM) - 18:00 Receptio

Ranking algorithms for implicit feedback

This report presents novel algorithms to use eye movements as an implicit relevance feedback in order to improve the performance of the searches. The algorithms are evaluated on "Transport Rank Five" Dataset which were previously collected in Task 8.3. We demonstrated that simple linear combination or tensor product of eye movement and image features can improve the retrieval accuracy

Adaptive sampling in context-aware systems: a machine learning approach

As computing systems become ever more pervasive, there is an increasing need for them to understand and adapt to the state of the environment around them: that is, their context. This understanding comes with considerable reliance on a range of sensors. However, portable devices are also very constrained in terms of power, and hence the amount of sensing must be minimised. In this paper, we present a machine learning architecture for context awareness which is designed to balance the sampling rates (and hence energy consumption) of individual sensors with the significance of the input from that sensor. This significance is based on predictions of the likely next context. The architecture is implemented using a selected range of user contexts from a collected data set. Simulation results show reliable context identification results. The proposed architecture is shown to significantly reduce the energy requirements of the sensors with minimal loss of accuracy in context identification

A nonlinear concept of electromagnetic energy harvester for rotational applications

Many industrial applications incorporate rotating shafts with fluctuating speeds around a required mean value. This often harmonic component of the shaft speed is generally detrimental, since it can excite components of the system, leading to large oscillations (and potentially durability issues), as well as to excessive noise generation. On the other hand, the addition of sensors on rotating shafts for system monitoring or control poses challenges due to the need to constantly supply power to the sensor and extract data from the system. In order to tackle the requirement of powering sensors for structure health monitoring or control applications, this work proposes a nonlinear vibration energy harvester design intended for use on rotating shafts with harmonic speed fluctuations. The essential nonlinearity of the harvester allows for increased operating bandwidth, potentially across the whole range of the shaft’s operating conditions

A nonlinear energy harvester for torsional oscillations

© Proceedings of ISMA 2018 - International Conference on Noise and Vibration Engineering and USD 2018 - International Conference on Uncertainty in Structural Dynamics. All rights reserved. Torsional fluctuations around the mean speed of a rotating shaft represent a typical source of undesirable energy losses in many industrial applications. Furthermore, many control and structural health monitoring systems in rotordynamics require an ever-increasing number of sensors. Currently, powering a wireless sensor mounted on a rotating shaft is feasible using either slip rings or batteries, both of which often incur high maintenance costs in applications with difficult access or when idle due to malfunction. In this paper, an electromagnetic energy harvester prototype is manufactured by adapting a commercially available permanent magnet DC motor. The energy harvesting capabilities of the device are preliminary tested and compared to theoretical predictions

BOOSTING

• Research ways to efficiently implement machinelearning algorithms on MIPS/PowerVR • Research possible extensions to MIP

Increased parental effort fails to buffer the cascading effects of warmer seas on common guillemot demographic rates

Research Funding Natural Environment Research Council Award. Grant Number: NE/R016429/1 UK-SCAPE Programme Delivering National Capability Joint Nature Conservation Committee EU ‘The Effect of Large-scale Industrial Fisheries On Non-Target Species’ FP5 Project ‘Interactions between the Marine environment, PREdators and Prey: Implications for Sustainable Sandeel Fisheries’. Grant Numbers: MS21-013, Q5RS-2000-30864 Ministry of Universities-University of ValenciaPeer reviewedPublisher PD