Spoken affect classification : algorithms and experimental implementation : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University, Palmerston North, New Zealand

Machine-based emotional intelligence is a requirement for natural interaction between humans and computer interfaces and a basic level of accurate emotion perception is needed for computer systems to respond adequately to human emotion. Humans convey emotional information both intentionally and unintentionally via speech patterns. These vocal patterns are perceived and understood by listeners during conversation. This research aims to improve the automatic perception of vocal emotion in two ways. First, we compare two emotional speech data sources: natural, spontaneous emotional speech and acted or portrayed emotional speech. This comparison demonstrates the advantages and disadvantages of both acquisition methods and how these methods affect the end application of vocal emotion recognition. Second, we look at two classification methods which have gone unexplored in this field: stacked generalisation and unweighted vote. We show how these techniques can yield an improvement over traditional classification methods

An information adaptive system study report and development plan

The purpose of the information adaptive system (IAS) study was to determine how some selected Earth resource applications may be processed onboard a spacecraft and to provide a detailed preliminary IAS design for these applications. Detailed investigations of a number of applications were conducted with regard to IAS and three were selected for further analysis. Areas of future research and development include algorithmic specifications, system design specifications, and IAS recommended time lines

MFC: An open-source high-order multi-component, multi-phase, and multi-scale compressible flow solver

MFC is an open-source tool for solving multi-component, multi-phase, and bubbly compressible flows. It is capable of efficiently solving a wide range of flows, including droplet atomization, shock–bubble interaction, and bubble dynamics. We present the 5- and 6-equation thermodynamically-consistent diffuse-interface models we use to handle such flows, which are coupled to high-order interface-capturing methods, HLL-type Riemann solvers, and TVD time-integration schemes that are capable of simulating unsteady flows with strong shocks. The numerical methods are implemented in a flexible, modular framework that is amenable to future development. The methods we employ are validated via comparisons to experimental results for shock–bubble, shock–droplet, and shock–water-cylinder interaction problems and verified to be free of spurious oscillations for material-interface advection and gas–liquid Riemann problems. For smooth solutions, such as the advection of an isentropic vortex, the methods are verified to be high-order accurate. Illustrative examples involving shock–bubble-vessel-wall and acoustic–bubble-net interactions are used to demonstrate the full capabilities of MFC

High-resolution tracking in a GEM-Emulsion detector

SHiP (Search for Hidden Particles) is a beam dump experiment proposed at the CERN SPS aiming at the observation of long lived particles very weakly coupled with ordinary matter mostly produced in the decay of charmed hadrons. The beam dump facility of SHiP is also a copious factory of neutrinos of all three kinds and therefore a dedicated neutrino detector is foreseen in the SHiP apparatus. The neutrino detector exploits the Emulsion Cloud Chamber technique with a modular structure, alternating walls of target units and planes of electronic detectors providing the time stamp to the event. GEM detectors are one of the possible choices for this task. This paper reports the results of the first exposure to a muon beam at CERN of a new hybrid chamber, obtained by coupling a GEM chamber and an emulsion detector. Thanks to the micrometric accuracy of the emulsion detector, the position resolution of the GEM chamber as a function of the particle inclination was evaluated in two configurations, with and without the magnetic fiel

A highly modular adaptive lattice algorithm for multichannel least squares filtering

In this paper a highly modular adaptive lattice algorithm for multichannel least squares FIR filtering and multivariable system identification is presented. Multichannel filters with different number of delay elements per input channel are allowed. The main features of the proposed multichannel adaptive lattice least squares algorithm is the use of scalar only operations, multiplications/divisions and additions, and the local communication which enables the development of a fully pipelining architecture. The tracking capability and the numerical stability and accuracy of the proposed technique are illustrated by simulations