This thesis addresses the problem of classifying an audio stream as either speech or music, an issue which is beginning to receive increasing attention due to its wide range of applications. Various techniques have been presented in last decade to discriminate between speech and music. However, their accuracy is still not sufficient since music can refer to a very broad class of signals due to the large number of musical instruments found in audio data. Performance can also be further compromised in noisy conditions, which are unavoidable in some practical situations. This thesis presents an analysis of feature extraction techniques and classifiers currently being used, followed by the proposal and evaluation of new features for improved classification. These include two novel cepstral features, delta cepstral energy and power spectrum deviation, along with amplitude and frequency modulation features. The modified group delay feature, initially proposed for speech recognition, is also investigated for speech and music discrimination. Experiments were performed using different sets of features, compared among themselves and with conventional MFCCs using error rate criteria and Detection Error Trade-off curves. It is shown that the proposed cepstral and modulation features result in an increase in the accuracy of the conventional MFCC based system. However, the modified group delay feature which has been shown to improve accuracy for speech classification problems, does not contribute much to the problem of speech and music discrimination. Among the ones presented here the optimum feature configuration, both modulation features with MFCC, resulted in overall error rate of 6.57% as compared to 7.43% for MFCC alone.

A generalized model which describes a family of antiferromagnetic Heisenberg magnets on a three-dimensional stacked triangular lattice is introduced. The model contains a constraint parameter which changes the details of the interactions but not the symmetry of the model. We investigate the question of whether a first or second order phase transition occurs in these systems using a short time dynamics method. This method does not suffer from the problem of critical slowing down which occurs in the usual equilibrium Monte Carlo simulations. The effective critical exponents are determined as a function of the constraint parameter. Our results provide strong evidence that the phase transition is first order. In addition, for a particular value of the constraint parameter, the model corresponds to an antiferromagnet on a stacked Kagome lattice. In this case, our results are not inconsistent with the existence of a finite temperature first order phase transition. / October 2007

The demand for high performance microelectronic products drives the development of 3-D chip-stacking structure. By the introduction of through-silicon-via (TSV) into 3-D flip-chip packages, microelectronic performance is improved by increasing circuit capacity and diminishing signal delay. However, TSV-embedded structure also raises concerns over many reliability issues that come with the steep thermal and mechanical transient responses, increasing numbers of bi-material interfaces and reduced component sizes. In this research, defect initiation induced by thermalmechanical phenomena is studied to establish the early failure modes within 3-D flip-chip packages. It is found that low amplitude but extremely high frequency thermal stress waves would occur and attenuate rapidly in the first hundreds of nanoseconds upon power-on. Although the amplitude of these waves is far below material yielding points, their intrinsic characteristics of high frequency and high power density are capable of compromising the integrity of all flip-chip components. By conducting spectral analysis of the stress waves and applying the methodology of accumulated damage evaluation, it is demonstrated that micron crack initiation and interconnect debond are highly probable in the immediate proximity of the heat source. Such a negative impact exerted by the stress wave in the early, while brief, transient period is recognized as the short time scale dynamic effect. Researched results strongly indicate that short-time scale effects would inflict very serious reliability issues in 3-D flip-chip packages. The fact that 3-D flip-chip packages accommodate a large amount of reduced-size interconnects makes it vulnerable to the attack of short time scale propagating stress waves. In addition, the stacking structure also renders shearing effect extremely detrimental to 3-D flip-chip integrity. Finally, several guidelines effective in discouraging short-time scale effects and thus improving TSV flip-chip package reliability are proposed

A generalized model which describes a family of antiferromagnetic Heisenberg magnets on a three-dimensional stacked triangular lattice is introduced. The model contains a constraint parameter which changes the details of the interactions but not the symmetry of the model. We investigate the question of whether a first or second order phase transition occurs in these systems using a short time dynamics method. This method does not suffer from the problem of critical slowing down which occurs in the usual equilibrium Monte Carlo simulations. The effective critical exponents are determined as a function of the constraint parameter. Our results provide strong evidence that the phase transition is first order. In addition, for a particular value of the constraint parameter, the model corresponds to an antiferromagnet on a stacked Kagome lattice. In this case, our results are not inconsistent with the existence of a finite temperature first order phase transition.

A generalized model which describes a family of antiferromagnetic Heisenberg magnets on a three-dimensional stacked triangular lattice is introduced. The model contains a constraint parameter which changes the details of the interactions but not the symmetry of the model. We investigate the question of whether a first or second order phase transition occurs in these systems using a short time dynamics method. This method does not suffer from the problem of critical slowing down which occurs in the usual equilibrium Monte Carlo simulations. The effective critical exponents are determined as a function of the constraint parameter. Our results provide strong evidence that the phase transition is first order. In addition, for a particular value of the constraint parameter, the model corresponds to an antiferromagnet on a stacked Kagome lattice. In this case, our results are not inconsistent with the existence of a finite temperature first order phase transition.

The wavelet transform technique has been frequently used in time-frequency analysis as a relatively new concept. Compared to the traditional technique Short-time Fourier Transform (STFT), which is theoretically based on the Fourier transform, the wavelet transform has its advantage on better locality in time and frequency domain, but not significant as the solutions in spectrum. Wavelet transform has dynamic ‘window functions’ to represent time-frequency positions of raw signals, and can get better resolutions in time-frequency analysis. In this report, we shall first briefly introduce fuzzy sets and related concepts. And then we will evaluate their similarities and differences by not only the theoretic comparisons between STFT and wavelet transform, but also the process of the de-nosing to a noisy recorded signal.

Published Article / The reproductive status of animals is of utmost importance to the modern farmer. Decisions concerning the management of the flock are influenced by the knowledge of the percentage of animals that are pregnant at any specific time. The aim of the project was to gain knowledge for the development of an instrument that is affordable and with which a farmer can do pregnancy determination himself/herself, thereby enabling him/her to make the correct management decisions. Experimental data were obtained from pregnant Dorper ewes with the aid of a portable Doppler instrument. Using real data as input, simulations of Wavelet and Short Time Fourier Transforms (STF) were done in MathCAD. In the simulations known levels of noise were added to the Doppler signals. Satisfactory results were obtained from the simulations of Wavelet Transforms. In the simulation of the Wavelet Transforms, signals with a SNR of -6.5 dB were successfully identified. It can thus be concluded that Wavelet Transforms can be used successfully for the detection of the fetal heartbeat in noisy ultrasonic Doppler signals.

The diffusion of a particle in a crowded environment typically proceeds through three regimes: for very short times the particle diffuses freely until it collides with an obstacle for the first time, while for very long times diffusion the motion is Fickian with a diffusion coefficient D that depends on the concentration and type of obstacles present in the system. For intermediate times, the mean-square displacement of the particle often increases approximately as t α , with α < 1, typical of what is generally called anomalous diffusion. However, it is not clear how one can identify or choose a time or displacement interval that would give a reliable estimate of α. In this paper, we use two exact numerical approaches to obtain diffusion data for a simple Lattice Monte Carlo model in both time limits. This allows us to propose an objective definition of the transient regime and a unique value for α. Furthermore, our methodology directly gives us the length scale over which the transient regime switches to the steady-state regime. We test our proposed approach using several types of obstacle systems, and we introduce the novel concept of excess diffusion lengths. Finally, we show that the values of the parameters describing the anomalous transient regime depend on the Monte Carlo moves used to describe the dynamics of the particle, and we propose a new algorithm that correctly models the short time diffusion of a particle on a lattice.

This report seeks to evaluate the flexibility in a number of Fortum’s hydropower stations. The deregulation of the Nordic electricity market has put an emphasis on revenue maximizing rather than cost minimizing and there are good indications that flexible assets will be even more valuable in the future when more wind power has been introduced into the system. Through interviews with people involved in the hydropower planning and operation process a number of factors with the potential of affecting the flexibility or causing deviations between planned and realized operation have been identified and explained. These interviews have also been used to identify main flexibility limitations in studied stations, and what potentially could be done to improve the flexibility. A data analysis has been performed where historical data from planned and realized operation and results from a model developed in Matlab has been studied. The developed linear programming model is used as a reference level of an idealized theoretical potential for flexibility. Volume weighted average prices have been used to measure and compare the flexibility of studied stations. The analysis shows that the studied stations can be divided into two groups with regards to their flexibility compared to the modeled flexibility. This result is somewhat confirmed by the interview findings. Factors related to constraints imposed by water rights seem to have the biggest single impact on the flexibility of hydropower stations. The potential for flexible operation varies with season and the planned and realized operation is closer to the modeled results during the winter. It is a general opinion within the organization that there is a potential for a more flexible utilization of many hydropower stations. Experience, resources, understanding in how to fully utilize the reservoirs, and how multiple stations in a river reach can be coordinated are keys to improving the flexibility.

Carbon Dioxide concentration in Earth’s atmosphere is currently at 417 Parts permillion (ppm) and keep rising. Historic CO2 levels and historic temperature levels hasbeen cycling over the past 800,000 years. To study the trend of CO2 and temperatureover past 800,00 years, one needs to find out the relation between historic CO2 andhistoric temperature levels. In this project, we will perform different tasks to identify thetrend influencer between CO2 and temperature. Cross correlation technique is used tofind out the relation between two random signals. Temperature and CO2 data areconsidered as two random signals. Re-sampling by Interpolation techniques are imposedon both CO2 and temperature data for the change of sampling rate. Short time crosscorrelation technique is employed on the CO2 and temperature data over the differenttime windows to find out the time lag. Time lag refers to how far the signals are offset.