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.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Systems & Processes Engineering Corporation (SPEC) compared compression and decompression algorithms and developed optimal forms of lossless and lossy compression for hyperspectral data. We examined the relationship between compression-induced distortion and additive noise, determined the effect of errors on the compressed data, and showed that the data could separate targets from clutter after more than 50:1 compression.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / In this paper, we extended the matched filter bound (MFB) of time-discrete multipath Rayleigh fading channels derived in [1,2] for multiscale wavelet signaling communication.

Forest disturbances and the spatial patterns they create affect ecosystem processes through their influence on forest vegetation from individual trees to landscapes. In the boreal and mixed-wood forests of eastern Canada the main agents of disturbance are logging, fire, and defoliation by the spruce budworm (SBW, Choristoneura fumiferana). These disturbances are similar in that they remove forest biomass and influence forest succession but also distinct in that logging creates patterns that are different than those created by natural disturbances. All disturbances are indirectly linked to each other through their mutual effects on forest spatial structure and succession. Through such feedbacks, spatial disturbance legacies can facilitate or constrain further disturbances, including forest management. Surprisingly, the long term spatial consequences of interactions among multiple natural and anthropogenic disturbances remain largely unexplored. This thesis investigates how, and at what spatial scale, legacies in forest composition and age structure influence natural disturbance dynamics, and how natural disturbances constrain forest management. I address four specific questions: (i) For how long do spatial legacies of different forest management strategies persist on the landscape? (ii) How do interactions among logging, fire, SBW, and succession affect timber availability and long term forest patterns in age and composition? (iii) How do these patterns differ from those created by each disturbance individually? And, (iv) How can management be used to reduce the extent and severity of fires and SBW defoliation through the manipulation of forest structure? The key scientific innovations of this thesis are: (i) Characterization of the duration and influence of spatial legacies on forest disturbances and sustainability; (ii) Development of a dynamic spatial forest simulation model that includes distinct successional rules that respond to different types of disturbance and shifts in disturbance regimes; and, (iii) Development and application of a wavelet-based significance testing framework to identify key scales of expression in forest spatial patterns. These innovations provide a scientific basis for landscape level forest management strategies designed to reduce the long term impacts of defoliating insects and to meet multiple objectives.

Over the past few decades, the demand for digital information has increased drastically. This enormous demand poses serious difficulties on the storage and transmission bandwidth of the current technologies. One possible solution to overcome this approach is to compress the amount of information by discarding all the redundancies. In multimedia technology, various lossy compression techniques are used to compress the raw image data to facilitate storage and to fit the transmission bandwidth. In this thesis, we propose a new approach using algebraic integers to reduce the complexity of the Daubechies-4 (D4) and Daubechies-6 (D6) Lifting based Discrete Wavelet Transforms. The resulting architecture is completely integer based, which is free from the round-off error that is caused in floating point calculations. The filter coefficients of the two transforms of Daubechies family are individually converted to integers by multiplying it with value of 2x, where, x is a random value selected at a point where the quantity of losses is negligible. The wavelet coefficients are then quantized using the proposed iterative individual-subband coding algorithm. The proposed coding algorithm is adopted from the well-known Embedded Zerotree Wavelet (EZW) coding. The results obtained from simulation shows that the proposed coding algorithm proves to be much faster than its predecessor, and at the same time, produces good Peak Signal to Noise Ratio (PSNR) at very low bit rates. Finally, the two proposed transform architectures are implemented on Virtex-E Field Programmable Gate Array (FPGA) to test the hardware cost (in terms of multipliers, adders and registers) and throughput rate. From the synthesis results, we see that the proposed algorithm has low hardware cost and a high throughput rate.

En procesamiento de señales y reconocimiento de voz, uno de los principales tópicos es la extracción de características. Dada una señal digitalizada esta se transforma a una más compacta de acuerdo a algún criterio dependiente del problema. Por ejemplo, la voz es extremadamente redundante, y contiene información de múltiples fuentes como identidad del locutor, estado emocional y secuencia fonética. En reconocimiento de voz, se desea extraer características que preserven la discriminabilidad entre unidades acústicas pero tengan menor dimensión que la señal original. En esta memoria se estudiarán metodologías de extracción de características para reconocimiento de voz utilizando criterios de discriminabilidad entre unidades fonéticas. Como punto de partida se considerará la técnica de extracción de características MFCC utilizada en los reconocedores estándar y con ellas se entrenará un reconocedor con el software HTK para la base de datos TIMIT. Los MFCC se implementan con bancos de filtros; usando esa motivación y aprovechando la gran colección de formas de representar señales que permite la transformada Wavelet Packet (WP), se propuso un método de selección de bancos de filtros WP considerando discriminación entre unidades acústicas. La memoria se centrará en aplicar esta metodología y las soluciones que esta entrega para proponer una alternativa a los MFCCs. La forma de evaluar los rendimientos de los distintos métodos será mediante el porcentaje de reconocimiento fonético en un subconjunto de test de TIMIT. La parte teórica del trabajo consiste en estudiar propiedades y formas de aplicar los WP, y como se construyen los sistemas de reconocimiento fonético. En la práctica, en implementar la transformada WP y los WPCC en C++, implementar reconocedores fonéticos en el software HTK, y unir todos estos bloques usando el lenguaje PERL, para correr experimentos en distintos escenarios. El aporte de este trabajo son las características Wavelet Packet Cepstral Coefficients (WPCC), se estipulan criterios concretos de diseño para los bancos de filtros WP, con el objetivo de obtener la mayor información para discriminar fonemas. Los WP obtenidos tienen alta selectividad en frecuencia y una estructura que privilegia la resolución en bajas frecuencias principalmente el rango [200Hz-1000Hz] del espectro acústico.

Ingeniero Civil en Computación / En el área de desarrollo de software para el control de aplicaciones y dispositivos electrónicos por voz, ha sido cada vez más común implementar mecanismos que cumplan esta función, considerando el procesamiento de señales sonoras para finalmente descubrir patrones que permitan la identificación y el uso de instrucciones. El objetivo de este trabajo es la exploración de un mecanismo que implemente el procesamiento de la voz humana, extrayendo sus características fundamentales y utilizando estos datos para la identificación de patrones en el tiempo. La hipótesis fundamental es que en la voz humana existen patrones en el tiempo, que podemos obtener y utilizar para la elaboración de instrucciones a ser ejecutadas por componentes de software. Para lograr el objetivo se tomó como base la implementación de un mecanismo de obtención de espectros de frecuencias de la voz humana, considerando algoritmos y técnicas basadas en análisis espectral. Para el procesamiento de los patrones se desarrolló un mecanismo basado en redes neuronales, dada la naturaleza vectorial de los datos. El trabajo, por tanto, se divide en dos grandes tareas. La primera es la obtención de los datos relevantes a la voz, de acuerdo con espectros de frecuencias obtenidos a partir de filtros basados en Wavelet transformadas. La segunda tarea es la implementación de una red neuronal no supervisada, basada en mapas auto-organizativos (SOM), que permita el registro e identificación de patrones en el tiempo. El resultado de este trabajo es un mecanismo que cumple parcialmente sus objetivos, dados los niveles de identificación de los fonemas y el costo computacional requerido.

In our work, we focus on the dynamics of the volatility and co-movement during the first year of public trading. We use the wavelet analysis to investigate the return volatility of the technology stocks an their co-movement with the market in the time-frequency space. We employ the data sampled on multiple frequencies, ranging from 1 second high-frequency to daily data. We present three main findings. First, we identify gradual decline of the return volatility on all but the shortest investment horizons. Second, we do not find a convincing evidence that the technology stocks synchronize with the rest of the market as they get mature. Third, the different nature of the synchronization with the NASDAQ and S&P 500 indices is also not confirmed. JEL Classification C22, C32, C58, G19 Keywords IPO, technology stocks, wavelet analysis Author's e-mail martin.kus@outlook.com Supervisor's e-mail vachal@utia.cas.cz

Rotating machinery is a critical asset of industrial plants worldwide. Bearings and gearboxes are two of the most common components found in rotating machinery of industrial plants. The malfunction of bearings and gearboxes lead the machine to fail and often these failures occur catastrophically leading to personnel injuries. Therefore it is of high importance to identify the deterioration at an early stage. Among the techniques applied to detect damage in rotating machinery, acoustic emission has been a prevalent field of research for its potential to detect defects at an earlier stage than other more established techniques such as vibration analysis and oil analysis. However, to reliably detect the fault at an early stage de-noising techniques often must be applied to reduce the AE noise generated by neighbouring components and normal component operation. For this purpose a novel signal processing algorithm has been developed combining Wavelet Packets as a pre-processor, Hilbert Transform, Autocorrelation function and Fast Fourier transform. The combination of these techniques allows identification of g repetitive patterns in the AE signal that are attributable to bearing and gear damage. The enhancement for early stage defect detection in bearings and gears provided by this method is beneficial in planning maintenance in advance, reducing machinery down-time and consequently reducing the costs associated with bearing breakdown. The effectiveness of the proposed method has been investigated experimentally using seeded and naturally developed defects in gears and bearings. In addition, research into the optimal Wavelet Packet node that offers the best de-noising results has been performed showing that the 250-750 kHz band gives the best SNR results. The detection of shaft angular misalignment using Acoustic Emission has been investigated and compared with acceleration spectra. The results obtained show enhancements of AE in detection shaft angular misalignment over vibration analysis in SNR and stability with varying operational conditions.

The overarching theme of my thesis is understanding observed variations of northern hemisphere atmospheric carbon dioxide (CO2) and methane (CH4) concentrations. I focus my analysis on high-latitude observations of these gases, as there are large stores of carbon in boreal vegetation and tundra which are vulnerable to rapid warming in the Arctic. My thesis is split into two parts. First, I use the wavelet transform to spectrally decompose observed multi-decadal timeseries for CO2 and CH4. I perform a series of numerical experiments based on synthetic data in order to characterise the errors associated with the analysis. For CO2, I analyse the phase and amplitude of the detrended seasonal cycle of CO2 to infer changes about carbon uptake by northern vegetation. I do not find a long-term change in the length of the carbon uptake period despite significant changes in the spring and autumn phase. I do find an increase in the rate of peak uptake which coincides with the observed increase in seasonal amplitude. These results suggest that the carbon uptake period of boreal vegetation has become more intense but has not changed in length, which provides evidence for an increase in net uptake of CO2 in the high latitudes. For CH4, I test the hypothesis that an increase in Arctic wetland emissions could result in a decrease in the seasonal amplitude of CH4 in the high latitudes. This hypothesis is based on the fact that the seasonal minima of CH4 roughly coincides with the peak of high latitude wetland CH4 emissions. I find that the CH4 seasonal amplitude has significantly decreased at a number of high-latitude sites. However I also find that atmospheric transport appears to drive much of the variability in high-latitude CH4 and that transport could also be responsible for the observed changes in amplitude. I show that an increase in wetland emissions is likely to have a more pronounced effect on the high-latitude CH4 seasonal cycle in the future. In the second section of my thesis, I describe a series of experiments in collaboration with the UK Astronomy Technology Centre, in which I characterise a new instrument technology for satellite applications to observe changes in CO2 from low-Earth orbit. The proof of concepts experiments were performed with a bench top hyperspectral imager. I show that the instrument is able to capture clean spectra at the wavelengths required for CO2 with low levels of scattered light between spectra.