In this work, we present a new method for seismic waveform characterization, which is aimed at extracting detailed litho-stratigraphical information from seismic data. We attempt to estimate the lithological attributes from seismic data according to our parametric representation of stratigraphical horizons, where the parameter values provide us with a direct link to nature of lithological transitions. We test our method on a seismic dataset with a strong diagenetic transition (opal-A to opal-CT transition). Given some information from cutting samples of well, we use a percolation-based model to construct the elastic profile of lithological transitions. Our goal is to match parametric representation for the diagenetic transition in both real data and synthetic data given by these elastic profiles. This match may be interpreted as a well-seismic tie, which reveals lithological information about stratigraphical horizons.

The past three decades has witnessed substantial progress towards the application of low-rate speech coders to civilian and military communications as well as computer-related voice applications. Central to this progress has been the development of new speech coders capable of producing high-quality speech at low data rates. Most of these coders incorporate mechanisms to: represent the spectral properties of speech, provide for speech waveform matching, and "optimize" the coder's performance for the human ear. A number of these coders have already been adopted in national and international cellular telephony standards. The objective of this paper is to provide a tutorial overview of speech coding methodologies with emphasis on those algorithms that are part of the recent low-rate standards for voice applications. Although the emphasis is on the new low-rate coders, we attempt to provide a comprehensive survey by covering some of the traditional methodologies as well. The paper starts with a historical perspective and continues with a brief discussion on the speech properties and performance measures. Then I proceed with descriptions of waveform coders, linear predictive vocoders, and analysis-by-synthesis linear predictive coders. At the end the system for computer-based stenographing is presented. Quality research and ways how to improve this system will be provided.

With the desire for energy conservation and lower costs, the application of pulse-width modulated (PWM) voltage source converter (VSC) drives has grown at an exponential rate. However, due to their high switching frequency and high dv/dt, increased dielectric stresses and thermal stresses are applied to the insulation system of the motors, which may lead to the failure of the insulation. In order to test the performance of the motor insulation under the above complex stress conditions, an IGBT-based pulse voltage generator which can produce high voltage square wave and PWM waveforms has been successfully developed in this research. The generator consists of IGBT switches and other wave shaping components. The special cascade connection circuit design enables the generator to produce the stable high voltage square wave and PWM waveforms. A microcontroller-based trigger signal generator is used to trigger the power electronic switches in the generator. In order to avoid false triggering from electromagnetic interference (EMI), optical fibre cables are used to connect the trigger signal generator to the switches which are located in a high electric potential area. The generator can produce square wave and PWM waveforms with a peak voltage up to 15 kV and with a switching frequency of 600 Hz to 6 kHz. The fundamental frequency of the PWM waveform is 20 Hz to 1200 Hz, the rise time is less than 200 ns, and the pulse width can be varied up to several milliseconds. A 4 kVrms form wound model stator coil was tested under different voltage waveforms: power frequency, exponential decay pulse, square wave, and sinusoidal pulse-width modulated (SPWM) waveform. Infrared images and the maximum temperature rise of the coil under different electrical stresses were recorded. The results show that both the square and SPWM voltage waveforms cause a significantly higher temperature rise than the power frequency and exponential decay pulse voltage waveforms. Since the actual VSCs generate transients similar to those of the square and PWM voltage waveforms, it is recommended that the stator coil insulation be analyzed using PWM voltage waveforms in order to simulate actual conditions.

In this work, we present a new method for seismic waveform characterization, which is aimed at extracting detailed litho-stratigraphical information from seismic data. We attempt to estimate the lithological attributes from seismic data according to our parametric representation of stratigraphical horizons, where the parameter values provide us with a direct link to nature of lithological transitions. We test our method on a seismic dataset with a strong diagenetic transition (opal-A to opal-CT transition). Given some information from cutting samples of well, we use a percolation-based model to construct the elastic profile of lithological transitions. Our goal is to match parametric representation for the diagenetic transition in both real data and synthetic data given by these elastic profiles. This match may be interpreted as a well-seismic tie, which reveals lithological information about stratigraphical horizons.

This thesis discusses mathematical optimization techniques for waveform design in cognitive radars. These techniques have been designed with an increasing level of sophistication, starting from a bistatic model (i.e. two transmitters and a single receiver) and ending with a cognitive network (i.e. multiple transmitting and multiple receiving radars). The environment under investigation always features strong signal-dependent clutter and noise. All algorithms are based on an iterative waveform-filter optimization. The waveform optimization is based on convex optimization techniques and the exploitation of initial radar waveforms characterized by desired auto and cross-correlation properties. Finally, robust optimization techniques are introduced to account for the assumptions made by cognitive radars on certain second order statistics such as the covariance matrix of the clutter. More specifically, initial optimization techniques were proposed for the case of bistatic radars. By maximizing the signal to interference and noise ratio (SINR) under certain constraints on the transmitted signals, it was possible to iteratively optimize both the orthogonal transmission waveforms and the receiver filter. Subsequently, the above work was extended to a convex optimization framework for a waveform design technique for bistatic radars where both radars transmit and receive to detect targets. The method exploited prior knowledge of the environment to maximize the accumulated target return signal power while keeping the disturbance power to unity at both radar receivers. The thesis further proposes convex optimization based waveform designs for multiple input multiple output (MIMO) based cognitive radars. All radars within the system are able to both transmit and receive signals for detecting targets. The proposed model investigated two complementary optimization techniques. The first one aims at optimizing the signal to interference and noise ratio (SINR) of a specific radar while keeping the SINR of the remaining radars at desired levels. The second approach optimizes the SINR of all radars using a max-min optimization criterion. To account for possible mismatches between actual parameters and estimated ones, this thesis includes robust optimization techniques. Initially, the multistatic, signal-dependent model was tested against existing worst-case and probabilistic methods. These methods appeared to be over conservative and generic for the considered signal-dependent clutter scenario. Therefore a new approach was derived where uncertainty was assumed directly on the radar cross-section and Doppler parameters of the clutters. Approximations based on Taylor series were invoked to make the optimization problem convex and {subsequently} determine robust waveforms with specific SINR outage constraints. Finally, this thesis introduces robust optimization techniques for through-the-wall radars. These are also cognitive but rely on different optimization techniques than the ones previously discussed. By noticing the similarities between the minimum variance distortionless response (MVDR) problem and the matched-illumination one, this thesis introduces robust optimization techniques that consider uncertainty on environment-related parameters. Various performance analyses demonstrate the effectiveness of all the above algorithms in providing a significant increase in SINR in an environment affected by very strong clutter and noise.

abstract: In this thesis, an adaptive waveform selection technique for dynamic target tracking under low signal-to-noise ratio (SNR) conditions is investigated. The approach is integrated with a track-before-detect (TBD) algorithm and uses delay-Doppler matched filter (MF) outputs as raw measurements without setting any threshold for extracting delay-Doppler estimates. The particle filter (PF) Bayesian sequential estimation approach is used with the TBD algorithm (PF-TBD) to estimate the dynamic target state. A waveform-agile TBD technique is proposed that integrates the PF-TBD with a waveform selection technique. The new approach predicts the waveform to transmit at the next time step by minimizing the predicted mean-squared error (MSE). As a result, the radar parameters are adaptively and optimally selected for superior performance. Based on previous work, this thesis highlights the applicability of the predicted covariance matrix to the lower SNR waveform-agile tracking problem. The adaptive waveform selection algorithm's MSE performance was compared against fixed waveforms using Monte Carlo simulations. It was found that the adaptive approach performed at least as well as the best fixed waveform when focusing on estimating only position or only velocity. When these estimates were weighted by different amounts, then the adaptive performance exceeded all fixed waveforms. This improvement in performance demonstrates the utility of the predicted covariance in waveform design, at low SNR conditions that are poorly handled with more traditional tracking algorithms. / Dissertation/Thesis / M.S. Electrical Engineering 2011

abstract: Waveform design that allows for a wide variety of frequency-modulation (FM) has proven benefits. However, dictionary based optimization is limited and gradient search methods are often intractable. A new method is proposed using differential evolution to design waveforms with instantaneous frequencies (IFs) with cubic FM functions whose coefficients are constrained to the surface of the three dimensional unit sphere. Cubic IF functions subsume well-known IF functions such as linear, quadratic monomial, and cubic monomial IF functions. In addition, all nonlinear IF functions sufficiently approximated by a third order Taylor series over the unit time sequence can be represented in this space. Analog methods for generating polynomial IF waveforms are well established allowing for practical implementation in real world systems. By sufficiently constraining the search space to these waveforms of interest, alternative optimization methods such as differential evolution can be used to optimize tracking performance in a variety of radar environments. While simplified tracking models and finite waveform dictionaries have information theoretic results, continuous waveform design in high SNR, narrowband, cluttered environments is explored. / Dissertation/Thesis / M.S. Electrical Engineering 2014

In this work, we present a new method for seismic waveform characterization, which is aimed at extracting detailed litho-stratigraphical information from seismic data. We attempt to estimate the lithological attributes from seismic data according to our parametric representation of stratigraphical horizons, where the parameter values provide us with a direct link to nature of lithological transitions. We test our method on a seismic dataset with a strong diagenetic transition (opal-A to opal-CT transition). Given some information from cutting samples of well, we use a percolation-based model to construct the elastic profile of lithological transitions. Our goal is to match parametric representation for the diagenetic transition in both real data and synthetic data given by these elastic profiles. This match may be interpreted as a well-seismic tie, which reveals lithological information about stratigraphical horizons. / Science, Faculty of / Earth, Ocean and Atmospheric Sciences, Department of / Graduate

High blood pressure is a major public health issue. However, there are many physical and non-physical factors that affect the measurement of blood pressure (BP) over very short time spans. Therefore, it is very difficult to write a mathematical equation which includes all relevant factors needed to estimate accurate BP values. As a result, a possible solution to overcome these limitations is the use of an artificial neural network (ANN). The aim of this research is to design and implement a new ANN approach, which correlates the arterial pulse waveform shape to BP values, for estimation of BP in a single heartbeat. To test the feasibility of this approach, a pilot study was performed on an arterial pulse waveform dataset obtained from 11 patients with normal BP and 11 patients with hypertension. It was found that the proposed method can accurately estimate BP in single heartbeats and satisfy the requirements of the ANSI/AAMI standard for non-invasive measurement of BP.

Information about the central venous pressure is important in evaluating several clinical conditions including cardiac failure and volume overload. The jugular veins serve as a primary route for the indirect estimation of the central venous pressure or waveform. The conventional methods for acquiring the central venous pressure in these veins have been through neck visualization and the insertion of catheters. Even though these procedures are effective if done properly, they have various downsides such as being invasive, inaccurate and time consuming. In this research, a sensor is proposed for the noninvasive detection of central venous waveforms within the jugular veins. The sensor is a reflectance configured probe which utilizes laser based on the photoplethysmography principle. The effectiveness of the sensor was tested in-vitro using a mock circulatory loop and was also tested on a single human subject. The results from the tests indicated a very good sensor response in estimating pressure waveforms.