Although Robert Cogan's New Images of Musical Sound won the Society of Music Theory's Outstanding Publication Award in 1987, his musical application of spectral analysis has seen little use over the past 25 years. Spectral images are most effective at illustrating the timbre of sound, and harmony, not timbre, is the key structural component of most Western music. There are, however, some compositions in which timbre plays a critical role. Chief among these is Edgard Varèse's Ionisation, an epic percussion ensemble piece built around 40 instruments and 13 musicians. Previous analyses by Jean-Charles François and Varèse protege Chou Wen-Chung have emphasized the importance of timbre to Ionisation's construction, but are limited in their exploration of timbral qualities. Modern digital spectral analysis allows for a more accurate picture of the individual timbres that make up Ionisation and define the broader textures and structures that give the piece meaning.

Demonstramos a Lei de Weyl sobre o comportamento assintótico dos autovalores do operador Laplaciano com condições de contorno de Dirichlet em domínios limitados e suaves com o auxílio do núcleo do calor. Para isso, fazemos um estudo dos operadores não-limitados, semigrupos e da transformada de Fourier. Por fim, expomos alguns resultados posteriores motivados pelo artigo de Mark Kac \"Can one hear the shape of a drum?\". / We prove the Weyl Law on the asymptotic behavior of eigenvalues of the Laplace operator with Dirichlet boundary conditions in smooth bounded domains with the help of the heat kernel. To that end, we study unbounded operators, semigroups and the Fourier transform. Lastly, we mention some further results motivated by Mark Kac\'s article \"Can one hear the shape of a drum?\".

Interpretation of ground penetrating radar (GPR) signals can be a key point in the overall operability of a GPR system. In stepped-frequency and Frequency-Modulated Continuous-Wave (FMCW)GPR systems in particular, the target or object of interest is often located by analysis of Fast Fourier Transform (FFT) derived data. Increasing the GPR system bandwidth can improve resolution, but at the cost of reduced penetrating depth. The challenge is to develop high-resolution signal processing strategies for GPR.A number of Fourier based methods are investigated. However, the main response over a target's position can make it difficult to recognise closely spaced targets. The Least-Suare method is found to be the best autoregression-based estimator. However the method requires high Signal-to-Noise ratio to achieve high- resolution. Furthermore a number of subspace-based methods are investigated. Although the MUItiple Signal Classification (MUSIC) method can theoretically offer infinite resolution, they must be seeded with the number of targets actually present. A superimposed MUSIC technique is proposed to suppress false targets. A novel windowed MUSIC (W-MUSIC) algorithm is developed, and it offers high resolution while still able to minimise spurious responses. Since the performance of any FMCW GPR is critically linked to the linearity of the sweep frequency, the non-linearity in the target range estimation is studied. A Novel Short-Time MUSIC method is proposed and higher time and frequency resolution is achieved than the conventional Short-Time Fourier Transform method. In addition a modified Adaptive Sampling method is proposed to solve the non-linear problem by utilising a reference channel in a GPR system.

Orientadores: Aldário Chrestani Bordonalli, Júlio César Rodrigues Fernandes de Oliveira / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-09-27T13:36:17Z (GMT). No. of bitstreams: 1 Carvalho_LuisHenriqueHeckerde_M.pdf: 8092940 bytes, checksum: da29699f8638d37de27fc0743c89a013 (MD5) Previous issue date: 2014 / Resumo: Uma das alternativas para os sistemas de transmissão óptica de próxima geração é o uso de múltiplos subcanais ópticos densamente multiplexados em frequência (supercanais) com alta eficiência espectral. Supercanais ópticos empregam o processamento paralelo de sinais para alcançar taxas de transmissão além dos limites da eletrônica. Atualmente, as técnicas CO-OFDM e Nyquist WDM são vistas como as principais para a implementação de supercanais ópticos. Neste trabalho, estudam-se abordagens para aumentar a capacidade de transmissão dos sistemas ópticos por meio da realização de supercanais. A implementação em laboratório, análise de desempenho e comparativo entre as técnicas CO-OFDM e Nyquist WDM são realizadas para sistemas operando a 400 Gb/s e 1 Tb/s por canal, com modulação DP-16QAM e eficiências espectrais que chegam a 6 b/s/Hz e acima. Os principais desafios e soluções para a implementação de sistemas de transmissão óptica de próxima geração baseados em supercanais são identificados / Abstract: One of the options for the next generation of optical transmission systems is the use of multiple optical subchannels densely multiplexed in frequency (superchannels) with high spectral efficiency. Optical superchannels employ parallel signal processing to achieve transmission rates beyond the limits of electronics. Currently, CO-OFDM and Nyquist WDM are seen as the main techniques to the implementation of optical superchannels. In this work, different ways to increase the capacity of the current optical systems by the realization of superchannels are studied. The experimental implementation, performance analysis, and comparison between CO-OFDM and Nyquist WDM techniques are performed for systems operating at 400 Gb/s and 1 Tb/s per-channel with DP-16QAM modulation and spectral efficiencies of 6 b/s/Hz and beyond. The main challenges and solutions for the implementation of next generation optical transmission systems based on superchannels are identified / Mestrado / Telecomunicações e Telemática / Mestre em Engenharia Elétrica

The progress of science is based to a large degree on experimentation. The scientist, engineer, or researcher is usually interested in the results of a single experiment only to the extent that he hopes to generalize the results to a class of similar experiments associated with an underlying phenomenon. The process by which this is done is called inductive inference and is always subject to uncertainty. The science of statistical inference can be used to make inductive inferences for which the degree of uncertainty can be measure in terms of probability. A second type of inference called deductive inference is conclusive. If the premises are true, deductive inference leads to true conclusions. Proving the theorems of mathematics is an example of deductive inference; while in the empirical sciences, inductive inference is used to find new knowledge. In engineering and physical science, analytical , i.e., deterministic techniques have been developed to provide deductive descriptions of the real world. Sometimes the assumptions required to make deterministic techniques appropriate are too restrictive since no provision is made for stochastic or uncertainty involved in concluding real world situations. In these situations, the science of statistics provides a basis for generalizing the results of experiments associated with the phenomena of interest. In order to make statistical inference sound, the experimenter must decide in advance which factors must be controlled in the experiment. The factors which are unknown or which cannot be controlled must be controlled by the device of randomization. Uncontrolled factors express themselves as experimental error in the experiment. Randomization is used in order to insure that the experimental error satisfies the probability requirements specified in the statistical model for the experiment, thereby making it possible for the experimenter to generalize the results of his experiment using significance and confidence probability statements. Much of statistics is devoted to situations for which experiments are conducted according to schemes of restricted randomization. Therefore, the experimental errors are independent and are assumed to have a common, yet unknown, probability distribution that can be characterized by estimating the mean and the variance. However, there are certain other types of experimental situations for which it is desirable to observe a physical phenomena with the observations ordered in time or space. The resulting observations can be called a time series.The experimental errors of a time series are likely to be correlated. Consequently, if an unknown probability distribution is to be characterized, covariances as well as the respective means and the variances must be estimated. A time series resulting from observation of a given physical phenomena may exhibit dominant deterministic properties if the experiment can be well controlled. Or, the time series may exhibit dominant statistical properties if it is impossible or impractical to isolate and control various influencing factors. Generally an experiment will consist of both deterministic and statistical elements in some degree in a real world situation. The procedures of analysis presented in Chapter III consider the statistical analysis of periodic and aperiodic digital (discrete) time series, in both the time and frequency domains, using Fourier analysis, covariance and correlation analysis, and the estimation of power and cross power spectral density functions. Time ordered observations are important in the analysis of engineering systems. Certain characteristics of engineering systems are discussed in Chapter IV, and the input-output concept of control system engineering introduced. The input-output technique is not limited to control system engineering problems, but may be applicable in other areas of science also. A deterministic method of ascertaining the output performance of an engineering system consists of subjecting the system to a sinusoidal input function of time, and then measuring the output function of time. If the engineering system is linear, the well-developed techniques are available for analysis; but if the system is nonlinear, then more specialized analysis procedures must be developed for specific problems. In a broad sense, the frequency-response approach consists of investigating the output of a linear system to sinusoidal oscillations of the input. If the system of nonlinear, then the frequency-response approach must be modified; one such modification is the describing function technique. These techniques are also discussed in Chapter IV. Under actual experimental conditions, the deterministic approach of subjecting a system to a sinusoidal input function for purposes of analysis is likely to be complicated by nonlinearities of the system and statistical characteristics of the data. The physical characteristics of the data will undoubtedly be obscured by random measuring errors introduced by transducers and recording devices, and uncontrollable environmental and manufacturing influences. Consequently, generalized procedures for analyzing nonlinear systems in the presence of statistical variation are likely to be required to estimate the input-output characteristics if the system is to work with inferential models applied to recorded data. Such procedures are presented in Chapter III and Chapter V. In Chapter V the empirical determination from input-output rocket test data of a deterministic and statistical model for predicting rocket nozzle control system requirements is complicated by the fact that the control system is nonlinear and the nozzle data is non-stationary consisting of both systematic and random variation. The analysis techniques developed are general enough for analysis of other types of nonlinear systems. If the nonlinear effect of coulomb friction can be estimated and the responses are adjusted accordingly, the nozzle system bears a close relationship to a linear second order differential equation consisting of an acceleration times moment of enertia component, a gas dynamic spring component and a viscous friction component. In addition, vibration loading is present in the data. Consequently, estimation of auto correlation and power spectral density functions is used to isolate these vibrations. Analysis of the control system data is also considered in terms of auto correlations, and in terms of a power spectral density functions. Random input functions rather than sinusoidal input functions may be required under more general experimental conditions. Chapter VI numerically illustrates the analysis procedures. The actual rocket test data used in developing the analysis was classified; consequently, only fictitious data are used in this paper to illustrate the procedures. Chapter VIII is concerned with illustrating the procedures of Chapter III utilizing various time series data. The last part of Chapter VII is concerned with estimation of the power spectral function using techniques of multiple regression; i.e., the model of the General Linear Hypothesis. A definite limitation is the model assumption concerning the residual error of the model. The assumption concerning the error of the model can probably be made more tenable by suitable transformation of either the original time series data or the autocovariances. In any even the spectral function developed by assuming the model for the General Linear Hypothesis gives the same spectral function as defined in Chapter III. However, such quantities as the variance, tests of hypotheses and variance of the spectral function can now be estimated, if the assumptions concerning residual error are valid. Chapter VIII summarizes the results of previous chapters.

Highly repetitive motion is associated with upper extremity musculoskeletal disorders among industrial workers.Current methods of estimating occupational exposure to "repetitiveness" provide information about the repetitiveness of joint motion, but fail to provide complete information about the repetitiveness of muscular exertion, a more biomechanically meaningful measure of repetition. This thesis introduces an innovative digital signal processing method, from which muscular exertion frequency was estimated. Specifically, time series recordings of muscle activity obtained with surface electromyography (sEMG) were processed with standard root-mean-square (RMS) amplitude calculations and then transformed from the time domain into the frequency domain. The mean power frequencies of the RMS-processed sEMG signals (MPF EMG) were then calculated to estimate muscular exertion frequency. In a laboratory-based validation study involving repetitive isometric hand gripping exertions, MPF EMG was compared to measures of muscular exertion frequency and joint motion frequency across a range of known exertion frequencies, intensities, and durations. Strong linear relationships were observed between MPF EMG and external measures of muscular exertion frequency. However, performance of MPF EMG as a measure of muscular exertion frequency may be improved with an increase of the signal to noise ratio in the sEMG data. Signal processing parameters were therefore investigated. Alternative processing parameters were suggested to minimize difference between MPF EMG and established methods of muscular exertion frequency. A second laboratory-based validation study compared MPF EMG to a measure of muscular exertion frequency and a measure of joint movement frequency during a simulated industrial task. Although a stronger linear relationship was observed between metrics of joint motion frequency and established measures of muscular exertion, the differences between measures were not meaningful and the relationship between MPF EMG and established measures was moderate-to-strong. The final phase of this thesis explored the application of the proposed techniques to field-based data collected during a study of ironworkers involved in construction stud-welding tasks. Limitations in data collection limited the analysis of MPF EMG in this study. The research presented in this thesis introduces a novel metric based on the frequency analysis of RMS processed sEMG data, and presents evidence that MPF EMG has potential to be a valuable assessment technique of exposure to repetitive muscular exertion.

In a shallow aquifer underlain by low permeable material, groundwater recharge (R), discharge to rivers or stream as baseflow (BF), and discharge to the atmosphere as evapotranspiration (ET) are related by a simple water balance equation, R - ET - BF = ΔS. Gathering information about these processes is difficult since these processes are hidden from view, yet these processes commingle with one another at the water table surface. The main objective of this dissertation project was to evaluate how main hydrological processes can be inferred from high-resolution water table measurements collected at various sites in Walnut Creek watershed located in southern Iowa. Water table monitoring data available for analysis in the project included three main sites, covering 2573 days between 1996 to 2008 and 61,714 individual water level monitoring points. Water table fluctuations were used to estimate R across an upland-floodplain chronosequence and plant ET under three riparian land covers. High resolution hydraulic head measurements were analyzed with spectral methods to evaluate potential surface and groundwater interaction. Detailed sedimentology and water table monitoring were combined to develop a conceptual model of nitrate leaching to in the near-stream riparian zone of an incised channel. Additional soil moisture and precipitation monitoring are recommended for improved application of methods to other sites. Results from this dissertation indicate that there is a considerable amount of information about key hydrological processes to be gained by measuring water table levels at a high frequency.

Traditional stochastic approaches for synthetic generation of weather variables often assume a prior functional form for the stochastic process, are often not capable of reproducing the probabilistic structure present in the data, and may not be uniformly applicable across sites. In an attempt to find a general framework for stochastic generation of weather variables, this study marks a unique departure from the traditional approaches, and ushers in the use of data-driven nonparametric techniques and demonstrates their utility. Precipitation is one of the key variables that drive hydrologic systems and hence warrants more focus . In this regard, two major aspects of precipitation modeling were considered: (I) resampling traces under the assumption of stationarity in the process, or with some treatment of the seasonality, and (2) investigations into interannual and secular trends in precipitation and their likely implications. A nonparametric seasonal wet/dry spell model was developed for the generation of daily precipitation. In this the probability density functions of interest are estimated using non parametric kernel density estimators. In the course of development of this model, various nonparametric density estimators for discrete and continuous data were reviewed, tested, and documented, which resulted in the development of a nonparametric estimator for discrete probability estimation. Variations in seasonality of precipitation as a function of latitude and topographic factors were seen through the non parametric estimation of the time-varying occurrence frequency. Nonparametric spectral analysis, performed on monthly precipitation, revealed significant interannual frequencies and coherence with known atmospheric oscillations. Consequently, a non parametric, nonhomogeneous Markov chain for modeling daily precipitation was developed that obviated the need to divide the year into seasons. Multivariate nonparametric resampling technique from the nonparametrically fitted probability density functions, which can be likened to a smoothed bootstrap approach, was developed for the simulation of other weather variables (solar radiation, maximum and minimum temperature, average dew point temperature, and average wind speed). In this technique the vector of variables on a day is generated by conditioning on the vector of these variables on the preceding day and the precipitation amount on the current day generated from the wet/dry spell model.

The research contained within this thesis was directed at examining the spectral properties of regolith-dominated terrains using airborne and proximal hyperspectral instruments. The focus of the investigation was to identify the mineralogy of the regolith and determine if surficial materials were indicative of the underlying bedrock in the regolithdominated terrain of the eastern Olary Domain, South Australia. The research area was constrained to a 250 km2 area around the Cu-Au mineralisation of the White Dam Prosect. Integrated remote sensing, using airborne hyperspectral datasets (HyMap), Landsat imagery and gamma-ray spectroscopy data, was performed to map regolith-landforms and extract information on surficial materials. Detailed calibration of the HyMap dataset, using a modified model-based/empirical line calibration technique, was required prior to information extraction. The White Dam area was able to be divided into: alluvial regolith-dominated; in situ regolith-dominated; and bedrock-dominated terrains, based on mineralogical interpretations of the regolith, using the remotely sensed hyperspectral data. Alluvial regions were characterised by large abundances of vegetation and soils with a hematite-rich mineralogy. Highly weathered areas of in situ material were discriminated by the presence of goethite and kaolinite of various crystallinities, whereas the bedrock-dominated regions displayed white mica-/muscovite-rich mineralogy. Areas flanking bedrock exposures commonly consisted of shallow muscovite-rich soils containing regolith carbonate accumulations. Traditional mineral mapping processes were performed on the HyMap data and were able to extract endmembers of regolith and other surficial materials. The Mixture Tuned Matched Filter un-mixing process was successful at classifying regolith materials and minerals. Spectral indices performed on masked data were effective at identifying the key regolith mineralogical features of the HyMap imagery and proved less time consuming than un-mixing processes. Processed HyMap imagery was able to identify weathering halos, highlighted in mineralogical changes, around bedrock exposures. Proximal spectral measurements and XRD analyses of samples collected from the White Dam Prospect were used to create detailed mineralogical dispersion maps of the surface and costean sections. Regolith materials of the logged sections were found to correlate with the spectrally-derived mineral dispersion profiles. The HyLogger drill core scanning instrument was used to examine the mineralogy of the fresh bedrock, which contrasted with the weathering-derived near-surface regolith materials. The overall outcomes of the thesis showed that hyperspectral techniques were useful for charactering the mineralogy of surficial materials and mapping regolith-landforms. / Thesis (Ph.D.)--School of Earth & Environmental Sciences, 2004.

In this thesis we propose a robust PID controller to regulate the hydrogen flow of proton exchange membrane fuel cells. The controller allows the so-called hydrogen excess ratio to track a desired value rapidly in order to achieve saving hydrogen and to avoid damage of the fuel cell when the power output of the fuel cell varies from one level to another. The fuel cell system is governed by a set of complicated nonlinear dynamical equations. To ease the control design task, we model the system, at each operating point, as a feedback interconnection of a linear time-invariant nominal part with a norm-bounded perturbation. We use the technique of system identification to acquire the transfer function representation of the nominal part and the size of the perturbation. To do this, the chirp signal is adopted to excite the system and the observed response is analyzed using spectral analysis to obtain the model. Based on the model, a $H_{infty}$ PID controller is designed for the fuel cell system. The design is tested on an experimental platform. The experimental results verify that the proposed controller can regulate the hydrogen excess ratio rapidly under load variation, and effectively reject the influence of external disturbances.