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The discovery of proteomic information through the use of mass spectrometry (MS) has been an active area of research in the diagnosis and prognosis of many types of cancer. This process involves feature selection through peak detection but is often complicated by many forms of non-biologicalbias. The need to extract biologically relevant peak information from MS data has resulted in the development of statistical techniques to aid in spectra pre-processing. Baseline estimation and normalization are important pre-processing steps because the subsequent quantification of peak heights depends on this baseline estimate. This dissertation introduces a mixture model to estimate the baseline and peak heights simultaneously through the expectation-maximization (EM) algorithm and a penalized likelihood approach. Our model-based pre-processing performs well in the presence of raw, unnormalized data, with few subjective inputs. We also propose a model-based normalization solution for use in subsequent classification procedures, where misclassification results compare favorably with existing methods of normalization. The performance of our pre-processing method is evaluated using popular matrix-assisted laser desorption and ionization (MALDI) and surface-enhanced laser desorption and ionization (SELDI) datasets as well as through simulation.

Dietary factors in early life (infant feeding practices and timing of introduction of solid foods) are the most potentially modifiable early life exposures associated with childhood growth, as compared to genetic determinants, co-morbidity, and other environmental influences. Yet, studies assessing the association of infant feeding with growth may be limited by out-of-date data and unable to compare results due to inconsistent definitions of infant feeding practices. Mixed feeding (i.e. breast and bottle feeding) calls for special attention due to the reality of mothers returning to work after childbirth in the US. This report used data from the National Children’s Study Formative Research in Physical Measurements. A discovery set of 300 participants were selected by ethnicity from the sample available when this report was developed. This report emphasized statistical methods as well as data pre-processing, which are critical but typically under-studied. This report is intended to contribute towards closing this gap by describing a study from design, data pre-processing, to analysis. Results showed that non-Hispanic Black children had the lowest rates of ever and exclusively breastfeeding, compared to Hispanics and non-Hispanic Whites. Mothers aged 30 years and over, married, and educated above the high school level exclusively breastfed more than other mothers. Mixed feeding was categorized into three and five subgroups according to maternal recalls of the extent or frequency of breast/formula-feeding and compared by mean durations of breast/formula-feeding. Mixed feeding groups may provide unique opportunities to assess the relationship between mixed feeding versus exclusively breast/formula-feeding and childhood linear growth in the author’s dissertation. The percentage of children who were breastfed less than 6 months differed from those breastfed more than 6 months by ethnicity, child’s birthweight, gestational age, maternal age at childbirth, education level, and marital status, which suggests 6 months as a reasonable cut-off for breastfeeding categorization. Children of low birthweight and born preterm were introduced to solid foods later than those of normal/high birthweight and those born on time/postterm, even after adjusting for ethnicity. Analyses on a re-test set will be performed and compared to this discovery set in the author’s dissertation. / text

Predictive maintenance is extremely important to fleet owners. On-duty automobile engine failures add cost of extra towing, gas and labor expenses which can add up to millions of dollars every year. Early knowledge of upcoming failures helps reduce these expenses. Thus, companies invest considerably in fault detection and diagnosis (FDD) systems to reduce unnecessary costs. Artificial Intelligence (AI) is getting increasingly used in the data driven signal based FDD industry because it requires less labor and equipment. It also results in higher productivity since it can operate continuously. This research offers Artificial Intelligence based solutions to detect and diagnose the degradation of three Internal Combustion Engine (ICE) parts which may cause on-duty failures: lead-acid accessory battery, spark plugs, and Exhaust Gas Recirculation (EGR) valve. Since the goal behind most FDD systems is cost reduction, it is important to reduce the cost of the FDD test. Therefore, all the FDD solutions proposed in this research are based on three types of built-in sensors: battery voltage sensor, knock sensors and speed sensor. Furthermore, the engine database, the Machine Learning (ML) and Deep Learning (DL) models, and the virtual operating machines were all stored and operated in the cloud. In this research, eight Machine Learning (ML) and Deep Learning (DL) models are proposed to detect degradations in three vehicle parts mentioned above. Additionally, novel advanced pre-processing approaches were designed to enhance the performance of the models. All the developed models showed excellent detection accuracies while classifying engine data obtained under artificially and physically induced fault conditions. Since some variant data samples could not be detected due to experimental flaws, defective sensors and changes in temperature and humidity, novel pre-processing methods were proposed for Long Short-Term Memory Networks (LSTM-RNN) and Convolutional Neural Networks (CNN) which solved the data variability problem and outperformed the previous ML/DL models. / Thesis / Doctor of Philosophy (PhD) / Predictive maintenance is extremely important to fleet owners. On-duty automobile engine failures add cost of extra towing, gas and labor expenses which can add up to millions of dollars every year. Early knowledge of upcoming failures helps reduce these expenses. Thus, companies invest considerably in fault detection and diagnosis (FDD) systems to reduce unnecessary costs. Artificial Intelligence (AI) is getting increasingly used in the data driven signal based FDD industry because it requires less labor and equipment. It also results in higher productivity since it can operate continuously. This research offers Artificial Intelligence based solutions to detect and diagnose the degradation of three Internal Combustion Engine (ICE) parts which may cause on-duty failures: lead-acid accessory battery, spark plugs, and Exhaust Gas Recirculation (EGR) valve. Since the goal behind most FDD systems is cost reduction, it is important to reduce the cost of the FDD test. Therefore, all the FDD solutions proposed in this research are based on three types of built-in sensors: battery voltage sensor, knock sensors and speed sensor. Furthermore, the engine database, the Machine Learning (ML) and Deep Learning (DL) models, and the virtual operating machines were all stored and operated in the cloud. In this research, eight Machine Learning (ML) and Deep Learning (DL) models are proposed to detect degradations in three vehicle parts mentioned above. Additionally, novel advanced pre-processing approaches were designed to enhance the performance of the models. All the developed models showed excellent detection accuracies while classifying engine data obtained under artificially and physically induced fault conditions. Since some variant data samples could not be detected due to experimental flaws, defective sensors and changes in temperature and humidity, novel pre-processing methods were proposed for Long Short-Term Memory Networks (LSTM-RNN) and Convolutional Neural Networks (CNN) which solved the data variability problem and outperformed the previous ML/DL models.

Automatic age and gender recognition for speech applications is very important for a number of reasons. One of the reasons is that it can improve human-machine interaction. For example, the advertisements can be specialized based on the age and the gender of the person on the phone. It also can help identify suspects in criminal cases or at least it can minimize the number of suspects. Some other uses of this system can be applied for adaptation of waiting queue music where a different type of music can be played according to the person's age and gender. And also using this age and gender recognition system, the statistics about age and gender information for a specific population can be learned. Machine learning is part of artificial intelligence which aims to learn from data. Machine Learning has a long history. But due to some limitations, for ex. , the cost of computation and due to some inefficient algorithms, it was not applied to speech recognition tasks. Only for a decade, researchers started to apply these algorithms to some real world tasks, for ex., speech recognition, computer vision, finance, banking, robotics etc. In this thesis, recognition of age and gender was done using a popular machine learning algorithm and the performance of the system was compared. Also the dataset included real -life examples, so that the system is adaptable to real world applications. To remove the noise and to get the features of speech examples, some digital signal processing techniques were used. Useful speech features that were used in this work were: pitch frequency and cepstral representations. The performance of the age and gender recognition system depends on the speech features used. As the first speech feature, the fundamental frequency was selected. Fundamental frequency is the main differentiating factor between male and female speakers. Also, fundamental frequency for each age group is different. So in order to build age and gender recognition system, fundamental frequency was used. To get the fundamental frequency of speakers, harmonic to sub harmonic ratio method was used. The speech was divided into frames and fundamental frequency for each frame was calculated. In order to get the fundamental frequency of the speaker, the mean value of all the speech frames were taken. It turns out that, fundamental frequency is not only a good discriminator gender, but also it is a good discriminator of age groups simply because there is a distinction between age groups and the fundamental frequencies. Mel Frequency Cepstral Coefficients (MFCC) is a good feature for speech recognition and so it was selected. Using MFCC, the age and gender recognition accuracies were satisfactory. As an alternative to MFCC, Shifted Delta Cepstral (SDC) was used as a speech feature. SDC is extracted using MFCC and the advantage of SDC is that, it is more robust under noisy data. It captures the essential information in noisy speech better. From the experiments, it was seen that SDC did not give better recognition rates because the dataset did not contain too much noise. Lastly, a combination of pitch and MFCC was used to get even better recognition rates. The final fused system has an overall recognition value of 64.20% on ELSDSR [32] speech corpus.

The recent evolution in multidisciplinary fields of Engineering, neuroscience, microelectronics, bioengineering and neurophysiology have reduced the gap between human and machine intelligence. Many methods and algorithms have been developed for analysis and classification of bio signals, 1 or 2-dimensional, in time or frequency distribution. The integration of signal processing with the electronic devices serves as a major root for the development of various biomedical applications. There are many ongoing research in this area to constantly improvise and build an efficient human- robotic system. Electroencephalography (EEG) technology is an efficient way of recording electrical activity of the brain. The advancement of EEG technology in biomedical application helps in diagnosing various brain disorders as tumors, seizures, Alzheimer’s disease, epilepsy and other malfunctions in human brain. The main objective of our thesis deals with acquiring and pre-processing of real time EEG signals using a single dry electrode placed on the forehead. The raw EEG signals are transmitted in a wireless mode (Bluetooth) to the local acquisition server and stored in the computer. Various machine learning techniques are preferred to classify EEG signals precisely. Different algorithms are built for analysing various signal processing techniques to process the signals. These results can be further used for the development of better Brain-computer interface systems.

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This paper describes the format of monitoring data files that are collected for external measuring sites and at laboratory experiments at the Institute for Building Climatology (IBK). The Monitoring Data Files are containers for storing time series or event driven data collected as input for transient heat and moisture transport simulations. Further applications are the documentation of real world behaviour, laboratory experiments or the collection of validation data sets for simulation results ( whole building / energy consumption / HAM ). The article also discusses the application interface towards measurement data verification tools as well as data storage solutions that can be used to archive measurement data files conveniently and efficiently.

This paper describes the format of monitoring data files that are collected for external measuring sites and at laboratory experiments at the Institute for Building Climatology (IBK). The Monitoring Data Files are containers for storing time series or event driven data collected as input for transient heat and moisture transport simulations. Further applications are the documentation of real world behaviour, laboratory experiments or the collection of validation data sets for simulation results ( whole building / energy consumption / HAM ). The article also discusses the application interface towards measurement data verification tools as well as data storage solutions that can be used to archive measurement data files conveniently and efficiently.:1 Introduction 2 File Name Conventions 3 Headers 3.1 Specifics on Time Series Header Files 3.2 Specifics s on Event Driven Header Files 4 Data Section Format Description 5 SI Unit Strings 6 Competition Law Advice 7 Liability for external Links