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Soil-rock probing (Jb-probing) is the most common probing method in Sweden. Due to the penetration capacity of the Jb-probing it can be performed in both soil and rock. However, the capacity also results in inherent limitations and uncertainties, such as the difficulty identifying the soil layer sequences of soft soils. In order to attain a more detailed soil layer sequence it is necessary to perform complementary probing and sampling methods, an inefficient and consequently costly procedure. By instead implementing non-interfering complementary methods performed simultaneously as the Jb-probing the method may be rationalized. The so-called acoustic Jb-probing method may be a potential complement to the Jb-probing. In this thesis a continued study of the acoustic Jb-probing method is performed by means of a case study in Gubbängen with the focus on the potential additional information that the spectrogram (a visual representation of the frequency spectra) may contain compared to the Jb-parameters alone. This was done by obtaining vibration signals during Jb-probing using a triaxle geophone installed four meters from the boreholes. Vibration signals were collected from 13 boreholes. The vibration signals were then analyzed in time- and frequency domain which were compared to corresponding Jb-parameters and classified soil types. The results showed that the clay layers held the most promise for discovering additional information in the spectrogram, however this does not exclude potential in other soil types. Additionally, it was shown that the geophone ought to be fastened in the ground in order to attain satisfactory data. Overall, the acoustic Jb-probing method is a favorable way of collecting and analyzing data, which with continued development of the operational and computational process may be an economical alternative to the conventional method.

<p>Torsional vibrations, resulting from the interaction between the engine, transmission, and other components, can lead to reduced driveline performance, increased fatigue, and compromised vehicle reliability. Thus, understanding and effectively managing these vibrations are crucial for ensuring optimal truck operation and safety. This thesis investigates the phenomenon of torsional vibrations in trucks and proposes an algorithm-based approach for detecting natural frequencies and controlling vibrations along the driveline. </p>

The objective of this present thesis was to determine whether GEV (Generalized Extreme Value) itself can be a more conservative distribution than LP3 (Log Pearson III) associated with other methods, such as the B17B weighting procedure with Single Grubbs-Beck (SGB) for low outliers, when determining the projected floods in a flood frequency analysis (FFA) for Santa Ana and San Gabriel regions and other urbanized stream gages present in California. In this work, USGS PeakFQ was utilized. From the results obtained, it was possible to state that GEV fitting results were directly affected by the length of the data. When the length of the record is short, it is not accurate to use a projection of 100-year return period, for example, to represent future projection. Comparing the LP3 and GEV CDFs, for the majority of the stream gages analyzed in this project, GEV proves to be the most conservative method, with smaller return periods.

No / Word spotting in continuous speech is considered a challenging issue due to dynamic nature of speech. Literature contains a variety of novel techniques for the isolated word recognition and spotting. Most of these techniques are based on pattern recognition and similarity measures. This paper amalgamates the use of different techniques that includes wavelet transform, feature extraction and Euclidean distance. Based on the acoustic features, the proposed system is capable of identifying and localizing a target (test) word in a continuous speech of any length. Wavelet transform is used for the time-frequency representation and filtration of speech signal. Only high intensity frequency components are passed to feature extraction and matching process resulting robust performance in terms of matching as well as computational cost.

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With technology and computers becoming more and more sophisticated and readily available, cars have followed suit by integrating more and more microcontrollers to handle tasks ranging from controlling the radio to the brakes and steering. Handling all of these separate processors is a communication system and protocol known as Controller Area Network (CAN) bus. While the CAN bus is a robust system for sending messages, allowing control of the car through the CAN bus presents an opportunity for an outside party to interfere with the operations of a car. Any number of different methods could be used to hack the bus and take control of a car, including hacking into the bus remotely, plugging a small device into the on-board diagnostics port to the CAN bus, or swapping an existing node on the CAN bus for one that has been tampered with. This presents obvious safety risks, so to guard against this possibility, this paper will present an algorithm designed to recognize nodes based on the noise content of their signal so that any messages coming from an improper source can be flagged as suspicious. The algorithm makes use of MATLAB and Python to perform various transformations on the data and calculate features of the noise in a signal. These features are then passed through a statistical analysis which provides each one a score for how much useful information it contains. The best performing features are run through both a multilayer perceptron neural network and a support vector machine, and the results are compared. Each algorithm gives strong prediction performance, with prediction accuracies of 99.9% and 99.8% for the neural network and support vector machine, respectively. / Master of Science / With technology and computers becoming more and more sophisticated and readily available, cars have followed suit by integrating more and more microcontrollers to handle tasks ranging from controlling the radio to the brakes. Handling all of these separate processors is a communication system and protocol known as Controller Area Network (CAN) bus. However, this presents an opportunity for an outside party to interfere with the operations of a car. An existing node for the CAN bus could be swapped out for one that has been tampered with, causing potentially fatal accidents. To guard against this possibility, this paper will present an algorithm designed to recognize nodes based on the noise content of their signal so that any new hardware will trigger a flag that an unrecognized source is trying to interfere. The algorithm makes use of the MATLAB and Python programming languages to calculate certain characteristics of the noise in the signal and pass those through a machine learning algorithm. This algorithm is able to learn through mathematical means what each node ”sounds like”. With over 99% accuracy, we were able to predict which node sent a given signal.

This work deals with the metods frequency and time frequency analysis of stabilometric signal. In the introroduction is described theory about posturography and posturographic measurment. The work contains describtion of stabilometric parametrs in time domain (1D and 2D parametrs) and in frequency domain. The aim is create review of basic metods used to processing and preprocessing of stabilometric signals and comparing this methods . In work is realized ferquency analysis used Frourier transfrmation and Burg method and time-frequency analysis used Short time Frourier transformation and Wavelet transformation. One part of program is aimed on comparison of this methods.

The thesis is concerned with development of efficient regional frequency analysis (RFA) approaches to estimate quantiles of hydrometeorological events. The estimates are necessary for various applications in water resources engineering. The classical approach to estimate quantiles involves fitting frequency distribution to at-site data. However, this approach cannot be used when data at target site are inadequate or unavailable to compute parameters of the frequency distribution. This impediment can be overcome through RFA, in which sites having similar attributes are identified to form a region, and information is pooled from all the sites in the region to estimate the quantiles at target site. The thesis proposes new approaches to RFA of precipitation, meteorological droughts and floods, and demonstrates their effectiveness. The approach proposed for RFA of precipitation overcomes shortcomings of conventional approaches with regard to delineation and validation of homogeneous precipitation regions, and estimation of precipitation quantiles in ungauged and data sparse areas. For the first time in literature, distinction is made between attributes/variables useful to form homogeneous rainfall regions and to validate the regions. Another important issue is that some of the attributes considered for regionalization vary dynamically with time. In conventional approaches, there is no provision to consider dynamic aspects of time varying attributes. This may lead to delineation of ineffective regions. To address this issue, a dynamic fuzzy clustering model (DFCM) is developed. The results obtained from application to Indian summer monsoon and annual rainfall indicated that RFA based on DFCM is more effective than that based on hard and fuzzy clustering models in arriving at rainfall quantile estimates. Errors in quantile estimates for the hard, fuzzy and dynamic fuzzy models based on the proposed approach are shown to be significantly less than those computed for Indian summer monsoon rainfall regions delineated in three previous studies. Overall, RFA based on DFCM and large scale atmospheric variables appeared promising. The performance of DFCM is followed by that of fuzzy and hard clustering models. Next, a new approach is proposed for RFA of meteorological droughts. It is suggested that homogeneous precipitation regions have to be delineated before proceeding to develop drought severity - areal extent - frequency (SAF) curves. Drought SAF curves are constructed at annual and summer monsoon time scales for each of the homogeneous rainfall regions that are newly delineated in India based on the proposed approach. They find use in assessing spatial characteristics and frequency of meteorological droughts. It overcomes shortcomings associated with classical approaches that construct SAF curves for political (e.g., state, country) and physiographic regions (e.g., river basin), based on spatial patterns of at-site values of drought indices in the study area, without testing homogeneity in rainfall. Advantage of the new approach can be noted especially in areas that have significant variations in temporal and spatial distribution of precipitation (possibly due to variations in topography, landscape and climate). The DFCM is extended to RFA of floods, and its effectiveness in prediction of flood quantiles is demonstrated by application to Godavari basin in India, considering precipitation as time varying attribute. Six new homogeneous regions are formed in Godavari basin and errors in quantile estimates based on those regions are shown to be significantly less than those computed based on sub-zones delineated in Godavari basin by Central Water Commission in a previous study.