The operating motion of a ground compactor uses high power vibrations to improve mechanical properties of a compacted ground. This motion gives a good base for the vibration analysis with an aid of Signal Processing. In this thesis, the motion of a bottom plate in a compactor is of the main interest. The thesis concerns usage of two main spectral analyzing tools, Power Spectrum estimators and Power Spectral Density estimators, presenting advantages and disadvantages in the application of a vibration analysis. Moreover, an influence of two window applications, a Flattop window, and a Hanning window, is described in relation to both analyzing approaches. The results present problems that occur when a vibration with a present modulated frequency is analyzed and how a Power Spectral Density estimator arise in a more consistent estimate over analyzed vibration spectrum. What is more, an Ordinary Deflection Shapes for a simplified bottom plate model, under different motion excitations, are presented at the end of this thesis, giving a better view of the operational motion of an analyzed system.

O Ruído de Baixa Frequência (LFN), tais como o ruído flicker e o Random Telegraph Noise (RTN), são limitadores de performance em muitos circuitos analógicos e digitais. Para transistores diminutos, a densidade espectral de potência do ruído pode variar muitas ordens de grandeza, impondo uma séria limitação na performance do circuito e também em sua confiabilidade. Nesta tese, nós propomos um novo modelo de RTN estatístico para descrever o ruído de baixa frequência em MOSFETs. Utilizando o modelo proposto, pode-se explicar e calcular o valor esperado e a variabilidade do ruído em função das polarizações, geometrias e dos parâmetros físicos do transistor. O modelo é validado através de inúmeros resultados experimentais para dispositivos com canais tipo n e p, e para diferentes tecnologias CMOS. É demonstrado que a estatística do ruído LFN dos dispositivos de canal tipo n e p podem ser descritos através do mesmo mecanismo. Através dos nossos resultados e do nosso modelo, nós mostramos que a densidade de armadilhas dos transistores de canal tipo p é fortemente dependente do nível de Fermi, enquanto para o transistor de tipo n a densidade de armadilhas pode ser considerada constante na energia. Também é mostrado e explicado, através do nosso modelo, o impacto do implante de halo nas estatísticas do ruído. Utilizando o modelo demonstra-se porque a variabilidade, denotado por σ[log(SId)], do RTN/LFN não segue uma dependência 1/√área; e fica demonstrado que o ruído, e sua variabilidade, encontrado em nossas medidas pode ser modelado utilizando parâmetros físicos. Além disso, o modelo proposto pode ser utilizado para calcular o percentil do ruído, o qual pode ser utilizado para prever ou alcançar certo rendimento do circuito. / Low Frequency Noise (LFN) and Random Telegraph Noise (RTN) are performance limiters in many analog and digital circuits. For small area devices, the noise power spectral density can easily vary by many orders of magnitude, imposing serious threat on circuit performance and possibly reliability. In this thesis, we propose a new RTN model to describe the statistics of the low frequency noise in MOSFETs. Using the proposed model, we can explain and calculate the Expected value and Variability of the noise as function of devices’ biases, geometry and physical parameters. The model is validated through numerous experimental results for n-channel and p-channel devices from different CMOS technology nodes. We show that the LFN statistics of n-channel and p-channel MOSFETs can be described by the same mechanism. From our results and model, we show that the trap density of the p-channel device is a strongly varying function of the Fermi level, whereas for the n-channel the trap density can be considered constant. We also show and explain, using the proposed model, the impact of the halo-implanted regions on the statistics of the noise. Using this model, we clarify why the variability, denoted by σ[log(SId)], of RTN/LFN doesn't follow a 1/√area dependence; and we demonstrate that the noise, and its variability, found in our measurements can be modeled using reasonable physical quantities. Moreover, the proposed model can be used to calculate the percentile quantity of the noise, which can be used to predict or to achieve certain circuit yield.

O Ruído de Baixa Frequência (LFN), tais como o ruído flicker e o Random Telegraph Noise (RTN), são limitadores de performance em muitos circuitos analógicos e digitais. Para transistores diminutos, a densidade espectral de potência do ruído pode variar muitas ordens de grandeza, impondo uma séria limitação na performance do circuito e também em sua confiabilidade. Nesta tese, nós propomos um novo modelo de RTN estatístico para descrever o ruído de baixa frequência em MOSFETs. Utilizando o modelo proposto, pode-se explicar e calcular o valor esperado e a variabilidade do ruído em função das polarizações, geometrias e dos parâmetros físicos do transistor. O modelo é validado através de inúmeros resultados experimentais para dispositivos com canais tipo n e p, e para diferentes tecnologias CMOS. É demonstrado que a estatística do ruído LFN dos dispositivos de canal tipo n e p podem ser descritos através do mesmo mecanismo. Através dos nossos resultados e do nosso modelo, nós mostramos que a densidade de armadilhas dos transistores de canal tipo p é fortemente dependente do nível de Fermi, enquanto para o transistor de tipo n a densidade de armadilhas pode ser considerada constante na energia. Também é mostrado e explicado, através do nosso modelo, o impacto do implante de halo nas estatísticas do ruído. Utilizando o modelo demonstra-se porque a variabilidade, denotado por σ[log(SId)], do RTN/LFN não segue uma dependência 1/√área; e fica demonstrado que o ruído, e sua variabilidade, encontrado em nossas medidas pode ser modelado utilizando parâmetros físicos. Além disso, o modelo proposto pode ser utilizado para calcular o percentil do ruído, o qual pode ser utilizado para prever ou alcançar certo rendimento do circuito. / Low Frequency Noise (LFN) and Random Telegraph Noise (RTN) are performance limiters in many analog and digital circuits. For small area devices, the noise power spectral density can easily vary by many orders of magnitude, imposing serious threat on circuit performance and possibly reliability. In this thesis, we propose a new RTN model to describe the statistics of the low frequency noise in MOSFETs. Using the proposed model, we can explain and calculate the Expected value and Variability of the noise as function of devices’ biases, geometry and physical parameters. The model is validated through numerous experimental results for n-channel and p-channel devices from different CMOS technology nodes. We show that the LFN statistics of n-channel and p-channel MOSFETs can be described by the same mechanism. From our results and model, we show that the trap density of the p-channel device is a strongly varying function of the Fermi level, whereas for the n-channel the trap density can be considered constant. We also show and explain, using the proposed model, the impact of the halo-implanted regions on the statistics of the noise. Using this model, we clarify why the variability, denoted by σ[log(SId)], of RTN/LFN doesn't follow a 1/√area dependence; and we demonstrate that the noise, and its variability, found in our measurements can be modeled using reasonable physical quantities. Moreover, the proposed model can be used to calculate the percentile quantity of the noise, which can be used to predict or to achieve certain circuit yield.

Abstract Cognitive Radio (CR) improves the efficiency of spectrum utilization by allowing non- licensed users to utilize bands when not occupied by licensed users. In this thesis, we address several challenges currently limiting the wide use of cognitive radios. These challenges include identification of unoccupied bands, energy consumption and other technical challenges. Improved accuracy edge detection techniques are developed for CR to mitigate both noise and estimation error variance effects. Next, a reduced complexity Simplified DFT (SDFT) is proposed for use in CR. Then, a sub-Nyquist rate A to D converter is introduced to reduce energy consumption. Finally, a novel multi-resolution PSD estimation based on expectation-maximization algorithm is introduced that can obtain a more accurate PSD within a specified sensing time.

The manufacturing of optical components, such as lenses or mirrors, consists of numeroussteps that are essential to the performance of the fnished optical system, such as the specifcation ofthe optical surface. For a longer period, the main focus has been in identifying and restricting thenegative effects of the low and high spatial frequency content of the surface. However, as technologyand optical equipment has become more advanced, the effects of the mid-spatial frequencies havebeen studied more, and continue to be a topic of research. As of now, there is still a need for methodsthat accurately predict and analyse the regime of mid-spatial frequencies, such that they can becontrolled during the specifcation phase, successfully limiting the need of post-processing steps.This work introduces a software tool, specifcally designed to approach this problem, which wasto be developed in Python as a contribution to the existing Optical Scripting Library at OHB. Byspecifying an optical component in terms of a Power Spectral Density function, together with thecontributions from different spatial frequency domains and the application of a ripple patterns, thissoftware tool can generate pseudo-random optical surfaces, which maintains the input specifcations.Furthermore, a Dynamic Link Library fle was developed, sharing the same functionality as thePython implementation, allowing for simulations using Zemax OpticStudio. Using the software tool,it was found that the relative error between input and output measurements were approximately0.78%, in terms of the Power Spectral Density Function. In addition, the result of analysing one of thetwo test cases indicate that the software tool is effective in predicting the infuence of mid-spatialfrequency errors, fulflling a previously measured predicition. The second test case proved that thesoftware tool can be used for mimicing surfaces of real measurements, holding the same specifcations.

Lawrence Livermore National Lab (LLNL) is home to the National Ignition Facility (NIF), the world’s largest and most energetic laser. Each of the 192 beamlines contains dozens of large optics, which require offline damage inspection using large, raster-scanning microscopes. The primary microscope used to measure and characterize the optical damage sites has a precision level of 1 µm. Mounted in a class 100 clean room with a raised tile floor, the microscope is supported by a steel stand that structurally connects the microscope to the concrete ground. Due to ambient vibrations experienced in the system, the microscope is only able to reliably reach a 10-µm level of precision. As NIF’s technology advances, there is a need to both increase optic measurement throughput and to measure damage sites at a higher level of precision. As a result, there is to be another microscope mounted into another clean room lab at LLNL. To assure the microscope can meet its specified level of precision, the stand on which it is mounted was designed to meet the rigorous Environmental Vibrational Criteria standards, or VC curves. Through the collection of random vibrational data using accelerometers and Power Spectral Density (PSD) analysis, the stand was designed to meet the VC-C curve requirement of velocities below 12.5 µm/sec. Furthermore, the stand design was optimized to avoid resonance at common vibrational signatures throughout the frequency spectrum, placing its first natural frequency at a sufficiently high level to minimize amplification.

Many research studies have analyzed the effect that whole-body vibration (WBV) has on sleep, and some have sought to use vibration to treat sleep disorders such as insomnia. It has been shown that low frequencies (f < 2Hz) are generally sleep inducing, but oscillations of this frequency are typically difficult to achieve using electromagnetic vibration drives. In the research that has been performed, optimal vibration parameters have not been determined, and the effects of multiple vibration sources vibrating at different frequencies to induce a low frequency traveling wave have not been explored. Insomnia affects millions of people worldwide, and non-pharmacological treatment options are limited. A bed excited with multiple vibration sources was used to explore beat frequency vibration as a non-pharmacological treatment for insomnia. A repeated measures design pilot study of 14 participants with mild-moderate insomnia symptom severity was conducted to determine the effects of beat frequency vibration, and traditional standing wave vibration on sleep latency and quality. Participants were monitored using high-density electroencephalography (HD-EEG). Sleep latency was compared between treatment conditions. Trends of a decrease in sleep latency due to beat frequency vibration were found (p ≤ 0.181 for AASM latency, and p ≤ 0.068 for unequivocal sleep latency). Neural complexity during wake, N1, and N2 stages were compared using Multi-Scale Sample Entropy (MSE), which demonstrated significantly lower MSE between wake and N2 stages (p ≤ 0.002). Lower MSE was found in the transition from wake to N1 stage sleep but did not reach significance (p ≤ 0.300). During N2 sleep, beat frequency vibration shows lower MSE than the control session in the left frontoparietal region. This indicates that beat frequency vibration may lead to a decrease of conscious awareness during deeper stages of sleep. Standing wave vibration caused reduced Alpha activity and increased Delta activity during wake. Beat frequency vibration caused increased Delta activity during N2 sleep. These preliminary results suggest that beat frequency vibration may help individuals with insomnia symptoms by decreasing sleep latency, by reducing their conscious awareness, and by increasing sleep drive expression during deeper stages of sleep. Standing wave vibration may be beneficial for decreasing expression of arousal and increasing expression of sleep drive during wake, implying that a dynamic vibration treatment may be beneficial. The application of vibration treatment as part of a heuristic sleep model is discussed.

No description available.

Encoding and consolidation of memories are two processes within the hippocampus, and connected cortical networks, that recruit different circuit level dynamics to effectively process and pass information from brain region to brain region. In the hippocampal CA1 pyramidal layer local field potential (LFP), these processes take the form of theta and sharp wave ripples (SPW-Rs) for encoding and consolidation, respectively. As an animal runs through an environment, neurons become active at specific locations in the environment (place cells) increasing their firing rate, functionally representing these specific locations. These firing rate increases are organized within the local theta oscillations and sequential activation of many place cells creates a map of the environment. Once the animal stops moving and begins consummatory behaviors, such as eating, drinking, or grooming, theta activity diminishes, and large irregular activity (LIA) begins to dominate the LFP. Spontaneously, with the LIA, the place cells active during the experience are replayed during SPW-Rs in the same spatial order they were encountered in the environment. Both theta and SPW-R oscillations and their associated neuronal firing are necessary for effective place recognition as well as learning and memory. As such, interruption or termination of SPW-R events results in decreased learning performance over days. During exploration, the associated theta and sequential place cell activity is thought to encode the experience. During quiet restfulness or slow wave sleep (SWS), SPW-R events, that replay experience specific place sequences, are thought to be the signal by which systems consolidation progresses and the hippocampus guides cortical synaptic reorganization. The granular retrosplenial cortex (gRSC) is an associational area that exhibits high frequency oscillations (HFOs) during both hippocampal theta and SPW-Rs, and is potentially a period when the gRSC interprets incoming content from the hippocampus during encoding and systems consolidation. However, the precise laminar organization of synaptic currents supporting HFOs, whether the local gRSC circuitry can support HFOs without patterned input, and the precise coupling of hippocmapla oscillations to gRSC HFOs across brain states remains unknown. We aimed to answer these questions using in vivo, awake electrophysiological recordings in head-fixed mice that were trained to run for water rewards in a 1D virtual environment. We show that gRSC synaptic currents supporting HFOs, across all awake brain states, are exclusively localized to layer 2/3 (L2/3), even when events are detected within layer 5 (L5). Using focal optogenetics, both L2/3 and L5 can generate induced HFOs given a strong enough broad stimulation. Spontaneous gRSC HFOs occurring outside of SPW-Rs are highly comodulated with medial entorhinal cortex (MEC) generated high gamma in hippocampal stratum lacunosum moleculare. gRSC HFOs may serve a necessary role in communication between the hippocampus during SPW-Rs states and between the hippocampus, gRSC, and MEC during theta states to support memory consolidation and memory encoding, respectively. / Doctor of Philosophy / As an animal moves through an environment, individual neurons in the hippocampus, known as place cells, increase and decrease their firing rate as the animal enters and exits specific locations in the environment. Within an environment, multiple neurons become active in different locations, this cooperation of spiking in various locations creates a place map of the environment. Now let's say when the animal moved from one corner of the environment to another, place cells 'A', 'C', 'B', 'E', and 'D' became active in that order. This means, at any given point in the environment, the animal is standing in a venn-diagram-esque overlap of place fields, or locations individual place cells represent. A key question that entranced researchers for many years was how do these neurons know when to be active to not impinge on their neighbor's locations? The answer to this question rested with population electrical activity, known as the local field potential (LFP), that place cell activity is paced to. During active navigation through an environment, place cells activity is coupled to the phase of a slow ~8 hertz (Hz) theta oscillation. Within one theta cycle, or peak to peak, multiple place cells are active, representing the venn diagram of location the animal is in. Importantly, this theta activity and encoding of place cell activity is largely seen during active running or rapid eye movement (REM) sleep. During slow wave sleep (SWS), after an animal has experienced a specific environment and has created a place map, place cells are reactivated in the same order the animal experienced them in. From our previous example, the content of this reactivation would be the place cells 'A', 'C', 'B', 'E', and 'D' which all would be reactivated in that same order. These reactivations or replays occur during highly synchronous and fast LFP oscillations known as sharp wave-ripples (SPW-Rs). SPW-Rs are thought to be a key LFP event that drives memory consolidation and the eventual conversion of short-term memory into long-term memory. However, for consolidation to occur, connected cortical regions need to be able to receive and interpret the information within SPW-Rs. The granular retrosplenial cortex (gRSC) is one proposed region that serves this role. During SPW-Rs the superficial gRSC has been shown to exhibit high frequency oscillations (HFOs), which potentially serve the purpose for interpreting SPW-R content. However, HFOs have been reported during hippocampal theta, suggesting HFOs serve multiple purposes in interregional communication across different states. In this study, we found that naturally occurring gRSC HFOs occur exclusively in layer 2/3 across all awake brain states. Using focal optogenetic excitation we were able to evoke HFOs in both layer 2/3 and 5. Spontaneous gRSC HFOs occurring without SPW-Rs were highly comodulated with medial entorhinal cortex (MEC) generated high gamma in hippocampal stratum lacunosum moleculare. gRSC HFOs may serve a general role in supporting hippocampo-cortical dialogue during SPW-R and theta brain states to support memory consolidation and encoding, respectively.

The spectral analysis of heart rate variability is a tool that gained more and more clinical importance in the latest years. It can be used in order to access autonomic function on the cardiovascular system through the evaluation of the different frequency bands of the HRV. So far different mathematical approaches have been used towards this aim, often with contradictory results. Therefore, the need for standardization of the methods seems more and more important. In this thesis 2 non-parametric, Fourier-based methods and two parametric based on autoregressive modeling were used in order to extract the power spectral density of patients with epilepsy. Their results were statistically compared to age matched controls. The analysis have shown that when a parametric method is used, a careful model order selection method must be used, and when this is accomplished, the power spectrum could more efficient highlight differences between controls and patients. The results between non-parametric and parametric methods were different, therefore these methods cannot be considered interchangeable. The analysis methodolgy established in this first part of the study has been used to analyse HRV signals from patients before and after deep brain stimulation. / Η φασματική ανάλυση της Μεταβλητότητας της Καρδιακής Συχνότητας (ΜΚΣ) χρησιμοποείται όλο και περισσότερο σε κλινικές μελέτες τα τελευταία χρόνια. Και αυτό γιατί μπορεί να δώσει πληροφορίες σχετικά με την λειτουργία του αυτόνομου νευρικού συστήματος πάνω στην καρδιά αναλύοντας το συχνοτικό περιεχόμενο των ΜΚΣ σημάτων σε διακριτές ζώνες συχνοτήτων. Μέχρι τώρα διαφορετικές μαθηματικές μέθοδοι έδωσαν διαφορετικά, συχνα αντικρουόμενα αποτελέσματα. Έτσι η ανάγκη λεπτομερής περιγραφής των μεθόδων φαίνεται όλο και περισσοτερο επιτακτική. Σε αυτή τη διπλωματική εργασία, δυο μη παραμετρικές μέθοδοι και δύο παραμετρικές βασισμένες σε μοντέλα αυτοπαλινδρόμησης (autoregressive modeling) εφαρμόστηκαν προκειμένου να υπολογιστεί το φάσμα ασθενών με χρόνια επιληψία. Τα αποτελέσματα συγκρίθηκαν με υγιείς εθελοντές ίδιου ηλικιακού προφίλ. Η ανάλυση έδειξε ότι όταν χρησιμοποιουνται παραμετρικές μέθοδοι, η επιλογή της τάξης του μοντέλου πρέπει να γίνεται με προσοχή και όταν αυτό γίνει, το φάσμα μπορεί να αναδείξει πιο αποτελεσματικά διαφορές μεταξύ ασθενών και υγειών εθελοντών. Τα αποτελέσματα μεταξύ παραμετρικών και μη παραμετρικών μεθόδων αποδείχθηκαν διαφορετικα, και κατά συνέπεια οι δύο αυτές κατηγορίες ανάλυσης δεν μπορούν να θεωρηθούν ίδιες. Η μεθοδολογία που αναπτύχθηκε στο πρώτο αυτό μέρος της εργασίας χρησιμοποιήθηκε για να αναλύσει σήματα ΜΚΣ από ασθενείς με Πάρκινσον πριν και μετά εν τω βάθει ερεθισμό (Deep brain simulation).