ABSTRACT Detection of and discrimination between different nanoparticles and biomolecules are vital steps in analytical, biochemical, and diagnostic biomedical procedures used in life sciences. Synthetic micro/nanopores in solid-state membranes form an emerging class of single-molecule detectors capable of detecting and probing the properties of particles and biomolecules with high throughput and resolution: The particles or biomolecules to be analyzed are added to an electrolyte solution in one of the two reservoirs of the detector system separated by a thin membrane containing a single micro/nanopore. An outer electric field induces an open-pore ionic current (Iopen) through the pore, dragging the particles with itself. Transient changes occur when a particle slightly smaller than the pore translocates through the pore. This electrical signal can be analyzed to derive information regarding to the particle or biomolecule size and even its morphology, concentration in the solution, and the affinity for the pore. Many detectors are based on self-assembled, naturally occurring protein pores in lipid bilayer membranes. Most solid-state pore-based detectors reported in literature use artificial pores in silicon nitride or silicon oxide membranes. Applying polymers as a membrane potentially offers advantages over the aforementioned types, including good electrical insulation, improved wettability thanks to higher hydrophilicity, and long-term stable yet low-cost and disposable devices. The present study aims at exploiting such advantages by developing the proof-of-concept for a single-material, all-polymer, nanopore detector allowing the continuous variation of target pore size in the range from micrometers to a few nanometers for best pore size adaption to the biomolecules to be investigated. The research comprises materials selection, system design, development of a fabrication and assembly sequence, device fabrication, and functional device testing. Poly (methyl methacrylate) (PMMA) was selected as it combines advantageous microfluidic properties know from competing materials, such as polyimide, polystyrene, polycarbonate, or polyethylene terephthalate, with outstanding micropatterning capabilities. The membrane thickness is set to be 1 µm, based on a compromise between robustness during fabrication and operation on one side, and electrochemical performance on the other. After spincoating the membrane onto a sacrificial wafer, pores with diameters of typically several hundred nanometers are patterned by electron beam lithography. In combination with thermal post processing leading to polymer reflow, diameters one order of magnitude smaller can be achieved. The present study focuses on 450 nm and 22 nm pores, respectively. Besides these pores fabricated in a top-down approach, self-assembled -hemolysin protein pores of 1.5 nm diameter are integrated in a combined top-down and bottom-up approach so that single digit, double digit, and triple digit nanometer pores are available. Systems integration is achieved by capillary-forced based release from the sacrificial substrate and the application of UV-initiated glue. Test sequences proved and qualified the device functionality: Electrical characterization was performed in aqueous KCl electrolyte solution. The devices exhibit a stable, time-independent ionic current. The current-voltage curves are linear and scale with the electrolyte concentration. System verification was performed using silica nanospheres of 100 nm and 150 nm diameter as known test particles. Translocation through a 450 nm pore induced current blockades for about 1 ms with an amplitude of 30 pA to 55 pA for 100 nm particles and in excess of 70 pA for 150 nm particles. This is in close agreement with results obtained by a mathematical model used in this study. Biomolecules relevant to many life science applications, double-stranded DNA (dsDNA) and bovine serum albumin (BSA) were subsequently analyzed to prove the device concept. Post-processed pores of 22 nm diameter were used at 600 mV driving voltage and 0.1 molar electrolyte in a slightly acidic regime of pH = 6. Typical current blockade amplitudes for complete translocations of dsDNA are Iblock = 22 pA for a translocation time of tD = 0.2 ms, and an almost threefold current blockade (Iblock = 60 pA) for the larger BSA molecules, respectively. The results demonstrate that the PMMA-based nanopores are sensitive enough to not only detect translocating biomolecules, but to also sense them by distinguishing between different biomolecules. The molecule-specific and distinct translocation signals through the pores using both, standardized silica nanoparticles and biomolecules of different dimensions, prove the concept of an all-PMMA electrophoretic flow detector with adjustable pore diameters. Devices with pore diameters covering three orders of magnitude in the nanometer range were successfully built, tested, and characterized. The results suggest such detectors are promising candidates for biomolecule detecting applications.

A análise de sinais mioelétricos provenientes do tubo gastro-intestinal de animais de laboratório (ratos), conseguidos por meio de eletrodos cronicamente implantados, é peça-chave no entendimento das desordens associadas ao sistema digestivo. Esta análise enfrenta consideráveis dificuldades quando realizadas por métodos clássicos, em especial os baseados na transformada de Fourier. A interação de várias componentes mioelétricas torna muito complicado e trabalhoso o acompanhamento destes sinais ao longo do tubo digestivo e a obtenção de parâmetros típicos como a velocidade de propagação entre eletrodos. Estuda-se aqui uma alternativa mais nova e promissora: a transformada Wavelet. Utilizando esta ferramenta matemática, torna-se possível obter uma melhor resolução tempo-freqüencial dos sinais estudados, permitindo encontrar padrões referentes à propagação do sinal mesmo em leituras ruidosas e compostas de várias freqüências. Foram analisados 82 leituras de 9 animais normais do Laboratório de Investigação Médica da Faculdade de Medicina da USP, sendo possível determinar dois parâmetros: a velocidade de propagação média entre eletrodos (cerca de 1.2 cm/s) e as componentes principais da freqüência basal (0.63 e 0.65 Hz). / The analysis of myoelectric signals from the gastro-intestinal tube of laboratory animals (mice), recorded by chronically implanted electrodes, is a key stone in understanding the disorders associated to the digestive system. This analysis meets considerable difficulties when done by classical methods, specially those based in the Fourier transform. The many myoelectric components interactions makes the following of these signals along the digestive tract and the retrieval of typical parameters (such as the propagation velocity between electrodes) a very complicated and laborious task. Here is studied a newer and more promising alternative: the Wavelet transform. Using this mathematical tool, it becomes possible to obtain a better time-frequency resolution of the studied signals, allowing to find patterns related to the signal propagation even in noisy and multifrequencial readings. 82 readings from 9 normal animals belonging to the Medical Investigation Laboratory of the Medicine Faculty of University of São Paulo were analyzed, becoming feasible to determine two parameters: the mean propagation velocity between electrodes (about 1.2 cm/s) and the main components of the basal frequency (0.63 e 0.65 Hz).

This work deal with analysis of LoRa wireless communication protocol and LoRaWAN MAC layer. Analysis aims to LoRa packet detection using software defined radio, wireless trafic and information which can be extracted (sniffed) from wireless communication between devices which uses LoRaWAN MAC layer. Next part of this work describes two wireless devices development. First one for demonstration purporses with some type of commercial LoRa modules and second to paralell sniffing of LoRa wireless communication on various frequency channels.

Les performances des plasmas de fusion confinés magnétiquement peuvent être dégradées par l'accumulation d'impuretés. Plus particulièrement, les impuretés lourdes accumulées au centre du plasma diluent les réactifs, et peuvent aussi conduire à un collapse radiatif du plasma par de fortes pertes par rayonnement. La compréhension du transport des impuretés lourdes produites lors de l'interaction plasma-paroi est donc devenue cruciale.Le coeur du plasma est sujet à une instabilité magnétohydrodynamique (MHD) appelée ‘kink interne’, conduisant à des oscillations de relaxation nommées ‘dents de scie’. Les dents de scie entraînent une relaxation périodique de densité et de température dans le coeur du plasma, et affectent significativement le transport radial. Notamment, les particules et la chaleur sont redistribuées pendant un crash dont la durée est très courte par rapport au temps de confinement.En l'absence des instabilités MHD, le transport des impuretés est porté par les collisions (transport néoclassique) et la turbulence. Il est établi que le transport néoclassique est important pour les impuretés lourdes dans la région centrale du plasma de tokamak. Cependant, des mesures expérimentales du tokamak ASDEX-Upgrade montrent que la dynamique des impuretés en présence des dents de scie est différente des prédictions faites par les codes de transport.Dans cette thèse, l'outil numérique utilisé pour simuler les dents de scie est le code XTOR-2F, qui est un code non-linéaire tridimensionnel résolvant les équations de la MHD. Les équations fluides modélisant le transport des impuretés dans un régime de collisionalité élevée (Pfirsch-Schlüter) ont été implémentées et couplées avec l'ensemble des équations de la MHD bi-fluide.Les simulations montrent que les profils de densité d'impuretés sont affectés par les dents de scie, en accord avec les observations expérimentales. Ceci résulte d'une compétition entre processus néoclassiques et relaxations dues aux dents de scie. / Impurity accumulation can degrade the performance of magnetically confined fusion plasmas. In particular, heavy impurities accumulated in the core plasma dilute fusion reactants and may also lead to a radiative collapse of the plasma due to excessive cooling by radiation. Therefore, understanding the transport of heavy impurities produced by plasma-wall interaction has become a subject of utmost importance.The plasma core is likely to be affected by a magnetohydrodynamic (MHD) instability called 'internal kink' that induces relaxation oscillations named 'sawteeth'. Sawteeth are responsible for periodic relaxations of the core density and temperature and affect significantly the radial transport. Especially, particles and heat are redistributed during the crash phase the duration of which is short compared to the confinement time.In absence of MHD instabilities, impurity transport is governed by collisions (neoclassical transport) and turbulence. It is shown that neoclassical transport is important for heavy impurities in the core region of tokamak plasmas. Meanwhile, experimental measurements in the ASDEX-Upgrade tokamak show that the impurity dynamics in presence of sawteeth differs from the predictions made by transport codes.In this thesis, the numerical tool used to simulate sawteeth is the XTOR-2F code, which is a non-linear tridimensional code solving MHD equations. Fluid equations that model the transport of impurities in a highly collisional (Pfirsch-Schlüter) regime have been implemented and coupled to the set of two-fluid MHD equations.The simulations show a difference between the impurity profiles with and without sawteeth, which is consistent with experimental observations. This results from a competition between neoclassical processes and sawtooth relaxations.

Pulse-shaped signals require a tool that can accurately analyse and identify the peak characteristics in the spectrum. One recently developed tool available to analyse non-stationary pulse-shaped waveforms with a suitable peak reconstruction is semiclassical signal analysis (SCSA). SCSA is a signal representation method that decomposes a real positive signal y(t) into a set of squared eigenfunctions through the discrete spectrum of the Schr¨odinger operator. In this study, we apply SCSA in two directions. First, we propose a new signal denoising method based on the signal curvature. We use this technique to show that denoising the pulse-shaped signal by regularizing its curvature can yield better peak-preserving performance than traditional filters, such as moving average filter or wavelet. Second, we apply SCSA to biomedical signal analysis. The localization abilities of L2 normalized squared eigenfunctions are used in blood pressure (BP) estimation. Based on existing properties, the systolic and diastolic phases are separated into photoplethysmograms (PPGs), which are then used as features for BP estimation. In addition, the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) database is used to test the application with more than 8000 subjects. Another application uses SCSA features to characterize EEG and MEG signals, leading to more accurate epileptic spike detection and diagnosis in epileptic patients. Both applications are validated using real datasets, which guarantees statistical reliability and motivates future work of this model in clinical applications and equipment designs.

We consider real zero-crossing analysis of the real/imaginary parts of the spectrum, namely, spectral zero-crossings (SZCs). The two major contributions are to show that: (i) SZCs provide enable temporal localization of transients; and (b) SZCs are suitable for modeling transient signals. We develop a spectral dual of Kedem’s result linking temporal zero-crossing rate (ZCR) to the spectral centroid. The key requirement is stationarity, which we achieve through random-phase modulations of the time-domain signal. Transient signals are not amenable to modelling in the time domain since they are bursts of energy localized in time and lack structure. We show that the spectrum of transient signals have a rich modulation structure, which leads to an amplitude-modulation – frequency-modulation (AM-FM) model of the spectrum. We generalize Kedem’s arc-cosine formula for lags greater than one. For the specific case of a sinusoid in white Gaussian noise, He and Kedem devised an iterative filtering algorithm, which leads to a contraction mapping. An autoregressive filter of order one is employed and the location of the pole is the parameter that is updated based on the filtered output. We use the higher-order property, which relates the autocorrelation to the expected ZCR of the filtered process, between lagged ZCR and higher-lag autocorrelation to develop an iterative higher-order autoregressive-filtering scheme, which stabilizes the ZCR and consequently provides robust estimates of the autocorrelation at higher lags. Next, we investigate ZC properties of critically sampled outputs of a maximally decimated M-channel power complementary analysis filterbank (PCAF) and derive the relationship between the ZCR of the input Gaussian process at lags that are integer multiples of M in terms of the subband ZCRs. Based on this result, we propose a robust autocorrelation estimator for a signal consisting of a sum of sinusoids of fixed amplitudes and uniformly distributed random phases. Robust subband ZCRs are obtained through iterative filtering and the subband variances are estimated using the method-of-moments estimator. We compare the performance of the proposed estimator with the sample auto-correlation estimate in terms of bias, variance, and mean-squared error, and show through simulations that the performance of the proposed estimator is better than the sample auto- correlation for medium to low SNR. We then consider the ZC statistics of the real/imaginary parts of the discrete Fourier spectrum. We introduce the notion of the spectral zero-crossing rate (SZCR) and show that, for transients, it gives information regarding the location of the transient. We also demonstrate the utility of SZCR to estimate interaural time delay between the left and right head-related impulse responses. The accuracy of interaural time delay plays a vital role in binaural synthesis and a comparison of the performance of the SZCR estimates with that of the cross-correlation estimates illustrate that spectral zeros alone contain enough information for accurately estimating interaural time delay. We provide a mathematical formalism for establishing the dual of the link between zero-crossing rate and spectral centroid. Specifically, we show that the expected SZCR of a stationary spectrum is a temporal centroid. For a deterministic sequence, we obtain the stationary spectrum by modulating the sequence with a random phase unit amplitude sequence and then computing the spectrum. The notion of a stationary spectrum is necessary for deriving counterparts of the results available in temporal zero-crossings literature. The robustness of location information embedded in SZCR is analyzed in presence of a second transient within the observation window, and also in the presence of additive white Gaussian noise. A spectral-domain iterative filtering scheme based on autoregressive filters is presented and improvement in the robustness of the location estimates is demonstrated. As an application, we consider epoch estimation in voiced speech signals and show that the location information is accurately estimated using spectral zeros than other techniques. The relationship between temporal centroid and SZCR also finds applications in frequency-domain linear prediction (FDLP), which is used in audio compression. The prediction coefficients are estimated by solving the Yule-Walker equations constructed from the spectral autocorrelation. We use the relationship between the spectral autocorrelation and temporal centroid to obtain the spectral autocorrelation directly by time-domain windowing without explicitly computing the spectrum. The proposed method leads to identical results as the standard FDLP method but with reduced computational load. We then develop a SZCs-based spectral-envelope and group-delay (SEGD) model, which finds applications in modelling of non-stationary signals such as Castanets. Taking into account the modulation structure and spectral continuity, local polynomial regression is performed to estimate the GD from the real spectral zeros. The SE is estimated based on the phase function computed from the estimated GD. Since the GD estimate is parametric, the degree of smoothness can be controlled directly. Simulation results based on synthetic transient signals are presented to analyze the noise-robustness of the SE-GD model. Applications to castanet modeling, transient compression, and estimation of the glottal closure instants in speech are shown.

<p>Condition monitoring of critical machine tool components and machining processes is a key factor to increase the availability of the machine tool and achieving a more robust machining process. Failures in the machining process and machine tool components may also have negative effects on the final produced part. Instabilities in machining processes also shortens the life time of the cutting edges and machine tool.</p><p>The condition monitoring system may utilise information from several sources to facilitate the detection of instabilities in the machining process. To avoid additional complexity to the machining system the use of internal sensors is considered. The focus in this thesis has been to investigate if information related to the machining process can be extracted directly from the internal sensors of the machine tool.</p><p>The main contibutions of this work is a further understanding of the direct response from both linear and angular position encoders due the variations in the machining process. The analysis of the response from unbalance testing of turn tables and two types of milling processes, i.e. disc-milling and slot-milling, is presented. It is shown that operational frequencies, such as cutter frequency and tooth-passing frequency, can be extracted from both active and inactive machine axes, but the response from an active machine axis involves a more complex analysis. Various methods for the analysis of the responses in time domain, frequency domain and phase space are presented.</p> / QC 20100518

Decision making can be a complicated process involving perception of the present situation, past experience and knowledge necessary to foresee a better future. This cognitive process is one of the essential human ability that is required from everyday walk of life to making major life choices. Although it may seem ambiguous to translate such a primitive process into quantifiable science, the goal of this thesis is to break it down to signal processing and quantifying the thought process with prominence of EEG signal power variance. This paper will discuss the cognitive science, the signal processing of brain signals and how brain activity can be quantifiable through data analysis. An experiment is analyzed in this thesis to provide evidence that theta frequency band activity is associated with stress and stress is negatively correlated with concentration and problem solving, therefore hindering decision making skill. From the results of the experiment, it is seen that theta is negatively correlated to delta and beta frequency band activity, thus establishing the fact that stress affects internal focus while carrying out a task.

Traditionally robots are controlled using devices like joysticks, keyboards, mice and other similar human computer interface (HCI) devices. Although this approach is effective and practical for some cases, it is restrictive only to healthy individuals without disabilities, and it also requires the user to master the device before its usage. It becomes complicated and non-intuitive when multiple robots need to be controlled simultaneously with these traditional devices, as in the case of Human Swarm Interfaces (HSI). This work presents a novel concept of using human bio-signals to control swarms of robots. With this concept there are two major advantages: Firstly, it gives amputees and people with certain disabilities the ability to control robotic swarms, which has previously not been possible. Secondly, it also gives the user a more intuitive interface to control swarms of robots by using gestures, thoughts, and eye movement. We measure different bio-signals from the human body including Electroencephalography (EEG), Electromyography (EMG), Electrooculography (EOG), using off the shelf products. After minimal signal processing, we then decode the intended control action using machine learning techniques like Hidden Markov Models (HMM) and K-Nearest Neighbors (K-NN). We employ formation controllers based on distance and displacement to control the shape and motion of the robotic swarm. Comparison for ground truth for thoughts and gesture classifications are done, and the resulting pipelines are evaluated with both simulations and hardware experiments with swarms of ground robots and aerial vehicles.

"This compendium-format dissertation (i.e., comprised mostly of published and in-process articles) primarily reports on system identification methods that relate the surface electromyogram (EMG)—the electrical activity of skeletal muscles—to mechanical kinetics. The methods focus on activities of the elbow and hand-wrist. The relationship between the surface EMG and joint impedance was initially studied. My work provided a complete second-order EMG-based impedance characterization of stiffness, viscosity and inertia over a complete range of nominal torques, from a single perturbation trial with slowly varied torque. A single perturbation trial provides a more convenient method for impedance evaluation. The RMS errors of the EMG-based method were 20.01% for stiffness and 7.05% for viscosity, compared with the traditional mechanical measurement. Three projects studied the relationship between EMG and force/torque, a topic that has been studied for a number of years. Optimal models use whitened EMG amplitude, combining multiple EMG channels and a polynomial equation to describe this relationship. First, we used three techniques to improve current models at the elbow joint. Three more features were extracted from the EMG (waveform length, slope sign change rate and zero crossing rate), in addition to EMG amplitude. Each EMG channel was used separately, compared to previous studies which combined multiple channels from biceps and, separately, from triceps muscles. Finally, an exponential power law model was used. Each of these improvement techniques showed better performance (P<0.05 and ~0.7 percent maximum voluntary contraction (%MVC) error reduction from a nominal error of 5.5%MVC) than the current “optimal” model. However, the combination of pairs of these techniques did not further improve results. Second, traditional prostheses only control 1 degree of freedom (DoF) at a time. My work provided evidence for the feasibility of controlling 2-DoF wrist movements simultaneously, with a minimum number of electrodes. Results suggested that as few as four conventional electrodes, optimally located about the forearm, could provide 2-DoF simultaneous, independent and proportional control with error ranging from 9.0–10.4 %MVC, which is similar to the 1-DoF approach (error from 8.8–9.8 %MVC) currently used for commercial prosthesis control. The third project was similar to the second, except that this project studied controlling a 1-DoF wrist with one hand DoF simultaneously. It also demonstrated good performance with the error ranging from 7.8-8.7 %MVC, compared with 1-DoF control. Additionally, I participated in two team projects—EMG decomposition and static wrist EMG to torque—which are described herein. "