Fractal descriptors based on Fourier spectrum applied to texture analysis

This work proposes the development and study of a novel technique lot the generation of fractal descriptors used in texture analysis. The novel descriptors are obtained from a multiscale transform applied to the Fourier technique of fractal dimension calculus. The power spectrum of the Fourier transform of the image is plotted against the frequency in a log-log scale and a multiscale transform is applied to this curve. The obtained values are taken as the fractal descriptors of the image. The validation of the proposal is performed by the use of the descriptors for the classification of a dataset of texture images whose real classes are previously known. The classification precision is compared to other fractal descriptors known in the literature. The results confirm the efficiency of the proposed method. (C) 2012 Elsevier B.V. All rights reserved.

Mitochondrial localization and structure-based phosphate activation mechanism of Glutaminase C with implications for cancer metabolism

Glutamine is an essential nutrient for cancer cell proliferation, especially in the context of citric acid cycle anaplerosis. In this manuscript we present results that collectively demonstrate that, of the three major mammalian glutaminases identified to date, the lesser studied splice variant of the gene gls, known as Glutaminase C (GAC), is important for tumor metabolism. We show that, although levels of both the kidney-type isoforms are elevated in tumor vs. normal tissues, GAC is distinctly mitochondrial. GAC is also most responsive to the activator inorganic phosphate, the content of which is supposedly higher in mitochondria subject to hypoxia. Analysis of X-ray crystal structures of GAC in different bound states suggests a mechanism that introduces the tetramerization-induced lifting of a "gating loop" as essential for the phosphate-dependent activation process. Surprisingly, phosphate binds inside the catalytic pocket rather than at the oligomerization interface. Phosphate also mediates substrate entry by competing with glutamate. A greater tendency to oligomerize differentiates GAC from its alternatively spliced isoform and the cycling of phosphate in and out of the active site distinguishes it from the liver-type isozyme, which is known to be less dependent on this ion.

Shape resonance spectrum of cytosine-guanine pairs.

Single and double strand breaks in DNA can be caused by low-energy electrons, the most abundant secondary products of the interaction of ionizing radiation to the biological matter. Attachment of these electrons to biomolecules lead to the formation of transient negative ions (TNIs) [1], often referred to as resonances, a process that may lead to significant vibrational excitation and dissociation. In the present study, we employ the parallel version [2] of the Schwinger Multichannel Method implemented with pseudopotentials [3] to obtain the shape resonance spectrum of cytosine-guanine (CG) pairs, with special attention to π* transient anion states. Recent experimental studies pointed out a quasi-continuum vibrational excitation spectrum for electron collisions against formic acid dimers [4], suggesting that electron attachment into π* valence orbitals could induce proton transfer in these dimers. In addition, our previous studies on the shape resonance spectra of the hydrogen-bonded complexes comprising formic acid and formamide units indicated interesting electron delocalization (localization) effects arising from the presence (absence) of inversion symmetry centers in the complexes [5]. In the present work, we extend the studies on hydrogen-bonded complexes to the CG pair, where localization of ¼¤ anions would be expected, based on the previous results. References [1]. B. Boudaïffa, P. Cloutier, D. Hunting, M. A. Huels, L. Sanche, Science 287, 1658 (2000). [2]. J. S. dos Santos, R. F. da Costa , M. T. do N. Varella, J. Chem. Phys. 136, 084307 (2012). [3]. M. H. F. Bettega, L. G. Ferreira, M. A. P. Lima, Phys. Rev. A 47, 1111 (1993). [4]. M. Allan, Phys. Rev. Lett. 98, 123201 (2007). [5]. T. C. Freitas, S. dA. Sanchez, M. T. do N. Varella, M. H. F. Bettega, Phys. Rev. A 84, 062714 (2011).

A low complexity system based on multiple weighted decision trees for indoor localization

[EN] Indoor position estimation has become an attractive research topic due to growing interest in location-aware services. Nevertheless, satisfying solutions have not been found with the considerations of both accuracy and system complexity. From the perspective of lightweight mobile devices, they are extremely important characteristics, because both the processor power and energy availability are limited. Hence, an indoor localization system with high computational complexity can cause complete battery drain within a few hours. In our research, we use a data mining technique named boosting to develop a localization system based on multiple weighted decision trees to predict the device location, since it has high accuracy and low computational complexity.

Spectrum Sensing Algorithms for Cognitive Radio Applications

Future wireless communications systems are expected to be extremely dynamic, smart and capable to interact with the surrounding radio environment. To implement such advanced devices, cognitive radio (CR) is a promising paradigm, focusing on strategies for acquiring information and learning. The first task of a cognitive systems is spectrum sensing, that has been mainly studied in the context of opportunistic spectrum access, in which cognitive nodes must implement signal detection techniques to identify unused bands for transmission. In the present work, we study different spectrum sensing algorithms, focusing on their statistical description and evaluation of the detection performance. Moving from traditional sensing approaches we consider the presence of practical impairments, and analyze algorithm design. Far from the ambition of cover the broad spectrum of spectrum sensing, we aim at providing contributions to the main classes of sensing techniques. In particular, in the context of energy detection we studied the practical design of the test, considering the case in which the noise power is estimated at the receiver. This analysis allows to deepen the phenomenon of the SNR wall, providing the conditions for its existence and showing that presence of the SNR wall is determined by the accuracy of the noise power estimation process. In the context of the eigenvalue based detectors, that can be adopted by multiple sensors systems, we studied the practical situation in presence of unbalances in the noise power at the receivers. Then, we shift the focus from single band detectors to wideband sensing, proposing a new approach based on information theoretic criteria. This technique is blind and, requiring no threshold setting, can be adopted even if the statistical distribution of the observed data in not known exactly. In the last part of the thesis we analyze some simple cooperative localization techniques based on weighted centroid strategies.

Diagnosis and Fault detection in Electrical Machines and Drives based on Advanced Signal Processing Techniques

In the present thesis, a new methodology of diagnosis based on advanced use of time-frequency technique analysis is presented. More precisely, a new fault index that allows tracking individual fault components in a single frequency band is defined. More in detail, a frequency sliding is applied to the signals being analyzed (currents, voltages, vibration signals), so that each single fault frequency component is shifted into a prefixed single frequency band. Then, the discrete Wavelet Transform is applied to the resulting signal to extract the fault signature in the frequency band that has been chosen. Once the state of the machine has been qualitatively diagnosed, a quantitative evaluation of the fault degree is necessary. For this purpose, a fault index based on the energy calculation of approximation and/or detail signals resulting from wavelet decomposition has been introduced to quantify the fault extend. The main advantages of the developed new method over existing Diagnosis techniques are the following: - Capability of monitoring the fault evolution continuously over time under any transient operating condition; - Speed/slip measurement or estimation is not required; - Higher accuracy in filtering frequency components around the fundamental in case of rotor faults; - Reduction in the likelihood of false indications by avoiding confusion with other fault harmonics (the contribution of the most relevant fault frequency components under speed-varying conditions are clamped in a single frequency band); - Low memory requirement due to low sampling frequency; - Reduction in the latency of time processing (no requirement of repeated sampling operation).

Fault detection, diagnosis and active fault tolerant control for a satellite attitude control system

Modern control systems are becoming more and more complex and control algorithms more and more sophisticated. Consequently, Fault Detection and Diagnosis (FDD) and Fault Tolerant Control (FTC) have gained central importance over the past decades, due to the increasing requirements of availability, cost efficiency, reliability and operating safety. This thesis deals with the FDD and FTC problems in a spacecraft Attitude Determination and Control System (ADCS). Firstly, the detailed nonlinear models of the spacecraft attitude dynamics and kinematics are described, along with the dynamic models of the actuators and main external disturbance sources. The considered ADCS is composed of an array of four redundant reaction wheels. A set of sensors provides satellite angular velocity, attitude and flywheel spin rate information. Then, general overviews of the Fault Detection and Isolation (FDI), Fault Estimation (FE) and Fault Tolerant Control (FTC) problems are presented, and the design and implementation of a novel diagnosis system is described. The system consists of a FDI module composed of properly organized model-based residual filters, exploiting the available input and output information for the detection and localization of an occurred fault. A proper fault mapping procedure and the nonlinear geometric approach are exploited to design residual filters explicitly decoupled from the external aerodynamic disturbance and sensitive to specific sets of faults. The subsequent use of suitable adaptive FE algorithms, based on the exploitation of radial basis function neural networks, allows to obtain accurate fault estimations. Finally, this estimation is actively exploited in a FTC scheme to achieve a suitable fault accommodation and guarantee the desired control performances. A standard sliding mode controller is implemented for attitude stabilization and control. Several simulation results are given to highlight the performances of the overall designed system in case of different types of faults affecting the ADCS actuators and sensors.

NLGA based Active Control in Aerospace: fault tolerability, disturbance rejection, and parameter estimation

A new control scheme has been presented in this thesis. Based on the NonLinear Geometric Approach, the proposed Active Control System represents a new way to see the reconfigurable controllers for aerospace applications. The presence of the Diagnosis module (providing the estimation of generic signals which, based on the case, can be faults, disturbances or system parameters), mean feature of the depicted Active Control System, is a characteristic shared by three well known control systems: the Active Fault Tolerant Controls, the Indirect Adaptive Controls and the Active Disturbance Rejection Controls. The standard NonLinear Geometric Approach (NLGA) has been accurately investigated and than improved to extend its applicability to more complex models. The standard NLGA procedure has been modified to take account of feasible and estimable sets of unknown signals. Furthermore the application of the Singular Perturbations approximation has led to the solution of Detection and Isolation problems in scenarios too complex to be solved by the standard NLGA. Also the estimation process has been improved, where multiple redundant measuremtent are available, by the introduction of a new algorithm, here called "Least Squares - Sliding Mode". It guarantees optimality, in the sense of the least squares, and finite estimation time, in the sense of the sliding mode. The Active Control System concept has been formalized in two controller: a nonlinear backstepping controller and a nonlinear composite controller. Particularly interesting is the integration, in the controller design, of the estimations coming from the Diagnosis module. Stability proofs are provided for both the control schemes. Finally, different applications in aerospace have been provided to show the applicability and the effectiveness of the proposed NLGA-based Active Control System.

Dynamics of small interfering RNA - FRET based integrity measurements of siRNA in the cuvette & inside cells

RNAi ist ein bedeutendes Werkzeug zur Funktionsanalyse von Genen und hat großes Potential für den Einsatz in der Therapie. Obwohl effiziente Knockdowns in der Zellkultur erzielt werden, erweist sich eine in vivo Anwendung als schwierig. Die großen Hürden sind dabei der Transport der siRNA ins Zielgewebe und deren voranschreitende Degradierung.rnMarkierte siRNA kann sowohl zur eigenen Integritätsmessung als auch zur Lokalisierung verwendet werden. Zwei Farbstoffe an den jeweiligen 3’- bzw. -5’-Enden des Sense- bzw. Antisense-Stranges erzeugen ein robustes FRET-System (Hirsch et al. 2012). Das Verhältnis von FRET- zu Donor-Signal, das R/G-Ratio, dient zur sensitiven Klassifizierung des Integritätslevels einer siRNA Probe (Järve et al. 2007; Hirsch et al. 2011; Kim et al. 2010). Mit diesem System kann eine Degradierung von weniger als 5 % in der Küvette und in Zellen nachgewiesen werden.rnDie vorliegende Arbeit beschäftigt sich mit der Evaluierung von potentiellen FRET Farbstoffpaaren hinsichtlich deren Eignung für in vitro und in vivo Anwendung. Verschiedenste FRET-Paare, die das gesamte sichtbare Spektrum abdecken, wurden evaluiert und ermöglichen nun die Auswahl eines geeigneten Paares für die jeweilige Anwendung oder Kombination mit anderen Farbstoffen.rnMit Hilfe von Alexa555/Atto647N siRNA wurde ein erfolgreicher Einschluss von siRNA in Liposomen beobachtet. Eine anschließende Evaluierung der RNase-Protektion ergab für Liposomen, Nanohydrogele und kationische Peptide hervorragende protektive Eigenschaften. Basierend auf den Ergebnisse können diese und andere Transportsysteme nun für eine zelluläre Aufnahme optimiert werden.rnAtto488/Atto590 zeigte die besten Eigenschaften für Echtzeit-Integritätsmessungen in der Lebendzellmikroskopie. Verringerte Bleicheigenschaften und minimaler spektraler “Cross-Talk” ermöglichten es, transfizierte Zellen über einen Zeitraum von bis zu 8 Stunden zu beobachten. Mittels Atto488/Atto590 siRNA wurde die Einschleusung und Freisetzung in Zellen in Echtzeit untersucht. Dabei konnten Freisetzung und Verteilung in einzelnen Zellen beobachtet und analysiert werden. rnAuf eine anfängliche Phase mit hoher Freisetzungsrate folgte eine Phase mit geringerer Rate für den restlichen Beobachtungszeitraum. Die durchschnittliche Verweildauer im Zytosol betrug 24 und 58 Minuten, wobei zwischen lang- und kurzanhaltenden Ereignissen unterschieden werden konnte. Obwohl ein Import von siRNA in den Zellkern beobachtet wurde, konnte kein Schema bzw. genauer Zeitpunkt, in Bezug auf den Transfektionszeitraum für diese Ereignisse bestimmt werden. Die beobachteten Freisetzungsprozesse fanden sporadisch statt und Änderungen in der zellulären Verteilung geschahen innerhalb von wenigen Minuten. Einmal freigesetzte siRNA verschwand mit der Zeit wieder aus dem Zytosol und es blieben nur kleine Aggregate von siRNA mit immer noch geringer Integrität zurück.rn

A shortened radiofrequency denervation method for cervical zygapophysial joint pain based on ultrasound localization of the nerves

Radiofrequency neurotomy is a recognized treatment for cervical zygapophysial joint pain. In several studies, the method has provided complete pain relief in 60-70% of the patients for approximately 9 months. The validated technique has the disadvantage of procedural times of 2-4 hours because several lesions are performed to take into account the variable nerve course. We tested the hypothesis that ultrasound localization of the nerves would enable us to reduce the number of lesions performed, while reaching the benchmark of at least 80% pain relief in 80% of patients with a median duration of 35 weeks, as achieved by a previous investigation using the standard method.

FPGA BASED DESIGN FOR ACCELERATED FAULT-TESTING OF INTEGRATED CIRCUITS

In the past few decades, integrated circuits have become a major part of everyday life. Every circuit that is created needs to be tested for faults so faulty circuits are not sent to end-users. The creation of these tests is time consuming, costly and difficult to perform on larger circuits. This research presents a novel method for fault detection and test pattern reduction in integrated circuitry under test. By leveraging the FPGA's reconfigurability and parallel processing capabilities, a speed up in fault detection can be achieved over previous computer simulation techniques. This work presents the following contributions to the field of Stuck-At-Fault detection: We present a new method for inserting faults into a circuit net list. Given any circuit netlist, our tool can insert multiplexers into a circuit at correct internal nodes to aid in fault emulation on reconfigurable hardware. We present a parallel method of fault emulation. The benefit of the FPGA is not only its ability to implement any circuit, but its ability to process data in parallel. This research utilizes this to create a more efficient emulation method that implements numerous copies of the same circuit in the FPGA. A new method to organize the most efficient faults. Most methods for determinin the minimum number of inputs to cover the most faults require sophisticated softwareprograms that use heuristics. By utilizing hardware, this research is able to process data faster and use a simpler method for an efficient way of minimizing inputs.

Towards Self-Learning Radio-Based Localization Systems

Location-awareness indoors will be an inseparable feature of mobile services/applications in future wireless networks. Its current ubiquitous availability is still obstructed by technological challenges and privacy issues. We propose an innovative approach towards the concept of indoor positioning with main goal to develop a system that is self-learning and able to adapt to various radio propagation environments. The approach combines estimation of propagation conditions, subsequent appropriate channel modelling and optimisation feedback to the used positioning algorithm. Main advantages of the proposal are decreased system set-up effort, automatic re-calibration and increased precision.

Compressed sensing based cyclic feature spectrum sensing for cognitive radios

Spectrum sensing is currently one of the most challenging design problems in cognitive radio. A robust spectrum sensing technique is important in allowing implementation of a practical dynamic spectrum access in noisy and interference uncertain environments. In addition, it is desired to minimize the sensing time, while meeting the stringent cognitive radio application requirements. To cope with this challenge, cyclic spectrum sensing techniques have been proposed. However, such techniques require very high sampling rates in the wideband regime and thus are costly in hardware implementation and power consumption. In this thesis the concept of compressed sensing is applied to circumvent this problem by utilizing the sparsity of the two-dimensional cyclic spectrum. Compressive sampling is used to reduce the sampling rate and a recovery method is developed for re- constructing the sparse cyclic spectrum from the compressed samples. The reconstruction solution used, exploits the sparsity structure in the two-dimensional cyclic spectrum do-main which is different from conventional compressed sensing techniques for vector-form sparse signals. The entire wideband cyclic spectrum is reconstructed from sub-Nyquist-rate samples for simultaneous detection of multiple signal sources. After the cyclic spectrum recovery two methods are proposed to make spectral occupancy decisions from the recovered cyclic spectrum: a band-by-band multi-cycle detector which works for all modulation schemes, and a fast and simple thresholding method that works for Binary Phase Shift Keying (BPSK) signals only. In addition a method for recovering the power spectrum of stationary signals is developed as a special case. Simulation results demonstrate that the proposed spectrum sensing algorithms can significantly reduce sampling rate without sacrifcing performance. The robustness of the algorithms to the noise uncertainty of the wireless channel is also shown.

Investigation on the evolution of an indoor robotic localization system based on wireless networks

This work addresses the evolution of an artificial neural network (ANN) to assist in the problem of indoor robotic localization. We investigate the design and building of an autonomous localization system based on information gathered from wireless networks (WN). The article focuses on the evolved ANN, which provides the position of a robot in a space, as in a Cartesian coordinate system, corroborating with the evolutionary robotic research area and showing its practical viability. The proposed system was tested in several experiments, evaluating not only the impact of different evolutionary computation parameters but also the role of the transfer functions on the evolution of the ANN. Results show that slight variations in the parameters lead to significant differences on the evolution process and, therefore, in the accuracy of the robot position.