A current-mode based Field-Programmable Analog Array for signal processing applications

This paper presents a new approach to develop Field Programmable Analog Arrays (FPAAs),(1) which avoids excessive number of programming elements in the signal path, thus enhancing the performance. The paper also introduces a novel FPAA architecture, devoid of the conventional switching and connection modules. The proposed FPAA is based on simple current mode sub-circuits. An uncompounded methodology has been employed for the programming of the Configurable Analog Cell (CAC). Current mode approach has enabled the operation of the FPAA presented here, over almost three decades of frequency range. We have demonstrated the feasibility of the FPAA by implementing some signal processing functions.

Experimental aspects of biomass fuels in a bubbling fluidized bed combustor

This work presents experimental information relevant to the combustion of biomass in a bubbling fluidized bed. The biomass distribution in a fluidized bed was studied through tests performed in a cold bed, while the volatiles released in the biomass pyrolysis, the burning rate of the resulting charcoal, and the combustion control regime, were studied through tests performed in a high temperature bed.Visual examination of photographs taken from a transparent walls bed, with a rectangular cross-section, showed that the large fuel particles, typical of biomass processing, were distributed in the bubbles, in the splash zone, and in the emulsion phase. The occurrence of biomass in the emulsion phase was favored by burning biomass particles of greater density and smaller size-expetimentally determined in each case. Decreasing the fuel particle size improved the biomass distribution inside the bed. The same was accomplished by increasing the superficial gas velocity as high as possible, compatibly with the acceptable elutriation.Burning tests showed that the biomass fuels have the advantage of reaching the diffusional regime at temperatures that can be lower than 1000 K, which ensures that the biomass fuels burn in a stable regime. (C) 2007 Elsevier B.V. All rights reserved.

Pattern recognition theory in nonlinear signal processing

A body of research has developed within the context of nonlinear signal and image processing that deals with the automatic, statistical design of digital window-based filters. Based on pairs of ideal and observed signals, a filter is designed in an effort to minimize the error between the ideal and filtered signals. The goodness of an optimal filter depends on the relation between the ideal and observed signals, but the goodness of a designed filter also depends on the amount of sample data from which it is designed. In order to lessen the design cost, a filter is often chosen from a given class of filters, thereby constraining the optimization and increasing the error of the optimal filter. To a great extent, the problem of filter design concerns striking the correct balance between the degree of constraint and the design cost. From a different perspective and in a different context, the problem of constraint versus sample size has been a major focus of study within the theory of pattern recognition. This paper discusses the design problem for nonlinear signal processing, shows how the issue naturally transitions into pattern recognition, and then provides a review of salient related pattern-recognition theory. In particular, it discusses classification rules, constrained classification, the Vapnik-Chervonenkis theory, and implications of that theory for morphological classifiers and neural networks. The paper closes by discussing some design approaches developed for nonlinear signal processing, and how the nature of these naturally lead to a decomposition of the error of a designed filter into a sum of the following components: the Bayes error of the unconstrained optimal filter, the cost of constraint, the cost of reducing complexity by compressing the original signal distribution, the design cost, and the contribution of prior knowledge to a decrease in the error. The main purpose of the paper is to present fundamental principles of pattern recognition theory within the framework of active research in nonlinear signal processing.

Hydrodynamic aspects of the amorphous alloy ribbon fabrication

Conditions for as-quenched amorphous ribbon fabrication by a single roll-casting method are analyzed from a hydrodynamic standpoint. The analysis is based on the investigation of the processing conditions for Fe 4 0Ni 40P 14B 6 amorphous ribbons. It is shown that the dependence of ribbon thickness on the ejection pressure for different roll angular velocities and different dimensions of crucible and orifice can be obtained from general considerations on the melt flow regime.

Neurophysiological aspects of musical auditory stimulation on cardiovascular system

Introduction: The literature has shown that musical stimulation can influence the cardiovascular system, however, the neurophysiological aspects of this influence are not yet fully elucidated. Objective: This study describes the influence of music on the neurophysiological mechanisms in the human body, specifically the variable blood pressure, as well as the neural mechanisms of music processing. Methods: Searches were conducted in Medline, PEDro, Lilacs and SciELO using the intersection of the keyword “music” with the keyword descriptors “blood pressure” and “neurophysiology”. Results: There were selected 11 articles, which indicated that music interferes in some aspects of physiological variables. Conclusion: Studies have indicated that music interferes on the control of blood pressure, heart and respiratory rate, through possible involvement of limbic brain areas which modulate hypothalamic-pituitary functions. Further studies are needed in order to identify the mechanisms by which this influence occurs.

Evolution of signal form

Visual signals, used for communication both within and between species, vary immensely in the forms that they take. How is it that all this splendour has evolved in nature? Since it is the receiver’s preferences that cause selective pressures on signals, elucidating the mechanism behind the response of the signal receiver is vital to gain a closer understanding of the evolutionary process. In my thesis I have therefore investigated how receivers, represented by chickens, Gallus gallus domesticus, respond to different stimuli displayed on a peck-sensitive computer screen. According to the receiver bias hypothesis, animals and humans often express biases when responding to certain stimuli. These biases develop as by-products of how the recognition mechanism categorises and discriminates between stimuli. Since biases are generated from general stimulus processing mechanisms, they occur irrespective of species and type of signal, and it is often possible to predict the direction and intensity of the biases. One of the results from the experiments in my thesis demonstrates that similar experience in different species may generate similar biases. By giving chickens at least some of the experience of human faces as humans presumably have, the chickens subsequently expressed preferences for the same faces as a group of human subjects. Another kind of experience generated a bias for symmetry. This bias developed in the context of training chickens to recognise two mirror images of an asymmetrical stimulus. Untrained chickens and chickens trained on only one of the mirror images expressed no symmetry preferences. The bias produced by the training regime was for a specific symmetrical stimulus which had a strong resemblance to the familiar asymmetrical exemplar, rather than a general preference for symmetry. A further kind of experience, training chickens to respond to some stimuli but not to others, generated a receiver bias for exaggerated stimuli, whereas chickens trained on reversed stimuli developed a bias for less exaggerated stimuli. To investigate the potential of this bias to drive the evolution of signals towards exaggerated forms, a simplified evolutionary process was mimicked. The stimuli variants rejected by the chickens were eliminated, whereas the selected forms were kept and evolved prior to the subsequent display. As a result, signals evolved into exaggerated forms in all tested stimulus dimensions: length, intensity and area, despite the inclusion of a cost to the sender for using increasingly exaggerated signals. The bias was especially strong and persistent for stimuli varying along the intensity dimension where it remained despite extensive training. All the results in my thesis may be predicted by the receiver bias hypothesis. This implies that biases, developed due to stimuli experience, may be significant mechanisms driving the evolution of signal form.

Adaptive multiscale biological signal processing

Biological processes are very complex mechanisms, most of them being accompanied by or manifested as signals that reflect their essential characteristics and qualities. The development of diagnostic techniques based on signal and image acquisition from the human body is commonly retained as one of the propelling factors in the advancements in medicine and biosciences recorded in the recent past. It is a fact that the instruments used for biological signal and image recording, like any other acquisition system, are affected by non-idealities which, by different degrees, negatively impact on the accuracy of the recording. This work discusses how it is possible to attenuate, and ideally to remove, these effects, with a particular attention toward ultrasound imaging and extracellular recordings. Original algorithms developed during the Ph.D. research activity will be examined and compared to ones in literature tackling the same problems; results will be drawn on the base of comparative tests on both synthetic and in-vivo acquisitions, evaluating standard metrics in the respective field of application. All the developed algorithms share an adaptive approach to signal analysis, meaning that their behavior is not dependent only on designer choices, but driven by input signal characteristics too. Performance comparisons following the state of the art concerning image quality assessment, contrast gain estimation and resolution gain quantification as well as visual inspection highlighted very good results featured by the proposed ultrasound image deconvolution and restoring algorithms: axial resolution up to 5 times better than algorithms in literature are possible. Concerning extracellular recordings, the results of the proposed denoising technique compared to other signal processing algorithms pointed out an improvement of the state of the art of almost 4 dB.

Vegetables biofortified with iodine: chemical, agronomic and qualitative aspects of raw and fried potatoes

Iodine is an essential microelement for human health because it is a constituent of the thyroid hormones that regulate growth and development of the organism. Iodine Deficiency Disorders (IDDs) are believed to be one of the commonest preventable human health problems in the world today, according to the World Health Organization: that diseases include endemic goiter, cretinism and fetal abnormalities, among others, and they are caused by lack of iodine in the diet, that is the main source of iodine. Since iodine intake from food is not enough respect to human needs, this can be remedied through dietary diversification, mineral supplementation, food fortification, or increasing the concentration and/or bioavailability of mineral elements in the edible portions of crops through agricultural intervention or genetic selection (biofortification). The introduction of iodized salt is a strategy widely used and accepted to eradicate iodine deficiency, because it is an inexpensive source of stable iodine. Since the intake of salt, though iodized, must still be limited according to the risk of cardiovascular disease, so the increase of iodine content in plants for the production of functional foods is representing a field of study of particular interest and a potential market. In Italy potatoes enriched with iodine are produced by a patented procedure of agronomic biofortification for the fresh market since several years, furthermore they are recently accepted and recommended by Italian Thyroid Association, as an alternative source of iodine. Researches performed during the PhD course intended to characterize this innovative vegetables products, focusing the attention on different aspects, such as chemistry, agriculture, and quality of fresh and fried potatoes. For this purpose, lipid fraction of raw material was firstly investigated, in order to assess whether the presence of iodine in plant metabolism can affect fatty acid or sterol biosynthesis, according to the hypothesis that iodine can be bounded to polyunsaturated fatty acids of cell membranes, protecting them from peroxydation; phytosterols of plant sterol are also studied because their importance in reducing serum cholesterol, especially in potato plant sterols are also involved in synthesis of glycoalkaloid, a family of steroidal toxic secondary metabolites present in plants of the Solanaceae family. To achieve this goal chromatographic analytical techniques were employed to identify and quantify fatty acids and sterols profile of common and iodine enriched row potatoes. Another aim of the project was to evaluate the effects of frying on the quality of iodine-enriched and common potatoes. Since iodine-enriched potatoes are nowadays produced only for the fresh market, preliminary trials of cultivation under controlled environment were carried out to verify if potato varieties suitable for processing were able to absorb and accumulate iodine in the tuber. In a successive phase, these varieties were grown in the field, to evaluate their potential productivity and quality at harvest and after storage. The best potato variety to be destined for processing purposes, was finally subjected to repeated frying cycles; the effects of lipid oxidation on the composition and quality of both potatoes and frying oil bath were evaluated by chromatographic and spectrophotometric analytical techniques. Special attention were paid on volatile compounds of fried potatoes.

Heterogeneous multicore systems for signal processing

This thesis explores the capabilities of heterogeneous multi-core systems, based on multiple Graphics Processing Units (GPUs) in a standard desktop framework. Multi-GPU accelerated desk side computers are an appealing alternative to other high performance computing (HPC) systems: being composed of commodity hardware components fabricated in large quantities, their price-performance ratio is unparalleled in the world of high performance computing. Essentially bringing “supercomputing to the masses”, this opens up new possibilities for application fields where investing in HPC resources had been considered unfeasible before. One of these is the field of bioelectrical imaging, a class of medical imaging technologies that occupy a low-cost niche next to million-dollar systems like functional Magnetic Resonance Imaging (fMRI). In the scope of this work, several computational challenges encountered in bioelectrical imaging are tackled with this new kind of computing resource, striving to help these methods approach their true potential. Specifically, the following main contributions were made: Firstly, a novel dual-GPU implementation of parallel triangular matrix inversion (TMI) is presented, addressing an crucial kernel in computation of multi-mesh head models of encephalographic (EEG) source localization. This includes not only a highly efficient implementation of the routine itself achieving excellent speedups versus an optimized CPU implementation, but also a novel GPU-friendly compressed storage scheme for triangular matrices. Secondly, a scalable multi-GPU solver for non-hermitian linear systems was implemented. It is integrated into a simulation environment for electrical impedance tomography (EIT) that requires frequent solution of complex systems with millions of unknowns, a task that this solution can perform within seconds. In terms of computational throughput, it outperforms not only an highly optimized multi-CPU reference, but related GPU-based work as well. Finally, a GPU-accelerated graphical EEG real-time source localization software was implemented. Thanks to acceleration, it can meet real-time requirements in unpreceeded anatomical detail running more complex localization algorithms. Additionally, a novel implementation to extract anatomical priors from static Magnetic Resonance (MR) scansions has been included.

Design and implementation of a novel multi-constellation FPGA-based dual frequency GNSS receiver for space applications

The PhD activity described in the document is part of the Microsatellite and Microsystem Laboratory of the II Faculty of Engineering, University of Bologna. The main objective is the design and development of a GNSS receiver for the orbit determination of microsatellites in low earth orbit. The development starts from the electronic design and goes up to the implementation of the navigation algorithms, covering all the aspects that are involved in this type of applications. The use of GPS receivers for orbit determination is a consolidated application used in many space missions, but the development of the new GNSS system within few years, such as the European Galileo, the Chinese COMPASS and the Russian modernized GLONASS, proposes new challenges and offers new opportunities to increase the orbit determination performances. The evaluation of improvements coming from the new systems together with the implementation of a receiver that is compatible with at least one of the new systems, are the main activities of the PhD. The activities can be divided in three section: receiver requirements definition and prototype implementation, design and analysis of the GNSS signal tracking algorithms, and design and analysis of the navigation algorithms. The receiver prototype is based on a Virtex FPGA by Xilinx, and includes a PowerPC processor. The architecture follows the software defined radio paradigm, so most of signal processing is performed in software while only what is strictly necessary is done in hardware. The tracking algorithms are implemented as a combination of Phase Locked Loop and Frequency Locked Loop for the carrier, and Delay Locked Loop with variable bandwidth for the code. The navigation algorithm is based on the extended Kalman filter and includes an accurate LEO orbit model.

Ultra-low power WSNs: distributed signal processing and dynamic resource management

This thesis presents several data processing and compression techniques capable of addressing the strict requirements of wireless sensor networks. After introducing a general overview of sensor networks, the energy problem is introduced, dividing the different energy reduction approaches according to the different subsystem they try to optimize. To manage the complexity brought by these techniques, a quick overview of the most common middlewares for WSNs is given, describing in detail SPINE2, a framework for data processing in the node environment. The focus is then shifted on the in-network aggregation techniques, used to reduce data sent by the network nodes trying to prolong the network lifetime as long as possible. Among the several techniques, the most promising approach is the Compressive Sensing (CS). To investigate this technique, a practical implementation of the algorithm is compared against a simpler aggregation scheme, deriving a mixed algorithm able to successfully reduce the power consumption. The analysis moves from compression implemented on single nodes to CS for signal ensembles, trying to exploit the correlations among sensors and nodes to improve compression and reconstruction quality. The two main techniques for signal ensembles, Distributed CS (DCS) and Kronecker CS (KCS), are introduced and compared against a common set of data gathered by real deployments. The best trade-off between reconstruction quality and power consumption is then investigated. The usage of CS is also addressed when the signal of interest is sampled at a Sub-Nyquist rate, evaluating the reconstruction performance. Finally the group sparsity CS (GS-CS) is compared to another well-known technique for reconstruction of signals from an highly sub-sampled version. These two frameworks are compared again against a real data-set and an insightful analysis of the trade-off between reconstruction quality and lifetime is given.

Aspects of poriferan (Suberites domuncula) apoptosis and innate immune system and evolutionary implications

Survivin, a unique member of the family of inhibitors of apoptosis (IAP) proteins, orchestrates intracellular pathways during cell division and apoptosis. Its central regulatory function in vertebrate molecular pathways as mitotic regulator and inhibitor of apoptotic cell death has major implications for tumor cell proliferation and viability, and has inspired several approaches that target survivin for cancer therapy. Analyses in early-branching Metazoa so far propose an exclusive role of survivin as a chromosomal passenger protein, whereas only later during evolution the second, complementary antiapoptotic function might have arisen, concurrent with increased organismal complexity. To lift the veil on the ancestral function(s) of this key regulatory molecule, a survivin homologue of the phylogenetically oldest extant metazoan taxon (phylum Porifera) was identified and functionally characterized. SURVL of the demosponge Suberites domuncula shares significant similarities with its metazoan homologues, ranging from conserved exon/intron structures to the presence of localization signal and protein-interaction domains, characteristic of IAP proteins. Whereas sponge tissue displayed a very low steady-state level, SURVL expression was significantly up-regulated in rapidly proliferating primmorph cells. In addition, challenge of sponge tissue and primmorphs with cadmium and the lipopeptide Pam3Cys-Ser-(Lys)4 stimulated SURVL expression, concurrent with the expression of newly discovered poriferan caspases (CASL and CASL2). Complementary functional analyses in transfected HEK-293 revealed that heterologous expression of poriferan survivin in human cells not only promotes cell proliferation but also augments resistance to cadmium-induced cell death. Taken together, these results demonstrate both a deep evolutionary conserved and fundamental dual role of survivin, and an equally conserved central position of this key regulatory molecule in interconnected pathways of cell cycle and apoptosis. Additionally, SDCASL, SDCASL2, and SDTILRc (TIR-LRR containing protein) may represent new components of the innate defense sentinel in sponges. SDCASL and SDCASL2 are two new caspase-homolog proteins with a singular structure. In addition to their CASc domains, SDCASL and SDCASL2 feature a small prodomain NH2-terminal (effector caspases) and a remarkably long COOH-terminal domain containing one or several functional double stranded RNA binding domains (dsrm). This new caspase prototype can characterize a caspase specialization coupling pathogen sensing and apoptosis, and could represent a very efficient defense mechanism. SDTILRc encompasses also a unique combination of domains: several leucine rich repeats (LRR) and a Toll/IL-1 receptor (TIR) domain. This unusual domain association may correspond to a new family of intracellular sensing protein, forming a subclass of pattern recognition receptors (PRR).

Efficient ultrasonic signal processing techniques for aided medical diagnostics

Ultrasound imaging is widely used in medical diagnostics as it is the fastest, least invasive, and least expensive imaging modality. However, ultrasound images are intrinsically difficult to be interpreted. In this scenario, Computer Aided Detection (CAD) systems can be used to support physicians during diagnosis providing them a second opinion. This thesis discusses efficient ultrasound processing techniques for computer aided medical diagnostics, focusing on two major topics: (i) Ultrasound Tissue Characterization (UTC), aimed at characterizing and differentiating between healthy and diseased tissue; (ii) Ultrasound Image Segmentation (UIS), aimed at detecting the boundaries of anatomical structures to automatically measure organ dimensions and compute clinically relevant functional indices. Research on UTC produced a CAD tool for Prostate Cancer detection to improve the biopsy protocol. In particular, this thesis contributes with: (i) the development of a robust classification system; (ii) the exploitation of parallel computing on GPU for real-time performance; (iii) the introduction of both an innovative Semi-Supervised Learning algorithm and a novel supervised/semi-supervised learning scheme for CAD system training that improve system performance reducing data collection effort and avoiding collected data wasting. The tool provides physicians a risk map highlighting suspect tissue areas, allowing them to perform a lesion-directed biopsy. Clinical validation demonstrated the system validity as a diagnostic support tool and its effectiveness at reducing the number of biopsy cores requested for an accurate diagnosis. For UIS the research developed a heart disease diagnostic tool based on Real-Time 3D Echocardiography. Thesis contributions to this application are: (i) the development of an automated GPU based level-set segmentation framework for 3D images; (ii) the application of this framework to the myocardium segmentation. Experimental results showed the high efficiency and flexibility of the proposed framework. Its effectiveness as a tool for quantitative analysis of 3D cardiac morphology and function was demonstrated through clinical validation.

Topological aspects of the human protein interaction network

It is currently widely accepted that the understanding of complex cell functions depends on an integrated network theoretical approach and not on an isolated view of the different molecular agents. Aim of this thesis was the examination of topological properties that mirror known biological aspects by depicting the human protein network with methods from graph- and network theory. The presented network is a partial human interactome of 9222 proteins and 36324 interactions, consisting of single interactions reliably extracted from peer-reviewed scientific publications. In general, one can focus on intra- or intermodular characteristics, where a functional module is defined as "a discrete entity whose function is separable from those of other modules". It is found that the presented human network is also scale-free and hierarchically organised, as shown for yeast networks before. The interactome also exhibits proteins with high betweenness and low connectivity which are biologically analyzed and interpreted here as shuttling proteins between organelles (e.g. ER to Golgi, internal ER protein translocation, peroxisomal import, nuclear pores import/export) for the first time. As an optimisation for finding proteins that connect modules, a new method is developed here based on proteins located between highly clustered regions, rather than regarding highly connected regions. As a proof of principle, the Mediator complex is found in first place, the prime example for a connector complex. Focusing on intramodular aspects, the measurement of k-clique communities discriminates overlapping modules very well. Twenty of the largest identified modules are analysed in detail and annotated to known biological structures (e.g. proteasome, the NFκB-, TGF-β complex). Additionally, two large and highly interconnected modules for signal transducer and transcription factor proteins are revealed, separated by known shuttling proteins. These proteins yield also the highest number of redundant shortcuts (by calculating the skeleton), exhibit the highest numbers of interactions and might constitute highly interconnected but spatially separated rich-clubs either for signal transduction or for transcription factors. This design principle allows manifold regulatory events for signal transduction and enables a high diversity of transcription events in the nucleus by a limited set of proteins. Altogether, biological aspects are mirrored by pure topological features, leading to a new view and to new methods that assist the annotation of proteins to biological functions, structures and subcellular localisations. As the human protein network is one of the most complex networks at all, these results will be fruitful for other fields of network theory and will help understanding complex network functions in general.