Analysis of product-separable 2-D IIR discrete filters with feedbacks for variable characteristics

In order to design 2-D digital systems with variable characteristics, product-separable IIR filters with feedbacks in each dimension are studied. The stability and response of such systems with different situations are analyzed and discussed. Belonging to one of the methods of generation of VSHP in z-domain, product-separable IIR filters with dimensional feedbacks offer variable characteristics. The variable feedback gain in one-dimension is analyzed with all the other coefficients based on Schussler's Theorem. The stable conditions for the feedback in terms of other coefficients are obtained and hence a stable range of the feedback gain is determined. Within the stable range of feedback in each dimension, different filters' 2-D frequency responses have been studied and observed with different situations. Significant variable characteristics and different 2-D symmetries are shown with stable responses. Relations between the feedback and response are discussed. Furthermore, to extend this analysis to higher order systems, a computerized analysis method and algorithm is proposed and implemented. This approximation method can work with any order of IIR or FIR filters and has low computation complexity. More complex systems with variable characteristics can be composed and examples of Image Processing with these filters are simulated as space-domain applications

Virtual Retina : a biological retina model and simulator, with contrast gain control

A detailed retina model is proposed, that transforms a video sequence into a set of spike trains, as those emitted by retinal ganglion cells. It includes a linear model of filtering in the Outer Plexiform Layer (OPL), a contrast gain control mechanism modeling the non-linear feedback of some amacrine cells on bipolar cells, and a spike generation process modeling ganglion cells. A strength of the model is that each of its features can be associated to a precise physiological signification and location. The resulting retina model can simulate physiological recordings on mammalian retinas, including such non-linearities as cat Y cells, or contrast gain control. Furthermore, the model has been implemented on a large-scale simulator that can emulate the spikes of up to 100,000 neurons

Application of active controls technology to aircraft bide smoothing systems

A critical review of past efforts in the design and testing of ride smoothing and gust alleviation systems is presented. Design trade offs involving sensor types, choice of feedback loops, human comfort, and aircraft handling-qualities criteria are discussed. Synthesis of a system designed to employ direct-lift and side-force producing surfaces is reported. Two STOL aircraft and an executive transport are considered. Theoretically predicted system performance is compared with hybrid simulation and flight test data. Pilot opinion rating, pilot workload, and passenger comfort rating data for the basic and augmented aircraft are included

Navigation and estimation of separative labelling curves

openIn questo lavoro si investiga la possibilità di un agente di navigare ed identificare una curva sconosciuta, lineare o non lineare, la quale divide due regioni dello spazio bidimensionale in cui i punti sono dotati di un'etichetta (e.g. +1 o -1) che identifica unicamente la regione di appartenenza. L'agente considerato è un integratore a tempo discreto che campiona periodicamente solo i punti in cui transita, misurandone l'etichetta, e in base a tale informazione aggiorna la sua strategia di controllo per muoversi attorno alla curva. Nelle misurazioni, dapprima si è considerato il caso ideale senza errori, poi si è inserito un modello di errore. Sono stati sviluppati e testati degli algoritmi che mirano alla convergenza o alla navigazione della curva, per poi usare dei classici metodi di stima per identificare la curva di separazione.In this work, we investigate the possibility of the navigation and idenitification of an unknown linear or non linear curve by an agent. Such a curve separates two regions of the two-dimensional space whose points have a label (e.g. +1 or -1) depending on which region they belong to. The considered agent is a discrete time integrator that periodically samples only the points where it transits, measuring the label, and with such information it updates its control strategy to move around the curve. In the measurements, we first consider the ideal case without errors and then we adopt a noise error model. Some algorithms that aim to the convergence or to the navigation around the curve have been developed and tested, and classical estimation methods have been used to finally identify the separative curve

Kinetic studies on the University of London nuclear reactor

Imperial Users onl

Massively Parallel Spiking Neural Circuits: Encoding, Decoding and Functional Identification

This thesis presents a class of massively parallel spiking neural circuit architectures in which neurons are modeled by dendritic stimulus processors cascaded with spike generators. We investigate how visual stimuli can be represented by the spike times generated by the massively parallel neural circuits, how the spike times can be used to reconstruct and process visual stimuli, and the conditions when visual stimuli can be faithfully represented/reconstructed. Functional identification of the massively parallel neural circuits from spike times and its evaluation are also investigated. Together, this thesis offers a comprehensive analytic framework of massively parallel spiking neural circuit architectures arising in the study of early visual systems. In encoding, modeling of visual stimuli in reproducing kernel Hilbert spaces is presented, recognizing the importance of studying visual encoding in a rigorous mathematical framework. For massively parallel neural circuits with biophysical spike generators, I/O characterization of the biophysical spike generators becomes possible by introducing phase response curve manifolds for the biophysical spike generators. I/O characterization of the entire neural circuit can then be interpreted as generalized sampling in the Hilbert space. Multi-component dendritic stimulus processors are introduced to model visual encoding in stereoscopic color vision. It is also shown that encoding of visual stimuli by an ensemble of complex cells has the complexity of Volterra dendritic stimulus processors. Based on the I/O characterization, reconstruction algorithms are derived to decode, from spike times, visual stimuli encoded by these massively parallel neural circuits. Decoding problems are first formulated as spline interpolation problems. Conditions on faithful reconstruction are presented, allowing the probe of information content carried by the spikes. Algorithms are developed to qualify the decoding in massively parallel settings. For stereoscopic color visual stimuli, demixing of individual channels from an unlabeled set of spike trains is demonstrated. For encoding with complex cells, decoding problems are formulated as rank minimization problems. It is shown that the decoding algorithm does not suffer from the curse of dimensionality and thereby allows for a visual representation using biologically realistic neural resources. The study of visual stimuli encoding and decoding enables the functional identification of massively parallel neural circuits. The duality between decoding and functional identification suggests that algorithms for functional identification of the projection of dendritic stimulus processors onto the space of input stimuli can be formulated similarly to the decoding algorithms. Functional identification of dendritic stimulus processors of neurons carrying stereoscopic color information as well as that of energy processing in complex cells is demonstrated. Furthermore, this duality also inspires a novel method to evaluate the quality of functional identification of massively parallel spiking neural circuits. By reconstructing novel stimuli using identified circuit parameters, the evaluation of the entire identified circuit is reduced to intuitive comparisons in stimulus space. The use of biophysical spike generators advances a methodology in the study of intrinsic noise sources in neurons and their effects on stimulus representation and on precision of functional identification. These effects are investigated using a class of nonlinear neural circuits consisting of both feedforward and feedback Volterra dendritic stimulus processors and biophysical spike generators. It is shown that encoding with neural circuits with intrinsic noise sources can be interpreted as generalized sampling with noisy measurements. Effects of noise on decoding and functional identification are derived theoretically and were systematically investigated by extensive simulations. Finally, the massively parallel neural circuit architectures are shown to enable the implementation of identity preserving transformations in the spike domain using a switching matrix regulating the connection between encoding and decoding. Two realizations of the architectures are developed, and extensive examples using continuous visual streams are provided. Implications of this result on the problem of invariant object recognition in the spike domain are discussed

Traffic Scene Perception for Automated Driving with Top-View Grid Maps

Ein automatisiertes Fahrzeug muss sichere, sinnvolle und schnelle Entscheidungen auf Basis seiner Umgebung treffen. Dies benötigt ein genaues und recheneffizientes Modell der Verkehrsumgebung. Mit diesem Umfeldmodell sollen Messungen verschiedener Sensoren fusioniert, gefiltert und nachfolgenden Teilsysteme als kompakte, aber aussagekräftige Information bereitgestellt werden. Diese Arbeit befasst sich mit der Modellierung der Verkehrsszene auf Basis von Top-View Grid Maps. Im Vergleich zu anderen Umfeldmodellen ermöglichen sie eine frühe Fusion von Distanzmessungen aus verschiedenen Quellen mit geringem Rechenaufwand sowie eine explizite Modellierung von Freiraum. Nach der Vorstellung eines Verfahrens zur Bodenoberflächenschätzung, das die Grundlage der Top-View Modellierung darstellt, werden Methoden zur Belegungs- und Elevationskartierung für Grid Maps auf Basis von mehreren, verrauschten, teilweise widersprüchlichen oder fehlenden Distanzmessungen behandelt. Auf der resultierenden, sensorunabhängigen Repräsentation werden anschließend Modelle zur Detektion von Verkehrsteilnehmern sowie zur Schätzung von Szenenfluss, Odometrie und Tracking-Merkmalen untersucht. Untersuchungen auf öffentlich verfügbaren Datensätzen und einem Realfahrzeug zeigen, dass Top-View Grid Maps durch on-board LiDAR Sensorik geschätzt und verlässlich sicherheitskritische Umgebungsinformationen wie Beobachtbarkeit und Befahrbarkeit abgeleitet werden können. Schließlich werden Verkehrsteilnehmer als orientierte Bounding Boxen mit semantischen Klassen, Geschwindigkeiten und Tracking-Merkmalen aus einem gemeinsamen Modell zur Objektdetektion und Flussschätzung auf Basis der Top-View Grid Maps bestimmt

Modèle et simulateur à grande échelle d'une rétine biologique, avec contrôle de gain

The retina is a complex neural structure. The characteristics of retinal processing are reviewed extensively in Part I of this work: It is a very ordered structure, which proceeds to band-pass spatio-temporal enhancements of the incoming light, along different parallel output pathways with distinct spatio-temporal properties. The spike trains emitted by the retina have a complex statistical structure, such that precise spike timings may play a role in the code conveyed by the retina. Several mechanisms of gain control provide a constant adaptation of the retina to luminosity and contrast. The retina model that we have defined and implemented in Part II can account for a good part of this complexity. It can model spatio-temporal band-pass behavior with adjustable filtering scales, with the inclusion of plausible mechanisms of contrast gain control and spike generation. The gain control mechanism proposed in the model provides a good fit to experimental data, and it can induce interesting effects of local renormalization in the output retinal image. Furthermore, a mathematical analysis confirms that the gain control behaves well under simple sinusoidal stimulation. Finally, the simulator /Virtual Retina/ implements the model on a large-scale, so that it can emulate up to around 100,000 cells with a processing speed of about 1/100 real time. It is ready for use in various applications, while including a number of advanced retinal functionalities which are too often overlooked.La rétine est une structure neuronale complexe, qui non seulement capte la lumière incidente au fond de l'oeil, mais procède également à des transformations importantes du signal lumineux. Dans la Partie I de ce travail, nous résumons en détail les caractéristiques fonctionnelles de la rétine des vertébrés: Il s'agit d'une structure très ordonnée, qui réalise un filtrage passe-bande du stimulus visuel, selon différents canaux parallèles d'information aux propriétés spatio-temporelles distinctes. Les trains de potentiels d'action émis par la rétine ont également une structure statistique complexe, susceptible de véhiculer une information importante. De nombreux mécanismes de contrôle de gain permettent une adaptation constante à la luminosité et au contraste. Le modèle de rétine défini et implémenté dans la Partie II de ce travail prend en compte une part importante de cette complexité. Il reproduit le comportement passe-bande, à l'aide de filtres linéaires spatio-temporels appropriés. Des mécanismes non-linéaires d'adaptation au contraste et de génération de potentiels d'action sont également inclus. Le mécanisme de contrôle du gain au contraste proposé permet une bonne reproduction des données expérimentales, et peut également véhiculer d'importants effets d'égalisation spatiale des contrastes en sortie de rétine. De plus, une analyse mathématique confirme que notre mécanisme a le comportement escompté en réponse à une stimulation sinusoïdale. Enfin, le simulateur /Virtual Retina/ implémente le modèle à grande échelle, permettant la simulation d'environ 100 000 cellules en un temps raisonnable (100 fois le temps réel)