Cellular Nonlinear Networks: optimized implementation on FPGA and applications to robotics

L'objectiu principal d'aquesta tesi consisteix a estudiar la factibilitat d'implementar un sensor càmera CNN amb plena funcionalitat basat en FPGA de baix cost adequat per a aplicacions en robots mòbils. L'estudi dels fonaments de les xarxes cel•lulars no lineals (CNNs) i la seva aplicació eficaç en matrius de portes programables (FPGAs) s'ha complementat, d'una banda amb el paral•lelisme que s'estableix entre arquitectura multi-nucli de les CNNs i els eixams de robots mòbils, i per l'altre banda amb la correlació dinàmica de CNNs i arquitectures memristive. A més, els memristors es consideren els substituts dels futurs dispositius de memòria flash per la seva capacitat d'integració d'alta densitat i el seu consum d'energia prop de zero. En el nostre cas, hem estat interessats en el desenvolupament d’FPGAs que han deixat de ser simples dispositius per a la creació ràpida de prototips ASIC per esdevenir complets dispositius reconfigurables amb integració de la memòria i els elements de processament general. En particular, s'han explorat com les arquitectures implementades CNN en FPGAs poden ser optimitzades en termes d’àrea ocupada en el dispositiu i el seu consum de potència. El nostre objectiu final ens ah portat a implementar de manera eficient una CNN-UM amb complet funcionament a un baix cost i baix consum sobre una FPGA amb tecnología flash. Per tant, futurs estudis sobre l’arquitectura eficient de la CNN sobre la FPGA i la interconnexió amb els robots comercials disponibles és un dels objectius d'aquesta tesi que se seguiran en les línies de futur exposades en aquest treball.El objetivo principal de esta tesis consiste en estudiar la factibilidad de implementar un sensor cámara CNN con plena funcionalidad basado en FPGA de bajo coste adecuado para aplicaciones en robots móviles. El estudio de los fundamentos de las redes celulares no lineales (CNNs) y su aplicación eficaz en matrices de puertas programables (FPGAs) se ha complementado, por un lado con el paralelismo que se establece entre arquitectura multi -núcleo de las CNNs y los enjambres de robots móviles, y por el otro lado con la correlación dinámica de CNNs y arquitecturas memristive. Además, los memristors se consideran los sustitutos de los futuros dispositivos de memoria flash por su capacidad de integración de alta densidad y su consumo de energía cerca de cero. En nuestro caso, hemos estado interesados en el desarrollo de FPGAs que han dejado de ser simples dispositivos para la creación rápida de prototipos ASIC para convertirse en completos dispositivos reconfigurables con integración de la memoria y los elementos de procesamiento general. En particular, se han explorado como las arquitecturas implementadas CNN en FPGAs pueden ser optimizadas en términos de área ocupada en el dispositivo y su consumo de potencia. Nuestro objetivo final nos ah llevado a implementar de manera eficiente una CNN-UM con completo funcionamiento a un bajo coste y bajo consumo sobre una FPGA con tecnología flash. Por lo tanto, futuros estudios sobre la arquitectura eficiente de la CNN sobre la FPGA y la interconexión con los robots comerciales disponibles es uno de los objetivos de esta tesis que se seguirán en las líneas de futuro expuestas en este trabajo.The main goal of this thesis consists in studying the feasibility to implement a full-functionality CNN camera sensor based on low-cost FPGA device suitable for mobile robotic applications. The study of Cellular Nonlinear Networks (CNNs) fundamentals and its efficient implementation on Field Programmable Gate Arrays (FPGAs) has been complemented, on one side with the parallelism established between multi-core CNN architecture and swarm of mobile robots, and on the other side with the dynamics correlation of CNNs and memristive architectures. Furthermore, memristors are considered the future substitutes of flash memory devices because of its capability of high density integration and its close to zero power consumption. In our case, we have been interested in the development of FPGAs that have ceased to be simple devices for ASIC fast prototyping to become complete reconfigurable devices embedding memory and processing elements. In particular, we have explored how the CNN architectures implemented on FPGAs can be optimized in terms of area occupied on the device or power consumption. Our final accomplishment has been implementing efficiently a fully functional reconfigurable CNN-UM on a low-cost low-power FPGA based on flash technology. Therefore, further studies on an efficient CNN architecture on FPGA and interfacing it with commercially-available robots is one of the objectives of this thesis that will be followed in the future directions exposed in this work

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

1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

Beam scanning by liquid-crystal biasing in a modified SIW structure

A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

Zur Invertierbarkeit von durch spatio-temporale Filter erzeugten Abbildungen mit besonderem Bezug zur Entwicklung einer lernfähigen Sehprothese

Für die Entwicklung wahrnehmungsbasierter Dialogverfahren für das Training lernfähiger Retina Encoder (RE) wird ein Inverter Modul benötigt, welches die spatio-temporale Abbildung, die durch den RE geleistet wird, umkehrt. In der vorliegenden Arbeit wird, ausgehend von einem speziellen Modell der visuellen Informationsverarbeitung beim Menschen, der Sehvorgang als Sequenz zweier Abbildungen betrachtet, die auf zueinander invers sein müssen. Mit der mathematischen Beschreibung dieser spatio-temporalen Filterung war es möglich, zwei unterschiedliche Inversionsverfahren, die Matrix-Methode und die Entscheidungsbaum-Methode, zu entwickeln, die einen Eingangsreiz perfekt aus dem RE-Filterergebnis rekonstruieren können. Die Matrix-Methode wurde durch Übertragung geeigneter Inversionsverfahren aus anderen Wissenschaftsdisziplinen auf die Retina Encoder Problemstellung realisiert. Die Entwicklung der Entscheidungsbaum-Methode basierte auf Erkenntnissen und Annahmen über die visuelle Informationsverarbeitung im Zentralen Sehsystem des Menschen sowie auf künstlich generierten Augenbewegung. Es stellte sich dabei heraus, dass die Invertierung der ST-Filterung typischerweise auf ein schlecht gestelltes Problem (ill-posed problem) bzw. schlecht konditioniertes Problem (ill-conditioned problem) führt. Aus diesem Grund musste die Matrix Methode durch Regularisierungsverfahren erweitert werden. Die Eignung der Inversionsverfahren für das wahrnehmungsbasierte RE-Training mit normalsichtigen Versuchspersonen wurde in Dialogverfahren unter Einsatz evolutionärer Algorithmen mit automatischer Selektion untersucht. Es stellte sich heraus, dass die Entscheidungsbaum-Methode der Matrix-Methode im Falle schlecht konditionierter RE-Abbildungen bei der Rekonstruktion reiner schwarz-weißer Reizmuster überlegen ist. Der Einsatz der Entscheidungsbaum-Methode im Inverter Modul zusammen mit einem ebenfalls neu entwickelten Lerndialogverfahren mit ortspezifischem Feedback (Position Sensitive Tuning) ermöglichte normalsichtigen Versuchspersonen ein schnelles und erfolgreiches RE-Training. </p

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Reports to the President

A compilation of annual reports for the 1990-1991 academic year, including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans