Automatic Pixel-Parallel Extraction of the Retinal Vascular Tree Algorithm Design, On-Chip Implementation an Applications

[Resumen] La tesis doctoral propone un nuevo algoritmo para la extracción del árbol arterio-venoso en imágenes digitales de retina usando sistemas pixel paralelo que le confiere un procesamiento a alta velocidad, Inicialmente el problema de la extracción del árbol arterio venoso se estudió desde el punto de vista del procesamiento de imágenes utilizando técnicas pixel paralelo, concretamente bajo el paradigma de las Cellular Neural Networks. Este algoritmo utiliza una técnica de contornos activos, los Pixel level snakes (PLS) que permiten aprovechar las ventajas de los contornos activos, como es su capacidad de funcionamiento con contornos borrosos así como su robustez ante el ruido, y al mismo tiempo todo ello procesándose a una alta velocidad de computación. Esta técnica permite también su proyección en un dispositivo hardware específico. La primera versión del algoritmo fue diseñada basándose en el paradigma CNN. Los resultados obtenidos eran buenos bajo el punto de vista del procesado de imagen. Sin embargo, la complejidad de algunas de las operaciones propuestas en esta versión eran de una alta complejidad para ser implementados en los chips pixel paralelos actuales con capacidades SIMD (Single Instruction Multiple Data). Esta versión ha sido redefinida para ser implementada en un chip SIMD. Esta última versión ha sido analizada desde un punto de vista del ajuste de los resultados y desde el punto de vista de la velocidad de ejecución. Para el primer análisis se ha hecho uso de una base de datos pública, concretamente la DRIVE (Digital Retinal Image for Vessel Extraction). Para el análisis de los tiempos de ejecución, se implementó el algoritmo en un chip específico, el SCAMP-3 vision system. El análisis de ambos aspectos ha permitido observar, que el ajuste obtenido sobre los resultados es alto, aunque existen algoritmos con un ajuste mejor, y el tiempo de ejecución es realmente rápido y no existe ningún algoritmo en la bibliografía que mejore el tiempo obtenido con la implementación propuesta en esta tesis. Asimismo se ha realizado un estudio de la mejora que se podría obtener utilizando una técnica de solapamiento, puesto que debido a la alta resolución de las imágenes utilizadas, estas se han tenido que dividir en subventanas para su procesamiento. Este análisis ha demostrado que la mejora obtenida es mínima en comparación con el notable incremento del tiempo de ejecución, siendo descartada su utilización. Una vez demostrado el funcionamiento del algoritmo se ha procedido a su inclusión en aplicaciones prácticas que se encontraban ya funcionando utilizando algoritmos clásicos para la extracción del árbol arterio venoso. Las aplicaciones corresponden a dos ámbitos diferentes con necesidades propias, el ámbito médico y la autenticación de personas. Para la autenticación de personas se observó que el funcionamiento es igual que usando las versiones clásicas, manteniendo un 100% de efectividad en la identificación de personas. En el caso de la aplicación médica, se incluyó dentro de un sistema de estimación del índice arterio-venoso, mostrando un funcionamiento con valores similares

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

NASA Tech Briefs, February 1991

Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

Technology 2002: the Third National Technology Transfer Conference and Exposition, Volume 1

The proceedings from the conference are presented. The topics covered include the following: computer technology, advanced manufacturing, materials science, biotechnology, and electronics

Efficient Object Detection in Mobile and Embedded Devices with Deep Neural Networks

[EN] Neural networks have become the standard for high accuracy computer vision. These algorithms can be built with arbitrarily large architectures to handle an ever growing complexity in the data they process. State of the art neural network architectures are primarily concerned with increasing the recognition accuracy when performing inference on an image, which creates an insatiable demand for energy and compute power. These models are primarily targeted to run on dense compute units such as GPUs. In recent years, demand to allow these models to execute in limited capacity environments such as smartphones, however even the most compact variations of these state of the art networks constantly push the boundaries of the power envelop under which they run. With the emergence of the Internet of Things, it is becoming a priority to enable mobile systems to perform image recognition at the edge, but with small energy requirements. This thesis focuses on the design and implementation of an object detection neural network that attempts to solve this problem, providing reasonable accuracy rates with extremely low compute power requirements. This is achieved by re-imagining the meta architecture of traditional object detection models and discovering a mechanism to classify and localize objects through a set of neural network based algorithms that are better aimed to mobile and embedded devices. The main contributions of this thesis are: (i) provide a better image processing algorithm that is more suitable at preparing data for consumption by taking advantage of the characteristics of the ISP available in these devices; (ii) provide a neural network architecture that maintains acceptable accuracy targets with minimal computational requirements by making efficient use of basic neural algorithms; and (iii) provide a programming framework for how these systems can be most efficiently implemented in a manner that is optimized for the underlying hardware units available in these devices by taking into account memory and computation restrictions

Survey of FPGA applications in the period 2000 – 2015 (Technical Report)

Romoth J, Porrmann M, Rückert U. Survey of FPGA applications in the period 2000 – 2015 (Technical Report).; 2017.Since their introduction, FPGAs can be seen in more and more different fields of applications. The key advantage is the combination of software-like flexibility with the performance otherwise common to hardware. Nevertheless, every application field introduces special requirements to the used computational architecture. This paper provides an overview of the different topics FPGAs have been used for in the last 15 years of research and why they have been chosen over other processing units like e.g. CPUs

Energy efficient enabling technologies for semantic video processing on mobile devices

Semantic object-based processing will play an increasingly important role in future multimedia systems due to the ubiquity of digital multimedia capture/playback technologies and increasing storage capacity. Although the object based paradigm has many undeniable benefits, numerous technical challenges remain before the applications becomes pervasive, particularly on computational constrained mobile devices. A fundamental issue is the ill-posed problem of semantic object segmentation. Furthermore, on battery powered mobile computing devices, the additional algorithmic complexity of semantic object based processing compared to conventional video processing is highly undesirable both from a real-time operation and battery life perspective. This thesis attempts to tackle these issues by firstly constraining the solution space and focusing on the human face as a primary semantic concept of use to users of mobile devices. A novel face detection algorithm is proposed, which from the outset was designed to be amenable to be offloaded from the host microprocessor to dedicated hardware, thereby providing real-time performance and reducing power consumption. The algorithm uses an Artificial Neural Network (ANN), whose topology and weights are evolved via a genetic algorithm (GA). The computational burden of the ANN evaluation is offloaded to a dedicated hardware accelerator, which is capable of processing any evolved network topology. Efficient arithmetic circuitry, which leverages modified Booth recoding, column compressors and carry save adders, is adopted throughout the design. To tackle the increased computational costs associated with object tracking or object based shape encoding, a novel energy efficient binary motion estimation architecture is proposed. Energy is reduced in the proposed motion estimation architecture by minimising the redundant operations inherent in the binary data. Both architectures are shown to compare favourable with the relevant prior art