Ultra fast CNN based hardware computing platform concepts for ADAS visual sensors and evolutionary mobile robots

Durch VerkehrsÃberwachung und -Steuerung kann die Sicherheit auf den StraÃen verbessert werden und somit die Kosten fÃr das Sozialsystem gesenkt werden. Es sterben in Europa noch immer 40000 Menschen jÃ$hrlich im StraÃenverkehr. Es gibt wesentliche zwei LÃsungsansÃ$tze um die Sicherheit auf den StraÃen zu erhÃhen: Die Verbesserung der Fahrsicherheitsausbildungsprogramme einerseits und die Entwicklung einer Reihe von Technologien, darunter die Fahrassistenzsysteme. Meist ist der Faktor Mensch schuld an AutounfÃ$llen. Wenn der Fahrer mÃde ist erhÃht sich die Unfallwahrscheinlichkeit dramatisch. Fahrassistenzsysteme sind eine praktische MÃglichkeit UnfÃ$lle durch ÉbermÃdung zu vermeiden. Diese Arbeit beantwortet im Wesentlichen die folgenden Kernforschungsfragen in Hinblick auf die potentiellen Verbesserungen von Fahrassistenzsystemen durch Echtzeitbildverarbeitung in dazugehÃrigen visuellen Sensoren: * Was sind die Echtzeit Bildverarbeitungs- und FlexibilitÃ$tsanforderungen fÃr Fahrassistenzsysteme? Woran scheitern traditionelle BildverarbeitungsansÃ$tze, um diese strenge Anforderungen zu erfÃllen? * Welches Potential haben "Neurocomputing" insbesondere herkÃmmliche Neuronale Netzwerke (NN) und Zellulare Neuronale Netzwerke (CNN) in der flexiblen Hochleistungs-Bildverarbeitung fÃr Fahrassistenzsysteme? Welche EinschrÃ$nkungen gibt es und wie kann man damit umgehen? * In wie weit kÃnnen Vorteile des altherkÃmmlichen "Analog Computing" Paradigma durch eine Emulation auf digitalen Hardware Plattform (FPGA) fÃr Echtzeit Bildverarbeitung genutzt werden? * Inwieweit kann eine effiziente CNN Implementierung auf FPGA oder GPU entwickelt und umgesetzt werden? * Inwieweit kann CNN fÃreine evolutionÃ$re Datenverarbeitung eingesetzt werden, mit mÃglichen Anwendungen in Robotik bzw. intelligenten Fahrzeugen? Um diese Forschungsfragen zu beantworten wurden verschiedene auf CNN basierende prototypisch demonstriert. Eine sehr hohe StabilitÃ$t und LeistungsfÃ$higkeit der eigenen Konzepte zu erreichen und deren Éberlegenheit konnte durch einen Vergleich mit herkÃmmlichen sequenziellen algorithmischen AnsÃ$tze untermauert.Monitoring and controlling traffic can improve the road safety and reduce the costs; but still every year 40,000 people die because of car accidents in Europe. There are two solutions for overcoming the critical issue of road safety: improving the driver safety education programs and improving the vehicle safety using advance technology like ADAS (Advanced Driver Assistance System). One of the main factors in car accidents and traffic safety is the human factor. If the driver is tired or asleep the probability of accident will dramatically increase. A convenient way to avoid these types of accidents is using an assistance system. This thesis answers the following seven research questions which are related to the potential performance improvement of ADAS technology with respect to the involved real-time image processing: * What are the hard requirements of ADAS concerning real-time image processing and design flexibility? How far do traditional approaches fail to satisfy these requirements? * What are the major limitations of traditional high performance computing approaches if used to ensure "real-time" image processing? * What is the huge potential of neurocomputing involving either traditional neural networks (NN) or cellular neural networks (CNN) for high-speed and flexible image processing for ADAS? Are there any limitations and how can these eventually be addressed? * What are the major template calculation schemes of relevance for CNN based image processing? How can these calculations be performed in a real-time high performance computing context? * How far can the advantages of "analog computing" be used through an emulation of analog computing on digital hardware platforms like FPGA (for the benefit of an ultrafast image processing)? * How far can an efficient implementation of CNN on FPGA and GPU be designed and implemented? * How far can CNN be involved in an evolutionary computing context example (for illustration)? To cover these research questions, we have conducted a survey concerning different ADAS concepts, different architecture and methodology to get more reliability and stability in our design. We have shown the limitations of traditional/sequential computing concepts for processing high quality images in the ADAS context and did propose a parallel processing model based on CNN. This thesis does address two main challenging issues in ADAS technology: a) a universal model and architecture for a real time visual processing; and b) the implementation of a prototype system on both GPU and FPGA.Alireza FasihAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersKlagenfurt, Alpen-Adria-Univ., Diss., 2011OeBB(VLID)241143

Optimization of ultrasonic assisted extraction of fatty acids from Borago Officinalis L. flower by central composite design

In the present study, the ultrasonic assisted extraction (UAE) of essential oils and fatty acids from Borago officinalis L. flower was developed by using n-hexane as extracting solvent. The obtained extracts were compared by hydrodistillation. Four parameters such as temperature, time, power of ultrasonic, and the ratio of extracting solvent volume to the weight of the plant were optimized using a central composite design after a full factorial design. Based on direct observation and analysis, the highest yields for UAE were obtained at a temperature of 48 °C, an extraction time of 30 min, minimum power of ultrasonic and in the ratio of extracting solvent volume to weight of plant 36:1 mL/g. The chemical compositions of the UAE extract were identified by GC–MS after derivation. The extraction yield base on ultrasonic assisted extraction varied in the range of 0.12–1.04% (w/w)

A novel real time emotion detection system for advanced driver assistance systems

This paper presents a real-time emotion recognition concept of voice streams. A comprehensive solution based on Bayesian Quadratic Discriminate Classifier(QDC) is developed. The developed system supports Advanced Driver Assistance Systems (ADAS) to detect the mood of the driver based on the fact that aggressive behavior on road leads to traffic accidents. We use only 12 features to classify between 5 different classes of emotions. We illustrate that the extracted emotion features are highly overlapped and how each emotion class is effecting the recognition ratio. Finally, we show that the Bayesian Quadratic Discriminate Classifier is an appropriate solution for emotion detection systems, where a real-time detection is deeply needed with a low number of features. Document type: Part of book or chapter of boo

Erratum to a novel real time emotion detection system for advanced driver assistance systems

Document type: Part of book or chapter of boo