Field programmable gate array based target detection and gesture recognition

The move from Standard Definition (SD) to High Definition (HD) represents a six times increases in data, which needs to be processed. With expanding resolutions and evolving compression, there is a need for high performance with flexible architectures to allow for quick upgrade ability. The technology advances in image display resolutions, advanced compression techniques, and video intelligence. Software implementation of these systems can attain accuracy with tradeoffs among processing performance (to achieve specified frame rates, working on large image data sets), power and cost constraints. There is a need for new architectures to be in pace with the fast innovations in video and imaging. It contains dedicated hardware implementation of the pixel and frame rate processes on Field Programmable Gate Array (FPGA) to achieve the real-time performance. ^ The following outlines the contributions of the dissertation. (1) We develop a target detection system by applying a novel running average mean threshold (RAMT) approach to globalize the threshold required for background subtraction. This approach adapts the threshold automatically to different environments (indoor and outdoor) and different targets (humans and vehicles). For low power consumption and better performance, we design the complete system on FPGA. (2) We introduce a safe distance factor and develop an algorithm for occlusion occurrence detection during target tracking. A novel mean-threshold is calculated by motion-position analysis. (3) A new strategy for gesture recognition is developed using Combinational Neural Networks (CNN) based on a tree structure. Analysis of the method is done on American Sign Language (ASL) gestures. We introduce novel point of interests approach to reduce the feature vector size and gradient threshold approach for accurate classification. (4) We design a gesture recognition system using a hardware/ software co-simulation neural network for high speed and low memory storage requirements provided by the FPGA. We develop an innovative maximum distant algorithm which uses only 0.39% of the image as the feature vector to train and test the system design. Database set gestures involved in different applications may vary. Therefore, it is highly essential to keep the feature vector as low as possible while maintaining the same accuracy and performance^

Enhanced student learning in accounting utilising web-based technology, peer-review feedback and reflective practices: A learning community approach to assessment

Higher education is becoming a major driver of economic competitiveness in an increasingly knowledge-driven global economy. Maintaining the competitive edge has seen an increase in public accountability of higher education institutions through the mechanism of ranking universities based on the quality of their teaching and learning outcomes. As a result, assessment processes are under scrutiny, creating tensions between standardisation and measurability and the development of creative and reflective learners. These tensions are further highlighted in the context of large undergraduate subjects, learner diversity and time-poor academics and students. Research suggests that high level and complex learning is best developed when assessment, combined with effective feedback practices, involves students as partners in these processes. This article reports on a four-phase, cross-institution and cross-discipline project designed to embed peer-review processes as part of the assessment in two large, undergraduate accounting classes. Using a social constructivist view of learning, which emphasises the role of both teacher and learner in the development of complex cognitive understandings, we undertook an iterative process of peer review. Successive phases built upon students’ feedback and achievements and input from language/learning and curriculum experts to improve the teaching and learning outcomes.

Array-based gene expression, CGH and tissue data defines a 12q24 gain in neuroblastic tumors with prognostic implication

Neuroblastoma has successfully served as a model system for the identification of neuroectoderm-derived oncogenes. However, in spite of various efforts, only a few clinically useful prognostic markers have been found. Here, we present a framework, which integrates DNA, RNA and tissue data to identify and prioritize genetic events that represent clinically relevant new therapeutic targets and prognostic biomarkers for neuroblastoma.

Micropillar Array Based Microchips for Electrospray Ionization Mass Spectrometry, Microreactors, and Liquid Chromatographic Separation

This dissertation deals with the design, fabrication, and applications of microscale electrospray ionization chips for mass spectrometry. The microchip consists of microchannel, which leads to a sharp electrospray tip. Microchannel contain micropillars that facilitate a powerful capillary action in the channels. The capillary action delivers the liquid sample to the electrospray tip, which sprays the liquid sample to gas phase ions that can be analyzed with mass spectrometry. The microchip uses a high voltage, which can be utilized as a valve between the microchip and mass spectrometry. The microchips can be used in various applications, such as for analyses of drugs, proteins, peptides, or metabolites. The microchip works without pumps for liquid transfer, is usable for rapid analyses, and is sensitive. The characteristics of performance of the single microchips are studied and a rotating multitip version of the microchips are designed and fabricated. It is possible to use the microchip also as a microreactor and reaction products can be detected online with mass spectrometry. This property can be utilized for protein identification for example. Proteins can be digested enzymatically on-chip and reaction products, which are in this case peptides, can be detected with mass spectrometry. Because reactions occur faster in a microscale due to shorter diffusion lengths, the amount of protein can be very low, which is a benefit of the method. The microchip is well suited to surface activated reactions because of a high surface-to-volume ratio due to a dense micropillar array. For example, titanium dioxide nanolayer on the micropillar array combined with UV radiation produces photocatalytic reactions which can be used for mimicking drug metabolism biotransformation reactions. Rapid mimicking with the microchip eases the detection of possibly toxic compounds in preclinical research and therefore could speed up the research of new drugs. A micropillar array chip can also be utilized in the fabrication of liquid chromatographic columns. Precisely ordered micropillar arrays offer a very homogenous column, where separation of compounds has been demonstrated by using both laser induced fluorescence and mass spectrometry. Because of small dimensions on the microchip, the integrated microchip based liquid chromatography electrospray microchip is especially well suited to low sample concentrations. Overall, this work demonstrates that the designed and fabricated silicon/glass three dimensionally sharp electrospray tip is unique and facilitates stable ion spray for mass spectrometry.

Ultrasmall microlens array based on vertically aligned carbon nanofibers.

An ultrasmall tunable microlens with a diameter of 1.5 μm is fabricated using nematic liquid crystals (electrically tunable medium) and vertically aligned carbon nanofibers (CNFs, electrodes). Individual CNFs are grown at the center of circular dielectric regions. This allows the CNFs to produce a more Gaussian electric field profile and hence more uniformity in lens array switching.

Field programmable gate array based predictive control system for spacecraft rendezvous in elliptical orbits

Copyright © 2014 John Wiley & Sons, Ltd. Copyright © 2014 John Wiley & Sons, Ltd. Summary A field programmable gate array (FPGA) based model predictive controller for two phases of spacecraft rendezvous is presented. Linear time-varying prediction models are used to accommodate elliptical orbits, and a variable prediction horizon is used to facilitate finite time completion of the longer range manoeuvres, whilst a fixed and receding prediction horizon is used for fine-grained tracking at close range. The resulting constrained optimisation problems are solved using a primal-dual interior point algorithm. The majority of the computational demand is in solving a system of simultaneous linear equations at each iteration of this algorithm. To accelerate these operations, a custom circuit is implemented, using a combination of Mathworks HDL Coder and Xilinx System Generator for DSP, and used as a peripheral to a MicroBlaze soft-core processor on the FPGA, on which the remainder of the system is implemented. Certain logic that can be hard-coded for fixed sized problems is implemented to be configurable online, in order to accommodate the varying problem sizes associated with the variable prediction horizon. The system is demonstrated in closed-loop by linking the FPGA with a simulation of the spacecraft dynamics running in Simulink on a PC, using Ethernet. Timing comparisons indicate that the custom implementation is substantially faster than pure embedded software-based interior point methods running on the same MicroBlaze and could be competitive with a pure custom hardware implementation.