Automatic Number Plate Recognition on FPGA

Automatic Number Plate Recognition (ANPR) systems have become one of the most important components in the current Intelligent Transportation Systems (ITS). In this paper, a FPGA implementation of a complete ANPR system which consists of Number Plate Localisation (NPL), Character Segmentation (CS), and Optical Character Recognition (OCR) is presented. The Mentor Graphics RC240 FPGA development board was used for the implementation, where only 80% of the available on-chip slices of a Virtex-4 LX60 FPGA have been used. The whole system runs with a maximum frequency of 57.6 MHz and is capable of processing one image in 11ms with a successful recognition rate of 93%

IMPROVED LICENSE PLATE LOCALIZATION ALGORITHM BASED ON MORPHOLOGICAL OPERATIONS

Automatic License Plate Recognition (ALPR) systems have become an important tool to track stolen cars, access control, and monitor traffic. ALPR system consists of locating the license plate in an image, followed by character detection and recognition. Since the license plate can exist anywhere within an image, localization is the most important part of ALPR and requires greater processing time. Most ALPR systems are computationally intensive and require a high-performance computer. The proposed algorithm differs significantly from those utilized in previous ALPR technologies by offering a fast algorithm, composed of structural elements which more precisely conducts morphological operations within an image, and can be implemented in portable devices with low computation capabilities. The proposed algorithm is able to accurately detect and differentiate license plates in complex images. This method was first tested through MATLAB with an on-line public database of Greek license plates which is a popular benchmark used in previous works. The proposed algorithm was 100% accurate in all clear images, and achieved 98.45% accuracy when using the entire database which included complex backgrounds and license plates obscured by shadow and dirt. Second, the efficiency of the algorithm was tested in devices with low computational processing power, by translating the code to Python, and was 300% faster than previous work

Interfacing of neuromorphic vision, auditory and olfactory sensors with digital neuromorphic circuits

The conventional Von Neumann architecture imposes strict constraints on the development of intelligent adaptive systems. The requirements of substantial computing power to process and analyse complex data make such an approach impractical to be used in implementing smart systems. Neuromorphic engineering has produced promising results in applications such as electronic sensing, networking architectures and complex data processing. This interdisciplinary field takes inspiration from neurobiological architecture and emulates these characteristics using analogue Very Large Scale Integration (VLSI). The unconventional approach of exploiting the non-linear current characteristics of transistors has aided in the development of low-power adaptive systems that can be implemented in intelligent systems. The neuromorphic approach is widely applied in electronic sensing, particularly in vision, auditory, tactile and olfactory sensors. While conventional sensors generate a huge amount of redundant output data, neuromorphic sensors implement the biological concept of spike-based output to generate sparse output data that corresponds to a certain sensing event. The operation principle applied in these sensors supports reduced power consumption with operating efficiency comparable to conventional sensors. Although neuromorphic sensors such as Dynamic Vision Sensor (DVS), Dynamic and Active pixel Vision Sensor (DAVIS) and AEREAR2 are steadily expanding their scope of application in real-world systems, the lack of spike-based data processing algorithms and complex interfacing methods restricts its applications in low-cost standalone autonomous systems. This research addresses the issue of interfacing between neuromorphic sensors and digital neuromorphic circuits. Current interfacing methods of these sensors are dependent on computers for output data processing. This approach restricts the portability of these sensors, limits their application in a standalone system and increases the overall cost of such systems. The proposed methodology simplifies the interfacing of these sensors with digital neuromorphic processors by utilizing AER communication protocols and neuromorphic hardware developed under the Convolution AER Vision Architecture for Real-time (CAVIAR) project. The proposed interface is simulated using a JAVA model that emulates a typical spikebased output of a neuromorphic sensor, in this case an olfactory sensor, and functions that process this data based on supervised learning. The successful implementation of this simulation suggests that the methodology is a practical solution and can be implemented in hardware. The JAVA simulation is compared to a similar model developed in Nengo, a standard large-scale neural simulation tool. The successful completion of this research contributes towards expanding the scope of application of neuromorphic sensors in standalone intelligent systems. The easy interfacing method proposed in this thesis promotes the portability of these sensors by eliminating the dependency on computers for output data processing. The inclusion of neuromorphic Field Programmable Gate Array (FPGA) board allows reconfiguration and deployment of learning algorithms to implement adaptable systems. These low-power systems can be widely applied in biosecurity and environmental monitoring. With this thesis, we suggest directions for future research in neuromorphic standalone systems based on neuromorphic olfaction

Development of scintillator based coded-aperture neutron imager for nuclear decommissioning

This thesis documents a proof-of-concept study of a novel, scintillator based, coded-aperture approach to neutron detection. Developments presented in this document suggest that coded-aperture approach, previously mainly associated with photon detectors, can be adapted for a small scale neutron detector. This work represents an innovative, scintillator based approach for small scale radiation detector aimed at nuclear decommissioning applications. A novel pixelated plastic scintillator was designed and built in this work. Scintillator cells 2.8 x 2.8 x 15 mm each), build of EJ-299-34 plastic were manufactured and arranged into a 13 x 13 array. The plastic scintillator which was used in this research was sensitive to both gamma and neutron fields. Experimental data were obtained for various solid scintillator samples and a comparison of a number of pulse shape discrimination techniques was performed. Prior to the experimental work, a simulation based study identified potential candidates for the scintillation material, as well as characterised the mixed-field environment, provided by 252Cf at Lancaster University, UK. Suitable coded-aperture materials were also computationally identified, and were subsequently used to manufacture a tungsten coded aperture, based on modified uniformly redundant array design technique. Pixelated nature of the coded-aperture based approach to radiation imaging allows the lateral resolution of the image to be improved, without affecting the signal-to-noise ratio. The focal point of this technique is located in the coded-aperture design and the scintillator. Modulation properties of the rank-7 coded aperture, made of tungsten using additive manufacturing techniques, were investigated. The experiment was performed using 137Cs gamma-ray calibration source at Lancaster University. Data obtained were subsequently used to perform the localisation of the point source used in this study. The idea of using tungsten coded aperture for dual-particle imaging was also simulated using Monte Carlo techniques (MCNPX) prior to the experimental work. The pulse shape discrimination performance of the pixelated organic plastic scintillator was investigated. The scintillator was exposed to a mixed-field environment provided by 252Cf and its performance was compared to that of a cylindrical plastic sample. Tests were also carried out in moderated neutron and gamma-ray fields of 252Cf. Suitable pixelated photodetectors, together with associated readout electronics circuitry, were also identified

Strategies for neural networks in ballistocardiography with a view towards hardware implementation

A thesis submitted for the degree of Doctor of Philosophy at the University of LutonThe work described in this thesis is based on the results of a clinical trial conducted by the research team at the Medical Informatics Unit of the University of Cambridge, which show that the Ballistocardiogram (BCG) has prognostic value in detecting impaired left ventricular function before it becomes clinically overt as myocardial infarction leading to sudden death. The objective of this study is to develop and demonstrate a framework for realising an on-line BCG signal classification model in a portable device that would have the potential to find pathological signs as early as possible for home health care. Two new on-line automatic BeG classification models for time domain BeG classification are proposed. Both systems are based on a two stage process: input feature extraction followed by a neural classifier. One system uses a principal component analysis neural network, and the other a discrete wavelet transform, to reduce the input dimensionality. Results of the classification, dimensionality reduction, and comparison are presented. It is indicated that the combined wavelet transform and MLP system has a more reliable performance than the combined neural networks system, in situations where the data available to determine the network parameters is limited. Moreover, the wavelet transfonn requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced. Overall, a methodology for realising an automatic BeG classification system for a portable instrument is presented. A fully paralJel neural network design for a low cost platform using field programmable gate arrays (Xilinx's XC4000 series) is explored. This addresses the potential speed requirements in the biomedical signal processing field. It also demonstrates a flexible hardware design approach so that an instrument's parameters can be updated as data expands with time. To reduce the hardware design complexity and to increase the system performance, a hybrid learning algorithm using random optimisation and the backpropagation rule is developed to achieve an efficient weight update mechanism in low weight precision learning. The simulation results show that the hybrid learning algorithm is effective in solving the network paralysis problem and the convergence is much faster than by the standard backpropagation rule. The hidden and output layer nodes have been mapped on Xilinx FPGAs with automatic placement and routing tools. The static time analysis results suggests that the proposed network implementation could generate 2.7 billion connections per second performance

Verification of Gynaecological Brachytherapy Treatments Using an End-to-End Phantom

High dose rate brachytherapy allows the delivery of radiation internally, with high-dose gradients creating a conformal distribution. The inherent drawback of this treatment exists within small uncertainties producing a large impact on safety and efficacy. Applicator displacement was ret-rospectively simulated for 29 cervical cancer treatments to determine a critical shift in applicator position. A 2 mm shift in the anterior and posterior directions was detrimental to the bladder and rectum, respectively and a 4 mm shift in all directions caused a critical reduction in HR-CTV cover-age. These findings indicate the importance of quality assurance practices that mitigate applicator displacement. Furthermore, the source localisation accuracy required for cervical brachytherapy was quantified. HDR gynaecological brachytherapy relies on 3D imaging, contouring, precise reconstruc-tion of applicator position and transfer of data to the afterloading device. To evaluate this process an end-to-end phantom was developed, which consists of a component that houses gynaecological applicators and the Magic Plate 987 (MP987), created by the Centre of Medical and Radiation Physics, University of Wollongong. The 21 × 22.5 cm2 silicon diode array facilitates source tracking at clinically relevant depths. A characterisation of the MP987 for HDR source tracking has been performed, producing an error in dwell time and position of 0.1s and 0.25 mm respectively, for dwell times greater than 5 s. Source tracking accuracy is a function of both dwell time and distance from detector to source. The End-to-end phantom has verified both vaginal and cervical treatments. For a vaginal treat-ment, the mean residual in dwell position is within (0.24 ± 0.01) mm for all directions, with the difference in dwell time being (0.10 ± 0.01) s. Catheter swap, indexer length and activity miscalibration errors were all detected within the vaginal therapy end-to-end test. Validation of the End-to-end phantom for a cervical brachytherapy treatment produced a mean difference of (3.49 ± 0.57) mm,(4.74 ± 0.77) mm, (6.14± 1) mm in the X, Y and Z directions respectively, with a dwell time differ-ence of (0.19 ± 0.03) s. The localisation accuracy achieved is below the critical displacement value established within the treatment planning study. Improvement in co-registration and Z localisation methodologies will provide better outcomes for cervical cases. The End-to-end phantom successfully verifies the procedure for HDR gynaecological brachytherapy treatments, enabling safe and effective patient care

An automated fluorescence lifetime imaging multiwell plate reader: application to high content imaging of protein interactions and label free readouts of cellular metabolism

This thesis reports on work performed in the development and application of an automated plate reading microscope implementing wide field time gated fluorescence lifetime imaging technology. High content analysis (HCA) imaging assays enabled by automated microscopy platforms allow hundreds of conditions to be tested in a single experiment. Though fluorescence lifetime imaging (FLIM) is established in life sciences applications as a method whereby quantitative information may be extracted from time-resolved fluorescence signals, FLIM has not been widely adopted in an HCA context. The FLIM plate reader developed throughout this PhD has been designed to allow HCA-FLIM experiments to be performed and has been demonstrated to be capable of recording multispectral, FLIM and bright field data from 600 fields of view in less than four hours. FLIM is commonly used as a means of reading out Förster resonance energy transfer (FRET) between fluorescent fusion proteins in cells. Using the FLIM plate reader to investigate large populations of cells per experimental condition without significant user input has allowed statistically significant results to be obtained in FRET experiments that present relatively small changes in mean fluorescent lifetime. This capability has been applied to investigations of FOXM1 SUMOylation in response to anthracycline treatment, and to studies of the spatiotemporal activation profiles of small GTPases. Furthermore, the FLIM plate reader allows FLIM-FRET to be applied to protein-protein interaction screening. The application of the instrument to screening RASSF proteins for interaction with MST1 is discussed. The FLIM plate reader was also configured to utilise ultraviolet excitation radiation and optimised for the measurement of autofluorescence lifetime for label-free assays of biological samples. Experiments investigating the autofluorescence lifetime of live cells under the influence of metabolic modulators are presented alongside the design considerations necessary when using UV excitation for HCA-FLIM.Open Acces

Development and application of fluorescence lifetime imaging and super-resolution microscopy

This PhD thesis reports the development and application of fluorescence imaging technologies for studying biological processes on spatial scales below the diffraction limit. Two strategies were addressed: firstly fluorescence lifetime imaging (FLIM) to study molecular processes, e.g. using Förster resonance energy transfer (FRET) to read out protein interactions, and secondly direct imaging of nanostructure using super-resolution microscopy (SRM). For quantitative FRET readouts, the development and characterisation of an automated multiwell plate FLIM microscope for high content analysis (HCA) is described. Open source software was developed for the data acquisition and analysis, and approaches to improve the performance of time-gated imaging for FLIM were evaluated including different methods to despeckle the laser illumination and testing of an enhanced detector. This instrument was evaluated using standard fluorescent dye samples and cells expressing fluorescent protein-based FRET constructs. It was applied to an assay of live cells expressing a FRET biosensor and to FRET readouts of aggregation of a membrane receptor (DDR1) in fixed cells. A novel instrument, combining structured illumination microscopy (SIM) with FLIM, was developed to explore the combination of SRM and FLIM-FRET readouts. This enabled the simultaneous mapping of molecular readouts with FLIM and super-resolved imaging. The SIM+FLIM system was applied to image collagen-stimulated DDR1 aggregation in cells, to image DNA structures during the cell cycle and to explore interactions between cell organelles. A novel SRM approach based on a stimulated emission of depletion (STED) microscope incorporating a spatial light modulator (SLM) was developed to provide straightforward robust alignment with collinear excitation/depletion beams, aberration correction, an extended field of view and multiple beam scanning for faster STED image acquisition. The performance of easySLM-STED was evaluated by imaging bead samples, labelled vimentin in Vero cells and the synaptonemal complex in homologs of C. elegans germlines.Open Acces