Efficient reconfigurable architectures for 3D medical image compression

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Recently, the more widespread use of three-dimensional (3-D) imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US) have generated a massive amount of volumetric data. These have provided an impetus to the development of other applications, in particular telemedicine and teleradiology. In these fields, medical image compression is important since both efficient storage and transmission of data through high-bandwidth digital communication lines are of crucial importance. Despite their advantages, most 3-D medical imaging algorithms are computationally intensive with matrix transformation as the most fundamental operation involved in the transform-based methods. Therefore, there is a real need for high-performance systems, whilst keeping architectures exible to allow for quick upgradeability with real-time applications. Moreover, in order to obtain efficient solutions for large medical volumes data, an efficient implementation of these operations is of significant importance. Reconfigurable hardware, in the form of field programmable gate arrays (FPGAs) has been proposed as viable system building block in the construction of high-performance systems at an economical price. Consequently, FPGAs seem an ideal candidate to harness and exploit their inherent advantages such as massive parallelism capabilities, multimillion gate counts, and special low-power packages. The key achievements of the work presented in this thesis are summarised as follows. Two architectures for 3-D Haar wavelet transform (HWT) have been proposed based on transpose-based computation and partial reconfiguration suitable for 3-D medical imaging applications. These applications require continuous hardware servicing, and as a result dynamic partial reconfiguration (DPR) has been introduced. Comparative study for both non-partial and partial reconfiguration implementation has shown that DPR offers many advantages and leads to a compelling solution for implementing computationally intensive applications such as 3-D medical image compression. Using DPR, several large systems are mapped to small hardware resources, and the area, power consumption as well as maximum frequency are optimised and improved. Moreover, an FPGA-based architecture of the finite Radon transform (FRAT)with three design strategies has been proposed: direct implementation of pseudo-code with a sequential or pipelined description, and block random access memory (BRAM)- based method. An analysis with various medical imaging modalities has been carried out. Results obtained for image de-noising implementation using FRAT exhibits promising results in reducing Gaussian white noise in medical images. In terms of hardware implementation, promising trade-offs on maximum frequency, throughput and area are also achieved. Furthermore, a novel hardware implementation of 3-D medical image compression system with context-based adaptive variable length coding (CAVLC) has been proposed. An evaluation of the 3-D integer transform (IT) and the discrete wavelet transform (DWT) with lifting scheme (LS) for transform blocks reveal that 3-D IT demonstrates better computational complexity than the 3-D DWT, whilst the 3-D DWT with LS exhibits a lossless compression that is significantly useful for medical image compression. Additionally, an architecture of CAVLC that is capable of compressing high-definition (HD) images in real-time without any buffer between the quantiser and the entropy coder is proposed. Through a judicious parallelisation, promising results have been obtained with limited resources. In summary, this research is tackling the issues of massive 3-D medical volumes data that requires compression as well as hardware implementation to accelerate the slowest operations in the system. Results obtained also reveal a significant achievement in terms of the architecture efficiency and applications performance.Ministry of Higher Education Malaysia (MOHE), Universiti Tun Hussein Onn Malaysia (UTHM) and the British Counci

Heterogeneous computing systems for vision-based multi-robot tracking

Irwansyah A. Heterogeneous computing systems for vision-based multi-robot tracking. Bielefeld: Universität Bielefeld; 2017

Heterogeneous multicore systems for signal processing

This thesis explores the capabilities of heterogeneous multi-core systems, based on multiple Graphics Processing Units (GPUs) in a standard desktop framework. Multi-GPU accelerated desk side computers are an appealing alternative to other high performance computing (HPC) systems: being composed of commodity hardware components fabricated in large quantities, their price-performance ratio is unparalleled in the world of high performance computing. Essentially bringing “supercomputing to the masses”, this opens up new possibilities for application fields where investing in HPC resources had been considered unfeasible before. One of these is the field of bioelectrical imaging, a class of medical imaging technologies that occupy a low-cost niche next to million-dollar systems like functional Magnetic Resonance Imaging (fMRI). In the scope of this work, several computational challenges encountered in bioelectrical imaging are tackled with this new kind of computing resource, striving to help these methods approach their true potential. Specifically, the following main contributions were made: Firstly, a novel dual-GPU implementation of parallel triangular matrix inversion (TMI) is presented, addressing an crucial kernel in computation of multi-mesh head models of encephalographic (EEG) source localization. This includes not only a highly efficient implementation of the routine itself achieving excellent speedups versus an optimized CPU implementation, but also a novel GPU-friendly compressed storage scheme for triangular matrices. Secondly, a scalable multi-GPU solver for non-hermitian linear systems was implemented. It is integrated into a simulation environment for electrical impedance tomography (EIT) that requires frequent solution of complex systems with millions of unknowns, a task that this solution can perform within seconds. In terms of computational throughput, it outperforms not only an highly optimized multi-CPU reference, but related GPU-based work as well. Finally, a GPU-accelerated graphical EEG real-time source localization software was implemented. Thanks to acceleration, it can meet real-time requirements in unpreceeded anatomical detail running more complex localization algorithms. Additionally, a novel implementation to extract anatomical priors from static Magnetic Resonance (MR) scansions has been included

Applying Artificial Intelligence Planning to Optimise Heterogeneous Signal Processing for Surface and Dimensional Measurement Systems

The need for in-process measurement has surpassed the processing capability of traditional computer hardware. As Industry 4.0 changes the way modern manufacturing occurs, researchers and industry are turning to hardware acceleration to increase the performance of their signal processing to allow real-time process and quality control. This thesis reviewed Industry 4.0 and the challenges that have arisen from transitioning towards a connected smart factory. It has investigated the different hardware acceleration techniques available and the bespoke nature of software that industry and researchers are being forced towards in the pursuit of greater performance. In addition, the application of hardware acceleration within surface and dimensional instrument signal processing was researched and to what extent it is benefitting researchers. The collection of algorithms that the field are using were examined finding significant commonality across multiple instrument types, with work being repeated many times over by different people. The first use of PDDL to optimise heterogenous signal processing within surface and dimensional measurements is proposed. Optical Signal Processing Workspace (OSPW) is presented as a self-optimising software package using GPGPU acceleration using Compute Unified Device Architecture (CUDA)for Nvidia GPUs. OSPW was designed from scratch to be easy to use with very little-to-no programming experience needed, unlike other popular systems such LabVIEW and MATLAB. It provides an intuitive and easy to navigate User Interface (UI) that allows a user to select the signal processing algorithms required, display system outputs, control actuation devices, and modify capture device properties. OSPW automatically profiles the execution time of the signal processing algorithms selected by the user and creates and executes a fully optimised version using an AI planning language, Planning Description Domain Language (PDDL), by selecting the optimum architecture for each signal processing function. OSPW was then evaluated against two case studies, Dispersed Reference Interferometry (DRI) and Line-Scanning Dispersed Interferometry (LSDI). These case studies demonstrated that OSPW can achieve at least21x greater performance than an identical MATLAB implementation with a further 13% improvement found using the PDDL’s heterogenous solution. This novel approach to providing a configurable signal processing library that is self-optimising using AI planning will provide considerable performance gains to researchers and industrial engineers. With some additional development work it will save both academia and industry time and money which can be reinvested to further advance surface and dimensional instrumentation research

Proceedings of the International Workshop on Medical Ultrasound Tomography: 1.- 3. Nov. 2017, Speyer, Germany

Ultrasound Tomography is an emerging technology for medical imaging that is quickly approaching its clinical utility. Research groups around the globe are engaged in research spanning from theory to practical applications. The International Workshop on Medical Ultrasound Tomography (1.-3. November 2017, Speyer, Germany) brought together scientists to exchange their knowledge and discuss new ideas and results in order to boost the research in Ultrasound Tomography

Exploration of cyber-physical systems for GPGPU computer vision-based detection of biological viruses

This work presents a method for a computer vision-based detection of biological viruses in PAMONO sensor images and, related to this, methods to explore cyber-physical systems such as those consisting of the PAMONO sensor, the detection software, and processing hardware. The focus is especially on an exploration of Graphics Processing Units (GPU) hardware for “General-Purpose computing on Graphics Processing Units” (GPGPU) software and the targeted systems are high performance servers, desktop systems, mobile systems, and hand-held systems. The first problem that is addressed and solved in this work is to automatically detect biological viruses in PAMONO sensor images. PAMONO is short for “Plasmon Assisted Microscopy Of Nano-sized Objects”. The images from the PAMONO sensor are very challenging to process. The signal magnitude and spatial extension from attaching viruses is small, and it is not visible to the human eye on raw sensor images. Compared to the signal, the noise magnitude in the images is large, resulting in a small Signal-to-Noise Ratio (SNR). With the VirusDetectionCL method for a computer vision-based detection of viruses, presented in this work, an automatic detection and counting of individual viruses in PAMONO sensor images has been made possible. A data set of 4000 images can be evaluated in less than three minutes, whereas a manual evaluation by an expert can take up to two days. As the most important result, sensor signals with a median SNR of two can be handled. This enables the detection of particles down to 100 nm. The VirusDetectionCL method has been realized as a GPGPU software. The PAMONO sensor, the detection software, and the processing hardware form a so called cyber-physical system. For different PAMONO scenarios, e.g., using the PAMONO sensor in laboratories, hospitals, airports, and in mobile scenarios, one or more cyber-physical systems need to be explored. Depending on the particular use case, the demands toward the cyber-physical system differ. This leads to the second problem for which a solution is presented in this work: how can existing software with several degrees of freedom be automatically mapped to a selection of hardware architectures with several hardware configurations to fulfill the demands to the system? Answering this question is a difficult task. Especially, when several possibly conflicting objectives, e.g., quality of the results, energy consumption, and execution time have to be optimized. An extensive exploration of different software and hardware configurations is expensive and time-consuming. Sometimes it is not even possible, e.g., if the desired architecture is not yet available on the market or the design space is too big to be explored manually in reasonable time. A Pareto optimal selection of software parameters, hardware architectures, and hardware configurations has to be found. To achieve this, three parameter and design space exploration methods have been developed. These are named SOG-PSE, SOG-DSE, and MOGEA-DSE. MOGEA-DSE is the most advanced method of these three. It enables a multi-objective, energy-aware, measurement-based or simulation-based exploration of cyber-physical systems. This can be done in a hardware/software codesign manner. In addition, offloading of tasks to a server and approximate computing can be taken into account. With the simulation-based exploration, systems that do not exist can be explored. This is useful if a system should be equipped, e.g., with the next generation of GPUs. Such an exploration can reveal bottlenecks of the existing software before new GPUs are bought. With MOGEA-DSE the overall goal—to develop a method to automatically explore suitable cyber-physical systems for different PAMONO scenarios—could be achieved. As a result, a rapid, reliable detection and counting of viruses in PAMONO sensor data using high-performance, desktop, laptop, down to hand-held systems has been made possible. The fact that this could be achieved even for a small, hand-held device is the most important result of MOGEA-DSE. With the automatic parameter and design space exploration 84% energy could be saved on the hand-held device compared to a baseline measurement. At the same time, a speedup of four and an F-1 quality score of 0.995 could be obtained. The speedup enables live processing of the sensor data on the embedded system with a very high detection quality. With this result, viruses can be detected and counted on a mobile, hand-held device in less than three minutes and with real-time visualization of results. This opens up completely new possibilities for biological virus detection that were not possible before

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems