Computer Vision System-On-Chip Designs for Intelligent Vehicles

Intelligent vehicle technologies are growing rapidly that can enhance road safety, improve transport efficiency, and aid driver operations through sensors and intelligence. Advanced driver assistance system (ADAS) is a common platform of intelligent vehicle technologies. Many sensors like LiDAR, radar, cameras have been deployed on intelligent vehicles. Among these sensors, optical cameras are most widely used due to their low costs and easy installation. However, most computer vision algorithms are complicated and computationally slow, making them difficult to be deployed on power constraint systems. This dissertation investigates several mainstream ADAS applications, and proposes corresponding efficient digital circuits implementations for these applications. This dissertation presents three ways of software / hardware algorithm division for three ADAS applications: lane detection, traffic sign classification, and traffic light detection. Using FPGA to offload critical parts of the algorithm, the entire computer vision system is able to run in real time while maintaining a low power consumption and a high detection rate. Catching up with the advent of deep learning in the field of computer vision, we also present two deep learning based hardware implementations on application specific integrated circuits (ASIC) to achieve even lower power consumption and higher accuracy. The real time lane detection system is implemented on Xilinx Zynq platform, which has a dual core ARM processor and FPGA fabric. The Xilinx Zynq platform integrates the software programmability of an ARM processor with the hardware programmability of an FPGA. For the lane detection task, the FPGA handles the majority of the task: region-of-interest extraction, edge detection, image binarization, and hough transform. After then, the ARM processor takes in hough transform results and highlights lanes using the hough peaks algorithm. The entire system is able to process 1080P video stream at a constant speed of 69.4 frames per second, realizing real time capability. An efficient system-on-chip (SOC) design which classifies up to 48 traffic signs in real time is presented in this dissertation. The traditional histogram of oriented gradients (HoG) and support vector machine (SVM) are proven to be very effective on traffic sign classification with an average accuracy rate of 93.77%. For traffic sign classification, the biggest challenge comes from the low execution efficiency of the HoG on embedded processors. By dividing the HoG algorithm into three fully pipelined stages, as well as leveraging extra on-chip memory to store intermediate results, we successfully achieved a throughput of 115.7 frames per second at 1080P resolution. The proposed generic HoG hardware implementation could also be used as an individual IP core by other computer vision systems. A real time traffic signal detection system is implemented to present an efficient hardware implementation of the traditional grass-fire blob detection. The traditional grass-fire blob detection method iterates the input image multiple times to calculate connected blobs. In digital circuits, five extra on-chip block memories are utilized to save intermediate results. By using additional memories, all connected blob information could be obtained through one-pass image traverse. The proposed hardware friendly blob detection can run at 72.4 frames per second with 1080P video input. Applying HoG + SVM as feature extractor and classifier, 92.11% recall rate and 99.29% precision rate are obtained on red lights, and 94.44% recall rate and 98.27% precision rate on green lights. Nowadays, convolutional neural network (CNN) is revolutionizing computer vision due to learnable layer by layer feature extraction. However, when coming into inference, CNNs are usually slow to train and slow to execute. In this dissertation, we studied the implementation of principal component analysis based network (PCANet), which strikes a balance between algorithm robustness and computational complexity. Compared to a regular CNN, the PCANet only needs one iteration training, and typically at most has a few tens convolutions on a single layer. Compared to hand-crafted features extraction methods, the PCANet algorithm well reflects the variance in the training dataset and can better adapt to difficult conditions. The PCANet algorithm achieves accuracy rates of 96.8% and 93.1% on road marking detection and traffic light detection, respectively. Implementing in Synopsys 32nm process technology, the proposed chip can classify 724,743 32-by-32 image candidates in one second, with only 0.5 watt power consumption. In this dissertation, binary neural network (BNN) is adopted as a potential detector for intelligent vehicles. The BNN constrains all activations and weights to be +1 or -1. Compared to a CNN with the same network configuration, the BNN achieves 50 times better resource usage with only 1% - 2% accuracy loss. Taking car detection and pedestrian detection as examples, the BNN achieves an average accuracy rate of over 95%. Furthermore, a BNN accelerator implemented in Synopsys 32nm process technology is presented in our work. The elastic architecture of the BNN accelerator makes it able to process any number of convolutional layers with high throughput. The BNN accelerator only consumes 0.6 watt and doesn\u27t rely on external memory for storage

Non-cancer Diseases of Korean Atomic Bomb Survivors in Residence at Hapcheon, Republic of Korea

Many Koreans, in addition to Japanese, were killed or injured by the atomic bombs detonated over Hiroshima and Nagasaki, Japan, in 1945. Our study examined non-cancer diseases of Korean A-bomb survivors in residence at Hapcheon, Republic of Korea and evaluated whether they had significantly higher prevalence of non-cancer diseases than non-exposed people. We evaluated a number of tests, including anthropometric measurements, blood pressure, blood chemistry, hepatitis B surface antigen, and urinalysis, of survivors (n=223) and controls (n=372). Univariate analysis revealed significantly lower fasting glucose and creatinine, and higher diastolic blood pressure, aspartate aminotransferase, alanine aminotransferase, and blood urea nitrogen levels in the survivors than in the controls. The calculation of crude prevalence ratios (PRs) revealed that A-bomb survivors had a significantly higher prevalence of hypertension (PR, 1.16; 95% CI, 1.00-1.35) and chronic liver disease (2.20; 1.59-3.06) than controls. After adjusting for covariates (age, sex, body mass index, marital status, education, alcohol consumption, and smoking), A-bomb survivors had a significantly higher prevalence of hypertension (1.24; 1.06-1.44), chronic liver disease (2.07; 1.51-2.84), and hypercholesterolemia (1.79; 1.11-2.90) than controls. This study suggests that A-bomb exposure is associated with a higher prevalence of non-cancer diseases in Korean survivors

Corporate responsibility, supply chain partnership and performance: An empirical examination

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2012 Elsevier B.V.Unlike corporate and business levels, there is little research examining corporate responsibility (CR) at the functional level of the firm including supply chain strategy. The results of a firm-level survey show that CR internal awareness, and monitoring CR performance are positively related to the supply chain partnership approach, however sharing CR best practices is negatively associated. Furthermore, the impact of CR on firm performance is mediated by the functional behaviour of supply chain partnership formation. Our study provides support for including CR awareness building and monitoring in the development of partnerships but cautions against imposing CR best practices on suppliers

Messing with nature? Exploring public perceptions of geoengineering in the UK

Anthropogenic influence on the climate – and possible societal responses to it – offers a unique window through which to examine the way people think about and relate to the natural world. This paper reports data from four, one-day deliberative workshops conducted with members of the UK public during early 2012. The workshops focused on geoengineering – the deliberate, large-scale manipulation of the planetary environment – as one of three possible responses to climate change (alongside mitigation and adaptation). Here, we explore one of the most pervasive and wide-ranging themes to emerge from the workshops: whether geoengineering represented an unprecedented human intervention into ‘nature’, and what the moral consequences of this might be. Using the concept of ‘messing with nature’ as an analytical lens, we explore public perceptions of geoengineering. We also reflect on why ‘messing with nature’ was such a focal point for debate and disagreement, and whether the prospect of geoengineering may reveal new dimensions to the way that people think about the natural world, and their relationship to it

Integrating skills profiling and purchasing portfolio management:an opportunity for building purchasing capability

Kralijc’s (1983) purchasing portfolio approach holds that different types of purchases need different sourcing strategies, underpinned by distinct sets of resources and practices. The approach is widely deployed in business and extensively researched, and yet little research has been conducted on how knowledge and skills vary across a portfolio of purchases. This study extends the body of knowledge on purchasing portfolio management, and its application in the strategic development of purchasing in an organization, and on human resource management in the purchasing function. A novel approach to profiling purchasing skills is proposed, which is well suited to dynamic environments which require flexibility. In a survey, experienced purchasing personnel described a specific purchase and profiled the skills required for effective performance in purchasing that item. Purchases were categorized according to their importance to the organization (internally-oriented evaluation of cost and production factors) and to the supply market (externally-oriented evaluation of commercial risk and uncertainty). Through cluster analysis three key types of purchase situations were identified. The skills required for effective purchasing vary significantly across the three clusters (for 22 skills, p<0.01). Prior research shows that global organizations use the purchasing portfolio approach to develop sourcing strategies, but also aggregate analyses to inform the design of purchasing arrangements (local vs global) and to develop their improvement plans. Such organizations would also benefit from profiling skills by purchase type. We demonstrate how the survey can be adapted to provide a management tool for global firms seeking to improve procurement capability, flexibility and performance

Protocol for Nearly Full-Length Sequencing of HIV-1 RNA from Plasma

Nearly full-length genome sequencing of HIV-1 using peripheral blood mononuclear cells (PBMC) DNA as a template for PCR is now a relatively routine laboratory procedure. However, this has not been the case when using virion RNA as the template and this has made full genome analysis of circulating viruses difficult. Therefore, a well-developed procedure for sequencing of full-length HIV-1 RNA directly from plasma was needed. Plasma from U.S. donors representing a range of viral loads (VL) was used to develop the assay. RNA was extracted from plasma and reverse-transcribed. Two or three overlapping regions were PCR amplified to cover the entire viral genome and sequenced for verification. The success of the procedure was sensitive to VL but was routinely successful for VL greater than 105 and the rate declined in proportion to the VL. While the two-amplicon strategy had an advantage of increasing the possibility of amplifying a single species of HIV-1, the three-amplicon strategy was more successful in amplifying samples with low viral loads. This protocol provides a useful tool for molecular analysis to understand the HIV epidemic and pathogenesis, as well as diagnosis, therapy and future vaccine strategies

Mapping the Future: Policy Applications of Climate Vulnerability Mapping in West Africa

We describe the development of climate vulnerability maps for three Sahelian countries – Mali, Burkina Faso, and Niger – and for coastal West Africa, with a focus on the way the maps were designed to meet decision-making needs and their ultimate influence and use in policy contexts. The paper provides a review of the literature on indicators and maps in the science-policy interface. We then assess the credibility, salience, and legitimacy of the maps as tools for decision-making. Results suggest that vulnerability maps are a useful boundary object for generating discussions among stakeholders with different objectives and technical backgrounds, and that they can provide useful input for targeting development assistance. We conclude with a discussion of the power of maps to capture policy maker attention, and how this increases the onus on map developers to communicate clearly uncertainties and limitations. The assessment of policy uptake in this paper is admittedly subjective; the article includes a discussion of ways to conduct more objective and rigorous assessments of policy impact so as to better evaluate the value and use of vulnerability mapping in decision-making processes

The design and testing of a dual fiber textile matrix for accelerating surface hemostasis

The standard treatment for severe traumatic injury is frequently compression and application of gauze dressing to the site of hemorrhage. However, while able to rapidly absorb pools of shed blood, gauze fails to provide strong surface (topical) hemostasis. The result can be excess hemorrhage-related morbidity and mortality. We hypothesized that cost-effective materials (based on widespread availability of bulk fibers for other commercial uses) could be designed based on fundamental hemostatic principles to partially emulate the wicking properties of gauze while concurrently stimulating superior hemostasis. A panel of readily available textile fibers was screened for the ability to activate platelets and the intrinsic coagulation cascade in vitro. Type E continuous filament glass and a specialty rayon fiber were identified from the material panel as accelerators of hemostatic reactions and were custom woven to produce a dual fiber textile bandage. The glass component strongly activated platelets while the specialty rayon agglutinated red blood cells. In comparison with gauze in vitro, the dual fiber textile significantly enhanced the rate of thrombin generation, clot generation as measured by thromboelastography, adhesive protein adsorption and cellular attachment and activation. These results indicate that hemostatic textiles can be designed that mimic gauze in form but surpass gauze in ability to accelerate hemostatic reactions