Polarimetric remote sensing system analysis: Digital Imaging and Remote Sensing Image Generation (DIRSIG) model validation and impact of polarization phenomenology on material discriminability

In addition to spectral information acquired by traditional multi/hyperspectral systems, passive electro optical and infrared (EO/IR) polarimetric sensors also measure the polarization response of different materials in the scene. Such an imaging modality can be useful in improving surface characterization; however, the characteristics of polarimetric systems have not been completely explored by the remote sensing community. Therefore, the main objective of this research was to advance our knowledge in polarimetric remote sensing by investigating the impact of polarization phenomenology on material discriminability. The first part of this research focuses on system validation, where the major goal was to assess the fidelity of the polarimetric images simulated using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. A theoretical framework, based on polarization vision models used for animal vision studies and industrial defect detection applications, was developed within which the major components of the polarimetric image chain were validated. In the second part of this research, a polarization physics based approach for improved material discriminability was proposed. This approach utilizes the angular variation in the polarization response to infer the physical characteristics of the observed surface by imaging the scene in three different view directions. The usefulness of the proposed approach in improving detection performance in the absence of apriori knowledge about the target geometry was demonstrated. Sensitivity analysis of the proposed system for different scene related parameters was performed to identify the imaging conditions under which the material discriminability is maximized. Furthermore, the detection performance of the proposed polarimetric system was compared to that of the hyperspectral system to identify scenarios where polarization information can be very useful in improving the target contrast

Industrial ecology and industry symbiosis for environmental sustainability - Definitions, Frameworks and Applications

Chapter 1: An Introduction to the Closed-loop Concept in Industrial Ecology. Abstract - This introductory chapter explains the fundamental problem of a linear transformation representation used in operations management (OM) to the development of environmental sustainability. Linear transformation thinking needs to be replaced by closed-loop system thinking. IE and IS can help to achieve this development. This chapter explores the basic concepts in relation to IE, including biological ecosystem, industrial ecosystem, sub-ecosystems and their interactions with the ecosystem of the Earth. IE considers the development of high level closed-loop industrial ecosystems as its ultimate goal through mimicking key principles of biological ecosystems. An industrial ecosystem needs to work towards high level closed-loop material exchanges and high efficiency of energy cascading. The system boundary is subject to study purposes and extended system thinking should be applied. Keywords: Linear transformation, Closed-loop system, Biological ecosystem, Industrial ecosystem, Industrial Ecology, Industrial Symbiosis Chapter 2: Industrial Ecology and Industrial Symbiosis - Definitions and Development Histories. Abstract - Various definitions of Industrial Ecology (IE) and Industrial Symbiosis (IS) have been provided in the literature over the past thirty years. These definitions have offered some insights but also confusion due to inconsistency. IE, as an interdisciplinary study field, develops and applies different approaches in its four interrelated areas: industrial ecosystem, IS, industrial metabolism (IM), and environmental legislation and regulations. The ultimate goal of IE is to develop nearly closed-loop industrial ecosystems to enhance environmental sustainability. IS focuses on the development of knowledge webs of novel material, energy and waste exchanges to facilitate the establishment of synergies to support the achievement of this IE goal. The difference between IE and IS lies in the focus, instead of the scale of economy. Keywords: Industrial Ecology, Industrial Symbiosis, Definitions, Development histories, Relationships between Industrial Ecology and Industrial Symbiosis Chapter 3 - Industrial Ecology Applications in the Four Areas. Abstract - Industrial ecology (IE) can be applied in four interrelated study areas: industrial ecosystem, industrial symbiosis (IS), industrial metabolism (IM), and legislation and regulations for IE applications. Different methods can be used to determine the boundary of an industrial ecosystem: material-based, product-based, and geographic-based. IS applications have shifted from a self-organising to planned or facilitated practice. IM establishes its position within IE in quantifying the efficiency and rates of material, waste and energy exchanges over the total corresponding flow to evaluate the closed-loop status of an industrial ecosystem. Legislation and regulations on waste need to reflect the IE’s view of waste as resources. The integration of these four areas is critical for the success of IE applications to fulfil its potential to improve environmental sustainability. Keywords: Applications of Industrial Ecology, Industrial ecosystem boundaries, Self-organised, planned, and facilitated Industrial Symbiosis, Efficiency of resource flows in Industrial Metabolism, Legislation and regulations for Industrial Ecology applications Chapter 4 - Applications of Industrial Symbiosis Abstract - Industrial Symbiosis (IS), a study area within Industrial Ecology (IE), focuses on the knowledge web establishment of novel exchanges for synergies among companies to develop industrial ecosystems. Three types of IS applications have been explored in the literature: regional community-based IS, national IS programmes, and eco-industrial parks (EIPs). These IS applications have offered valuable lessons. Critical success factors drawn from these practices are: an IS coordinating centre, economic and environmental gains in the vision, a large database of knowledge webs for potential symbiotic exchanges, early involvement of participating companies, and government investment at the start. IS applications need not be restricted by geographic proximity. Industrial clusters also need to be transformed into eco-industrial clusters. The transformation requires planned and facilitated IS and long-term vision. Key words: Industrial Symbiosis Applications, Kalundborg Industrial Symbiosis (IS), the UK national IS programme (NISP), Eco-Industrial Parks (EIPs), Geographic Proximity, Eco-Industrial Clusters (EICs)   Chapter 5 - Life Cycle Thinking and Analysis, Design for Environment, and Industrial Ecology Frameworks. Abstract - This chapter explores the product life cycle, life cycle analytical tools, and design for the environment (DfE) methodology. The product life cycle from an operations management (OM) perspective includes material acquisition, manufacturing, distribution, use, and after-use. DfE explores eco-design options at each stage of this product life cycle to proactively reduce the impact of industrial activities on the environment. This chapter also presents two Industrial Ecology (IE) frameworks, one at a factor level and the other one at a supply chain level. These two frameworks illustrate the importance of integration and collaboration among different parts and parties within and across industrial ecosystems to increase levels of closed-loop material, energy and waste flows, which reduce their interaction with natural systems, hence the reduced impact. Key words: Life cycle thinking/analysis, design for environment (DfE), industrial ecology (IE) frameworks Chapter 6 - Challenges for Applying Industrial Ecology And Industrial Ecology Future Development. Abstract - This chapter explores four challenges for Industrial Ecology (IE) applications: paradigm shift from linear to closed-loop thinking, restriction lift in legislation and regulations on waste, establishment of knowledge webs, and development of symbiotic and recycling networks. Future development of IE is reflected in each of its study areas. In the area of ‘industrial ecosystem’, features and limitations of different types of industrial ecosystems require further exploration and extended system thinking. For IS, development of knowledge webs, symbiotic networks, and infrastructure of end-life-waste collection process are further research agendas. For IM, quantification methods of resource flows in industrial ecosystems require further development. For environmental legislation and regulations, alignment with policy-makers needs to be explored in order to support IE applications on a much larger scale. Keywords: Challenges for applying Industrial Ecology (IE) and Industrial Symbiosis (IS), Future development of Industrial Ecology (IE

High Performance Customizable Architecture for Machine Vision Applications

Vision based applications are present anywhere. A special market is industry, allowing to improve product quality and to reduce manufacturing costs. The vision systems applied to industries are known as machine vision systems. These systems must meet time constraints to operate in real time. Generally the production lines are more and more fasters, and the time to process and bring a response is minimal. For this reasons, dedicated architectures are emplaced. In this work a review of several commercial systems is presented, as well a proposed architecture is depicted. The architecture is concern as a customizable platform, avoiding having knowledge in hardware description languages. It is based on massive parallelism to achieve the maximum processing performance. Several optimizations at different levels are applied to increase the final system speedup. Also, time and area metrics are reported, showing that the architecture is well suitable for real time video processing in industrial applications.Facultad de Informátic

A single-chip real-Time range finder

Range finding are widely used in various industrial applications, such as machine vision, collision avoidance, and robotics. Presently most range finders either rely on active transmitters or sophisticated mechanical controllers and powerful processors to extract range information, which make the range finders costly, bulky, or slowly, and limit their applications. This dissertation is a detailed description of a real-time vision-based range sensing technique and its single-chip CMOS implementation. To the best of our knowledge, this system is the first single chip vision-based range finder that doesn't need any mechanical position adjustment, memory or digital processor. The entire signal processing on the chip is purely analog and occurs in parallel. The chip captures the image of an object and extracts the depth and range information from just a single picture. The on-chip, continuous-time, logarithmic photoreceptor circuits are used to couple spatial image signals into the range-extracting processing network. The photoreceptor pixels can adjust their operating regions, simultaneously achieving high sensitivity and wide dynamic range. The image sharpness processor and Winner-Take-All circuits are characterized and analyzed carefully for their temporal bandwidth and detection performance. The mathematical and optical models of the system are built and carefully verified. A prototype based on this technique has been fabricated and tested. The experimental results prove that the range finder can achieve acceptable range sensing precision with low cost and excellent speed performance in short-to-medium range coverage. Therefore, it is particularly useful for collision avoidance

Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition

Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically cornbine botton-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from incorrect labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs (cns.bu.edulvisionlab and cns.bu.edu/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.Air Force Office of Scientific Research (F40620-01-1-0423); National Geographic-Intelligence Agency (NMA 201-001-1-2016); National Science Foundation (SBE-0354378; BCS-0235298); Office of Naval Research (N00014-01-1-0624); National Geospatial-Intelligence Agency and the National Society of Siegfried Martens (NMA 501-03-1-2030, DGE-0221680); Department of Homeland Security graduate fellowshi