Enhanced spectral modeling of sparse aperture imaging systems

The remote sensing community continues to pursue advanced sensor designs and post processing techniques that improve upon the spatial quality of collected overhead imagery. Unfortunately, spaceborne applications frequently encounter launch vehicle fairing and weight constraints that limit the size of the primary aperture that can be utilized for a given application. Sparse aperture telescopes provide a potential avenue for overcoming some of the size and weight issues associated with deploying a large monolithic mirror system. These telescope systems are constructed of smaller subapertures which are phased to form a common image field and thereby synthesize a larger effective primary diameter to obtain higher spatial resolution than that achievable with a single subaperture. Much of the research conducted to date in this sparse aperture arena has focused on the panchromatic image quality performance of various optical configurations through approaches that make use of resampled, gray-scale imagery products. The research effort performed in conjunction with this dissertation focused on laying the groundwork for synthetic model-based approaches for evaluating the optical performance of sparse aperture collection systems with enhanced spectral fidelity and a polychromatic object scene. It entailed a fundamental investigation and demonstration of the first-principles physics required to model such imaging systems. This theoretical development ultimately led to the generation of a modeling concept that more rigorously addresses the spectral characteristics of classic sparse aperture optical configurations used in remote sensing applications. To demonstrate the proposed theoretical foundation, a proof-of-concept digital model was implemented that incorporates essential components of the fundamental physical processes involved with typical sparse aperture collection systems, including any potential spectral effects unique to these design configurations. In addition to modeling the detected imagery derived from the collection system, there was also an interest in exploring the quality implications of image restoration techniques typically required for sparse aperture imaging systems. Several variations of the classic Wiener-Helstrom filter were implemented and investigated in response to this research objective. The basic restoration methodologies pursued in this effort provide a foundation for research into more advanced techniques in the future. Finally, a top-level sensitivity study of the image quality performance of various sparse aperture pupil configurations subjected to varying levels of subaperture dephasing and/or aberrations was performed. This exploration of the trade space focused on a panchromatic detection scenario and attempted to bound the performance region where unique spectral quality issues are observed for the unconventional collection telescopes targeted through this research effort

Using Multispectral Information to Decrease the Spectral Artifacts in Sparse-Aperture Imagery

Optical sparse-aperture telescopes represent a promising new technology to increase the effective diameter of an optical system while reducing its weight and stowable size. The sub-apertures of a sparse-aperture system are phased to synthesize a telescope system that has a larger effective aperture than any of the independentsub-apertures. Sparse-apertures have mostly been modeled to date using a gray-world approximation where the input is a grayscale image. The gray-world model makes use of a polychromatic optical transfer function (OTF) where the spectral OTFs are averaged to form a single OTF. This OTF is then convolved with the grayscale image to create the resultant sparse-aperture image. The model proposed here uses a spectral image-cube as the input to create a panchromatic or multispectral result. These outputs better approximate an actual system because there is a higher spectral fidelity present than a gray-world model. Unlike its Cassegrain counterpart that has a well behaved OTF, the majority of sparse-aperture OTFs have very oscillatory and attenuated natures. When a spectral sparse-aperture model is used, spectral artifacts become apparent when thephasing errors increase beyond a certain threshold. This threshold can be based in part on the type of phasing error (i.e. piston, tip/tilt, and the amount present in each sub-aperture), as well as the collection conditions, including configuration, signal-to-noise ratio (SNR), and fill factor.This research addresses whether integrating a restored multispectral sparse-aperture image into a panchromatic image will decrease the amount of spectral artifacts present. The restored panchromatic image created from integrating multispectral images is compared to a conventional panchromatic sparse-aperture image. Conclusionsare drawn through image quality analysis and the change in spectral artifacts

The Climate CoLab: Large scale model-based collaborative planning

The Climate CoLab is a system to help thousands of people around the world collectively develop plans for what humans should do about global climate change. This paper shows how the system combines three design elements (model-based planning, on-line debates, and electronic voting) in a synergistic way. The paper also reports early usage experience showing that: (a) the system is attracting a continuing stream of new and returning visitors from all over the world, and (b) the nascent community can use the platform to generate interesting and high quality plans to address climate change. These initial results indicate significant progress towards an important goal in developing a collective intelligence system - the formation of a large and diverse community collectively engaged in solving a single problem.Cisco Systems, Inc.Argosy FoundationMIT Energy InitiativeMIT Sloan Sustainability Initiativ

Wisdom of Stakeholder Crowds in Complex Social-ecological Systems

Sustainable management of natural resources requires adequate scientific knowledge about complex relationships between human and natural systems. Such understanding is difficult to achieve in many contexts due to data scarcity and knowledge limitations. We explore the potential of harnessing the collective intelligence of resource stakeholders to overcome this challenge. Using a fisheries example, we show that by aggregating the system knowledge held by stakeholders through graphical mental models, a crowd of diverse resource users produces a system model of social–ecological relationships that is comparable to the best scientific understanding. We show that the averaged model from a crowd of diverse resource users outperforms those of more homogeneous groups. Importantly, however, we find that the averaged model from a larger sample of individuals can perform worse than one constructed from a smaller sample. However, when averaging mental models within stakeholder-specific subgroups and subsequently aggregating across subgroup models, the effect is reversed. Our work identifies an inexpensive, yet robust way to develop scientific understanding of complex social–ecological systems by leveraging the collective wisdom of non-scientist stakeholders

Harnessing the Collective Intelligence of Stakeholders for Conservation

Incorporating relevant stakeholder input into conservation decision making is fundamentally challenging yet critical for understanding both the status of, and human pressures on, natural resources. Collective intelligence (CI), defined as the ability of a group to accomplish difficult tasks more effectively than individuals, is a growing area of investigation, with implications for improving ecological decision making. However, many questions remain about the ways in which emerging internet technologies can be used to apply CI to natural resource management. We examined how synchronous social‐swarming technologies and asynchronous “wisdom of crowds” techniques can be used as potential conservation tools for estimating the status of natural resources exploited by humans. Using an example from a recreational fishery, we show that the CI of a group of anglers can be harnessed through cyber‐enabled technologies. We demonstrate how such approaches – as compared against empirical data – could provide surprisingly accurate estimates that align with formal scientific estimates. Finally, we offer a practical approach for using resource stakeholders to assist in managing ecosystems, especially in data‐poor situations

Laboratory Techniques Used to Diagnose Constitutional Platelet Dysfunction

International audiencePlatelets play a major role in primary hemostasis, where activated platelets form plugs to stop hemorrhaging in response to vessel injuries. Defects in any step of the platelet activation process can cause a variety of platelet dysfunction conditions associated with bleeding. To make an accurate diagnosis, constitutional platelet dysfunction (CPDF) should be considered once von Willebrand disease and drug intake are ruled out. CPDF may be associated with thrombocytopenia or a genetic syndrome. CPDF diagnosis is complex, as no single test enables the analysis of all aspects of platelet function. Furthermore, the available tests lack standardization, and repeat tests must be performed in specialized laboratories especially for mild and moderate forms of the disease. In this review, we provide an overview of the laboratory tests used to diagnose CPDF, with a focus on light transmission platelet aggregation (LTA), flow cytometry (FC), and granules assessment. Global tests, mainly represented by LTA, are often initially performed to investigate the consequences of platelet activation on platelet aggregation in a single step. Global test results should be confirmed by additional analytical tests. FC represents an accurate, simple, and reliable test to analyze abnormalities in platelet receptors, and granule content and release. This technique may also be used to investigate platelet function by comparing resting- and activated-state platelet populations. Assessment of granule content and release also requires additional specialized analytical tests. High-throughput sequencing has become increasingly useful to diagnose CPDF. Advanced tests or external research laboratory techniques may also be beneficial in some cases