Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Spatially resolved X-ray micro/nano-spectroscopy and imaging on the model organism Daphnia magna using laboratory and synchrotron sources

This work demonstrates and further develops spatially resolved laboratory and synchrotron X-ray spectroscopic and imaging experiments to answer specific ecotoxicological questions on the model organism Daphnia magna. The first phase of this work consisted of developing appropriate sample preparation procedures for this organism in order to be able to perform X-ray analyses on Daphnia magna subjected to ecotoxicological experiments. In the second phase, X-ray set-ups at different laboratories and synchrotron facilities were optimized and characterized in order to be able to perform the X-ray spectral imaging experiments of interest on Daphnia magna. Finally, various data reduction strategies were employed to process the vast amount of spectral data for evaluation and interpretation

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research

Research reports: 1990 NASA/ASEE Summer Faculty Fellowship Program

Reports on the research projects performed under the NASA/ASEE Summer Faculty Fellowship Program are presented. The program was conducted by The University of Alabama and MSFC during the period from June 4, 1990 through August 10, 1990. Some of the topics covered include: (1) Space Shuttles; (2) Space Station Freedom; (3) information systems; (4) materials and processes; (4) Space Shuttle main engine; (5) aerospace sciences; (6) mathematical models; (7) mission operations; (8) systems analysis and integration; (9) systems control; (10) structures and dynamics; (11) aerospace safety; and (12) remote sensin

Single image super resolution for spatial enhancement of hyperspectral remote sensing imagery

Hyperspectral Imaging (HSI) has emerged as a powerful tool for capturing detailed spectral information across various applications, such as remote sensing, medical imaging, and material identification. However, the limited spatial resolution of acquired HSI data poses a challenge due to hardware and acquisition constraints. Enhancing the spatial resolution of HSI is crucial for improving image processing tasks, such as object detection and classification. This research focuses on utilizing Single Image Super Resolution (SISR) techniques to enhance HSI, addressing four key challenges: the efficiency of 3D Deep Convolutional Neural Networks (3D-DCNNs) in HSI enhancement, minimizing spectral distortions, tackling data scarcity, and improving state-of-the-art performance. The thesis establishes a solid theoretical foundation and conducts an in-depth literature review to identify trends, gaps, and future directions in the field of HSI enhancement. Four chapters present novel research targeting each of the aforementioned challenges. All experiments are performed using publicly available datasets, and the results are evaluated both qualitatively and quantitatively using various commonly used metrics. The findings of this research contribute to the development of a novel 3D-CNN architecture known as 3D Super Resolution CNN 333 (3D-SRCNN333). This architecture demonstrates the capability to enhance HSI with minimal spectral distortions while maintaining acceptable computational cost and training time. Furthermore, a Bayesian-optimized hybrid spectral spatial loss function is devised to improve the spatial quality and minimize spectral distortions, combining the best characteristics of both domains. Addressing the challenge of data scarcity, this thesis conducts a thorough study on Data Augmentation techniques and their impact on the spectral signature of HSI. A new Data Augmentation technique called CutMixBlur is proposed, and various combinations of Data Augmentation techniques are evaluated to address the data scarcity challenge, leading to notable enhancements in performance. Lastly, the 3D-SRCNN333 architecture is extended to the frequency domain and wavelet domain to explore their advantages over the spatial domain. The experiments reveal promising results with the 3D Complex Residual SRCNN (3D-CRSRCNN), surpassing the performance of 3D-SRCNN333. The findings presented in this thesis have been published in reputable conferences and journals, indicating their contribution to the field of HSI enhancement. Overall, this thesis provides valuable insights into the field of HSI-SISR, offering a thorough understanding of the advancements, challenges, and potential applications. The developed algorithms and methodologies contribute to the broader goal of improving the spatial resolution and spectral fidelity of HSI, paving the way for further advancements in scientific research and practical implementations.Hyperspectral Imaging (HSI) has emerged as a powerful tool for capturing detailed spectral information across various applications, such as remote sensing, medical imaging, and material identification. However, the limited spatial resolution of acquired HSI data poses a challenge due to hardware and acquisition constraints. Enhancing the spatial resolution of HSI is crucial for improving image processing tasks, such as object detection and classification. This research focuses on utilizing Single Image Super Resolution (SISR) techniques to enhance HSI, addressing four key challenges: the efficiency of 3D Deep Convolutional Neural Networks (3D-DCNNs) in HSI enhancement, minimizing spectral distortions, tackling data scarcity, and improving state-of-the-art performance. The thesis establishes a solid theoretical foundation and conducts an in-depth literature review to identify trends, gaps, and future directions in the field of HSI enhancement. Four chapters present novel research targeting each of the aforementioned challenges. All experiments are performed using publicly available datasets, and the results are evaluated both qualitatively and quantitatively using various commonly used metrics. The findings of this research contribute to the development of a novel 3D-CNN architecture known as 3D Super Resolution CNN 333 (3D-SRCNN333). This architecture demonstrates the capability to enhance HSI with minimal spectral distortions while maintaining acceptable computational cost and training time. Furthermore, a Bayesian-optimized hybrid spectral spatial loss function is devised to improve the spatial quality and minimize spectral distortions, combining the best characteristics of both domains. Addressing the challenge of data scarcity, this thesis conducts a thorough study on Data Augmentation techniques and their impact on the spectral signature of HSI. A new Data Augmentation technique called CutMixBlur is proposed, and various combinations of Data Augmentation techniques are evaluated to address the data scarcity challenge, leading to notable enhancements in performance. Lastly, the 3D-SRCNN333 architecture is extended to the frequency domain and wavelet domain to explore their advantages over the spatial domain. The experiments reveal promising results with the 3D Complex Residual SRCNN (3D-CRSRCNN), surpassing the performance of 3D-SRCNN333. The findings presented in this thesis have been published in reputable conferences and journals, indicating their contribution to the field of HSI enhancement. Overall, this thesis provides valuable insights into the field of HSI-SISR, offering a thorough understanding of the advancements, challenges, and potential applications. The developed algorithms and methodologies contribute to the broader goal of improving the spatial resolution and spectral fidelity of HSI, paving the way for further advancements in scientific research and practical implementations

Research and technology, 1993. Salute to Skylab and Spacelab: Two decades of discovery

A summary description of Skylab and Spacelab is presented. The section on Advanced Studies includes projects in space science, space systems, commercial use of space, and transportation systems. Within the Research Programs area, programs are listed under earth systems science, space physics, astrophysics, and microgravity science and applications. Technology Programs include avionics, materials and manufacturing processes, mission operations, propellant and fluid management, structures and dynamics, and systems analysis and integration. Technology transfer opportunities and success are briefly described. A glossary of abbreviations and acronyms is appended as is a list of contract personnel within the program areas

Electronic countermeasures applied to passive radar

Passive Radar (PR) is a form of bistatic radar that utilises existing transmitter infrastructure such as FM radio, digital audio and video broadcasts (DAB and DVB-T/T2), cellular base station transmitters, and satellite-borne illuminators like DVB-S instead of a dedicated radar transmitter. Extensive research into PR has been performed over the last two decades across various industries with the technology maturing to a point where it is becoming commercially viable. Nevertheless, despite the abundance of PR literature, there is a scarcity of open literature pertaining to electronic countermeasures (ECM) applied to PR. This research makes the novel contribution of a comprehensive exploration and validation of various ECM techniques and their effectiveness when applied to PR. Extensive research has been conducted to assess the inherent properties of the lluminators of Opportunity to identify their possible weaknesses for the purpose of applying targeted ECM. Similarly, potential jamming signals have also been researched to evaluate their effectiveness as bespoke ECM signals. Whilst different types of PR exist, this thesis focuses specifically on ECM applied to FM radio and DVB-T2 based PR. The results show noise jamming to be effective against FM radio based PR where jamming can be achieved with relatively low jamming power. A waveform study is performed to determine the optimal jamming waveform for an FM radio based PR. The importance of an effective direct signal interference (DSI) canceller is also shown as a means of suppressing the jamming signal. A basic overview of counter-ECM (ECCM) is discussed to counter potential jamming of FM based PR. The two main processing techniques for DVB-T2 based PR, mismatched and inverse filtering, have been investigated and their performance in the presence of jamming evaluated. The deterministic components of the DVB-T2 waveform are shown to be an effective form of attack for both mismatched filtering and inverse filtering techniques. Basic ECCM is also presented to counter potential pilot attacks on DVB-T2 based PR. Using measured data from a PR demonstrator, the application and effectiveness of each jamming technique is clearly demonstrated, evaluated and quantified

Fifteenth Space Simulation Conference: Support the Highway to Space Through Testing

The Institute of Environmental Sciences Fifteenth Space Simulation Conference, Support the Highway to Space Through Testing, provided participants a forum to acquire and exchange information on the state-of-the-art in space simulation, test technology, thermal simulation and protection, contamination, and techniques of test measurements

Vision-Inertial SLAM using Natural Features in Outdoor Environments

Simultaneous Localization and Mapping (SLAM) is a recursive probabilistic inferencing process used for robot navigation when Global Positioning Systems (GPS) are unavailable. SLAM operates by building a map of the robot environment, while concurrently localizing the robot within this map. The ultimate goal of SLAM is to operate anywhere using the environment's natural features as landmarks. Such a goal is difficult to achieve for several reasons. Firstly, different environments contain different types of natural features, each exhibiting large variance in its shape and appearance. Secondly, objects look differently from different viewpoints and it is therefore difficult to always recognize them. Thirdly, in most outdoor environments it is not possible to predict the motion of a vehicle using wheel encoders because of errors caused by slippage. Finally, the design of a SLAM system to operate in a large-scale outdoor setting is in itself a challenge. The above issues are addressed as follows. Firstly, a camera is used to recognize the environmental context (e. g. , indoor office, outdoor park) by analyzing the holistic spectral content of images of the robot's surroundings. A type of feature (e. g. , trees for a park) is then chosen for SLAM that is likely observable in the recognized setting. A novel tree detection system is introduced, which is based on perceptually organizing the content of images into quasi-vertical structures and marking those structures that intersect ground level as tree trunks. Secondly, a new tree recognition system is proposed, which is based on extracting Scale Invariant Feature Transform (SIFT) features on each tree trunk region and matching trees in feature space. Thirdly, dead-reckoning is performed via an Inertial Navigation System (INS), bounded by non-holonomic constraints. INS are insensitive to slippage and varying ground conditions. Finally, the developed Computer Vision and Inertial systems are integrated within the framework of an Extended Kalman Filter into a working Vision-INS SLAM system, named VisSLAM. VisSLAM is tested on data collected during a real test run in an outdoor unstructured environment. Three test scenarios are proposed, ranging from semi-automatic detection, recognition, and initialization to a fully automated SLAM system. The first two scenarios are used to verify the presented inertial and Computer Vision algorithms in the context of localization, where results indicate accurate vehicle pose estimation for the majority of its journey. The final scenario evaluates the application of the proposed systems for SLAM, where results indicate successful operation for a long portion of the vehicle journey. Although the scope of this thesis is to operate in an outdoor park setting using tree trunks as landmarks, the developed techniques lend themselves to other environments using different natural objects as landmarks