Application of Deep Learning Approaches for Enhancing Mastcam Images

There are two mast cameras (Mastcam) onboard the Mars rover Curiosity. Both Mastcams are multispectral imagers with nine bands in each. The right Mastcam has three times higher resolution than the left. In this chapter, we apply some recently developed deep neural network models to enhance the left Mastcam images with help from the right Mastcam images. Actual Mastcam images were used to demonstrate the performance of the proposed algorithms

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

Online Laser Diagnostics for High-Temperature Chemistry in Biomass Combustion

Increasing concern over environment and new energy policies are driving the thermal heat and power industry towards new CO2 neutral fuels, such as biomass, and novel combustion schemes. Therefore new operational control and monitoring concepts are required to provide information of the combustion processes. Alkali elements and compounds have been identified to be one of the greatest challenges associated with thermal conversion of biomass as they cause severe operational problems in power plant boilers. In this Thesis, a new method to monitor temperature and O2 concentration during thermal conversion of biomass is developed. Collinear Photofragmentation and Atomic Absorption Spectroscopy (CPFAAS) is utilized to measure potassium reaction kinetics in lean combustion conditions, which provides valuable information for high temperature reaction models and simulations. The new information on potassium reaction kinetics with O2 enables online monitoring of temperature and O2 concentration utilizing the CPFAAS signal.Microwave-Assisted Laser-Induced Breakdown Spectroscopy (MW-LIBS) is demonstrated for the first time at ambient atmospheric conditions with impressive 93fold enhancement in limit of detection (LOD). MW-LIBS is further applied for online elemental monitoring during thermal conversion of biomass fuels as it improves detection of trace elements and reduces adverse self-absorption effects in high-concentration conditions. To enable the benefits of MW-LIBS, a novel burner for flame calibration is introduced. The burner allows calibration of LIBS for extended concentration range enabling quantitative elemental release monitoring during thermal conversion of different biomass fuels with varying elemental content. The elemental release behavior of biomass fuels is paramount for thermal conversion models and simulations that provide boiler operators and manufacturers crucial information on how to optimize the thermal processes and mitigate the alkali associated problems. Furthermore, as the novel MW-LIBS approach requires no or minimal sample preparation, it has great application potential for online elemental monitoring in different fields of science where low LOD or high sensitivity is required.The novel CPFAAS and MW-LIBS approaches provide simple and versatile methods for online high-temperature chemistry monitoring from laboratory-scale systems up to full-scale power plant boilers. Laser diagnostics will play a significant role in optimization and in process control of future thermal power generation as it enables development of online sensor networks to monitor and forecast the plant behavior

Exploiting Cross Domain Relationships for Target Recognition

Cross domain recognition extracts knowledge from one domain to recognize samples from another domain of interest. The key to solving problems under this umbrella is to find out the latent connections between different domains. In this dissertation, three different cross domain recognition problems are studied by exploiting the relationships between different domains explicitly according to the specific real problems. First, the problem of cross view action recognition is studied. The same action might seem quite different when observed from different viewpoints. Thus, how to use the training samples from a given camera view and perform recognition in another new view is the key point. In this work, reconstructable paths between different views are built to mirror labeled actions from one source view into one another target view for learning an adaptable classifier. The path learning takes advantage of the joint dictionary learning techniques with exploiting hidden information in the seemingly useless samples, making the recognition performance robust and effective. Second, the problem of person re-identification is studied, which tries to match pedestrian images in non-overlapping camera views based on appearance features. In this work, we propose to learn a random kernel forest to discriminatively assign a specific distance metric to each pair of local patches from the two images in matching. The forest is composed by multiple decision trees, which are designed to partition the overall space of local patch-pairs into substantial subspaces, where a simple but effective local metric kernel can be defined to minimize the distance of true matches. Third, the problem of multi-event detection and recognition in smart grid is studied. The signal of multi-event might not be a straightforward combination of some single-event signals because of the correlation among devices. In this work, a concept of ``root-pattern\u27\u27 is proposed that can be extracted from a collection of single-event signals, but also transferable to analyse the constituent components of multi-cascading-event signals based on an over-complete dictionary, which is designed according to the ``root-patterns\u27\u27 with temporal information subtly embedded. The correctness and effectiveness of the proposed approaches have been evaluated by extensive experiments

Compressed Sensing in Resource-Constrained Environments: From Sensing Mechanism Design to Recovery Algorithms

Compressed Sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. It is promising that CS can be utilized in environments where the signal acquisition process is extremely difficult or costly, e.g., a resource-constrained environment like the smartphone platform, or a band-limited environment like visual sensor network (VSNs). There are several challenges to perform sensing due to the characteristic of these platforms, including, for example, needing active user involvement, computational and storage limitations and lower transmission capabilities. This dissertation focuses on the study of CS in resource-constrained environments. First, we try to solve the problem on how to design sensing mechanisms that could better adapt to the resource-limited smartphone platform. We propose the compressed phone sensing (CPS) framework where two challenging issues are studied, the energy drainage issue due to continuous sensing which may impede the normal functionality of the smartphones and the requirement of active user inputs for data collection that may place a high burden on the user. Second, we propose a CS reconstruction algorithm to be used in VSNs for recovery of frames/images. An efficient algorithm, NonLocal Douglas-Rachford (NLDR), is developed. NLDR takes advantage of self-similarity in images using nonlocal means (NL) filtering. We further formulate the nonlocal estimation as the low-rank matrix approximation problem and solve the constrained optimization problem using Douglas-Rachford splitting method. Third, we extend the NLDR algorithm to surveillance video processing in VSNs and propose recursive Low-rank and Sparse estimation through Douglas-Rachford splitting (rLSDR) method for recovery of the video frame into a low-rank background component and sparse component that corresponds to the moving object. The spatial and temporal low-rank features of the video frame, e.g., the nonlocal similar patches within the single video frame and the low-rank background component residing in multiple frames, are successfully exploited