A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Applied Aerial Autonomy for Reliable Indoor Flight and 3D Mapping(2024) Shastry, Animesh Kumar; Paley, Derek; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Uncrewed Aerial Systems (UAS) are essential for safely exploring indoor environments damaged by shelling, fire, floods, and structural collapse. These systems can gather critical visual and locational data, aiding in hazard assessment and rescue planning without risking human lives. Reliable UAS deployments requires advanced sensors and robust algorithms for real-time data processing and safe navigation, even in GPS-denied and windy conditions. This dissertation details three research projects to improve UAS performance: (1) in-flight calibration to improve estimation and control, (2) system identification for wind rejection, and (3) indoor aerial 3D mapping. The dissertation begins with introducing a comprehensive nonlinear filtering framework for UAV parameter estimation, which considers factors such as external wind, drag coefficients, IMU bias, and center of pressure. Additionally, it establishes optimized flight trajectories for parameter estimation through empirical observability. Moreover, an estimation and control framework is implemented, utilizing the mean of state and parameter estimates to generate suitable control inputs for vehicle actuators. By employing a square-root unscented Kalman filter (sq-UKF), this framework can derive a 23-dimensional state vector from 9-dimensional sensor data and 4-dimensional control inputs. Numerical results demonstrate enhanced tracking performance through the integration of the estimation framework with a conventional model-based controller. The estimation of unsteady winds results in improved gust rejection capabilities of the onboard controller as well. Closely related to parameter estimation is system identification. Combining with the previous work a comprehensive system identification framework with both linear offline and nonlinear online methods is introduced. Inertial parameters are estimated using frequency-domain linear system identification, incorporating control data from motor-speed sensing and state estimates from automated frequency sweep maneuvers. Additionally, drag-force coefficients and external wind are recursively estimated during flight using a sq-UKF. A custom flight controller is developed to manage the computational demands of online estimation and control. Flight experiments demonstrate the tracking performance of the nonlinear controller and its improved capability in rejecting gust disturbances. Aside from wind rejection, aerial indoor 3D mapping is also required for indoor navigation, and therefore, the dissertation introduces a comprehensive pipeline for real-time mapping and target detection in indoor environments with limited network access. Seeking a best-in-class UAS design, it provides detailed analysis and evaluation of both hardware and software components. Experimental testing across various indoor settings demonstrates the system's efficacy in producing high-quality maps and detecting targets.Item WIRELESS SENSING AND ANALYTICS FOR MOTION MONITORING AND MAPPING(2023) Zhu, Guozhen; Liu, K. J. Ray; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Environmental perception is pivotal for intelligent systems, enabling them to adeptly capture, interpret, and act upon contextual cues. Grasping the intricacies of the environment—its objects, occupants, floor plan, and dynamics—is fundamental for the effective deployment of technologies, including robotics, the Internet of Things (IoT), and augmented reality. Traditional perception mechanisms, such as video surveillance and sensor-based monitoring, are often hampered by privacy concerns, substantial infrastructural costs, energy inefficiencies, and limited coverage. In contrast, WiFi sensing stands out for its non-intrusive, cost-effective, and pervasive attributes. Capitalizing on ubiquitous WiFi signals that permeate both indoor and outdoor spaces, WiFi sensing delivers unparalleled advantages over its traditional counterparts, sidestepping the need for extra hardware yet offering profound environmental insights. Its capability to penetrate walls and other obstructions further broadens its range, covering areas beyond the reach of conventional sensors. These unique edges of WiFi sensing elevate its value across diverse applications, spanning smart homes, health monitoring, location-based services, and security systems. Amplifying environmental perception via WiFi sensing is more than just an innovation in ubiquitous computing; it's a leap towards forging safer, more efficient, and smarter environments. This dissertation explores monitoring and mapping environments leveraging motion analytics based on commodity WiFi. In the first part of this dissertation, we introduce an efficient and cost-effective system for precise floor plan construction by integrating RF and inertial sensing techniques. The proposed system harnesses detailed insights from RF tracking and broad context from inertial metrics, such as magnetic field strength, to produce an accurate map. The system employs a robot for trajectory collection and requires only a single Access Point to be arbitrarily installed in space, both of which are widely available nowadays. Impressively, the system can produce detailed maps even with minimal data, making it adaptable for diverse structures such as shopping centers, offices, and residences without significant expenses. We validated the efficacy of the proposed system using a Dji RoboMaster S1 robot equipped with standard WiFi across three distinct buildings, demonstrating its capability to produce reliable maps for the intended regions. Given the widespread presence of WiFi setups and the increasing prevalence of domestic robots, the proposed approach paves the way for universal intelligent systems offering indoor mapping services. In the second and third parts, we present two innovative strategies leveraging WiFi to identify the motion of human and various non-human subjects. Initially, we detail a novel passive, non-intrusive methodology tailored for edge devices. By extracting and analyzing motion's physically and statistically plausible features, our system recognizes human and diverse non-human subjects through walls using a singular WiFi link. Experimental results from four distinct buildings with various moving subjects validate its efficiency on edge devices. Advancing to more intricate cases, we put forth a deep learning-based WiFi sensing paradigm. This delves into the efficacy of diverse deep learning models on human and non-human object recognition and probes the feasibility of transferring image-trained models to fulfill the WiFi sensing task. Designed with a robust statistic invariant to the environment and position, this system efficiently adapts to new surroundings. Comprehensive experimental evaluations affirm our framework's precision in pinpointing intricate human and non-human subjects, and readiness for integration into prevalent intelligent systems, thereby boosting their perceptual capacities. In the final part of this dissertation, we propose a pioneering through-wall indoor intrusion detection system that adeptly filters out interference from non-human subjects using ubiquitous WiFi signals. A novel deep learning architecture is proposed for single-link WiFi signal analysis. It employs a ResNet-18-based module to extract features of indoor moving subjects and an LSTM-based module to incorporate temporal information for efficient intrusion detection. Notably, the system is invariant to environmental changes, angles, and positions, enabling swift deployment in new environments without additional training. Evaluation in five indoor environments with various interference yielded high intrusion detection accuracy and a low false alarm rate, even without model tuning for unseen settings. The results underscore the system's exceptional adaptability, positioning it as a top contender for widespread intelligent indoor security applications.Item HIERARCHICAL MAPPING TECHNIQUES FOR SIGNAL PROCESSING SYSTEMS ON PARALLEL PLATFORMS(2014) Wang, Lai-Huei; Bhattacharyya, Shuvra S.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Dataflow models are widely used for expressing the functionality of digital signal processing (DSP) applications due to their useful features, such as providing formal mechanisms for description of application functionality, imposing minimal data-dependency constraints in specifications, and exposing task and data level parallelism effectively. Due to the increased complexity of dynamics in modern DSP applications, dataflow-based design methodologies require significant enhancements in modeling and scheduling techniques to provide for efficient and flexible handling of dynamic behavior. To address this problem, in this thesis, we propose an innovative framework for mode- and dynamic-parameter-based modeling and scheduling. We apply, in a systematically integrated way, the structured mode-based dataflow modeling capability of dynamic behavior together with the features of dynamic parameter reconfiguration and quasi-static scheduling. Moreover, in our proposed framework, we present a new design method called parameterized multidimensional design hierarchy mapping (PMDHM), which is targeted to the flexible, multi-level reconfigurability, and intensive real-time processing requirements of emerging dynamic DSP systems. The proposed approach allows designers to systematically represent and transform multi-level specifications of signal processing applications from a common, dataflow-based application-level model. In addition, we propose a new technique for mapping optimization that helps designers derive efficient, platform-specific parameters for application-to-architecture mapping. These parameters help to maximize system performance on state-of-the-art parallel platforms for embedded signal processing. To further enhance the scalability of our design representations and implementation techniques, we present a formal method for analysis and mapping of parameterized DSP flowgraph structures, called topological patterns, into efficient implementations. The approach handles an important class of parameterized schedule structures in a form that is intuitive for representation and efficient for implementation. We demonstrate our methods with case studies in the fields of wireless communication and computer vision. Experimental results from these case studies show that our approaches can be used to derive optimized implementations on parallel platforms, and enhance trade-off analysis during design space exploration. Furthermore, their basis in formal modeling and analysis techniques promotes the applicability of our proposed approaches to diverse signal processing applications and architectures.Item Theory, Design, and Implementation of Landmark Promotion Cooperative Simultaneous Localization and Mapping(2011) Karvounis, John George; Blankenship, Gilmer; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Simultaneous Localization and Mapping (SLAM) is a challenging problem in practice, the use of multiple robots and inexpensive sensors poses even more demands on the designer. Cooperative SLAM poses specific challenges in the areas of computational efficiency, software/network performance, and robustness to errors. New methods in image processing, recursive filtering, and SLAM have been developed to implement practical algorithms for cooperative SLAM on a set of inexpensive robots. The Consolidated Unscented Mixed Recursive Filter (CUMRF) is designed to handle non-linear systems with non-Gaussian noise. This is accomplished using the Unscented Transform combined with Gaussian Mixture Models. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis (PCA) and the X84 outlier rejection rule. Forgetful SLAM is a local SLAM technique that runs in nearly constant time relative to the number of visible landmarks and improves poor performing sensors through sensor fusion and outlier rejection. Forgetful SLAM correlates all measured observations, but stops the state from growing over time. Hierarchical Active Ripple SLAM (HAR-SLAM) is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple robots, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of robots, landmarks, and robots poses. This dissertation presents explicit methods for closing-the-loop, joining multiple robots, and active updates. Landmark Promotion SLAM is a hierarchy of new SLAM methods, using the Robust Kalman Filter, Forgetful SLAM, and HAR-SLAM. Practical aspects of SLAM are a focus of this dissertation. LK-SURF is a new image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking. Typical stereo correspondence techniques fail at providing descriptors for features, or fail at temporal tracking. Several calibration and modeling techniques are also covered, including calibrating stereo cameras, aligning stereo cameras to an inertial system, and making neural net system models. These methods are important to improve the quality of the data and images acquired for the SLAM process.Item Nucleic Acid Extraction and Detection Across Two-Dimensional Tissue Samples(2010) Armani, Michael Daniel; Shapiro, Benjamin; Smela, Elisabeth; Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Visualizing genetic changes throughout tissues can explain basic biological functions and molecular pathways in disease. However, over 90% of mammalian messenger RNA (mRNA) is in low abundance (<15 copies per cell) making them hard to see with existing techniques, such as in-situ hybridization (ISH). In the example of diagnosing cancer, a disease caused by genetic mutations, only a few cancer-associated mRNAs can be visualized in the clinic due to the poor sensitivity of ISH. To improve the detection of low-abundance mRNA, many researchers combine the cells across a tissue sample by taking a scrape. Mixing cells provides only one data point and masks the inherent heterogeneity of tissues. To address these challenges, we invented a sensitive method for mapping nucleic acids across tissues called 2D-PCR. 2D-PCR transfers a tissue section into an array of wells, confining and separating the tissue into subregions. Chemical steps are then used to free nucleic acids from the tissues subregions. If the freed genetic material is mRNA, a purification step is also performed. One or more nucleic acids are then amplified using PCR and detected across the tissue to produce a map. As an initial proof of concept, a DNA map was made from a frozen tissue section using 2D-PCR at the resolution of 1.6 mm per well. The technique was improved to perform the more challenging task of mapping three mRNA molecules from a frozen tissue section. Because the majority of clinical tissues are stored using formalin fixation and not freezing, 2D-PCR was improved once more to detect up to 24 mRNAs from formalin-fixed tissue microarrays. This last approach was used to validate genetic profiles in human normal and tumor prostate samples faster than with existing techniques. In conclusion, 2D-PCR is a robust method for detecting genetic changes across tissues or from many tissue samples. 2D-PCR can be used today for studying differences in nucleic acids between tumor and normal specimens or differences in subregions of the brain.