A new method for positional accuracy control for nonnormal errors applied to airborne laser scanner data

18 p.A new statistical method for the quality control of the positional accuracy, useful in a wide range of data sets, is proposed and its use is illustrated through its application to airborne laser scanner (ALS) data. The quality control method is based on the use of a multinomial distribution that categorizes cases of errors according to metric tolerances. The use of the multinomial distribution is a very novel and powerful approach to the problem of evaluating positional accuracy, since it allows for eliminating the need for a parametric model for positional errors. Three different study cases based on ALS data (infrastructure, urban, and natural cases) that contain non-normal errors were used. Three positional accuracy controls with different tolerances were developed. In two of the control cases, the tolerances were defined by a Gaussian model, and in the third control case, the tolerances were defined from the quantiles of the observed error distribution. The analysis of the test results based on the type I and type II errors show that the method is able to control the positional accuracy of freely distributed data.S

Navigation Recommender:Real-Time iGNSS QoS Prediction for Navigation Services

Global Navigation Satellite Systems (GNSSs), especially Global Positioning System (GPS), have become commonplace in mobile devices and are the most preferred geo-positioning sensors for many location-based applications. Besides GPS, other GNSSs under development or deployment are GLONASS, Galileo, and Compass. These four GNSSs are planned to be integrated in the near future. It is anticipated that integrated GNSSs (iGNSSs) will improve the overall satellite-based geo-positioning performance. However, one major shortcoming of any GNSS and iGNSSs is Quality of Service (QoS) degradation due to signal blockage and attenuation by the surrounding environments, particularly in obstructed areas. GNSS QoS uncertainty is the root cause of positioning ambiguity, poor localization performance, application freeze, and incorrect guidance in navigation applications. In this research, a methodology, called iGNSS QoS prediction, that can provide GNSS QoS on desired and prospective routes is developed. Six iGNSS QoS parameters suitable for navigation are defined: visibility, availability, accuracy, continuity, reliability, and flexibility. The iGNSS QoS prediction methodology, which includes a set of algorithms, encompasses four modules: segment sampling, point-based iGNSS QoS prediction, tracking-based iGNSS QoS prediction, and iGNSS QoS segmentation. Given that iGNSS QoS prediction is data- and compute-intensive and navigation applications require real-time solutions, an efficient satellite selection algorithm is developed and distributed computing platforms, mainly grids and clouds, for achieving real-time performance are explored. The proposed methodology is unique in several respects: it specifically addresses the iGNSS positioning requirements of navigation systems/services; it provides a new means for route choices and routing in navigation systems/services; it is suitable for different modes of travel such as driving and walking; it takes high-resolution 3D data into account for GNSS positioning; and it is based on efficient algorithms and can utilize high-performance and scalable computing platforms such as grids and clouds to provide real-time solutions. A number of experiments were conducted to evaluate the developed methodology and the algorithms using real field test data (GPS coordinates). The experimental results show that the methodology can predict iGNSS QoS in various areas, especially in problematic areas

Collaborative Workspaces within Distributed Virtual Environments

In warfare, be it a training simulation or actual combat, a commander\u27s time is one of the most valuable and fleeting resources of a military unit. Thus, it is natural for a unit to have a plethora of personnel to analyze and filter information to the decision-maker. This dynamic exchange of ideas between analyst and commander is currently not available within the distributed interactive simulation (DIS) community. This lack of exchange limits the usefulness of the DIS experience to the commander and his troops. This thesis addresses the commander\u27s isolation problem through the integration of a collaborative workspace within AFIT\u27s Synthetic BattleBridge (SBB) as a technique to improve situational awareness. The SBB\u27s Collaborative Workspace enhances battlespace awareness through CSCW (computer supported cooperative work) enabling communication technologies. The SBB\u27s Collaborative Workspace allows the user to interact with other SBB users through the transmission and reception of public bulletins, private email, real-time chat sessions, shared viewpoints, shared video, and shared annotations to the virtual environment. Collaborative communication between SBB occurs through the use of standard and experimental DIS-compliant protocol data units. The SBB\u27s Collaborative Workspace gives the battlespace commander the widest range of communication options available within a DIS virtual environment today

Robotic Assembly Using 3D and 2D Computer Vision

The content of this thesis concerns the development and evaluation of a robotic cell used for automated assembly. The automated assembly is made possible by a combination of an eye-inhand 2D camera and a stationary 3D camera used to automatically detect objects. Computer vision, kinematics and programming is the main topics of the thesis. Possible approaches to object detection has been investigated and evaluated in terms of performance. The kinematic relation between the cameras in the robotic cell and robotic manipulator movements has been described. A functioning solution has been implemented in the robotic cell at the Department of Production and Quality Engineering laboratory. Theory with significant importance to the developed solution is presented. The methods used to achieve each part of the solution is anchored in theory and presented with the decisions and guidelines made throughout the project work in order to achieve the final solution. Each part of the system is presented with associated results. The combination of these results yields a solution which proves that the methods developed to achieve automated assembly works as intended. Limitations, challenges and future possibilities and improvements for the solution is then discussed. The results from the experiments presented in this thesis demonstrates the performance of the developed system. The system fulfills the specifications defined in the problem description and is functioning as intended considering the instrumentation used

Ultrasonic sensor platforms for non-destructive evaluation

Robotic vehicles are receiving increasing attention for use in Non-Destructive Evaluation (NDE), due to their attractiveness in terms of cost, safety and their accessibility to areas where manual inspection is not practical. A reconfigurable Lamb wave scanner, using autonomous robotic platforms is presented. The scanner is built from a fleet of wireless miniature robotic vehicles, each with a non-contact ultrasonic payload capable of generating the A0 Lamb wave mode in plate specimens. An embedded Kalman filter gives the robots a positional accuracy of 10mm. A computer simulator, to facilitate the design and assessment of the reconfigurable scanner, is also presented. Transducer behaviour has been simulated using a Linear Systems approximation (LS), with wave propagation in the structure modelled using the Local Interaction Simulation Approach (LISA). Integration of the LS and LISA approaches were validated for use in Lamb wave scanning by comparison with both analytical techniques and more computationally intensive commercial finite element/diference codes. Starting with fundamental dispersion data, the work goes on to describe the simulation of wave propagation and the subsequent interaction with artificial defects and plate boundaries. The computer simulator was used to evaluate several imaging techniques, including local inspection of the area under the robot and an extended method that emits an ultrasonic wave and listens for echos (B-Scan). These algorithms were implemented in the robotic platform and experimental results are presented. The Synthetic Aperture Focusing Technique (SAFT) was evaluated as a means of improving the fidelity of B-Scan data. It was found that a SAFT is only effective for transducers with reasonably wide beam divergence, necessitating small transducers with a width of approximately 5mm. Finally, an algorithm for robot localisation relative to plate sections was proposed and experimentally validated

A deep learning digitisation framework to mark up corrosion circuits in piping and instrumentation diagrams.

Corrosion circuit mark up in engineering drawings is one of the most crucial tasks performed by engineers. This process is currently done manually, which can result in errors and misinterpretations depending on the person assigned for the task. In this paper, we present a semi-automated framework which allows users to upload an undigitised Piping and Instrumentation Diagram, i.e. without any metadata, so that two key shapes, namely pipe specifications and connection points, can be localised using deep learning. Afterwards, a heuristic process is applied to obtain the text, orient it and read it with minimal error rates. Finally, a user interface allows the engineer to mark up the corrosion sections based on these findings. Experimental validation shows promising accuracy rates on finding the two shapes of interest and enhance the functionality of optical character recognition when reading the text of interest

New Mechatronic Systems for the Diagnosis and Treatment of Cancer

Both two dimensional (2D) and three dimensional (3D) imaging modalities are useful tools for viewing the internal anatomy. Three dimensional imaging techniques are required for accurate targeting of needles. This improves the efficiency and control over the intervention as the high temporal resolution of medical images can be used to validate the location of needle and target in real time. Relying on imaging alone, however, means the intervention is still operator dependent because of the difficulty of controlling the location of the needle within the image. The objective of this thesis is to improve the accuracy and repeatability of needle-based interventions over conventional techniques: both manual and automated techniques. This includes increasing the accuracy and repeatability of these procedures in order to minimize the invasiveness of the procedure. In this thesis, I propose that by combining the remote center of motion concept using spherical linkage components into a passive or semi-automated device, the physician will have a useful tracking and guidance system at their disposal in a package, which is less threatening than a robot to both the patient and physician. This design concept offers both the manipulative transparency of a freehand system, and tremor reduction through scaling currently offered in automated systems. In addressing each objective of this thesis, a number of novel mechanical designs incorporating an remote center of motion architecture with varying degrees of freedom have been presented. Each of these designs can be deployed in a variety of imaging modalities and clinical applications, ranging from preclinical to human interventions, with an accuracy of control in the millimeter to sub-millimeter range

Deep learning models for predicting RNA degradation via dual crowdsourcing

Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition ("Stanford OpenVaccine") on Kaggle, involving single-nucleotide resolution measurements on 6043 102-130-nucleotide diverse RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1588 nucleotides) with improved accuracy compared to previously published models. Top teams integrated natural language processing architectures and data augmentation techniques with predictions from previous dynamic programming models for RNA secondary structure. These results indicate that such models are capable of representing in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for data set creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales