Robocart Machine Vision Framework

This project documents a framework for an extensible, flexible machine vision software implementation for the Robocart project. It uses a distributed mobile computing framework in order to best leverage the scalability of machine vision. This process aims to improve upon current machine vision implementations in commercial autonomous vehicles, as well as provide a basis for further development of RobocartÂ’s autonomous navigation systems. This framework is tested with the use case of road detection

Marine biodiversity assessments using aquatic internet of things

While Ubiquitous Computing remains vastly applied in urban environments, it is still scarce in oceanic environments. Current equipment used for biodiversity assessments remain at a high cost, being still inaccessible to wider audiences. More accessible IoT (Internet of Things) solutions need to be implemented to tackle these issues and provide alternatives to facilitate data collection in-the-wild. While the ocean remains a very harsh environment to apply such devices, it is still providing the opportunity to further explore the biodiversity, being that current marine taxa is estimated to be 200k, while this number can be actually in millions. The main goal of this thesis is to provide an apparatus and architecture for aerial marine biodiversity assessments, based on low-cost MCUs (Microcontroller unit) and microcomputers. In addition, the apparatus will provide a proof of concept for collecting and classifying the collected media. The thesis will also explore and contribute to the latest IoT and machine learning techniques (e.g. Python, TensorFlow) when applied to ocean settings. The final product of the thesis will enhance the state of the art in technologies applied to marine biology assessments.A computação ubíqua é imensamente utilizada em ambientes urbanos, mas ainda é escassa em ambientes oceânicos. Os equipamentos atuais utilizados para o estudo de biodiversidade são de custo alto, e geralmente inacessíveis para o público geral. Uma solução IoT mais acessível necessita de ser implementada para combater estes problemas e fornecer alternativas para facilitar a recolha de dados na natureza. Embora o oceano seja um ambiente severo para aplicar estes dispositivos, este fornece mais oportunidades para explorar a biodiversidade, sendo que a taxa de marinha atual é estimada ser 200 mil, mas este número pode estar nos milhões. O objetivo principal desta tese é fornecer um aparelho e uma arquitetura para estudos aéreos de biodiversidade marinha, baseado em microcontroladores low-cost e microcomputadores. Adi cionalmente, este aparelho irá fornecer uma prova de conceito para coletar e classificar a mídia coletada. A tese irá também explorar e contribuir para as técnicas mais recentes de machine learn ing (e.g. Python, TensorFlow) quando aplicadas num cenário de oceano. O produto final desta tese vai elevar o estado da arte em tecnologias aplicadas a estudos de biologia marinha

Visual control of multi-rotor UAVs

Recent miniaturization of computer hardware, MEMs sensors, and high energy density batteries have enabled highly capable mobile robots to become available at low cost. This has driven the rapid expansion of interest in multi-rotor unmanned aerial vehicles. Another area which has expanded simultaneously is small powerful computers, in the form of smartphones, which nearly always have a camera attached, many of which now contain a OpenCL compatible graphics processing units. By combining the results of those two developments a low-cost multi-rotor UAV can be produced with a low-power onboard computer capable of real-time computer vision. The system should also use general purpose computer vision software to facilitate a variety of experiments. To demonstrate this I have built a quadrotor UAV based on control hardware from the Pixhawk project, and paired it with an ARM based single board computer, similar those in high-end smartphones. The quadrotor weights 980 g and has a flight time of 10 minutes. The onboard computer capable of running a pose estimation algorithm above the 10 Hz requirement for stable visual control of a quadrotor. A feature tracking algorithm was developed for efficient pose estimation, which relaxed the requirement for outlier rejection during matching. Compared with a RANSAC- only algorithm the pose estimates were less variable with a Z-axis standard deviation 0.2 cm compared with 2.4 cm for RANSAC. Processing time per frame was also faster with tracking, with 95 % confidence that tracking would process the frame within 50 ms, while for RANSAC the 95 % confidence time was 73 ms. The onboard computer ran the algorithm with a total system load of less than 25 %. All computer vision software uses the OpenCV library for common computer vision algorithms, fulfilling the requirement for running general purpose software. The tracking algorithm was used to demonstrate the capability of the system by per- forming visual servoing of the quadrotor (after manual takeoff). Response to external perturbations was poor however, requiring manual intervention to avoid crashing. This was due to poor visual controller tuning, and to variations in image acquisition and attitude estimate timing due to using free running image acquisition. The system, and the tracking algorithm, serve as proof of concept that visual control of a quadrotor is possible using small low-power computers and general purpose computer vision software

SEEKING A COMMON THEME: A STUDY OF CERAMIC EFFIGY ARTIFACTS IN THE PRE-HISPANIC AMERICAN SOUTHWEST AND NORTHERN MEXICO USING COMPUTER IMAGE PATTERN RECOGNITION AND PHYLOGENETIC ANALYSIS

Effigy artifacts are found throughout the Pre-Hispanic American Southwest and Northern Mexico (PHASNM), as well as in other cultures around the world, with many sharing the same forms and design features. The earliest figurines within the PHASNM were partial anthropomorphic figurines made from fired clay, dating to between A.D. 287 and A.D. 312 (Morss 1954:27). They were found in a pit house village of Bluff Ruin in the Forestdale Valley of eastern Arizona, and they appeared to be associated with the Mogollon culture. The temporal range of the samples examined in this study is from approximately 200 A.D. to 1650 A.D., and the geographical range includes the Southwestern United States (Arizona, New Mexico, Texas, Colorado, and Utah) and the northcentral section of Mexico (Casas Grandes and the surrounding area). This research looks at the similarities among the markings of ceramic effigy artifacts from the PHASNM, using computer image pattern recognition, design analysis, and phylogenetics, to determine whether their ceramic traditions share a common theme and whether the specific method of social learning responsible for the transmission of information relating to ceramic effigy decoration can be identified. Transmission is possible in one of three ways: vertical transmission, where parents/teachers distribute information by encouraging imitation and sharing learned traditions with children/students (Richerson and Boyd 2005; Shennan 2002); horizontal transmission, where information is transmitted among peers, either from within the individual’s group or from interaction with peers from neighboring populations (Borgerhoff Mulder et al. 2006), and where the individual comes into contact with a wide range of attributes related to the item of interest and then adopts those that allow for the fastest, most economical methods of production and distribution (Eerkens et al 2006; Rogers 1983); and oblique transmission, where information is transmitted by adults, masters, or institutions of elite or higher social status, either internally or externally to the adopting cultural Type (Jensen 2016; Jordan 2014), and where particular traits are adopted or left out in disproportionate ways, creating patterns in localized traditions that can be empirically identified. Horizontal transmission can be broken into two types: unlimited, where contact is not confined to a particular group; and limited, where contact is restricted to a particular set of contacts. Using criteria for each of the categories as set forth by the New Mexico Office of Archaeological Studies Pottery Typology Project, the samples were classified in terms of cultural area (culture), branch, tradition, ware, and type. The research v group consisted of 360 photographic samples represented by 868 images that were resized to a 640x640 pixel format. The images were then examined through computer image pattern recognition (using YOLOv5) and through manual observation. This study resulted in a database representing 230 traits. These traits were assembled into groups by cultural area, branch, tradition, ware, and type, and phylogenetic analysis was applied to show how the different entities transfer information among each other

Object tracking using a many-core embedded system

Object localization and tracking is essential for many practical applications, such as mancomputer interaction, security and surveillance, robot competitions, and Industry 4.0. Because of the large amount of data present in an image, and the algorithmic complexity involved, this task can be computationally demanding, mainly for traditional embedded systems, due to their processing and storage limitations. This calls for investigation and experimentation with new approaches, as emergent heterogeneous embedded systems, that promise higher performance, without compromising energy e ciency. This work explores several real-time color-based object tracking techniques, applied to images supplied by a RGB-D sensor attached to di erent embedded platforms. The main motivation was to explore an heterogeneous Parallella board with a 16-core Epiphany coprocessor, to reduce image processing time. Another goal was to confront this platform with more conventional embedded systems, namely the popular Raspberry Pi family. In this regard, several processing options were pursued, from low-level implementations specially tailored to the Parallella, to higher-level multi-platform approaches. The results achieved allow to conclude that the programming e ort required to e - ciently use the Epiphany co-processor is considerable. Also, for the selected case study, the performance attained was bellow the one o ered by simpler approaches running on quad-core Raspberry Pi boards.A localização e o seguimento de objetos são essenciais para muitas aplicações práticas, como interação homem-computador, segurança e vigilância, competições de robôs e Industria 4.0. Devido `a grande quantidade de dados presentes numa imagem, e a` complexidade algorítmica envolvida, esta tarefa pode ser computacionalmente exigente, principalmente para os sistemas embebidos tradicionais, devido às suas limitações de processamento e armazenamento. Desta forma, ´e importante a investigação e experimentação com novas abordagens, tais como sistemas embebidos heterogéneos emergentes, que trazem consigo a promessa de melhor desempenho, sem comprometer a eficiência energética. Este trabalho explora várias t´técnicas de seguimento de objetos em tempo real baseado em imagens a cores adquiridas por um sensor RBD-D, conectado a diferentes sistemas em- bebidos. A motivação principal foi a exploração de uma placa heterogénea Parallella com um co-processador Epiphany de 16 núcleos, a fim de reduzir o tempo de processamento das imagens. Outro objetivo era confrontar esta plataforma com sistemas embebidos mais convencionais, nomeadamente a popular família Raspberry Pi. Nesse sentido, foram prosseguidas diversas opções de processamento, desde implementações de baixo nível, específicas da placa Parallella, até abordagens multi-plataforma de mais alto nível. Os resultados alcançados permitem concluir que o esforço de programação necessário para utilizar eficientemente o co-processador Epiphany é considerável. Adicionalmente, para o caso de estudo deste trabalho, o desempenho alcançado fica aquém do conseguido por abordagens mais simples executando em sistemas Raspberry Pi com quatro núcleos

Towards Developing Computer Vision Algorithms and Architectures for Real-world Applications

abstract: Computer vision technology automatically extracts high level, meaningful information from visual data such as images or videos, and the object recognition and detection algorithms are essential in most computer vision applications. In this dissertation, we focus on developing algorithms used for real life computer vision applications, presenting innovative algorithms for object segmentation and feature extraction for objects and actions recognition in video data, and sparse feature selection algorithms for medical image analysis, as well as automated feature extraction using convolutional neural network for blood cancer grading. To detect and classify objects in video, the objects have to be separated from the background, and then the discriminant features are extracted from the region of interest before feeding to a classifier. Effective object segmentation and feature extraction are often application specific, and posing major challenges for object detection and classification tasks. In this dissertation, we address effective object flow based ROI generation algorithm for segmenting moving objects in video data, which can be applied in surveillance and self driving vehicle areas. Optical flow can also be used as features in human action recognition algorithm, and we present using optical flow feature in pre-trained convolutional neural network to improve performance of human action recognition algorithms. Both algorithms outperform the state-of-the-arts at their time. Medical images and videos pose unique challenges for image understanding mainly due to the fact that the tissues and cells are often irregularly shaped, colored, and textured, and hand selecting most discriminant features is often difficult, thus an automated feature selection method is desired. Sparse learning is a technique to extract the most discriminant and representative features from raw visual data. However, sparse learning with \textit{L1} regularization only takes the sparsity in feature dimension into consideration; we improve the algorithm so it selects the type of features as well; less important or noisy feature types are entirely removed from the feature set. We demonstrate this algorithm to analyze the endoscopy images to detect unhealthy abnormalities in esophagus and stomach, such as ulcer and cancer. Besides sparsity constraint, other application specific constraints and prior knowledge may also need to be incorporated in the loss function in sparse learning to obtain the desired results. We demonstrate how to incorporate similar-inhibition constraint, gaze and attention prior in sparse dictionary selection for gastroscopic video summarization that enable intelligent key frame extraction from gastroscopic video data. With recent advancement in multi-layer neural networks, the automatic end-to-end feature learning becomes feasible. Convolutional neural network mimics the mammal visual cortex and can extract most discriminant features automatically from training samples. We present using convolutinal neural network with hierarchical classifier to grade the severity of Follicular Lymphoma, a type of blood cancer, and it reaches 91\% accuracy, on par with analysis by expert pathologists. Developing real world computer vision applications is more than just developing core vision algorithms to extract and understand information from visual data; it is also subject to many practical requirements and constraints, such as hardware and computing infrastructure, cost, robustness to lighting changes and deformation, ease of use and deployment, etc.The general processing pipeline and system architecture for the computer vision based applications share many similar design principles and architecture. We developed common processing components and a generic framework for computer vision application, and a versatile scale adaptive template matching algorithm for object detection. We demonstrate the design principle and best practices by developing and deploying a complete computer vision application in real life, building a multi-channel water level monitoring system, where the techniques and design methodology can be generalized to other real life applications. The general software engineering principles, such as modularity, abstraction, robust to requirement change, generality, etc., are all demonstrated in this research.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Control tools for flow chemistry processing and their application to the synthesis of bromodomain inhibitors

Flow chemistry and continuous processing techniques are now frequently used in synthetic laboratories, taking advantage of the ability to contain reactive or hazardous intermediates and to perform moderate scale-up processes for important compounds. However, only a limited number of methods and tools for connecting flow synthesis steps into a single protocol have been described, and as a result manual interventions are frequently required between consecutive stages. There are two main challenges to overcome. Work-up operations such as solvent extractions and filtrations are invariably needed to ensure high purity of the intermediates. Solutions for achieving this are well established within industrial facilities for continuous production, but adapting such machinery for laboratory use is rarely straightforward. Secondly, the combination of multiple steps tends to result in a more elaborate reactor configuration. The control procedures required to achieve optimum performance may then be beyond the capabilities of a single researcher. Computer control and remote monitoring can help to make such experiments more practical; but commercially-available systems are often highly specialised, and purpose-built at high cost for a particular system, and so are not suitable for laboratory scientists to use routinely. This work describes the development of software tools to enable rapid prototyping of control systems that can integrate multiple instruments and devices (in Chapter 2). These are applied to three multi-step synthesis projects, which also make use of enabling technologies such as heterogeneous reagents and in-line work-up techniques so that material can be passed directly from one stage to the next: In Chapter 1, a series of analogues of a precursor to imatinib, a tyrosine kinase inhibitor used for the treatment of chronic myeloid leukaemia, are prepared. A “catch-react-release” technique for solid-phase synthesis is used, with computer-controlled operation of the reactors. In Chapter 3, a two-step procedure for the synthesis of piperazine-2-carboxamide, a valuable 3D building block, is developed. A computer control system enabled extended running and the integration of several machines to perform optimisation experiments. In Chapter 4, improvements to the continuous synthesis of 2-aminoadamantane-2-carboxylic acid are discussed. This includes an integrated sequence of three reactions and three workup operations. The final chapter describes a project to evaluate the application of control techniques to a medicinal chemistry project. New ligands for BRD9 and CECR2, proteins involved in the recognition of acetylated histone proteins, are produced. A number of triazolopyridazine compounds were synthesised and tested using a number of assay techniques, including a frontal-affinity chromatography system under development within our group. Pleasingly, the qualitative FAC data showed a good correlation with biological assessments made using established assay techniques. Further work using the FAC method is ongoing

Научная инициатива иностранных студентов и аспирантов : сборник докладов II Международной научно-практической конференции, Томск, 26-28 апреля 2022 г.

Сборник представляет интерес для специалистов и исследователей в области математики, механики, электротехники, информатики и вычислительных систем, физики, химии, геологии, гуманитарных наук и экономики