Self-organising an indoor location system using a paintable amorphous computer

This thesis investigates new methods for self-organising a precisely defined pattern of intertwined number sequences which may be used in the rapid deployment of a passive indoor positioning system's infrastructure.A future hypothetical scenario is used where computing particles are suspended in paint and covered over a ceiling. A spatial pattern is then formed over the covered ceiling. Any small portion of the spatial pattern may be decoded, by a simple camera equipped device, to provide a unique location to support location-aware pervasive computing applications.Such a pattern is established from the interactions of many thousands of locally connected computing particles that are disseminated randomly and densely over a surface, such as a ceiling. Each particle has initially no knowledge of its location or network topology and shares no synchronous clock or memory with any other particle.The challenge addressed within this thesis is how such a network of computing particles that begin in such an initial state of disarray and ignorance can, without outside intervention or expensive equipment, collaborate to create a relative coordinate system. It shows how the coordinate system can be created to be coherent, even in the face of obstacles, and closely represent the actual shape of the networked surface itself. The precision errors incurred during the propagation of the coordinate system are identified and the distributed algorithms used to avoid this error are explained and demonstrated through simulation.A new perimeter detection algorithm is proposed that discovers network edges and other obstacles without the use of any existing location knowledge. A new distributed localisation algorithm is demonstrated to propagate a relative coordinate system throughout the network and remain free of the error introduced by the network perimeter that is normally seen in non-convex networks. This localisation algorithm operates without prior configuration or calibration, allowing the coordinate system to be deployed without expert manual intervention or on networks that are otherwise inaccessible.The painted ceiling's spatial pattern, when based on the proposed localisation algorithm, is discussed in the context of an indoor positioning system

Geometric, Semantic, and System-Level Scene Understanding for Improved Construction and Operation of the Built Environment

Recent advances in robotics and enabling fields such as computer vision, deep learning, and low-latency data passing offer significant potential for developing efficient and low-cost solutions for improved construction and operation of the built environment. Examples of such potential solutions include the introduction of automation in environment monitoring, infrastructure inspections, asset management, and building performance analyses. In an effort to advance the fundamental computational building blocks for such applications, this dissertation explored three categories of scene understanding capabilities: 1) Localization and mapping for geometric scene understanding that enables a mobile agent (e.g., robot) to locate itself in an environment, map the geometry of the environment, and navigate through it; 2) Object recognition for semantic scene understanding that allows for automatic asset information extraction for asset tracking and resource management; 3) Distributed coupling analysis for system-level scene understanding that allows for discovery of interdependencies between different built-environment processes for system-level performance analyses and response-planning. First, this dissertation advanced Simultaneous Localization and Mapping (SLAM) techniques for convenient and low-cost locating capabilities compared with previous work. To provide a versatile Real-Time Location System (RTLS), an occupancy grid mapping enhanced visual SLAM (vSLAM) was developed to support path planning and continuous navigation that cannot be implemented directly on vSLAM’s original feature map. The system’s localization accuracy was experimentally evaluated with a set of visual landmarks. The achieved marker position measurement accuracy ranges from 0.039m to 0.186m, proving the method’s feasibility and applicability in providing real-time localization for a wide range of applications. In addition, a Self-Adaptive Feature Transform (SAFT) was proposed to improve such an RTLS’s robustness in challenging environments. As an example implementation, the SAFT descriptor was implemented with a learning-based descriptor and integrated into a vSLAM for experimentation. The evaluation results on two public datasets proved the feasibility and effectiveness of SAFT in improving the matching performance of learning-based descriptors for locating applications. Second, this dissertation explored vision-based 1D barcode marker extraction for automated object recognition and asset tracking that is more convenient and efficient than the traditional methods of using barcode or asset scanners. As an example application in inventory management, a 1D barcode extraction framework was designed to extract 1D barcodes from video scan of a built environment. The performance of the framework was evaluated with video scan data collected from an active logistics warehouse near Detroit Metropolitan Airport (DTW), demonstrating its applicability in automating inventory tracking and management applications. Finally, this dissertation explored distributed coupling analysis for understanding interdependencies between processes affecting the built environment and its occupants, allowing for accurate performance and response analyses compared with previous research. In this research, a Lightweight Communications and Marshalling (LCM)-based distributed coupling analysis framework and a message wrapper were designed. This proposed framework and message wrapper were tested with analysis models from wind engineering and structural engineering, where they demonstrated the abilities to link analysis models from different domains and reveal key interdependencies between the involved built-environment processes.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155042/1/lichaox_1.pd

Digital watermarking and novel security devices

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

SYNERGY OF BUILDING CYBERSECURITY SYSTEMS

The development of the modern world community is closely related to advances in computing resources and cyberspace. The formation and expansion of the range of services is based on the achievements of mankind in the field of high technologies. However, the rapid growth of computing resources, the emergence of a full-scale quantum computer tightens the requirements for security systems not only for information and communication systems, but also for cyber-physical systems and technologies. The methodological foundations of building security systems for critical infrastructure facilities based on modeling the processes of behavior of antagonistic agents in security systems are discussed in the first chapter. The concept of information security in social networks, based on mathematical models of data protection, taking into account the influence of specific parameters of the social network, the effects on the network are proposed in second chapter. The nonlinear relationships of the parameters of the defense system, attacks, social networks, as well as the influence of individual characteristics of users and the nature of the relationships between them, takes into account. In the third section, practical aspects of the methodology for constructing post-quantum algorithms for asymmetric McEliece and Niederreiter cryptosystems on algebraic codes (elliptic and modified elliptic codes), their mathematical models and practical algorithms are considered. Hybrid crypto-code constructions of McEliece and Niederreiter on defective codes are proposed. They can significantly reduce the energy costs for implementation, while ensuring the required level of cryptographic strength of the system as a whole. The concept of security of corporate information and educational systems based on the construction of an adaptive information security system is proposed. ISBN 978-617-7319-31-2 (on-line)ISBN 978-617-7319-32-9 (print) ------------------------------------------------------------------------------------------------------------------ How to Cite: Yevseiev, S., Ponomarenko, V., Laptiev, O., Milov, O., Korol, O., Milevskyi, S. et. al.; Yevseiev, S., Ponomarenko, V., Laptiev, O., Milov, O. (Eds.) (2021). Synergy of building cybersecurity systems. Kharkiv: РС ТЕСHNOLOGY СЕNTЕR, 188. doi: http://doi.org/10.15587/978-617-7319-31-2 ------------------------------------------------------------------------------------------------------------------ Indexing:                    Розвиток сучасної світової спільноти тісно пов’язаний з досягненнями в області обчислювальних ресурсів і кіберпростору. Формування та розширення асортименту послуг базується на досягненнях людства у галузі високих технологій. Однак стрімке зростання обчислювальних ресурсів, поява повномасштабного квантового комп’ютера посилює вимоги до систем безпеки не тільки інформаційно-комунікаційних, але і до кіберфізичних систем і технологій. У першому розділі обговорюються методологічні основи побудови систем безпеки для об'єктів критичної інфраструктури на основі моделювання процесів поведінки антагоністичних агентів у систем безпеки. У другому розділі пропонується концепція інформаційної безпеки в соціальних мережах, яка заснована на математичних моделях захисту даних, з урахуванням впливу конкретних параметрів соціальної мережі та наслідків для неї. Враховуються нелінійні взаємозв'язки параметрів системи захисту, атак, соціальних мереж, а також вплив індивідуальних характеристик користувачів і характеру взаємовідносин між ними. У третьому розділі розглядаються практичні аспекти методології побудови постквантових алгоритмів для асиметричних криптосистем Мак-Еліса та Нідеррейтера на алгебраїчних кодах (еліптичних та модифікованих еліптичних кодах), їх математичні моделі та практичні алгоритми. Запропоновано гібридні конструкції криптокоду Мак-Еліса та Нідеррейтера на дефектних кодах. Вони дозволяють істотно знизити енергетичні витрати на реалізацію, забезпечуючи при цьому необхідний рівень криптографічної стійкості системи в цілому. Запропоновано концепцію безпеки корпоративних інформаційних та освітніх систем, які засновані на побудові адаптивної системи захисту інформації. ISBN 978-617-7319-31-2 (on-line)ISBN 978-617-7319-32-9 (print) ------------------------------------------------------------------------------------------------------------------ Як цитувати: Yevseiev, S., Ponomarenko, V., Laptiev, O., Milov, O., Korol, O., Milevskyi, S. et. al.; Yevseiev, S., Ponomarenko, V., Laptiev, O., Milov, O. (Eds.) (2021). Synergy of building cybersecurity systems. Kharkiv: РС ТЕСHNOLOGY СЕNTЕR, 188. doi: http://doi.org/10.15587/978-617-7319-31-2 ------------------------------------------------------------------------------------------------------------------ Індексація:                 &nbsp

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Automation and Robotics: Latest Achievements, Challenges and Prospects

This SI presents the latest achievements, challenges and prospects for drives, actuators, sensors, controls and robot navigation with reverse validation and applications in the field of industrial automation and robotics. Automation, supported by robotics, can effectively speed up and improve production. The industrialization of complex mechatronic components, especially robots, requires a large number of special processes already in the pre-production stage provided by modelling and simulation. This area of research from the very beginning includes drives, process technology, actuators, sensors, control systems and all connections in mechatronic systems. Automation and robotics form broad-spectrum areas of research, which are tightly interconnected. To reduce costs in the pre-production stage and to reduce production preparation time, it is necessary to solve complex tasks in the form of simulation with the use of standard software products and new technologies that allow, for example, machine vision and other imaging tools to examine new physical contexts, dependencies and connections

Computer vision based classification of fruits and vegetables for self-checkout at supermarkets

The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be divided into many significant fields namely computer vision, data sciences, real time analytics and Natural Language Processing (NLP). All these applications are being used to help computer based systems to operate more usefully in everyday contexts. Computer vision research is currently active in a wide range of areas such as the development of autonomous vehicles, object recognition, Content Based Image Retrieval (CBIR), image segmentation and terrestrial analysis from space (i.e. crop estimation). Despite significant prior research, the area of object recognition still has many topics to be explored. This PhD thesis focuses on using advanced machine learning approaches to enable the automated recognition of fresh produce (i.e. fruits and vegetables) at supermarket self-checkouts. This type of complex classification task is one of the most recently emerging applications of advanced computer vision approaches and is a productive research topic in this field due to the limited means of representing the features and machine learning techniques for classification. Fruits and vegetables offer significant inter and intra class variance in weight, shape, size, colour and texture which makes the classification challenging. The applications of effective fruit and vegetable classification have significant importance in daily life e.g. crop estimation, fruit classification, robotic harvesting, fruit quality assessment, etc. One potential application for this fruit and vegetable classification capability is for supermarket self-checkouts. Increasingly, supermarkets are introducing self-checkouts in stores to make the checkout process easier and faster. However, there are a number of challenges with this as all goods cannot readily be sold with packaging and barcodes, for instance loose fresh items (e.g. fruits and vegetables). Adding barcodes to these types of items individually is impractical and pre-packaging limits the freedom of choice when selecting fruits and vegetables and creates additional waste, hence reducing customer satisfaction. The current situation, which relies on customers correctly identifying produce themselves leaves open the potential for incorrect billing either due to inadvertent error, or due to intentional fraudulent misclassification resulting in financial losses for the store. To address this identified problem, the main goals of this PhD work are: (a) exploring the types of visual and non-visual sensors that could be incorporated into a self-checkout system for classification of fruits and vegetables, (b) determining a suitable feature representation method for fresh produce items available at supermarkets, (c) identifying optimal machine learning techniques for classification within this context and (d) evaluating our work relative to the state-of-the-art object classification results presented in the literature. An in-depth analysis of related computer vision literature and techniques is performed to identify and implement the possible solutions. A progressive process distribution approach is used for this project where the task of computer vision based fruit and vegetables classification is divided into pre-processing and classification techniques. Different classification techniques have been implemented and evaluated as possible solution for this problem. Both visual and non-visual features of fruit and vegetables are exploited to perform the classification. Novel classification techniques have been carefully developed to deal with the complex and highly variant physical features of fruit and vegetables while taking advantages of both visual and non-visual features. The capability of classification techniques is tested in individual and ensemble manner to achieved the higher effectiveness. Significant results have been obtained where it can be concluded that the fruit and vegetables classification is complex task with many challenges involved. It is also observed that a larger dataset can better comprehend the complex variant features of fruit and vegetables. Complex multidimensional features can be extracted from the larger datasets to generalise on higher number of classes. However, development of a larger multiclass dataset is an expensive and time consuming process. The effectiveness of classification techniques can be significantly improved by subtracting the background occlusions and complexities. It is also worth mentioning that ensemble of simple and less complicated classification techniques can achieve effective results even if applied to less number of features for smaller number of classes. The combination of visual and nonvisual features can reduce the struggle of a classification technique to deal with higher number of classes with similar physical features. Classification of fruit and vegetables with similar physical features (i.e. colour and texture) needs careful estimation and hyper-dimensional embedding of visual features. Implementing rigorous classification penalties as loss function can achieve this goal at the cost of time and computational requirements. There is a significant need to develop larger datasets for different fruit and vegetables related computer vision applications. Considering more sophisticated loss function penalties and discriminative hyper-dimensional features embedding techniques can significantly improve the effectiveness of the classification techniques for the fruit and vegetables applications

Encoded hydrogel microparticles for high-throughput molecular diagnostics and personalized medicine

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 141-161).The ability to accurately detect and quantify biological molecules in complex mixtures is crucial in basic research as well as in clinical settings. Advancements in genetic analysis, molecular diagnostics, and patient-tailored medicine require robust detection technologies that can obtain high-density information from a range of physiological samples in a rapid and cost-effective manner. Compared to conventional microarrays and methods based on polymerase chain reaction (PCR), suspension (particle-based) arrays offer several advantages in the multiplexed detection of biomolecules, including higher rates of sample processing, reduced consumption of sample and reagent, and rapid probe-set modification for customizable assays. This thesis expands the utility of a novel hydrogel-based microparticle array through (1) the creation of a microfluidic, flow-through fluorescence scanner for high-throughput particle analysis, (2) the development of a suite of techniques for the highly sensitive and specific detection of microRNA (miRNA) biomarkers, and (3) the investigation of new methods for directly measuring biomolecules at the single-cell level. Graphically-encoded hydrogel microparticles synthesized from non-fouling, bioinert poly(ethylene glycol) (PEG) and functionalized with biomolecule probes offer great promise in the development of high-performance, multiplexed bioassays. To extend this platform to applications in high-throughput analysis, particle design was optimized to ensure mechanical stability in high-velocity flow systems, and a single-color microfluidic scanner was constructed for the rapid fluorescence interrogation of each particle's spatially-segregated "code" and "probe" regions. The detection advantages of three-dimensional, probe-laden hydrogel scaffolds and the operational efficiencies of suspension array technology were then leveraged for the rapid multiplexed expression profiling of miRNA. The graphical encoding method and ligationbased labeling scheme implemented here allowed for scalable multiplexing with a simple workflow and an unprecedented combination of sensitivity, flexibility, and throughput. Through the rolling circle amplification of a labeling oligonucleotide, it was possible to further enhance the system's sensitivity and resolve single-molecule miRNA binding events on particle surfaces, enabling the first direct detection of low-abundance miRNA in human serum without the need for RNA extraction or target amplification. Finally, by arraying cells and gel particles in polydimethylsiloxane (PDMS) microwells, it was possible to dramatically improve the particles' target capture efficiency and thereby move closer to a regime in which miRNAs and other biological molecules may be directly detected without target amplification from single cells.by Stephen Clifford Chapin.Ph.D

LIPIcs, Volume 258, SoCG 2023, Complete Volume

LIPIcs, Volume 258, SoCG 2023, Complete Volum