The fusion and integration of virtual sensors

There are numerous sensors from which to choose when designing a mobile robot: ultrasonic, infrared, radar, or laser range finders, video, collision detectors, or beacon based systems such as the Global Positioning System. In order to meet the need for reliability, accuracy, and fault tolerance, mobile robot designers often place multiple sensors on the same platform, or combine sensor data from multiple platforms. The combination of the data from multiple sensors to improve reliability, accuracy, and fault tolerance is termed Sensor Fusion.;The types of robotic sensors are as varied as the properties of the environment that need to be sensed. to reduce the complexity of system software, Roboticists have found it highly desirable to adopt a common interface between each type of sensor and the system responsible for fusing the information. The process of abstracting the essential properties of a sensor is called Sensor Virtualization.;Sensor virtualization to date has focused on abstracting the properties shared by sensors of the same type. The approach taken by T. Henderson is simply to expose to the fusion system only the data from the sensor, along with a textual label describing the sensor. We extend Henderson\u27s work in the following manner. First, we encapsulate both the fusion algorithm and the interface layer in the virtual sensor. This allows us to build multi-tiered virtual sensor hierarchies. Secondly, we show how common fusion algorithms can be encapsulated in the virtual sensor, facilitating the integration and replacement of both physical and virtual sensors. Finally, we provide a physical proof of concept using monostatic sonars, vector sonars, and a laser range-finder

A Survey on Interpretable Cross-modal Reasoning

In recent years, cross-modal reasoning (CMR), the process of understanding and reasoning across different modalities, has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare diagnostics. As the deployment of AI systems becomes more ubiquitous, the demand for transparency and comprehensibility in these systems' decision-making processes has intensified. This survey delves into the realm of interpretable cross-modal reasoning (I-CMR), where the objective is not only to achieve high predictive performance but also to provide human-understandable explanations for the results. This survey presents a comprehensive overview of the typical methods with a three-level taxonomy for I-CMR. Furthermore, this survey reviews the existing CMR datasets with annotations for explanations. Finally, this survey summarizes the challenges for I-CMR and discusses potential future directions. In conclusion, this survey aims to catalyze the progress of this emerging research area by providing researchers with a panoramic and comprehensive perspective, illuminating the state of the art and discerning the opportunities

Active Perception for Autonomous Systems : In a Deep Space Navigation Scenario

Autonomous systems typically pursue certain goals for an extended amount of time in a self-sustainable fashion. To this end, they are equipped with a set of sensors and actuators to perceive certain aspects of the world and thereupon manipulate it in accordance with some given goals. This kind of interaction can be thought of as a closed loop in which a perceive-reason-act process takes place. The bi-directional interface between an autonomous system and the outer world is then given by a sequence of imperfect observations of the world and corresponding controls which are as well imperfectly actuated. To be able to reason in such a setting, it is customary for an autonomous system to maintain a probabilistic state estimate. The quality of the estimate -- or its uncertainty -- is, in turn, dependent on the information acquired within the perceive-reason-act loop described above. Hence, this thesis strives to investigate the question of how to actively steer such a process in order to maximize the quality of the state estimate. The question will be approached by introducing different probabilistic state estimation schemes jointly working on a manifold-based encapsuled state representation. On top of the resultant state estimate different active perception approaches are introduced, which determine optimal actions with respect to uncertainty minimization. The informational value of the particular actions is given by the expected impact of measurements on the uncertainty. The latter can be obtained by different direct and indirect measures, which will be introduced and discussed. The active perception schemes for autonomous systems will be investigated with a focus on two specific deep space navigation scenarios deduced from a potential mining mission to the main asteroid belt. In the first scenario, active perception strategies are proposed, which foster the correctional value of the sensor information acquired within a heliocentric navigation approach. Here, the expected impact of measurements is directly estimated, thus omitting counterfactual updates of the state based on hypothetical actions. Numerical evaluations of this scenario show that active perception is beneficial, i.e., the quality of the state estimate is increased. In addition, it is shown that the more uncertain a state estimate is, the more the value of active perception increases. In the second scenario, active autonomous deep space navigation in the vicinity of asteroids is investigated. A trajectory and a map are jointly estimated by a Graph SLAM algorithm based on measurements of a 3D Flash-LiDAR. The active perception strategy seeks to trade-off the exploration of the asteroid against the localization performance. To this end, trajectories are generated as well as evaluated in a novel twofold approach specifically tailored to the scenario. Finally, the position uncertainty can be extracted from the graph structure and subsequently be used to dynamically control the trade-off between localization and exploration. In a numerical evaluation, it is shown that the localization performance of the Graph SLAM approach to navigation in the vicinity of asteroids is generally high. Furthermore, the active perception strategy is able to trade-off between localization performance and the degree of exploration of the asteroid. Finally, when the latter process is dynamically controlled, based on the current localization uncertainty, a joint improvement of localization as well as exploration performance can be achieved. In addition, this thesis comprises an excursion into active sensorimotor object recognition. A sensorimotor feature is derived from biological principles of the human perceptual system. This feature is then employed in different probabilistic classification schemes. Furthermore, it enables the implementation of an active perception strategy, which can be thought of as a feature selection process in a classification scheme. It is shown that those strategies might be driven by top-down factors, i.e., based on previously learned information, or by bottom-up factors, i.e., based on saliency detected in the currently considered data. Evaluations are conducted based on real data acquired by a camera mounted on a robotic arm as well as on datasets. It is shown that the integrated representation of perception and action fosters classification performance and that the application of an active perception strategy accelerates the classification process

A diagnostic expert system as a tool for technology improvement support

This dissertation focuses on design, modelling and development of a diagnostic expert system, which was implemented as a tool (named Capability Diagnostic) in FutureSME project (and web portal) as one of the tools for self-diagnostic of SMEs, where according to analysis of current state the output data were generated. These data were used for creation of an action plan, which serves as a list of improvements that need to be done to solve crucial processes of the company. After improvements in company processes were completed, a new diagnostic process was initiated and a comparison with previous results was performed. This tool was evaluated by companies partnering to the project as one of the most contributive results of the project. Design, modelling and development of the system were focused on general use of the diagnostic system for company processes and improvement support.Tato disertační práce se zabývá návrhem, modelováním, vývojem a realizací obecného diagnostického expertního systému, který byl jako nástroj (nazván jako Capability Diagnostic) nasazen v rámci projektu (a portálu) FutureSME jako jeden z nástrojů pro diagnostiku malých a středních podniků, kde na základě analýzy aktuálního stavu generoval výstupní data, která byla použita pro vytvoření akčního plánu, na základě kterého byly provedeny zásahy do chodu firmy, které měly napomoci k řešení klíčových procesů jejího fungování. Po zavedení opatření byla opakovaně provedena diagnostika a výsledek byl porovnán s původním či předchozím stavem. Tento nástroj byl partnery projektu (majiteli či řediteli firem) hodnocen jako jeden z nejpřínosnějších v tomto projektu. Návrh, model i vývoj systému byl zaměřen na obecné využití i v jiných oblastech (nejen průmyslových) pro zlepšování firemních procesů.352 - Katedra automatizační techniky a řízenívyhově

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving

CLiFF Notes: Research in the Language Information and Computation Laboratory of The University of Pennsylvania

This report takes its name from the Computational Linguistics Feedback Forum (CLIFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science, Psychology, and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. With 48 individual contributors and six projects represented, this is the largest LINC Lab collection to date, and the most diverse

Automated Deduction – CADE 28

This open access book constitutes the proceeding of the 28th International Conference on Automated Deduction, CADE 28, held virtually in July 2021. The 29 full papers and 7 system descriptions presented together with 2 invited papers were carefully reviewed and selected from 76 submissions. CADE is the major forum for the presentation of research in all aspects of automated deduction, including foundations, applications, implementations, and practical experience. The papers are organized in the following topics: Logical foundations; theory and principles; implementation and application; ATP and AI; and system descriptions

Inferring Complex Activities for Context-aware Systems within Smart Environments

The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered