1,490 research outputs found
Pervasive Monitoring - An Intelligent Sensor Pod Approach for Standardised Measurement Infrastructures
Geo-sensor networks have traditionally been built up in closed monolithic systems, thus limiting trans-domain usage of real-time measurements. This paper presents the technical infrastructure of a standardised embedded sensing device, which has been developed in the course of the Live Geography approach. The sensor pod implements data provision standards of the Sensor Web Enablement initiative, including an event-based alerting mechanism and location-aware Complex Event Processing functionality for detection of threshold transgression and quality assurance. The goal of this research is that the resultant highly flexible sensing architecture will bring sensor network applications one step further towards the realisation of the vision of a âdigital skin for planet earthâ. The developed infrastructure can potentially have far-reaching impacts on sensor-based monitoring systems through the deployment of ubiquitous and fine-grained sensor networks. This in turn allows for the straight-forward use of live sensor data in existing spatial decision support systems to enable better-informed decision-making.Seventh Framework Programme (European Commission) (FP7 project GENESIS no. 223996)Austria. Federal Ministry of Transport, Innovation and TechnologyERA-STAR Regions Project (G2real)Austria. Federal Ministry of Science and Researc
A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks
In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs
Advances in Technologies and Methods for Behavior, Emotion, and Health Monitoring in Pets
This research offers a detailed descriptions of existing technologies and approaches for monitoring pets in the areas of behavior, emotion, and health. The first section discusses behavior and emotion monitoring. It includes wearable devices like smart collars that are fitted with sensors for monitoring heart rate, activity levels, and temperature. These devices communicate with AI-based anomaly detection systems that send real-time alerts through various channels such as SMS, email, and mobile app notifications. Additionally, smart cameras and sound capturing devices are employed to analyze behavior and emotional states. The second section discusses health monitoring and assistance. Users can input data such as pet breed, age, and observed behaviors into dashboards. Subsequent AI algorithms analyze the data, providing health forecasts and preventive measures. Moreover, imaging technologies employ image acquisition, preprocessing, and feature extraction to detect abnormalities, the results of which are stored in databases and can trigger alerts to medical staff. The review identifies distinct modules for each sector, including data capture, processing, and alerting mechanisms. While each module specializes in specific tasks, common functionalities such as real-time alerting and data storage are pervasive across both sectors. The study asserts that current technological advancements have significantly enhanced the ability to monitor pets in real-time, providing actionable insights for pet owners and veterinary professionals
Architectures for embedded multimodal sensor data fusion systems in the robotics : and airport traffic suveillance ; domain
Smaller autonomous robots and embedded sensor data fusion systems often suffer from limited
computational and hardware resources. Many âReal Timeâ algorithms for multi modal sensor data
fusion cannot be executed on such systems, at least not in real time and sometimes not at all, because
of the computational and energy resources needed, resulting from the architecture of the
computational hardware used in these systems. Alternative hardware architectures for generic
tracking algorithms could provide a solution to overcome some of these limitations. For tracking and
self localization sequential Bayesian filters, in particular particle filters, have been shown to be able to
handle a range of tracking problems that could not be solved with other algorithms. But particle filters
have some serious disadvantages when executed on serial computational architectures used in most
systems. The potential increase in performance for particle filters is huge as many of the computational
steps can be done concurrently. A generic hardware solution for particle filters can relieve the central
processing unit from the computational load associated with the tracking task.
The general topic of this research are hardware-software architectures for multi modal sensor data
fusion in embedded systems in particular tracking, with the goal to develop a high performance
computational architecture for embedded applications in robotics and airport traffic surveillance
domain. The primary concern of the research is therefore: The integration of domain specific concept
support into hardware architectures for low level multi modal sensor data fusion, in particular
embedded systems for tracking with Bayesian filters; and a distributed hardware-software tracking
systems for airport traffic surveillance and control systems.
Runway Incursions are occurrences at an aerodrome involving the incorrect presence of an aircraft,
vehicle, or person on the protected area of a surface designated for the landing and take-off of aircraft.
The growing traffic volume kept runway incursions on the NTSBâs âMost Wantedâ list for safety
improvements for over a decade. Recent incidents show that problem is still existent. Technological
responses that have been deployed in significant numbers are ASDE-X and A-SMGCS. Although these
technical responses are a significant improvement and reduce the frequency of runway incursions,
some runway incursion scenarios are not optimally covered by these systems, detection of runway
incursion events is not as fast as desired, and they are too expensive for all but the biggest airports.
Local, short range sensors could be a solution to provide the necessary affordable surveillance accuracy
for runway incursion prevention. In this context the following objectives shall be reached. 1) Show the
feasibility of runway incursion prevention systems based on localized surveillance. 2) Develop a design
for a local runway incursion alerting system. 3) Realize a prototype of the system design using the
developed tracking hardware.Kleinere autonome Roboter und eingebettete Sensordatenfusionssysteme haben oft mit stark
begrenzter RechenkapazitÀt und eingeschrÀnkten Hardwareressourcen zu kÀmpfen. Viele
Echtzeitalgorithmen fĂŒr die Fusion von multimodalen Sensordaten können, bedingt durch den hohen
Bedarf an RechenkapazitĂ€t und Energie, auf solchen Systemen ĂŒberhaupt nicht ausgefĂŒhrt werden,
oder zu mindesten nicht in Echtzeit. Der hohe Bedarf an Energie und RechenkapazitÀt hat seine
Ursache darin, dass die Architektur der ausfĂŒhrenden Hardware und der ausgefĂŒhrte Algorithmus
nicht aufeinander abgestimmt sind. Dies betrifft auch Algorithmen zu Spurverfolgung. Mit Hilfe von
alternativen Hardwarearchitekturen fĂŒr die generische AusfĂŒhrung solcher Algorithmen könnten sich
einige der typischerweise vorliegenden EinschrĂ€nkungen ĂŒberwinden lassen. Eine Reihe von Aufgaben,
die sich mit anderen Spurverfolgungsalgorithmen nicht lösen lassen, lassen sich mit dem Teilchenfilter,
einem Algorithmus aus der Familie der Bayesschen Filter lösen. Bei der AusfĂŒhrung auf traditionellen
Architekturen haben Teilchenfilter gegenĂŒber anderen Algorithmen einen signifikanten Nachteil,
allerdings ist hier ein groĂer Leistungszuwachs durch die nebenlĂ€ufige AusfĂŒhrung vieler
Rechenschritte möglich. Eine generische Hardwarearchitektur fĂŒr Teilchenfilter könnte deshalb die
oben genannten Systeme stark entlasten.
Das allgemeine Thema dieses Forschungsvorhabens sind Hardware-Software-Architekturen fĂŒr die
multimodale Sensordatenfusion auf eingebetteten Systemen - speziell fĂŒr Aufgaben der
Spurverfolgung, mit dem Ziel eine leistungsfĂ€hige Architektur fĂŒr die Berechnung entsprechender
Algorithmen auf eingebetteten Systemen zu entwickeln, die fĂŒr Anwendungen in der Robotik und
VerkehrsĂŒberwachung auf FlughĂ€fen geeignet ist. Das Augenmerk des Forschungsvorhabens liegt
dabei auf der Integration von vom Einsatzgebiet abhÀngigen Konzepten in die Architektur von
Systemen zur Spurverfolgung mit Bayeschen Filtern, sowie auf verteilten Hardware-Software
Spurverfolgungssystemen zur Ăberwachung und FĂŒhrung des Rollverkehrs auf FlughĂ€fen.
Eine âRunway Incursionâ (RI) ist ein Vorfall auf einem Flugplatz, bei dem ein Fahrzeug oder eine Person
sich unerlaubt in einem Abschnitt der Start- bzw. Landebahn befindet, der einem Verkehrsteilnehmer
zur Benutzung zugewiesen wurde. Der wachsende Flugverkehr hat dafĂŒr gesorgt, das RIs seit ĂŒber
einem Jahrzehnt auf der âMost Wantedâ-Liste des NTSB fĂŒr Verbesserungen der Sicherheit stehen.
JĂŒngere VorfĂ€lle zeigen, dass das Problem noch nicht behoben ist. Technologische MaĂnahmen die in
nennenswerter Zahl eingesetzt wurden sind das ASDE-X und das A-SMGCS. Obwohl diese MaĂnahmen
eine deutliche Verbesserung darstellen und die Zahl der RIs deutlich reduzieren, gibt es einige RISituationen
die von diesen Systemen nicht optimal abgedeckt werden. AuĂerdem detektieren sie RIs
ist nicht so schnell wie erwĂŒnscht und sind - auĂer fĂŒr die gröĂten FlughĂ€fen - zu teuer. Lokale Sensoren
mit kurzer Reichweite könnten eine Lösung sein um die fĂŒr die zuverlĂ€ssige Erkennung von RIs
notwendige PrĂ€zision bei der Ăberwachung des Rollverkehrs zu erreichen. Vor diesem Hintergrund
sollen die folgenden Ziele erreicht werden. 1) Die Machbarkeit eines Runway Incursion
Vermeidungssystems, das auf lokalen Sensoren basiert, zeigen. 2) Einen umsetzbaren Entwurf fĂŒr ein
solches System entwickeln. 3) Einen Prototypen des Systems realisieren, das die oben gennannte
Hardware zur Spurverfolgung einsetzt
Ambient-aware continuous care through semantic context dissemination
Background: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e. g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data.
Methods: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability.
Results: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered.
Conclusions: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results
Environmental Application with Multi Sensor Network
This paper aimed to monitor temperature, humidity, and CO gas level using environmental application with multi sensor network (MSN). This system was applied in real life and real time, to be able to obtain data and information through mobile devices and other on internet network. In this research, environmental application is monitored remotely using displays on the web and sensors as device. This research obtained data in outdoor and indoor parking area also with obstacles and without obstacles, so it obtained the results from each of the different environmental conditions.  
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