Bibliographies thématiques / Ambient Intelligence (AmI)

Littérature scientifique sur le sujet « Ambient Intelligence (AmI) »

Auteur : Grafiati
Publié le 11 mars 2023

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Articles de revues sur le sujet "Ambient Intelligence (AmI)"

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Muhammad Zunnurain Hussain, Zaka Ullah, Taimoor Hassan et Muhammad Zulkifl Hasan. « Ambient Intelligence ». Lahore Garrison University Research Journal of Computer Science and Information Technology 2, no 4 (31 décembre 2018) : 15–20. http://dx.doi.org/10.54692/lgurjcsit.2018.020456.

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Ambient intelligence is electronic environment which are sensitive to the presence of people. It is the light of future technologies. All AMI subsystems are interact with each other to accomplish their goal. AMI applications which are directly interact to define the environment. It shows that human environment which are not produced this process. The AMI systems should identify the various emotions of human experience. The AMI systems can also highlight the communication of the user. The AMI systems which is consists of robots and sensors.
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Gams, Matjaz, et Martin Gjoreski. « Artificial Intelligence and Ambient Intelligence ». Electronics 10, no 8 (15 avril 2021) : 941. http://dx.doi.org/10.3390/electronics10080941.

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Traynor, Declan, Ermai Xie et Kevin Curran. « Context-Awareness in Ambient Intelligence ». International Journal of Ambient Computing and Intelligence 2, no 1 (janvier 2010) : 13–23. http://dx.doi.org/10.4018/jaci.2010010102.

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Ambient Intelligence (AmI) deals with the issue of how we can create context-aware, electronic environments which foster seamless human-computer interaction. Ambient Intelligence encompasses the fields of ubiquitous computing, artificially intelligent systems, and context awareness among others. This paper discusses context-awareness and examines how discoveries in this area will be key in propelling the development of true AmI environments. This will be done by examining the background and reasoning behind this particular strand of AmI research along with an overview of the technologies being explored alongside possible applications of context awareness in computing as well as technological and socio- ethical challenges in this field.
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Dunne, Rob, Tim Morris et Simon Harper. « A Survey of Ambient Intelligence ». ACM Computing Surveys 54, no 4 (mai 2021) : 1–27. http://dx.doi.org/10.1145/3447242.

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Ambient Intelligence (AmI) is the application and embedding of artificial intelligence into everyday environments to seamlessly provide assistive and predictive support in a multitude of scenarios via an invisible user interface. These can be as diverse as autonomous vehicles, smart homes, industrial settings, and healthcare facilities—referred to as Ambient Assistive Living. This survey gives an overview of the field; defines key terms; discusses social, cultural, and ethical issues; and outlines the state of the art in AmI technology, and where opportunities for further research exist. We guide the reader through AmI from its inception more than 20 years ago, focussing on the important topics and research achievements of the past 10 years since the last major survey, before finally detailing the most recents research trends and forecasting where this technology is likely to develop. This survey covers domains, use cases, scenarios, and datasets; cultural concerns and usability issues; security, privacy, and ethics; interaction and recognition; prediction and intelligence; and hardware, infrastructure, and mobile devices. This survey serves as an introduction for researchers and the technical layperson into the topic of AmI and identifies notable opportunities for further research.
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Quadar, Nordine, Abdellah Chehri, Gwanggil Jeon, Mohammad Mehedi Hassan et Giancarlo Fortino. « Cybersecurity Issues of IoT in Ambient Intelligence (AmI) Environment ». IEEE Internet of Things Magazine 5, no 3 (septembre 2022) : 140–45. http://dx.doi.org/10.1109/iotm.001.2200009.

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Crutzen, C. K. M. « Invisibility and the Meaning of Ambient Intelligence ». International Review of Information Ethics 6 (1 décembre 2006) : 52–62. http://dx.doi.org/10.29173/irie140.

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A vision of future daily life is explored in Ambient Intelligence (AmI). It contains the assumption that intelligent technology should disappear into our environment to bring humans an easy and entertaining life. The mental, physical, methodical invisibility of AmI will have an effect on the relation between design and use activities of both users and designers. Especially the ethics discussions of AmI, privacy, identity and security are moved into the foreground. However in the process of using AmI, it will go beyond these themes. The infiltration of AmI will cause the construction of new meanings of privacy, identity and security because the "visible" acting of people will be preceded, accompanied and followed by the invisible and visible acting of the AmI technology and their producers. A question in this paper is: How is it possible to create critical transformative rooms in which doubting will be possible under the circumstances that autonomous 'intelligent agents' surround humans? Are humans in danger to become just objects of artificial intelligent conversations? Probably the relation between mental, physical, methodical invisibility and visibility of AmI could give answers.
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Chambers, Anna. « Models, AmI-Creator and A-Methodology for Ambient Intelligence Environments ». Journal of Software Engineering and Applications 07, no 04 (2014) : 311–46. http://dx.doi.org/10.4236/jsea.2014.74030.

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Cinque, Marcello, Antonio Coronato et Alessandro Testa. « On Dependability Issues in Ambient Intelligence Systems ». International Journal of Ambient Computing and Intelligence 3, no 3 (juillet 2011) : 18–27. http://dx.doi.org/10.4018/jaci.2011070103.

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Ambient Intelligence (AmI) is the emerging computing paradigm used to build next-generation smart environments. It provides services in a flexible, transparent, and anticipative manner, requiring minimal skills for human-computer interaction. Recently, AmI is being adapted to build smart systems to guide human activities in critical domains, such as, healthcare, ambient assisted living, and disaster recovery. However, the practical application to such domains generally calls for stringent dependability requirements, since the failure of even a single component may cause dangerous loss or hazard to people and machineries. Despite these concerns, there is still little understanding on dependability issues in Ambient Intelligent systems and on possible solutions. This paper provides an analysis of the AmI literature dealing with dependability issues and to propose an innovative architectural solution to such issues, based on the use of runtime verification techniques.
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García, Óscar, Ricardo S. Alonso, Dante I. Tapia et Juan M. Corchado. « Using ZigBee in Ambient Intelligence Learning Scenarios ». International Journal of Ambient Computing and Intelligence 4, no 3 (juillet 2012) : 33–45. http://dx.doi.org/10.4018/jaci.2012070103.

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The inclusion of Information and Communication Technologies, especially mobile devices, in learning environments has allowed both the emergence of new ways of learning and the adaptation of traditional teaching methods. In this sense, Ambient Intelligence (AmI) paradigm represents a promising approach that can be successfully applied to education. Pervasive computing, context and location awareness are AmI features that can allow students to receive customized information in a transparent way. Fortunately, there are several technologies that can help to gather such information. In this regard, Real-Time Locating Systems (RTLS) is a key technology that can improve context-awareness in AmI-based systems. This paper presents the use of a novel RTLS based on ZigBee technology that provides users’ positions in order to enhance context information in learning applications. This way, this system allows customizing the content offered to the users without their explicit interaction, as well as the granularity level provided by the system.
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Fotopoulos, Vasileios, Apostolos Zarras et Panos Vassiliadis. « Schedule-Aware Transactions for Ambient Intelligence Environments ». International Journal of Ambient Computing and Intelligence 2, no 4 (octobre 2010) : 55–69. http://dx.doi.org/10.4018/jaci.2010100106.

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In this paper, the authors investigate the concept of designing user-centric transaction protocols toward achieving dependable coordination in AmI environments. As a proof-of-concept, this paper presents a protocol that takes into account the schedules of roaming users, which move from one AmI environment to another, avoiding abnormal termination of transactions when users leave an environment for a short time and return later. The authors compare the proposed schedule-aware protocol against a schedule-agnostic one. Findings show that the use of user-centric information in such situations is quite beneficial.
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Thèses sur le sujet "Ambient Intelligence (AmI)"

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el, Sayed Mewafy Sherif. « Investigation into the creation of an ambient intelligent physiology measurement environment to facilitate modelling of the human wellbeing ». Thesis, University of South Wales, 2014. https://pure.southwales.ac.uk/en/studentthesis/investigation-into-the-creation-of-an-ambient-intelligent-physiology-measurement-environment-to-facilitate-modelling-of-the-human-wellbeing(27f3cf96-357d-49fa-a19f-cf60e9ae0347).html.

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The elderly population worldwide has an increasing expectation of wellbeing and life expectancy. The monitoring of the majority of elderly people on an individual basis, in a medical sense, will not be a viable proposition in the future due to the projected numbers of individuals requiring such activity. The expectation is that the infrastructure available will not be adequate to meet all the anticipated requirements and subsequently people will have to live at home with inadequate care. A new global objective that aims towards enhancing the quality of life of the elderly is being supported by extensive research. This research has been taking place in the field of ambient intelligence (AmI), considering factors including more comfort, improved health, enhanced security for the elderly, and facilitating the living in their homes longer. Prior research has shown a need for accelerated expansion in the ambient intelligence domain. To that end this work presents a novel learning technique for intelligent agents that can be used in Ambient Intelligent Environments (AIEs). The main objective of this work is to add knowledge to the AmI domain and to explore the practical applications within this research field. The added knowledge is accomplished through the development of an ambient intelligent health care environment that allows a practical assessment of the human well-being to take place. This is achieved by transforming the elderly living environment into an intelligent pseudo robot within which they reside to better understand the human wellbeing. The system developed aims to provide evidence that a level of automated care is both possible and practical. This care is for those with chronic physical or mental disabilities who have difficulty in their interactions with standardised living spaces. The novel integrated hardware and software architecture provides personalised environmental monitoring. It also provides control facilities based on the patient‘s physical and emotional wellness in their home. Entitled Health Adaptive Online Emotion Fuzzy Agent (HAOEFA), the system provides a non-invasive, self-learning, intelligent controlling system that constantly adapts to the requirements of an individual. The system has the ability to model and learn the user behaviour in order to control the environment on their behalf. This is achieved with respect to the changing environmental conditions as well as the user‘s health and emotional states being detected. A change of emotion can have a direct impact on the system‘s control taking place in the environment. Thus HAOEFA combines an emotion recognition system within a fuzzy logic learning and adaptation based controller. The emotion recogniser detects the occupant‘s emotions upon the changes of the physiological data being monitored. In addition to acting as an output to the occupant‘s physiological changes, the detected emotion also acts as input to the whole situation being observed by HAOEFA. This allows HAOEFA to control the Glam i-HomeCare on the user‘s behalf with respect to their emotional status. The system developed incorporates real-time, continuous adaptations to facilitate any changes to the occupant‘s behaviour within the environment. It also allows the rules to be adapted and extended online, assisting a life-long learning technique as the environmental conditions change and the user behaviour adjusts with it. HAOEFA uses the fuzzy c-means clustering methodology for extracting membership functions (MFs) before building its set of fuzzy rules. These MFs together with the rules base constitute a major part of the proposed system. It has the ability to learn and model the individual human behaviour with respect to their emotional status. Following the provided literature review and the presentation of Fuzzy logic MFs (see section 3.3). The thesis presents two chosen unobtrusive self-learning techniques that are used in the development of the intelligent fuzzy system. Each approach combines an emotion recogniser with a fuzzy logic learning and adaptation based technique for systems that can be used in AIEs. A comparison of two different MFs designs is contrasted showing the impact they have on the system learning ability. A number of carefully designed experiments were performed by volunteers in the Glam i-HomeCare test-bed at the University of South Wales to examine the system‘s ability to learn the occupant‘s behaviour with respect to their health and emotional states. The experimental procedures were performed twice by each volunteer, while maintaining the same behavioural actions to compare how much the design of fuzzy membership functions can impact the learning process and the number of rules created by the system. Besides evaluating both systems‘ emotion recognition accuracies and comparing them to one another for each occupant, the empirical outcomes show the potential of the approach in assisting the extension of independent living. The results demonstrate how the type-1 fuzzy system both learnt and adapted to each occupant‘s behaviour with respect to their health and emotional state whilst assessing multiple environmental conditions.
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Gabel, Oliver. « Entwurf von QoS-adaptiven Regelungen für AmI-Systeme mit heuristischen Methoden ». Aachen Shaker, 2008. http://d-nb.info/989384535/04.

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Bibri, Simon Elias. « A Critical Reading of the Scholarly and ICT Industry’s Construction of Ambient Intelligence for Societal Transformation of Europe ». Thesis, Malmö högskola, Fakulteten för kultur och samhälle (KS), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-23791.

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Ambient Intelligence (AmI) refers to a vision of the information society where everyday human environments will be permeated by intelligent technology: people will be surrounded and accompanied by intelligent interfaces supported by computing and wireless networking technology that is ubiquitous, embedded in virtually all kinds of everyday objects. These computationally augmented, smart environments - composed of a myriad of invisible, distributed, networked, connected, interactive, and always-on computing devices - are aware of human context; sensitive to people's needs; adaptive to, and anticipatory of, their behavior; personalized to their requirements; and responsive to their emotion and presence, thereby intelligently supporting their daily and social lives by providing limitless services in a seamless and unobtrusive way. The vision of AmI assumes a paradigmatic shift in both computing and society – far-reaching societal implications. The challenge lies in developing AmI forms that acclimatise to societal change and the diversity of European socio-cultural life. Indeed, one of the most fundamental views in the prevailing AmI vision is a radical and technology-driven change to social environments and people’s lives. Research emphasizes the fundamental role the ISTAG, a group of scholars and ICT industry experts, plays in the reproduction of AmI as a positive force for societal change. Therefore, the objective of this study is to carry out a critical reading of the scholarly and ICT industry’s construction of AmI in relation to societal transformation. To achieve this objective, a discourse analytical approach was employed to examine the selected empirical material: three reports published by the ISTAG in 2001, 2003 and 2006. The approach consists of seven stages: (1) surface elements and organizational structure, (2) discursive constructions, (3) social actors, (4) language and rhetoric, (5) framing as power and operation, (6) positioning and legitimation, and (7) ideological viewpoints.The AmI discourse (vision) construction tends to be deterministic, i.e. it assumes that the ‘amization’ of society will lead to radical social transformations, and has an unsophisticated account of how social change occurs. It is also inclined to be rhetorical - it promises revolutionary social changes without really having a holistic strategy for achieving the goal. Moreover, topicalization is accomplished in correspondence with the preferred mental models and social representations. Furthermore, the discourse is exclusionary: many issues (pertaining to trust, social sustainability, human-centred design, healthcare, and community life) are left out with the intention to advance the idea of the eventual societal acceptance of AmI. It additionally plays a role in wider processes of legitimation of social agents and structures on the basis of normative and political reasons, and it offers different subject positions: between ISTAG and Europe and European citizens, and between citizens and ICT designers and producers. Likewise, it plays a major role in constructing the image of social actors – ISTAG, ICT industry, research community and EU – as well as in defining their relations and identities in ways that reallocate roles and reflect new attributes. A great highlight and space is awarded to represent these actors, and their views dominate the reports. They are the prime definer of the represented reality. As to ideological reproduction, the discourse perpetuates power relations, serves the interest of certain stakeholders in European society, and reconstructs ideological claims. This discursive endeavor provides a valuable reference for social researchers or scientists in related research communities. Until now, there has been, to the best of one’s knowledge, no comprehensive discursive research of AmI in relation to societal transformation, more specifically the potential of AmI in modernizing the European social model and in shaping Europe’s future.
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Benea, Marius-Tudor. « Collective Intelligence in AmI Environments ». Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS142.

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La thèse intersecte le domaine de l'Intelligence Ambiante (AmI) avec le domaine de l'Intelligence Collective (IC) dans un point qui facilite le développement des environnements AmI à grande échelle, caractérisés par un haut degré de l'intelligence. Le modèle CAmIE est introduit, pour le développement ascendant des environnements AmI complexes, à grande échelle, avec un niveau élevé de l'IC émergente. Il offre des outils pour le développement facile des applications AmI et il facilite les interactions entre leurs back-ends. De plus, CAmIE encourage la diversité, un des aspects clés pour l'émergence de l'IC. CAmIE motive tous les acteurs humains de l'environnement à contribuer, afin que les environnements résultés intègrent leurs IC, et il offre un langage de programmation orienté-agents, par défaut, nommé CAmI, pour la création d'applications AmI basées sur des scénarios particuliers, qui est compatible avec tous les autres principes du modèle. Il est facile à apprendre, modulaire et il offre de nombreux avantages par rapport à l'AmI, comme l'approche orientée agents, la conscience du contexte, l'indépendance des formalismes de représentation des connaissances et comme des fonctionnalités facilitant les interactions et l’émergence de l'IC. Quatre études de cas sont offert, soulignant les points forts du modèle introduit, du point de vue du développement des applications AmI autonomes, de faire interagir les applications pour améliorer l'expérience de l'utilisateur, de l’émergence de l'IC dans les grands environnements, étude réalisée à l'aide de simulations multi-agents, et de la création d'une communauté
The thesis intersects the field of Ambient Intelligence (AmI) with the field of Collective Intelligence (CI) in a point that facilitates the development of large scale AmI environments characterized by a high degree of intelligence. The CAmIE model is introduced, for the bottom-up development of complex, large-scale, AmI environments characterized by a high level of emergent CI. It offers the tools to easily develop AmI applications and it facilitates the interactions between the back-ends of these applications. Moreover, CAmIE encourages diversity, one of the key aspects for the emergence of CI. CAmIE motivates all the human actors from the environment to contribute, so that the resulting environments integrate their CI and it offers a default agent-oriented programming language, CAmI, for developing AmI applications based on particular scenarios and that is compatible with all the other principles behind the model. It is easy to learn and understand, modular and it offers many advantages with respect to AmI, like the agent-oriented approach, context awareness, independence from any knowledge representation formalisms and features that facilitate interactions and the emergence of CI in the AmI environments. Four case studies are offered, highlighting the strengths of the model introduced, from the perspectives of developing stand-alone AmI applications, of making the applications interact to enhance user's experience, of leading to an emergent CI in large environments, study realized through multi-agent simulations, and of creating a community, as having a community of users and developers is desired for a framework implementing CAmIE to work to its full potential
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Koelemeijer, Dorien. « The Design and Evaluation of Ambient Displays in a Hospital Environment ». Thesis, Malmö högskola, Fakulteten för kultur och samhälle (KS), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-23601.

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Hospital environments are ranked as one of the most stressful contemporary work environments for their employees, and this especially concerns nurses (Nejati et al. 2016). One of the core problems comprises the notion that the current technology adopted in hospitals does not support the mobile nature of medical work and the complex work environment, in which people and information are distributed (Bardram 2003). The employment of inadequate technology and the strenuous access to information results in a decrease in efficiency regarding the fulfilment of medical tasks, and puts a strain on the attention of the medical personnel. This thesis proposes a solution to the aforementioned problems through the design of ambient displays, that inform the medical personnel with the health statuses of patients whilst requiring minimal allocation of attention. The ambient displays concede a hierarchy of information, where the most essential information encompasses an overview of patients’ vital signs. Data regarding the vital signs are measured by biometric sensors and are embodied by shape-changing interfaces, of which the ambient displays consist. User-authentication permits the medical personnel to access a deeper layer within the hierarchy of information, entailing clinical data such as patient EMRs, after gesture-based interaction with the ambient display. The additional clinical information is retrieved on the user’s PDA, and can subsequently be viewed in more detail, or modified at any place within the hospital.In this thesis, prototypes of shape-changing interfaces were designed and evaluated in a hospital environment. The evaluation was focused on the interaction design and user-experience of the shape-changing interface, the capabilities of the ambient displays to inform users through peripheral awareness, as well as the remote communication between patient and healthcare professional through biometric data. The evaluations indicated that the required attention allocated for the acquisition of information from the shape-changing interface was minimal. The interaction with the ambient display, as well as with the PDA when accessing additional clinical data, was deemed intuitive, yet comprised a short learning curve. Furthermore, the evaluations in situ pointed out that for optimised communication through the ambient displays, an overview of the health statuses of approximately eight patients should be displayed, and placed in the corridors of the hospital ward.
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FILHO, JOSE VITERBO. « DECENTRALIZED REASONING IN AMBIENT INTELLIGENCE ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16428@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
A computação ubíqua prevê a integração de sistemas computacionais ao nosso dia-a-dia para prover informações e funcionalidades em qualquer momento e lugar. Sistemas desse tipo englobam diferentes tipos de sensores e dispositivos móveis interconectados através de uma combinação de várias tecnologias de rede sem fio. Uma tendência particular nessa área é explorar o paradigma dos Ambientes Inteligentes (AmI), o qual visa à integração de tecnologias inovativas para criar ambientes mediados por computador que, através de serviços específicos, auxiliam os usuários em suas atividades com mínima intervenção. Em sistemas de AmI, mecanismos de inferência são fundamentais para disparar ações ou adaptações de acordo com situações que podem ser significativas e relevantes para aplicações. Muitos sistemas adotam uma abordagem centralizada para seus mecanismos de inferência. Em AmI, entretanto, essas operações podem ter que avaliar dados de contexto coletados de fontes distribuídas e armazenados em diferentes dispositivos, uma vez que geralmente nem todo dado de contexto está prontamente disponível para os serviços de inferência. O objetivo desta tese é propor uma abordagem descentralizada para executar inferência de contexto baseada em regras. Para isso, definimos um modelo de contexto assumindo que os dados de contexto são distribuídos em dois lados, o lado do usuário, representado pelos usuários e seus dispositivos móveis, e o lado do ambiente, representado pela infrastrutura computacional fixa e os serviços do ambiente. Formalizamos a operação de inferência cooperativa - na qual duas entidades cooperam para executar a inferência descentralizada baseada em regras - e definimos um processo completo para realizar esta operação. Finalmente, para mostrar que essa abordagem é possível, projetamos, implementamos e avaliamos o protótipo de um serviço de middleware que executa inferência baseada no processo de inferência cooperativa.
Ubiquitous computing features the seamless integration of computer systems into the everyday lives of users to provide information and functionalities anytime and anywhere. Such systems encompass different kinds of sensors and mobile devices interconnected through a combination of several wireless network technologies. A particular trend in this area is exploring the Ambient Intelligence (AmI) paradigm, which aims at the integration of innovative technologies to create computer-mediated environments that support user activities through specific services, with minimal user intervention. In AmI systems, reasoning is fundamental for triggering actions or adaptations according to specific situations that may be meaningful and relevant to some applications. Most middleware systems adopt a centralized approach for their reasoning mechanisms. In AmI environments, however, these reasoning operations may need to evaluate context data collected from distributed sources and stored in different devices, as usually not all context data is readily available to the reasoners within a ubiquitous system. The goal of this thesis is to propose a decentralized reasoning approach for performing rule-based reasoning about context data targeting AmI systems. For this sake, we defined a context model assuming that in AmI environments context data is distributed over two sides, the user side, represented by the users and their mobile devices, and the ambient side, represented by the fixed computational infrastructure and ambient services. We formalized the cooperative reasoning operation - in which two entities cooperate to perform decentralized rule-based reasoning - and defined a complete process to perform this operation. Finally, to show the feasibility of this approach, we designed, implemented and evaluated a middleware service supporting decentralized reasoning based cooperative reasoning process.
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Nulu, Sujatha. « A GENERIC MOBILE AGENT FRAMEWORK FOR AMBIENT INTELLIGENCE ». Available to subscribers only, 2009. http://proquest.umi.com/pqdweb?did=1796330261&sid=1&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Demrozi, Florenc. « An IoT based Virtual Coaching System (VSC) for Assisting Activities of Daily Life ». Doctoral thesis, 2020. http://hdl.handle.net/11562/1016173.

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Nowadays aging of the population is becoming one of the main concerns of theworld. It is estimated that the number of people aged over 65 will increase from 461million to 2 billion in 2050. This substantial increment in the elderly population willhave significant consequences in the social and health care system. Therefore, in thecontext of Ambient Intelligence (AmI), the Ambient Assisted Living (AAL) has beenemerging as a new research area to address problems related to the aging of the population. AAL technologies based on embedded devices have demonstrated to be effectivein alleviating the social- and health-care issues related to the continuous growing of theaverage age of the population. Many smart applications, devices and systems have beendeveloped to monitor the health status of elderly, substitute them in the accomplishment of activities of the daily life (especially in presence of some impairment or disability),alert their caregivers in case of necessity and help them in recognizing risky situations.Such assistive technologies basically rely on the communication and interaction be-tween body sensors, smart environments and smart devices. However, in such contextless effort has been spent in designing smart solutions for empowering and supportingthe self-efficacy of people with neurodegenerative diseases and elderly in general. Thisthesis fills in the gap by presenting a low-cost, non intrusive, and ubiquitous VirtualCoaching System (VCS) to support people in the acquisition of new behaviors (e.g.,taking pills, drinking water, finding the right key, avoiding motor blocks) necessary tocope with needs derived from a change in their health status and a degradation of theircognitive capabilities as they age. VCS is based on the concept of extended mind intro-duced by Clark and Chalmers in 1998. They proposed the idea that objects within theenvironment function as a part of the mind. In my revisiting of the concept of extendedmind, the VCS is composed of a set of smart objects that exploit the Internet of Things(IoT) technology and machine learning-based algorithms, in order to identify the needsof the users and react accordingly. In particular, the system exploits smart tags to trans-form objects commonly used by people (e.g., pillbox, bottle of water, keys) into smartobjects, it monitors their usage according to their needs, and it incrementally guidesthem in the acquisition of new behaviors related to their needs. To implement VCS, thisthesis explores different research directions and challenges. First of all, it addresses thedefinition of a ubiquitous, non-invasive and low-cost indoor monitoring architecture byexploiting the IoT paradigm. Secondly, it deals with the necessity of developing solu-tions for implementing coaching actions and consequently monitoring human activitiesby analyzing the interaction between people and smart objects. Finally, it focuses on the design of low-cost localization systems for indoor environment, since knowing theposition of a person provides VCS with essential information to acquire information onperformed activities and to prevent risky situations. In the end, the outcomes of theseresearch directions have been integrated into a healthcare application scenario to imple-ment a wearable system that prevents freezing of gait in people affected by Parkinson’sDisease.
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Nazari, Shirehjini Ali Asghar [Verfasser]. « Interaktion in ambient intelligence : Konzeption eines intuitiven Assistenten zur ganzheitlichen und konfliktfreien Interaktion in adaptiven Umgebungen / von Ali Asghar Nazari Shirehjini ». 2008. http://d-nb.info/998376981/34.

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Livres sur le sujet "Ambient Intelligence (AmI)"

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Aarts, E. H. L. (Emile H. L.), dir. Ambient intelligence : European conference, AmI 2008, Nuremberg, Germany, November 19-22, 2008 ; proceedings. Berlin : Springer, 2008.

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AmI 2009 (2009 Salzburg, Austria). Ambient intelligence : European conference, AmI 2009, Salzburg, Austria, November 18-21, 2009 : proceedings. Berlin : Springer, 2009.

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1968-, Schiele Bernt, dir. Ambient intelligence : European conference, AmI 2007, Darmstadt, Germany, November 7-10, 2007 ; proceedings. Berlin : Springer, 2007.

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Ruyter, Boris de. Ambient Intelligence : First International Joint Conference, AmI 2010, Malaga, Spain, November 10-12, 2010. Proceedings. Berlin, Heidelberg : Springer Berlin Heidelberg, 2010.

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Mühlhäuser, Max. Constructing Ambient Intelligence : AmI 2007 Workshops Darmstadt, Germany, November 7-10, 2007 Revised Papers. Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg, 2008.

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Kristof, Laerhoven, Gelissen Jean et SpringerLink (Online service), dir. Constructing Ambient Intelligence : AmI 2011 Workshops, Amsterdam, The Netherlands, November 16-18, 2011. Revised Selected Papers. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012.

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Gabrielli, Silvia. Ambient Media and Systems : Second International ICST Conference, AMBI-SYS 2011, Porto, Portugal, March 24-25, 2011, Revised Selected Papers. Berlin, Heidelberg : Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Chatzimisios, Periklis, Boris De Ruyter, Irene Mavrommati et Achilles Kameas. Ambient Intelligence : 12th European Conference, AmI 2015, Athens, Greece, November 11-13, 2015, Proceedings. Springer London, Limited, 2015.

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Chatzigiannakis, Ioannis, Boris De Ruyter et Irene Mavrommati. Ambient Intelligence : 15th European Conference, AmI 2019, Rome, Italy, November 13–15, 2019, Proceedings. Springer, 2019.

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Chatzimisios, Periklis, Boris De Ruyter, Irene Mavrommati et Achilles Kameas. Ambient Intelligence : 12th European Conference, AmI 2015, Athens, Greece, November 11-13, 2015, Proceedings. Springer, 2015.

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Chapitres de livres sur le sujet "Ambient Intelligence (AmI)"

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Costa, Ricardo, Luís Calçada, Diva Jesus, Luís Lima et Luís C. Lima. « AmI : Monitoring Physical Activity ». Dans Ambient Intelligence - Software and Applications, 233–39. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07596-9_26.

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Sharpanskykh, Alexei, et Kashif Zia. « Grouping Behaviour in AmI-Enabled Crowd Evacuation ». Dans Ambient Intelligence - Software and Applications, 233–40. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19937-0_30.

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Muñoz, Antonio, Francisco Sánchez-Cid, Paul El Khoury et Luca Compagna. « XACML as a Security and Dependability Pattern for Access Control in AmI environments ». Dans Developing Ambient Intelligence, 143–55. Paris : Springer Paris, 2008. http://dx.doi.org/10.1007/978-2-287-78544-3_14.

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Spallazzo, Davide, Martina Sciannamè, Marco Ajovalasit, Mauro Ceconello et Venanzio Arquilla. « Exploring AI-Infused Products Qualities to Unleash AmI ». Dans Ambient Intelligence – Software and Applications – 12th International Symposium on Ambient Intelligence, 35–45. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06894-2_4.

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Sorici, Alexandru, Gauthier Picard, Olivier Boissier et Adina Florea. « Policy-Based Adaptation of Context Provisioning in AmI ». Dans Ambient Intelligence - Software and Applications, 33–43. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19695-4_4.

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Fernández, Javier Martínez, Juan Carlos Augusto, Ralf Seepold et Natividad Martínez Madrid. « AmI Support for the Trading Process : Self-aware Trader Model ». Dans Ambient Intelligence - Software and Applications, 1–8. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19937-0_1.

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Griol, David, Javier Carbó et José Manuel Molina. « Optimizing Dialog Strategies for Conversational Agents Interacting in AmI Environments ». Dans Ambient Intelligence - Software and Applications, 93–100. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28783-1_12.

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Bibri, Simon Elias. « AmI and the IoT and Environmental and Societal Sustainability : Risks, Challenges, and Underpinnings ». Dans Atlantis Ambient and Pervasive Intelligence, 163–215. Paris : Atlantis Press, 2015. http://dx.doi.org/10.2991/978-94-6239-142-0_6.

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García, Óscar, Ricardo S. Alonso, Dante I. Tapia et Juan M. Corchado. « CAFCLA : An AmI-Based Framework to Design and Develop Context-Aware Collaborative Learning Activities ». Dans Ambient Intelligence - Software and Applications, 41–48. Heidelberg : Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00566-9_6.

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Joe Steinhauer, H., et Jonas Mellin. « Automatic Early Risk Detection of Possible Medical Conditions for Usage Within an AMI-System ». Dans Ambient Intelligence - Software and Applications, 13–21. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19695-4_2.

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Actes de conférences sur le sujet "Ambient Intelligence (AmI)"

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Rui, Chen, Hou Yibin, Huang Zhangqin et He Jian. « Data Management Model for Ambient Intelligence : AmI-Data ». Dans 2009 WRI International Conference on Communications and Mobile Computing (CMC). IEEE, 2009. http://dx.doi.org/10.1109/cmc.2009.194.

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Torres, Armengol. « Project for an Ambient Intelligence SpaceLab (AmI SpaceLab) ». Dans 57th International Astronautical Congress. Reston, Virigina : American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.iac-06-e5.2.04.

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Ahmed, Shaftab, Mohammad Ilyas et M. Yasin Akhtar Raja. « Smart Living : Ubiquitous Services Powered by Ambient Intelligence (AmI) ». Dans 2019 IEEE 16th International Conference on Smart Cities : Improving Quality of Life Using ICT & IoT and AI (HONET-ICT). IEEE, 2019. http://dx.doi.org/10.1109/honet.2019.8908054.

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Ballestas, Caseysimone, Euiyoung Kim, Jesuël Lanoy et Jules Janssens. « Design-Engineers’ Selection of Agency : Harm Mitigation in Ambient Intelligent Environments ». Dans ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-91063.

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Abstract The computing paradigm where sensor and actuator technology work in tandem to track and act on events in real Euclidean space, known as ambient intelligence (AmI), is likely to become increasingly common due to the rapid maturation of computing technology. Installing AmI in the built environment creates ambient intelligent environments (AmIE), which strive to make the places we inhabit (invisibly) sensitive and responsive to our presence, needs, wants, and preferences. Given that built environments and the goings-on therein are complicated in an of them selves, implementing AmI for (increasingly) complicated tasks in (increasingly) complicated scenarios, increases the difficulty of managing the outcomes in AmIEs. Our previous research indicates that industry practitioners attribute the agency of AmI artifacts as responsible for these outcomes; especially when harm perpetuation is (one of) the outcome(s), which we codified as the Agency/Intelligence Axis [1]. Due to the nascence of AmI, research on best practices for the design-engineering of AmI is still emerging. This research seeks to add to this literature by evaluating our formerly identified Agency/Intelligence Axis in the context of AmIE through a case study of VyZee, a retail company working on transitioning their retail stores to “smart” stores. Our findings highlight that while VyZee seems largely unaware of any relationship between agency and perpetuating un-anticipated/-desired outcomes, they do implement an array of levels of AmI agency in their retail stores, and their justifications for their choices are presented in the discussion. Finally, coding the data revealed more nuance then previously documented in the Agency/Intelligence Axis, and a new Ambient Intelligent Agent Model, which suggests that AmI agents have six properties, is proposed.
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Rui, Chen, Hou Yi-bin, Huang Zhang-qin, Zhang Yong et Li Hui. « Framework for Local Ambient Intelligence Space : The AmI-Space Project ». Dans 31st Annual International Computer Software and Applications Conference - Vol. 2 - (COMPSAC 2007). IEEE, 2007. http://dx.doi.org/10.1109/compsac.2007.120.

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Plentz, Patricia D. M., et Edson Roberto De Pieri. « An Overview on Real-Time Constraints for Ambient Intelligence (AmI) ». Dans 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA). IEEE, 2018. http://dx.doi.org/10.1109/aiccsa.2018.8612871.

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Alhaijawy, Feras sameer, et Adina magda Florea. « USING AMBIENT INTELLIGENCE IN HEALTHCARE : CASE STUDIES AND SCENARIOS AND EMPLOYING E-TRAINING IN HEALTHCARE ». Dans eLSE 2018. Carol I National Defence University Publishing House, 2018. http://dx.doi.org/10.12753/2066-026x-18-093.

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While there is a progress in different sciences, researchers in machine learning working to make the people life more comfortable by interpreting this progress in various life sphere, they introduced new field called Ambient Intelligence (AmI). The main idea behind AmI is to enroll technology in every aspect of our lives to interact with societies in a smooth manner and improves the quality of our lives. During the last two decades, Ambient Intelligence evolved fast toward suitable developing real-life solutions, causing mass evolutions in every aspect of life as learning, transportation, manufacturing, healthcare, etc. The paper constrained on the healthcare domain due to its exciting paradigm. In this paradigm, the digital environments will not only interact with human events through sensing and interpreting, but it will also learn and adapt actions to the inhabitant needs, gestures, habits, and emotions. This paper summarizes the requirements to achieve the vision of Ambient Intelligence in healthcare. The paper will analyze and compare some previous case studies and scenarios in the Ambient Intelligence area. Finally, the paper proposes a scenario that focuses on the slice of elderly and disabled people those who are physically disabled and have neurological problems to help them to enhance the facilities of using Ambient Intelligence technologies in healthcare and an associated architecture and support them to interact with the community. The paper proposes an architecture that converts the user’s home to look like smart-homes because of the scattered sensors and devices in it and connecting them all together with some sensors on the user’s body besides providing the user with giraff-robot with a screen with different tasks. Some of the giraff-robot responsibilities are to do some actions according to the sensed data, and to work as an online trainer by displaying videos or displaying an avatar tells the user how to do it. The paper tries to present the benefit of using the AmI and the advantage of E-learning -by providing the online training- to ease the life of this slice of people. The architecture tries to respond to the requirements needed by the user those identified during our work.
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Götzelmann, Timo, Julian Kreimeier, Johannes Schwabl, Pascal Karg, Christina Oumard et Florian Büttner. « AmI-VR : An Accessible Building Information System as Case Study Towards the Applicability of Ambient Intelligence in Virtual Reality ». Dans MuC '21 : Mensch und Computer 2021. New York, NY, USA : ACM, 2021. http://dx.doi.org/10.1145/3473856.3474032.

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Chandrasekaran, K., I. R. Ramya et R. Syama. « Ambi Graph : Modeling Ambient Intelligent System ». Dans 2009 International Conference on Computer Engineering and Technology (ICCET). IEEE, 2009. http://dx.doi.org/10.1109/iccet.2009.203.

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« AmIE - Towards Ambient Intelligence for the Ageing Citizens ». Dans International Conference on Health Informatics. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0001433404210424.

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