162,284 research outputs found

    On-Body Channel Measurement Using Wireless Sensors

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    © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This post-acceptance version of the paper is essentially complete, but may differ from the official copy of record, which can be found at the following web location (subscription required to access full paper): http://dx.doi.org/10.1109/TAP.2012.219693

    The Informational Model of Consciousness: Mechanisms of Embodiment/Disembodiment of Information

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    It was shown recently that information is the central concept which it is to be considered to understand consciousness and its properties. Arguing that consciousness is a consequence of the operational activity of the informational system of the human body, it was shown that this system is composed by seven informational components, reflected in consciousness by corresponding cognitive centers. It was argued also that consciousness can be connected to the environment not only by the common senses, but also by a special connection pole to the bipolar properties of the universe, allowing to explain the associated phenomena of the near-death experiences and other special phenomena. Starting from the characteristics of this model, defined as the Informational Model of Consciousness and to complete the info-communication panorama, in this paper it is analyzed the info-connectivity of the informational system with the body itself. The brain areas where the activity of each informational component are identified, and a definition of consciousness in terms of information is proposed. As the electrical connectivity by means of the nervous system was already proved, allowing the application of the analysis and developing tools of the information science, a particular attention is paid to the non-electrical mechanisms implied in the internal communication. For this, it is shown that the key mechanisms consists in embodiment/disembodiment processes of information during the inter and intra communication of the cells. This process can be modeled also by means of, and in correlation with specific concepts of the science and technology of information, referred to network communication structures, and is represented by epigenetic mechanisms, allowing the acquired trait transmission to the offspring generation. From the perspective of the informational model of consciousness, the human organism appears therefore as a dynamic reactive informational system, actuating in correlation with matter for adaptation, by embodiment/disembodiment processes of information

    Personal area technologies for internetworked services

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    Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

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    Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

    AROMA: Automatic Generation of Radio Maps for Localization Systems

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    WLAN localization has become an active research field recently. Due to the wide WLAN deployment, WLAN localization provides ubiquitous coverage and adds to the value of the wireless network by providing the location of its users without using any additional hardware. However, WLAN localization systems usually require constructing a radio map, which is a major barrier of WLAN localization systems' deployment. The radio map stores information about the signal strength from different signal strength streams at selected locations in the site of interest. Typical construction of a radio map involves measurements and calibrations making it a tedious and time-consuming operation. In this paper, we present the AROMA system that automatically constructs accurate active and passive radio maps for both device-based and device-free WLAN localization systems. AROMA has three main goals: high accuracy, low computational requirements, and minimum user overhead. To achieve high accuracy, AROMA uses 3D ray tracing enhanced with the uniform theory of diffraction (UTD) to model the electric field behavior and the human shadowing effect. AROMA also automates a number of routine tasks, such as importing building models and automatic sampling of the area of interest, to reduce the user's overhead. Finally, AROMA uses a number of optimization techniques to reduce the computational requirements. We present our system architecture and describe the details of its different components that allow AROMA to achieve its goals. We evaluate AROMA in two different testbeds. Our experiments show that the predicted signal strength differs from the measurements by a maximum average absolute error of 3.18 dBm achieving a maximum localization error of 2.44m for both the device-based and device-free cases.Comment: 14 pages, 17 figure

    Information and Meaning in Life, Humans and Robots

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    Information and meaning exist around us and within ourselves, and the same information can correspond to different meanings. This is true for humans and animals, and is becoming true for robots. We propose here an overview of this subject by using a systemic tool related to meaning generation that has already been published (C. Menant, Entropy 2003). The Meaning Generator System (MGS) is a system submitted to a constraint that generates a meaningful information when it receives an incident information that has a relation with the constraint. The content of the meaningful information is explicited, and its function is to trigger an action that will be used to satisfy the constraint of the system. The MGS has been introduced in the case of basic life submitted to a "stay alive" constraint. We propose here to see how the usage of the MGS can be extended to more complex living systems, to humans and to robots by introducing new types of constraints, and integrating the MGS into higher level systems. The application of the MGS to humans is partly based on a scenario relative to the evolution of body self-awareness toward self-consciousness that has already been presented (C. Menant, Biosemiotics 2003, and TSC 2004). The application of the MGS to robots is based on the definition of the MGS applied to robots functionality, taking into account the origins of the constraints. We conclude with a summary of this overview and with themes that can be linked to this systemic approach on meaning generation
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