Ultra-wideband MIMO radio channel characterisation for body-centric wireless communication

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Visualization in cyber-geography: reconsidering cartography's concept of visualization in current usercentric cybergeographic cosmologies

This article discusses some epistemological problems of a semiotic and cybernetic character in two current scientific cosmologies in the study of geographic information systems (GIS) with special reference to the concept of visualization in modern cartography. Setting off from Michael Batty’s prolegomena for a virtual geography and Michael Goodchild’s “Human-Computer-Reality-Interaction” as the field of a new media convergence and networking of GIS-computation of geo-data, the paper outlines preliminarily a common field of study, namely that of cybernetic geography, or just “cyber-geography) owing to the principal similarities with second order cybernetics. Relating these geographical cosmologies to some of Science’s dominant, historical perceptions of the exploring and appropriating of Nature as an “inventory of knowledge”, the article seeks to identify some basic ontological and epistemological dimensions of cybernetic geography and visualization in modern cartography. The points made is that a generalized notion of visualization understood as the use of maps, or more precisely as cybergeographic GIS-thinking seems necessary as an epistemological as well as a methodological prerequisite to scientific knowledge in cybergeography. Moreover do these generalized concept seem to lead to a displacement of the positions traditionally held by the scientist and lay-man citizen, that is not only in respect of the perception of the matter studied, i.e. the field of geography, but also of the manner in which the scientist informs the lay-man citizen in the course of action in the public participation in decision making; a displacement that seems to lead to a more critical, or perhaps even quasi-scientific approach as concerns the lay-man user

USING AUDIENCE-CENTRIC DESIGN AND COMMUNITY FEEDBACK TO MANAGE COMPLEX PRIVACY SETTINGS

Today, technology is enabling people to share information on an unprecedented scale. Although much of this information is intended to be shared with a large group of people or even the public, some disclosure is intended for smaller audiences—a subset of a larger group. People may want to limit information visibility because the information is private or sensitive, or they may feel others would not be interested in the content. When people want to selectively share to different audiences, many technologies fail to provide usable mechanisms to manage these more complex sharing situations. In many cases, people lack understanding about which audiences are able to see what items of information. Additionally, the effort to manage audiences and control access to information adds some extra physical and cognitive burden. This research suggests two methods to help people better understand and control sharing. The first examines audience-centric design: using mechanisms that integrate with the primary task and allow sharing to multiple audiences to improve understanding of how information flows to multiple groups of people. The second method examines using community feedback to enhance privacy/sharing default settings thereby lessening the user’s configuration burden. This knowledge contributes to existing research by understanding the extent of how users share information to multiple audiences and react to community feedback mechanisms designed to ease configuration burden

Diversity performance of off-body MB-OFDM UWB-MIMO

This paper introduces a novel formalism to improve the performance of an off-body system by deploying multiple ultra wideband (UWB) antennas, positioned strategically on the body. A methodology is presented for determining the optimal positions of UWB antennas on the body, necessary to provide a reliable multiband orthogonal frequency division multiplexing (MB-OFDM) UWB diversity antenna system operating in the Federal Communications Commission frequency band between 3.1 and 10.6 GHz. By evaluating the diversity metric, using simulation and measurement data, it is shown that the performance of such a system is stable throughout the entire investigated frequency band for both indoor and outdoor environments. There is a good agreement between the simulated and measured diversity values with a deviation of less than 9%. Therefore, the proposed technique optimizes the antennas' positions for maximum diversity performance within a very broad frequency band, independent of the used wireless communication standard. Thus, the obtained diversity system might be used in any kind of wireless communication link within that frequency band, e.g., UWB-OFDM, UWBMB-OFDM, UWB, or even narrowband transmission

ShadowNet for Data-Centric Quantum System Learning

Understanding the dynamics of large quantum systems is hindered by the curse of dimensionality. Statistical learning offers new possibilities in this regime by neural-network protocols and classical shadows, while both methods have limitations: the former is plagued by the predictive uncertainty and the latter lacks the generalization ability. Here we propose a data-centric learning paradigm combining the strength of these two approaches to facilitate diverse quantum system learning (QSL) tasks. Particularly, our paradigm utilizes classical shadows along with other easily obtainable information of quantum systems to create the training dataset, which is then learnt by neural networks to unveil the underlying mapping rule of the explored QSL problem. Capitalizing on the generalization power of neural networks, this paradigm can be trained offline and excel at predicting previously unseen systems at the inference stage, even with few state copies. Besides, it inherits the characteristic of classical shadows, enabling memory-efficient storage and faithful prediction. These features underscore the immense potential of the proposed data-centric approach in discovering novel and large-scale quantum systems. For concreteness, we present the instantiation of our paradigm in quantum state tomography and direct fidelity estimation tasks and conduct numerical analysis up to 60 qubits. Our work showcases the profound prospects of data-centric artificial intelligence to advance QSL in a faithful and generalizable manner