11,132 research outputs found
Ultra-wideband MIMO radio channel characterisation for body-centric wireless communication
No abstract available
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 Battyâs prolegomena for a virtual geography and Michael
Goodchildâs âHuman-Computer-Reality-Interactionâ 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
âcyber-geography) owing to the principal similarities with second order cybernetics.
Relating these geographical cosmologies to some of Scienceâs dominant, historical
perceptions of the exploring and appropriating of Nature as an âinventory of
knowledgeâ, 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 audiencesâa 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 userâs 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
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