43,246 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
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
One-shot ultraspectral imaging with reconfigurable metasurfaces
One-shot spectral imaging that can obtain spectral information from thousands
of different points in space at one time has always been difficult to achieve.
Its realization makes it possible to get spatial real-time dynamic spectral
information, which is extremely important for both fundamental scientific
research and various practical applications. In this study, a one-shot
ultraspectral imaging device fitting thousands of micro-spectrometers (6336
pixels) on a chip no larger than 0.5 cm, is proposed and demonstrated.
Exotic light modulation is achieved by using a unique reconfigurable
metasurface supercell with 158400 metasurface units, which enables 6336
micro-spectrometers with dynamic image-adaptive performances to simultaneously
guarantee the density of spectral pixels and the quality of spectral
reconstruction. Additionally, by constructing a new algorithm based on
compressive sensing, the snapshot device can reconstruct ultraspectral imaging
information (/~0.001) covering a broad (300-nm-wide)
visible spectrum with an ultra-high center-wavelength accuracy of 0.04-nm
standard deviation and spectral resolution of 0.8 nm. This scheme of
reconfigurable metasurfaces makes the device can be directly extended to almost
any commercial camera with different spectral bands to seamlessly switch the
information between image and spectral image, and will open up a new space for
the application of spectral analysis combining with image recognition and
intellisense
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