77,624 research outputs found
Programmable photonics : an opportunity for an accessible large-volume PIC ecosystem
We look at the opportunities presented by the new concepts of generic programmable photonic integrated circuits (PIC) to deploy photonics on a larger scale. Programmable PICs consist of waveguide meshes of tunable couplers and phase shifters that can be reconfigured in software to define diverse functions and arbitrary connectivity between the input and output ports. Off-the-shelf programmable PICs can dramatically shorten the development time and deployment costs of new photonic products, as they bypass the design-fabrication cycle of a custom PIC. These chips, which actually consist of an entire technology stack of photonics, electronics packaging and software, can potentially be manufactured cheaper and in larger volumes than application-specific PICs. We look into the technology requirements of these generic programmable PICs and discuss the economy of scale. Finally, we make a qualitative analysis of the possible application spaces where generic programmable PICs can play an enabling role, especially to companies who do not have an in-depth background in PIC technology
Power Scaling of Uplink Massive MIMO Systems with Arbitrary-Rank Channel Means
This paper investigates the uplink achievable rates of massive multiple-input
multiple-output (MIMO) antenna systems in Ricean fading channels, using
maximal-ratio combining (MRC) and zero-forcing (ZF) receivers, assuming perfect
and imperfect channel state information (CSI). In contrast to previous relevant
works, the fast fading MIMO channel matrix is assumed to have an arbitrary-rank
deterministic component as well as a Rayleigh-distributed random component. We
derive tractable expressions for the achievable uplink rate in the
large-antenna limit, along with approximating results that hold for any finite
number of antennas. Based on these analytical results, we obtain the scaling
law that the users' transmit power should satisfy, while maintaining a
desirable quality of service. In particular, it is found that regardless of the
Ricean -factor, in the case of perfect CSI, the approximations converge to
the same constant value as the exact results, as the number of base station
antennas, , grows large, while the transmit power of each user can be scaled
down proportionally to . If CSI is estimated with uncertainty, the same
result holds true but only when the Ricean -factor is non-zero. Otherwise,
if the channel experiences Rayleigh fading, we can only cut the transmit power
of each user proportionally to . In addition, we show that with an
increasing Ricean -factor, the uplink rates will converge to fixed values
for both MRC and ZF receivers
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Recovering Sparse Signals Using Sparse Measurement Matrices in Compressed DNA Microarrays
Microarrays (DNA, protein, etc.) are massively parallel affinity-based biosensors capable of detecting and quantifying a large number of different genomic particles simultaneously. Among them, DNA microarrays comprising tens of thousands of probe spots are currently being employed to test multitude of targets in a single experiment. In conventional microarrays, each spot contains a large number of copies of a single probe designed to capture a single target, and, hence, collects only a single data point. This is a wasteful use of the sensing resources in comparative DNA microarray experiments, where a test sample is measured relative to a reference sample. Typically, only a fraction of the total number of genes represented by the two samples is differentially expressed, and, thus, a vast number of probe spots may not provide any useful information. To this end, we propose an alternative design, the so-called compressed microarrays, wherein each spot contains copies of several different probes and the total number of spots is potentially much smaller than the number of targets being tested. Fewer spots directly translates to significantly lower costs due to cheaper array manufacturing, simpler image acquisition and processing, and smaller amount of genomic material needed for experiments. To recover signals from compressed microarray measurements, we leverage ideas from compressive sampling. For sparse measurement matrices, we propose an algorithm that has significantly lower computational complexity than the widely used linear-programming-based methods, and can also recover signals with less sparsity
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