736 research outputs found
Pushing towards the Limit of Sampling Rate: Adaptive Chasing Sampling
Measurement samples are often taken in various monitoring applications. To
reduce the sensing cost, it is desirable to achieve better sensing quality
while using fewer samples. Compressive Sensing (CS) technique finds its role
when the signal to be sampled meets certain sparsity requirements. In this
paper we investigate the possibility and basic techniques that could further
reduce the number of samples involved in conventional CS theory by exploiting
learning-based non-uniform adaptive sampling.
Based on a typical signal sensing application, we illustrate and evaluate the
performance of two of our algorithms, Individual Chasing and Centroid Chasing,
for signals of different distribution features. Our proposed learning-based
adaptive sampling schemes complement existing efforts in CS fields and do not
depend on any specific signal reconstruction technique. Compared to
conventional sparse sampling methods, the simulation results demonstrate that
our algorithms allow less number of samples for accurate signal
reconstruction and achieve up to smaller signal reconstruction error
under the same noise condition.Comment: 9 pages, IEEE MASS 201
Communication channel analysis and real time compressed sensing for high density neural recording devices
Next generation neural recording and Brain-
Machine Interface (BMI) devices call for high density or distributed
systems with more than 1000 recording sites. As the
recording site density grows, the device generates data on the
scale of several hundred megabits per second (Mbps). Transmitting
such large amounts of data induces significant power
consumption and heat dissipation for the implanted electronics.
Facing these constraints, efficient on-chip compression techniques
become essential to the reduction of implanted systems power
consumption. This paper analyzes the communication channel
constraints for high density neural recording devices. This paper
then quantifies the improvement on communication channel
using efficient on-chip compression methods. Finally, This paper
describes a Compressed Sensing (CS) based system that can
reduce the data rate by > 10x times while using power on
the order of a few hundred nW per recording channel
Efficient high-dimensional entanglement imaging with a compressive sensing, double-pixel camera
We implement a double-pixel, compressive sensing camera to efficiently
characterize, at high resolution, the spatially entangled fields produced by
spontaneous parametric downconversion. This technique leverages sparsity in
spatial correlations between entangled photons to improve acquisition times
over raster-scanning by a scaling factor up to n^2/log(n) for n-dimensional
images. We image at resolutions up to 1024 dimensions per detector and
demonstrate a channel capacity of 8.4 bits per photon. By comparing the
classical mutual information in conjugate bases, we violate an entropic
Einstein-Podolsky-Rosen separability criterion for all measured resolutions.
More broadly, our result indicates compressive sensing can be especially
effective for higher-order measurements on correlated systems.Comment: 10 pages, 7 figure
Sparsity-Based Super Resolution for SEM Images
The scanning electron microscope (SEM) produces an image of a sample by
scanning it with a focused beam of electrons. The electrons interact with the
atoms in the sample, which emit secondary electrons that contain information
about the surface topography and composition. The sample is scanned by the
electron beam point by point, until an image of the surface is formed. Since
its invention in 1942, SEMs have become paramount in the discovery and
understanding of the nanometer world, and today it is extensively used for both
research and in industry. In principle, SEMs can achieve resolution better than
one nanometer. However, for many applications, working at sub-nanometer
resolution implies an exceedingly large number of scanning points. For exactly
this reason, the SEM diagnostics of microelectronic chips is performed either
at high resolution (HR) over a small area or at low resolution (LR) while
capturing a larger portion of the chip. Here, we employ sparse coding and
dictionary learning to algorithmically enhance LR SEM images of microelectronic
chips up to the level of the HR images acquired by slow SEM scans, while
considerably reducing the noise. Our methodology consists of two steps: an
offline stage of learning a joint dictionary from a sequence of LR and HR
images of the same region in the chip, followed by a fast-online
super-resolution step where the resolution of a new LR image is enhanced. We
provide several examples with typical chips used in the microelectronics
industry, as well as a statistical study on arbitrary images with
characteristic structural features. Conceptually, our method works well when
the images have similar characteristics. This work demonstrates that employing
sparsity concepts can greatly improve the performance of SEM, thereby
considerably increasing the scanning throughput without compromising on
analysis quality and resolution.Comment: Final publication available at ACS Nano Letter
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