7,124 research outputs found
Compressive Sampling for Remote Control Systems
In remote control, efficient compression or representation of control signals
is essential to send them through rate-limited channels. For this purpose, we
propose an approach of sparse control signal representation using the
compressive sampling technique. The problem of obtaining sparse representation
is formulated by cardinality-constrained L2 optimization of the control
performance, which is reducible to L1-L2 optimization. The low rate random
sampling employed in the proposed method based on the compressive sampling, in
addition to the fact that the L1-L2 optimization can be effectively solved by a
fast iteration method, enables us to generate the sparse control signal with
reduced computational complexity, which is preferable in remote control systems
where computation delays seriously degrade the performance. We give a
theoretical result for control performance analysis based on the notion of
restricted isometry property (RIP). An example is shown to illustrate the
effectiveness of the proposed approach via numerical experiments
How to find real-world applications for compressive sensing
The potential of compressive sensing (CS) has spurred great interest in the
research community and is a fast growing area of research. However, research
translating CS theory into practical hardware and demonstrating clear and
significant benefits with this hardware over current, conventional imaging
techniques has been limited. This article helps researchers to find those niche
applications where the CS approach provides substantial gain over conventional
approaches by articulating lessons learned in finding one such application; sea
skimming missile detection. As a proof of concept, it is demonstrated that a
simplified CS missile detection architecture and algorithm provides comparable
results to the conventional imaging approach but using a smaller FPA. The
primary message is that all of the excitement surrounding CS is necessary and
appropriate for encouraging our creativity but we all must also take off our
"rose colored glasses" and critically judge our ideas, methods and results
relative to conventional imaging approaches.Comment: 10 page
Compressive sensing based velocity estimation in video data
This paper considers the use of compressive sensing based algorithms for
velocity estimation of moving vehicles. The procedure is based on sparse
reconstruction algorithms combined with time-frequency analysis applied to
video data. This algorithm provides an accurate estimation of object's velocity
even in the case of a very reduced number of available video frames. The
influence of crucial parameters is analysed for different types of moving
vehicles.Comment: 4 pages, 5 figure
Visualization on colour based flow vector of thermal image for movement detection during interactive session
Recently thermal imaging is exploited in applications such as motion and face detection. It has drawn attention many researchers to build such technology to improve lifestyle. This work proposed a technique to detect and identify a motion in sequence images for the application in security monitoring system or outdoor surveillance. Conventional system might cause false information with the present of shadow. Thus, methods employed in this work are Canny edge detector method, Lucas Kanade and Horn Shunck algorithms, to overcome the major problem when using thresholding method, which is only intensity or pixel magnitude is considered instead of relationships between the pixels. The results obtained could be observed in flow vector parameter and the segmentation colour based image for the time frame from 1 to 10 seconds. The visualization of both the parameters clarified the movement and changes of pixel intensity between two frames by the supportive colour segmentation, either in smooth or rough motion. Thus, this technique may contribute to others application such as biometrics, military system, and surveillance machine
Video Compressive Sensing for Dynamic MRI
We present a video compressive sensing framework, termed kt-CSLDS, to
accelerate the image acquisition process of dynamic magnetic resonance imaging
(MRI). We are inspired by a state-of-the-art model for video compressive
sensing that utilizes a linear dynamical system (LDS) to model the motion
manifold. Given compressive measurements, the state sequence of an LDS can be
first estimated using system identification techniques. We then reconstruct the
observation matrix using a joint structured sparsity assumption. In particular,
we minimize an objective function with a mixture of wavelet sparsity and joint
sparsity within the observation matrix. We derive an efficient convex
optimization algorithm through alternating direction method of multipliers
(ADMM), and provide a theoretical guarantee for global convergence. We
demonstrate the performance of our approach for video compressive sensing, in
terms of reconstruction accuracy. We also investigate the impact of various
sampling strategies. We apply this framework to accelerate the acquisition
process of dynamic MRI and show it achieves the best reconstruction accuracy
with the least computational time compared with existing algorithms in the
literature.Comment: 30 pages, 9 figure
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