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Thursday, March 11, 2010

CS: Ear-Phone, Sparse NARMA Identification, High-quality Quantum Imaging, some blog news and more.


The Wired article on Compressed Sensing has now been featured in the mainstream news. I am not sure how pristine the information remains.


Zainul Charbiwala pointed to the following paper about using distributed sensor to monitor city-wide noise, interesting! Ear-Phone: An End-to-End Participatory Urban Noise Mapping System by Rajib Kumar Rana, Chun Tung Chou, Salil S. Kanhere, Nirupama Bulusu, Wen Hu. The abstract reads:
A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps. In this paper, we present the design, implementation and performance evaluation of an end-to-end participatory urban noise mapping system called Ear-Phone. Ear-Phone, for the first time, leverages Compressive Sensing to address the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection. Ear-Phone, implemented on Nokia N95 and HP iPAQ mobile devices, also addresses the challenge of collecting accurate noise pollution readings at a mobile device. Extensive simulations and outdoor experiments demonstrate that Ear-Phone is a feasible platform to assess noise pollution, incurring reasonable system resource consumption at mobile devices and providing high reconstruction accuracy of the noise map.
Thanks Zainul !

Greedy algorithms form an essential tool for compressed sensing. However, their inherent batch mode discourages their use in time-varying environments due to significant complexity and storage requirements. In this paper a powerful greedy scheme developed in [1, 2] is converted into an adaptive algorithm which is applied to estimation of nonlinear channels. Performance is assessed via computer simulations on a variety of linear and nonlinear channels; all confirm significant improvements over conventional methods.

I just found the following paper linking the Hanbury Brown-Twiss effect to compressive sensing: High-quality Quantum Imaging Algorithm and Experiment Based on Compressive Sensing by Liu Jiying, Zhu Jubo, Lu Chuan, and Huang Shisheng. The abstract reads:
Quantum imaging has some unique advantages, such as nonlocal imaging manner and
enhanced space resolution. However, the quality of the reconstructed images and the time of data acquisition leave much to be desired. Based on the framework of compressive sensing, we propose an optimization criterion for high-quality quantum imaging whereby total variation restriction is specifically utilized for noise suppression. The corresponding reported algorithm uses a combination of a greedy strategy and the interactive re-weight least square method. The simulation and the actual imaging experiment both show a significant improvement of the proposed algorithm over previous imaging method.
I am not sure how the set-up is different from the Compressive ghost imaging by Ori Katz, Yaron Bromberg, Yaron Silberberg. The algorithm is indeed a little different in its use of the TV term.

Several blogs covered some aspect of compressive sensing, namely:

Also found on the interweb:



Credit: NASA / JPL / SSI / colorized by Emily Lakdawalla Pointing at Helene.

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