Nuit Blanche: CS: Irregular tiling in retina, Recognition using information-optimal adaptive feature-specific imaging, other CS hardware encoding.

Saturday, April 11, 2009

CS: Irregular tiling in retina, Recognition using information-optimal adaptive feature-specific imaging, other CS hardware encoding.

I wanted to do an entry on it, but David Brady beat me to it in his blog entry entitled Irregular Image Sampling about the recent PloS paper entitled: Receptive Fields in Primate Retina Are Coordinated to Sample Visual Space More Uniformly by Jeffrey L. Gauthier, Greg D. Field, Alexander Sher, Martin Greschner, Jonathon Shlens, Alan M. Litke, E. J. Chichilnisky. It echoes previous entries such as these:

Pawan Baheti, a reader of this blog, let me know of some of his papers. The "work in this [first] paper is based on compressive imaging architecture; however it does not uses random projections rather figures out the optimal basis based on a priori information and already measured projections data."

Here they are:

Recognition using information-optimal adaptive feature-specific imaging by Pawan Baheti and Mark A. Neifeld. The abstract reads:

We present an information-theoretic adaptive feature-specific imaging (AFSI) system for a M-class recognition task. The proposed system utilizes the recently developed task-specific information (TSI) framework to incorporate the knowledge from previous measurements and adapt the projection matrix at each step. The decision-making framework is based on sequential hypothesis testing. We quantify the number of measurements required to achieve a specified probability of misclassification (P_e), and we compare the performances of three approaches: the new TSI-based AFSI system, a previously reported statistical AFSI system, and static FSI (SFSI). The TSI-based AFSI system exhibits significant improvement compared with SFSI and statistical AFSI at low signal-to-noise ratio (SNR). It is shown that for M=4 hypotheses, SNR=−20 dB and desired P_e=10⁻², TSI-based AFSI requires 3 times fewer measurements than statistical AFSI, and 16 times fewer measurements than SFSI. We also describe an extension of the proposed method that is suitable for recognition in the presence of nuisance parameters such as illumination conditions and target orientations.

Pawan has told me the following:

...I am currently at Qualcomm research and have applied compressed sensing framework to biomedical signals like PPG, ECG for reducing acquisition power, task-specific estimation and concealment from channel-errors. Some of the work is published in 2009 Bodynets and BSN conferences.

Here are the paper titles: Heart Rate and Blood Pressure Estimation from Compressively Sensed Photoplethysmograph by Pawan Baheti and Harinath Garudadri. The abstract reads:

In this paper we consider the problem of low power ${\textmd{S}}_{p}{\textmd{O}}_{2}$ sensors for acquiring Photoplethysmograph (PPG) signals. Most of the power in ${\textmd{S}}_{p}{\textmd{O}}_{2}$ sensors goes to lighting red and infra-red LEDs. We use compressive sensing to lower the amount of time LEDs are lit, thereby reducing the signal acquisition power. We observe power savings by a factor that is comparable to the sampling rate. At the receiver, we reconstruct the signal with sufficient integrity for a given task. Here we consider the tasks of heart rate (HR) and blood pressure (BP) estimation. For BP estimation we use ECG signals along with the reconstructed PPG waveform. We show that the reconstruction quality can be improved at the cost of increasing compressed sensing bandwidth and receiver complexity for a given task. We present HR and BP estimation results using the MIMIC database.

and An Ultra Low Power Pulse Oximeter Sensor Based On Compressed Sensing.

Thanks Pawan !

One more word, while papers are properties of most journals, journals also allow authors to post their preprints/postprints on their website for archival purposes. For instance, this is the case for the JOSAa.