Biblio

The main limitation of acoustic particle separation for microfluidic application is its low sorting efficiency. This is due to the weak coupling of surface acoustic waves (SAWs) into the microchannel. In this work, we demonstrate bulk acoustic wave (BAW) particle sorting using capacitive micromachined ultrasonic transducers (CMUTs) for the first time. A collapsed mode CMUT was driven in air to generate acoustic pressure within the silicon substrate in the in-plane direction of the silicon die. This acoustic pressure was coupled into a water droplet, positioned at the side of the CMUT die, and measured with an optical hydrophone. By using a beam steering approach, the ultrasound generated from 32 CMUT elements were added in-phase to generate a maximum peak-to-peak pressure of 0.9 MPa. Using this pressure, 10 µm latex beads were sorted almost instantaneously.

We propose and experimentally demonstrate a compressive sensing scheme based on optical coherent receiver that recovers sparse optical arbitrary signals with an analog bandwidth up to 25GHz. The proposed scheme uses 16x lower sampling rate than the Nyquist theorem and spectral resolution of 24.4MHz.

Compressive Covariance Sampling (CCS) is a strategy used to recover the covariance matrix (CM) directly from compressive measurements. Several works have proven the advantages of CSS in Compressive Spectral Imaging (CSI) but most of these algorithms require multiple random projections of the scene to obtain good reconstructions. However, several low-resolution copies of the scene can be captured in a single snapshot through a lenslet array. For this reason, this paper proposes a sensing protocol and a single snapshot CCS optical architecture using a lenslet array based on the Dual Dispersive Aperture Spectral Imager(DD-CASSI) that allows the recovery of the covariance matrix with a single snapshot. In this architecture uses the lenslet array allows to obtain different projections of the image in a shot due to the special coded aperture. In order to validate the proposed approach, simulations evaluated the quality of the recovered CM and the performance recovering the spectral signatures against traditional methods. Results show that the image reconstructions using CM have PSNR values about 30 dB, and reconstructed spectrum has a spectral angle mapper (SAM) error less than 15° compared to the original spectral signatures.

We present an object tracking framework which fuses multiple unstable video-based methods and supports automatic tracker initialization and termination. To evaluate our system, we collected a large dataset of hand-annotated 5-minute traffic surveillance videos, which we are releasing to the community. To the best of our knowledge, this is the first publicly available dataset of such long videos, providing a diverse range of real-world object variation, scale change, interaction, different resolutions and illumination conditions. In our comprehensive evaluation using this dataset, we show that our automatic object tracking system often outperforms state-of-the-art trackers, even when these are provided with proper manual initialization. We also demonstrate tracking throughput improvements of 5× or more vs. the competition.