It is anticipated that in some extreme situations, autonomous cars will benefit from the intervention of a ״Remote Driver״. The vehicle computer may discover a failure and decide to request remote assistance for safe roadside parking. In a more extreme scenario, the vehicle may require a complete remote-driver takeover due to malfunctions or an inability to resolve unknown decision logic. In such cases, the remote driver will need a sufficiently good quality real-time video stream of the vehicle cameras to respond quickly and accurately enough to the situation at hand. Relaying such a video stream to the remote Command and Control (C&C) center is especially challenging when considering the varying wireless channel bandwidths expected in these scenarios. This paper proposes an innovative end-to-end content-sensitive video compression scheme to allow efficient and satisfactory video transmission from autonomous vehicles to the remote C&C center.
It has been shown that sub-diffraction structures can be resolved in acoustic resolution photoacoustic imaging thanks to norm-based iterative reconstruction algorithms exploiting prior knowledge of the point spread function (PSF) of the imaging system. Here, we demonstrate that super-resolution is still achievable when the receiving ultrasonic probe has much fewer elements than used conventionally (8 against 128). To this end, a proof-of-concept experiment was conducted. A microfluidic circuit containing five parallel microchannels (channel’s width 40μm, center-to center distance 180μm) filled with dye was exposed to 5ns laser pulses (=532nm, fluence=3.0mJ/cm2, PRF=100Hz). Photoacoustic signals generated by the sample were captured by a linear ultrasonic array (128 elements, pitch=0.1mm, fc=15MHz) connected to an acquisition device. The forward problem is modelled in a matrix form Y=AX, where Y are the measured photoacoustic signals and X is the object to reconstruct. The matrix A contained the PSFs at all points of the reconstruction grid, and was derived from a single PSF acquired experimentally for a 10-μm wide microchannel. For the reconstruction, we used a sparsity-based minimization algorithm. While the conventional image obtained by beamforming the signals measured with all the 128 elements of the probe cannot resolve the individual microchannels, our sparsity-based reconstruction leads to super-resolved images with only 8 elements of the probe (regularly spaced over the full probe aperture), with an image quality comparable to that obtained with all the 128 elements. These results pave the way towards super-resolution in 3D photoacoustic imaging with sparse transducers arrays.
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