We report the development of a broadband rotary joint for high-speed ultrahigh-resolution endoscopic optical coherence tomography (OCT) imaging in the 800 nm spectral range. This rotary joint features a pair of achromatic doublets in order to achieve broadband operation for a 3 dB bandwidth over 150 nm. The measured one-way throughput of the rotary joint is greater than 80%, while the fluctuation of the double-pass coupling efficiency during 360 deg rotation is less than ±5% at a near video-rate speed of 20 revolutions/s (rps). The rotary joint is used in conjunction with a diffractive-optics-based endoscope and 800 nm spectral domain OCT system and achieved an ultrahigh axial resolution of ∼2.4 μm in air. The imaging performance is demonstrated by 3D circumferential imaging of a mouse colon in vivo.

We present a high-speed single pixel flow imager based on an all-optical Haar wavelet transform of moving objects. Spectrally-encoded wavelet measurement patterns are produced by chirp processing of broad-bandwidth mode-locked laser pulses. A complete wavelet pattern set serially illuminates the object via a spectral disperser. This high-rate structured illumination transforms the scene into a set of sparse coefficients. We show that complex scenes can be compressed to less than 30% of their Nyquist rate by thresholding and storing the most significant wavelet coefficients. Moreover by employing temporal multiplexing of the patterns we are able to achieve pixel rates in excess of 360 MPixels/s.

In this paper, we propose a simplified and robust model for Place cell generation based on the Oscillatory Interference (OI) model concept. Aiming toward hardware implementation in bio-inspired Simultaneous Localization and Mapping (SLAM) systems for mobile robotics, we base our model on logic operations that reduce its computational complexity. The model compensates for parameter variations in the behaviors of the population of constituent Theta cells, and allows the Theta cells to have square-wave oscillation profiles. The robustness of the model, with respect to mismatch in the Theta cell’s base oscillation frequency and gain—as a function of modulatory inputs—is demonstrated. Place cell composed of 48 Theta cells with base frequency variations with a 25% standard deviation from the mean and a gain error with 20% standard deviation from the mean only result in a 20% deformations within the place field and 0.24% outer side lobes, and an overall pattern with 0.0015 mean squared error on average. We also present how the model can be used to achieve the localization and path-tracking functionalities of SLAM. Hence, we propose a model for spatial cell formation using Theta cells with behaviors that are biologically plausible and hardware implementable for real world application in neurally-inspired SLAM.

A single-pixel compressively sensed architecture is exploited to simultaneously achieve a 10× reduction in acquired data compared with the Nyquist rate, while alleviating limitations faced by conventional widefield temporal focusing microscopes due to scattering of the fluorescence signal. Additionally, we demonstrate an adaptive sampling scheme that further improves the compression and speed of our approach.

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