Danyal Ahsanullah

Temporally varying speckle patterns, produced by light-matter interaction encode valuable information about inhomogeneities embedded within a scattering medium. These speckle fluctuations arise either from the tuning of the emission frequency of a laser illuminating a static scattering medium or from the microscopic motion of scatterers within a dynamically scattering medium. In this work, we detect embedded inhomogeneities by probing static and dynamic scattering media with coherent light and leveraging the statistical distribution of temporal speckle differences. In addition, we utilize the insights from the speckle differencing paradigm, to present the first experimental results of detecting inhomogeneities embedded within a scattering medium using bio-inspired neuromorphic sensors. The proposed neuromorphic approach simplifies the optical and electronic design, and significantly reduces data throughput by capturing only the differential information in the form of 1-bit spikes.

Indirect imaging correlography (IIC) is a coherent imaging technique that provides access to the autocorrelation of the albedo of objects obscured from line-of-sight. This technique is used to recover sub-mm resolution images of obscured objects at large standoffs in non-line-of-sight (NLOS) imaging. However, predicting the exact resolving power of IIC in any given NLOS scene is complicated by the interplay between several factors, including object position and pose. This work puts forth a mathematical model for the imaging operator in IIC to accurately predict the images of objects in NLOS imaging scenes. Using the imaging operator, expressions for the spatial resolution as a function of scene parameters such as object position and pose are derived and validated experimentally. In addition, a self-supervised deep neural network framework to reconstruct images of objects from their autocorrelation is proposed. Using this framework, objects with ≈ 250 μm features, located at 1 mt standoffs in an NLOS scene, are successfully reconstructed.

It was originally stated that convolution operations were required to implement adjoint sensitivity in the time domain. In this paper, we revisit time-domain adjoint sensitivity with a circuit theoretic approach and an efficient solution is clearly stated in terms of device level. Key is the linearization of the energy storage elements (e.g., capacitance and inductance) and nonlinear memoryless elements (e.g., MOS, BJT DC characteristics) at each time step. Due to the finite precision of computation, numerical errors that accumulate across timesteps can arise in nonlinear elements. A methodology to suppress that error is introduced. Numerical results demonstrate that the proposed method achieves accuracy while significantly reducing computational run time.

A passive method for extending the native contrast range of semiconductor light sources using spectral filters is presented from both a theoretical framework including performance bounds. The approach is experimentally verified.

The Light Scaled Space-Time Bandwidth Product is proposed as a means of quantifying the performance space of Neuromorphic/Event-Based sensors. Inspired by this metric, the sensor is demonstrated to successfully detect vibrating objects behind scattering media.

This work recovers images of voids embedded inside a scattering medium using a tunable laser and a CMOS camera by adapting ideas from intensity interferometry and photon migration through disordered media.