We have previously introduced a high throughput multiplexing computational spectral imaging device. The device measures scalar projections of pseudo-arbitrary spectral filters at each spatial pixel. This paper discusses simulation and initial experimental progress in performing computational spectral unmixing by taking advantage of the natural sparsity commonly found in the fractional abundances. The simulation results show a lower unmixing error compared to traditional spectral imaging devices. Initial experimental results demonstrate the ability to directly perform spectral unmixing with less error than multiplexing alone.

Computational sensing has demonstrated the ability to ameliorate or eliminate many trade-offs in traditional sensors. Rather than attempting to form a perfect image, then sampling at the Nyquist rate, and reconstructing the signal of interest prior to post-processing, the computational sensor attempts to utilize a priori knowledge, active or passive coding of the signal-of-interest combined with a variety of algorithms to overcome the trade-offs or to improve various task-specific metrics. While it is a powerful approach to radically new sensor architectures, published research tends to focus on architecture concepts and positive results. Little attention is given towards the practical issues when faced with implementing computational sensing prototypes. I will discuss the various practical challenges that I encountered while developing three separate applications of computational sensors. The first is a compressive sensing based object tracking camera, the SCOUT, which exploits the sparsity of motion between consecutive frames while using no moving parts to create a psuedo-random shift variant point-spread function. The second is a spectral imaging camera, the AFSSI-C, which uses a modified version of Principal Component Analysis with a Bayesian strategy to adaptively design spectral filters for direct spectral classification using a digital micro-mirror device (DMD) based architecture. The third demonstrates two separate architectures to perform spectral unmixing by using an adaptive algorithm or a hybrid techniques of using Maximum Noise Fraction and random filter selection from a liquid crystal on silicon based computational spectral imager, the LCSI. All of these applications demonstrate a variety of challenges that have been addressed or continue to challenge the computational sensing community. One issue is calibration, since many computational sensors require an inversion step and in the case of compressive sensing, lack of redundancy in the measurement data. Another issue is over multiplexing, as more light is collected per sample, the finite amount of dynamic range and quantization resolution can begin to degrade the recovery of the relevant information. A priori knowledge of the sparsity and or other statistics of the signal or noise is often used by computational sensors to outperform their isomorphic counterparts. This is demonstrated in all three of the sensors I have developed. These challenges and others will be discussed using a case-study approach through these three applications.

No description available.

Numerous pigment protein complexes exist in natural systems to harvest light energy such as photosystem II and Nanosalina xenorhodopsin. However, the mechanisms of these lipid embedded photo-active complexes have yet to be fully understood. Photosystem II is of interest due to being a compact complex which can perform the three initial key steps of photosynthesis: absorb light, transfer the excitation from the antennae to reaction center, and perform efficient charge separation. Despite considerable theoretical and experimental effort the exact mechanism of this process remains uncertain. Nanosalina xenorhodopsin is a more recently discovered inwards proton pump with minimal studies into the inwards proton pumping mechanism. Nanosalina xenorhodopsin is of interest as it contrasts with other known and well studied rhodopsins which serve as outwards proton pumps, moving H+ ions out of a cell. In this work, we use the Hamiltonian ensemble method to construct the first fully ab initio computational models of these systems which will be used to determine the mechanisms of these systems. To construct these models we first investigated the effect of the modeled surrounding membrane and simulated temperature. The effect of the extended modeled environment on calculated results is often overlooked but important for the construction of an accurate ab initio model. Our models showed that both membrane composition and temperature result in significant changes in the behavior of the extended membrane system, relative excitation energies of chromophores, and energy dynamics of a pigment protein complex. The absolute excitation energies of chromophores, absorption spectra, and linear dichroism spectra were comparatively insensitive to changes in the modeled environment. With the effect of the environment established, we present a preliminary method to extend our photosystem II model to include charge transfer states, and a preliminary model of Nanosalina xenorhodopsin which can determine the photocycle states through validation of calculated spectra against experimental results.