Data compressive paradigm for spectral sensing and classification using electrically tunable detectors

This dissertation contains three major parts: (1) demonstration of the algorithmic spectrometry in the mid-IR sensing regime using spectrally tunable quantum dots-in-a-well (DWELL) IR detector without employing any spectral filters; (2) further demonstration of the spectral-classification capability of tunable DWELL IR focal-plane array (FPA), again without using any spectral filters; and (3) development of a generalized filter-free data-compressive spectral sensing paradigm using the DWELL detector that enables arbitrarily specified MS sensing (e.g., spectral matched filtering, slope sensing, multicolor sensing, etc.) without using any spectral filters and possibly under constrained acquisition times

Continuous time-varying biasing approach for spectrally tunable infrared detectors

In a recently demonstrated algorithmic spectral-tuning technique by Jang et al. [Opt. Express 19, 19454-19472, (2011)], the reconstruction of an object’s emissivity at an arbitrarily specified spectral window of interest in the long-wave infrared region was achieved. The technique relied upon forming a weighted superposition of a series of photocurrents from a quantum dots-in-a-well (DWELL) photodetector operated at discrete static biases that were applied serially. Here, the technique is generalized such that a continuously varying biasing voltage is employed over an extended acquisition time, in place using a series of fixed biases over each sub-acquisition time, which totally eliminates the need for the post-processing step comprising the weighted superposition of the discrete photocurrents. To enable this capability, an algorithm is developed for designing the time-varying bias for an arbitrary spectral-sensing window of interest. Since continuous-time biasing can be implemented within the readout circuit of a focal-plane array, this generalization would pave the way for the implementation of the algorithmic spectral tuning in focal-plane arrays within in each frame time without the need for on-sensor multiplications and additions. The technique is validated by means of simulations in the context of spectrometry and object classification while using experimental data for the DWELL under realistic signal-to-noise ratios

Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks

Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector

Rapid and selective concentration of bacteria, viruses, and proteins using alternating current signal superimposition on two coplanar electrodes

Dielectrophoresis (DEP) is usually effective close to the electrode surface. Several techniques have been developed to overcome its drawbacks and to enhance dielectrophoretic particle capture. Here we present a simple technique of superimposing alternating current DEP (high-frequency signals) and electroosmosis (EO; low-frequency signals) between two coplanar electrodes (gap: 25 mu m) using a lab-made voltage adder for rapid and selective concentration of bacteria, viruses, and proteins, where we controlled the voltages and frequencies of DEP and EO separately. This signal superimposition technique enhanced bacterial capture (Escherichia coli K-12 against 1-mu m-diameter polystyrene beads) more selectively (>99%) and rapidly (similar to 30 s) at lower DEP (5 Vpp) and EO (1.2 Vpp) potentials than those used in the conventional DEP capture studies. Nanometer-sized MS2 viruses and troponin I antibody proteins were also concentrated using the superimposed signals, and significantly more MS2 and cTnI-Ab were captured using the superimposed signals than the DEP (10 Vpp) or EO (2 Vpp) signals alone (p < 0.035) between the two coplanar electrodes and at a short exposure time (1 min). This technique has several advantages, such as simplicity and low cost of electrode fabrication, rapid and large collection without electrolysis

Comb-rooted multi-channel synthesis of ultra-narrow optical frequencies of few Hz linewidth

We report a multi-channel optical frequency synthesizer developed to generate extremely stable continuous wave lasers directly out of the optical comb of an Er-doped fiber oscillator. Being stabilized to a high-finesse cavity with a fractional frequency stability of $3.8\times10^{-15}$ at 0.1 s, the comb-rooted synthesizer produces multiple optical frequencies of ultra-narrow linewidth of 1.0 Hz at 1 s concurrently with an output power of tens of mW per each channel. Diode-based stimulated emission by injection locking is a key mechanism that allows comb frequency modes to sprout up with sufficient power amplification but no loss of original comb frequency stability. Channel frequencies are individually selectable with a 0.1 GHz increment over the entire comb bandwidth spanning 4.25 THz around a 1550 nm center wavelength. A series of out-of-loop test results is discussed to demonstrate that the synthesizer is able to provide stable optical frequencies with the potential for advancing diverse ultra-precision applications such as optical clocks comparison, atomic line spectroscopy, photonic microwaves generation, and coherent optical telecommunications.Comment: 19 pages, 4 figure

Multispectral Classification With Bias-Tunable Quantum Dots-in-a-Well Focal Plane Arrays

Mid-wave and long-wave infrared (IR) quantum-dots-in-a-well (DWELL) focal plane arrays (FPAs) are promising technology for multispectral (MS) imaging and sensing. The DWELL structure design provides the detector with a unique property that allows the spectral response of the detector to be continuously, albeit coarsely, tuned with the applied bias. In this paper, a MS classification capability of the DWELL FPA is demonstrated. The approach is based upon: 1) imaging an object repeatedly using a sequence of bias voltages in the tuning range of the FPA and then 2) applying a classification algorithm to the totality of readouts, over multiple biases, at each pixel to identify the “class” of the material. The approach is validated for two classification problems: separation among different combinations of three IR filters and discrimination between rocks. This work is the first demonstration of the MS classification capability of the DWELL FPA