The donor, 2,4-bis[4-(N,N-diisobutylamino)-2,6-dihydroxyphenyl] squaraine (SQ) is used with the acceptor, [6,6]-phenyl C70 butyric acid methyl ester (PC70BM) to result in efficient, solution-processed, small-molecule bulk heterojunction photovoltaic cells. The distribution of the donor nanoparticles in the acceptor matrix as a function of relative concentrations results in a trade-off between exciton dissociation and hole mobility (and hence, cell series resistance). A bulk heterojunction solar cell consisting of an active region with a component ratio of SQ to PC70BM of 1:6 has a power conversion efficiency of 2.7 +/- 0.1% with a 8.85 +/- 0.22 mA/cm(2) short-circuit current density and an open-circuit voltage of 0.89 +/- 0.01 V obtained under simulated 1 sun (100 mW/cm(2)) air mass 1.5 global (AM1.5 G) solar illumination. This is a decrease from 3.3 +/- 0.3% at 0.2 sun intensity, and is less than that of a control planar heterojunction SQ/C60 cell with 4.1 +/- 0.2% at 1 sun, suggesting that the nanoparticle morphology introduces internal resistance into the solution-based thin film. The nanomorphology and hole mobility in the films is strongly dependent on the SQ-to-PC70BM ratio, increasing by greater than 2 orders of magnitude as the ratio increases from 28% to 100% SQ.

Object detection, a critical task in computer vision, has been revolutionized by Deep Learning technologies, especially convolutional neural networks (CNN). These techniques are increasingly deployed in infrared imaging systems for long-range target detection, localization, and identification. Its performance is highly dependent on the training procedure, network architecture and computing resources. In contrast, human-in-the-loop task performance can be reliably predicted using well-established models. Here we model the performance of a CNN developed for MWIR and LWIR sensors and compare against human perception models. We focus on tower detection relevant to vision-based geolocation tasks which present novel high-aspect ratio, unresolved and low-clutter scenarios.

A priori estimation of the expected achievable quality for an uncrewed aerial vehicle (UAV) based imaging system can help validate the choice of components for the system's implementation. For uncrewed airborne imaging systems coupling the sensor to the UAV platform is relatively simple. Quantifying the expected quality of collected data can, on the other hand, be less clear and often require trial and error. The central problem for these platforms is blur. The blur produced by the various rotational modalities of the aircraft can range from overwhelming to trivial but in most cases can be mitigated. This leaves the combination of the aircraft's linear motion, its altitude and the imaging device's instantaneous field of view (IFOV) and integration time as the determining factors for the blur produced in the image. In addition, there are significant differences in speeds obtainable between multi-rotor and fixed wing UAVs. In this paper we develop mathematical models for predicting blur based on these factors. We then compare these models with field data obtained from cameras mounted to fixed wing and multi-rotor UAVs. Conclusions regarding camera characteristics best suited for both types of UAV as well as the best image acquisition parameters such as altitude and speed, are discussed.

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.Drawing from techniques used to dehaze visible, three-channel RGB images, we propose an approach to separate emissive and reflected radiance in images at short distances with a microbolometer-based longwave infrared (LWIR) camera system. The best case for optimal contrast and with the most descriptive information about the scene in an LWIR image would be where no external sources are radiating toward the scene. We introduce the concept of a blackbody channel prior (BCP) to multiband LWIR imaging to describe pixels that represent objects that behave most similarly to perfect blackbodies with an emissivity near unity. Most LWIR images of outdoor scenes are degraded largely in part by reflected sky path radiance. We can estimate scene radiance with a minimized reflective component producing images with enhanced contrast. Experiments on a number of multiband images are present to demonstrate this spectral-based BCP technique and show its potential for preserving scene information while achieving contrast enhancement.