Unsupervised Stereo Matching with Surface Normal Assistance for Indoor Depth Estimation

To obtain more accurate depth information with stereo cameras, various learning-based stereo-matching algorithms have been developed recently. These algorithms, however, are significantly affected by textureless regions in indoor applications. To address this problem, we propose a new deep-neural-network-based data-driven stereo-matching scheme that utilizes the surface normal. The proposed scheme includes a neural network and a two-stage training strategy. The neural network involves a feature-extraction module, a normal-estimation branch, and a disparity-estimation branch. The training processes of the feature-extraction module and the normal-estimation branch are supervised while the training of the disparity-estimation branch is performed unsupervised. Experimental results indicate that the proposed scheme is capable of estimating the surface normal accurately in textureless regions, leading to improvement in the disparity-estimation accuracy and stereo-matching quality in indoor applications involving such textureless regions.

Cognitive Radio with Machine Learning to Increase Spectral Efficiency in Indoor Applications on the 2.5 GHz Band

Due to the propagation characteristics in the 2.5 GHz band, the signal is significantly degraded by building entry loss (BEL), making coverage in indoor environments in some cases non-existent. Signal degradation inside buildings is a challenge for planning engineers, but it can be seen as a spectrum usage opportunity for a cognitive radio communication system. This work presents a methodology based on statistical modeling of data collected by a spectrum analyzer and the application of machine learning (ML) to leverage the use of those opportunities by autonomous and decentralized cognitive radios (CRs), independent of any mobile operator or external database. The proposed design targets using as few narrowband spectrum sensors as possible in order to reduce the cost of the CRs and sensing time, as well as improving energy efficiency. Those characteristics make our design especially interesting for internet of things (IoT) applications or low-cost sensor networks that may use idle mobile spectrum with high reliability and good recall.

Mechanically Stable Flexible Organic Photovoltaics with Silver Nanomesh for Indoor Applications

Enhanced device performance of flexible organic solar cells (FOSCs) was achieved according to the development of organic solar cells (OSCs). OSCs are promising candidates as energy sources for low-power supply systems such as the Internet of Things (IoT) under indoor lighting environments. To apply FOSCs to flexible or wearable applications, they must be mechanically stable. In this study, we fabricated FOSCs with silver nanomesh (AgNM) as the bottom transparent conductive electrode (TCE). Instead of indium tin oxide (ITO), AgNMs were prepared using three pitches of 25, 50, and 100 μm with a square pattern, using a poly(ethylene terephthalate) (PET) substrate. Notably, the device using AgNMs with a pitch of 25 μm exhibited a power conversion efficiency (PCE) of 14.93% under 1 sun illumination and 17.91% under 1000 lux of light-emitting diode (LED) light conditions. Flexible devices using AgNMs maintained over 92% of their initial PCE under 1 sun illumination (PCE decreased to 12.98 from 14.04%) and over 92% when tested under 1000 lux of LED light illumination (PCE decreased to 16.57 from 17.91%) after 1000 instances of bending. These results demonstrate the advantages of using AgNMs as an alternative TCE under both 1 sun and indoor lightning environments and are promising candidates for flexible applications.

Suppressing Energetic Disorder Enables Efficient Indoor Organic Photovoltaic Cells With a PTV Derivative

Indoor organic photovoltaics (IOPVs) cells have attracted considerable attention in the past few years. Herein, two PTV-derivatives, PTVT-V and PTVT-T, were used as donor materials to fabricate IOPV cells with ITCC as the acceptor. The preferred orientation of the crystals changed from edge-on to face-on after replacing the ethylene in the backbones of PTVT-V by the thiophene in that of PTVT-T. Besides, it was found that, the energetic disorder of the PTVT-T:ITCC based system is 58 meV, which is much lower than that of PTVT-V:ITCC-based system (70 meV). The lower energetic disorder in PTVT-T:ITCC leads to an efficient charge transfer, charge transport, and thus the weak charge recombination. As a result, a PCE of 9.60% under AM 1.5 G and a PCE of 24.27% under 1,000 lux (LED 2,700 K) with a low non-radiative energy loss of 0.210 eV were obtained based on PTVT-T:ITCC blend. The results indicate that to improve the PTV-derivatives photovoltaic properties by suppressing the energetic disorder is a promising way to realize low-cost IOPV cells.

Alkoxybenzothiadiazole-Based Fullerene and Nonfullerene Polymer Solar Cells with High Shunt Resistance for Indoor Photovoltaic Applications

We synthesized three semicrystalline polymers (PTTBT_BO, PDTBT_BO, and P2FDTBT_BO) by modulating the intra- and intermolecular noncovalent Coulombic interactions and investigated their photovoltaic characteristics under various light intensities. Low series (R_s) and high shunt (R_sh) resistances are essential prerequisites for good device properties under standard illumination (100 mW cm^-2). Considering these factors, among three polymers, PDTBT_BO polymer solar cells (PSCs) exhibited the most desirable characteristics, with peak power conversion efficiencies (PCE) of 7.52 and 9.60% by being blended with PC₇₁BM under standard and dim light (2.5 mW cm^-2), respectively. P2FDTBT_BO PSCs exhibited a low PCE of 3.69% under standard light due to significant charge recombination with high R_s (9.42 Ω cm²). However, the PCE was remarkably improved by 2.3 times (8.33% PCE) under dim light, showing negligible decrease in open-circuit voltage and remarkable increase in fill factor, which is due to an exceptionally high R_sh of over 1000 kΩ cm². R_s is less significant under dim light because the generated current is too small to cause noticeable R_s-induced voltage losses. Instead, high R_sh becomes more important to avoid leakage currents. This work provides important tips to further optimize PSCs for indoor applications with low-power electronic devices such as Internet of things sensors.