Interfacial Connections between Organic Perovskite/n+ Silicon/Catalyst that Allow Integration of Solar Cell and Catalyst for Hydrogen Evolution from Water

The rapidly increasing solar conversion efficiency (PCE) of hybrid organic–inorganic perovskite (HOIP) thin-film semiconductors has triggered interest in their use for direct solar-driven water splitting to produce hydrogen. However, application of these low-cost, electronic-structure-tunable HOIP tandem photoabsorbers has been hindered by the instability of the photovoltaic-catalyst-electrolyte (PV+E) interfaces. Here, photolytic water splitting is demonstrated using an integrated configuration consisting of an HOIP/n⁺silicon single junction photoabsorber and a platinum (Pt) thin film catalyst. An extended electrochemical (EC) lifetime in alkaline media is achieved using titanium nitride on both sides of the Si support to eliminate formation of insulating silicon oxide, and as an effective diffusion barrier to allow high-temperature annealing of the catalyst/TiO₂-protected-n⁺silicon interface necessary to retard electrolytic corrosion. Halide composition is examined in the (FA_1-xCs_x)PbI₃ system with a bandgap suitable for tandem operation. A fill factor of 72.5% is achieved using a Spiro-OMeTAD-hole-transport-layer (HTL)-based HOIP/n⁺Si solar cell, and a high photocurrent density of −15.9 mA cm⁻² (at 0 V vs reversible hydrogen electrode) is attained for the HOIP/n⁺Si/Pt photocathode in 1 m NaOH under simulated 1-sun illumination. While this thin-film design creates stable interfaces, the intrinsic photo- and electro-degradation of the HOIP photoabsorber remains the main obstacle for future HOIP/Si tandem PEC devices.     

A new dynamic cellular learning automata-based skin detector

Skin detection is a difficult and primary task in many image processing applications. Because of the diversity of various image processing tasks, there exists no optimum method that can perform properly for all applications. In this paper, we have proposed a novel skin detection algorithm that combines color and texture information of skin with cellular learning automata to detect skin-like regions in color images. Skin color regions are first detected, by using a committee structure, from among several explicit boundary skin models. Detected skin-color regions are then fed to a texture analyzer which extracts texture features via their color statistical properties and maps them to a skin probability map. This map is then used by cellular learning automata to adaptively make a decision on skin regions. Conducted experiments show that the proposed algorithm achieves the true positive rate of about 86.3% and the false positive rate of about 9.2% on Compaq skin database which shows its efficiency.     

Color PCA eigenimages and their application to compression and watermarking

From the birth of multi-spectral imaging techniques, there has been a tendency to consider and process this new type of data as a set of parallel gray-scale images, instead of an ensemble of an n-D realization. However, it has been proved that using vector-based tools leads to a more appropriate understanding of color images and thus more efficient algorithms for processing them. Such tools are able to take into consideration the high correlation of the color components and thus to successfully carry out energy compaction. In this paper, a novel method is proposed to utilize the principal component analysis in the neighborhoods of an image in order to extract the corresponding eigenimages. These eigenimages exhibit high levels of energy compaction and thus are appropriate for such operations as compression and watermarking. Subsequently, two such methods are proposed in this paper and their comparison with available approaches is presented.     

Modification of fast-cure ethylene–propylene diene terpolymer rubber by maleic anhydride and effect of electron donor

Grafting of maleic anhydride (MA) onto fast-cure ethylene–propylene diene terpolymer rubber was studied. The effect of the amount of the MA, initiator (dicumyl peroxide), and electron donor (stearamide) on graft content are described. The potentiometric method and Fourier transform infrared spectroscopy are used to obtained the graft content. The stearamide suppresses the side reactions, such as crosslinking and chain scission. © 1998 John Wiley & Sons, Inc. J Appl Polym Sci 70: 1–5, 1998     

Efficient scale estimation methods using lightweight deep convolutional neural networks for visual tracking

Neural Computing and Applications - In recent years, visual tracking methods that are based on discriminative correlation filters (DCFs) have been very promising. However, most of these methods...     

Multiple human 3D pose estimation from multiview images

Multimedia Tools and Applications - Multiple human 3D pose estimation is a challenging task. It is mainly because of large variations in the scale and pose of humans, fast motions, multiple persons...     

An Adaptive Approach to Singular Point Detection in Fingerprint Images

This paper presents a novel algorithm for detection of singular points, the core and delta points, in fingerprint images. The number and location of singular points, are used to classify fingerprint images into five general groups; and therefore to narrow down the search space in large fingerprint databases. Using the proposed directional masks in the first step, we detect the neighborhood of the singular points. In the second stage by implementing the proposed algorithm, an adaptive singular point detection method, is designed to extract the exact location of core and delta points. Usage of the proposed directional masks speeds up the process and the proposed adaptive singular point detection method increases the accuracy of the algorithm.     

A novel fingerprint image compression technique using wavelets packets and pyramid lattice vector quantization

A novel compression algorithm for fingerprint images is introduced. Using wavelet packets and lattice vector quantization , a new vector quantization scheme based on an accurate model for the distribution of the wavelet coefficients is presented. The model is based on the generalized Gaussian distribution. We also discuss a new method for determining the largest radius of the lattice used and its scaling factor , for both uniform and piecewise-uniform pyramidal lattices. The proposed algorithms aim at achieving the best rate-distortion function by adapting to the characteristics of the subimages. In the proposed optimization algorithm, no assumptions about the lattice parameters are made, and no training and multi-quantizing are required. We also show that the wedge region problem encountered with sharply distributed random sources is resolved in the proposed algorithm. The proposed algorithms adapt to variability in input images and to specified bit rates. Compared to other available image compression algorithms, the proposed algorithms result in higher quality reconstructed images for identical bit rates.