Constructing quasi-layered and self-hole doped SnSe oriented films to achieve excellent thermoelectric power factor and output power density.

Thermoelectric (TE) technology can achieve the mutual conversion between electric energy and waste heat, and it has exhibited great prospects in multifunctional energy applications to alleviate the energy crisis. In the recent decade, SnSe has been explored widely because of its potentially high energy harvesting efficiency, green nature, and low cost. However, the relatively poor power factor (PF) derived from the intrinsic low carrier concentration (∼10 cm) limits the output power density of the stoichiometric SnSe devices.

A 2D-SnSe film with ferroelectricity and its bio-realistic synapse application.

Catering to the general trend of artificial intelligence development, simulating humans' learning and thinking behavior has become the research focus. Second-order memristors, which are more analogous to biological synapses, are the most promising devices currently used in neuromorphic/brain-like computing. However, few second-order memristors based on two-dimensional (2D) materials have been reported, and the inherent bionic physics needs to be explored.

Enhanced Light-Induced Transverse Thermoelectric Effect in Tilted BiCuSeO Film via the Ultra-thin AuNPs Layer.

Significant enhancement of light-induced transverse thermoelectric (LITT) effect in tilted BiCuSeO film has been achieved via introduction of an ultra-thin layer of gold nanoparticles (AuNPs) with the thickness of a few nanometers. In both cases of pulsed and continuous light irradiation, about two times increment in the LITT voltage sensitivity is observed for the BiCuSeO film coated with 4-nm-thick AuNPs layer. This can be ascribed to the increased photo-thermal conversion efficiency in the LITT effect owing to the efficient usage of the incident light of AuNPs layer.

Illumination normalization of face image based on illuminant direction estimation and improved Retinex.

Illumination normalization of face image for face recognition and facial expression recognition is one of the most frequent and difficult problems in image processing. In order to obtain a face image with normal illumination, our method firstly divides the input face image into sixteen local regions and calculates the edge level percentage in each of them. Secondly, three local regions, which meet the requirements of lower complexity and larger average gray value, are selected to calculate the final illuminant direction according to the error function between the measured intensity and the calculated intensity, and the constraint function for an infinite light source model.

Illuminant direction estimation for a single image based on local region complexity analysis and average gray value.

Illuminant direction estimation is an important research issue in the field of image processing. Due to low cost for getting texture information from a single image, it is worthwhile to estimate illuminant direction by employing scenario texture information. This paper proposes a novel computation method to estimate illuminant direction on both color outdoor images and the extended Yale face database B.