Multimodal Sensor Fusion In Single Thermal image Super-Resolution

With the fast growth in the visual surveillance and security sectors, thermal infrared images have become increasingly necessary ina large variety of industrial applications. This is true even though IR sensors are still more expensive than their RGB counterpart having the same resolution. In this paper, we propose a deep learning solution to enhance the thermal image resolution. The following results are given:(I) Introduction of a multimodal, visual-thermal fusion model that ad-dresses thermal image super-resolution, via integrating high-frequency information from the visual image. (II) Investigation of different net-work architecture schemes in the literature, their up-sampling methods,learning procedures, and their optimization functions by showing their beneficial contribution to the super-resolution problem. (III) A bench-mark ULB17-VT dataset that contains thermal images and their visual images counterpart is presented. (IV) Presentation of a qualitative evaluation of a large test set with 58 samples and 22 raters which shows that our proposed model performs better against state-of-the-arts

SMOOTHNESS AND ROUGHNESS: CHARACTERISTICS OF FABRIC-TO-FABRIC SELF-FRICTION PROPERTIES

Smoothness and roughness of fabric materials are important fabric tactile properties for engineering design of many textile products including medical textiles, hygiene and healthcare products, sportswear, underwear, lingerie and other consumer products having special requirements in sensitive surface tactile properties. They are assessed by human fingers and hands in subjective evaluations to form personal perceptions of the fabrics, and they are usually characterised by using the friction coefficient and surface roughness profile during human skin (or artificial human skin/finger/probes) sliding against fabric surfaces in haptic science. In this paper, the friction coefficient and its spectrum during a fabric surface sliding against the fabric surface itself in a fabric self-friction process are used to characterise the fabric smoothness and roughness. The dynamic friction coefficients and its frequency analysis of its variations of three different fabric materials are assessed against their surface morphological profile. The application of such characteristics of fabric-to-fabric frictional properties in the engineering design of fabric surface structures and their uses in the objective evaluation of fabric hand and discrimination of fabric are also discussed

Fe-doping induced superconductivity in charge-density-wave system 1T-TaS2

We report the interplay between charge-density-wave (CDW) and superconductivity of 1$T$-Fe$_{x}$Ta$_{1-x}$S$_{2}$ ($0\leq x \leq 0.05$) single crystals. The CDW order is gradually suppressed by Fe-doping, accompanied by the disappearance of pseudogap/Mott-gap as shown by the density functional theory (DFT) calculations. The superconducting state develops at low temperatures within the CDW state for the samples with the moderate doping levels. The superconductivity strongly depends on $x$ within a narrow range, and the maximum superconducting transition temperature is 2.8 K as $x=0.02$. We propose that the induced superconductivity and CDW phases are separated in real space. For high doping level ($x>0.04$), the Anderson localization (AL) state appears, resulting in a large increase of resistivity. We present a complete electronic phase diagram of 1$T$-Fe$_{x}$Ta$_{1-x}$S$_{2}$ system that shows a dome-like $T_{c}(x)$