A versatile scanning photocurrent mapping system to characterize optoelectronic devices based on 2D materials

The investigation of optoelectronic devices based on two-dimensional materials and their heterostructures is a very active area of investigation with both fundamental and applied aspects involved. We present a description of a home-built scanning photocurrent microscope that we have designed and developed to perform electronic transport and optical measurements of two-dimensional materials based devices. The complete system is rather inexpensive (<10000 EUR) and it can be easily replicated in any laboratory. To illustrate the setup we measure current-voltage characteristics, in dark and under global illumination, of an ultra-thin PN junction formed by the stacking of an n-doped few-layer MoS2 flake onto a p-type MoS2 flake. We then acquire scanning photocurrent maps and by mapping the short circuit current generated in the device under local illumination we find that at zero bias the photocurrent is generated mostly in the region of overlap between the n-type and p-type flakes.Comment: 9 pages, 3 figures, 1 table, supporting informatio

Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection

Multispectral pedestrian detection has received extensive attention in recent years as a promising solution to facilitate robust human target detection for around-the-clock applications (e.g. security surveillance and autonomous driving). In this paper, we demonstrate illumination information encoded in multispectral images can be utilized to significantly boost performance of pedestrian detection. A novel illumination-aware weighting mechanism is present to accurately depict illumination condition of a scene. Such illumination information is incorporated into two-stream deep convolutional neural networks to learn multispectral human-related features under different illumination conditions (daytime and nighttime). Moreover, we utilized illumination information together with multispectral data to generate more accurate semantic segmentation which are used to boost pedestrian detection accuracy. Putting all of the pieces together, we present a powerful framework for multispectral pedestrian detection based on multi-task learning of illumination-aware pedestrian detection and semantic segmentation. Our proposed method is trained end-to-end using a well-designed multi-task loss function and outperforms state-of-the-art approaches on KAIST multispectral pedestrian dataset

Iranian cashes recognition using mobile

In economical societies of today, using cash is an inseparable aspect of human life. People use cashes for marketing, services, entertainments, bank operations and so on. This huge amount of contact with cash and the necessity of knowing the monetary value of it caused one of the most challenging problems for visually impaired people. In this paper we propose a mobile phone based approach to identify monetary value of a picture taken from cashes using some image processing and machine vision techniques. While the developed approach is very fast, it can recognize the value of cash by average accuracy of about 95% and can overcome different challenges like rotation, scaling, collision, illumination changes, perspective, and some others.Comment: arXiv #13370