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With the ever-increasing operating frequency in integrated circuit, it is very essentialto assess the radiation used to help the IC designer. Based on the similarity of electromagnetic patterns obtained from the radiation of ICs and their nonlinear edge, we develop a post-processing technique to group the electromagnetic patterns. A near field scanning is performed to obtain and extract the electromagnetic pattern that is used to validate the technique. Experiment results show that it can accurately group the electromagnetic patterns by which radiation assessment can be performed.

With the development of deep learning, various fields of computer vision have made huge progress. Among them, depth estimation is an important part of scene perception, therefore receives much interest and is widely used in daily life with the assistance of GPUs. Besides, the ways to obtain depth maps have also been improved, from using multiple images to a single image to obtain depth, which is called monocular depth estimation task. In this paper, we design a convolutional neural network called ERF-PSPNet to perform the task. We prove that by using unsupervised training, monocular depth estimation’s result learned from large-scale dataset is close to the result of stereo matching. We also show that the monocular depth estimation model proposed in this paper can achieve a satisfying precision while maintaining a certain real-time frame rate for day-night driving scenes, which confirms the practical applicability of our design and result.

Positioning is one of the most urgent problems for assisting visually impaired people, especially in indoor environments where GPS signals are weak. In this paper, we present a positioning framework based on panoramic visual odometry for assisting visually impaired people. We introduce panoramic annular lens to visual odometry and use the Taylor camera model to describe its projection rules. Some critical techniques in visual odometry, including direct image alignment and stereo matching, are extended to fit this camera model. Besides, an easy-to-maintain coordinate alignment method based on multi-pinhole image rectification and marker detection is also proposed to unify the results of visual odometry in the world coordinate system. We evaluate our system on both synthetic and real-world datasets in a comparative set of experiments and compare against state-of-the-art algorithms. The experiment results show that the robustness of positioning has been significantly improved by the proposed visual odometry algorithm with panoramic annular lens and the system has the ability to provide reliable positioning results in indoor environments.

With the development of computer vision, wearable computing technologies not only have changed our lifestyle, but also have provided much convenience for vulnerable road users, especially the Visually Impaired (VI) pedestrians. VI people have difficulties in locating and socializing due to the limitations of traditional assistive tools, e.g., the inability to recognize text. Text plays a significant role in various aspects, which can convey abundant semantic information of the scene. In recent years, text detection and recognition has made huge progress which makes it possible for VI people to understand the surroundings by using scene text information. In this paper, a text recognition system is proposed to help VI people to perceive store sign text. Firstly, we locate the text on the sign with the aim of leading VI pedestrian to reach the destination store. Towards this end, an objection detection network is integrated into the system to extract Regions of Interest (ROI) in complex real-world scenarios. In order to fulfil real-time assistance, an efficient detection network named Single Shot MultiBox Detector (SSD) has been made light-weight and embedded in the wearable system. Secondly, we leverage an open-source optical character recognition (OCR) instrument to recognize the detected text. Afterwards, we introduce the collected dataset and critical training tips for the task. Finally, a comprehensive set of experiments on our dataset demonstrates that our approach significantly improves the precision and make the recognition robust even in real-world settings. Based on our approach, the wearable system can feedback the recognized text in real time and assist the VI people during their every independent navigation.

There are a very few people who have the ability to “see” the surroundings by the echoes, which is called echolocation. The study of the brain mechanism of echolocation can not only help to improve the blind assistance device, but also provides a window into the research of brain’s plasticity. In this paper, we developed a wearable system to transform the spatial information captured by camera into a voice description and fed it back to blind users which is inspired by echolocation. After our online virtual scene training, users can easily discriminate object location in the camera’s view, motion of the objects, even shape of the objects. Compared with natural echolocation, it’s easier to learn and be applied in daily life. In addition, the device achieves high spacial resolution. In this study, two trained blind subjects and two non-trained sighted subjects were tested by using functional Magnetic Resonance Imaging (fMRI). We obtain the fMRI images of the subjects’ brain activity when they were listening to the sound of the wearable prototype. Intriguingly, we find that after training with the blind assistance system, the blind’ visual area of the brain have been activated when they are dealing with the acoustic feedback from the device.

A new method for long focal-length measurements is proposed. In this method, we employ divergent beam and two Ronchi gratings of different periods, as the alternative to collimated beams and two identical gratings in traditional method, to realize achieve higher accuracy. Moreover, with divergent beam, the full-aperture measurement is easily realized when detect large diameter lens. The experiments demonstrated the proposed method features high accuracy and repeatability.