Mechanism for graphene-based optoelectronic switches by tuning surface plasmon-polaritons in monolayer graphene

It is shown that one can explore the optical conductivity of graphene, together with the ability of controlling its electronic density by an applied gate voltage, in order to achieve resonant coupling between an external electromagnetic radiation and surface plasmon-polaritons in the graphene layer. This opens the possibility of electrical control of the intensity of light reflected inside a prism placed on top of the graphene layer, by switching between the regimes of total reflection and total absorption. The predicted effect can be used to build graphene-based opto-electronic switches.Comment: 5 page

Domain Adaptation For Vehicle Detection In Traffic Surveillance Images From Daytime To Nighttime

Vehicle detection in traffic surveillance images is an important approach to obtain vehicle data and rich traffic flow parameters. Recently, deep learning based methods have been widely used in vehicle detection with high accuracy and efficiency. However, deep learning based methods require a large number of manually labeled ground truths (bounding box of each vehicle in each image) to train the Convolutional Neural Networks (CNN). In the modern urban surveillance cameras, there are already many manually labeled ground truths in daytime images for training CNN, while there are little or much less manually labeled ground truths in nighttime images. In this paper, we focus on the research to make maximum usage of labeled daytime images (Source Domain) to help the vehicle detection in unlabeled nighttime images (Target Domain). For this purpose, we propose a new method based on Faster R-CNN with Domain Adaptation (DA) to improve the vehicle detection at nighttime. With the assistance of DA, the domain distribution discrepancy of Source and Target Domains is reduced. We collected a new dataset of 2,200 traffic images (1,200 for daytime and 1,000 for nighttime) of 57,059 vehicles for training and testing CNN. In the experiment, only using the manually labeled ground truths of daytime data, Faster R- CNN obtained 82.84% as F-measure on the nighttime vehicle detection, while the proposed method (Faster R-CNN+DA) achieved 86.39% as F-measure on the nighttime vehicle detection

Detecting phone-related pedestrian distracted behaviours via a two-branch convolutional neural network

The distracted phone-use behaviours among pedestrians, like Texting, Game Playing and Phone Calls, have caused increasing fatalities and injuries. However, the research of phonerelated distracted behaviour by pedestrians has not been systemically studied. It is desired to improve both the driving and pedestrian safety by automatically discovering the phonerelated pedestrian distracted behaviours. Herein, a new computer vision-based method is proposed to detect the phone-related pedestrian distracted behaviours from a view of intelligent and autonomous driving. Specifically, the first end-to-end deep learning based Two-Branch Convolutional Neural Network (CNN) is designed for this task. Taking one synchronised image pair by two front on-car GoPro cameras as the inputs, the proposed two-branch CNN will extract features for each camera, fuse the extracted features and perform a robust classification. This method can also be easily extended to video-based classification by confidence accumulation and voting. A new benchmark dataset of 448 synchronised video pairs of 53,760 images collected on a vehicle is proposed for this research. The experimental results show that using two synchronised cameras obtained better performance than using one single camera. Finally, the proposed method achieved an overall best classification accuracy of 84.3% on the new benchmark when compared to other methods

Impact of data aggregation approaches on the relationships between operating speed and traffic safety

The impact of operating speed on traffic crash occurrence has been a controversial topic in the traffic safety discipline as some studies reported a positive association whereas others indicated a negative relationship between speed and crashes. Two major issues thought to be accountable for such conflicting findings are the application of inappropriate statistical methods and the use of sample datasets with varying levels of aggregation. The main objective of this study is therefore to investigate the impacts of data aggregation schemes on the relationships between operating speed and traffic safety. A total of three aggregation approaches were examined: (1) a segment-based dataset in which crashes are grouped by roadway segment, (2) a scenario-based dataset where crashes are aggregated by traffic operating scenarios, and (3) a disaggregated crash-level dataset consisting of information from individual crashes. The first two aggregation approaches were used in examining the relationships between operating speed and crash frequency using Bayesian random-effects negative binomial models. The third disaggregated crash risk analysis was conducted utilizing Bayesian random-effects logistic regression models. From the modeling results, it has been concluded that the scenario-based approach shared similar findings with those of the disaggregated crash risk analysis approach in which a U-shaped relationship between operating speed and crash occurrence was identified. However, the commonly adopted segment-based aggregation approach revealed a monotonous negative relationship between speed and crash frequency. The implications of the different analyses results and the potential applications of the results on speed management systems have therefore been discussed

Identification of Proteins Related to Nickel Homeostasis in Helicobater pylori by Immobilized Metal Affinity Chromatography and Two-Dimensional Gel Electrophoresis

Helicobacter pylori (H. pylori) is a widespread human pathogen causing peptic ulcers and chronic gastritis. Maintaining nickel homeostasis is crucial for the establishment of H. pylori infection in humans. We used immobilized-nickel affinity chromatography to isolate Ni-related proteins from H. pylori cell extracts. Two-dimensional gel electrophoresis and mass spectrometry were employed to separate and identify twenty two Ni-interacting proteins in H. pylori. These Ni-interacting proteins can be classified into several general functional categories, including cellular processes (HspA, HspB, TsaA, and NapA), enzymes (Urease, Fumarase, GuaB, Cad, PPase, and DmpI), membrane-associated proteins (OM jhp1427 and HpaA), iron storage protein (Pfr), and hypothetical proteins (HP0271, HP jhp0216, HP jhp0301, HP0721, HP0614, and HP jhp0118). The implication of these proteins in nickel homeostasis is discussed