arXiv subjects

Ting Wu

Publications and source records attributed to Ting Wu.

40 records · Page 3Linked to original sources

arXiv2018

Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Computer-generated graphics (CGs) are images generated by computer software. The~rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN) and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN)-based model, these images---CGs and NIs---are clipped into image patches. Furthermore, three high-pass filters (HPFs) are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The~experiments have demonstrated that (1) the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2) the proposed method with three HPFs achieves 100\% accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.

cs.MM↗

arXiv2017

DeepTFP: Mobile Time Series Data Analytics based Traffic Flow Prediction

Traffic flow prediction is an important research issue to avoid traffic congestion in transportation systems. Traffic congestion avoiding can be achieved by knowing traffic flow and then conducting transportation planning. Achieving traffic flow prediction is challenging as the prediction is affected by many complex factors such as inter-region traffic, vehicles' relations, and sudden events. However, as the mobile data of vehicles has been widely collected by sensor-embedded devices in transportation systems, it is possible to predict the traffic flow by analysing mobile data. This study proposes a deep learning based prediction algorithm, DeepTFP, to collectively predict the traffic flow on each and every traffic road of a city. This algorithm uses three deep residual neural networks to model temporal closeness, period, and trend properties of traffic flow. Each residual neural network consists of a branch of residual convolutional units. DeepTFP aggregates the outputs of the three residual neural networks to optimize the parameters of a time series prediction model. Contrast experiments on mobile time series data from the transportation system of England demonstrate that the proposed DeepTFP outperforms the Long Short-Term Memory (LSTM) architecture based method in prediction accuracy.

cs.LG↗

arXiv2017

Bose-Einstein condensates in the presence of Weyl spin-orbit coupling

We consider two-component Bose-Einstein condensates subject to Weyl spin-orbit coupling. We obtain mean-field ground state phase diagram by variational method. In the regime where interspecies coupling is larger than intraspecies coupling, the system is found to be fully polarized and condensed at a finite momentum lying along the quantization axis. We characterize this phase by studying the excitation spectrum, the sound velocity, the quantum depletion of condensates, the shift of ground state energy, and the static structure factor. We find that spin-orbit coupling and interspecies coupling generally leads to competing effects.

cond-mat.quant-gas↗

arXiv2015

The Human Body and Millimeter-Wave Wireless Communication Systems: Interactions and Implications

With increasing interest in millimeter wave wireless communications, investigations on interactions between the human body and millimeter wave devices are becoming important. This paper gives examples of current regulatory requirements, and provides an example for a 60 GHz transceiver. Also, the propagation characteristics of millimeter-waves in the presence of the human body are studied, and four models representing different body parts are considered to evaluate thermal effects of millimeter-wave radiation on the body. Simulation results show that about 34% to 42% of the incident power is reflected at the skin surface at 60 GHz. This paper shows that power density is not suitable to determine exposure compliance when millimeter wave devices are used very close to the body. A temperature-based technique for the evaluation of safety compliance is proposed in this paper.

cs.ET↗