Fast and robust deep neural networks design

In the past few years, we have witnessed a rapid development of deep neural networks in computer vision, from basic image classiffcation tasks to some more advanced applications e.g. object detection and semantic segmentation. Inspire of its great success, there exists two challenges of deep neural networks real-world applications: its computational cost and vulnerability. Thus we are aimed to deal with these two problems in this thesis. To speed up deep networks, we propose a L1-Norm based low-rank approximation method to reduce oat operations based on the alternating direction method (ADM) in Chapter 2. Our experimental results on public datasets, including CIFAR-10 and ImageNet, demonstrate that this new decomposition scheme outperforms the recently developed L2-norm based nonlinear decomposition method. To defend against adversarial examples, we develop a novel pre-processing alogrithm based on image restoration to remove adversarial attack noise in Chapter 3. We detect high-sensitivity which have signiffcant contributions to the image classiffcation performance. Then we partition the image pixels into the two groups: high-sensitivity and low-sensitivity keypoints. For the low-sensitivity pixels, we use the existing total variation (TV) norm-based image smoothing. For the high-sensitivity pixels, we develop a structure-preserving low-rank image completion methods. Based on matrix analysis and optimization, we have derived an iterative solution for this optimization problem. This high-sensitivity points detection helps us to improve the defense against white-box attack BPDA. However, in our keypoints defense we only remove and recover a few part of pixels, which indicates there are still many perturbation over the whole image. In Chapter 4, we propose a novel image completion algorithm structure-preserving progressive lowrank image completion (SPLIC ) based on smoothed rank function (SRF) in which we can reconstruct a image with over 50% removed pixels. In SPLIC, we randomly remove over 50% pixels on the image and then do matrix completion by low-rank approximation to remain the global structure of the image. Differ from other lowrank methods, we replace nuclear norm by smoothed rank function (SRF) for its closer rank function approximation. We introduce total variance (TV) regularization to improve image reconstruction, and then combine total variance (TV) norm de-noising to further remove the perturbation over the whole image. Then we train the network on the SPLIC images. The experimental results show our SPLIC outperforms other pre-processing methods in image reconstruction, gray-box and black-box scenario.Includes bibliographical references (pages 102-119)

Few-Mode Fibers With Uniform Differential Mode Group Delay for Microwave Photonic Signal Processing

We present a novel design of few-mode fiber (FMF) with a uniform differential mode group delay (U-DMGD) among propagating modes and apply the FMF to a single-fiber delay line module for implementing microwave photonic finite impulse response (FIR) filters. By optimizing key parameters such as the core grading exponent and the dimension of the trench of FMF, a U-DMGD between adjacent modes among four modes (LP 01 , LP 11 , LP 02 and LP 31 ) over the entire C band is achieved. Wavelength dependence is entirely removed. An FIR microwave photonic filter (MPF) implemented using the designed 1-km FMF is investigated through numerical simulations. The free spectral range (FSR) of the MPF is 5.7 GHz, the 3-dB bandwidth is 1.26 GHz, and the main lobe-to-side lobe ratio (MSR) is 10.42 dB. Discussions on fabrication aspects have also been presented. The proposed single-fiber delay line structure based on FMF can significantly reduce the system complexity of microwave photonic signal processing

Quality assessment metric of stereo images considering cyclopean integration and visual saliency

In recent years, there has been great progress in the wider use of three-dimensional (3D) technologies. With increasing sources of 3D content, a useful tool is needed to evaluate the perceived quality of the 3D videos/images. This paper puts forward a framework to evaluate the quality of stereoscopic images contaminated by possible symmetric or asymmetric distortions. Human visual system (HVS) studies reveal that binocular combination models and visual saliency are the two key factors for the stereoscopic image quality assessment (SIQA) metric. Therefore inspired by such findings in HVS, this paper proposes a novel saliency map in SIQA metric for the cyclopean image called “cyclopean saliency”, which avoids complex calculations and produces good results in detecting saliency regions. Moreover, experimental results show that our metric significantly outperforms conventional 2D quality metrics and yields higher correlations with human subjective judgment than the state-of-art SIQA metrics. 3D saliency performance is also compared with “cyclopean saliency” in SIQA. It is noticed that the proposed metric is applicable to both symmetric and asymmetric distortions. It can thus be concluded that the proposed SIQA metric can provide an effective evaluation tool to assess stereoscopic image quality

Spring Flood Forecasting Based on the WRF-TSRM Mode

The snowmelt process is becoming more complex in the context of global warming, and the current existing studies are not effective in using the short-term prediction model to drive the distributed hydrological model to predict snowmelt floods. In this study, we selected the Juntanghu Watershed in Hutubi County of China on the north slope of the Tianshan Mountains as the study area with which to verify the snowmelt flood prediction accuracy of the coupling model. The weather research and forecasting (WRF) model was used to drive a double-layer distributed snowmelt runoff model called the Tianshan Snowmelt Runoff Model (TSRM), which is based on multi-year field snowmelt observations. Moreover, the data from NASA’s moderate resolution imaging spectroradiometer (MODIS) was employed to validate the snow water equivalent during the snow-melting period. Results show that, based on the analysis of the flow lines in 2009 and 2010, the WRF-driven TSRM has an overall 80% of qualification ratios (QRs), with determination coefficients of 0.85 and 0.82 for the two years, respectively, which demonstrates the high accuracy of the model. However, due to the influence of the ablation of frozen soils, the forecasted flood peak is overestimated. This problem can be solved by an improvement to the modeled frozen soil layers. The conclusion reached in this study suggests that the WRF-driven TSRM can be used to forecast short-term snowmelt floods on the north slope of the Tianshan Mountains, which can effectively improve the local capacity for the forecasting and early warning of snowmelt floods