Directional Bilateral Filters

We propose a bilateral filter with a locally controlled domain kernel for directional edge-preserving smoothing. Traditional bilateral filters use a range kernel, which is responsible for edge preservation, and a fixed domain kernel that performs smoothing. Our intuition is that orientation and anisotropy of image structures should be incorporated into the domain kernel while smoothing. For this purpose, we employ an oriented Gaussian domain kernel locally controlled by a structure tensor. The oriented domain kernel combined with a range kernel forms the directional bilateral filter. The two kernels assist each other in effectively suppressing the influence of the outliers while smoothing. To find the optimal parameters of the directional bilateral filter, we propose the use of Stein's unbiased risk estimate (SURE). We test the capabilities of the kernels separately as well as together, first on synthetic images, and then on real endoscopic images. The directional bilateral filter has better denoising performance than the Gaussian bilateral filter at various noise levels in terms of peak signal-to-noise ratio (PSNR)

Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey

This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Computational Methods for Matrix/Tensor Factorization and Deep Learning Image Denoising

Feature learning is a technique to automatically extract features from raw data. It is widely used in areas such as computer vision, image processing, data mining and natural language processing. In this thesis, we are interested in the computational aspects of feature learning. We focus on rank matrix and tensor factorization and deep neural network models for image denoising. With respect to matrix and tensor factorization, we first present a technique to speed up alternating least squares (ALS) and gradient descent (GD) − two commonly used strategies for tensor factorization. We introduce an efficient, scalable and distributed algorithm that addresses the data explosion problem. Instead of a computationally challenging sub-step of ALS and GD, we implement the algorithm on parallel machines by using only two sparse matrix-vector products. Not only is the algorithm scalable but it is also on average 4 to 10 times faster than competing algorithms on various data sets. Next, we discuss our results of non-negative matrix factorization for hyperspectral image data in the presence of noise. We introduce a spectral total variation regularization and derive four variants of the alternating direction method of multiplier algorithm. While all four methods belong to the same family of algorithms, some perform better than others. Thus, we compare the algorithms using stimulated Raman spectroscopic image will be demonstrated. For deep neural network models, we focus on its application to image denoising. We first demonstrate how an optimal procedure leveraging deep neural networks and convex optimization can combine a given set of denoisers to produce an overall better result. The proposed framework estimates the mean squared error (MSE) of individual denoised outputs using a deep neural network; optimally combines the denoised outputs via convex optimization; and recovers lost details of the combined images using another deep neural network. The framework consistently improves denoising performance for both deterministic denoisers and neural network denoisers. Next, we apply the deep neural network to solve the image reconstruction issues of the Quanta Image Sensor (QIS), which is a single-photon image sensor that oversamples the light field to generate binary measures

A policy compliance detection architecture for data exchange infrastructures

Data sharing and federation can significantly increase efficiency and lower the cost of digital collaborations. It is important to convince the data owners that their outsourced data will be used in a secure and controlled manner. To achieve this goal, constructing a policy governing concrete data usage rule among all parties is essential. More importantly, we need to establish digital infrastructures that can enforce the policy. In this thesis, we investigate how to select optimal application-tailored infrastructures and enhance policy compliance capabilities. First, we introduce a component linking the policy to the infrastructure patterns. The mechanism selects digital infrastructure patterns that satisfy the collaboration request to a maximal degree by modelling and closeness identification. Second, we present a threat-analysis driven risk assessment framework. The framework quantitatively assesses the remaining risk of an application delegated to digital infrastructure. The optimal digital infrastructure for a specific data federation application is the one which can support the requested collaboration model and provides the best security guarantee. Finally, we present a distributed architecture that detects policy compliance when an algorithm executes on the data. A profile and an IDS model are built for each containerized algorithm and are distributed to endpoint execution platforms via a secure channel. Syscall traces are monitored and analysed in endpoint points platforms. The machine learning based IDS is retrained periodically to increase generalization. A sanitization algorithm is implemented to filter out malicious samples to further defend the architecture against adversarial machine learning attacks