Xidian University

About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Computer science. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.

Sparsity-based image denoising via dictionary learning and structural clustering

Abstract: Where does the sparsity in image signals come from? Local and nonlocal image models have supplied complementary views toward the regularity in natural images — the former attempts to construct or learn a dictionary of basis functions that promotes the sparsity; while the latter connects the sparsity with the self-similarity of the image source by clustering. In this paper, we present a variational framework for unifying the above two views and propose a new denoising algorithm built upon clustering-based sparse representation (CSR). Inspired by the success of l 1 -optimization, we have formulated a double-header l 1 -optimization problem where the regularization involves both dictionary learning and structural structuring. A surrogate-function based iterative shrinkage solution has been developed to solve the double-header l 1 -optimization problem and a probabilistic interpretation of CSR model is also included. Our experimental results have shown convincing improvements over state-of-the-art denoising technique BM3D on the class of regular texture images. The PSNR performance of CSR denoising is at least comparable and often superior to other competing schemes including BM3D on a collection of 12 generic natural images.

Smart Contract-Based Access Control for the Internet of Things

Abstract: This paper investigates a critical access control issue in the Internet of Things (IoT). In particular, we propose a smart contract-based framework, which consists of multiple access control contracts (ACCs), one judge contract (JC), and one register contract (RC), to achieve distributed and trustworthy access control for IoT systems. Each ACC provides one access control method for a subject-object pair, and implements both static access right validation based on predefined policies and dynamic access right validation by checking the behavior of the subject. The JC implements a misbehavior-judging method to facilitate the dynamic validation of the ACCs by receiving misbehavior reports from the ACCs, judging the misbehavior and returning the corresponding penalty. The RC registers the information of the access control and misbehavior-judging methods as well as their smart contracts, and also provides functions (e.g., register, update, and delete) to manage these methods. To demonstrate the application of the framework, we provide a case study in an IoT system with one desktop computer, one laptop and two Raspberry Pi single-board computers, where the ACCs, JC, and RC are implemented based on the Ethereum smart contract platform to achieve the access control.

First-in-human liver-tumour surgery guided by multispectral fluorescence imaging in the visible and near-infrared-I/II windows

Abstract: The second near-infrared wavelength window (NIR-II, 1,000–1,700 nm) enables fluorescence imaging of tissue with enhanced contrast at depths of millimetres and at micrometre-scale resolution. However, the lack of clinically viable NIR-II equipment has hindered the clinical translation of NIR-II imaging. Here, we describe an optical-imaging instrument that integrates a visible multispectral imaging system with the detection of NIR-II and NIR-I (700–900 nm in wavelength) fluorescence (by using the dye indocyanine green) for aiding the fluorescence-guided surgical resection of primary and metastatic liver tumours in 23 patients. We found that, compared with NIR-I imaging, intraoperative NIR-II imaging provided a higher tumour-detection sensitivity (100% versus 90.6%; with 95% confidence intervals of 89.1%–100% and 75.0%–98.0%, respectively), a higher tumour-to-normal-liver-tissue signal ratio (5.33 versus 1.45) and an enhanced tumour-detection rate (56.41% versus 46.15%). We infer that combining the NIR-I/II spectral windows and suitable fluorescence probes might improve image-guided surgery in the clinic. An optical-imaging instrument that integrates a visible multispectral imaging system with the detection of near-infrared fluorescence in the first and second windows aids the fluorescence-guided surgical resection of liver tumours in patients.

Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification

Abstract: The employed dictionary plays an important role in sparse representation or sparse coding based image reconstruction and classification, while learning dictionaries from the training data has led to state-of-the-art results in image classification tasks. However, many dictionary learning models exploit only the discriminative information in either the representation coefficients or the representation residual, which limits their performance. In this paper we present a novel dictionary learning method based on the Fisher discrimination criterion. A structured dictionary, whose atoms have correspondences to the subject class labels, is learned, with which not only the representation residual can be used to distinguish different classes, but also the representation coefficients have small within-class scatter and big between-class scatter. The classification scheme associated with the proposed Fisher discrimination dictionary learning (FDDL) model is consequently presented by exploiting the discriminative information in both the representation residual and the representation coefficients. The proposed FDDL model is extensively evaluated on various image datasets, and it shows superior performance to many state-of-the-art dictionary learning methods in a variety of classification tasks.

Intelligent 5G: When Cellular Networks Meet Artificial Intelligence

Abstract: 5G cellular networks are assumed to be the key enabler and infrastructure provider in the ICT industry, by offering a variety of services with diverse requirements. The standardization of 5G cellular networks is being expedited, which also implies more of the candidate technologies will be adopted. Therefore, it is worthwhile to provide insight into the candidate techniques as a whole and examine the design philosophy behind them. In this article, we try to highlight one of the most fundamental features among the revolutionary techniques in the 5G era, i.e., there emerges initial intelligence in nearly every important aspect of cellular networks, including radio resource management, mobility management, service provisioning management, and so on. However, faced with ever-increasingly complicated configuration issues and blossoming new service requirements, it is still insufficient for 5G cellular networks if it lacks complete AI functionalities. Hence, we further introduce fundamental concepts in AI and discuss the relationship between AI and the candidate techniques in 5G cellular networks. Specifically, we highlight the opportunities and challenges to exploit AI to achieve intelligent 5G networks, and demonstrate the effectiveness of AI to manage and orchestrate cellular network resources. We envision that AI-empowered 5G cellular networks will make the acclaimed ICT enabler a reality.