Graph Construction for Hyperspectral Data Unmixing

This chapter presents graph construction for hyperspectral data and associated unmixing methods based on graph regularization. Graph is a ubiquitous mathematical tool for modeling relations between objects under study. In the context of hyperspectral image analysis, constructing graphs can be useful to relate pixels in order to perform corporative analysis instead of analyzing each pixel individually. In this chapter, we review fundamental elements of graphs and present different ways to construct graphs in both spatial and spectral senses for hyperspectral images. By incorporating a graph regularization, we then formulate a general hyperspectral unmixing problem that can be important for applications such as remote sensing and environment monitoring. Alternating direction method of multipliers (ADMM) is also presented as a generic tool for solving the formulated unmixing problems. Experiments validate the proposed scheme with both synthetic data and real remote sensing data

Distributed Private Online Learning for Social Big Data Computing over Data Center Networks

With the rapid growth of Internet technologies, cloud computing and social networks have become ubiquitous. An increasing number of people participate in social networks and massive online social data are obtained. In order to exploit knowledge from copious amounts of data obtained and predict social behavior of users, we urge to realize data mining in social networks. Almost all online websites use cloud services to effectively process the large scale of social data, which are gathered from distributed data centers. These data are so large-scale, high-dimension and widely distributed that we propose a distributed sparse online algorithm to handle them. Additionally, privacy-protection is an important point in social networks. We should not compromise the privacy of individuals in networks, while these social data are being learned for data mining. Thus we also consider the privacy problem in this article. Our simulations shows that the appropriate sparsity of data would enhance the performance of our algorithm and the privacy-preserving method does not significantly hurt the performance of the proposed algorithm.Comment: ICC201

An Extensive Game-Based Resource Allocation for Securing D2D Underlay Communications

Device-to-device (D2D) communication has been increasingly attractive due to its great potential to improve cellular communication performance. While resource allocation optimization for improving the spectrum efficiency is of interest in the D2D-related work, communication security, as a key issue in the system design, has not been well investigated yet. Recently, a few studies have shown that D2D users can actually serve as friendly jammers to help enhance the security of cellular user communication against eavesdropping attacks. However, only a few studies considered the security of D2D communications. In this paper, we consider the secure resource allocation problem, particularly, how to assign resources to cellular and the D2D users to maximize the system security. To solve this problem, we propose an extensive game-based algorithm aiming at strengthening the security of both cellular and the D2D communications via system resource allocation. Finally, the simulation results show that the proposed method is able to efficiently improve the overall system security when compared to existing studies

Dynamic incentive strategy for voluntary demand response based on TDP scheme

Abstract-The enhanced real-time metering and communication capabilities from smart meters and their associated advanced metering infrastructure make it possible for utility company to extend demand response (DR) to small customers through timedependent pricing (TDP). Considering the economic reason and infrastructure cost, the utility company has to design an incentive scheme to attract the traditional flat pricing (FP) users to be engaged in the TDP scheme. In this process, the utility company may share its revenue from the TDP scheme to those TDP users. It is found, with properly analyzing the energy procurement cost and user elasticity, a dynamic incentive strategy can be considered in dual-tariffs system when flat pricing (FP) and TDP pricing are co-existed. This dynamic incentive strategy gives appropriate stimulus to the users who are involved into the TDP program, and guarantee the utility company's profit at the same time

Randomized Rank-Revealing QLP for Low-Rank Matrix Decomposition

The pivoted QLP decomposition is computed through two consecutive pivoted QR decompositions, and provides an approximation to the singular value decomposition. This work is concerned with a partial QLP decomposition of low-rank matrices computed through randomization, termed Randomized Unpivoted QLP (RU-QLP). Like pivoted QLP, RU-QLP is rank-revealing and yet it utilizes random column sampling and the unpivoted QR decomposition. The latter modifications allow RU-QLP to be highly parallelizable on modern computational platforms. We provide an analysis for RU-QLP, deriving bounds in spectral and Frobenius norms on: i) the rank-revealing property; ii) principal angles between approximate subspaces and exact singular subspaces and vectors; and iii) low-rank approximation errors. Effectiveness of the bounds is illustrated through numerical tests. We further use a modern, multicore machine equipped with a GPU to demonstrate the efficiency of RU-QLP. Our results show that compared to the randomized SVD, RU-QLP achieves a speedup of up to 7.1 times on the CPU and up to 2.3 times with the GPU

Networked Twins and Twins of Networks : an Overview on the Relationship Between Digital Twins and 6G

Digital Twin (DT) is a promising technology for the new immersive digital life with a variety of applications in areas such as Industry 4.0, aviation, and healthcare. Proliferation of this technology requires higher data rates, reliability, resilience, and lower latency beyond what is currently offered by 5G. Thus, DT can become a major driver for 6G research and development. Alternatively, 6G network development can benefit from Digital Twin technology and its powerful features such as modularity and remote intelligence. Using DT, a 6G network (or some of its components) will have the opportunity to use Artificial Intelligence more proactively in order to enhance its resilience. DT's application in telecommunications is still in its infancy. In this article we highlight some of the most promising research and development directions for this technology

Spectral techniques for multiple-valued logic

83 p.The research work discussed in this thesis concerns spectral techniques for multiple-valued logic. The main objectives are the development of novel discrete transformations and fast algorithms for binary and multiple-valued switching circuits which find various potential applications in communication system, digital signal processing and in particular, the classical logic synthesis.Doctor of Philosophy (EEE

A DPSK microwave link for wireless LANs

This report describes the implementation of a digital communication system for wireless LAN. The system employs differential phase shift keying modulation (DPSK). A Manchester encoder and decoder are added to the system to simulate the Ethernet network data which is Manchester encoded. The resultant signaling, MDPSK, is first theoretically investigated. In the theoretical analysis, the power spectral density of the MDPSK signaling is uniquely derived. The power spectral density of the MDPSK signals contains line spectra.Master of Engineerin