Carrier Frequency Offset Estimation for OFDM Systems using Repetitive Patterns

This paper deals with Carrier Frequency Offset (CFO) estimation for OFDM systems using repetitive patterns in the training symbol. A theoretical comparison based on Cramer Rao Bounds (CRB) for two kinds of CFO estimation methods has been presented in this paper. Through the comparison, it is shown that the performance of CFO estimation can be improved by exploiting the repetition property and the exact training symbol rather than exploiting the repetition property only. The selection of Q (number of repetition patterns) is discussed for both situations as well. Moreover, for exploiting the repetition and the exact training symbol, a new numerical procedure for the Maximum-Likelihood (ML) estimation is designed in this paper to save computational complexity. Analysis and numerical result are also given, demonstrating the conclusions in this paper

Understanding size dependence of phase stability and band gap in CsPbI3 perovskite nanocrystals.

Inorganic halide perovskites CsPbX3 (X = Cl, Br, I) have been widely studied as colloidal quantum dots for their excellent optoelectronic properties. Not only is the long-term stability of these materials improved via nanostructuring, their optical bandgaps are also tunable by the nanocrystal (NC) size. However, theoretical understanding of the impact of the NC size on the phase stability and bandgap is still lacking. In this work, the relative phase stability of CsPbI3 as a function of the crystal size and the chemical potential is investigated by density functional theory. The optically active phases (α- and γ-phase) are found to be thermodynamically stabilized against the yellow δ-phase by reducing the size of the NC below 5.6 nm in a CsI-rich environment. We developed a more accurate quantum confinement model to predict the change in bandgaps at the sub-10 nm regime by including a finite-well effect. These predictions have important implications for synthesizing ever more stable perovskite NCs and bandgap engineering

Detection of denial-of-service attacks based on computer vision techniques

University of Technology, Sydney. Faculty of Engineering and Information Technology.A Denial-of-Service (DoS) attack is an intrusive attempt, which aims to force a designated resource (e.g., network bandwidth, processor time or memory) to be unavailable to its intended users. This attack is launched either by deliberately exploiting system vulnerabilities of a victim (e.g., a host, a router, or an entire network) or by flooding a victim with large volume of useless network traffic. Since 1990s, DoS attacks have emerged as a type of the most severe network intrusive behaviours and have posed serious threats to the infrastructures of computer networks and various network-based services. This thesis aims to provide an intelligent and effective solution for DoS attack detection. Unlike the related works based on machine learning and statistical analysis, this thesis suggests to treat network traffic records as images and to redefine the DoS attack detection problem as a computer vision task. To achieve the aforementioned objectives, this thesis first conducts a detailed literature review on the state of the art in DoS attack detection. Then, it analyses and chooses the most appropriate mechanisms for DoS attack detection. Afterwards, it designs a general system framework for DoS attack detection with respect to the chosen mechanisms. Furthermore, two Multivariate Correlation Analysis (MCA) approaches are proposed based on two techniques, namely Euclidean distance and triangle area. These two proposed MCA approaches provide accurate description for network traffic records and facilitate conversion of network traffic into the respective images. In addition, this thesis proposes a DoS attack detection system, in which the images of network traffic are served as the observed objects and the task of DoS attack detection is reformulated as a computer vision problem, namely image retrieval. This proposed DoS attack detection system applies a widely used dissimilarity measure, namely the Earth Mover’s Distance (EMD), to object classification. The EMD takes cross-bin matching into account and provides a more accurate evaluation on the dissimilarity between distributions than some other well-known dissimilarity measures, such as Minkowski-form distance Lp and X² statistics. The merits of the EMD facilitate the capability of our proposed system with effective detection. Last but not least, our intelligent and effective solutions, including the two proposed MCA approaches and the EMD-based DoS attack detection system, are evaluated using the KDD Cup 99 dataset. The evaluation results illustrate that our proposed MCA approaches provide accurate characterisation for network traffic, and the proposed detection system can detect unknown DoS attacks and outperforms two state-of-the-art approaches

Analysis of RFID adoption in China

Radio-frequency identification (RFID) is an emerging technology for automatic data capturing, enabling real time information visibility. It promises great potentials in many industries to improve logistics operational efficiency, to help reduce inventory, and to automate asset/item track and trace, etc. The RFID adoption in China is a highly concerned topic as China has become a world manufacturing center. In this paper, we have presented an overview for China's current RFID adoption status. Based on Rogers's DOI theory, a methodology is developed for analyzing RFID adoption in China. With this methodology, China's RFID adoption status is identified, and ways to speed up the rate of adoption are also suggested. © 2007 IEEE.published_or_final_versio