Wenyu Qu

An effective solution for trademark image retrieval by combining shape description and feature matching

Trademark image retrieval (TIR), a branch of content-based image retrieval (CBIR), is playing an important role in multimedia information retrieval. This paper proposes an effective solution for TIR by combining shape description and feature matching. We first present an effective shape description method which includes two shape descriptors. Second, we propose an effective feature matching strategy to compute the dissimilarity value between the feature vectors extracted from images. Finally, we combine the shape description method and the feature matching strategy to realize our solution. We conduct a large number of experiments on a standard image set to evaluate our solution and the existing solutions. By comparison of their experimental results, we can see that the proposed solution outperforms existing solutions for the widely used performance metrics.

A hybrid collaborative filtering recommendation mechanism for P2P networks

With the increasing number of commerce facilities using peer-to-peer (P2P) networks, challenges exist in recommending interesting or useful products and services to a particular customer. Collaborative Filtering (CF) is one of the most successful techniques that attempts to recommend items (such as music, movies, web sites) which are likely to be of interest to the people. However, conventional collaborative filtering encounters a number of challenges on its recommendation accuracy. One of the most important challenges may be due to the sparse attributes inherent to the rating data. Another important challenge is that existing CF methods consider mainly user-based or item-based ratings respectively. In this paper a P2P-based hybrid collaborative filtering mechanism for the support of combining user-based and item attribute-based ratings is considered. We take advantage of the inherent item attributes to construct a Boolean matrix to predict the blank elements for a sparse user-item matrix. Furthermore, a Hybrid collaborative filtering (HCF) algorithm is presented to improve the predictive accuracy. Case studies and experiment results illustrate that our approaches not only contribute to predicting the unrated blank data for a sparse matrix but also improve the prediction accuracy as expected.

Efficient Unknown Tag Identification Protocols in Large-Scale RFID Systems

Owing to its attractive features such as fast identification and relatively long interrogating range over the classical barcode systems, radio-frequency identification (RFID) technology possesses a promising prospect in many practical applications such as inventory control and supply chain management. However, unknown tags appear in RFID systems when the tagged objects are misplaced or unregistered tagged objects are moved in, which often causes huge economic losses. This paper addresses an important and challenging problem of unknown tag identification in large-scale RFID systems. The existing protocols leverage the Aloha-like schemes to distinguish the unknown tags from known tags at the slot level, which are of low time-efficiency, and thus can hardly satisfy the delay-sensitive applications. To fill in this gap, two filtering-based protocols (at the bit level) are proposed in this paper to address the problem of unknown tag identification efficiently. Theoretical analysis of the protocol parameters is performed to minimize the execution time of the proposed protocols. Extensive simulation experiments are conducted to evaluate the performance of the protocols. The results demonstrate that the proposed protocols significantly outperform the currently most promising protocols.

Completely Pinpointing the Missing RFID Tags in a Time-Efficient Way

Radio Frequency Identification (RFID) technology has been widely used in inventory management in many scenarios, e.g., warehouses, retail stores, hospitals, etc. This paper investigates a challenging problem of complete identification of missing tags in large-scale RFID systems. Although this problem has attracted extensive attention from academy and industry, the existing work can hardly satisfy the stringent real-time requirements. In this paper, a Slot Filter-based Missing Tag Identification (SFMTI) protocol is proposed to reconcile some expected collision slots into singleton slots and filter out the expected empty slots as well as the unreconcilable collision slots, thereby achieving the improved time-efficiency. The theoretical analysis is conducted to minimize the execution time of the proposed SFMTI. We then propose a cost-effective method to extend SFMTI to the multi-reader scenarios. The extensive simulation experiments and performance results demonstrate that the proposed SFMTI protocol outperforms the most promising Iterative ID-free Protocol (IIP) by reducing nearly 45% of the required execution time, and is just within a factor of 1.18 from the lower bound of the minimum execution time.

Detecting DDoS attacks against data center with correlation analysis

Distributed denial-of-service (DDoS) attacks pose a great threat to the data center, and many defense mechanisms have been proposed to detect it. On one hand, many services deployed in data center can easily lead to corresponding DDoS attacks. On the other hand, attackers constantly modify their tools to bypass these existing mechanisms, and researchers in turn modify their approaches to handle new attacks. Thus, the DDoS against data center is becoming more and more complex. In this paper, we first analyze the correlation information of flows in data center. Second, we present an effective detection approach based on CKNN (K-nearest neighbors traffic classification with correlation analysis) to detect DDoS attacks. The approach exploits correlation information of training data to improve the classification accuracy and reduce the overhead caused by the density of training data. Aiming at solving the huge cost, we also present a grid-based method named r-polling method for reducing training data involved in the calculation. Finally, we evaluate our approach with the Internet traffic and data center traffic trace. Compared with the traditional methods, our approach is good at detecting abnormal traffic with high efficiency, low cost and wide detection range.

Xen Live Migration with Slowdown Scheduling Algorithm

With the increasing number of technology areas using Virtual Machine (VM) platforms, challenges exist in Virtual Machine migrating from one physical host to another. However, the complexity of these virtualized environments presents additional management challenges. Unfortunately, many traditional approaches may be either not effective well for reducing downtime or migration time, or not suitable well for Xen VMs platforms. This paper presents the design and implementation of a novel Slowdown Scheduling Algorithm (SSA) for Xen live VM migration. In our SSA methodology, the CPU resources which have been assigned to migration domain are decrease properly. That is, the dirtying page rate is reduced according to the decrease of CPU activity. Experimental results illustrate that our SSA approach can shorten both the total migration time and downtime obviously under high dirty page rate environment.

Energy-Efficient Tree-Based Multipath Power Control for Underwater Sensor Networks

Due to the use of acoustic channels with limited available bandwidth, Underwater Sensor Networks (USNs) often suffer from significant performance restrictions such as low reliability, low energy-efficiency, and high end-to-end packet delay. The provisioning of reliable, energy-efficient, and low-delay communication in USNs has become a challenging research issue. In this paper, we take noise attenuation in deep water areas into account and propose a novel layered multipath power control (LMPC) scheme in order to reduce the energy consumption as well as enhance reliable and robust communication in USNs. To this end, we first formalize an optimization problem to manage transmission power and control data rate across the whole network. The objective is to minimize energy consumption and simultaneously guarantee the other performance metrics. After proving that this optimization problem is NP-complete, we solve the key problems of LMPC including establishment of the energy-efficient tree and management of energy distribution and further develop a heuristic algorithm to achieve the feasible solution of the optimization problem. Finally, the extensive simulation experiments are conducted to evaluate the network performance under different working conditions. The results reveal that the proposed LMPC scheme outperforms the existing mechanism significantly.

A Multiple Hashing Approach to Complete Identification of Missing RFID Tags

Owing to its superior properties, such as fast identification and relatively long interrogating range over barcode systems, Radio Frequency Identification (RFID) technology has promising application prospects in inventory management. This paper studies the problem of complete identification of missing RFID tag, which is important in practice. Time efficiency is the key performance metric of missing tag identification. However, the existing protocols are ineffective in terms of execution time and can hardly satisfy the requirements of realtime applications. In this paper, a Multi-hashing based Missing Tag Identification (MMTI) protocol is proposed, which achieves better time efficiency by improving the utilization of the time frame used for identification. Specifically, the reader recursively sends bitmaps that reflect the current slot occupation state to guide the slot selection of the next hashing process, thereby changing more empty or collision slots to the expected singleton slots. We investigate the optimal parameter settings to maximize the performance of the MMTI protocol. Furthermore, we discuss the case of channel error and propose the countermeasures to make the MMTI workable in the scenarios with imperfect communication channels. Extensive simulation experiments are conducted to evaluate the performance of MMTI, and the results demonstrate that this new protocol significantly outperforms other related protocols reported in the current literature.

Tour-Guide: Providing Location-Based Tourist Information on Mobile Phones

Mobile phone has become a powerful platform for people-centric computing. It is now a necessary part of everyday life for many people. A growing number of mobile computing applications are on the rise, centered around the user’s daily life. In those emerging applications, location dependent systems have been identified as an important component. This paper presents the architecture and implementation of such a location-based application, called Tour-Guide. Implemented on iPhone, the system is designed to provide tour information services; therefore people can get tour guidance information that they need anytime and anywhere.

A Survey of Mobile Agent-Based Fault-Tolerant Technology

This paper surveys the state of the art of agentbased fault tolerance techniques. Existing mobile agent-based fault-tolerant techniques are identified on prevent mobile agents from being blocked by a failure.