We propose a new visual tracking method, where the target appearance is represented by combining color distribution and keypoints. Firstly, the object is localized via a keypoint-based tracking and matching strategy, where a new clustering method is presented to remove outliers. Secondly, the tracking confidence is evaluated by the color template. According to the tracking confidence, the local and global keypoints matching can be performed adaptively. Finally, we propose a target appearance update method in which the new appearance can be learned and added to the target model. The proposed tracker is compared with five state-of-the-art tracking methods on a recent benchmark dataset. Both qualitative and quantitative evaluations show that our method has favorable performance.

Designing secure revocable storage systems for a large number of users in a cloud-based environment is important. Cloud storage systems should allow its users to dynamically join and leave the storage service. Further, the rights of the users to access the data should be changed accordingly. Recently, Liang et al. proposed a cloud-based revocable identity-based proxy re-encryption (CR-IB-PRE) scheme that supports user revocation and delegation of decryption rights. Moreover, to reduce the size of the key update token, they employed a public key broadcast encryption system as a building block. In this paper, we show that the CR-IB-PRE scheme with the reduced key update token size is not secure against collusion attacks.

Determining an effective way to reduce computation complexity is an essential task for adaptive echo cancellation applications. Recently, a family of partial update (PU) adaptive algorithms has been proposed to effectively reduce computational complexity. However, because a PU algorithm updates only a portion of the weights of the adaptive filters, the rate of convergence is reduced. To address this issue, this paper proposes an enhanced switching-based variable step-size (ES-VSS) approach to the M-max PU least mean square (LMS) algorithm. The step-size is determined by the correlation between the error signals and their noise-free versions. Noise-free error signals are approximated according to the level of convergence achieved during the adaptation process. The approximation of the noise-free error signals switches among four modes, such that the resulting step-size is as close to its optimal value as possible. Simulation results show that when only a half of all taps are updated in a single iteration, the proposed method significantly enhances the convergence rate of the M-max PU LMS algorithm.

In this paper, we propose Affine Combination Lattice Algorithm (ACLA) as a new lattice-based adaptive notch filtering algorithm. The ACLA makes use of the affine combination of Regalia's Simplified Lattice Algorithm (SLA) and Lattice Gradient Algorithm (LGA). It is proved that the ACLA has faster convergence speed than the conventional lattice-based algorithms. We conduct this proof by means of theoretical analysis of the mean update term. Specifically, we show that the mean update term of the ACLA is always larger than that of the conventional algorithms. Simulation examples demonstrate the validity of this analytical result and the utility of the ACLA. In addition, we also derive the step-size bound for the ACLA. Furthermore, we show that this step-size bound is characterized by the gradient of the mean update term.

It is very difficult to know evolution state of ACO in its working. To solve the problem, we propose using colony entropy and mean colony entropy to monitor the algorithm. The two functions show fluctuation and declining trends depended on time t in a tour and iteration number. According to the principle, that each updated edge will get the same increment is improper. Then a weighted algorithm is proposed to calculate each arc's increment based on its selected probability. The strategy can provide more exploration to help to find the global optimum value, and experiments show its improved performance.

Operating system (OS) reboots are an essential part of updating kernels and applications on laptops and desktop PCs. Long downtime during OS reboots severely disrupts users' computational activities. This long disruption discourages the users from conducting OS reboots, failing to enforce them to conduct software updates. Although the dynamic updatable techniques have been widely studied, making the system “reboot-free” is still difficult due to their several limitations. As a result, users cannot benefit from new functionality or better performance, and even worse, unfixed vulnerabilities can be exploited by attackers. This paper presents ShadowReboot, a virtual machine monitor (VMM)-based approach that shortens downtime of OS reboots in software updates. ShadowReboot conceals OS reboot activities from user's applications by spawning a VM dedicated to an OS reboot and systematically producing the rebooted state where the updated kernel and applications are ready for use. ShadowReboot provides an illusion to the users that the guest OS travels forward in time to the rebooted state. ShadowReboot offers the following advantages. It can be used to apply patches to the kernels and even system configuration updates. Next, it does not require any special patch requiring detailed knowledge about the target kernels. Lastly, it does not require any target kernel modification. We implemented a prototype in VirtualBox 4.0.10 OSE. Our experimental results show that ShadowReboot successfully updated software on unmodified commodity OS kernels and shortened the downtime of commodity OS reboots on five Linux distributions (Fedora, Ubuntu, Gentoo, Cent, and SUSE) by 91 to 98%.

We present a low-complexity complementary pair affine projection (CP-AP) adaptive filter which employs the intermittent update of the filter coefficients. To achieve both a fast convergence rate and a small residual error, we use a scheme combining fast and slow AP filters, while significantly reducing the computational complexity. By employing an evolutionary method which automatically determines the update intervals, the update frequencies of the two constituent filters are significantly decreased. Experimental results show that the proposed CP-AP adaptive filter has an advantage over conventional adaptive filters with a parallel structure in that it has a similar convergence performance with a substantial reduction in the total number of updates.

Infrastructure as a Service (IaaS), a form of cloud computing, is gaining attention for its ability to enable efficient server administration in dynamic workload environments. In such environments, however, updating the software stack or content files of virtual machines (VMs) is a time-consuming task, discouraging administrators from frequently enhancing their services and fixing security holes. This is because the administrator has to upload the whole new disk image to the cloud platform via the Internet, which is not yet fast enough that large amounts of data can be transferred smoothly. Although the administrator can apply incremental updates directly to the running VMs, he or she has to carefully consider the type of update and perform operations on all running VMs, such as application restarts. This is a tedious and error-prone task. This paper presents a technique for synchronizing VMs with less time and lower administrative burden. We introduce the Virtual Disk Image Repository, which runs on the cloud platform and automatically updates the virtual disk image and the running VMs with only the incremental update information. We also show a mechanism that performs necessary operations on the running VM such as restarting server processes, based on the types of files that are updated. We implement a prototype on Linux 2.6.31.14 and Amazon Elastic Compute Cloud. An experiment shows that our technique can synchronize VMs in an order-of-magnitude shorter time than the conventional disk-image-based VM method. Also, we discuss limitations of our technique and some directions for more efficient VM updates.

This paper provides a novel normalized sign least-mean square (NSLMS) algorithm which updates only a part of the filter coefficients and simultaneously performs sparse updates with the goal of reducing computational complexity. A combination of the partial-update scheme and the set-membership framework is incorporated into the context of L∞-norm adaptive filtering, thus yielding computational efficiency. For the stabilized convergence, we formulate a robust update recursion by imposing an upper bound of a step size. Furthermore, we analyzed a mean-square stability of the proposed algorithm for white input signals. Experimental results show that the proposed low-complexity NSLMS algorithm has similar convergence performance with greatly reduced computational complexity compared to the partial-update NSLMS, and is comparable to the set-membership partial-update NLMS.

The performance of a mobile database management system (DBMS) in which most queries are made up of random data accesses if the NAND flash memory is used as storage media of the DBMS is degraded. The reason for this is that the performance of NAND flash memory is good for writing sequentially but poor when writing randomly. Thus, a new storage structure and querying policies are needed in mobile DBMS when flash memory is used as the storage media. In this letter, we propose a new policy of database page management to enhance the frequent random update performance, and then evaluate the performance experimentally.

High-speed IP address lookup with fast prefix update is essential for designing wire-speed packet forwarding routers. The developments of optical fiber and 100 Gbps interface technologies have placed IP address lookup as the major bottleneck of high performance networks. In this paper, we propose a novel structure named Compressed Multi-way Prefix Tree (CMPT) based on B+ tree to perform dynamic and scalable high-speed IP address lookup. Our contributions are to design a practical structure for high-speed IP address lookup suitable for both IPv4 and IPv6 addresses, and to develop efficient algorithms for dynamic prefix insertion and deletion. By investigating the relationships among routing prefixes, we arrange independent prefixes as the search indexes on internal nodes of CMPT, and by leveraging a nested prefix compression technique, we encode all the routing prefixes on the leaf nodes. For any IP address, the longest prefix matching can be made at leaf nodes without backtracking. For a forwarding table with u independent prefixes, CMPT requires O(logmu) search time and O(mlogmu) dynamic insert and delete time. Performance evaluations using real life IPv4 forwarding tables show promising gains in lookup and dynamic update speeds compared with the existing B-tree structures.

The applications of dynamic content updates for a group of users, for example weather reports and news broadcast, have been shown to benefit significantly from Delay Tolerant Networks (DTNs) communication mechanisms. In this paper, we study the performance of dynamic content updates over DTNs by focusing on the latest content distribution, which is an important factor of the system energy consumption and content update efficiency. By characterizing the content generating process and content sharing process, we obtain an explicit expression for the latest content distribution, and prove it theoretically. Moreover, through simulations based on two synthetical mobility models and a real-world scenario, we demonstrate the accuracy and correctness of the theoretically obtained result.

In this letter, we apply dynamic software updating to long-lived applications on the DDS middleware while minimizing service interruption and satisfying Quality of Service (QoS) requirements. We dynamically updated applications which run on a commercial DDS implementation to demonstrate the applicability of our approach to dynamic updating. The results show that our update system does not impose an undue performance overhead–all patches could be injected in less than 350 ms and the maximum CPU usage is less than 17%. In addition, the overhead on application throughput due to dynamic updates ranged from 0 to at most 8% and the deadline QoS of the application was satisfied while updating.

We propose a novel re-ranking method for content-based medical image retrieval based on the idea of pseudo-relevance feedback (PRF). Since the highest ranked images in original retrieval results are not always relevant, a naive PRF based re-ranking approach is not capable of producing a satisfactory result. We employ a two-step approach to address this issue. In step 1, a Pearson's correlation coefficient based similarity update method is used to re-rank the high ranked images. In step 2, after estimating a relevance probability for each of the highest ranked images, a fuzzy SVM ensemble based approach is adopted to re-rank the images. The experiments demonstrate that the proposed method outperforms two other re-ranking methods.

The panorama image obtained by image stitching can have visible artifacts due to the limitation of alignment accuracy and defects of the optical systems. Moreover, conventional image stitching algorithms cannot be directly applied to a real-time video stitching due to its complexity and waving artifacts. In this paper, we propose a real-time content-aware stitching algorithm which not only finds a seam by using path searching based on the greedy method, but also adaptively updates the seam by detecting objects moving toward the seam. Experimental results show that the proposed algorithm can successfully produce stitched video sequences without the waving and ghost artifacts commonly found in conventional stitching algorithms.

Recently, we have proposed a new prefix lookup algorithm which would use the prefixes as scalar numbers. This algorithm could be applied to different tree structures such as Binary Search Tree and some other balanced trees like RB-tree, AVL-tree and B-tree with minor modifications in the search, insert and/or delete procedures to make them capable of finding the prefixes of an incoming string e.g. an IP address. As a result, the search procedure complexity would be O(log n) where n is the number of prefixes stored in the tree. More important, the search complexity would not depend on the address length w i.e. 32 for IPv4 and 128 for IPv6. Here, it is assumed that interface to memory is wide enough to access the prefix and some simple operations like comparison can be done in O(1) even for the word length w. Moreover, insertion and deletion procedures of this algorithm are much simpler and faster than its competitors. In what follows, we report the software implementation results of this algorithm and compare it with other solutions for both IPv4 and IPv6. It also reports on a simple hardware implementation of the algorithm for IPv4. Comparison results show better lookup and update performances or superior storage requirements for Scalar Prefix Search in both average and worst cases.

DTDs are continuously updated according to changes in the real world. Let t be an XML document valid against a DTD D, and suppose that D is updated by an update script s. In general, we cannot uniquely "infer" a transformation of t from s, i.e., we cannot uniquely determine the elements in t that should be deleted and/or the positions in t that new elements should be inserted into. In this paper, we consider inferring K optimum transformations of t from s so that a user finds the most desirable transformation more easily. We first show that the problem of inferring K optimum transformations of an XML document from an update script is NP-hard even if K = 1. Then, assuming that an update script is of length one, we show an algorithm for solving the problem, which runs in time polynomial of |D|, |t|, and K.

Continuous processing and servicing of a new incoming session in LTE systems demands optimal tracking of the mobile user. In this letter, an optimal, information-theoretic framework is developed for tracking area update for next-generation LTE cellular systems. Shannon's entropy is used to characterize the location uncertainty of mobile user. The framework captures users' mobility patterns online and performs profile-based paging for optimizing the tracking area update cost. Simulation results demonstrate reductions in both update and paging costs in comparison to existing LTE systems.

Codebook based multiple-input multiple-output (MIMO) precoding can significantly improve the system spectral efficiency with limited feedback and has been accepted as one of the most promising techniques for the Evolved UTRA (E-UTRA). Compared with single-user (SU) MIMO, multi-user (MU) MIMO can further improve the system spectral efficiency due to increased multi-user diversity gain. MU-MIMO is preferred for the case of a large number of users,when the total feedback overhead will become a problem. In order to reduce the feedback overhead, feedback of single channel quality indicator (CQI), e.g. rank 1 CQI, is required in E-UTRA currently. The main challenge is how to obtain CQIs of other ranks at Node B for rank adaptation with single CQI feedback. In this paper, an adaptive CQI update scheme at Node B based on statistical characteristics of CQI of various ranks is proposed. To further increase the accuracy of CQI at Node B for data transmission, an adaptive CQI feedback scheme is then proposed in which single CQI with the rank same as previously scheduled is fed back. Simulation results show that our proposed CQI update scheme can achieve 2.5-5% gain compared with the conventional method with fixed backoff. Moreover, with the proposed adaptive feedback scheme, 20-40% performance gain can be obtained and the performance can approach the upper bound.

Several hardware-based pattern matching engines for network intrusion/prevention detection systems (NIDS/NIPSs) can achieve high throughput with less hardware resources. However, their flexibility to update new patterns is limited and still challenging. This paper describes a PAttern Matching Engine with Limited-time updAte (PAMELA) engine using a recently proposed hashing algorithm called Cuckoo Hashing. PAMELA features on-the-fly pattern updates without reconfiguration, more efficient hardware utilization, and higher performance compared with other works. First, we implement the improved parallel exact pattern matching with arbitrary length based on Cuckoo Hashing and linked-list technique. Second, while PAMELA is being updated with new attack patterns, both stack and FIFO are utilized to bound insertion time due to the drawback of Cuckoo Hashing and to avoid interruption of input data stream. Third, we extend the system for multi-character processing to achieve higher throughput. Our engine can accommodate the latest Snort rule-set, an open source NIDS/NIPS, and achieve the throughput up to 8.8 Gigabit per second while consuming the lowest amount of hardware. Compared to other approaches, ours is far more efficient than any other implemented on Xilinx FPGA architectures.