An Indexing Method for Answering Queries on Moving Objects

We consider the problem of indexing a set of objects moving in d-dimensional spaces along linear trajectories. A simple external-memory indexing scheme is proposed to efficiently answer general range queries. The following are examples of the queries that can be answered by the proposed method: report all moving objects that will (i) pass between two given points within a specified time interval; (ii) become within a given distance from some or all of a given set of other moving objects. Our scheme is based on mapping the objects to a dual space, where queries about moving objects are transformed into polyhedral queries concerning their speeds and initial locations. We then present a simple method for answering such polyhedral queries, based on partitioning the space into disjoint regions and using a B+-tree to index the points in each region. By appropriately selecting the boundaries of each region, we guarantee an average search time that matches a known lower bound for the problem. Specifically, for a fixed d, if the coordinates of a given set of N points are statistically independent, the proposed technique answers polyhedral queries, on the average, in O((N/B)^1−1/d⋅(log _B N)^1/d+K/B) I/O's using O(N/B) space, where B is the block size, and K is the number of reported points. Our approach is novel in that, while it provides a theoretical upper bound on the average query time, it avoids the use of complicated data structures, making it an effective candidate for practical applications. The proposed index is also dynamic in the sense that it allows object insertion and deletion in an amortized update cost of log _B(N) I/O's. Experimental results are presented to show the superiority of the proposed index over other methods based on R-trees. recommend Ahmed Elmagarmid     

Handover management based on user’s mobility specification

Considering the highly dynamic nature of wireless networks, the major challenge in this environment is the provision of QoS guarantees that different applications demand. Recent research is interested in advance resource reservation to provide the necessary QoS to the mobile terminals. Another way to obtain a better use of the resources in this environment is to determine the future locations of the mobile terminal. This paper describes a user’s mobility profile management based approach for advance resource reservation in wireless networks; it is based on theNsis signaling. This reservation is based on an objectMspec (Mobility Specification) which determines the future location of the mobile terminal. The mspec object is a part of a user’s mobility profile; this last is determined by the mobile terminal. Another applicability of our user’s mobility profile is in the 4G wireless systems, in this case; the mobile terminal supports various access technologies and the user’s mobility profile is used to adapt the vertical and the diagonal handover to the user’s needs.     

A novel look-ahead optimization strategy for trie-based approximate string matching

This paper deals with the problem of estimating a transmitted string X ^* by processing the corresponding string Y, which is a noisy version of X ^*. We assume that Y contains substitution, insertion, and deletion errors, and that X ^* is an element of a finite (but possibly, large) dictionary, H. The best estimate X ⁺ of X ^*, is defined as that element of H which minimizes the generalized Levenshtein distance D(X, Y) between X and Y such that the total number of errors is not more than K, for all X ∈H. The trie is a data structure that offers search costs that are independent of the document size. Tries also combine prefixes together, and so by using tries in approximate string matching we can utilize the information obtained in the process of evaluating any one D(X _i , Y), to compute any other D(X _j , Y), where X _i and X _j share a common prefix. In the artificial intelligence (AI) domain, branch and bound (BB) schemes are used when we want to prune paths that have costs above a certain threshold. These techniques have been applied to prune, for example, game trees. In this paper, we present a new BB pruning strategy that can be applied to dictionary-based approximate string matching when the dictionary is stored as a trie. The new strategy attempts to look ahead at each node, c, before moving further, by merely evaluating a certain local criterion at c. The search algorithm according to this pruning strategy will not traverse inside the subtrie(c) unless there is a “hope” of determining a suitable string in it. In other words, as opposed to the reported trie-based methods (Kashyap and Oommen in Inf Sci 23(2):123–142, 1981; Shang and Merrettal in IEEE Trans Knowledge Data Eng 8(4):540–547, 1996), the pruning is done a priori before even embarking on the edit distance computations. The new strategy depends highly on the variance of the lengths of the strings in H. It combines the advantages of partitioning the dictionary according to the string lengths, and the advantages gleaned by representing H using the trie data structure. The results demonstrate a marked improvement (up to 30% when costs are of a 0/1 form, and up to 47% when costs are general) with respect to the number of operations needed on three benchmark dictionaries.     

A new real-time retinal tracking system for image-guided laser treatment

A new system is proposed for tracking sensitive areas in the retina for computer-assisted laser treatment of choroidal neovascularization (CNV). The system consists of a fundus camera using red-free illumination mode interfaced to a computer that allows real-time capturing of video input. The first image acquired is used as the reference image and utilized by the treatment physician for treatment planning. A grid of seed contours over the whole image is initiated and allowed to deform by splitting and/or merging according to preset criteria until the whole vessel tree is demarcated. Then, the image is filtered using a one-dimensional Gaussian filter in two perpendicular directions to extract the core areas of such vessels. Faster segmentation can be obtained for subsequent images by automatic registration to compensate for eye movement and saccades. An efficient registration technique is developed whereby some landmarks are detected in the reference frame then tracked in the subsequent frames. Using the relation between these two sets of corresponding points, an optimal transformation can be obtained. The implementation details of proposed strategy are presented and the obtained results indicate that it is suitable for real-time location determination and tracking of treatment positions.     

Disposition of the flavonoid quercetin in rats after single intravenous and oral doses

The pharmacokinetic and mean time tissue distribution parameters, after a single 50-mg/kg dose of quercetin administered as intravenous bolus, oral solution, and oral suspension, were determined using rat as an animal model. Following intravenous administration, the elimination rate constant and the elimination half-life were found to be 0.0062 min^-1 and 111 min, respectively. Examining the mean time tissue distribution parameters reflected a strong binding affinity of the drug molecules to both plasma and tissue proteins. In addition, the low permeability rate of drug molecules in the peripheral system was demonstrated. Following the oral administration of the drug, the extent of absorption was greater from solution than from suspension. Moreover, the solution showed a shorter T_max and a higher C_max than suspension. The absolute bioavailability for the solution was 0.275 and that for suspension was 0.162. The mean residence time (MRT) and the mean absorption time (MAT) were higher for suspension, reflecting the need for dissolving the drug in order to be absorbed. The mean (in-vivo) dissolution time (MDT_in-vivo) was 34.5 min. Thus, an oral quercetin formulation that can readily form a drug solution in the gastrointestinal tract may enhance the absorption of the drug.     

A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled. However, multiple kernel clustering for incomplete data is a critical yet challenging task. Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task, they may fail when data has a high value-missing rate, and they may easily fall into a local optimum. To address these problems, in this paper, we propose an absent multiple kernel clustering (AMKC) method on incomplete data. The AMKC method first clusters the initialized incomplete data. Then, it constructs a new multiple-kernel-based data space, referred to as K-space, from multiple sources to learn kernel combination coefficients. Finally, it seamlessly integrates an incomplete-kernel-imputation objective, a multiple-kernel-learning objective, and a kernel-clustering objective in order to achieve absent multiple kernel clustering. The three stages in this process are carried out simultaneously until the convergence condition is met. Experiments on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is significantly better than state-of-the-art competitors. Meanwhile, the proposed method gains fast convergence speed.     

From triadic closure to conference closure: the role of academic conferences in promoting scientific collaborations

An academic conference is not only a venue for publishing papers but also a nursery room for new scientific encounters. While previous research has investigated scientific collaboration mechanisms based on the triadic closure and focal closure, in this paper, we propose a new collaboration mechanism named conference closure. Conference closure means that scholars involved in a common conference may collaborate with each other in the future. We analyze the extent to which scholars will meet new collaborators from both the individual and community levels by using 22 conferences in the field of data mining extracted from DBLP digital library. Our results demonstrate the existence of conference closure and this phenomenon is more remarkable in conferences with high field rating and large scale attendees. Scholars involved in multiple conferences will encounter more collaborators from the conferences. Another interesting finding is that although most conference attendees are junior scholars with few publications, senior scholars with fruitful publications may gain more collaborations during the conference. Meanwhile, the conference closure still holds if we control the productivity homophily. Our study will shed light on evaluating the impact of a conference from the social function perspective based on the index of conference closure.     

Evaluation of Grid-Connected Photovoltaic Plants Based on Clustering Methods

Photovoltaic (PV) systems are electric power systems designed to supply usable solar power by means of photovoltaics, which is the conversion of light into electricity using semiconducting materials. PV systems have gained much attention and are a very attractive energy resource nowadays. The substantial advantage of PV systems is the usage of the most abundant and free energy from the sun. PV systems play an important role in reducing feeder losses, improving voltage profiles and providing ancillary services to local loads. However, large PV grid-connected systems may have a destructive impact on the stability of the electric grid. This is due to the fluctuations of the output AC power generated from the PV systems according to the variations in the solar energy levels. Thus, the electrical distribution system with high penetration of PV systems is subject to performance degradation and instabilities. For that, this project attempts to enhance the integration process of PV systems into electrical grids by analyzing the impact of installing grid-connected PV plants. To accomplish this, an indicative representation of solar irradiation datasets is used for planning and power flow studies of the electric network prior to PV systems installation. Those datasets contain lengthy historical observations of solar energy data, that requires extensive analysis and simulations. To overcome that the lengthy historical datasets are reduced and clustered while preserving the original data characteristics. The resultant clusters can be utilized in the planning stage and simulation studies. Accordingly, studies related to PV systems integration into the electric grid are conducted in an efficient manner, avoiding computing resources and processing times with easier and practical implementation.     

Early detection of Alzheimer’s disease based on the state-of-the-art deep learning approach: a comprehensive survey

Multimedia Tools and Applications - Alzheimer’s disease (AD) is a form of brain disorder that causes functions’ loss in a person’s daily activity. Due to the tremendous progress...     

A Survey on IP Watermarking Techniques

Intellectual property (IP) block reuse is essential for facilitating the design process of system-on-a-chip. Sharing IP designs poses significant high security risks. Recently, digital watermarking emerged as a candidate solution for copyright protection of IP blocks. In this paper, we survey and classify different techniques used for watermarking IP designs. To this end, we defined several evaluation criteria, which can also be used as a benchmark for new IP watermarking developments. Furthermore, we established a comprehensive set of requirements for future IP watermarking techniques.