Most of the existing algorithms cannot effectively solve the data sparse problem of trajectory prediction. This paper proposes a novel sparse trajectory prediction method based on L-Z entropy estimation. Firstly, the moving region of trajectories is divided into a two-dimensional plane grid graph, and then the original trajectories are mapped to the grid graph so that each trajectory can be represented as a grid sequence. Secondly, an L-Z entropy estimator is used to calculate the entropy value of each grid sequence, and then the trajectory which has a comparatively low entropy value is segmented into several sub-trajectories. The new trajectory space is synthesised by these sub-trajectories based on trajectory entropy. The trajectory synthesis can not only resolve the sparse problem of trajectory data, but also make the new trajectory space more credible. In addition, the trajectory scale is limited in a certain range. Finally, under the new trajectory space, Markov model and Bayesian Inference is applied to trajectory prediction with data sparsity. The experiments based on the taxi trajectory dataset of Microsoft Research Asia show the proposed method can make an effective prediction for the sparse trajectory. Compared with the existing methods, our method needs a smaller trajectory space and provides much wider predicting range, faster predicting speed and better predicting accuracy.

Considering the obvious bias of the traditional interpolation method, a novel time delay estimation (TDE) interpolation method with sub-sample accuracy is presented in this paper. The proposed method uses a generalized extended approximation method to obtain the objection function. Then the optimized interpolation curve is generated by Second-order Cone programming (SOCP). Finally the optimal TDE can be obtained by interpolation curve. The delay estimate of proposed method is not forced to lie on discrete samples and the sample points need not to be on the interpolation curve. In the condition of the acceptable computation complexity, computer simulation results clearly indicate that the proposed method is less biased and outperforms the other interpolation algorithms in terms of estimation accuracy.

A method for increasing alignment tolerance in simple multiple-stream transmission is described. Its use of π-shifted antenna directivity phase enables it to cancel interference even when antenna placement deviations occur. The interference cancellation by using π-shifted directivities provides higher alignment tolerance than that with conventional fixed weight methods. It also provides smaller channel gain variation than can be obtained using fixed weights even when antenna displacement occurs. An objective function is described that is determined by the alignment tolerance. The function is defined to maximize the alignment tolerance. The method's validity is confirmed by an experimental analysis of two-stream transmission in which the alignment tolerance of the proposed method is compared to that of conventional fixed weight methods.

Distributed compressive video sensing (DCVS) is an emerging low-complexity video coding framework which integrates the merits of distributed video coding (DVC) and compressive sensing (CS). In this paper, we propose a novel rate-distortion optimized DCVS codec, which takes advantage of a rate-distortion optimization (RDO) model based on the estimated correlation noise (CN) between a non-key frame and its side information (SI) to determine the optimal measurements allocation for the non-key frame. Because the actual CN can be more accurately recovered by our DCVS codec, it leads to more faithful reconstruction of the non-key frames by adding the recovered CN to the SI. The experimental results reveal that our DCVS codec significantly outperforms the legacy DCVS codecs in terms of both objective and subjective performance.

Designing a backbone IP network, especially to support both unicast and multicast traffic under delay constraints, is a difficult problem. Real network design must consider cost, performance and reliability. Therefore, a simulator can help a network designer to test the functionality of the network before the implementation. This paper proposes a heuristic design algorithm called D-MENTOR, and the algorithm was developed by programming based on Mesh Network Topological Optimization and Routing Version 2 (MENTOR-II) to integrate as a new module of DElite tool. The simulation results show that, in almost all test cases, the proposed algorithm yields lower installation cost.

Some diffusive and recurrence properties of Lorentz Lattice Gas Cellular Automata (LLGCA) have been expensively studied in terms of the densities of some of the left/right static/flipping mirrors/rotators. In this paper, for any combination S of these well known scatters, we study the computational complexity of the following problem which we call PERIODICITY on the S-model: given a finite configuration that distributes only those scatters in S, whether a particle visits the starting position periodically or not. Previously, the flipping mirror model and the occupied flipping rotator model have been shown unbounded, i.e. the process is always diffusive [17]. On the other hand, PERIODICITY is shown PSPACE-complete in the unoccupied flipping rotator model [21]. In this paper, we show that PERIODICITY is PSPACE-compete in any S-model that is neither occupied, unbounded, nor static. Particularly, we prove that PERIODICITY in any unoccupied and bounded model containing flipping mirror is PSPACE-complete.

An r-gathering of customers C to facilities F is an assignment A of C to open facilities F' ⊂ F such that r (≥ 2) or more customers are assigned to each open facility. (Each facility needs enough number of customers for its opening.) Then the r-gathering problem finds an r-gathering minimizing a designated cost. Armon gave a simple 3-approximation algorithm for the r-gathering problem and proved that with assumption P ≠ NP the problem cannot be approximated within a factor of less than 3 for any r ≥ 3. The running time of the 3-approximation algorithm is O(|C||F|+r|C|+|C|log|C|)). In this paper we improve the running time of the algorithm by (1) removing the sort in the algorithm and (2) designing a simple but efficient data structure.

A generalized covariance matrix taper (GCMT) model is proposed to enhance the performance of knowledge-aided space-time adaptive processing (KA-STAP) under sea clutter environments. In KA-STAP, improving the accuracy degree of the a priori clutter covariance matrix is a fundamental issue. As a crucial component in the a priori clutter covariance matrix, the taper matrix is employed to describe the internal clutter motion (ICM) or other subspace leakage effects, and commonly constructed by the classical covariance matrix taper (CMT) model. This work extents the CMT model into a generalized CMT (GCMT) model with a greater degree of freedom. Comparing it with the CMT model, the proposed GCMT model is more suitable for sea clutter background applications for its improved flexibility. Simulation results illustrate the efficiency of the GCMT model under different sea clutter environments.

Optimizating the deployment of wireless sensor networks, which is one of the key issues in wireless sensor networks research, helps improve the coverage of the networks and the system reliability. In this paper, we propose an evolutionary algorithm based on modified t-distribution for the wireless sensor by introducing a deployment optimization operator and an intelligent allocation operator. A directed perturbation operator is applied to the algorithm to guide the evolution of the node deployment and to speed up the convergence. In addition, with a new geometric sensor detection model instead of the old probability model, the computing speed is increased by 20 times. The simulation results show that when this algorithm is utilized in the actual scene, it can get the minimum number of nodes and the optimal deployment quickly and effectively.Compared with the existing mainstream swarm intelligence algorithms, this method has satisfied the need for convergence speed and better coverage, which is closer to the theoretical coverage value.

We have studied ultra-low-power superconductor circuits using adiabatic quantum flux parametron (AQFP) logic. Latches, which store logic data in logic circuits, are indispensable logic elements in the realization of AQFP computing systems. Among them, feedback latches, which hold data by using a feedback loop, have advantages in terms of their wide operation margins and high stability. Their drawbacks are their large junction counts and long latency. In this paper, we propose a majority gate-based feedback latch for AQFP logic with a reduced number of junctions. We designed and fabricated the proposed AQFP latches using a standard National Institute of Advanced Industrial Science and Technology (AIST) process. The measurement results showed that the feedback latches operate with wide operation margins that are comparable with circuit simulation results.

Connection Service Providers (CSP) are wishing to increase their Return on Investment (ROI) by utilizing the data assets generated by tracking subscriber behaviors. This results in the ability to apply personalized policies, monitor and control the service traffic to subscribers and gain more revenue through the usage of subscriber data with ad networks. In this paper, a system is proposed to monitor and analyze the Internet access of the subscribers of a regional SP in order to classify the subscribers into interest categories from the Interactive Advertising Bureau (IAB) categories. The study employs the categorization engine to build category vectors for all individuals using Internet services through the subscription. The proposal makes it easy to detect changes in the interests of individuals/subscribers over time.

This paper presents analysis and simple design guideline for ThruChip Interface (TCI) as located by LC-VCO which is used in high-speed SoC. The electromagnetic interference (EMI) from TCI channels to LC-VCO is analyzed and evaluated. The accuracy of the analysis and design guidelines is verified through the test-chip verification.

In this paper, we investigate a preference-aware multicast mechanism in active array aided LTE (Long Term Evolution) networks. An active antenna system can direct vertical beams in different horizontal and vertical directions, so the amount of energy delivered is more concentrated on the target users. The active array provides each multicast group with an individual beam with specific downtilt delivering shared video to all users in the group. For the multicast system, the objective of our proposed resource allocation scheme is to maximize the total throughput, subject to the constraints of power, subcarrier and antenna downtilt, as well as horizontal angles and the vertical half power bandwidth. To solve the problem, individual beams are steered for multicast groups. Furthermore, a novel subcarrier assignment scheme is proposed to enhance the spectrum resource utilization, and the optimal power allocation is obtained by virtue of Lagrangian method. Simulation results demonstrate the throughput and the spectral efficiency enhancement of our proposed scheme over other conditional schemes.

User-based and item-based collaborative filtering (CF) are two of the most important and popular techniques in recommender systems. Although they are widely used, there are still some limitations, such as not being well adapted to the sparsity of data sets, failure to consider the hierarchical structure of the items, and changes in users' interests when calculating the similarity of items. To overcome these shortcomings, we propose an evolutionary approach based on hierarchical structure for dynamic recommendation system named Hierarchical Temporal Collaborative Filtering (HTCF). The main contribution of the paper is displayed in the following two aspects. One is the exploration of hierarchical structure between items to improve similarity, and the other is the improvement of the prediction accuracy by utilizing a time weight function. A unique feature of our method is that it selects neighbors mainly based on hierarchical structure between items, which is more reliable than co-rated items utilized in traditional CF. To the best of our knowledge, there is little previous work on researching CF algorithm by combining object implicit or latent object-structure relations. The experimental results show that our method outperforms several current recommendation algorithms on recommendation accuracy (in terms of MAE).

We describe a calculation tool and modeling methods to find self and mutual inductance and current distribution in superconductive multilayer circuit layouts. Accuracy of the numerical solver is discussed and compared with experimental measurements. Effects of modeling parameter selection on calculation results are shown, and we make conclusions on the selection of modeling parameters for fast but sufficiently accurate calculations when calibration methods are used. Circuit theory for the calculation of branch impedances from the output of the numerical solver is discussed, and compensation for solution difficulties is shown through example. We elaborate on the construction of extraction models for superconductive integrated circuits, with and without resistive branches. We also propose a method to calculate current distribution in a multilayer circuit with multiple bias current feed points. Finally, detailed examples are shown where the effects of stacked vias, bias pillars, coupling, ground connection stacks and ground return currents in circuit layouts for the AIST advanced process (ADP2) and standard process (STP2) are analyzed. We show that multilayer inductance and current distribution extraction in such circuits provides much more information than merely branch inductance, and can be used to improve layouts; for example through reduced coupling between conductors.

Residents living in a nursing home usually have established medical histories in multiple sources, and most previous medicine management systems have only focused on the integration of prescriptions and the identification of repeated drug uses. Therefore, a comprehensive medicine management system is proposed to integrate medical information from different sources. The proposed system not only detects inappropriate drugs automatically but also allows users to input such information for any non-prescription medicines that the residents take. Every participant can fully track the residents' latest medicine use online and in real time. Pharmacists are able to issue requests for suggestions on medicine use, and residents can also have a comprehensive understanding of their medicine use. The proposed scheme has been practically implemented in a nursing home in Taiwan. The evaluation results show that the average time to detect an inappropriate drug use and complete a medicine record is reduced. With automatic and precise comparisons, the repeated drugs and drug side effects are identified effectively such that the amount of medicine cost spent on the residents is also reduced. Consequently, the proactive feedback, real-time tracking, and interactive consulting mechanisms bind all parties together to realize a comprehensive medicine management system.

For a service-oriented architecture-based system, the problem of synthesizing a concrete model (i.e., a behavioral model) for each peer configuring the system from an abstract specification — which is referred to as choreography — is known as the choreography realization problem. In this paper, we consider the condition for the behavioral model when choreography is given by an acyclic relation. A new notion called re-constructible decomposition of acyclic relations is introduced, and a necessary and sufficient condition for a decomposed relation to be re-constructible is shown. The condition provides lower and upper bounds of the acyclic relation for the behavioral model. Thus, the degree of freedom for behavioral models increases; developing algorithms for synthesizing an intelligible model for users becomes possible. It is also expected that the condition is applied to the case where choreography is given by a set of acyclic relations.

We consider the problem of deciding whether a query can be rewritten by a nondeterministic view. It is known that rewriting is decidable if views are given by single-valued non-copying devices such as compositions of single-valued extended linear top-down tree transducers with regular look-ahead, and queries are given by deterministic MSO tree transducers. In this paper, we extend the result to the case that views are given by nondeterministic devices that are not always single-valued. We define two variants of rewriting: universal preservation and existential preservation, and discuss the decidability of them.