Processing Optimal Sequenced Route Queries Using Voronoi Diagrams

The Optimal Sequenced Route (OSR) query strives to find a route of minimum length starting from a given source location and passing through a number of typed locations in a specific sequence imposed on the types of the locations. In this paper, we propose a pre-computation approach to OSR query in both vector and metric spaces. We exploit the geometric properties of the solution space and theoretically prove its relation to additively weighted Voronoi diagrams. Our approach recursively accesses these diagrams to incrementally build the OSR. Introducing the analogous diagrams for the space of road networks, we show that our approach is also efficiently applicable to this metric space. Our experimental results verify that our pre-computation approach outperforms the previous index-based approaches in terms of query response time. This research has been funded in part by NSF grants EEC-9529152 (IMSC ERC), IIS-0238560 (PECASE), IIS-0324955 (ITR), IIS-0534761, and unrestricted cash gifts from Google and Microsoft. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.

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Super-Streaming: A New Object Delivery Paradigm for Continuous Media Servers

A number of studies have focused on the design of continuous media, CM, (e.g., video and audio) servers to support the real-time delivery of CM objects. These systems have been deployed in local environments such as hotels, hospitals and cruise ships to support media-on-demand applications. They typically stream CM objects to the clients with the objective of minimizing the buffer space required at the client site. This objective can now be relaxed due to the availability of inexpensive storage devices at the client side. Therefore, we propose a Super-streaming paradigm that can utilize the client side resources in order to improve the utilization of the CM server. To support super-streaming, we propose a technique to enable the CM servers to deliver CM objects at a rate higher than their display bandwidth requirement. We also propose alternative admission control policies to downgrade super-streams in favor of regular streams when the resources are scarce. We demonstrate the superiority of our paradigm over streaming with both analytical and simulation models.Moreover, new distributed applications such as distant-learning, digital libraries, and home entertainment require the delivery of CM objects to geographically disbursed clients. For quality purposes, recently many studies proposed dedicated distributed architectures to support these types of applications. We extend our super-streaming paradigm to be applicable in such distributed architectures. We propose a sophisticated resource management policy to support super-streaming in the presence of multiple servers, network links and clients. Due to the complexity involved in modeling these architectures, we only evaluate the performance of super-streaming by a simulation study.     

On the performance of distributed space-time coding systems with one and two non-regenerative relays

Spatial diversity can be induced by using wireless relay stations, which cooperate by amplifying and retransmitting the information received from a source to a destination station. In this context we propose a distributed space-time coding (DSTC) system based on the Alamouti codes. We characterize the symbol error rate of systems with one and two non-regenerative relays using bounds and high signal-to-noise ratio (SNR) approximations. The asymptotic (high SNR) symbol error probability formulas are used to optimize the power allocation in the DSTC system. Furthermore, using the asymptotic symbol error probability formulas we argue that the DSTC system has at least 1.5 times the diversity achieved by point-to-point transmissions with the same bandwidth. Simulations show not only that the DSTC outperforms the amplify-and-forward cooperative system with orthogonal transmissions, but also convolutional encoded one-hop transmissions with the same information rate as the DSTC system. Assuming full channel knowledge at the source and the relays, we find an optimum cooperative system by minimizing the bit error rate of the DSTC system with one and two non-regenerative relays subject to fixed transmit energy constraints at each radio. Numerical results show that the DSTC system with two relays performs very close to the optimum cooperative system.     

Diagnosing attention disorders in a virtual classroom

The virtual classroom provides an efficient and scalable tool for conducting attention testing. The University of Southern California integrated media systems center and digital media works have partnered to develop virtual reality technology applications for the study, assessment, and rehabilitation of cognitive and functional processes. This work primarily focuses on the development of systems that address the special needs of clinical populations with central nervous system dysfunctions such as brain injury, learning disabilities, or neurological disorders.     

Feature subset selection and feature ranking for multivariate time series

Feature subset selection (FSS) is a known technique to preprocess the data before performing any data mining tasks, e.g., classification and clustering. FSS provides both cost-effective predictors and a better understanding of the underlying process that generated the data. We propose a family of novel unsupervised methods for feature subset selection from multivariate time series (MTS) based on common principal component analysis, termed CLeVer. Traditional FSS techniques, such as recursive feature elimination (RFE) and Fisher criterion (FC), have been applied to MTS data sets, e.g., brain computer interface (BCI) data sets. However, these techniques may lose the correlation information among features, while our proposed techniques utilize the properties of the principal component analysis to retain that information. In order to evaluate the effectiveness of our selected subset of features, we employ classification as the target data mining task. Our exhaustive experiments show that CLeVer outperforms RFE, FC, and random selection by up to a factor of two in terms of the classification accuracy, while taking up to 2 orders of magnitude less processing time than RFE and FC.     

Gas Sensing of NiO‐SCCNT Core–Shell Heterostructures: Optimization by Radial Modulation of the Hole‐Accumulation Layer

Hierarchical core–shell (C–S) heterostructures composed of a NiO shell deposited onto stacked‐cup carbon nanotubes (SCCNTs) are synthesized by atomic layer deposition (ALD). A film of NiO particles (0.80–21.8 nm in thickness) is uniformly deposited onto the inner and outer walls of the SCCNTs. The electrical resistance of the samples is found to increase of many orders of magnitude with the increasing of the NiO thickness. The response of NiO–SCCNT sensors toward low concentrations of acetone and ethanol at 200 °C is studied. The sensing mechanism is based on the modulation of the hole‐accumulation region in the NiO shell layer upon chemisorption of the reducing gas molecules. The electrical conduction mechanism is further studied by the incorporation of an Al₂O₃ dielectric layer at NiO and SCCNT interfaces. The investigations on NiO–Al₂O₃–SCCNT, Al₂O₃–SCCNT, and NiO–SCCNT coaxial heterostructures reveal that the sensing mechanism is strictly related to the NiO shell layer. The remarkable performance of the NiO–SCCNT sensors toward acetone and ethanol benefits from the conformal coating by ALD, large surface area of the SCCNTs, and the optimized p‐NiO shell layer thickness followed by the radial modulation of the space‐charge region.     

On a Distributed Cognitive MAC Protocol for IEEE 802.11s Wireless Mesh Networks

A distributed frequency agile medium access control (MAC) extension to the IEEE 802.11s for the next generation wireless mesh networks is proposed. The introduced protocol enhancements are capable of concurrent deployment of existing frequency opportunities in order to coordinate simultaneous data transmissions. The root concept is mainly based on the deployment of well-known ISM frequency bands, where the legacy 802.11-based wireless equipments operate, as the common control channel in order to establish contemporaneous transmissions. We apply the aforementioned key concept to the IEEE 802.11s common channel framework to attain two important goals: To improve the channel utilization using the concept of cognitive radio, and to lower the access delay. Through extensive event-driven simulations, taking into account primary user appearance in non-ISM frequency bands, performance of the proposed MAC enhancement is evaluated showing its higher efficiency compared to the existing solutions, in addition to its better wireless medium management.     

An efficient hybrid approach based on Harris Hawks optimization and imperialist competitive algorithm for structural optimization

In this paper, a new hybrid algorithm is introduced, combining two Harris Hawks Optimizer (HHO) and the Imperialist Competitive Algorithm (ICA) to achieve a better search strategy. HHO is a new population-based, nature-inspired optimization algorithm that mimics Harris Hawks cooperative behavior and chasing style in nature called surprise pounce HHO. It is a robust algorithm in exploitation, but has an unfavorable performance in exploring the search space, while ICA has a better performance in exploration; thus, combining these two algorithms produces an effective hybrid algorithm. The hybrid algorithm is called Imperialist Competitive Harris Hawks Optimization (ICHHO). The proposed hybrid algorithm's effectiveness is examined by comparing other nature-inspired techniques, 23 mathematical benchmark problems, and several well-known structural engineering problems. The results successfully indicate the proposed hybrid algorithm's competitive performance compared to HHO, ICA, and some other well-established algorithms.

     

Calculation of maximum surface settlement induced by EPB shield tunnelling and introducing most effective parameter

This study aims to predict ground surface settlement due to shallow tunneling and introduce the most affecting parameters on this phenomenon.Based on data collected from Shanghai LRT Line 2 project undertaken by TBM-EPB method,this research has considered the tunnel's geometric,strength,and operational factors as the dependent variables.At first,multiple regression(MR) method was used to propose equations based on various parameters.The results indicated the dependency of surface settlement on many parameters so that the interactions among different parameters make it impossible to use MR method as it leads to equations of poor accuracy.As such,adaptive neuro-fuzzy inference system(ANFIS),was used to evaluate its capabilities in terms of predicting surface settlement.Among generated ANFIS models,the model with all input parameters considered produced the best prediction,so as its associated R~2 in the test phase was obtained to be 0.957.The equations and models in which operational factors were taken into consideration gave better prediction results indicating larger relative effect of such factors.For sensitivity analysis of ANFIS model,cosine amplitude method(CAM) was employed; among other dependent variables,fill factor of grouting(n) and grouting pressure(P) were identified as the most affecting parameters.     

Application of a fuzzy TOPSIS method base on modified preference ratio and fuzzy distance measurement in assessment of traffic police centers performance

In this paper we have presented a TOPSIS approach based on preference ratio and an efficient fuzzy distance measurement for a Fuzzy Multiple Criteria Group Decision-Making Problem (FMCGDMP). Preference ratio with a moderate modification for negative fuzzy numbers was used as an efficient ranking method for fuzzy numbers in a relative manner. As human reasoning persuades that distances between two fuzzy numbers should be a fuzzy measure, so all distances between fuzzy numbers (i.e. distances between alternatives, Fuzzy Positive Ideal solutions, and Fuzzy Negative Ideal solutions) have been calculated as fuzzy numbers using an efficient fuzzy distance measurement. The aforementioned arguments make the proposed algorithm unique and well posed for real-life problem modeling. Moreover, the main novelties of the proposed procedure (i.e. the fuzzy distance measurement and Preference Ratio) have been developed for Generalized Fuzzy Numbers (GFNs). The proposed algorithm has efficiently been applied in assessment of traffic police centers which is treated as a FMCGDMP.