Information-Bearing Noncoherently Modulated Pilots for MIMO Training

In this correspondence, we deal with noncoherent communications over multiple-input-multiple-output (MIMO) wireless links. For a Rayleigh flat block-fading channel with M transmit- and N receive-antennas and a channel coherence interval of length T, it is well known that for TGtM, or, at high signal-to-noise-ratio (SNR) rhoGt1 and Mlesmin{N,lfloorT/2rfloor}, unitary space-time modulation (USTM) is capacity-achieving, but incurs exponential demodulation complexity in T. On the other hand, conventional training-based schemes that rely on known pilot symbols for channel estimation simplify the receiver design, but they induce certain SNR loss. To achieve desirable tradeoffs between performance and complexity, we propose a novel training approach where USTM symbols over a short length T_tau(tau is a small fraction of T, and recovers part of the SNR loss experienced by the conventional training-based schemes. When rhorarrinfin and TgesT_tau ges2M=2Nrarrinfin, but the ratios alpha=M/T, alpha₁ =T_tau/T are fixed, we obtain analytical expressions of the asymptotic SNR loss for both the conventional and new training-based approaches, serving as a guideline for practical designs     

Evaluation of ageing in asphalt cores using low-field nuclear magnetic resonance

This paper introduces an innovative methodology for estimating the ageing of asphalt concrete cores without extracting the binder. Asphalt concrete samples at different ageing stages (unaged, 3-month and 6-month aged) and with different percent air voids (4%, 7% and 10%) were analysed with low-field nuclear magnetic resonance (NMR). The transverse relaxation time T₂ and relative hydrogen index (RHI) obtained from NMR measurements were related to the viscosity of the asphalt binder. The samples were analysed during cooling from 70°C to room temperature, showing increase in viscosity with decreasing temperature. There was a clear trend indicating higher viscosities in samples that were aged for a longer period and samples with higher percent air voids. The RHI and T₂ values obtained from low-field NMR measurements and the viscosity data calculated from measurements using a dynamic shear rheometer were correlated to develop a model that relates viscosity with RHI.     

Multiscale quality assessment of Global Human Settlement Layer scenes against reference data using statistical learning

A method for quality assessment of the Global Human Settlement Layer scenes against reference data is presented. It relies on two settlement metrics; the local average and gradient functions that quantify the notions of settlement density and flexible settlement limits respectively. They are both utilized as generalization functions for increasing the level of abstraction of the sets under comparison. Generalization compensates for inaccuracies of the automatic target extraction method and can be computed at multiple scales. The comparison between the target built-up layers and the reference data employs an ordered multi-scale, linear regression computing the goodness of fit measure R²$R^2$. An optimized assessment procedure is investigated in a pilot study and is further employed in a big data exercise. A newly introduced quality metric returns the agreement between automatically extracted built-up from a set of 13605 scenes and the MODIS 500 urban layer, that was found too be as high as 91% for selected sensors. A final experiment attempts a performance increase at lower scales by correlating the target layer with automatically selected training subsets. At 50 m the adjusted R²$R^2$ increases by 3% with a mean squared error improvement of 2% compared to the performance achieved without statistical learning. The experiment suggests that the GHSL assessment at a global scale can be carried out based on limited high resolution reference data of minimal spatial coverage.     

A PATH PLANNING METHOD BASED ON CELLULAR AUTOMATA FOR COOPERATIVE ROBOTS

A Cellular Automaton-based technique suitable for solving the path planning problem in a distributed robot team is outlined. Real-time path planning is a challenging task that has many applications in the fields of artificial intelligence, moving robots, virtual reality, and agent behavior simulation. The problem refers to finding a collision-free path for autonomous robots between two specified positions in a configuration area. The complexity of the problem increases in systems of multiple robots. More specifically, some distance should be covered by each robot in an unknown environment, avoiding obstacles found on its route to the destination. On the other hand, all robots must adjust their actions in order to keep their initial team formation immutable. Two different formations were tested in order to study the efficiency and the flexibility of the proposed method. Using different formations, the proposed technique could find applications to image processing tasks, swarm intelligence, etc. Furthermore, the presented Cellular Automaton (CA) method was implemented and tested in a real system using three autonomous mobile minirobots called E-pucks. Experimental results indicate that accurate collision-free paths could be created with low computational cost. Additionally, cooperation tasks could be achieved using minimal hardware resources, even in systems with low-cost robots.     

Model‐Driven and Risk‐Based Performance Analysis of Industrial Emergency Plans

This paper presents a methodology for the assessment of the performance of industrial hazardous materials emergency plans. This approach is based on a structuro‐functional model of the emergency plan, which highlights the plan's functions, resources, support services and interactions. A resource taxonomy is used to manage the complexity of the emergency response system. The model can be used both as a planning guide and for the analysis of the performance of the emergency response system, based on the risk assessment of the system. The failure probability is estimated through the plan's functions and resources fault trees. The failure severity of each function is determined by using the facility's hazard study. The failure criticality of each function is hence obtained.     

Securing Recommender Systems Against Shilling Attacks Using Social-Based Clustering

Recommender systems (RS) have been found supportive and practical in e-commerce and been established as useful aiding services. Despite their great adoption in the user communities, RS are still vulnerable to unscrupulous producers who try to promote their products by shilling the systems. With the advent of social networks new sources of information have been made available which can potentially render RS more resistant to attacks. In this paper we explore the information provided in the form of social links with clustering for diminishing the impact of attacks. We propose two algorithms, CluTr and WCluTr, to combine clustering with "trust" among users. We demonstrate that CluTr and WCluTr enhance the robustness of RS by experimentally evaluating them on data from a public consumer recommender system Epinions.com.     

Layer 1 virtual private networks: driving forces and realization by GMPLS

This article describes an emerging service for next-generation networks, layer 1 virtual private networks. L1VPNs allow customers desiring to connect multiple sites to be supported over a single shared layer 1 network. In the article we first describe the transport network's evolution and the shift in expectations of both service providers and customers. We provide an overview of the motivation for L1VPNs and examples of network usage. We follow by reviewing existing GMPLS mechanisms (addressing, discovery, and signaling) for realizing L1VPN functionality and identifying other work areas.     

Carrier‐sense‐assisted adaptive learning MAC protocols for distributed wireless LANs

A Carrier‐sense‐assisted adaptive learning MAC protocol for wireless LANs, capable of operating efficiently in bursty traffic wireless networks with unreliable channel feedback, is introduced. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by means of learning automata. At each station, the learning automaton takes into account the network feedback information in order to update the choice probability of each mobile station. The proposed protocol utilizes carrier sensing in order to reduce the collisions that are caused by different decisions at the various mobile stations due to the unreliable channel feedback. Simulation results show satisfactory performance of the proposed protocol compared to similar MAC protocols. Copyright © 2005 John Wiley & Sons, Ltd.