346,872 research outputs found
Performance Analysis and Design of Maximum Ratio Combining in Channel-Aware MIMO Decision Fusion
In this paper we present a theoretical performance analysis of the maximum
ratio combining (MRC) rule for channel-aware decision fusion over
multiple-input multiple-output (MIMO) channels for (conditionally) dependent
and independent local decisions. The system probabilities of false alarm and
detection conditioned on the channel realization are derived in closed form and
an approximated threshold choice is given. Furthermore, the channel-averaged
(CA) performances are evaluated in terms of the CA system probabilities of
false alarm and detection and the area under the receiver operating
characteristic (ROC) through the closed form of the conditional moment
generating function (MGF) of the MRC statistic, along with Gauss-Chebyshev (GC)
quadrature rules. Furthermore, we derive the deflection coefficients in closed
form, which are used for sensor threshold design. Finally, all the results are
confirmed through Monte Carlo simulations.Comment: To appear in IEEE Transactions on Wireless Communication
Wavelet Packet Division Multiplexing (WPDM)-Aided Industrial WSNs
Industrial Internet-of-Things (IIoT) involve multiple groups of sensors, each
group sending its observations on a particular phenomenon to a central
computing platform over a multiple access channel (MAC). The central platform
incorporates a decision fusion center (DFC) that arrives at global decisions
regarding each set of phenomena by combining the received local sensor
decisions. Owing to the diverse nature of the sensors and heterogeneous nature
of the information they report, it becomes extremely challenging for the DFC to
denoise the signals and arrive at multiple reliable global decisions regarding
multiple phenomena. The industrial environment represents a specific indoor
scenario devoid of windows and filled with different noisy electrical and
measuring units. In that case, the MAC is modelled as a large-scale shadowed
and slowly-faded channel corrupted with a combination of Gaussian and impulsive
noise. The primary contribution of this paper is to propose a flexible, robust
and highly noise-resilient multi-signal transmission framework based on Wavelet
packet division multiplexing (WPDM). The local sensor observations from each
group of sensors are waveform coded onto wavelet packet basis functions before
reporting them over the MAC. We assume a multi-antenna DFC where the
waveform-coded sensor observations can be separated by a bank of linear filters
or a correlator receiver, owing to the orthogonality of the received waveforms.
At the DFC we formulate and compare fusion rules for fusing received multiple
sensor decisions, to arrive at reliable conclusions regarding multiple
phenomena. Simulation results show that WPDM-aided wireless sensor network
(WSN) for IIoT environments offer higher immunity to noise by more than 10
times over performance without WPDM in terms of probability of false detection
Query-Constraint-Based Mining of Association Rules for Exploratory Analysis of Clinical Datasets in the National Sleep Research Resource
Background: Association Rule Mining (ARM) has been widely used by biomedical researchers to perform exploratory data analysis and uncover potential relationships among variables in biomedical datasets. However, when biomedical datasets are high-dimensional, performing ARM on such datasets will yield a large number of rules, many of which may be uninteresting. Especially for imbalanced datasets, performing ARM directly would result in uninteresting rules that are dominated by certain variables that capture general characteristics.
Methods: We introduce a query-constraint-based ARM (QARM) approach for exploratory analysis of multiple, diverse clinical datasets in the National Sleep Research Resource (NSRR). QARM enables rule mining on a subset of data items satisfying a query constraint. We first perform a series of data-preprocessing steps including variable selection, merging semantically similar variables, combining multiple-visit data, and data transformation. We use Top-k Non-Redundant (TNR) ARM algorithm to generate association rules. Then we remove general and subsumed rules so that unique and non-redundant rules are resulted for a particular query constraint.
Results: Applying QARM on five datasets from NSRR obtained a total of 2517 association rules with a minimum confidence of 60% (using top 100 rules for each query constraint). The results show that merging similar variables could avoid uninteresting rules. Also, removing general and subsumed rules resulted in a more concise and interesting set of rules.
Conclusions: QARM shows the potential to support exploratory analysis of large biomedical datasets. It is also shown as a useful method to reduce the number of uninteresting association rules generated from imbalanced datasets. A preliminary literature-based analysis showed that some association rules have supporting evidence from biomedical literature, while others without literature-based evidence may serve as the candidates for new hypotheses to explore and investigate. Together with literature-based evidence, the association rules mined over the NSRR clinical datasets may be used to support clinical decisions for sleep-related problems
Cluster-based cooperative subcarrier sensing using antenna diversity-based weighted data fusion
Cooperative spectrum sensing (CSS) is used in cognitive radio (CR) networks to improve the spectrum sensing performance in shadow fading environments. Moreover, clustering in CR networks is used to reduce reporting time and bandwidth overhead during CSS. Thus, cluster-based cooperative spectrum sensing (CBCSS) has manifested satisfactory spectrum sensing results in harsh environments under processing constraints. On the other hand, the antenna diversity of multiple input multiple output CR systems can be exploited to further improve the spectrum sensing performance. This paper presents the CBCSS performance in a CR network which is comprised of single- as well as multiple-antenna CR systems. We give theoretical analysis of CBCSS for orthogonal frequency division multiplexing signal sensing and propose a novel fusion scheme at the fusion center which takes into account the receiver antenna diversity of the CRs present in the network. We introduce the concept of weighted data fusion in which the sensing results of different CRs are weighted proportional to the number of receiving antennas they are equipped with. Thus, the receiver diversity is used to the advantage of improving spectrum sensing performance in a CR cluster. Simulation results show that the proposed scheme outperforms the conventional CBCSS scheme
Cooperative subcarrier sensing using antenna diversity based weighted virtual sub clustering
The idea of cooperation and the clustering amongst cognitive radios (CRs) has recently been focus of attention of research community, owing to its potential to improve performance of spectrum sensing (SS) schemes. This focus has led to the paradigm of cluster based cooperative spectrum sensing (CBCSS). In perspective of high date rate 4th generation wireless systems, which are characterized by orthogonal frequency division multiplexing (OFDM) and spatial diversity, there is a need to devise effective SS strategies. A novel CBCSS scheme is proposed for OFDM subcarrier detection in order to enable the non-contiguous OFDM (NC-OFDM) at the physical layer of CRs for efficient utilization of spectrum holes. Proposed scheme is based on the energy detection in MIMO CR network, using equal gain combiner as diversity combining technique, hard combining (AND, OR and Majority) rule as data fusion technique and antenna diversity based weighted clustering as virtual sub clustering algorithm. Results of proposed CBCSS are compared with conventional CBCSS scheme for AND, OR and Majority data fusion rules. Moreover the effects of antenna diversity, cooperation and cooperating clusters are also discussed
On Properties of Policy-Based Specifications
The advent of large-scale, complex computing systems has dramatically
increased the difficulties of securing accesses to systems' resources. To
ensure confidentiality and integrity, the exploitation of access control
mechanisms has thus become a crucial issue in the design of modern computing
systems. Among the different access control approaches proposed in the last
decades, the policy-based one permits to capture, by resorting to the concept
of attribute, all systems' security-relevant information and to be, at the same
time, sufficiently flexible and expressive to represent the other approaches.
In this paper, we move a step further to understand the effectiveness of
policy-based specifications by studying how they permit to enforce traditional
security properties. To support system designers in developing and maintaining
policy-based specifications, we formalise also some relevant properties
regarding the structure of policies. By means of a case study from the banking
domain, we present real instances of such properties and outline an approach
towards their automatised verification.Comment: In Proceedings WWV 2015, arXiv:1508.0338
Segregatory Coordination and Ellipsis in Text Generation
In this paper, we provide an account of how to generate sentences with
coordination constructions from clause-sized semantic representations. An
algorithm is developed to generate sentences with ellipsis, gapping,
right-node-raising, and non-constituent coordination constructions. Various
examples from linguistic literature will be used to demonstrate that the
algorithm does its job well.Comment: 7 pages, uses colacl.st
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