Distributed Clustering in Cognitive Radio Ad Hoc Networks Using Soft-Constraint Affinity Propagation

Absence of network infrastructure and heterogeneous spectrum availability in cognitive radio ad hoc networks (CRAHNs) necessitate the self-organization of cognitive radio users (CRs) for efficient spectrum coordination. The cluster-based structure is known to be effective in both guaranteeing system performance and reducing communication overhead in variable network environment. In this paper, we propose a distributed clustering algorithm based on soft-constraint affinity propagation message passing model (DCSCAP). Without dependence on predefined common control channel (CCC), DCSCAP relies on the distributed message passing among CRs through their available channels, making the algorithm applicable for large scale networks. Different from original soft-constraint affinity propagation algorithm, the maximal iterations of message passing is controlled to a relatively small number to accommodate to the dynamic environment of CRAHNs. Based on the accumulated evidence for clustering from the message passing process, clusters are formed with the objective of grouping the CRs with similar spectrum availability into smaller number of clusters while guaranteeing at least one CCC in each cluster. Extensive simulation results demonstrate the preference of DCSCAP compared with existing algorithms in both efficiency and robustness of the clusters

STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger demand prediction based on a graph and use a hierarchical graph convolutional structure to capture both spatial and temporal correlations simultaneously. Our model consists of three parts: 1) a long-term encoder to encode historical passenger demands; 2) a short-term encoder to derive the next-step prediction for generating multi-step prediction; 3) an attention-based output module to model the dynamic temporal and channel-wise information. Experiments on three real-world datasets show that our model consistently outperforms many baseline methods and state-of-the-art models.Comment: 7 page

Coulomb Drag of Massless Fermions in Graphene

Using a novel structure, consisting of two, independently contacted graphene single layers separated by an ultra-thin dielectric, we experimentally measure the Coulomb drag of massless fermions in graphene. At temperatures higher than 50 K, the Coulomb drag follows a temperature and carrier density dependence consistent with the Fermi liquid regime. As the temperature is reduced, the Coulomb drag exhibits giant fluctuations with an increasing amplitude, thanks to the interplay between coherent transport in the graphene layer and interaction between the two layers.Comment: 5 pages, 5 figure

Substitution principle for CLT of linear spectral statistics of high-dimensional sample covariance matrices with applications to hypothesis testing

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CLT for linear spectral statistics of a rescaled sample precision matrix

postprin

CLT for eigenvalue statistics of large-dimensional general Fisher matrices with applications

Random Fisher matrices arise naturally in multivariate statistical analysis and understanding the properties of its eigenvalues is of primary importance for many hypothesis testing problems like testing the equality between two covariance matrices, or testing the independence between sub-groups of a multivariate random vector. Most of the existing work on random Fisher matrices deals with a particular situation where the population covariance matrices are equal. In this paper, we consider general Fisher matrices with arbitrary population covariance matrices and develop their spectral properties when the dimensions are proportionally large compared to the sample size. The paper has two main contributions: first the limiting distribution of the eigenvalues of a general Fisher matrix is found and second, a central limit theorem is established for a wide class of functionals of these eigenvalues. Applications of the main results are also developed for testing hypotheses on high-dimensional covariance matrices.published_or_final_versio

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subjects active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems, is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large- scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the- art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.Comment: 10 page

Numerical simulation study on the influence of current stabilizer on tundish flow field

The software ProCAST is used to simulate the flow field in the tundish. In the tundish without current regulator, the long nozzle injection impacts the tundish bottom directly, which is easy to cause damage to the refractory. The molten steel flow diffuses far along the bottom, and the disturbance is large near the submerged nozzle. The maximum speed at the bottom can reach 0.8m/s, and the general flow trend is that the injection flow direction is upward from the bottom to both sides, which is not conducive to the upward floating of inclusions. In the tundish with current stabilizer, the velocity of the injection decreases rapidly under the action of the liquid steel contained in the current stabilizer. Due to the attenuation of injection kinetic energy, the scouring effect on the bottom of tundish is obviously smaller and the flow field is more stable

Effect of fire exposure on cracking, spalling and residual strength of fly ash geopolymer concrete

Fly ash based geopolymer is an emerging alternative binder to cement for making concrete. The cracking, spalling and residual strength behaviours of geopolymer concrete were studied in order to understand its fire endurance, which is essential for its use as a building material. Fly ash based geopolymer and ordinary portland cement (OPC) concrete cylinder specimens were exposed to fires at different temperatures up to 1000 °C, with a heating rate of that given in the International Standards Organization (ISO) 834 standard. Compressive strength of the concretes varied in the range of 39–58 MPa. After the fire exposures, the geopolymer concrete specimens were found to suffer less damage in terms of cracking than the OPC concrete specimens. The OPC concrete cylinders suffered severe spalling for 800 and 1000 °C exposures, while there was no spalling in the geopolymer concrete specimens. The geopolymer concrete specimens generally retained higher strength than the OPC concrete specimens. The Scanning Electron Microscope (SEM) images of geopolymer concrete showed continued densification of the microstructure with the increase of fire temperature. The strength loss in the geopolymer concrete specimens was mainly because of the difference between the thermal expansions of geopolymer matrix and the aggregates