Physical layer security jamming: Theoretical limits and practical designs in wireless networks

Physical layer security has been recently recognized as a promising new design paradigm to provide security in wireless networks. In addition to the existing conventional cryptographic methods, physical layer security exploits the dynamics of fading channels to enhance secured wireless links. In this approach, jamming plays a key role by generating noise signals to confuse the potential eavesdroppers, and significantly improves quality and reliability of secure communications between legitimate terminals. This article presents theoretical limits and practical designs of jamming approaches for physical layer security. In particular, the theoretical limits explore the achievable secrecy rates of user cooperation based jamming whilst the centralized, and game theoretic based precoding techniques are reviewed for practical implementations. In addition, the emerging wireless energy harvesting techniques are exploited to harvest the required energy to transmit jamming signals. Future directions of these approaches, and the associated research challenges are also briefly outlined

The Dimerization of H₂NO

H₂NO is the prototype of aminoxyls, kinetically persistent free radicals. The potential dimerization and reaction modes of H₂NO are examined. The dimer potential energy surface features a barely metastable O–O bound species and several locally bound dimeric structures. One of these, a rectangular or rhomboid O–N–O-N ring, is a characteristic structural feature of more stable aminoxyls in the solid state. Its electronic structure is related to other four-center six-electron systems. A general picture of the weak dimer binding is constructed for these and other H₂NO dimers from a balance of four-electron repulsions between NO π electrons, and two-electron attractive interaction between the singly occupied π* orbitals of the diradical. The most stable diradical structure is a surprisingly strongly hydrogen bonded dimer diradical. The barriers separating the other isomers from this global minimum are calculated to be small

Renormalized Coupled Cluster Approaches in the Cluster-in-Molecule Framework: Predicting Vertical Electron Binding Energies of the Anionic Water Clusters (H₂O)_<i>n</i>^–

Anionic water clusters are generally considered to be extremely challenging to model using fragmentation approaches due to the diffuse nature of the excess electron distribution. The local correlation coupled cluster (CC) framework cluster-in-molecule (CIM) approach combined with the completely renormalized CR-CC­(2,3) method [abbreviated CIM/CR-CC­(2,3)] is shown to be a viable alternative for computing the vertical electron binding energies (VEBE). CIM/CR-CC­(2,3) with the threshold parameter ζ set to 0.001, as a trade-off between accuracy and computational cost, demonstrates the reliability of predicting the VEBE, with an average percentage error of ∼15% compared to the full ab initio calculation at the same level of theory. The errors are predominantly from the electron correlation energy. The CIM/CR-CC­(2,3) approach provides the ease of a black-box type calculation with few threshold parameters to manipulate. The cluster sizes that can be studied by high-level ab initio methods are significantly increased in comparison with full CC calculations. Therefore, the VEBE computed by the CIM/CR-CC­(2,3) method can be used as benchmarks for testing model potential approaches in small-to-intermediate-sized water clusters

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Supplemental material 2 for Automatic band selection algorithm for envelope analysis

<p>Supplemental material 2 for Automatic band selection algorithm for envelope analysis by Peng Xu, Ahmad Ghasemloonia and Qiao Sun in Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science</p

The Kappa coefficients of 5×10-fold cross validations with BLDA and EBLDA for the experiment dataset.

<p>The feature vectors are obtained by one-versus-one CSP methods. The performance of BLDA and EBLDA classification methods are estimated with different training sizes.</p

Cross-Interface Emulsification for Generating Size-Tunable Droplets

We report cross-interface emulsification (XiE), a simple method for the generation of monodisperse droplets of controllable volumes from picoliter to nanoliter. A device is set up in which a fused-silica capillary is vibrating across the surface of the continuous phase (mineral oil) in a reservoir, and the flow of the dispersed phase (aqueous solution) in the capillary is segmented into monodisperse droplets at the air/oil interface. We find that the volume of droplets is mainly dominated by the flow rate and vibrating frequency and not significantly influenced by other factors, such as the viscosity of the continuous phase and dispersed phase, the inner diameter of the capillary (20–100 μm), or the shape of the tip (tapered or flat). These features reflect high robustness, flexibility, and precision of XiE for on-demand volume control of droplets. The droplets automatically assemble into planar monolayer droplet arrays (PMDA) in flat-bottomed microwells of 96-well plates, offering excellent convenience for imaging of droplets. As a representative application, we carry out digital loop-mediated isothermal amplification using PMDAs with multivolume droplets for the absolute quantification of nucleic acids. Our results demonstrate that XiE is simple and controllable for the production of monodisperse size-tunable droplets, and it offers opportunities for common laboratories, even without microfabrication facilities, to perform digital quantification, single cell analysis, and other biochemical assays with high throughput

Cross-Interface Emulsification for Generating Size-Tunable Droplets

We report cross-interface emulsification (XiE), a simple method for the generation of monodisperse droplets of controllable volumes from picoliter to nanoliter. A device is set up in which a fused-silica capillary is vibrating across the surface of the continuous phase (mineral oil) in a reservoir, and the flow of the dispersed phase (aqueous solution) in the capillary is segmented into monodisperse droplets at the air/oil interface. We find that the volume of droplets is mainly dominated by the flow rate and vibrating frequency and not significantly influenced by other factors, such as the viscosity of the continuous phase and dispersed phase, the inner diameter of the capillary (20–100 μm), or the shape of the tip (tapered or flat). These features reflect high robustness, flexibility, and precision of XiE for on-demand volume control of droplets. The droplets automatically assemble into planar monolayer droplet arrays (PMDA) in flat-bottomed microwells of 96-well plates, offering excellent convenience for imaging of droplets. As a representative application, we carry out digital loop-mediated isothermal amplification using PMDAs with multivolume droplets for the absolute quantification of nucleic acids. Our results demonstrate that XiE is simple and controllable for the production of monodisperse size-tunable droplets, and it offers opportunities for common laboratories, even without microfabrication facilities, to perform digital quantification, single cell analysis, and other biochemical assays with high throughput

Activated regions of different imaging tasks of Subject K1 for the four motor imaging tasks, left hand, right hand, foot and tongue movements.

<p>The values (<i>r²</i>) in the figures are calculated according to equation (15). The optimal discrimination channels of different tasks were found to be located at C4 for left hand, C3 for right hand, Cz for foot and CP6 for tongue, respectively.</p

Neighbor-Joining (NJ) tree of four antimicrobial peptide cDNA gene families of <i>A. cerana</i> and <i>A. mellifera</i>.

<p>All the cDNA sequences for <i>A. cerana</i> were edited to begin at the initiation codon and end at the stop codon (containing complete cds). All the cDNA sequences for <i>A. mellifera</i> are downloaded from the NCBI website and edited to end in the stop codon (containing complete or partial cds). Numbers at nodes are bootstrap values based on 1000 replications. The cDNA gene names, GenBank accession numbers and the encoding peptide names are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004239#pone-0004239-t001" target="_blank">Table 1</a>.</p