Coexistence of continuous variable QKD with intense DWDM classical channels

We demonstrate experimentally the feasibility of continuous variable quantum key distribution (CV-QKD) in dense-wavelength-division multiplexing networks (DWDM), where QKD will typically have to coexist with several co- propagating (forward or backward) C-band classical channels whose launch power is around 0dBm. We have conducted experimental tests of the coexistence of CV-QKD multiplexed with an intense classical channel, for different input powers and different DWDM wavelengths. Over a 25km fiber, a CV-QKD operated over the 1530.12nm channel can tolerate the noise arising from up to 11.5dBm classical channel at 1550.12nm in forward direction (9.7dBm in backward). A positive key rate (0.49kb/s) can be obtained at 75km with classical channel power of respectively -3dBm and -9dBm in forward and backward. Based on these measurements, we have also simulated the excess noise and optimized channel allocation for the integration of CV-QKD in some access networks. We have, for example, shown that CV-QKD could coexist with 5 pairs of channels (with nominal input powers: 2dBm forward and 1dBm backward) over a 25km WDM-PON network. The obtained results demonstrate the outstanding capacity of CV-QKD to coexist with classical signals of realistic intensity in optical networks.Comment: 19 pages, 9 figures. Revised version, to appear in New Journal of Physic

Numerical modeling of pressure transient behavior of fractured coal samples

In the current study the pressure transient behavior of various coal samples was investigated by using the finite element approach. Finite element analyses were performed on fourteen coal samples with different physical dimensions. Two pressure transients, namely Sine-6 and A-Spike pressure pulses, were used in the research study. Fluid compressibility values of CO2 and Argon were used to perform the analyses. A fracture width of 1mm was considered for each sample to investigate the influence of fluid type on coal permeability. The influence of elastic modulus of coal sample, fracture porosity, fracture width, and fluid compressibility were investigated. The finite element analyses for each sample were performed in two different ways: (a) without considering a fracture in the coal sample and (b) considering a fracture in the coal sample. The permeability of each sample was determined by comparing numerical results with available experimental data.;The calibrated finite element models were extended to determine the permeability of fractured coal samples. The numerically determined fracture permeability is much higher than the reported permeability values obtained by assuming a homogeneous medium. The results obtained from the numerical models compare well with the available experimental data on coal permeability

Spoof detection using time-delay shallow neural network and feature switching

Detecting spoofed utterances is a fundamental problem in voice-based biometrics. Spoofing can be performed either by logical accesses like speech synthesis, voice conversion or by physical accesses such as replaying the pre-recorded utterance. Inspired by the state-of-the-art \emph{x}-vector based speaker verification approach, this paper proposes a time-delay shallow neural network (TD-SNN) for spoof detection for both logical and physical access. The novelty of the proposed TD-SNN system vis-a-vis conventional DNN systems is that it can handle variable length utterances during testing. Performance of the proposed TD-SNN systems and the baseline Gaussian mixture models (GMMs) is analyzed on the ASV-spoof-2019 dataset. The performance of the systems is measured in terms of the minimum normalized tandem detection cost function (min-t-DCF). When studied with individual features, the TD-SNN system consistently outperforms the GMM system for physical access. For logical access, GMM surpasses TD-SNN systems for certain individual features. When combined with the decision-level feature switching (DLFS) paradigm, the best TD-SNN system outperforms the best baseline GMM system on evaluation data with a relative improvement of 48.03\% and 49.47\% for both logical and physical access, respectively

Full-duplex quantum coherent communication

High bandwidth requirements for data communications are currently being met by classical coherent communication using multi-level modulation of amplitude and phase of light. Alternatively, down at the level of quantum signals, coherent communication enables establishment of cryptographic keys between two legitimate users, and shows higher key exchange throughput compare to single-photon-based systems. In this work, we will examine the feasibility of full duplex quantum coherent communication, where both the transmitter and the receiver engage in quantum signal recovery as well as secure key generation

Webometric Analysis of National Library Websites of SAARC Countries

This study explored the Web Presence and Visibility of National Library Websites of SAARC Countries. Based on WISER ranking method, National library websites of SAARC countries were ranked. National Library of India secured top position in terms of Web presence. National Library of Bangladesh outscored other libraries in Webometric ranking and occupied the top position. The websites of all National libraries considered for the study lack rich files and scholarly content. Afghanistan was excluded from the study since its National library website could not be ascertained. The study recommends that Web masters should publish more content in the form of rich files and make available more scholarly content so as to improve the Web presence and visibility

Recurrent Highway Networks

Many sequential processing tasks require complex nonlinear transition functions from one step to the next. However, recurrent neural networks with 'deep' transition functions remain difficult to train, even when using Long Short-Term Memory (LSTM) networks. We introduce a novel theoretical analysis of recurrent networks based on Gersgorin's circle theorem that illuminates several modeling and optimization issues and improves our understanding of the LSTM cell. Based on this analysis we propose Recurrent Highway Networks, which extend the LSTM architecture to allow step-to-step transition depths larger than one. Several language modeling experiments demonstrate that the proposed architecture results in powerful and efficient models. On the Penn Treebank corpus, solely increasing the transition depth from 1 to 10 improves word-level perplexity from 90.6 to 65.4 using the same number of parameters. On the larger Wikipedia datasets for character prediction (text8 and enwik8), RHNs outperform all previous results and achieve an entropy of 1.27 bits per character.Comment: 12 pages, 6 figures, 3 table