Localisation determines the optimal noise rate for quantum transport

Environmental noise plays a key role in determining the efficiency of transport in quantum systems. However, disorder and localisation alter the impact of such noise on energy transport. To provide a deeper understanding of this relationship we perform a systematic study of the connection between eigenstate localisation and the optimal dephasing rate in 1D chains. The effects of energy gradients and disorder on chains of various lengths are evaluated and we demonstrate how optimal transport efficiency is determined by both size-independent, as well as size-dependent factors. By discussing how size-dependent influences emerge from finite size effects we establish when these effects are suppressed, and show that a simple power law captures the interplay between size-dependent and size-independent responses. Moving beyond phenomenological pure dephasing, we implement a finite temperature Bloch-Redfield model that captures detailed balance. We show that the relationship between localisation and optimal environmental coupling strength continues to apply at intermediate and high temperature but breaks down in the low temperature limit.

Characterization of Silicon-Photomultipliers for a Cosmic Muon Veto detector

A Cosmic Muon Veto (CMV) detector using extruded scintillators is being designed around the mini-Iron Calorimeter detector at the transit campus of the India-based Neutrino Observatory, Madurai for measuring its efficiency at shallow depth underground experiments. The scintillation signal is transmitted through a Wavelength Shifting (WLS) fibre and readout by Hamamatsu Silicon-Photomultipliers (SiPMs). A Light Emitting Diode (LED) system is included on the front-end readout for in-situ calibration of the gain of each SiPM. A characterization system was developed for the measurement of gain and choice of the overvoltage (V ov) of SiPMs using the LED as well as a cosmic muon telescope. The V ov is obtained by studying the noise rate, the gain of the SiPM, and the muon detection efficiency. In case of any malfunction of the LED system during the operation, the SiPM can also be calibrated with the noise data as well as using radioactive sources. This paper describes the basic characteristics of the SiPM and the comparison of the calibration results using all three methods, as well as the V ov of the SiPMs and muon selection criteria for the veto detector.

De-noising of an Image using Fuzzy Inference System and Performance Comparison with the Conventional system

Noise prevailing in the image can diminish the physical appearance of the objects existing within the image and make them frail. Present research emphasizes a fuzzy inference system eradicating several types of noise from the images. The investigation implies the utilization of different levels of Salt & Pepper noise. Followed by the pixel determination applying a mask, the disparity between the focused pixel's intensity with the minimum, average, and maximum power of the chosen window has been determined. Since two fuzzy valued outputs have been obtained to match them, the one provided by a low noise rate would demonstrate the more accurate filter for the selected window. Utilizing Matlab the Peak Signal-to-Noise ratio (PSNR) and Mean Square Error (MSE) are determined for evaluating the noise reduction performance. However, these values of PSNR and MSE obtained from this research are also compared with the conventional fuzzy filtering system.

Identification of Dynamic Models by Using Metaheuristic Algorithms

A modified versions of metaheuristic algorithms are presented to compare their performance in identifying the structural dynamic systems. Genetic algorithm, biogeography based optimization algorithm, ant colony optimization algorithm and artificial bee colony algorithm are heuristic algorithms that have robustness and ease of implementation with simple structure. Different algorithms were selected some from evolution algorithms and other from swarm algorithms to boost the equilibrium of global searches and local searches, to compare the performance and investigate the applicability of proposed algorithms to system identification; three cases are suggested under different conditions concerning data availability, different noise rate and previous familiarity of parameters. Simulation results show these proposed algorithms produce excellent parameter estimation, even with little measurements and a high noise rate.

Pediatric voice-related quality of life and acoustic analysis of voice: a study in schoolchildren

ABSTRACT Purpose: to assess the self-reported voice-related quality of life of schoolchildren without voice complaints and correlate it to acoustic parameters of voice. Methods: the research population comprised 31 children, mean age 6.5 (±0.17) years. The children’s perception of their voice-related quality of life was verified with the Pediatric Voice-Related Quality of Life Survey, which has 10 closed-ended questions and three domains. The acoustic parameters assessed were fundamental frequency, jitter, shimmer, glottal-to-noise excitation ratio, and noise rate, besides the phonatory deviation diagram, based on the analysis of the emission of the sustained vowel /ɛ/ for 5 seconds. Results: all the children obtained scores close to 100% in the three domains of the Pediatric Voice-Related Quality of Life Survey. As for the acoustic parameters, most of them presented abnormal values in the phonatory deviation diagram and in shimmer. There was a difference between girls and boys only in fundamental frequency. Conclusion: the pediatric self-reported voice-related quality of life of the children studied had a positive impact, despite the acoustic changes found in the voices. There was no correlation between the pediatric voice-related quality of life and the acoustic parameters in voice in the group studied.

On The Performance of MIMO AF Full-Duplex Joint Antenna and Relay Selection Network

This paper investigates a multi-input multi-output (MIMO) multi-relay amplify-and-forward (AF) full-duplex (FD) network using optimal joint relay and antenna selection over Rayleigh block fading channels. For system performance analysis, outage probability (OP) is theoretically analyzed to obtain exact and asymptotic expressions of OP where asymptotic derivations using the assumption of high signal-to-noise rate (SNR) regime. Numerical results are provided to verify the validity of the derived OP expressions by comparing with the related empirical performance. The paper shows that under different scenarios the exact OP expression values are close the related empirical ones while the approximated OP expression values approach to the empirical OP under high SNR conditions.

An Optimization Method for Simultaneous Wireless Power and Data Transfer Systems

Simultaneous wireless power and data transfer (SWPDT) technology is usually needed in wireless power transfer (WPT) systems. In this paper, to achieve the efficient and stable SWPDT, the signal to noise rate (SNR) is first optimized through the analysis of data transmission gain and the interference of power transfer on data transfer. In order to reduce the interference of power channel to data transfer channel, the compensation topology of double-sided LCC and the band-stop networks are used. To solve the problem of SNR limitation, the coupling relationships between each circuit are taken into consideration and a matching method of resonance parameters to improve the SNR is presented. Based on the relationship between SNR and the parameters of data transfer channel, an optimization design method focused on parameters of data transfer channel for improving the SNR is presented. Finally, the feasibility and the correctness of the parameter design method proposed in this paper is verified by simulation and experiment.

Nonlinear Dynamics of a Cavity Containing a Two-Mode Coherent Field Interacting with Two-Level Atomic Systems

This study analytically explored two coupled two-level atomic systems (TLAS) as two qubits interacting with two modes of an electromagnetic field (EMF) cavity via two-photon transitions in the presence of dipole–dipole interactions between the atoms and intrinsic damping. Using special unitary su(1,1) Lie algebra, the general solution of an intrinsic noise model is obtained when an EMF is initially in a generalized coherent state. We investigated the population inversion of two TLAS and the generated quantum coherence of some partitions (including the EMF, two TLAS, and TLAS–EMF). It is possible to generate quantum coherence (mixedness and entanglement) from the initial pure state. The robustness of the quantum coherence produced and the sudden appearance and disappearance of coherence depended not only on dipole–dipole coupling but also on the intrinsic noise rate. The growth of mixedness and entanglement may be enhanced by increasing dipole–dipole coupling, leading to more robustness against intrinsic noise.

Performance of LED for line-of-sight (LoS) underwater wireless optical communication system

Underwater wireless optical communications (UWOC) recently emerge as a solution to the problem of underwater communication for link with a high data rate, low delay, safety, and high immunity. In this study, the line of sight (LoS) method based on LED used for UWOC with different modulation schemes. The bit error rate (BER), signal-to-noise rate, and quality factor are used for assessing system performance and link quality. Besides the effect of transmitting angle, distance link (d), and transmitting power (PT) are analysed. Results show that 8-pulse position modulation (PPM) is the best modulation scheme for achieving a good link in the LoS method.

On defending against label flipping attacks on malware detection systems

Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and Internet of Thing (IoT). Recent work in the literature has suggested using the K-nearest neighboring algorithm to defend against such attacks. However, such an approach can suffer from low to miss-classification rate accuracy. In this paper, we design an architecture to tackle the Android malware detection problem in IoT systems. We develop an attack mechanism based on silhouette clustering method, modified for mobile Android platforms. We proposed two convolutional neural network-type deep learning algorithms against this Silhouette Clustering-based Label Flipping Attack. We show the effectiveness of these two defense algorithms—label-based semi-supervised defense and clustering-based semi-supervised defense—in correcting labels being attacked. We evaluate the performance of the proposed algorithms by varying the various machine learning parameters on three Android datasets: Drebin, Contagio, and Genome and three types of features: API, intent, and permission. Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19% accuracy when compared with the state-of-the-art method in the literature.