Monte Carlo Simulation of Stochastic Differential Equation to Study Information Geometry

Information Geometry is a useful tool to study and compare the solutions of a Stochastic Differential Equations (SDEs) for non-equilibrium systems. As an alternative method to solving the Fokker–Planck equation, we propose a new method to calculate time-dependent probability density functions (PDFs) and to study Information Geometry using Monte Carlo (MC) simulation of SDEs. Specifically, we develop a new MC SDE method to overcome the challenges in calculating a time-dependent PDF and information geometric diagnostics and to speed up simulations by utilizing GPU computing. Using MC SDE simulations, we reproduce Information Geometric scaling relations found from the Fokker–Planck method for the case of a stochastic process with linear and cubic damping terms. We showcase the advantage of MC SDE simulation over FPE solvers by calculating unequal time joint PDFs. For the linear process with a linear damping force, joint PDF is found to be a Gaussian. In contrast, for the cubic process with a cubic damping force, joint PDF exhibits a bimodal structure, even in a stationary state. This suggests a finite memory time induced by a nonlinear force. Furthermore, several power-law scalings in the characteristics of bimodal PDFs are identified and investigated.     

Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas

This paper studies the privacy of wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect transmissions of interest. There exists one transmitter that transmits to its receiver in the presence of an eavesdropper. In the meantime, a cooperative jammer (CJ) with multiple antennas transmits carefully crafted adversarial perturbations over the air to fool the eavesdropper into classifying the received superposition of signals as noise. While generating the adversarial perturbation at the CJ, multiple antennas are utilized to improve the attack performance in terms of fooling the eavesdropper. Two main points are considered while exploiting the multiple antennas at the adversary, namely the power allocation among antennas and the utilization of channel diversity. To limit the impact on the bit error rate (BER) at the receiver, the CJ puts an upper bound on the strength of the perturbation signal. Performance results show that this adversarial perturbation causes the eavesdropper to misclassify the received signals as noise with a high probability while increasing the BER at the legitimate receiver only slightly. Furthermore, the adversarial perturbation is shown to become more effective when multiple antennas are utilized.     

Enhanced output voltage of nano energy harvester with diverse textiles

Abstract

We demonstrate the electrical properties of nano energy harvesters (NEHs) with various textiles for smart textiles that can be applied to the next generation wearable electronics. Output voltages and currents of NEHs with various fabrics, such as a cotton, rayon and wool that have different triboelectricity were measured. Cotton, rayon and wool shows the maximum output voltages of 1.250, 3.313 and 4.063 V, respectively. In addition, output currents of those textiles were 0.75, 4.4, 1.063 μA, respectively. Wool, in particular, which has the highest triboelectricity of 350 V exhibits the highest output voltage.     

A New Reversible Colorimetric Chemosensor Based on Julolidine Moiety for Detecting F−

Journal of Fluorescence - We synthesized an original reversible colorimetric chemosensor PDJ...     

Investigation on the magnetic and Mössbauer spectroscopy of 57Fe doped LiMnPO4

Journal of Radioanalytical and Nuclear Chemistry - The 57Fe doped LiMnPO4 cathode with potential applications in Li-ion batteries was prepared by solid-state reaction. The magnetic susceptibility...     

Accuracy analysis on concentration determination of molecular iodine in gamma irradiated solutions

Journal of Radioanalytical and Nuclear Chemistry - The gamma irradiated aqueous samples containing I2 and I? were measured by two different analysis methods using an UV–VIS...     

Effect of gamma irradiation on polypropylene yarns under air and water

Journal of Radioanalytical and Nuclear Chemistry - Polypropylene (PP) filters are used for the treatment of radioactive liquid waste containing gamma nuclides such as Co-60, and filter physical and...     

Choline chloride based deep eutectic solvents for the lignocellulose nanofibril production from Mongolian oak (Quercus mongolica)

Cellulose - Lignocellulose nanofibrils (LCNFs) were produced from the biomass of Mongolian oak for the first time using deep eutectic solvent pretreatment and mechanical defibrillation. Three...     

The recent progress of functionally graded CNT reinforced composites and structures

In the last decade,the functionally graded carbon nanotube reinforced composites(FG-CNTRCs)have attracted considerable interest due to their excellent mechanical properties,and the structures made of FG-CNTRCs have found broad potential applications in aerospace,civil and ocean engineering,automotive industry,and smart structures.Here we review the literature regarding the mechanical analysis of bulk CNTR nanocomposites and FG-CNTRC structures,aiming to provide a clear picture of the mechanical modeling and properties of FG-CNTRCs as well as their composite structures.The review is organized as follows:(1)a brief introduction to the functionally graded materials(FGM),CNTRCs and FG-CNTRCs;(2)a literature review of the mechanical modeling methodologies and properties of bulk CNTRCs;(3)a detailed discussion on the mechanical behaviors of FG-CNTRCs;and(4)conclusions together with a suggestion of future research trends.