Nanostructured Dielectric Fractals on Resonant Plasmonic Metasurfaces for Selective and Sensitive Optical Sensing of Volatile Compounds

Advances in the understanding and fabrication of plasmonic nanostructures have led to a plethora of unprecedented optoelectronic and optochemical applications. Plasmon resonance has found widespread use in efficient optical transducers of refractive index changes in liquids. However, it has proven challenging to translate these achievements to the selective detection of gases, which typically adsorb non‐specifically and induce refractive index changes below the detection limit. Here, it's shown that integration of tailored fractals of dielectric TiO₂ nanoparticles on a plasmonic metasurface strongly enhances the interaction between the plasmonic field and volatile organic molecules and provides a means for their selective detection. Notably, this superior optical response is due to the enhancement of the interaction between the dielectric fractals and the plasmonic metasurface for thickness of up to 1.8 μm, much higher than the evanescent plasmonic near‐field (≈30 nm) . Optimal dielectric–plasmonic structures allow measurements of changes in the refractive index of the gas mixture down to <8 × 10^?6 at room temperature and selective identification of three exemplary volatile organic compounds. These findings provide a basis for the development of a novel family of dielectric–plasmonic materials with application extending from light harvesting and photocatalysts to contactless sensors for noninvasive medical diagnostics.     

New attacks on secret sharing-based data outsourcing: toward a resistant scheme

In this paper, two new practical attacks on some secret sharing-based data outsourcing schemes are first introduced, and several other security and performance issues with the existing schemes are also explored. The existing and new attacks exploit the information about the share range boundaries or the correspondences between the secret values and shares. A range expansion technique is then proposed to thwart one of the attacks. It expands the ranges in every range predicate in the submitted queries in order to hide the share range boundaries from any query observer. Next, a mapping method is proposed to thwart the other attacks. It maps each secret value to a mapping value using a secret one-to-many mapping with a finite set of linear mapping rules so that the tuples of shares are generated from the mapping values rather than directly from the secret values. The proposed mapping method works as an additional layer of security and addresses any attack based on the correspondences between the secret values and shares. At the same time, it preserves the homomorphism property of secret sharing. Finally, a new secure data outsourcing scheme is elaborated on secret sharing, the proposed mapping method, and the proposed range expansion technique. The proposed scheme is resistant to various attacks and also some inferences. It supports the fully server-side or a partially server-side query execution of most types of queries. The experimental results confirm that the proposed scheme is quite practical and efficient.

     

A flexible energy management approach for smart healthcare on the internet of patients (IoP)

Considering the importance of biosensors on the Internet of the patient body that collect vital signs and transmit them to the coordinator, energy consumption and network lifetime are essential challenges in these networks. This paper, it has been tried to present a method based on adapting sampling rate through patient’s risk and discovered pattern by employing an intelligence method based on adaptive neuro-fuzzy inference system, interpolation function, and a biosensor patron. It causes restricting sensed and transmitted data to the coordinator. In the proposed schema, three methods containing Grid partitioning, Subtractive Clustering and fuzzy c-means have been used in two modes, including hybrid and error backpropagation, to predict the individual’s behavioral pattern and determine the patient's risk, attentively. The simulation results in MATLAB R2018b show that the proposed method reduces the network communications. It has improved energy consumption by up to three times and also reduced traffic by more than 80% compared to similar methods.

     

Improving the readability and saliency of abstractive text summarization using combination of deep neural networks equipped with auxiliary attention mechanism

Rapid and exponential development of textual data in recent years has yielded to the need for automatic text summarization models which aim to automatically condense a piece of text into a shorter version. Although various unsupervised and machine learning-based approaches have been introduced for text summarization during the last decades, the emergence of deep learning has made remarkable progress in this field. However, deep learning-based text summarization models are still in their early steps of development and their potential has yet to be fully explored. Accordingly, a novel abstractive summarization model is proposed in this paper which utilized the combination of convolutional neural network and long short-term memory integrated with auxiliary attention in its encoder to increase the saliency and coherency of generated summaries. The proposed model was validated on CNN\Daily Mail and DUC-2004 datasets and empirical results indicated that not only the proposed model outperformed existing models in terms of ROUGE metric but also its generated summaries had higher saliency and readability compared to the baseline model according to human evaluation.

     

Consensus tracking for second-order nonlinear multi-agent systems with switching topologies and a time-varying reference state

In this study, the authors consider the consensus tracking problem for second-order nonlinear multi-agent systems with switching topologies and a time-varying reference state. The dynamics of each agent consists of nonlinear inherent dynamics. In order to reach consensus tracking, a class of nonsmooth control protocols is proposed which only depends on the agent's own information and its neighbours’ information. With the aid of algebraic graph theory, matrix theory, and Lyapunov theory, some corresponding sufficient conditions guaranteeing consensus tracking under the proposed control protocols are derived. Finally, the numerical simulations are given to illustrate the effectiveness of the developed theoretical results.     

Consensus with a reference state for fractional-order multi-agent systems

This paper investigates consensus of fractional-order multi-agent systems (MASs) with a reference state. First, a consensus control law with a constant reference state is given using graph theory and stability analysis of fractional-order. Then, a general control law and a particular one for consensus of fractional-order MASs with a time-varying reference state are proposed. Next, the above control laws are extended to solve formation tracking problem. Finally, several simulations are presented to verify the effectiveness of the obtained results.     

Robust distributed H∞ filtering over an uncertain sensor network with multiple fading measurements and varying sensor delays

In this paper, the problem of robust distributed H_∞ filtering is investigated for state‐delayed discrete‐time linear systems over a sensor network with multiple fading measurements, random time‐varying communication delays, and norm‐bounded uncertainties in all matrices of the system. The diagonal matrices, whose elements are individual independent random variables, are utilized to describe the multiple fading measurements. Furthermore, the Bernoulli‐distributed white sequences are introduced to model the random occurrence of time‐varying communication delays. In the proposed filtering approach, the stability of the estimation error system is first shown by the Lyapunov stability theory and the H_∞ performance is then achieved using a linear matrix inequality method. Finally, two numerical examples are given to show the effectiveness and performance of the proposed approach.     

New Sliding Mode Control of 2-DOF Robot Manipulator Based on Extended Grey Wolf Optimizer

International Journal of Control, Automation and Systems - This paper presents a novel sliding mode (NSMC) to control of a 2-DOF robot manipulator based on the extended grey wolf optimizer (EGWO)....     

Reversible Thermal Tuning of All‐Dielectric Metasurfaces

All‐dielectric metasurfaces provide a powerful platform for a new generation of flat optical devices, in particular, for applications in telecommunication systems, due to their low losses and high transparency in the infrared. However, active and reversible tuning of such metasurfaces remains a challenge. This study experimentally demonstrates and theoretically justifies a novel scenario of the dynamical reversible tuning of all‐dielectric metasurfaces based on the temperature‐dependent change of the refractive index of silicon. How to design an all‐dielectric metasurface with sharp resonances by achieving interference between magnetic dipole and electric quadrupole modes of constituted nanoparticles arranged in a 2D lattice is shown. Thermal tuning of these resonances can cause drastic but reciprocal changes in the directional scattering of the metasurface in a spectral window of 75 nm. This change can result in a 50‐fold enhancement of the radiation directionality. This type of reversible tuning can play a significant role in novel flat optical devices including the metalenses and metaholograms.     

Electrophoretic deposition of ZnO–CeO2 mixed oxide nanoparticles

ZnO–CeO₂ nanoparticles were synthesized by microwave combustion and deposited by electrophoretic deposition (EPD). The effect of using two different alcohols, ethanol and methanol, was investigated on EPD behavior and morphology of deposited film. Moreover, the effect of concentration of nanoparticles and applied voltage on the mass of deposit and the variation in the current density were investigated. With a change in the alcohol type, the surface morphology of deposition changed and some voids were observed on the deposition surface in ethanol. In all cases, with increasing concentration of nanoparticles in suspension, the number of developed cracks increased. Besides, a rise in voltage led to an increase in the number of cracks. The EPD processes in ethanol and methanol suspension were simulated over time using different zero boundary conditions. Hemi‐spherical morphology was seen for the nanoparticles deposited in ethanol. This kind of growth was simulated based on the changes in electrical field.