Further criterion for stochastic stability analysis of semi‐Markovian jump linear systems

This article is devoted to provide further criterion for stochastic stability analysis of semi‐Markovian jump linear systems (S‐MJLSs), in which more generic transition rates (TRs) will be studied. As is known, the time‐varying TR is one of the key issues to be considered in the analysis of S‐MJLS. Therefore, this article is to investigate general cases for the TRs that covered almost all types, especially for the type that the jumping information from one mode to another is fully unknown, which is merely investigated before. By virtue of stochastic functional theory, sufficient conditions are developed to check stochastic stability of the underlying systems via linear matrix inequalities formulation combined with a maximum optimization algorithm. Finally, a numerical example is given to verify the validity and effectiveness of the obtained results.     

PVT‐compensated low‐voltage and low‐power CMOS LNA for IoT applications

In this paper, a low‐noise amplifier (LNA) with process, voltage, and temperature (PVT) compensation for low power dissipation applications is designed. When supply voltage and LNA bias are close to the subthreshold, voltage has significant impact on power reduction. At this voltage level, the gain is reduced and various circuit parameters become highly sensitive to PVT variations. In the proposed LNA circuit, in order to enhance efficiency at low supply voltage, the cascade technique with g_m boosting is used. To improve circuit performance when in the subthreshold area, the forward body bias technique is used. Also, a new PVT compensator is suggested to reduce sensitivity of different circuit's parameters to PVT changes. The suggested PVT compensator employs a current reference circuit with constant output regarding temperature and voltage variations. This circuit produces a constant current by subtracting two proportional to absolute temperature currents. At a supply voltage of 0.35 V, the total power consumption is 585 μW. In different process corners, in the proposed LNA with PVT compensator, gain and noise figure (NF) variations are reduced 10.3 and 4.6 times, respectively, compared to a conventional LNA with constant bias. With a 20% deviation in the supply voltage, the gain and noise NF variations decrease 6.5 and 34 times, respectively.     

Deep Learning Neural Network for Unconventional Images Classification

Neural Processing Letters - The pornographic materials including videos and images are easily in reach for everyone, including under-age youths, allover Internet. It is also an aim for popular...     

Neighborhood min distance descriptor for kinship verification

Multimedia Tools and Applications - The verification of parental and family relationships based on the facial appearance of subjects is a recent topic that attracted the attention of the computer...     

Elderly fall detection based on multi-stream deep convolutional networks

Multimedia Tools and Applications - Fall is the biggest threat to seniors, with significant emotional, physical and financial implications. It is the major cause of serious injuries, disabilities,...     

Numerical solution of optimal control problems governed by integro‐differential equations

In this paper, an efficient hybrid approximation scheme for solving optimal control problems governed by integro‐differential equations is proposed. The current approach is based on a generalization of the hybrid of block‐pulse functions and Legendre's polynomials. An upper bound for the generalized hybrid functions with respect to the maximum norm is acquired and its convergence is demonstrated. The optimal control problem under study is transcribed to a mathematical programming one. Two illustrative examples are considered to verify the capability and reliability of the proposed procedure.     

Control of large scale interconnected systems with input and state delays using decentralized adaptive state observers

This paper investigates the problem of decentralized model reference adaptive control (MRAC) for a class of large scale interconnected systems with both state and input delays. The upper bounds of the interconnection terms are considered to be unknown. Time varying delays in the nonlinear interconnection terms are bounded and nonnegative continuous functions and their derivatives are not necessarily less than one. For exact prediction, a decentralized adaptive state observer is designed and a nested predictor based approach is established to predict the future states of the input delay compensation. It is shown that the solutions of uncertain large‐scale time‐delay interconnected systems converge uniformly exponentially to a desired small ball. The effectiveness of the proposed approaches is illustrated by two examples.     

A Multi-Model Nonstationary Rainfall-Runoff Modeling Framework: Analysis and Toolbox

Water Resources Management - We present a framework and toolbox for multi-model (one at a time) nonstationary modeling of rainfall-runoff (RR) transformation. The designed time-varying nature of...     

AK-GWO: a novel hybrid optimization method for accurate optimum hierarchical stiffened shells

Engineering with Computers - An attempt has been made to propose a novel prediction model based on the Gaussian process regression (GPR) approach. The proposed GPR was used to predict blast-induced...     

Presenting a hybrid model in social networks recommendation system architecture development

There are many studies conducted on recommendation systems, most of which are focused on recommending items to users and vice versa. Nowadays, social networks are complicated due to carrying vast arrays of data about individuals and organizations. In today’s competitive environment, companies face two significant problems: supplying resources and attracting new customers. Even the concept of supply-chain management in a virtual environment is changed. In this article, we propose a new and innovative combination approach to recommend organizational people in social networks based on organizational communication and SCM. The proposed approach uses a hybrid strategy that combines basic collaborative filtering and demographic recommendation systems, using data mining, artificial neural networks, and fuzzy techniques. The results of experiments and evaluations based on a real dataset collected from the LinkedIn social network showed that the hybrid recommendation system has higher accuracy and speed than other essential methods, even substantially has eliminated the fundamental problems with such systems, such as cold start, scalability, diversity, and serendipity.