Short‐Time Linear Quadratic Form Technique for Estimating Fast‐Varying Parameters in Feedback Loops

The precision of a closed‐loop controller system designed for an uncertain plant depends strongly upon the maximum extent to which it is possible to track the trend of time‐varying parameters of the plant. The aim of this study is to describe a new parameter estimation algorithm that is able to follow fast‐varying parameters in closed‐loop systems. The short‐time linear quadratic form (STLQF) estimation algorithm introduced in this paper is a technique for tracking time‐varying parameters based on short‐time analysis of the regressing variables in order to minimize locally a linear quadratic form cost function. The established cost function produces a linear combination of errors with several delays. To meet this objective, mathematical development of the STLQF estimation algorithm is described. To implement the STLQF algorithm, the algorithm is applied to a planar mobile robot with fast‐varying parameters of inertia and viscous and coulomb frictions. Next, performance of the proposed algorithm is assessed against noise effects and variation in the type of parameters.     

Preparation,characterization and anticorrosive properties of a novel polyaniline/clinoptilolite nanocomposite

Nanocomposite of polyaniline (PANI) with natural clinoptilolite (Clino) was prepared. Formation of nanocomposite and incorporation of polyaniline in the clinoptilolite channels was confirmed and characterized using FTIR spectroscopy studies, X-ray diffraction (XRD) pattern, scanning electron microscopy (SEM) and cyclic voltammetry techniques. The anticorrosive properties of a 20 μm thickness coating of PANI/Clino nanocomposite with various weight ratios (1, 3 and 5%, w/w) of clinoptilolite content on iron coupons was evaluated and compared with pure polyaniline coating. According to the results in acidic environments PANI/Clino nanocomposite has enhanced corrosion protection effect in comparison to pure polyaniline coating. Comparative experiments revealed that PANI/Clino nanocomposite with 3% (w/w) clinoptilolite content has the best protective properties. Further experiments showed that the PANI/Clino nanocomposite has considerably different corrosion protection efficiencies in various corrosive environments.     

Study of Quantitative Structure Equilibrating Interaction of Retention Indices of Monomethylalkanes in Fossil Fuels by Multiple Linear Regression and Vector

Theoretical Foundations of Chemical Engineering - A support vector machine model in quantitative structure–property interaction was developed for predicting retention indices of...     

Frequency selective millimeter‐wave channel estimation based on subspace enhancement algorithms

In millimeter wave (mmW) communication systems, hybrid architecture, including the analog‐digital precoder and combiner matrices, is employed to take advantage of the multistream transceiver. In practice, mmW channel is assumed to be frequency‐selective, since the signal bandwidth is larger than the coherence bandwidth. Hence, orthogonal frequency‐division multiplexing signaling can be remedial. So far, most of the previous works on the frequency‐selective channel estimation have focused on the single measurement vector (SMV) form, whereas finding and exploiting the proper multimeasurement vector (MMV) model can improve upon the estimation procedure based on compressive sensing (CS) concepts. In fact, the estimation procedure based on the MMV model has a faster convergence speed than the SMV method specially, when the training frames are small. In this paper, we first extract the MMV model of the channel. In this model, the rank‐deficiency occurs as the number of training frames is less or equal to the sparsity level. Thus, the conventional estimation methods fail to provide the desirable performance. To overcome this issue, we propose two rank‐aware algorithms based on the enhancement of the observed signal subspace. The first algorithm assumes to know the sparsity level, while the second faces to the lack of knowledge about the sparsity level. The simulation results corroborate the fact that the proposed methods outperform the conventional CS algorithms such as Simultaneous Orthogonal Matching Pursuit.     

Theoretical Prediction of an Antimony-Silicon Monolayer $$(\hbox {penta-Sb}_{2}\hbox {Si})$$: Band Gap Engineering by Strain Effect

In this paper, using a first principles calculation, a two-dimensional structure of silicon-antimony named penta-Sb\(_{2}\)Si is predicted. The structural, kinetic, and thermal stabilities of the predicted monolayer are confirmed by the cohesive energy calculation, phonon dispersion analysis, and first principles molecular dynamic simulation, respectively. The electronic properties investigation shows that the pentagonal Sb\(_{2}\)Si monolayer is a semiconductor with an indirect band gap of about 1.53 eV (2.1 eV) from GGA-PBE (PBE0 hybrid functional) calculations which can be effectively engineered by employing external biaxial compressive and tensile strain. Furthermore, the optical characteristics calculation indicates that the predicted monolayer has considerable optical absorption and reflectivity in the ultraviolet region. The results suggest that a Sb\(_{2}\)Si monolayer has very good potential applications in new nano-optoelectronic devices.     

The off‐grid frequency selective millimeter wave channel estimation

Broadband millimeter wave (mmW) systems are a promising pioneer of cellular communication for next generation which is utilizing the hybrid baseband/analog beamforming structures along with the miniature massive antenna arrays at both sides of the communication link. mmW channel with an available unlicensed spread spectrum is frequency selective because the signal bandwidth can be larger than the coherence bandwidth. Due to the sparse nature of mmW channel, extracting compressive sensing model of the system is preferable. In fact, exploiting the sparse structure will lead to the reduction of the computational complexity, because there is a reduction in the channel training length compared with the conventional methods such as least square estimation. Most of the prior works have considered on‐grid quantized departure/arrival angles in the input/output antennas to obtain a sparse virtual channel model. However, the sparse angles in the physical channel model are continuous where this continuity indicates a mismatch between the physical angles and the on‐grid angles. Such a mismatch will contribute to unwanted components in the virtual channel model. Given these extra components, the conventional compressive sensing tools are unable to recover the channel. In this paper, we propose two solutions for overcoming the problem caused by off‐grid angle selection. The first is based on the vector shaping, and the second one is based on the sparse total least square concepts. Simulation results demonstrate that the proposed methods both could obtain an adequate channel recovery and are preferable regarding computational complexity concerning the newly developed surrogate method.