A compact lowpass - dual bandpass diplexer with high output ports isolation

Bavandpour, Soheil Karimi; Roshani, Saeed; Pirasteh, Ali; Roshani, Sobhan; Seyedi, Hosein

doi:10.1016/j.aeue.2021.153748

Cited by 23 publications

(5 citation statements)

References 14 publications

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“…Within the last decade, various computational methods have been utilized to electromagnetic and microwave designs, such as finite element [ 56 ], finite‐difference frequency domain [ 57 ], and finite difference time domain [ 58 ]. Here, for simulating the S-parameters, Electromagnetic (EM) software tool HFSS v9 is utilized and measured the S-parameter achieved from prototype is given in Fig.…”

Section: Resultsmentioning

confidence: 99%

A parasitic patch loaded staircase shaped UWB MIMO antenna having notch band for WBAN applications

Islam,

Ali,

Awan

et al. 2024

Heliyon

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Section: Resultsmentioning

confidence: 99%

A parasitic patch loaded staircase shaped UWB MIMO antenna having notch band for WBAN applications

Islam,

Ali,

Awan

et al. 2024

Heliyon

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“…Over the recent years, various computational approaches, such as finite element, finite difference time domain, finite-difference frequency-domain, and Lyapunov stability analysis have been successfully applied to a variety of control systems, including photonic crystals [ 73 , 74 , 75 , 76 , 77 , 78 ], high-frequency passive components [ 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 ], electromagnetic devices [ 88 , 89 , 90 , 91 , 92 , 93 , 94 ], fluid mechanical engineering [ 95 , 96 ], and MIMO control systems [ 97 , 98 ]. Simulation studies using MATLAB are carried out to evaluate the performances of the proposed MANNC in tracking setpoints, reducing unwanted overshoots or undershoots, and securing the global stability of a closed-loop system during the system’s entire control process.…”

Section: Simulation Resultsmentioning

confidence: 99%

Introducing a Novel Model-Free Multivariable Adaptive Neural Network Controller for Square MIMO Systems

Mehrafrooz

Lalbakhsh

2022

Sensors

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In this study, a novel Multivariable Adaptive Neural Network Controller (MANNC) is developed for coupled model-free n-input n-output systems. The learning algorithm of the proposed controller does not rely on the model of a system and uses only the history of the system inputs and outputs. The system is considered as a ‘black box’ with no pre-knowledge of its internal structure. By online monitoring and possessing the system inputs and outputs, the parameters of the controller are adjusted. Using the accumulated gradient of the system error along with the Lyapunov stability analysis, the weights’ adjustment convergence of the controller can be observed, and an optimal training number of the controller can be selected. The Lyapunov stability of the system is checked during the entire weight training process to enable the controller to handle any possible nonlinearities of the system. The effectiveness of the MANNC in controlling nonlinear square multiple-input multiple-output (MIMO) systems is demonstrated via three simulation studies covering the cases of a time-invariant nonlinear MIMO system, a time-variant nonlinear MIMO system, and a hybrid MIMO system, respectively. In each case, the performance of the MANNC is compared with that of a properly selected existing counterpart. Simulation results demonstrate that the proposed MANNC is capable of controlling various types of square MIMO systems with much improved performance over its existing counterpart. The unique properties of the MANNC will make it a suitable candidate for many industrial applications.

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“…In calculating ref S (27), the solar incidence angle for different components of transmitted solar radiation reaching the inner side of the greenhouse cover cannot be ascertained, thus, the reflectance of the cover  () is taken as 0.1 for the inside part of the cover [46]. The term t F in ( 26) and ( 27) is the total solar fraction and it is explained in the next subsection.…”

Section: Calculating Greenhouse Cover Transmittance For Solar Radiati...mentioning

confidence: 99%

“…In a recent research, Mahjoob Karambasti et al [24] determined the optimal orientation and roof inclination angle of an even-span greenhouse with fixed floor area by employing a multiobjective optimization (MOGA) method and solar radiation distribution model for northern sections of Iran. It should be mentioned that apart from GA and PSO approaches, other nature-based and numerical algorithms, [25][26][27][28][29][30], [31][32][33], [34][35][36][37], such as gray wolf optimization, ant colony, neural networks and other types of natured based and numerical algorithms can be adopted for greenhouse optimal condition improvement.…”

Section: Introductionmentioning

confidence: 99%

Optimal Solar Greenhouses Design Using Multiobjective Genetic Algorithm

et al. 2022

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In greenhouse horticulture, solar energy is the key to extending the greenhouse using seasonal or daily thermal storage technology and decreasing the energy consumption rate, especially in subtropical climate areas. Therefore, the present research aims to reach the optimal physical parameters of common greenhouses in northern areas of Iran to harness the best possible amount of solar energy in a year. Three common types of greenhouses, including even-span, modified arch and Quonset types, are considered. Threefloor area sizes are investigated for each shape to find the optimal physical parameters based on each greenhouse size. A mathematical model is proposed that uses the total solar fraction to compute the radiation

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A compact lowpass - dual bandpass diplexer with high output ports isolation

Cited by 23 publications

References 14 publications

A parasitic patch loaded staircase shaped UWB MIMO antenna having notch band for WBAN applications

A parasitic patch loaded staircase shaped UWB MIMO antenna having notch band for WBAN applications

Introducing a Novel Model-Free Multivariable Adaptive Neural Network Controller for Square MIMO Systems

Optimal Solar Greenhouses Design Using Multiobjective Genetic Algorithm

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