A Wilkinson power divider with harmonic suppression through low-pass filter for GSM and LTE applications.

Conventional Wilkinson power dividers (WPDs) perform satisfactorily near the intended operation frequency. Nonetheless, these WPDs demonstrate subpar performance in the stopband and necessitate a significant physical space. To enhance the existing level of advancement and in order to improve on the current state-of-the-art, a modified WPD is designed and fabricated, demonstrating a significant improvement in stopband and superior isolation between output ports. To improve the stopband and suppress unwanted harmonics, a low-pass filter (LPF) structure is placed in the both branches of the conventional WPD. The proposed modified WPD depicts a wide stopband bandwidth (fSB > 17.25 GHz) from 2.75 to over 20 GHz with an attenuation level of 20 dB, suppressing 2nd to 11th harmonics. According to measured results, the input return loss (|S11|), insertion loss (|S21|) and output isolation (|S32|) at f = 1.8 GHz are better than 33 dB, 3.2 dB and 21 dB, respectively. Indeed, the proposed modified WPD exhibits a magnitude imbalance of 0.00018, a phase imbalance of 1.25 degrees and a group delay of 0.5 ns. The proposed WPD depicts a compact size of 35 mm × 25 mm (0.38 λg × 0.27 λg), where λg is the guided wavelength at f = 1.8 GHz. There is a good agreement between the simulated and measured results. According to the obtained results, the proposed modified WPD shows a desirable performance for modern LTE and GSM communication applications.

Mutual Coupling Reduction in Antenna Arrays Using Artificial Intelligence Approach and Inverse Neural Network Surrogates.

This paper presents a novel approach to reducing undesirable coupling in antenna arrays using custom-designed resonators and inverse surrogate modeling. To illustrate the concept, two standard patch antenna cells with 0.07λ edge-to-edge distance were designed and fabricated to operate at 2.45 GHz. A stepped-impedance resonator was applied between the antennas to suppress their mutual coupling. For the first time, the optimum values of the resonator geometry parameters were obtained using the proposed inverse artificial neural network (ANN) model, constructed from the sampled EM-simulation data of the system, and trained using the particle swarm optimization (PSO) algorithm. The inverse ANN surrogate directly yields the optimum resonator dimensions based on the target values of its S-parameters being the input parameters of the model. The involvement of surrogate modeling also contributes to the acceleration of the design process, as the array does not need to undergo direct EM-driven optimization. The obtained results indicate a remarkable cancellation of the surface currents between two antennas at their operating frequency, which translates into isolation as high as -46.2 dB at 2.45 GHz, corresponding to over 37 dB improvement as compared to the conventional setup.

A Miniaturized Dual-Band Diplexer Design with High Port Isolation for UHF/SHF Applications Using a Neural Network Model.

In this paper, a compact dual-band diplexer is proposed using two interdigital filters. The proposed microstrip diplexer correctly works at 2.1 GHz and 5.1 GHz. In the proposed diplexer, two fifth-order bandpass interdigital filters are designed to pass the desired frequency bands. Applied interdigital filters with simple structures pass the 2.1 GHz and 5.1 GHz frequencies and suppress other frequency bands with high attenuation levels. The dimensions of the interdigital filter are obtained using the artificial neural network (ANN) model, constructed from the EM-simulation data. The desired filter and diplexer parameters, such as operating frequency, bandwidth, and insertion loss, can be obtained using the proposed ANN model. The insertion loss parameter of the proposed diplexer is 0.4 dB, and more than 40 dB output port isolation is obtained (for both operating frequencies). The main circuit has the small size of 28.5 mm × 23 mm (0.32 λg × 0.26 λg). The proposed diplexer, with the achieved desired parameters, is a good candidate for UHF/SHF applications.

A Compact Rat-Race Coupler with Harmonic Suppression for GSM Applications: Design and Implementation Using Artificial Neural Network.

In this paper, a compact microstrip rat-race coupler at a 950 MHz operating frequency is designed, simulated, and fabricated. New branches are proposed in this design using high-/low- impedance open-ended resonators. In the conventional rat-race coupler, there are three long λ/4 branches and a 3λ/4 branch, and they occupy a very large area. In the presented designed, three compact branches are proposed for use instead of three λ/4 branches and an ultra-compact branch is suggested for use instead of the 3λ/4 branch. Additionally, an artificial neural network (ANN) approach is incorporated to improve the performance of the resonators using a radial basis function (RBF) network. The proposed compact structure has achieved a reduction of more than 82% compared with the size of the conventional coupler structures. Additionally, the proposed coupler can suppress the 2nd up to the 5th harmonic to improve the performance of the device.

Design of a Compact Quad-Channel Microstrip Diplexer for L and S Band Applications.

In this paper, two novel dual-band bandpass filters (BPFs) and a compact quad-channel diplexer working at 1.7/3.3 GHz and 1.9/3.6 GHz are proposed. In the proposed diplexer design, triangular loop resonators and rectangular loop resonators are used together to reduce the circuit size and improve diplexer performances. Insertion loss (IL) and return loss (RL) of the proposed diplexer are better than 0.8 dB and 21 dB, respectively, at these four operating frequencies. Output ports isolation parameter is better than 30 dB. With the achieved specifications, the proposed diplexer can be used in L and S band applications.

Evolution of Sensor Research for Clarifying the Dynamics and Properties of Future Directions.

The principal goal of this study is to analyze the evolution of sensor research and technologies from 1990 to 2020 to clarify outlook and future directions. This paper applies network analysis to a large dataset of publications concerning sensor research covering a 30-year period. Results show that the evolution of sensors is based on growing scientific interactions within networks, between different research fields that generate co-evolutionary pathways directed to develop general-purpose and/or specialized technologies, such as wireless sensors, biosensors, fiber-optic, and optical sensors, having manifold applications in industries. These results show new directions of sensor research that can drive R&D investments toward promising technological trajectories of sensors, exhibiting a high potential of growth to support scientific, technological, industrial, and socioeconomic development.

Scientific laws of research funding to support citations and diffusion of knowledge in life science.

One of the main problems in scientometrics is to explore the factors that affect the growth of citations in publications to identify best practices of research policy to increase the diffusion of scientific research and knowledge in science and society. The principal purpose of this study is to analyze how research funding affects the citation-based performance of scientific output in vital research fields of life science, which is a critical province (area of knowledge) in science to improve the wellbeing of people. This study uses data from the Scopus database in 2015 (to assess the impact on citations in 2021, after more than 5 years) concerning different disciplines of life science, given by "agricultural and biological sciences", "biochemistry, genetics, and molecular biology", "Immunology and microbiology", "neuroscience" and "pharmacology, toxicology and pharmaceutics". Results demonstrate that although journals publish un-funded articles more than funded publications in all disciplines of life science, the fraction of total citations in funded papers is higher than the share in the total number of publications. In short, funded documents receive more citations than un-funded papers in all research fields of life science under study. Findings also support that citations of total (funded + un-funded), funded, and un-funded published papers have a power-law distribution in all five research fields of life science. Original results here reveal a general property in scientific development: funded research has a higher scaling potential than un-funded publications. Critical implications of research policy, systematized in a decision-making matrix, suggest that R&D investments in "Neuroscience" can generate a positive impact of scientific results in science and society-in terms of citations-higher than other research fields in medicine. Overall, then, results here can explain some characteristics driving scientific change and help policymakers and scholars to allocate resources towards research fields that facilitate the development and diffusion of scientific research and knowledge in life science for positive societal impact.

Scientific Developments and New Technological Trajectories in Sensor Research.

Scientific developments and new technological trajectories in sensors play an important role in understanding technological and social change. The goal of this study is to develop a scientometric analysis (using scientific documents and patents) to explain the evolution of sensor research and new sensor technologies that are critical to science and society. Results suggest that new directions in sensor research are driving technological trajectories of wireless sensor networks, biosensors and wearable sensors. These findings can help scholars to clarify new paths of technological change in sensors and policymakers to allocate research funds towards research fields and sensor technologies that have a high potential of growth for generating a positive societal impact.

Size reduction and performance improvement of a microstrip Wilkinson power divider using a hybrid design technique.

In the design of a microstrip power divider, there are some important factors, including harmonic suppression, insertion loss, and size reduction, which affect the quality of the final product. Thus improving each of these factors contributes to a more efficient design. In this respect, a hybrid technique to reduce the size and improve the performance of a Wilkinson power divider (WPD) is introduced in this paper. The proposed method includes a typical series LC circuit, a miniaturizing inductor, and two transmission lines, which make an LC branch. Accordingly, two quarter-wavelength branches of the conventional WPD are replaced by two proposed LC branches. Not only does this modification lead to a 100% size reduction, an infinite number of harmonics suppression, and high-frequency selectivity theoretically, but it also results in a noticeable performance improvement practically compared to using quarter-wavelength branches in the conventional microstrip power dividers. The main important contributions of this technique are extreme size reduction and harmonic suppression for the implementation of a filtering power divider (FPD). Furthermore, by tuning the LC circuit, the arbitrary numbers of unwanted harmonics are blocked while the operating frequency, the stopband bandwidth, and the operating bandwidth are chosen optionally. The experimental result verifies the theoretical and simulated results of the proposed technique and demonstrates its potential for improving the performance and reducing the size of other similar microstrip components.

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment.

COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.