Author
Bahare Mohamadzade
Other affiliations: Semnan University, University of Tehran
Bio: Bahare Mohamadzade is an academic researcher from Macquarie University. The author has contributed to research in topics: Antenna (radio) & Patch antenna. The author has an hindex of 9, co-authored 21 publications receiving 395 citations. Previous affiliations of Bahare Mohamadzade include Semnan University & University of Tehran.
Papers
More filters
••
University of West Bohemia1, Macquarie University2, Tehran University of Medical Sciences3, Razi University4, Islamic Azad University5, Edinburgh Napier University6, University of Wisconsin-Madison7, Louisiana State University8, Texas A&M University–Kingsville9, University of Toronto10, Babol University of Medical Sciences11
TL;DR: A response to combat the virus through Artificial Intelligence (AI) is rendered 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.
Abstract: 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.
358 citations
••
TL;DR: An operational perspective of recent advances fabrication methods for flexible antennas is presented, while analyzing the strengths and limitations of each in the microwave as well as millimeter-wave regions.
Abstract: Antennas are a vital component of the wireless body sensor networks devices. A wearable antenna in this system can be used as a communication component or energy harvester. This paper presents a detailed review to recent advances fabrication methods for flexible antennas. Such antennas, for any applications in wireless body sensor networks, have specific considerations such as flexibility, conformability, robustness, and ease of integration, as opposed to conventional antennas. In recent years, intriguing approaches have demonstrated antennas embroidered on fabrics, encapsulated in polymer composites, printed using inkjets on flexible laminates and a 3-D printer and, more interestingly, by injecting liquid metal in microchannels. This article presents an operational perspective of such advanced approaches and beyond, while analyzing the strengths and limitations of each in the microwave as well as millimeter-wave regions. Navigating through recent developments in each area, mechanical and electrical constitutive parameters are reviewed, and finally, some open challenges are presented as well for future research directions.
96 citations
••
66 citations
••
TL;DR: An intelligent design methodology of microstrip filters in which a dynamic neural network model based on Bayesian Regularization Back-Propagation (BRBP) learning algorithm is used, suggesting an excellent in and out-of-band performance.
Abstract: This paper presents an intelligent design methodology of microstrip filters in which a dynamic neural network model based on Bayesian Regularization Back-Propagation (BRBP) learning algorithm is used. In this approach, a Low-Pass Filter (LPF) composed of multiple open stubs, and stepped impedance resonators is initially designed for which an Artificial Neural Network (ANN) is trained to improve the performance of the filter. The predicted and measured results of the filter verify the effectiveness of the presented method, suggesting an excellent in and out-of-band performance. According to the measurement, the filter has a very small transition band from 2.087 to 2.399 GHz with 3 and 40 dB attenuation points, respectively, leading to a sharp roll-off rate of 118.6 dB/GHz. In addition the optimized filter has an ultra-wide stopband, extending from 2.399 to 15.01 GHz with attenuation level of 22 dB are The overall size of the fabricated filter is only 0.190 λ g × 0.094 λ g , where λ g is the guided wavelength at 3 dB cut-off frequency (2.087 GHz). A performance comparison with some of the recent published LPFs presented, showing the superiority of the proposed filter.
56 citations
••
TL;DR: In this paper, a planar compact electromagnetic bandgap (EBG) structure with the potential to reduce the mutual coupling between the elements of a microstrip antenna array was proposed.
Abstract: This research work presents a planar compact electromagnetic bandgap (EBG) structure with the potential to reduce the mutual coupling between the elements of a microstrip antenna array The proposed structure is investigated at 559 GHz, which is the centre frequency of the wireless local area network band To achieve the highest radiation performance for microstrip antenna arrays, with minimal inter-element spacing and mutual coupling, different unit cell arrangements were considered along with two adjacent patch elements The simulations and measurement results for the proposed arrangements indicate that the mutual coupling tends to diminish significantly For instance, when adjacent patches are spaced by 04
λ
, the mutual coupling improves by ~25 dB For the particular spacing of 04
λ
, it is favourably observed that the proposed EBG cells can also improve the antenna gain by ~25 dB Such improvements can be attributed to the compactness of the cells (~
λ
/8 × λ
/10) and their remarkable ability to suppress the surface waves
50 citations
Cited by
More filters
•
3,940 citations
••
TL;DR: In this paper, a comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field is provided, and the challenges and suggested solutions to help researchers understand the existing research gaps.
Abstract: In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.
1,084 citations
••
22 Mar 2021TL;DR: In this paper, the authors present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application and highlight the challenges and potential research directions based on their study.
Abstract: In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.
659 citations
••
University of West Bohemia1, Macquarie University2, Tehran University of Medical Sciences3, Razi University4, Islamic Azad University5, Edinburgh Napier University6, University of Wisconsin-Madison7, Louisiana State University8, Texas A&M University–Kingsville9, University of Toronto10, Babol University of Medical Sciences11
TL;DR: A response to combat the virus through Artificial Intelligence (AI) is rendered 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.
Abstract: 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.
358 citations
••
University of Rome Tor Vergata1, Monash University2, London Metropolitan University3, Institut national de la recherche scientifique4, Monterrey Institute of Technology and Higher Education5, Edinburgh Napier University6, Université catholique de Louvain7, University of Bradford8, Universidad Pública de Navarra9
TL;DR: It is shown that the mutual-coupling reduction methods inspired by MTM and MTS concepts can provide a higher level of isolation between neighbouring radiating elements using easily realizable and cost-effective decoupling configurations that have negligible consequence on the array’s characteristics such as bandwidth, gain and radiation efficiency, and physical footprint.
Abstract: Nowadays synthetic aperture radar (SAR) and multiple-input-multiple-output (MIMO) antenna systems with the capability to radiate waves in more than one pattern and polarization are playing a key role in modern telecommunication and radar systems. This is possible with the use of antenna arrays as they offer advantages of high gain and beamforming capability, which can be utilized for controlling radiation pattern for electromagnetic (EM) interference immunity in wireless systems. However, with the growing demand for compact array antennas, the physical footprint of the arrays needs to be smaller and the consequent of this is severe degradation in the performance of the array resulting from strong mutual-coupling and crosstalk effects between adjacent radiating elements. This review presents a detailed systematic and theoretical study of various mutual-coupling suppression (decoupling) techniques with a strong focus on metamaterial (MTM) and metasurface (MTS) approaches. While the performance of systems employing antenna arrays can be enhanced by calibrating out the interferences digitally, however it is more efficient to apply decoupling techniques at the antenna itself. Previously various simple and cost-effective approaches have been demonstrated to effectively suppress unwanted mutual-coupling in arrays. Such techniques include the use of defected ground structure (DGS), parasitic or slot element, dielectric resonator antenna (DRA), complementary split-ring resonators (CSRR), decoupling networks, P.I.N or varactor diodes, electromagnetic bandgap (EBG) structures, etc. In this review, it is shown that the mutual-coupling reduction methods inspired By MTM and MTS concepts can provide a higher level of isolation between neighbouring radiating elements using easily realizable and cost-effective decoupling configurations that have negligible consequence on the array’s characteristics such as bandwidth, gain and radiation efficiency, and physical footprint.
226 citations