Surrogate Modeling for Designing and Optimizing MIMO Antennas

This papers presents the design and optimization of multiple-input and multiple-output (MIMO) antennas through intelligent methods namely as: surrogate modeling. The optimization process is performed automatically with the combination of Microwave Studio (Dassault Systèmes) and MATLAB numerical analyzer. The proposed optimization method aims to find the optimal solution for the total active reflection coefficient (TARC) specification, S 11 , and S 12 by using shallow neural network. This methodology leads to efficiently size the design parameters of MIMO antenna and to optimize S-parameters and TARC specification jointly. To validate the proposed method, an ultra wideband MIMO antenna in the frequency band of 3.1 GHz to 10.6 GHz is designed and optimized

A Case Study for Improving Performance of Frequency Selective Surface through Union of Sub-Sets and Particle Swarm Optimization

Frequency Selective Surfaces (FSSs) consist of a repetition of a given pattern in a periodic way; typically, a dielectric substrate supports this arrangement giving rise to a two-dimensional array. Although relatively simple in structure, designing an FSS that exhibits large bandwidth and stable response to oblique incidence is not straightforward and requires special attention and significant computational effort. To address this problem, this study presents a methodology whereby an initial configuration of the FSS pattern is subjected to an optimization method for sizing the geometrical parameters. Consequently, the initial unit cell is first broken down into subsections, specifically as a “union of subsets”, then particle swarm optimization is used to achieve optimal design parameters that further improves the overall FSS performances. To validate the proposed method, an X-band FSS is proposed and optimized in a commercial simulation environment (Microwave Studio, Dassault Systèmes)

Prediction of Class-Amplifiers with the Aid of Neural Network

This paper presents a strategy addressing the problem of selection of the class of the amplifiers to be used in future wireless communication systems. The proposed methodology uses a scheme based on neural networks (NN): the characteristics of each class of amplifier (i.e., A, B, AB, C, D, F, G, J, S, T , etc.) are determined and then the ‘classification NN’ is constructed for distinguishing various classes from each other. To validate the method, firstly the designs of various class-amplifiers are collected from the recently published literature, and then the specifications of the amplifiers are extracted in terms of voltage (V), current (I) and efficiency; finally with these data the classification NN is trained. After building this black-box NN, providing the required specifications of each amplifier, designer are informed about the class of amplifier that is predicated by the classification NN and that better fits the characteristics of the considered application. This methodology is important as it leads the way of amplifier class selection in the complex communication systems

Deep Learning and its Benefits in Prediction of Patients Through Medical Images

The ability to comprehend the medical images and make prediction on diseases, significantly depends on any medical doctors' experiences. In the wireless medical communications, this process is not developing effectively, and significant tasks are required to make it of high accuracy. Hence, advanced methods are required for accurately diagnosing the various diseases and in the shortest time. Use of deep learning techniques can be a proper solution due to their suitable accuracy in the image segmentation giving rise to pathologic prediction by considering the medical images. In this paper, we employ the deep neural network for predicting the various cysts that can be exist in the human's brain. This intelligent method can estimate and predict the types of brain cysts by the provided medical images. The experimental results demonstrate the well-performance of the presented method to be used for predicting the patients with affections by the help of scanned medical images

Conjointly Electromagnetic Simulations for Bended Microstrip Antenna Designs

Investigative electromagnetic (EM) simulations for bended antenna designs, also used for wearable devices, plays an important role in the design process. The simulation for conformal antennas is time consuming also considering the effects of the presence of the feeding/beamforming network on the antenna performances. To tackle this drawback, a new simulation environment is created, where Keysight ADS tool is employed for modeling the initial microstrip antenna of which shape is determined using a bottom-up optimization (BUO) method. The employed BUO in the ADS environment significantly helps the designer in generating the antenna geometries that exhibit the required performances in terms of bandwidth and radiation patterns. Afterwards, the CST Microwave Studio (Dassault Systèmes) is used for bending the previously designed flat microstrip antenna, and accurately evaluate its performances by numerical simulations. To verify the efficiency of the proposed methodology, one bended microstrip antenna in the frequency band of 8.8-9.4 GHz is designed and the radiation pattern responses are depicted

A review of recent innovations in remote health monitoring

The development of remote health monitoring systems has focused on enhancing healthcare services’ efficiency and quality, particularly in chronic disease management and elderly care. These systems employ a range of sensors and wearable devices to track patients’ health status and offer real-time feedback to healthcare providers. This facilitates prompt interventions and reduces hospitalization rates. The aim of this study is to explore the latest developments in the realm of remote health monitoring systems. In this paper, we explore a wide range of domains, spanning antenna designs, small implantable antennas, on-body wearable solutions, and adaptable detection and imaging systems. Our research also delves into the methodological approaches used in monitoring systems, including the analysis of channel characteristics, advancements in wireless capsule endoscopy, and insightful investigations into sensing and imaging techniques. These advancements hold the potential to improve the accuracy and efficiency of monitoring, ultimately contributing to enhanced health outcomes for patients.Publisher's VersionQ2WOS:001130630400001PMID:3813832

Amplifiers in Biomedical Engineering: A Review from Application Perspectives

Continuous monitoring and treatment of various diseases with biomedical technologies and wearable electronics has become significantly important. The healthcare area is an important, evolving field that, among other things, requires electronic and micro-electromechanical technologies. Designed circuits and smart devices can lead to reduced hospitalization time and hospitals equipped with high-quality equipment. Some of these devices can also be implanted inside the body. Recently, various implanted electronic devices for monitoring and diagnosing diseases have been presented. These instruments require communication links through wireless technologies. In the transmitters of these devices, power amplifiers are the most important components and their performance plays important roles. This paper is devoted to collecting and providing a comprehensive review on the various designed implanted amplifiers for advanced biomedical applications. The reported amplifiers vary with respect to the class/type of amplifier, implemented CMOS technology, frequency band, output power, and the overall efficiency of the designs. The purpose of the authors is to provide a general view of the available solutions, and any researcher can obtain suitable circuit designs that can be selected for their problem by reading this survey

Key Generation of Biomedical Implanted Antennas Through Artificial Neural Networks

This paper presents an accurate and efficient optimization-based approach for modelling and sizing implanted antennas automatically. The proposed method employs the long short-term memory (LSTM) artificial neural network (ANN) for predicting the design specifications in not only one frequency but also in a large frequency band. The entire process is performed in an automated environment that is the combination of electronic design automation (EDA) tools and the numerical analyzer. Based on this intelligent method, the difficulty of designing electromagnetic (EM)-based antennas is solved to the most degrees and the design parameters can be achieved in the easiest way. To validate the efficiency of the presented ANN, two implanted antennas are designed; they and realized on a grounded biocompatible substrate and covered by bone, muscle, fat, and skin tissues, respectively. These implanted antennas are optimized in terms of input scattering parameter, E-plane and H-plane radiation pattern (RP) specifications and the suitable design parameters are provided automatically. The modelled implanted antennas are appropriate to be used at the industrial, scientific, and medical (ISM) frequency band between 2.4 GHz and 2.5 GHz

Deep neural learning based optimization for automated high performance antenna designs

The present paper introduces an optimization-oriented method here practiced for designing high performance single antennas in a fully automated environment. The proposed method comprises two sequential major steps. The first one devotes configuring the shape of antenna and determining the feeding point by employing the bottom-up optimization (BUO) method. In this algorithm, the number of microstrip transmission lines (TLs) used to model the radiator is increased consecutively and the shape of the antenna is revised up to finding the initial satisfying results. Secondly, for determining the best design parameters of the configured antenna shape in the first step (i.e., width and length of TLs), deep neural network (DNN) that is based on Thompson sampling efficient multi-objective optimization (TSEMO) is applied. The recommended optimization method is successfully attracted as a problem solver for designers to tackle the subject for antenna design such as the complexity and large dimensions of structures. Hence, the main advantage of the implemented optimization method in this article is to noticeably decrease the required designer’s involvement automatically generating valid layouts. For validating the suggested method, two wideband antennas are designed, prototyped and subjected to experiment. The first optimized antenna covers the frequency band 8.8 - 10.1 GHz (13.75 % bandwidth) characterized by a maximum gain of 7.13 dB while the second one covers the frequency band 11.3 - 13.16 GHz (15.2 % bandwidth) which exhibits a maximum gain of 7.8 dB

Optimization Process for Bending a Periodic Structure: Start Ahead with Neural Networks

Optimizing high-dimensional designs requires essential efforts; bending of a radiating device is one of such examples. Such appearance appears in various applications like medical, space, radar, etc. Consequently, strong numerical methods are becoming necessary. In the present work, an optimized bent periodic structure, multiple-input and multiple-output (MIMO) antenna designed through a neural network (NN) is presented. The main goal of this optimization is to focus on the curvature of the antenna to find the suitable results for S-parameters both in terms of reflection S 11 , and transmission S 21 coefficients, and also total active reflection coefficient (TARC). Through the proposed method, the antenna configuration loaded by meandered slot and surrounded by printed meander line of different periodicities is designed and optimized in three frequency bands within the 12.94 GHz to 25 GHz range, namely, 12.94 - 14.79 GHz, 16.09 - 17.25 GHz, and 19.17 - 25 GHz