Circuit Model for Microstrip Array Antenna with Defected Ground Structures for Mutual Coupling Reduction and Beamforming Applications

A microstrip array antenna (MAA) structure incorporated with an orthogonal I-shaped defected ground structure (OI-DGS) was proposed and investigated and its equivalent circuit was created. Reflection losses were simulated and verified with the proposed circuit model using CST Commercial and AWR Microwave Office software. The optimized S11 parameter of the model was obtained by tuning the dimensions of the microstrip patch elements in the MAA and the lengths and widths of the slots of defected ground structure (DGS). The proposed equivalent circuit is expected to be useful as a model for the DGS design and to study its behavior. Finally, two prototypes of MAA, without and with OI-DGS, were fabricated by the milling technology and tested. The simulated results showed that -5.53 dB mutual coupling reduction and the measured around -3 dB. The simulated results demonstrate that main beam shifted 43° while the measured main beam shifted 36°

Front-end deep learning web apps development and deployment: a review

Machine learning and deep learning models are commonly developed using programming languages such as Python, C++, or R and deployed as web apps delivered from a back-end server or as mobile apps installed from an app store. However, recently front-end technologies and JavaScript libraries, such as TensorFlow.js, have been introduced to make machine learning more accessible to researchers and end-users. Using JavaScript, TensorFlow.js can define, train, and run new or existing, pre-trained machine learning models entirely in the browser from the client-side, which improves the user experience through interaction while preserving privacy. Deep learning models deployed on front-end browsers must be small, have fast inference, and ideally be interactive in real-time. Therefore, the emphasis on development and deployment is different. This paper aims to review the development and deployment of these deep-learning web apps to raise awareness of the recent advancements and encourage more researchers to take advantage of this technology for their own work. First, the rationale behind the deployment stack (front-end, JavaScript, and TensorFlow.js) is discussed. Then, the development approach for obtaining deep learning models that are optimized and suitable for front-end deployment is then described. The article also provides current web applications divided into seven categories to show deep learning potential on the front end. These include web apps for deep learning playground, pose detection and gesture tracking, music and art creation, expression detection and facial recognition, video segmentation, image and signal analysis, healthcare diagnosis, recognition, and identification

Landcover classification using ERS SAR/INSAR data over tropical areas

International Geoscience and Remote Sensing Symposium (IGARSS)2813-815IGRS

Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network

Many researchers have proposed algorithms to improve the network performance of vehicular ad hoc network (VANET) clustering techniques for different applications. The effectiveness of the clustering model is the most important challenge. The K-Means clustering algorithm is an effective algorithm for multi-clusters that can be used in VANETs. The problems with the K-Means algorithm concern the selection of a suitable number of clusters, the creation of a highly reliable cluster, and achieving high similarity within a cluster. To address these problems, a novel method combining a covering rough set and a K-Means clustering algorithm (RK-Means) was proposed in this paper. Firstly, RK-Means creates multi-groups of vehicles using a covering rough set based on effective parameters. Secondly, the K-value-calculating algorithm computes the optimal number of clusters. Finally, the classical K-Means algorithm is applied to create the vehicle clusters for each covering rough set group. The datasets used in this work were imported from Simulation of Urban Mobility (SUMO), representing two highway scenarios, high-density and low-density. Four evaluation indexes, namely, the root mean square error (RMSE), silhouette coefficient (SC), Davies–Bouldin (DB) index, and Dunn index (DI), were used directly to test and evaluate the results of the clustering. The evaluation process was implemented on RK-Means, K-Means++, and OK-Means models. The result of the compression showed that RK-Means had high cluster similarity, greater reliability, and error reductions of 32.5% and 24.2% compared with OK-Means and K-Means++, respectively

Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network

Many researchers have proposed algorithms to improve the network performance of vehicular ad hoc network (VANET) clustering techniques for different applications. The effectiveness of the clustering model is the most important challenge. The K-Means clustering algorithm is an effective algorithm for multi-clusters that can be used in VANETs. The problems with the K-Means algorithm concern the selection of a suitable number of clusters, the creation of a highly reliable cluster, and achieving high similarity within a cluster. To address these problems, a novel method combining a covering rough set and a K-Means clustering algorithm (RK-Means) was proposed in this paper. Firstly, RK-Means creates multi-groups of vehicles using a covering rough set based on effective parameters. Secondly, the K-value-calculating algorithm computes the optimal number of clusters. Finally, the classical K-Means algorithm is applied to create the vehicle clusters for each covering rough set group. The datasets used in this work were imported from Simulation of Urban Mobility (SUMO), representing two highway scenarios, high-density and low-density. Four evaluation indexes, namely, the root mean square error (RMSE), silhouette coefficient (SC), Davies–Bouldin (DB) index, and Dunn index (DI), were used directly to test and evaluate the results of the clustering. The evaluation process was implemented on RK-Means, K-Means++, and OK-Means models. The result of the compression showed that RK-Means had high cluster similarity, greater reliability, and error reductions of 32.5% and 24.2% compared with OK-Means and K-Means++, respectively

LEARNING BASIC MECHATRONICS THROUGH HELICOPTER WORKSHOP Special Issue: International Conference on Teaching and Learning in Education

ABSTRACT In recent years, technologies related to mechatronics and robotics is available even to elementary level students. It is now common to see schools in Malaysia usin

Influence of Sputtering Temperature of TiO2 Deposited onto Reduced Graphene Oxide Nanosheet as Efficient Photoanodes in Dye-Sensitized Solar Cells

Renewable solar energy is the key target to reduce fossil fuel consumption, minimize global warming issues, and indirectly minimizes erratic weather patterns. Herein, the authors synthesized an ultrathin reduced graphene oxide (rGO) nanosheet with ~47 nm via an improved Hummer’s method. The TiO2 was deposited by RF sputtering onto an rGO nanosheet with a variation of temperature to enhance the photogenerated electron or charge carrier mobility transport for the photoanode component. The morphology, topologies, element composition, crystallinity as well as dye-sensitized solar cells’ (DSSCs) performance were determined accordingly. Based on the results, FTIR spectra revealed presence of Ti-O-C bonds in every rGO-TiO2 nanocomposite samples at 800 cm–1. Besides, XRD revealed that a broad peak of anatase TiO2 was detected at ~25.4° after incorporation with the rGO. Furthermore, it was discovered that sputtering temperature of 120 °C created a desired power conversion energy (PCE) of 7.27% based on the J-V plot. Further increase of the sputtering temperature to 160 °C and 200 °C led to excessive TiO2 growth on the rGO nanosheet, thus resulting in undesirable charge recombination formed at the photoanode in the DSSC device