A comparative analysis of compact multiband bio-inspired Asymmetric microstrip fed antennas (BioAs-MPAs) is presented in this paper for the first time. The proposed antennas are based on semi-Carica papaya-leaf shaped, semi-Monstera deliciosa-leaf shaped, semi-Vitis vinifera shaped, Defected Ground Structure (DGS) and L-slit techniques. The antennas are built on a 33&#x2009;&#xd7;&#x2009;15&#x2009;mm2 Rogers duroid 5880 substrate. The modelling equations for resonant frequencies of the proposed arbitrarily shaped radiating patch is based on modified circular patch modelling equations. The semi-Carica papaya-leaf antenna operates at 2.4&#x2009;GHz and 4.4&#x2009;GHz, Monstera deliciosa-leaf antenna operates at 2.6&#x2009;GHz, 4.4&#x2009;GHz and 5.5&#x2009;GHz, while Vine-leaf antenna operates at 2.5&#x2009;GHz and 5.4&#x2009;GHz. The proposed BioAs-MPAs antennas radiation patterns at E-plane are Bi-directional in all the operating frequencies with suitable X-Pol purity and have Omnidirectional radiation patterns at H-Plane in all the operating frequencies. As a result of the analysis, it was found that each of the bio-inspired structures has its unique merit over the others. Owing to the small size, stable radiation pattern, acceptable gain and high radiation efficiency, the proposed BioAs-MPAs antennas are suitable for ISM band, Bluetooth, Wi-Fi, WiMAX, LTE, UMTS, Sub6&#x2009;GHz 5&#x2009;G band, ZigBee and RF-Altimeter used in unmanned aerial vehicle and Aviation industry.

Jeremiah O. Abolade

Dominic B. O. Konditi

Vasant M. Dharmadhikary

A Comparative Study of Compact Multiband Bio-Inspired Asymmetric Microstrip Fed Antennas (BioAs-MPAs) for Wireless Applications

The number of medical images being stored and communicated daily is rapidly increasing, according to the need for these images in medical diagnoses. Hence, the storage space and bandwidths required to store and communicate these images are exponentially increasing, which has brought attention toward compressing these images. In this study, a new compression method is proposed for medical images based on convolutional neural networks. The proposed neural network consists of two main stages: a segmentation stage and an autoencoder. The segmentation stage is used to recognize the Region of Interest (ROI) in the image and provide it to the autoencoder stage, so more emphasis on the information of the ROI is applied. The autoencoder part of the neural network contains a bottleneck layer that has one-eighth of the dimensions of the input image. The values in this layer are used to represent the image, while the following layers are used to decompress the images, after training the neural network. The proposed method is evaluated using the CLEF MED 2009 dataset, where the evaluation results show that the method has significantly better performance, compared to the existing state-of-the-art methods, by providing more visually similar images using less data.

Ali Ibrahim Khaleel

Nik Adilah Hanin Zahri

Muhammad Imran Ahmad

A Hybrid Compression Method for Medical Images Based on Region of Interest Using Artificial Neural Networks

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Journal of Engineering publishes research in several areas of engineering, including chemical engineering, civil engineering, computer engineering, electrical engineering, industrial engineering and mechanical engineering.

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Table of Contents

Table of Contents: 2021

A Comparative Study of Compact Multiband Bio-Inspired Asymmetric Microstrip Fed Antennas (BioAs-MPAs) for Wireless Applications

A Hybrid Compression Method for Medical Images Based on Region of Interest Using Artificial Neural Networks