2.46 ghz helical antenna design for wimax and wifi application / Dayang Suhaida Awang Damit, Samihah Abdullah and Rohaiza Baharudin

This project is to focus on to design of the basic geometry of helical antenna in small size at frequency 2.46 GHz. Several parameters dimensions and helical profiles were studied in order to obtain a suitable frequency range. The software that was applied for this simulation is the CST Microwave Studio (CST MWS) which is analytical tool that provides an accurate 3D EM simulation results for high frequency designs. From the simulation designs, the return losses were obtained. There were less than -23.9 dB. The results conclude that the helical antenna performed the properties of the 2.46 GHz frequency range. The simulation results obtained were S parameter, farfield, bandwidth and gain of the antenna. The measurement result shown that there was a slightly differences between the simulation results and measurement results. In a nutshell, all the results of revealed that the designed helical antenna performed at 2.46 GHz (Ultra High Frequency (UHF). At this 2.46 GHz can be applied to serve for a number of wireless applications including for WiMAX (Worldwide Interoperability for microwave Access) technologies

Automated DeepLabV3+ based model for left ventricle segmentation on short-axis late gadolinium enhancement-magnetic cardiac resonance imaging images

Accurate segmentation of myocardial scar tissue on late gadolinium enhancement-magnetic cardiac resonance imaging (LGE-CMR) is exceptionally vital for clinical applications, enabling precise diagnosis and effective treatment of various cardiac diseases, such as myocardial infarction and cardiomyopathies. However, the ventricle (LV) variations in the size and shape, artifacts, and image resolution of LGE-CMR has made automatic segmentation of myocardial scar tissue more challenging. While many existing approaches delineate the LV myocardium region using multi-modal segmentation, these models may be computationally complex and suffer from misalignment. Therefore, this study proposed an automatic dual-stage DeepLabV3+ based approach tailored for myocardial scar segmentation on short-axis LGE-MRI exclusively. To segment myocardial scar tissue, the second stage employs the segmented LV chamber from the previous stage. The encoder part of the framework utilizes a MobileNetV2 and ResNet50 backbone for the first and second segmentation, respectively, aiming for optimal resolution of feature maps. Both stages tailor an improved Atrous Spatial Pyramid Pooling module in the DeepLabV3+ model with fine-tuned dilated atrous rates to effectively extract the LV chamber and myocardial scar from the complex LGE-MRI background. Based on the results, the proposed dual-stage network recorded an outstanding segmentation performance, with mean Dice score of 96.02% for LV chamber segmentation and 68.01% for scar tissue extraction