An Economic Low-profile Elliptical Microstrip Antenna-to-RWG Transition for Microwave Laboratory and X-Band Applications.. [DOI: 10.21203/rs.3.rs-2357185/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

The work presents a low-cost compact microstrip line (MSL)-to-rectangular waveguide (RWG) transition for space communication, satellite communication, RADAR, amateur radio; microwave laboratory X-band applications. A 90˚ MSL-to-RWG transition comprises a MSL and a RWG (WR-90). An elliptical microstrip antenna is used as an RF energy launcher in RWG which is fabricated on a low-cost lossy FR4 epoxy substrate with a loss tangent of 0.02 and a permittivity of 4.4. An impedance mismatch between the 50 Ω MSL and 500 Ω RWG is a challenging task and it has been achieved by changes in the probe geometry, microstrip line (width and length) and by adjusting the dimensions (height, width, and length) of the waveguide slot cut at the bottom end. The impedance matching results in an improved -10dB return loss (RL) with insertion loss (IL) of 2.1 dB at 10.12 GHz and a relative bandwidth of approximately 27% (9.84 GHz-10.38 GHz). An extremely unidirectional with directivity (9.97 dBi) and high gain microstrip antenna (8.24 dBi) has been utilized for electromagnetic energy launch into the WR-90. Therefore the designed MSL-to-RWG transition is a suitable candidate for X-band applications such as microwave laboratory experiment purposes, front end in transmitter/receiver of Space and Satellite applications, RADAR, amateur radio, as a connector for MMIC/MIC circuits and devices. The transition-measured results are also presented.

Automatic detection of COVID-19 in chest radiographs using serially concatenated deep and handcrafted features

Since the infectious disease occurrence rate in the human community is gradually rising due to varied reasons, appropriate diagnosis and treatments are essential to control its spread. The recently discovered COVID-19 is one of the contagious diseases, which infected numerous people globally. This contagious disease is arrested by several diagnoses and handling actions. Medical image-supported diagnosis of COVID-19 infection is an approved clinical practice. This research aims to develop a new Deep Learning Method (DLM) to detect the COVID-19 infection using the chest X-ray. The proposed work implemented two methods namely, detection of COVID-19 infection using (i) a Firefly Algorithm (FA) optimized deep-features and (ii) the combined deep and machine features optimized with FA. In this work, a 5-fold cross-validation method is engaged to train and test detection methods. The performance of this system is analyzed individually resulting in the confirmation that the deep feature-based technique helps to achieve a detection accuracy of > 92% with SVM-RBF classifier and combining deep and machine features achieves > 96% accuracy with Fine KNN classifier. In the future, this technique may have potential to play a vital role in testing and validating the X-ray images collected from patients suffering from the infection diseases.

Effective fuzzy c-means based kernel function in segmenting medical images

The objective of this paper is to develop an effective robust fuzzy c-means for a segmentation of breast and brain magnetic resonance images. The widely used conventional fuzzy c-means for medical image segmentations has limitations because of its squared-norm distance measure to measure the similarity between centers and data objects of medical images which are corrupted by heavy noise, outliers, and other imaging artifacts. To overcome the limitations this paper develops a novel objective function based standard objective function of fuzzy c-means that incorporates the robust kernel-induced distance for clustering the corrupted dataset of breast and brain medical images. By minimizing the novel objective function this paper obtains effective equation for optimal cluster centers and equation to achieve optimal membership grades for partitioning the given dataset. In order to solve the problems of clustering performance affected by initial centers of clusters, this paper introduces a specialized center initialization method for executing the proposed algorithm in segmenting medical images. Experiments are performed with synthetic, real breast and brain images to assess the performance of the proposed method. Further the validity of clustering results is obtained using silhouette method and this paper compares the results with the results of other recent reported fuzzy c-means methods. The experimental results show the superiority of the proposed clustering results.

Infundibular metastasis and panhypopituitarism

A patient with no known history of primary neoplasia presented with panhypopituitarism secondary to metastasis. Complete anterior pituitary failure was documented by pituitary reserve testing. However, at autopsy there was extensive carcinomatous invasion of the hypothalamus and infundibulum but no adenohypophyseal involvement. The possibility of metastatic disease should be considered in patients presenting with panhypopituitarism. The literature covering this topic is reviewed.