Search

This paper presents three prototypes of cavity-backed slot antennas integrated with solar panels. The antenna design is straightforward and requires minimal alteration on the solar panel’s geometry. The antennas and solar cells are on the same surface and are effectively independent of each other. This eliminates the need for custom designed solar cells required in previous studies. The integrated solar panel antennas were demonstrated using all commercial-off-the-shelf space-qualified components and printed circuit board technology. The presented modular design method is valuable for CubeSats primarily built with standardized commercial components and where the surface area is limited to house solar panels and antennas separately. The integrated solar-antenna panels include a circularly polarized antenna, a linear two-slot antenna, and a dual band antenna. Measurements were performed to validate both antennas’ and solar panels’ functionality and the results are outstanding when compared to the antenna design data and solar cells’ specifications.

Background: Cancer ranks second among the causes of mortality worldwide, following cardiovascular diseases. Brain cancer, in particular, has the lowest survival rate of any form of cancer. Brain tumors vary in their morphology, texture, and location, which determine their classification. The accurate diagnosis of tumors enables physicians to select the optimal treatment strategies and potentially prolong patients’ lives. Researchers who have implemented deep learning models for the diagnosis of diseases in recent years have largely focused on deep neural network optimization to enhance their performance. This involves implementing models with the best performance and incorporating various network architectures by configuring their hyperparameters. Methods: This paper presents a novel hybrid approach for improved brain tumor classification by combining CNNs and EfficientNetV2B3 for feature extraction, followed by K-nearest neighbors (KNN) for classification, which has been described as one of the simplest machine learning algorithms based on supervised learning techniques. The KNN algorithm assumes similarities between new cases and available cases and assigns new cases to the category that most closely resembles the available categories. Results: To evaluate the recommended method’s efficacy, two widely known benchmark MRI datasets were utilized in the experiments. The initial dataset consisted of 3064 MRI images depicting meningiomas, gliomas, and pituitary tumors. Images from two classes, consisting of healthy brains and brain tumors, were included in the second dataset, which was obtained from Kaggle. Conclusions: In order to enhance the performance even further, this study concatenated the CNN and EfficientNetV2B3’s flattened outputs before feeding them into the KNN classifier. The proposed framework was run on these two different datasets and demonstrated outstanding performance, with accuracy of 99.51% and 99.8%, respectively.