Frequency Reconfigurable Microstrip Patch Antenna for Multiband Applications with Shunt-Series MEMS Switch

The wireless communication system is well developed and lots of antennas are designed and fabricated for this application. Still, the evolution of the communication system, the performance of the antenna is required to enhance to adopt the present era. The design of the antenna is most important for the performance of the antenna. Therefore, this work is designed and developed a novel antenna design for wideband application by utilizing frequency reconfigurable technique. The proposed work utilized microstrip patch antenna for the application of wideband and the Shun-series MEMS switch is applied for switching the frequency. The proposed antenna is designed with two switches and investigated with the switching conditions like ON-ON, OFF-ON, and OFF-OFF. The performance of the proposed antenna is validated by utilizing the antenna performance metrices such as Return loss, bandwidth, gain, VSWR, and radiation pattern for each switching condition. The simulation results are shows that the proposed antenna design with shunt-series MEMS switch is effectively performed and it is most suitable for the application of wireless communication system.

Optimized Deep Belief Neural Network for Semantic Change Detection in Multi-Temporal Image

Nowadays, a massive quantity of remote sensing images is utilized from tremendous earth observation platforms. For processing a wide range of remote sensing data to be transferred based on knowledge and information of them. Therefore, the necessity for providing the automated technologies to deal with multi-spectral image is done in terms of change detection. Multi-spectral images are associated with plenty of corrupted data like noise and illumination. In order to deal with such issues several techniques are utilized but they are not effective for sensitive noise and feature correlation may be missed. Several machine learning-based techniques are introduced to change detection but it is not effective for obtaining the relevant features. In other hand, the only limited datasets are available in open-source platform; therefore, the development of new proposed model is becoming difficult. In this work, an optimized deep belief neural network model is introduced based on semantic modification finding for multi-spectral images. Initially, input images with noise destruction and contrast normalization approaches are applied. Then to notice the semantic changes present in the image, the Semantic Change Detection Deep Belief Neural Network (SCD-DBN) is introduced. This research focusing on providing a change map based on balancing noise suppression and managing the edge of regions in an appropriate way. The new change detection method can automatically create features for different images and improve search results for changed regions. The projected technique shows a lower missed finding rate in the Semantic Change Detection dataset and a more ideal rate than other approaches