Development of universal software radio peripheral amplifier for underwater acoustic platform using software defined radio

First and foremost, the development of a modem using the USRP has applications in oceanographic monitoring and communication. Improved acoustic connecting would allow more efficient transfer of information between Underwater Acoustic (UWA) equipment such as autonomous vehicles, piloted vehicles, and underwater profilers. Therefore it can easily be modified in order to be employed for the testing of different UWA. This project describes the full system of an underwater acoustic modem with underwater wireless connection starting with the most critical component of the system which is the USRP amplifier for the receiver and the transceiver. In this project we focused on the development of the USRP amplifier. This amplifier is expected to enhance the signal of the transceiver to Universal Software Radio Peripheral (USRP) modem and the GNU radio. The platform that we proposed uses the Software Defined Radio (SDR) as the main controller. This is due to its flexibility in modulation and able to support coding. Since this is an initial stage, in this work we only use Gaussian Minimum Shift Keying (GMSK) as the modulation techniques. The performance of the UWA platform had been tested and we found that as the frequency increases the attenuation increased as well but with the USRP amplifier we have managed to decrease it. UWA communication research will benefit greatly from the adaption of the USRP as an underwater acoustic modem. The USRP amplifier amplifies the signal that has send by the transceiver and detected by the receiver will effectively demodulate the signal and analyze the received data in the USRP modem. GNU radio and USRP SDR has been successfully implemented. The results demonstrate that the objectives of this research are archived. It is proved that by implementing GNU radio and USRP SDR in the new generation of underwater acoustic communication technology, and improves the utilization of the underwater communication. We conclude that the proper design of the USRP Amplifier is crucial to obtain high quality performance. This project has successfully developed a USRP amplifier and the underwater acoustic communication testbed with expected results

IoT-based Smart Campus Monitoring Based on an Improved Chimp Optimization-Based Deep Belief Neural Network

Internet of Things (IoT) is a fast emerging technology that gained momentum steadily and shaped the future of the smart world. It has been created from the curiosity of human beings to provide comfortable and connected lifestyles with the mitigation of labor and therein promptly reduces the errors. This led to the usage of smart devices in everyday activities and thus enhances the efficacy of all smart applications. Smart applications include smart farming, healthcare, smart grid, smart city, and more. The application of IoT in monitoring the smart campus is an inevitable one to monitor the attendance of students and monitoring other activitieson the campus to protect the students and improve the education standards. Most education institutes use smart classrooms to achieve the aforementioned quality. Smart classrooms include audio-visual aids, multimedia, and smart boards along with these it is ineluctable to monitor the activities such as students’ attendance, analyzing the students-faculty performance, and content deliveries. To record the students’ attendance automatically we propose a Bluetooth-enabled IoT smart system for the positing of students with low energy utilization. The attendance can be recorded in the cloud environment by the Received signal strength indicator (RSSI). To achieve this we propose a novel IoT-based Deep Belief Neural Network (DBN) based Improved Chimp Optimization algorithm (ICO) for monitoring the attendance and positioning of the students’. An experimental study is conducted on Raspberry Pi with the deployment of Python and shows that our proposed approach provides better accuracy even with high interference signals