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The ionospheric Equinoctial Asymmetry (EA) and signifies ionospheric behavior has been studied over the Grid-Point (Geographic 134.05° E and 34.95° N) during the period 1997 to 2016. The Singular Spectrum Analysis (SSA) characterizes the EA of Ionospheric Total Electron Content (TEC). The behavior of the ionospheric TEC to the EA response depends on the season and local time, location, and solar activity. The data selection of TEC is based on the solar activity index (F10.7) F10.7 ≥ 105 and the magnetic activity index (AP) Ap ≤ 25. TEC datasets of F10.7 ≥ 105 and Ap ≤ 25 days from 1997 to 2016 are used for model development. A strong dependence on solar activity was explained by the first dominant SSA mode, which is a function of solar activity that increased with increasing solar activity throughout the period. The results showed that the increase in the variability of the ionosphere and the solar activity used in equinoctial asymmetry analysis could affect TEC's daytime and nighttime availability. In this scenario, the EA variability of the ionosphere TEC is a daytime phenomenon and occurs at night during the period 1997 to 2016. Moreover, the overview of statistical analysis is added to our examination to elaborate further on annual and semi-annual phenomena in the ionosphere at mid-latitudes during the day and night. [ABSTRACT FROM AUTHOR]

In this paper, research on an autonomous car that can recognise its surroundings using sensors is conducted. It does not require any assistance from passengers and is guided by the advanced car system. The intelligent system makes the car self-driving, and its future technology could have ramifications in a variety of industries and situations. In the proposed scenario, the Convolutional Neural Network (CNN), TensorFlow Lite, and OpenCV are used. A fully automatic self-driving car based on level-5 self-driving technology is being considered. Correspondingly, three algorithms were used to build the autonomous vehicle: lane detection, traffic sign detection, and object detection. Lane detection detects the steering angle of road lanes, traffic signs detection detects traffic signs, and object detection detects objects using a camera image as input. Each algorithm is created a module and combined these algorithms to make the best decision possible. The module can also sense its surroundings, such as estimating the distance between the vehicle and nearby objects, navigating the vehicle, detecting light, and collecting images from the environment. The vehicle was both manual and self-driving modes. The manual mode is used to train the model. Self-driving mode is used to drive the car automatically based on the steering angle, traffic signs, and objects. The self-driving mode is used to operate the car automatically based on the instructions of the algorithms, such as steering angle, traffic signals, and recognised objects. [ABSTRACT FROM AUTHOR]