The efficiency of the metal detection method using deep learning with data obtained from multiple magnetic impedance (MI) sensors was investigated. The MI sensor is a passive sensor that detects metal objects and magnetic field changes. However, when detecting a metal object, the amount of change in the magnetic field caused by the metal is small and unstable with noise. Consequently, there is a limit to the detectable distance. To effectively detect and analyze this distance, a method using deep learning was applied. The detection performances of a convolutional neural network (CNN) and a recurrent neural network (RNN) were compared from the data extracted from a self-impedance sensor. The RNN model showed better performance than the CNN model. However, in the shallow stage, the CNN model was superior compared to the RNN model. The performance of a deep-learning-based (DLB) metal detection network using multiple MI sensors was compared and analyzed. The network was detected using long short-term memory and CNN. The performance was compared according to the number of layers and the size of the metal sheet. The results are expected to contribute to sensor-based DLB detection technology.

This report describes recent progresses in development and production of GaN HEMT based power amplifiers in Wavice Inc., such as discrete transistors, s-band internally matched packaged transistors, 5W 3.5 GHz Doherty amplifiers, S-band Tx module and 4kW C-band high power amplifiers. Especially for s-band internally matched transistors and power amplifiers, more than 1500 products have been produced and qualified. The RF performance variations and reliability data from the qualification of power amplifiers are summarized.

As highly immersive virtual reality (VR) content, 360° video allows users to observe all viewpoints within the desired direction from the position where the video is recorded. In 360° video content, virtual objects are inserted into recorded real scenes to provide a higher sense of immersion. These techniques are called 3D composition. For a realistic 3D composition in a 360° video, it is important to obtain the internal (focal length) and external (position and rotation) parameters from a 360° camera. Traditional methods estimate the trajectory of a camera by extracting the feature point from the recorded video. However, incorrect results may occur owing to stitching errors from a 360° camera attached to several high-resolution cameras for the stitching process, and a large amount of time is spent on feature tracking owing to the high-resolution of the video. We propose a new method for pre-visualization and 3D composition that overcomes the limitations of existing methods. This system achieves real-time position tracking of the attached camera using a ZED camera and a stereo-vision sensor, and real-time stabilization using a Kalman filter. The proposed system shows high time efficiency and accurate 3D composition.