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This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their sound data. The purpose of work is to contribute to a system for detecting drones used for malicious purposes, such as for terrorism. Specifically, we present a method capable of detecting the presence of commercial hobby drones as a binary classification problem based on sound event detection. We recorded the sound produced by a few popular commercial hobby drones, and then augmented this data with diverse environmental sound data to remedy the scarcity of drone sound data in diverse environments. We investigated the effectiveness of state-of-the-art event sound classification methods, i.e., a Gaussian Mixture Model (GMM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), for drone sound detection. Our empirical results, which were obtained with a testing dataset collected on an urban street, confirmed the effectiveness of these models for operating in a real environment. In summary, our RNN models showed the best detection performance with an F-Score of 0.8009 with 240 ms of input audio with a short processing time, indicating their applicability to real-time detection systems.

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In this Letter, we propose a fast speckle noise reduction method with only a single reconstructed image based on convolutional neural networks. The proposed network has multi-sized kernels that can capture the speckle noise component effectively from digital holographic images. For robust noise reduction performance, the network is trained with a large noisy image dataset that has object-dependent noise and a wide range of noise levels. The experimental results show the fast, robust, and outstanding speckle noise reduction performance of the proposed approach.

Microactuators for micromirror system have found many applications in various areas including projection displays, optical switches, RF switches and so on. In this paper we demonstrated micromirror actuator using ionic polymer metal composites (IPMC) that is a suitable candidate, since it has many attractive qualities such as durability, aquatic, miniature and light-weighted. Specially, IPMC has extraordinary advantages which are simple bending motion for low driving voltage (1-2 V), low power consumption, and simple structure. The IPMC actuator is made of Nafion NE-1110 (Dupont Co, Ltd., 260 lm thick) layer and electrode (platinum) layers and driven by 1-4 V. The displacement measured vertically is 0.25 mm and tilting angle is 11.3°. The angular motion, which is more than 10°, is a good advantage in the field of display module. This paper shows that the IPMC actuator has enough possibility for other applications.