Development of a privacy-aware multi-sensing data analysis system

2017 Fiscal Year Final Research Report

• Project/Area Number: 26330255
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Intelligent informatics
• Research Institution: Kyushu University
• Principal Investigator: Takano Shigeru 九州大学, 共進化社会システム創成拠点, 准教授 (70336064)
• Project Period (FY): 2014-04-01 – 2018-03-31
• Keywords: マルチセンシングデータ解析 / サイバーフィジカルシステム / プライバシー制御 / ウェーブレット変換 / スマートセンサ情報システム

Outline of Final Research Achievements

In this research, we propose a design method of lifting wavelet filter adaptive to each characteristic in order to analyze enormous signal data acquired from various kinds of sensors at high speed. In the proposed method, a new compact deep neural network　(DNN) architecture based on lifting complex wavelets is proposed. The proposed DNN architecture (LcwtNet) is composed of multiple layers in addition to a CNN architecture. Complex wavelet and lifting wavelet layers are introduced as the lower layers of LcwtNet, which can reduce the number of parameters while maintaining high performance similar to that of CNN models. In simulations, the effectiveness of LcwtNet is demonstrated by several test results using the MNIST dataset. By virtue of the proposed method, we can realize efficient extraction of features on the sensor device side and development of a privacy-aware multi-sensing data analysis system.

Free Research Field

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