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Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.

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Natural fibers become most important reinforcement materials for commercial thermoplastic as it can easily biodegraded, high specific strength, abundant, renewable and easy processing. In this study, a partially biodegradable composite was prepared by melt-mixing of low density polyethylene (LDPE) with Pandanus amaryllifolius fiber (PAF). The effect of the PAF content on composite properties was investigated. Result demonstrated that the increasing of PAF content increased the density and water absorption of the composite. Tensile modulus of the composite also increase from 10.3 MPa (PAF content = 0 wt%) to 121 MPa (PAF content = 50 wt%). However the increasing of PAF content also resulted in a decreasing of tensile strength, impact strength and elongation at break of the composite.

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