All-in-one encoder/decoder approach for non-destructive identification of 3D-printed objects.

This paper presents an all-in-one encoder/decoder approach for the nondestructive identification of three-dimensional (3D)-printed objects. The proposed method consists of three parts: 3D code insertion, terahertz (THz)-based detection, and code extraction. During code insertion, a relevant one-dimensional (1D) identification code is generated to identify the 3D-printed object. A 3D barcode corresponding to the identification barcode is then generated and inserted into a blank bottom area inside the object's stereolithography (STL) file. For this objective, it is necessary to find an appropriate area of the STL file and to merge the 3D barcode and the model within the STL file. Next the information generated inside the object is extracted by using THz waves that are transmitted and reflected by the output 3D object. Finally, the resulting THz signal from the target object is detected and analyzed to extract the identification information. We implemented and tested the proposed method using a 3D graphic environment and a THz time-domain spectroscopy system. The experimental results indicate that one-dimensional barcodes are useful for identifying 3D-printed objects because they are simple and practical to process. Furthermore, information efficiency can be increased by using an integral fast Fourier transform to identify any code located in areas deeper within the object. As 3D printing is used in various fields, the proposed method is expected to contribute to the acceleration of the distribution of 3D printing empowered by the integration of the internal code insertion and recognition process.

Cross-Device Computation Coordination for Mobile Collocated Interactions with Wearables.

Mobile devices, wearables and Internet-of-Things are crammed into smaller form factors and batteries, yet they encounter demanding applications such as big data analysis, data mining, machine learning, augmented reality and virtual reality. To meet such high demands in the multi-device ecology, multiple devices should communicate collectively to share computation burdens and stay energy-efficient. In this paper, we present a cross-device computation coordination method for scenarios of mobile collocated interactions with wearables. We formally define a cross-device computation coordination problem and propose a method for solving this problem. Lastly, we demonstrate the feasibility of our approach through experiments and exemplar cases using 12 commercial Android devices with varying computation capabilities.