Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation

Segmentation stands at the forefront of many high-level vision tasks. In this study, we focus on segmenting finger bones within a newly introduced semi-supervised self-taught deep learning framework which consists of a student network and a stand-alone teacher module. The whole system is boosted in a life-long learning manner wherein each step the teacher module provides a refinement for the student network to learn with newly unlabeled data. Experimental results demonstrate the superiority of the proposed method over conventional supervised deep learning methods.Comment: IEEE BHI 2019 accepte

Assessing the social interconnection of retirement village : a framework of landscape design

Due to the population ageing across the world, retirement villages have been playing an increasingly important role in providing appropriate housing alternatives for the seniors. It has been argued that social interconnection influences the quality of life of senior residents. However, little landscape literature has been delivered to address the social interconnection of retirement villages. This research aims to propose a framework of landscape design to assess and enhance the social interconnection of retirement villages. A theoretical baseline of the landscape design of the social interconnection in retirement villages is developed, analogue to the previous literature. Subsequently, three retirement villages in Geelong, Australia are assessed and compared against the theoretical baseline. The major social-interconnection features not being addressed by the landscape design are identified, including respect and social inclusion; civic participation and employment; and communication and information in retirement villages. Meanwhile, few retirement villages have embedded landscape narratives into the landscape design. This research argues that landscape narratives shall create unique meaning in terms of their former life, living environment and experiences and contribute to their elder life in retirement villages. This research presents a novel measure to enhance the social interconnection of the retirement villages within the landscape context

DNN-DANM: A High-Accuracy Two-Dimensional DOA Estimation Method Using Practical RIS

Reconfigurable intelligent surface (RIS) or intelligent reflecting surface (IRS) has been an attractive technology for future wireless communication and sensing systems. However, in the practical RIS, the mutual coupling effect among RIS elements, the reflection phase shift, and amplitude errors will degrade the RIS performance significantly. This paper investigates the two-dimensional direction-of-arrival (DOA) estimation problem in the scenario using a practical RIS. After formulating the system model with the mutual coupling effect and the reflection phase/amplitude errors of the RIS, a novel DNNDANM method is proposed for the DOA estimation by combining the deep neural network (DNN) and the decoupling atomic norm minimization (DANM). The DNN step reconstructs the received signal from the one with RIS impairments, and the DANM step exploits the signal sparsity in the two-dimensional spatial domain. Additionally, a semi-definite programming (SDP) method with low computational complexity is proposed to solve the atomic minimization problem. Finally, both simulation and prototype are carried out to show estimation performance, and the proposed method outperforms the existing methods in the two-dimensional DOA estimation with low complexity in the scenario with practical RIS.Comment: 11 pages, 12 figure