http://oar.a-star.edu.sg:80/jspui/handle/123456789/109 2017-06-24T05:19:48Z http://oar.a-star.edu.sg:80/jspui/handle/123456789/2096 Title: A queue analytics system for taxi service using mobile crowd sensing Authors: Lu, Yu; Xiang, Shili; Wu, Wei; Wu, Huayu Abstract: Passengers waiting queues and taxis waiting queues are commonly seen in many urban cities. Our poster presents a queue analytics system, which collaboratively uses the mobile data from taxis and smartphones, to detect both passenger queues and taxi queues. In particular, the system firstly determines the existence of taxi queues by analyzing the taxi data, and then make a soft inference on passenger queues. Meanwhile, the passenger side adopts the smartphone-based crowd sensing strategy to detect the personal-scale queuing activities. Lastly, the system aggregates the detection results and validates passenger queues. The extensive empirical experiments demonstrate our system can accurately and effectively achieve the design objectives. Moreover, the system envisions a novel crowd sensing way to perform online analysis using data from heterogeneous sources. Description: © ACM 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, http://dx.doi.org/10.1145/2800835.2800887. 2015-09-07T00:00:00Z http://oar.a-star.edu.sg:80/jspui/handle/123456789/2092 Title: Cascade Subspace Clustering Authors: Xi, Peng; Jiashi, Feng; Jiwen, Lu; Wei-Yun, Yau; Zhang, Yi Abstract: In this paper, we recast the subspace clustering as a verification problem. Our idea comes from an assumption that the distribution between a given sample x and cluster centers Omega is invariant to different distance metrics on the manifold, where each distribution is defined as a probability map (i.e. soft-assignment) between x and Omega. To verify this so-called invariance of distribution, we propose a deep learning based subspace clustering method which simultaneously learns a compact representation using a neural network and a clustering assignment by minimizing the discrepancy between pair-wise sample-centers distributions. To the best of our knowledge, this is the first work to reformulate clustering as a verification problem. Moreover, the proposed method is also one of the first several cascade clustering models which jointly learn representation and clustering in end-to-end manner. Extensive experimental results show the effectiveness of our algorithm comparing with 11 state-of-the-art clustering approaches on four data sets regarding to four evaluation metrics. Description: Full paper can be downloaded from the Publisher's URL provided. 2017-02-04T00:00:00Z http://oar.a-star.edu.sg:80/jspui/handle/123456789/2091 Title: A new noise annoyance measurement metric for urban noise sensing and evalution Authors: Zhou, Huan; Yu, Rongshan; Song, Ying; Shu, Haiyan Abstract: This paper investigates the problem of noise-induced annoyance level evaluation, and proposes a novel annoyance measurement metric for more efficient and accurate evaluation of annoyance level of different types of noises. Results from a large-scale subjective listening test using 90 different noise clips and 96 subjects show that the proposed method can produce more consistent and reliable annoyance ratings than the widely adopted ISO method. Based on the subjective test results, we further develop an objective noise annoyance level measurement model based on the selected psychoacoustic features extracted from the noise samples. Our evaluation results show that the objective model produces satisfactory prediction accuracy on noise annoyance level. 2017-01-01T00:00:00Z http://oar.a-star.edu.sg:80/jspui/handle/123456789/2089 Title: Design and Fabrication of Microfiber Containing Gold Nanoparticles Authors: Jørgensen, Mette Marie; Wang, Guanghui; Hu, Dora Juan Juan; Shum, Ping Abstract: We present a simple fabrication method for embedding gold nanoparticles (GNPs) in a microfiber with two main advantages. The GNPs are positioned within the microfiber securing maximum enhancement of the electrical field and protection of the GNPs from the surroundings; moreover incoupling losses to the microfiber are reduced significantly by integrating a single mode fiber in the device. The average enhancement factor of the electric field within one unit cell of the microfiber due to the GNPs is estimated to 17.4. This enhancement leads to an enhanced Kerr effect near the plasmon resonance frequency. The fabrication method is demonstrated without GNPs. Description: (c) 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. 2010-12-14T00:00:00Z