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Lee C.-S.,National Chiao Tung University | Chung W.-H.,Research Center for Information Technology Innovation | Lee T.-S.,National Chiao Tung University
2015 IEEE Wireless Communications and Networking Conference, WCNC 2015 | Year: 2015

In this paper, the distributed channel access schemes for ALOHA-based cognitive radio networks are considered. In the considered system, time is divided into frames, which are further divided into sensing phase and transmission phase. We derive channel sensing policy in the sensing phase, and channel access policy in the transmission phase for a secondary user (SU) to maximize its throughput. To mitigate high complexities of the above scheme, we propose a threshold-based channel access scheme. In this scheme, an appropriate threshold is set based on channel occupancy information and channel state information, and only channels providing potentially high throughput will be sensed and accessed. The proposed schemes are fully distributed, i.e., no extra information exchange is needed among SUs, which is a highly desired and beneficial property. Simulation results confirm that the proposed schemes outperform prior random access schemes. © 2015 IEEE.

You C.-W.,Research Center for Information Technology Innovation | Kao H.-L.C.,National Taiwan University | Ho B.-J.,National Taiwan University | Chen Y.-H.T.,Massachusetts Institute of Technology | And 4 more authors.
IEEE Pervasive Computing | Year: 2014

Systematically and quantitatively determining patterns in consumer flow is an important problem in marketing research. Identifying these patterns can facilitate an understanding of where and when consumers purchase products and services at physical retail shops. Collecting data on real consumers who shop at retail stores is one of the most challenging and expensive aspects of these studies. This article introduces ConvenienceProbe, a phone-based data collection system for retail trade-area analysis. The proposed method targets local residents shopping at neighborhood convenience stores. This study deploys and tests the system by collecting real customer flow data in neighborhood convenience stores. Results show that the consumer flow data collected from the ConvenienceProbe system is comparable to that from a traditional face-to-face interview method. © 2002-2012 IEEE.

Yang M.-C.,National Taiwan University | Yang M.-C.,Research Center for Information Technology Innovation | Chu C.-T.,Research Center for Information Technology Innovation | Wang Y.-C.F.,Research Center for Information Technology Innovation | Wang Y.-C.F.,Academia Sinica, Taiwan
Proceedings - International Conference on Image Processing, ICIP | Year: 2010

Learning-based approaches for super-resolution (SR) have been studied in the past few years. In this paper, a novel single-image SR framework based on the learning of sparse image representation with support vector regression (SVR) is presented. SVR is known to offer excellent generalization ability in predicting output class labels for input data. Given a low resolution image, we approach the SR problem as the estimation of pixel labels in its high resolution version. The feature considered in this work is the sparse representation of different types of image patches. Prior studies have shown that this feature is robust to noise and occlusions present in image data. Experimental results show that our method is quantitatively more effective than prior work using bicubic interpolation or SVR methods, and our computation time is significantly less than that of existing SVR-based methods due to the use of sparse image representations. © 2010 IEEE.

Su C.-H.,Academia Sinica, Taiwan | Su C.-H.,National Taiwan University | Shih C.-H.,Academia Sinica, Taiwan | Chang T.-H.,Ohio State University | And 3 more authors.
Genomics | Year: 2010

In budding yeast, approximately a quarter of adjacent genes are divergently transcribed (divergent gene pairs). Whether genes in a divergent pair share the same regulatory system is still unknown. By examining transcription factor (TF) knockout experiments, we found that most TF knockout only altered the expression of one gene in a divergent pair. This prompted us to conduct a comprehensive analysis in silico to estimate how many divergent pairs are regulated by common sets of TFs (cis-regulatory modules, CRMs) using TF binding sites and expression data. Analyses of ten expression datasets show that only a limited number of divergent gene pairs share CRMs in any single dataset. However, around half of divergent pairs do share a regulatory system in at least one dataset. Our analysis suggests that genes in a divergent pair tend to be co-regulated in at least one condition; however, in most conditions, they may not be co-regulated. © 2010 Elsevier Inc.

Lee S.-Y.,Research Center for Information Technology Innovation | Chang M.-K.,National Chung Hsing University | Yang D.-N.,Research Center for Information Technology Innovation | Yang D.-N.,Academia Sinica, Taiwan
IEEE Wireless Communications and Networking Conference, WCNC | Year: 2013

The grid-connected photovoltaic (PV) power generator has been demonstrated as a promising solution of hybrid energy to leverage instantaneous renewable energy generation for reducing the energy acquired from electrical grid. In light of this technology trend and opportunity, this paper takes an initial attempt to explore the relay selection and power allocation for green cooperative cellular networks with grid-connected PV power generators. By properly considering the distributions of PV power, we propose an opportunistic relay selection scheme to minimize the grid power consumption. We also analyze the performance on the expected total grid power consumption in the whole network and derive a corresponding upper bound. The simulation results demonstrate that more than 53% of grid power can be saved by the exploitation of PV power with the proposed scheme. © 2013 IEEE.

Tsai Z.T.-Y.,Academia Sinica, Taiwan | Tsai Z.T.-Y.,National Yang Ming University | Huang G.T.-W.,Academia Sinica, Taiwan | Huang G.T.-W.,Carnegie Mellon University | And 3 more authors.
Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011 | Year: 2011

Identifying transcription factor binding sites (TFBSs) is crucial for understanding the mechanism of transcriptional regulation. It is known that transcription factors (TFs) often cooperate to regulate genes. While traditional approaches can be used to discover binding motifs of a group of co-regulated genes, they often fail to accurately assign motifs to the corresponding TFs. Here, we consider two TFs together to infer their TFBSs and their synergistic relationship simultaneously. The basic idea is that if two TFs interact, their TFBSs, if distinct, would be conserved across species and coincided in the promoter regions of the genes they co-regulated. Applying our method to Saccharomyces cerevisiae chromatin immunoprecipitation data, we predicted 110 TF pairs with statistically significant motif assignments. A majority of these TF pairs have literature support to be synergistic, and the designated motifs to TFs match well with their known consensus. We further examined the synergism of predicted TF pairs in seven experimental conditions using ANOVA, and identified significant interactions. © 2011 IEEE.

Tsai T.-H.,Research Center for Information Technology Innovation | Cheng W.-H.,Research Center for Information Technology Innovation | You C.-W.,Research Center for Information Technology Innovation | Hu M.-C.,National Cheng Kung University | And 2 more authors.
IEEE Transactions on Image Processing | Year: 2014

Camera-enabled mobile devices are commonly used as interaction platforms for linking the user's virtual and physical worlds in numerous research and commercial applications, such as serving an augmented reality interface for mobile information retrieval. The various application scenarios give rise to a key technique of daily life visual object recognition. On-premise signs (OPSs), a popular form of commercial advertising, are widely used in our living life. The OPSs often exhibit great visual diversity (e.g., appearing in arbitrary size), accompanied with complex environmental conditions (e.g., foreground and background clutter). Observing that such real-world characteristics are lacking in most of the existing image data sets, in this paper, we first proposed an OPS data set, namely OPS-62, in which totally 4649 OPS images of 62 different businesses are collected from Google's Street View. Further, for addressing the problem of real-world OPS learning and recognition, we developed a probabilistic framework based on the distributional clustering, in which we proposed to exploit the distributional information of each visual feature (the distribution of its associated OPS labels) as a reliable selection criterion for building discriminative OPS models. Experiments on the OPS-62 data set demonstrated the outperformance of our approach over the state-of-the-art probabilistic latent semantic analysis models for more accurate recognitions and less false alarms, with a significant 151.28% relative improvement in the average recognition rate. Meanwhile, our approach is simple, linear, and can be executed in a parallel fashion, making it practical and scalable for large-scale multimedia applications. © 2014 IEEE.

Hung S.-H.,Application Security | Hung S.-H.,Research Center for Information Technology Innovation | Tzeng T.-T.,Application Security | Wu G.-D.,Application Security | Shieh J.-P.,Application Security
Software - Practice and Experience | Year: 2015

Summary Smart mobile devices and wireless networks are changing the way people execute applications and access information. In the meantime, more and more personal data are thus spread around different data silos in the Internet. One day, you will find that you may want to access your own data, but the service providers may not allow or the vendors do not provide any application for your need. It means your personal data are locked in by the service vendors. While the advances in the hardware/software technology of the smart mobile devices enable more complicated application than the user can image a couple of years ago, those devices still fall short of big storage, powerful computing and more battery capacity for better user experience. The user may expect a device with secure and unlimited storage for his personal data and run his application as he wishes without incurring more energy consumption. So far, no simple solutions have been proposed to enable compute cloud for code offloading Android application and trusted storage cloud for personal data. The work described in this paper enhances current Android application framework to address the aforementioned issues. We introduce the MobileFBP framework to augment the compute part for the Android device and propose to leverage the central storage part with personal data store, that is, PDS, for trusted usage. In addition to the design and implementation of the enhanced framework, our preliminary experimental results are illustrated as well. Copyright © 2014 John Wiley & Sons, Ltd.

Lu X.,Japan National Institute of Information and Communications Technology | Tsao Y.,Research Center for Information Technology Innovation | Shen P.,Japan National Institute of Information and Communications Technology | Hori C.,Japan National Institute of Information and Communications Technology
Proceedings of the 9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014 | Year: 2014

In most algorithms for acoustic event detection (AED), frame based acoustic representations are used in acoustic modeling. Due to lack of context information in feature representation, large model confusions may occur during modeling. We have proposed a feature learning and representation algorithm to explore context information from temporal-frequency patches of signal for AED. With the algorithm, a sparse feature was extracted based on an acoustic dictionary composed of a bag of spectral patches. In our previous algorithm, the feature was obtained based on a definition of Euclidian distance between input signal and acoustic dictionary. In this study, we formulate the sparse feature extraction as l1 regularization in signal reconstruction. The sparsity of the representation is efficiently controlled via varying a regularization parameter. A support vector machine (SVM) classifier was built on the extracted sparse feature for AED. Our experimental results showed that the spectral patch based sparse representation effectively improved the performance by incorporating temporal-frequency context information in modeling. © 2014 IEEE.

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