The Grid 2: Blueprint for a New Computing Infrastructure

The questionnaires from the field were received, checked and stored by the data processing personnel. They checked: 1. The completeness of the questionnaires 2. The correct bubbling 3. The correct number of questionnaires per household, if total males + total females > 8 as the questionnaire ONLY accommodated maximum of 8 household members. 4. The reference number appears in all the 10 pages of the questionnaires.

Population and Housing Census

Hawaii International Conference on System Sciences

The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

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Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

This letter reports a performance analysis of a dual-hop relay system composed of asymmetric radio-frequency and free-space optics (RF/FSO) links. This approach is based on the fact that FSO links can provide even wider bandwidths as compared to RF ones. In particular, an exact closed-form expression for the end-to-end outage probability of the RF/FSO relay links is derived. Overall, the RF/FSO links show a slightly worse performance than the RF/RF links, but the performance gap is gradually reduced as the signal-to-noise ratio (SNR) increases. Our mathematical analysis results were verified by exactly matching Monte Carlo simulation results.

Performance Analysis of the Asymmetric Dual-Hop Relay Transmission With Mixed RF/FSO Links

This article presents the seven-step process involved in building a practical Wi-Fi-based indoor navigation system, which was implemented at the COEX complex in Seoul, Korea, in October 2010. The article describes the primary activities in each step using the COEX example. More than 200,000 users have downloaded the system since its first release. The successful launch of the COEX indoor navigation system suggests that indoor navigation is becoming a reality.

Building a Practical Wi-Fi-Based Indoor Navigation System

Embodiments for performing an indoor navigation are disclosed. The location based service system includes: a plurality of access points installed in an indoor environment and configured to send Wi-Fi signals including access point identification information; a mobile terminal configured to receive a Wi-Fi signal from at least one of the access points and form a first Wi-Fi fingerprint based on the received Wi-Fi signal; and a navigation service server configured to construct a plurality of Wi-Fi radio maps based on a plurality of second Wi-Fi fingerprints acquired at locations of the indoor environment, select a Wi-Fi radio map for estimating a location of the mobile terminal among the plurality of Wi-Fi radio maps, estimate the location the mobile terminal by using a second Wi-Fi fingerprint corresponding to the first Wi-Fi fingerprint based on the selected Wi-Fi radio map, and form location information including the estimated location.

Location based service system and method for performing indoor navigation

Wireless Local Area Network (WLAN) location fingerprinting has become a prevalent approach to indoor localization. However, its widespread adoption has been hindered by the need for manual efforts to collect location-labeled fingerprints for the calibration of a localization model. Several semi-supervised learning methods have been applied to reduce such manual efforts by exploiting unlabeled fingerprints, but they still require some amount of labeled fingerprints for initializing the learning process. In this research, in order to obviate the need for location labels or references, we propose a novel unsupervised learning method that calibrates a localization model using unlabeled fingerprints based on a hybrid global-local optimization scheme. The method determines the optimal placement of fingerprint sequences on an indoor map, under the constraint imposed by the inner structure shown on the map such as walls and partitions. An efficient interaction between a global and a local optimization in the hybrid scheme drastically reduces the complexity of the learning task. Experiments carried out in a single- and a multi-story building revealed that the proposed method could successfully build a precise localization model without any location reference or explicit efforts to collect labeled samples.

Unsupervised Learning for Crowdsourced Indoor Localization in Wireless Networks

With the accumulation of protein and its related data on the Internet, many domain-based computational techniques to predict protein interactions have been developed. However, most techniques still have many limitations when used in real fields. They usually suffer from low accuracy in prediction and do not provide any interaction possibility ranking method for multiple protein pairs. In this paper, we propose a probabilistic framework to predict the interaction probability of proteins and develop an interaction possibility ranking method for multiple protein pairs. Using the ranking method, one can discern the protein pairs that are more likely to interact with each other in multiple protein pairs. The validity of the prediction model was evaluated using an interacting set of protein pairs in yeast and an artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in the DIP (Database of Interacting Proteins) was used as a learning set of interacting protein pairs, high sensitivity (77%) and specificity (95%) were achieved for the test groups containing common domains with the learning set of proteins within our framework. The stability of the prediction model was also evident when tested over DIP CORE, HMS-PCI and TAP data. In the validation of the ranking method, we reveal that some correlations exist between the interacting probability and the accuracy of the prediction.

Dongsoo Han

Papers

Performance Analysis of the Asymmetric Dual-Hop Relay Transmission With Mixed RF/FSO Links

Building a Practical Wi-Fi-Based Indoor Navigation System

Location based service system and method for performing indoor navigation

Unsupervised Learning for Crowdsourced Indoor Localization in Wireless Networks

PreSPI: a domain combination based prediction system for protein–protein interaction