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

With the popularization of smartphones, the needs for indoor location information are rapidly growing these days. Wi-Fi based positioning technique has been widely used to provide positioning information indoors. In particular, fingerprint-based localization is preferred because of its advantage in accuracy. However, the accuracy of localization gradually degrades as the Wi-Fi environment changes. In order to prevent the accuracy degradation, a radio map, which stores the Wi-Fi environment information, should be updated to accommodate the changes. Recalibration is commonly used to update a radio map, but it usually requires considerable time and effort. In this paper, we propose a method that can update the radio map automatically by using fingerprints collected from numerous users. In order to tag the locations of the collected fingerprints more accurately, the data from various sensors such as accelerometer and gyroscope are used. The proposed method also uses optimization algorithms along with a filtering method to remove erroneous data. The evaluation results showed that the accuracy achieved by the method was comparable to that of manual calibration in spite of using user feedback data. This indicates that without recalibration, the fingerprint-based indoor positioning system can keep up its accuracy if it is used by many users.

Crowdsourcing-based radio map update automation for wi-fi positioning systems

In this paper, we propose a probabilistic framework to predict the interaction probability of proteins. The notion of domain combination and domain combination pair is newly introduced and the prediction model in the framework takes domain combination pair as a basic unit of protein interactions to overcome the limitations of the conventional domain pair based prediction systems. The framework largely consists of prediction preparation and service stages. In the prediction preparation stage, two appearance probability matrices are constructed. Each matrix holds information on appearance frequencies of domain combination pairs in the interacting and non-interacting sets of protein pairs, respectively. Based on the appearance probability matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range of 0 to 1. Two distributions of interacting and non-interacting sets of protein pairs are obtained using the equation. In the prediction service stage, the interaction probability of a protein pair is predicted using the distributions and the equation. The validity of the prediction model is evaluated for the interacting set of protein pairs in a Yeast organism and artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in DIP (Database of Interacting Proteins) is used as a learning set of interacting protein pairs, very high sensitivity (86%) and moderate specificity (56%) are achieved within our framework.

A Domain Combination-based Probabilistic Framework for Protein-Protein Interaction Prediction

A domain combination based probabilistic framework for protein-protein interaction prediction

Recently, Wi-Fi fingerprints are often used for constructing indoor positioning systems. Wi-Fi fingerprint is a vector of Received Signal Strength (RSS) values at a particular location. Radio map is the collection of Wi-Fi fingerprints and their collected location at an area or a building. Positioning systems, mounted on top of the radio map, estimate locations using the information in the radio map. Many Wi-Fi fingerprint-based positioning algorithms have been developed. K-Nearest Neighbor(KNN), probabilistic method, fuzzy logic, neural network, multilayer perceptron are the examples. However, this field has not yet fully benefited from the potential of deep learning approaches. The sequence of Wi-Fi fingerprints implies that the deep recurrent network approaches, especially designed to handle sequential data, can play a vital role to enhance the performance of fingerprint-based positioning systems. In this paper, deep and recurrent approaches are studied rigorously for the improvement of the accuracy of positioning systems. We focus mainly on Long Short-Term Memory (LSTM) networks. An LSTM-based approach was compared with other state of the art approaches. A complete explanation to select the best hyper parameters is presented so that they can be referenced by the researchers in this field. A simple vanilla LSTM architecture is also compared with a stacked LSTM architecture on a Wi-Fi fingerprint dataset.

Dongsoo Han

Papers

Crowdsourcing-based radio map update automation for wi-fi positioning systems

A Domain Combination-based Probabilistic Framework for Protein-Protein Interaction Prediction

A domain combination based probabilistic framework for protein-protein interaction prediction

An LSTM-based Indoor Positioning Method Using Wi-Fi Signals

THE-MUSS: Mobile u-health service system