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

The operating system in a page-based distributed shared memory (DSM) system gives the illusion to the users that the entire cluster of computers is a single system. In this paper, we track the performance issues in the UnixWare2/mk operating system that was developed for a page-based DSM machine. Firstly, we examine the design philosophy and the implementation details of the operating system. Then we address the performance issues in the operating system for the page-based DSM by showing the performance results of the following well-known benchmark programs: Ousterhout, Bonnie, and Networking Benchmark.

Performance issues in the operating system for the page-based distributed shared memory machine

Class-incremental few-shot learning, where new sets of classes are provided sequentially with only a few training samples, presents a great challenge due to catastrophic forgetting of old knowledge and overfitting caused by lack of data. During finetuning on new classes, the performance on previous classes deteriorates quickly even when only a small fraction of parameters are updated, since the previous knowledge is broadly associated with most of the model parameters in the original parameter space. In this paper, we introduce WaRP, the weight space rotation process, which transforms the original parameter space into a new space so that we can push most of the previous knowledge compactly into only a few important parameters. By properly identifying and freezing these key parameters in the new weight space, we can finetune the remaining parameters without affecting the knowledge of previous classes. As a result, WaRP provides an additional room for the model to effectively learn new classes in future incremental sessions. Experimental results confirm the effectiveness of our solution and show the improved performance over the state-of-the-art methods.

Class-incremental few-shot learning

In this paper, we propose a new agent-based flexible videoconference system (AVCS) by modifying videoconference manger (VCM) agent in a conventional flexible videoconference system (FVCS). The proposed AVCS can more flexibly cope with changes in working conditions during videoconferencing than the conventional FVCS. It is because an automatic parameter tuning algorithm is imbedded to VCM dynamically adapt QoS (Quality of Service) parameters in the sense that the current working condition can meet with the desired working condition of the user, which can change in time during videoconferencing. In the experimental section, we design a new structure of the VCM with the automatic parameter tuning module, imbed to the prototype of FVCS and implement the new AVCS. Also, it is shown that the proposed AVCS outperforms the existing FVCS in the experiment.

Agent-based flexible videoconference system with automatic QoS parameter tuning

PROBLEM TO BE SOLVED: To provide a method and system for automated WLAN radio map constructionSOLUTION: An automated WLAN radio map construction method includes the steps of: collecting WLAN fingerprints obtained by a mobile device in an indoor space; machine-learning a learning model generated on the basis of a state diagram that shows divided areas of an indoor map by location states; placing the collected WLAN fingerprints in corresponding location states; and storing a result of the placement Accordingly, collection locations of WLAN fingerprints collected by a plurality of unspecified smartphones without any reference location information source, such as GPS signals, can be automatically labeledSELECTED DRAWING: Figure 2

Automated wlan radio map construction method and automated wlan radio map construction system

WLAN fingerprinting has been extensively studied due to the widespread deployment of WLAN infrastructure. However, extensive calibration effort is required to collect fingerprints as are necessary for radio map construction, which is time-consuming and labor-intensive. Although many studies have been conducted to reduce the calibration efforts, it is inevitable to be applied in limited circumstances because it requires some location labels for initializing the learning models or requires continuous use of sensor data. In this research, we introduce a practical radio map construction method in a multi-story building utilizing only limited uses of sensors. The method determines the unlabeled data of each floor by clustering method utilizes the correlation of Wi-Fi and barometer data, and a radio map is constructed through a semi-supervised learning technique using candidates of location labels, PDR sensor data, and WLAN fingerprints. The extensive experiments carried out in three large multi-story buildings demonstrate that the method successfully builds accurate radio maps without any explicit effort to collect location reference.

Dongsoo Han

Papers

Performance issues in the operating system for the page-based distributed shared memory machine

Class-incremental few-shot learning

Agent-based flexible videoconference system with automatic QoS parameter tuning

Automated wlan radio map construction method and automated wlan radio map construction system

Location-labeling of crowdsourced fingerprints for indoor localization in multi-story buildings