Seng-Yong Lau

TUIC: enabling tangible interaction on capacitive multi-touch displays

We present TUIC, a technology that enables tangible interaction on capacitive multi-touch devices, such as iPad, iPhone, and 3Ms multi-touch displays, without requiring any hardware modifications. TUIC simulates finger touches on capacitive displays using passive materials and active modulation circuits embedded inside tangible objects, and can be used with multi-touch gestures simultaneously. TUIC consists of three approaches to sense and track objects: spatial, frequency, and hybrid (spatial plus frequency). The spatial approach, also known as 2D markers, uses geometric, multi-point touch patterns to encode object IDs. Spatial tags are straightforward to construct and are easily tracked when moved, but require sufficient spacing between the multiple touch points. The frequency approach uses modulation circuits to generate high-frequency touches to encode object IDs in the time domain. It requires fewer touch points and allows smaller tags to be built. The hybrid approach combines both spatial and frequency tags to construct small tags that can be reliably tracked when moved and rotated. We show three applications demonstrating the above approaches on iPads and 3Ms multi-touch displays.

A measurement study of zigbee-based indoor localization systems under RF interference

With an expected market value of 2.71 billion in 2016, supporting daily use of real-time location systems in households and commercial buildings is an increasingly important subject of study. A growing problem in providing robust indoor location estimations in real time is the use of wireless transmissions in RF frequencies. Having implemented a simple RSSI-signature-based location system on a 24-node IEEE 802.15.4-based sensor network testbed, we are able to analyze the effect of background IEEE 802.11 traffic on localization error. The measurement results demonstrate that the 80th-percentile of the localization error may increase by 141% at worst in an office building with active use of IEEE 802.11 for data. Such performance degradation results from RSSI reading loss as the beacon messages collide with background traffic.

Impact of sensor-enhanced mobility prediction on the design of energy-efficient localization

Energy efficiency and positional accuracy are often contradictive goals. We propose to decrease power consumption without sacrificing significant accuracy by developing an energy-aware localization that adapts the sampling rate to targets mobility level. In this paper, an energy-aware adaptive localization system based on signal strength fingerprinting is designed, implemented, and evaluated. Promising to satisfy an applications requirements on positional accuracy, our system tries to adapt its sampling rate to reduce its energy consumption. The contribution of this paper is fourfold. (1) We have developed a model to predict the positional error of a real working positioning engine under different mobility levels of mobile targets, estimation error from the positioning engine, processing and networking delay in the location infrastructure, and sampling rate of location information. (2) In a real test environment, our energy-saving method solves the mobility estimation error problem by utilizing additional sensors on mobile targets. The result is that we can improve the prediction accuracy by 56.34% on average, comparing to algorithms without utilizing additional sensors. (3) We further enhance our sensor-enhanced mobility prediction algorithm by detecting the targets moving foot step and then estimate the targets velocity. This method can improve the mobility prediction accuracy by 49.81% on an average, comparing to previous sensor-enhanced mobility prediction algorithm. (4) We implemented our energy-saving methods inside a working localization infrastructure and conducted performance evaluation in a real office environment. Our performance results show as much as 68.92% reduction in power consumption.

Sensor-Enhanced Mobility Prediction for Energy-Efficient Localization

Energy efficiency and positional accuracy are often contradictive goals. We propose to decrease power consumption without sacrificing significant accuracy by developing an energy-aware localization that adapts the sampling rate to targets mobility level. In this paper, an energy-aware adaptive localization system based on signal strength fingerprinting is designed, implemented, and evaluated. Promising to satisfy an applications requirements on positional accuracy, our system tries to adapt its sampling rate to reduce its energy consumption. The contribution of this paper is three-fold. (1) We have developed a model to predict the positional error of a real working positioning engine under different mobility levels of mobile targets, estimation error from the positioning engine, processing and networking delay in the location infrastructure, and sampling rate of location information. (2) In a real test environment, our energy-saving method solves the mobility estimation error problem by utilizing additional sensors on mobile targets. The result is that we can improve the prediction accuracy by as much as 37.01%. (3) We implemented our energy-saving methods inside a working localization infrastructure and conducted performance evaluation in a real office environment. Our performance results show as much as 49.76 % reduction in power consumption

Sensor networks for everyday use: the BL-Live experience

With 250,000 NTD invested, 6 man power allocated, and 9 month time elapsed, we present BL-Live. The seemingly dumb, senseless BL Hall at National Taiwan University is transformed to an intelligent office building by a 30+ node sensor network. Two everyday services, Elevator Report and Smart Office, are implemented to better utilize the resources of the building and of the building residents. Our experience allows us to re-examine technical issues such as manufacturing cost, form factor, deployment, data communication, and energy efficiency. Many of the lessons we learn are not only surprising but also inspiring, which in turn leads us to think that more deployment experience and experience sharing are critical to the advances and commercialization of sensor networks

Impact of Beacon Packet Losses to RSSI-Signature-Based Indoor Localization Sensor Networks

We present an experimental study on an RSSI-signature-based localization system. The two major findings are: (1) Switching from a clean channel to a busy one, we observe, in our experiments, a 300% increase in the 80-percentile location estimation error; (2) RSSI value and packet reception rate are not correlated.

BioScope: an extensible bandage system for facilitating data collection in nursing assessments

To facilitate the collection of patient biosignals, designing extensible sensing devices in which sensor management is simplified is essential. This paper presents BioScope, an extensible sensing system that facilitates collecting data used in nursing assessments. We conducted experiments to demonstrate the potential of the system. The results obtained in this study can be applied in improving the design, thus enabling BioScope to facilitate data collection in numerous potential applications.

GEN02-3: On the Search of Internet AS-level Topology Invariants

There has been a significant amount of work analyzing the Internet AS (Autonomous System)-level topology which gives rise to a number of topology models. Although each of these models is being refined to fit better a particular set of graph properties over time, there is a more fundamental need in knowing the topology invariants. To address this need, we examine how the Internet AS-level topology evolves, using a variety of graph metrics,-a super set of what has been used in recent works. We discover that the AS-level topology shows a clear converging trend only in the normalized Laplacian spectrum (nls). From the theory of nls, we discover further that the converging trend indicates a stabilizing ratio of leaf to core ASes (or customer to provider ASes) on the Internet.