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NextMe: Localization Using Cellular Traces in Internet of Things
No full textThe Internet of Things (IoT) opens up tremendous opportunities to location-based industrial applications that leverage both Internet-resident resources and phones\u27 processing power and sensors to provide location information. Location-based service is one of the vital applications in commercial, economic, and public domains. In this paper, we propose a novel localization scheme called NextMe, which is based on cellular phone traces. We find that the mobile call patterns are strongly correlated with the co-locate patterns. We extract such correlation as social interplay from cellular calls, and use it for location prediction from temporal and spatial perspectives. NextMe consists of data preprocessing, call pattern recognition, and a hybrid predictor. To design the call pattern recognition module, we introduce the notions of critical calls and corresponding patterns. In addition, NextMe does not require that the cell tower addresses should be bounded with concrete coordinates, e.g., global positioning system (GPS) coordinates. We validate NextMe across MIT Reality Mining Dataset, involving 500 000 h of continuous behavior information and 112 508 cellular calls. Experimental results show that NextMe achieves fine-grained prediction accuracy at cell tower level in the forthcoming 1-6 h with 12% accuracy enhancement averagely from cellular calls
Comprehensive Two-Dimensional Gas Chromatography with Pattern Modulation
No full textComprehensive two-dimensional gas chromatography (GC×GC) modulators normally transfer primary column effluent to the head of the secondary column as a series of sharp pulses. Such pulses are produced with time-varying temperature gradients in thermal modulation or with time-varying flow patterns in flow modulation. Thermal modulators produce narrow peaks at optimal flow rates, but require large amounts of consumables or a highly engineered heating/cooling system. Flow modulators involve simpler hardware and no additional consumables. However, flow modulators require a large increase in secondary column flow or transfer only a small portion of the primary effluent to the secondary column. This study examines a new method of producing GC×GC separations with a flow modulator. Instead of injecting narrow pulses, the modulator transfers primary effluent to the secondary column in the form of an intricate injection pattern. The detector signal is deconvoluted and converted to a two-dimensional chromatogram. The high duty cycle of the technique (\u3e50%) leads to deconvoluted peaks with twenty times greater intensity than those produced by conventional modulation with a Deans switch modulator. Pattern modulation can be produced without requiring elevated carrier flows. This study evaluates the efficacy of pattern modulation GC×GC by analyzing a standard mixture of 43 oxygenated organic compounds and an E85 fuel sample
Flint International Statistics Conference Announcement
Get PDFCONFERENCE ANNOUNCEMENT POSTER:
Kettering University is organizing this international conference to celebrate the IYS 2013 and the 175th anniversary of the American Statistical Association.
The main focus of this conference will be on STATISTICAL METHODS & STUDIES OF HISTORICAL DATA.
Participants may use any data. Data on Flint—consisting of up to 100 years of demographic, health, labor, census and crime records will be summarized and made available to participants. Sessions will include presentations of the statistical achievements and perspectives, followed by several talks on current results
Queues with interruptions: A survey
No full textIn this paper we survey work related to queues with interruptions that occur due to many reasons such as server breakdowns, servers taking emergency breaks, and customers having incomplete information or getting distracted. We look at both continuous and discrete time queueing models with interruptions in this survey