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Age of Context: Mobile, Sensors, Data and the Future of Privacy by Robert Scoble, Shel Israel
Albert Einstein, Apple II, augmented reality, call centre, Chelsea Manning, cloud computing, connected car, Edward Snowden, Elon Musk, factory automation, Filter Bubble, Google Earth, Google Glasses, Internet of things, job automation, Kickstarter, Mars Rover, Menlo Park, New Urbanism, PageRank, pattern recognition, RFID, ride hailing / ride sharing, Saturday Night Live, self-driving car, sensor fusion, Silicon Valley, Skype, smart grid, social graph, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Tesla Model S, Tim Cook: Apple, urban planning, Zipcar
An environmentally friendly company, Sensible Self, makes GreenGoose, cute little wireless stickers containing motion sensors that allow you to track anything that moves, from a pet or child to your phone, or even to check if your spouse left the toilet seat up. Melanie Martella, Executive Editor of Sensors magazine, introduced us to the concept of sensor fusion, a fast-emerging technology that takes data from disparate sources to come up with more accurate, complete and dependable data. Sensor fusion enables the same sense of depth that is available in 3D modeling, which is used for all modern design and construction, as well as the magic of special effects in movies. Sensors will understand if you are pilfering office supplies or engaging in a clandestine office affair. If you are a burglar, your phone might end up bearing witness against you and, in fact, your car will be able to testify if you were parked in an area you deny having visited—and it will be able to report when you were there, and if it was you in the car.
Data Mining in Time Series Databases by Mark Last, Abraham Kandel, Horst Bunke
4chan, call centre, computer vision, discrete time, information retrieval, iterative process, NP-complete, p-value, pattern recognition, random walk, sensor fusion, speech recognition, web application
Interesting applications of the median concept have been demonstrated in dealing with 2D shapes [16, 33], binary feature maps , 3D rotation , geometric features (points, lines, or 3D frames) , brain models , anatomical structures , and facial images . In this paper we discuss the adaptation of the median concept to the domain of strings. The median concept is useful in various contexts. It represents a fundamental quantity in statistics. In sensor fusion, multisensory measurements of some quantity are averaged to produce the best estimate. Averaging the results of several classiﬁers is used in multiple classiﬁer systems in order to achieve more reliable classiﬁcations. The outline of the chapter is as follows. We ﬁrst formally introduce the median string problem in Section 2 and provide some related theoretical results in Section 3. Sections 4 and 5 are devoted to algorithmic procedures for eﬃciently computing set median and generalized median strings.