Catálogo de publicaciones - libros

CarpetLAN is a novel indoor wireless(-like) broad-band networking and positioning system. It uses the floor surface and the human body as an Ethernet-cable, and weak electric fields as the transmission media. Portable and wearable devices can connect to the network while the user stands or walks on the floor; connection speed is 10Mbps. Home and office appliances can also access the network if they are just put on the floor. CarpetLAN also provides an indoor positioning function, which is urgently needed for realizing “ubiquitous” communication. This electric field based transmission system yields ultra-micro communication cells, so the positions of humans and appliances can be detected with about 1 meter accuracy.

This paper introduces a novel way to allow non-expert users to create smart surroundings. Non-smart everyday objects such as furniture and appliances found in homes and offices can be converted to smart ones by attaching computers, sensors, and devices. In this way, non-smart components that form non-smart objects are made smart in advance. For our first prototype, we have developed u-Texture, a self-organizable universal panel that works as a building block. The u-Texture can change its own behavior autonomously through recognition of its location, its inclination, and surrounding environment by assembling these factors physically. We have demonstrated several applications to confirm that u-Textures can create smart surroundings easily without expert users.

We present , a novel toolkit which automatically generates interfaces for ubiquitous applications. Designers need only specify declarative models of the interface and desired hardware device and uses decision-theoretic optimization to automatically generate a concrete rendering for that device. This paper provides an overview of our system and describes key extensions that barred the previous version (reported in [3]) from practical application. Specifically, we describe a functional modeling language capable of representing complex applications. We propose a new adaptation strategy, , which speeds access to common interface features without disorienting the user. We present a that allows designers and end users to override ’s automatic rendering decisions. We describe a distributed architecture which enables computationally-impoverished devices to benefit from interfaces. Finally, we present experiments and a preliminary user-study that demonstrate the practicality of our approach.

The paper reports the results of the first stage of our work on an automatic dietary monitoring system. The work is part of a large European project on using ubiquitous systems to support healthy lifestyle and cardiovascular disease prevention. We demonstrate that sound from the user’s mouth can be used to detect that he/she is eating. The paper also shows how different kinds of food can be recognized by analyzing chewing sounds. The sounds are acquired with a microphone located inside the ear canal. This is an unobtrusive location widely accepted in other applications (hearing aids, headsets). To validate our method we present experimental results containing 3500 seconds of chewing data from four subjects on four different food types typically found in a meal. Up to 99% accuracy is achieved on eating recognition and between 80% to 100% on food type classification.

With the ubiquity of camera phones, it is now possible to capture digital still and moving images anywhere, raising a legitimate concern for many organizations and individuals. Although legal and social boundaries can curb the capture of sensitive information, it sometimes is neither practical nor desirable to follow the option of confiscating the capture device from an individual. We present the design and proof of concept implementation of a capture-resistant environment that prevents the recording of still and moving images without requiring any cooperation on the part of the capturing device or its operator. Our solution involves a tracking system that uses computer vision for locating any number of retro-reflective CCD or CMOS camera sensors in a protected area. A pulsing light is then directed at the lens, distorting any imagery the camera records. Although the directed light interferes with the camera’s operation, it can be designed to minimally impact the sight of other humans in the environment.

Location systems that are based on scanning for nearby radio sources can estimate the position of a mobile device with reasonable accuracy and high coverage. These systems require a calibration step in which a map is built from radio-readings taken on a location-aware device. War driving, for example, calibrates the positions of WiFi access points using a GPS-equipped laptop. In this paper we introduce an algorithm for that minimizes or even eliminates explicit calibration by allowing the location system to build this radio map as the system is used. Using nearly 100 days of trace data, we evaluate self-mapping’s accuracy when the map is seeded by three realistic data sources: public war-driving databases, WiFi hotspot finders, and sporadic GPS connectivity. On average, accuracy and coverage are shown to be comparable to those achieved with an explicit war-driven radio map.

The ubiquitous computing community has widely researched the use of 802.11 for the purpose of location inference. Meanwhile, Bluetooth is increasingly widely deployed due to its low power consumption and cost. This paper describes a study of Bluetooth radio propagation using an accurate indoor location system to conduct fine-grained signal strength surveys. We discuss practical problems and requirements encountered setting up the infrastructure using the ultrasonic Active Bat indoor location system, and limitations of the commodity Bluetooth devices used. We conclude that Bluetooth is poorly suited to the purpose of fine-grained, low latency location inference due to specification and hardware limitations, and note that the movement speed of mobile devices is an important factor in calculating available bandwidth. We publish our data sets of signal strength samples for the community to freely use in future research.

Ubiquitous computing technologies which are cheap and easy to use are more likely to be adopted by users beyond the ubiquitous computing community. We present an ultrasonic-only tracking system that is cheap to build, self-calibrating and self-orientating, and has a convenient form factor. The system tracks low-power tags in three dimensions. The tags are smaller than AAA batteries and last up to several years on their power source. The system can be configured to track either multiple near-stationary objects or a single fast moving object. Full test results are provided and use of the system within a home application is discussed.

Accurate indoor localization has long been an objective of the ubiquitous computing research community, and numerous indoor localization solutions based on 802.11, Bluetooth, ultrasound and infrared technologies have been proposed. This paper presents the first accurate GSM indoor localization system that achieves median accuracy of 5 meters in large multi-floor buildings. The key idea that makes accurate GSM-based indoor localization possible is the use of signal-strength fingerprints. In addition to the 6-strongest cells traditionally used in the GSM standard, the wide fingerprint includes readings from additional cells that are strong enough to be detected, but too weak to be used for efficient communication. Experiments conducted on three multi-floor buildings show that our system achieves accuracy comparable to an 802.11-based implementation, and can accurately differentiate between floors in both wooden and steel-reinforced concrete structures.

Location-enhanced mobile devices are becoming common, but applications built for these devices find themselves suffering a mismatch between the latitude and longitude that location sensors provide and the colloquial place label that applications need. Conveying my location to my spouse, for example as (48.13641N, 11.57471E), is less informative than saying “at home.” We introduce an algorithm called BeaconPrint that uses WiFi and GSM radio fingerprints collected by someone’s personal mobile device to automatically learn the places they go and then detect when they return to those places. BeaconPrint does not automatically assign names or semantics to places. Rather, it provides the technological foundation to support this task. We compare BeaconPrint to three existing algorithms using month-long trace logs from each of three people. Algorithmic results are supplemented with a survey study about the places people go. BeaconPrint is over 90% accurate in learning and recognizing places. Additionally, it improves accuracy in recognizing places visited infrequently or for short durations—a category where previous approaches have fared poorly. BeaconPrint demonstrates 63% accuracy for places someone returns to only once or visits for less than 10 minutes, increasing to 80% accuracy for places visited twice.