Probabilistic models for mobile phone trajectory estimation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 157-161).This dissertation is concerned with the problem of determining the track or trajectory of a mobile device - for example, a sequence of road segments on an outdoor map, or a sequence of rooms visited inside a building - in an energy-efficient and accurate manner. GPS, the dominant positioning technology today, has two major limitations. First, it consumes significant power on mobile phones, making it impractical for continuous monitoring. Second, it does not work indoors. This dissertation develops two ways to address these limitations: (a) subsampling GPS to save energy, and (b) using alternatives to GPS such as WiFi localization, cellular localization, and inertial sensing (with the accelerometer and gyroscope) that consume less energy and work indoors. The key challenge is to match a sequence of infrequent (from sub-sampling) and inaccurate (from WiFi, cellular or inertial sensing) position samples to an accurate output trajectory. This dissertation presents three systems, all using probabilistic models, to accomplish this matching. The first, VTrack, uses Hidden Markov Models to match noisy or sparsely sampled geographic (lat, lon) coordinates to a sequence of road segments on a map. We evaluate VTrack on 800 drive hours of GPS and WiFi localization data collected from 25 taxicabs in Boston. We find that VTrack tolerates significant noise and outages in location estimates, and saves energy, while providing accurate enough trajectories for applications like travel-time aware route planning. CTrack improves on VTrack with a Markov Model that uses "soft" information in the form of raw WiFi or cellular signal strengths, rather than geographic coordinates. It also uses movement and turn "hints" from the accelerometer and compass to improve accuracy. We implement CTrack on Android phones, and evaluate it on cellular signal data from over 126 (1,074 miles) hours of driving data. CTrack can retrieve over 75% of a user's drive accurately on average, even from highly inaccurate (175 metres raw position error) GSM data. iTrack uses a particle filter to combine inertial sensing data from the accelerometer and gyroscope with WiFi signals and accurately track a mobile phone indoors. iTrack has been implemented on the iPhone, and can track a user to within less than a metre when walking with the phone in the hand or pants pocket, over 5 x more accurately than existing WiFi localization approaches. iTrack also requires very little manual effort for training, unlike existing localization systems that require a user to visit hundreds or thousands of locations in a building and mark them on a map.by Arvind Thiagarajan.Ph.D

Representing and querying regression models in a relational database management system

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 77-79).Curve fitting is a widely employed, useful modeling tool in several financial, scientific, engineering and data mining applications, and in applications like sensor networks that need to tolerate missing or noisy data. These applications need to both fit functions to their data using regression, and pose relational-style queries over regression models. Unfortunately, existing DBMSs are ill suited for this task because they do not include support for creating, representing and querying functional data, short of brute-force discretization of functions into a collection of tuples. This thesis describes FunctionDB, a novel DBMS that extends the state of the art. FunctionDB treats functions output by regression as first-class citizens that can be queried declaratively and manipulated like traditional database relations. The key contributions of FunctionDB are a compact, algebraic representation for regression models as piecewise functions, and an algebraic query processor that executes declarative queries directly on this representation as combinations of algebraic operations like function inversion, zero finding and symbolic integration. FunctionDB is evaluated on two real world data sets: measurements from a temperature sensor network, and traffic traces from cars driving on Boston roads. The results show that operating in the functional domain has substantial accuracy advantages (over 15% for some queries) and order of magnitude (10x-100x) performance gains over existing approaches that represent models as discrete collections of points. The thesis also describes an algorithm to maintain regression models online, as new raw data is inserted into the system. The algorithm supports a sustained insertion rate of the order of a million records per second, while generating models no less compact than a clairvoyant (offline) strategy.by Arvind Thiagarajan.S.M

Code In The Air: Simplifying Sensing and Coordination Tasks on Smartphones

A growing class of smartphone applications are tasking applications that run continuously, process data from sensors to determine the user's context (such as location) and activity, and optionally trigger certain actions when the right conditions occur. Many such tasking applications also involve coordination between multiple users or devices. Example tasking applications include location-based reminders, changing the ring-mode of a phone automatically depending on location, notifying when friends are nearby, disabling WiFi in favor of cellular data when moving at more than a certain speed outdoors, automatically tracking and storing movement tracks when driving, and inferring the number of steps walked each day. Today, these applications are non-trivial to develop, although they are often trivial for end users to state. Additionally, simple implementations can consume excessive amounts of energy. This paper proposes Code in the Air (CITA), a system which simplifies the rapid development of tasking applications. It enables non-expert end users to easily express simple tasks on their phone, and more sophisticated developers to write code for complex tasks by writing purely server-side scripts. CITA provides a task execution framework to automatically distribute and coordinate tasks, energy-efficient modules to infer user activities and compose them, and a push communication service for mobile devices that overcomes some shortcomings in existing push services.National Science Foundation (U.S.) (Grant 0931550

First Report on Infection of Argulus quadristriatus (Arthropoda: Crustacea: Branchiura) on Marine Fish Cobia in Brood Stock Pond Culture [2019]

A total of 30 specimens of fish cobia Rachycentron canadum (Total Length = 45–120 cm, Weight = 3.0–25 kg) were stocked at the density of 1 kg/m³ in the polythene lined earthen pond. After 3 months of stocking, fish cobia was found with infection of ectoparasites. Then fishes were sampled at fortnight interval to find the percentage distribution of ectoparasites in different parts of the body for a year and also any pathological symptoms. Identification of the parasite was made through light and electron microscopies. The parasite was identified as Argulus quadristriatus Devaraj and Ameer Hamsa, 1977 (Crustacea: Branchiura: Argulidae) commonly called as fish lice. The maximum distribution of pathogenic argulid was observed on the head and operculum of cobia and was found high in summer months from April to June. Pathological symptoms were observed on cobia as erratic swimming, rubbing against substrate in the pond and lesions of epithelial tissues on the infected regions. It must be due to continuous rupturing and feeding of argulids on the skin of cobia using its powerful antennae. Scanning electron micrographs revealed some important morphological features of A. quadristriatus. This is a first report of A. quadristriatus infection on cobia reared in a land-based pond ecosystem

Querying continuous functions in a database system

Many scientific, financial, data mining and sensor network applications need to work with continuous, rather than discrete data e.g., temperature as a function of location, or stock prices or vehicle trajectories as a function of time. Querying raw or discrete data is unsatisfactory for these applications – e.g., in a sensor network, it is necessary to interpolate sensor readings to predict values at locations where sensors are not deployed. In other situations, raw data can be inaccurate owing to measurement errors, and it is useful to fit continuous functions to raw data and query the functions, rather than raw data itself – e.g., fitting a smooth curve to noisy sensor readings, or a smooth trajectory to GPS data containing gaps or outliers. Existing databases do not support storing or querying continuous functions, short of brute-force discretization of functions into a collection of tuples. We present FunctionDB, a novel databas

Automatically generating interesting events with LifeJoin

This demo will showcase LifeJoin, a system that collects raw sensor data from phones and laptop computers to generate interesting events. Given the raw sensor data, LifeJoin implements a number of activity recognition algorithms to generate higher-level events. Furthermore, it uses supervised learning techniques to learn from users' feedback to generate only events of interest. In this demo, the audience will get to interact with the LifeJoin system and be able to examine the internals of LifeJoin