Outdoor location tracking of mobile devices in cellular networks

This paper presents a technique and experimental validation for anonymous outdoor location tracking of all users residing on a mobile cellular network. The proposed technique does not require any intervention or cooperation on the mobile side but runs completely on the network side, which is useful to automatically monitor traffic, estimate population movements, or detect criminal activity. The proposed technique exploits the topology of a mobile cellular network, enriched open map data, mode of transportation, and advanced route filtering. Current tracking algorithms for cellular networks are validated in optimal or controlled environments on a small dataset or are merely validated by simulations. In this work, validation data consisting of millions of parallel location estimations from over a million users are collected and processed in real time, in cooperation with a major network operator in Belgium. Experiments are conducted in urban and rural environments near Ghent and Antwerp, with trajectories on foot, by bike, and by car, in the months May and September 2017. It is shown that the mode of transportation, smartphone usage, and environment impact the accuracy and that the proposed AMT location tracking algorithm is more robust and outperforms existing techniques with relative improvements up to 88%. Best performances were obtained in urban environments with median accuracies up to 112 m

Minimal Infrastructure Radio Frequency Home Localisation Systems

The ability to track the location of a subject in their home allows the provision of a number of location based services, such as remote activity monitoring, context sensitive prompts and detection of safety critical situations such as falls. Such pervasive monitoring functionality offers the potential for elders to live at home for longer periods of their lives with minimal human supervision. The focus of this thesis is on the investigation and development of a home roomlevel localisation technique which can be readily deployed in a realistic home environment with minimal hardware requirements. A conveniently deployed Bluetooth ® localisation platform is designed and experimentally validated throughout the thesis. The platform adopts the convenience of a mobile phone and the processing power of a remote location calculation computer. The use of Bluetooth ® also ensures the extensibility of the platform to other home health supervision scenarios such as wireless body sensor monitoring. Central contributions of this work include the comparison of probabilistic and nonprobabilistic classifiers for location prediction accuracy and the extension of probabilistic classifiers to a Hidden Markov Model Bayesian filtering framework. New location prediction performance metrics are developed and signicant performance improvements are demonstrated with the novel extension of Hidden Markov Models to higher-order Markov movement models. With the simple probabilistic classifiers, location is correctly predicted 80% of the time. This increases to 86% with the application of the Hidden Markov Models and 88% when high-order Hidden Markov Models are employed. Further novelty is exhibited in the derivation of a real-time Hidden Markov Model Viterbi decoding algorithm which presents all the advantages of the original algorithm, while producing location estimates in real-time. Significant contributions are also made to the field of human gait-recognition by applying Bayesian filtering to the task of motion detection from accelerometers which are already present in many mobile phones. Bayesian filtering is demonstrated to enable a 35% improvement in motion recognition rate and even enables a floor recognition rate of 68% using only accelerometers. The unique application of time-varying Hidden Markov Models demonstrates the effect of integrating these freely available motion predictions on long-term location predictions

Local cellular neighbourhood controls proliferation in cell competition

Cell competition is a quality control mechanism through which tissues eliminate unfit cells. Cell competition can result from short-range biochemical inductions or long-range mechanical cues. However, little is known about how cell-scale interactions give rise to population shifts in tissues, due to the lack of experimental and computational tools to efficiently characterise interactions at the single-cell level. Here, we address these challenges by combining long-term automated microscopy with deep learning image analysis to decipher how single-cell behaviour determines tissue make-up during competition. Using our high-throughput analysis pipeline, we show that competitive interactions between MDCK wild-type cells and cells depleted of the polarity protein scribble are governed by differential sensitivity to local density and the cell-type of each cell's neighbours. We find that local density has a dramatic effect on the rate of division and apoptosis under competitive conditions. Strikingly, our analysis reveals that proliferation of the winner cells is upregulated in neighbourhoods mostly populated by loser cells. These data suggest that tissue-scale population shifts are strongly affected by cellular-scale tissue organisation. We present a quantitative mathematical model that demonstrates the effect of neighbour cell-type dependence of apoptosis and division in determining the fitness of competing cell lines

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research

Live cell kinetics of erbB dimerization reveals influences of activation state and membrane organization

The erbB1 receptor regulates cellular programs including proliferation, migration, and differentiation and is the prototypical receptor tyrosine kinase (RTK). The erbB family consists of four homologous transmembrane receptors (erbB1/HER1/EGFR, erbB2/HER2, erbB3/HER3, erbB4). Canonically, ligand binding leads to an extracellular conformational change that promotes the formation of a receptor-mediated back-to-back dimer, asymmetric orientation of the catalytic kinase domains, and downstream transphosphorylation of cytoplasmic tyrosine residues. Exceptions to this paradigm are the orphan erbB2 and the kinase defective erbB3. The erbB receptors are implicated in mechanisms of carcinogenesis and are, thus, important therapeutic targets. Antibodies and small molecule inhibitors have been used to target cancer cells expressing erbB1 and erbB2, however, tumors often become resistant to treatment. Recent evidence implicates erbB3 in escape from erbB1- and erbB2-targeted pharmacological agents. Therefore, understanding the function of these receptors and their interactions with each other is important for designing better therapeutics. Here, we investigated erbB dimer formation and lifetime using live cell imaging and an analytical three-state Hidden Markov Model (HMM). First, multi-color quantum dot (QD) based probes that label resting or activated receptors were used to directly observe dimerization and quantify diffusion and correlated motion. Second, pairwise analyses of single particle trajectories in our HMM are used to characterize transition rates between free, confined, and dimerized states. We examined preformed, unliganded erbB1 homodimers and demonstrate that these do not display correlated motion and that observed dimers are short lived. Interestingly, liganded erbB1 dimers have the same off rate regardless of the activation status of the kinase domain. We further describe features of membrane organization, in particular demonstrating differential partitioning of activated receptors that alters mobility and permits repeated interactions within domains. Important mechanistic insight comes from our novel observations of short lived erbB2-erbB3 heterodimers and long lived erbB3 homodimers. Prior biochemical studies suggested that the erbB2-erbB3 heterodimer was the functional signaling unit. Our single particle tracking results are consistent with a new model for an active erbB3 kinase domain that is dependent on interactions with erbB2. Furthermore, our data indicate that erbB3 dimers and, ultimately, oligomers may be the principal signaling complex. This work demonstrates the importance of membrane architecture and reorganization in signal transduction and sheds new light on mechanisms of erbB activation with unprecedented spatial and temporal resolution

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