Appearance-based indoor localization: a comparison of patch descriptor performance

Vision is one of the most important of the senses, and humans use it extensively during navigation. We evaluated different types of image and video frame descriptors that could be used to determine distinctive visual landmarks for localizing a person based on what is seen by a camera that they carry. To do this, we created a database containing over 3 km of video-sequences with ground-truth in the form of distance travelled along different corridors. Using this database, the accuracy of localization - both in terms of knowing which route a user is on - and in terms of position along a certain route, can be evaluated. For each type of descriptor, we also tested different techniques to encode visual structure and to search between journeys to estimate a user's position. The techniques include single-frame descriptors, those using sequences of frames, and both colour and achromatic descriptors. We found that single-frame indexing worked better within this particular dataset. This might be because the motion of the person holding the camera makes the video too dependent on individual steps and motions of one particular journey. Our results suggest that appearance-based information could be an additional source of navigational data indoors, augmenting that provided by, say, radio signal strength indicators (RSSIs). Such visual information could be collected by crowdsourcing low-resolution video feeds, allowing journeys made by different users to be associated with each other, and location to be inferred without requiring explicit mapping. This offers a complementary approach to methods based on simultaneous localization and mapping (SLAM) algorithms.Comment: Accepted for publication on Pattern Recognition Letter

Crowdsourcing-Based Fingerprinting for Indoor Location in Multi-Storey Buildings

POCI-01-0247-FEDER-033479The number of available indoor location solutions has been growing, however with insufficient precision, high implementation costs or scalability limitations. As fingerprinting-based methods rely on ubiquitous information in buildings, the need for additional infrastructure is discarded. Still, the time-consuming manual process to acquire fingerprints limits their applicability in most scenarios. This paper proposes an algorithm for the automatic construction of environmental fingerprints on multi-storey buildings, leveraging the information sources available in each scenario. It relies on unlabelled crowdsourced data from users’ smartphones. With only the floor plans as input, a demand for most applications, we apply a multimodal approach that joins inertial data, local magnetic field andWi-Fi signals to construct highly accurate fingerprints. Precise movement estimation is achieved regardless of smartphone usage through Deep Neural Networks, and the transition between floors detected from barometric data. Users’ trajectories obtained with Pedestrian Dead Reckoning techniques are partitioned into clusters with Wi-Fi measurements. Straight sections from the same cluster are then compared with subsequence Dynamic Time Warping to search for similarities. From the identified overlapping sections, a particle filter fits each trajectory into the building’s floor plans. From all successfully mapped routes, fingerprints labelled with physical locations are finally obtained. Experimental results from an office and a university building show that this solution constructs comparable fingerprints to those acquired manually, thus providing a useful tool for fingerprinting-based solutions automatic setup.publishersversionpublishe

Crowdsourcing error impact on indoor positioning

Nowadays, with the rapid development of communication technology, plenty of new applications of 5G and IoT have appeared which requires high accuracy positioning skills. Wi-Fi based fingerprinting method is one of the most promising approaches for indoor positioning. Crowdsourcing is an appropriate fingerprint data collecting method on one hand. However, it is vulnerable to different kinds of crowdsourcing errors which add errors to the fingerprint database and can decrease the accuracy of positioning on another hand. The main target of this thesis is to statistically analyze the behavior of the crowdsourcing data collected by different devices, and the effects of different kinds of intentionally or unintentionally added errors through MATLAB. From the analysis results, it can be concluded that two different kinds of manually added errors perform complete differently. Data modified with all constant RSS values, out of author’s expectation, achieves a decent accuracy similar to the original data. While data modified with only position error shows a behavior that the positioning accuracy drops with the increase of modified data proportion. Most of the distributions are closest to the Burr type XII distribution, which is particularly useful for modeling histograms

Gravity Spy and X-Pypeline: A multidisciplinary approach to characterizing and understanding non-astrophysical gravitational wave data and its impact on searches for unmodelled signals

With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) has initiated a new field of astronomy by providing an alternate means of sensing the Universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of aLIGO from non-gravitational-wave disturbances. Nonetheless, aLIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate that the possibility of accidental coincidence between the two aLIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are allowed by the design sensitivity of aLIGO. For this reason, over the past few years, glitch classification techniques have been developed to help make this task easier. Specifically, I explore the effect of glitches, and their suppression, on key gravitational-wave searches such as that for a Galactic supernova. Moreover, I explore the impact of including machine learning techniques in the post-processing stage of the gravitational-wave search algorithm, “X-Pypeline”. When performing a two detector network search for a gravitational wave from a Galactic supernova, this thesis finds that including information about glitch families and using machine learning techniques in the post-processing stages of the analysis can improve the sensitive range of the search by 10-15 percent over the standard post-processing method

Crowdsourcing for Engineering Design: Objective Evaluations and Subjective Preferences

Crowdsourcing enables designers to reach out to large numbers of people who may not have been previously considered when designing a new product, listen to their input by aggregating their preferences and evaluations over potential designs, aiming to improve ``good'' and catch ``bad'' design decisions during the early-stage design process. This approach puts human designers--be they industrial designers, engineers, marketers, or executives--at the forefront, with computational crowdsourcing systems on the backend to aggregate subjective preferences (e.g., which next-generation Brand A design best competes stylistically with next-generation Brand B designs?) or objective evaluations (e.g., which military vehicle design has the best situational awareness?). These crowdsourcing aggregation systems are built using probabilistic approaches that account for the irrationality of human behavior (i.e., violations of reflexivity, symmetry, and transitivity), approximated by modern machine learning algorithms and optimization techniques as necessitated by the scale of data (millions of data points, hundreds of thousands of dimensions). This dissertation presents research findings suggesting the unsuitability of current off-the-shelf crowdsourcing aggregation algorithms for real engineering design tasks due to the sparsity of expertise in the crowd, and methods that mitigate this limitation by incorporating appropriate information for expertise prediction. Next, we introduce and interpret a number of new probabilistic models for crowdsourced design to provide large-scale preference prediction and full design space generation, building on statistical and machine learning techniques such as sampling methods, variational inference, and deep representation learning. Finally, we show how these models and algorithms can advance crowdsourcing systems by abstracting away the underlying appropriate yet unwieldy mathematics, to easier-to-use visual interfaces practical for engineering design companies and governmental agencies engaged in complex engineering systems design.PhDDesign ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133438/1/aburnap_1.pd

First results from sonification and exploratory citizen science of magnetospheric ULF waves: Long-lasting decreasing-frequency poloidal field line resonances following geomagnetic storms

Magnetospheric ultra-low frequency (ULF) waves contribute to space weather in the solar wind - magnetosphere - ionosphere system. The monitoring of these waves by space- and ground-based instruments, however, produces "big data" which is difficult to navigate, mine and analyse effectively. We present sonification, the process of converting an oscillatory time-series into audible sound, and citizen science, where members of the public contribute to scientific investigations, as a means to potentially help tackle these issues. Magnetometer data in the ULF range at geostationary orbit has been sonified and released to London high schools as part of exploratory projects. While this approach reduces the overall likelihood of useful results from any particular group of citizen scientists compared to typical citizen science projects, it promotes independent learning and problem solving by all participants and can result in a small number of unexpected research outcomes. We present one such example, a case study identified by a group of students -of decreasing-frequency poloidal field line resonances over multiple days found to occur during the recovery phase of a CME-driven geomagnetic storm. Simultaneous plasma density measurements show that the decreasing frequencies were due to the refilling of the plasmasphere following the storm. The waves were likely generated by internal plasma processes. Further exploration of the audio revealed many similar events following other major storms, thus they are much more common than previously thought. We therefore highlight the potential of sonification and exploratory citizen science in addressing some of the challenges facing ULF wave research

Crowd sourced self beacon mapping with isolated signal aware bluetooth low energy positioning

In the past few decades, there has been an increase in the demand for positioning and navigation systems in various fields. Location-based service (LBS) usage covers a range of different variations from advertising and navigation to social media. Positioning based on a global navigation satellite system (GNSS) is the commonly used technology for positioning nowadays. However, the GNSS has a limitation of needing the satellites to be in line-of-sight (LOS) to provide an accurate position. Given this limitation, several different approaches are employed for indoor positioning needs. Bluetooth low energy (BLE) is one of the wireless technologies used for indoor positioning. However, BLE is well-known for having unstable signals, which will affect an estimated distance. Moreover, unlike Wi-Fi, BLE is not commonly and widely used, and BLE beacons must thus be placed to enable a venue with BLE positioning. The need to deploy the beacons results in a lengthy process to place and record the position of each placed beacon. This thesis proposes several solutions to solve these problems. A filter based on a Fourier transform is proposed to stabilise a BLE signal to obtain a more reliable reading. This allows the BLE signals to be less affected by internal variation than unfiltered signal. An obstruction-aware algorithm is also proposed using a statistical approach, which allows for the detection of non-line-of-sight (NLOS). These proposed solutions allow for a more stable BLE signal, which will result in a more reliable estimation of distance using the signal. The proposed solutions will enable accurate distance estimation, which will translate into improved positioning accuracy. An improvement in 88% of the test points is demonstrated by implementing the proposed solutions. Furthermore, to reduce the calibration needed when deploying the BLE beacons, a beacon-mapping algorithm is proposed that can be used to determine the position of BLE beacons. The proposed algorithm is based on trilateration with added information about direction. It uses the received signal strength (RSS) and the estimated distance to determine the error range, and a direction line is drawn based on the estimated error range. Finally, to further reduce the calibration needed, a crowdsource approach is proposed. This approach is proposed alongside a complete system to map the location of unknown beacons. The proposed system uses three phases to determine the user location, determine the beacons’ position, and recalculate BLE scans that have insufficient number of known BLE beacons. Each beacon and user’s position determined is assigned a weight to represent the reliability of that position. This is important to ensure that the position generated from a more reliable source will be emphasised. The proposed system demonstrates that the beacon-mapping system can map beacons with a root mean squared error (RMSE) of 4.64 m and a mean of absolute error (MAE) of 4.28 m