A map-matching algorithm dealing with sparse cellular fingerprint observations

The widespread availability of mobile communication makes mobile devices a resource for the collection of data about mobile infrastructures and user mobility. In these contexts, the problem of reconstructing the most likely trajectory of a device on the road network on the basis of the sequence of observed locations (map-matching problem) turns out to be particularly relevant. Different contributions have demonstrated that the reconstruction of the trajectory of a device with good accuracy is technically feasible even when only a sparse set of GNSS positions is available. In this paper, we face the problem of coping with sparse sequences of cellular fingerprints. Compared to GNSS positions, cellular fingerprints provide coarser spatial information, but they work even when a device is missing GNSS positions or is operating in an energy saving mode. We devise a new map-matching algorithm, that exploits the well-known Hidden Markov Model and Random Forests to successfully deal with noisy and sparse cellular observations. The performance of the proposed solution has been tested over a medium-sized Italian city urban environment by varying both the sampling of the observations and the density of the fingerprint map as well as by including some GPS positions into the sequence of fingerprint observations

Road Traffic Monitoring System Based on Mobile Devices and Bluetooth Low Energy Beacons

The paper proposes a method, which utilizes mobile devices (smartphones) and Bluetooth beacons, to detect passing vehicles and recognize their classes. The traffic monitoring tasks are performed by analyzing strength of radio signal received by mobile devices from beacons that are placed on opposite sides of a road. This approach is suitable for crowd sourcing applications aimed at reducing travel time, congestion, and emissions. Advantages of the introduced method were demonstrated during experimental evaluation in real-traffic conditions. Results of the experimental evaluation confirm that the proposed solution is effective in detecting three classes of vehicles (personal cars, semitrucks, and trucks). Extensive experiments were conducted to test different classification approaches and data aggregation methods. In comparison with state-of-the-art RSSI-based vehicle detection methods, higher accuracy was achieved by introducing a dedicated ensemble of random forest classifiers with majority voting

Investigation into biological and biomimetic transmembrane systems

Membranes are essential components of living organisms, which serve as effective barriers that separate distinct chemical environments on either side of the membrane. Chemists have designed biological and synthetic systems to functionalise membrane-embedded systems for a variety of applications such as sensing, sequencing, reaction mechanistic studies, and therapeutics. The continuous interest in functionalising membranes, combined with incomplete understanding of the underlying factors determining their mechanisms inspired the investigations undertaken in this work. This Thesis employs both experimental and computational methods to explore two distinct applications for sequencing and therapeutics, respectively. (1) Engineered biological nanopores have found great success in DNA sequencing. The Bayley group previously reported a molecular hopper, which makes sub-nanometer steps by thiol-disulfide interchange along a track with cysteine footholds within a protein nanopore. In Chapter 2, the hopping rate was optimized with a view towards rapid enzymeless biopolymer characterization during translocation within nanopores. I first used a nanopore approach to systematically profile the reactivity of individual cysteine footholds along an engineered protein track at the single-molecule level. Using this approach, I calculated the pKa of cysteine thiols and the pH-independent rate constants for the reaction between thiolates and a disulfide molecule. This reactivity profile guided site-specific mutagenesis. Together with the optimization of experimental conditions, the overall stepping rate of a DNA cargo along a five-cysteine track was accelerated. This work extends the practical application of this enzymeless system as a sequencing method for biopolymers beyond DNA. (2) Synthetic anion transporters have attracted significant attention as promising therapeutics for ion channel diseases. In Chapter 3, I use computational modelling to investigate the chloride binding and transmembrane transport mechanisms of E-/Z-switchable synthetic transporters. Using a model system,I developed a workflow to construct full energy profiles for the transmembrane transport process. These results revealed the importance of pre-organization of the Z-isomer and the balance between the energy barrier of transport and the solubility of the transporter. Additionally, in Chapter 4, I present a predictive machine-learning (ML) approach for estimating the chloride transport activity of a variety of synthetic chloride transporters. The ML models, employing both classification and regression frameworks, exhibited remarkable performance across a diverse range of systems. Moreover, they offered insights crucial for future design efforts, e.g., identifying key structural features and experimental conditions that influence the observed transport activity. Overall, this work bridges biological and molecular design, computational modelling and data-driven approaches to advance the development of two applications to functionalise membranes for sequencing and therapeutics. It provides interpretable molecular models as well as structure-activity relationships that will aid hypothesis generation and contribute to synthetic advances in both fields

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

Study of macromolecular interactions using computational solvent mapping

The term "binding hot spots" refers to regions of a protein surface with large contributions to the binding free energy. Computational solvent mapping serves as an analog to the major experimental techniques developed for the identification of such hot spots using X-ray and nuclear magnetic resonance (NMR) methods. Applications of the fast Fourier-transform-based mapping algorithm FTMap show that similar binding hot spots also occur in DNA molecules and interact with small molecules that bind to DNA with high affinity. Solvent mapping results on B-DNA, with or without Hoogsteen (HG) base pairing, have revealed the significance of "HG breathing" on the reactivity of DNA with formaldehyde. Extending the method to RNA molecules, I applied the FTMap algorithm to flexible structures of HIV-1 transactivation response element (TAR) RNA and Tau exon 10 RNA. Results show that despite the extremely flexible nature of these small RNA molecules, nucleic acid bases that interact with ligands consistently have high hit rates, and thus binding sites can be successfully identified. Based on this experience as well as the prior work on DNA, I extended the FTMap algorithm to mapping nucleic acids and implemented it in an automated online server available to the research community. FTSite, a related server for finding binding sites of proteins, was also extended to develop PeptiMap, an accurate and robust protocol that can determine peptide binding sites on proteins. Analyses of structural ensembles of ligand-free proteins using solvent mapping have shown that such ensembles contain pre-existing binding hot spots, and that such hot spots can be identified without any a priori knowledge of the ligand-bound structure. Furthermore, the structures in the ensemble having the highest binding-site hit rate are closest to the ligand-bound structure, and a higher hit rate implies improved structural similarity between the unbound protein and its bound state, resulting in high correlation coefficient between the two measures. These advances should greatly enhance researchers' ability to identify functionally important interactions among biomolecules in silico

A review of smartphones based indoor positioning: challenges and applications

The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning. This article is dedicated to review the most recent and interesting smartphones based indoor navigation systems, ranging from electromagnetic to inertia to visible light ones, with an emphasis on their unique challenges and potential real-world applications. A taxonomy of smartphones sensors will be introduced, which serves as the basis to categorise different positioning systems for reviewing. A set of criteria to be used for the evaluation purpose will be devised. For each sensor category, the most recent, interesting and practical systems will be examined, with detailed discussion on the open research questions for the academics, and the practicality for the potential clients

Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies

Owing to the intrinsic polypharmacological nature of most small-molecule kinase inhibitors, there is a need for computational models that enable systematic exploration of the chemogenomic landscape underlying druggable kinome toward more efficient kinome-profiling strategies. We implemented Virtual-KinomeProfiler, an efficient computational platform that captures distinct representations of chemical similarity space of the druggable kinome for various drug discovery endeavors. By using the computational platform, we profiled approximately 37 million compound-kinase pairs and made predictions for 151,708 compounds in terms of their repositioning and lead molecule potential, against 248 kinases simultaneously. Experimental testing with biochemical assays validated 51 of the predicted interactions, identifying 19 small-molecule inhibitors of EGFR, HCK, FLT1, and MSK1 protein kinases. The prediction model led to a 1.5-fold increase in precision and 2.8-fold decrease in false-discovery rate, when compared with traditional single-dose biochemical screening, which demonstrates its potential to drastically expedite the kinome-specific drug discovery process.Peer reviewe