Indoor Positioning System

The purpose of our project is to develop a complementary system to the current GPS with a focus on indoor localization and navigation. The current need for localization extends beyond what GPS can provide in today’s state of technology. Radio signals used in the global system are vast but weak, unable to penetrate obstacles and buildings in high density, populous areas of the world. Our system is designed to solve this problem by implementing an Indoor Localization System using a stronger ultra-wideband signal in the frequency spectrum. At a high level, the system is modeled after the architecture of the global positioning system by utilizing anchors as the satellites and tags as the receivers. With the use of up to date cloud technology, an end-to-end system is created through the Internet of Things with the inclusion of information security and a fully developed front-end user interface. The packaging is encapsulated within a miniature PCB design at a low cost, aimed as a plug-and-play integration within other systems in need of indoor detection. Applications of our IPS design include domains such as navigation (room-to-room assistance in a building), national defense (search and rescue operations, underground tracking, surveillance), commercialized zones (indicators for specific products on shelf, asset tracking in warehouses), and robotics (autonomous vehicles, drone detection). We demonstrate that all the components mentioned are essential to effectively carry out successful indoor localization with a focus on user flexibility and efficiency in response. We are able to use the system to enable an indoor drone show

Meta-Stock: Task-Difficulty-Adaptive Meta-learning for Sub-new Stock Price Prediction

Sub-new stock price prediction, forecasting the price trends of stocks listed less than one year, is crucial for effective quantitative trading. While deep learning methods have demonstrated effectiveness in predicting old stock prices, they require large training datasets unavailable for sub-new stocks. In this paper, we propose Meta-Stock: a task-difficulty-adaptive meta-learning approach for sub-new stock price prediction. Leveraging prediction tasks formulated by old stocks, our meta-learning method aims to acquire the fast generalization ability that can be further adapted to sub-new stock price prediction tasks, thereby solving the data scarcity of sub-new stocks. Moreover, we enhance the meta-learning process by incorporating an adaptive learning strategy sensitive to varying task difficulties. Through wavelet transform, we extract high-frequency coefficients to manifest stock price volatility. This allows the meta-learning model to assign gradient weights based on volatility-quantified task difficulty. Extensive experiments on datasets collected from three stock markets spanning twenty-two years prove that our Meta-Stock significantly outperforms previous methods and manifests strong applicability in real-world stock trading. Besides, we evaluate the reasonability of the task difficulty quantification and the effectiveness of the adaptive learning strategy

Theoretical study of the rotationally and vibrationally inelastic collision dynamics of small molecules

Rotationally and vibrationally inelastic collision dynamics of several small molecules are investigated through ab initio calculations of potential energy surfaces (PESs) and time-independent close-coupling scattering calculations. The scattering resonances in the collision energy dependent rotationally inelastic cross sections of OH in collisions with He and Ne, and NH3 in collisions with H2 were computed and analyzed. Both shape and Feshbach resonances were identified and the prospects for experimentally observing scattering resonances using Stark decelerated beams of OH radicals were discussed. A new PES for the interaction between CH3 with different umbrella displacements and a He atom were computed and the collisional vibrational relaxation of the $\nu_2$ mode of CH3 were studied. The vibrational relaxation rate constant was found to be two orders of magnitude smaller than the pure-rotational relaxation between two lower levels. Differential cross sections for the rotationally inelastic scattering of CH3 and CD3 with He, Ar, and H2 were computed and compared with results of velocity map imaging experiments conducted by Orr-Ewing and coworkers. In general, good agreement was found between theory and experiment, confirming the accuracy of our theoretical approach. Also, new sets of PESs describing the interaction between OH and H2 were computed, and bound-state calculations and scattering calculations were performed for this system. The computed dissociation energy of OH--\emph{ortho}-H2 complex and state-to-state cross sections of OH in collisions with H2 are in excellent agreement with earlier experimental results

Perturbation-based Self-supervised Attention for Attention Bias in Text Classification

In text classification, the traditional attention mechanisms usually focus too much on frequent words, and need extensive labeled data in order to learn. This paper proposes a perturbation-based self-supervised attention approach to guide attention learning without any annotation overhead. Specifically, we add as much noise as possible to all the words in the sentence without changing their semantics and predictions. We hypothesize that words that tolerate more noise are less significant, and we can use this information to refine the attention distribution. Experimental results on three text classification tasks show that our approach can significantly improve the performance of current attention-based models, and is more effective than existing self-supervised methods. We also provide a visualization analysis to verify the effectiveness of our approach

Probabilistic Calibration and Catheter Tracking with Robotic Systems

A significant boost in robotics technology has been observed in recent years and more and more tasks are being automated by robots such as robotic surgery, autonomous driving, package delivery, etc. Not only has the precision of robots been improved, but the number of robots involved in a specific task has also grown in many scenarios. An important part in a robotic automated task involves the relative pose estimation among objects, and this often boils down to calibration and tracking. The dissertation begins with a robotic catheter tracking system and then focuses on calibration of robotic systems. The presentation first introduces a novel robotic catheter tracking system which uses an embedded active piezoelectric element at the tip of the catheter. Catheter intervention procedure is performed exclusively with X-ray, while ultrasound comes as an alternative modality which is radiation free. However, the catheter tip is usually very small and hard to be differentiated from human tissue in an ultrasound image. Moreover, an ultrasound photographer needs to hold the ultrasound probe during the procedure which can easily last for over an hour. The proposed system can tackle these issues using a robot arm and the active echo signal, and is, to the best knowledge of the author, the first robotic catheter tracking system using ultrasound. It is demonstrated in both the simulation and experiment that a robotic arm holding the ultrasound probe can track the catheter tip without image input. To better assist the tracking process, other procedures can be automated such as catheter insertion and phantom localization, etc. All these require introducing an extra robot and a precise calibration between robots and targets of interest. Out of many calibration approaches, the most classical one is called the hand-eye calibration problem formulated as AX = XB which takes in data from sensors in different locations to solve for an unknown rigid-body transformation. A generalization of this problem is the AX = YB robot-world and hand-eye calibration, where two unknowns need to be recovered simultaneously. The above two approaches mainly deal with the calibration of a single robot system. For multi-robot systems, a problem cast as the AXB = YCZ formulation arises where three unknowns need to be solved given three sensor data streams. The second portion of the presentation investigates in the probabilistic approaches toward all three problems above. Different methods based on the probabilistic theory on Lie group are developed to show their superior performance over non-probabilistic equivalents when there is partial knowledge of the correspondence among sensor data

Parametric analysis of pitch angle scattering and losses of relativistic electrons by oblique EMIC waves

This study analyzes the effects of electromagnetic ion cyclotron (EMIC) waves on relativistic electron scattering and losses in the Earth’s outer radiation belt. EMIC emissions are commonly observed in the inner magnetosphere and are known to reach high amplitudes, causing significant pitch angle changes in primarily >1 MeV electrons via cyclotron resonance interactions. We run test-particle simulations of electrons streaming through helium band waves with different amplitudes and wave normal angles and assess the sensitivity of advective and diffusive scattering behaviors to these two parameters, including the possibility of very oblique propagation. The numerical analysis confirms the importance of harmonic resonances for oblique waves, and the very oblique waves are observed to efficiently scatter both co-streaming and counter-streaming electrons. However, strong finite Larmor radius effects limit the scattering efficiency at high pitch angles. Recently discussed force-bunching effects and associated strong positive advection at low pitch angles are, surprisingly, shown to cause no decrease in the phase space density of precipitating electrons, and it is demonstrated that the transport of electrons into the loss cone balances out the scattering out of the loss cone. In the case of high-amplitude obliquely propagating waves, weak but non-negligible losses are detected well below the minimum resonance energy, and we identify them as the result of non-linear fractional resonances. Simulations and theoretical analysis suggest that these resonances might contribute to subrelativistic electron precipitation but are likely to be overshadowed by non-resonant effects

Evaluation of pre-rigor proteases injections on cooked beef volatiles at 1 day and 21 days post-mortem

The fact that tenderness plays a major role in consumer acceptance of meat has been known for many years. After appearance and tenderness, flavour is another important component influencing meat palatability. Although proteases are widely used in the meat industry to tenderize meat, they can also contribute to the formation of amino acids that act as precursors for volatile flavour formation in cooked meat. This research was carried out to determine the effects of pre-rigor injection of beef with nine proteases from plant and microbial sources, after 1 day and 21 days post-mortem storage, on the volatile profile of cooked beef using solid phase microextraction (SPME) in combination with gas chromatography (GC) and mass spectrometry (MS) analysis. The topside of beef was injected with papain (PA), bromelain (BA), actinidin (Ac), zingibain (ZI), Fungal 31 protease (F31), Fungal 60 protease (F60), bacterial protease (BA), kiwi fruit juice (KJ), and Asparagus protease (ASP). A non-injected control (C) treatment was also included. In this study, a total of 56 key volatile compounds were found in cooked pre-rigor beef meat injected with proteases at 1 day and 21 days post mortem storage. This included 23 aldehydes, 5 ketones, 3 furans, 8 nitrogen and sulphur compounds, 4 alkanes, 7 alcohols and 6 terpenes. Eleven volatile compounds including camphene, 1,8-cineole, terpineol, citronellol, citral, geraniol, geranial, α-curcumene, zingiberene, α-farnesene, and β-sesquiphellandrene, were only detected in meat treated with ZI at 1 day and 21 days post-mortem storage. 3-methylbutanal and benzaldehyde were significantly increased (p<0.05) in the KJ 21 days treated sample. Aldehydes were the main chemical compounds that significantly changed with protease treatments and post mortem storage. Benzaldehyde was significantly increased (p<0.05) only in F31 and ASP treated samples from 1 day to 21 days post-mortem storage. A significant increase (p<0.05) in 3-methylbutanal was observed in KJ, BA, BR and F31 treated samples at 21 days post-mortem storage. Treatments with BR, PA, ASP, AC, and KJ (except KJ 21 days) proteases underwent fewer changes in the volatile compounds leading to a flavour profile closer to that of the control beef sample

Machine Learning Interpretability of Outer Radiation Belt Enhancement \& Depletion Events

We investigate the response of outer radiation belt electron fluxes to different solar wind and geomagnetic indices using an interpretable machine learning method. We reconstruct the electron flux variation during 19 enhancement and 7 depletion events and demonstrate a feature attribution analysis on the superposed epoch results for the first time. We find that the intensity and duration of the substorm sequence following an initial dropout determine the overall enhancement or depletion of electron fluxes, while the solar wind pressure drives the initial dropout in both types of events. Further statistical results from a dataset with 71 events confirm this and show a significant correlation between the resulting flux levels and the average AL index, indicating that the observed "depletion" event can be more accurately described as a "non-enhancement" event. Our novel SHAP-Enhanced Superposed Epoch Analysis (SHESEA) method can be used as an insight discovery tool in various physical systems