Advanced Sensors for Real-Time Monitoring Applications

It is impossible to imagine the modern world without sensors, or without real-time information about almost everything—from local temperature to material composition and health parameters. We sense, measure, and process data and act accordingly all the time. In fact, real-time monitoring and information is key to a successful business, an assistant in life-saving decisions that healthcare professionals make, and a tool in research that could revolutionize the future. To ensure that sensors address the rapidly developing needs of various areas of our lives and activities, scientists, researchers, manufacturers, and end-users have established an efficient dialogue so that the newest technological achievements in all aspects of real-time sensing can be implemented for the benefit of the wider community. This book documents some of the results of such a dialogue and reports on advances in sensors and sensor systems for existing and emerging real-time monitoring applications

Atomic imaging of complex molecular

One of the significant challenges of modern science is to track and image chemical reactions as they occur. The molecular movies, the precise spatiotemporal tracking of changes in their molecular dynamics, will provide a wealth of actionable insights into how nature works. Experimental techniques need to resolve the relevant molecular motions in atomic resolution, which includes (10^(-10) m) spatial dimensions and few- to hundreds of femtoseconds (10^(-15) s) temporal resolution. Laser-induced electron diffraction (LIED), a laser-based electron diffraction technique, images even singular molecular structures with combined sub-atomic picometre and femto-to attosecond spatiotemporal resolution. Here, a laser-driven attosecond electron wave packet scatters the parent’s ion after photoionization. The measured diffraction pattern of the electrons provides a unique fingerprint of molecular structure. Taking snapshots of molecular dynamics via the LIED technique is proved to be a potent tool to understand the intertwining of molecules and how they react, change, break, bend, etc. This thesis is especially interested in exploiting advanced LIED imaging techniques to retrieve large complex molecular structures. So far, LIED has successfully retrieved molecular information from small gas-phase molecules like oxygen (O2), nitrogen (N2), acetylene (C2H2), carbon disulfide (CS2), ammonia (NH3) and carbonyl sulfide (OCS). Nevertheless, most biology interesting organic molecules typically exist as liquid or solid at room temperature. In order to accomplish the final goal to extract these larger complex molecular structural information, we need to overcome two main challenges: delivering the liquid or solid samples as a gas-phase jet with sufficient gas density in the experiment and developing a new retrieval algorithm to extract the geometrical information from the diffraction pattern. We tested one of the most simple liquid molecules - water H2O in the reaction chamber as a primary step. We traced the variation of H2O+ cation structure under the different electric fields. To solve the problem of unsatisfactory gas density, we present a novel delivery system utilizing Tesla valves that generates more than an order-of-magnitude denser gaseous beam. Machine learning is well qualified to solve difficulties with manifold degrees of freedom. We use convolutional neural networks (CNNs) combined with LIED techniques to enable atomic-resolution imaging of the complex chiral molecule Fenchone (C10H16O).Uno de los desafíos importantes de la ciencia moderna es rastrear y obtener imágenes de las reacciones químicas a medida que ocurren. Las películas moleculares, el seguimiento espaciotemporal preciso de los cambios en su dinámica molecular, proporcionarán una gran cantidad de conocimientos prácticos sobre cómo funciona la naturaleza. Las técnicas experimentales necesitan resolver los movimientos moleculares relevantes en resolución atómica, que incluye ($10^{-10}$ m) dimensional espacial y resolución temporal de pocos a cientos de femtosegundos ($10^{-15}$ s). La difracción de electrones inducida por láser (LIED-Laser-induced electron diffraction), una técnica de difracción de electrones basada en láser, crea imágenes incluso de estructuras moleculares singulares con una resolución espaciotemporal subatómica combinada de picómetro y femto a attosegundo. Aquí, un paquete de ondas de electrones de attosegundos impulsado por láser dispersa el ion del padre después de la fotoionización. El patrón de difracción medido de los electrones proporciona una huella única de la estructura molecular. Se ha demostrado que tomar instantáneas de la dinámica molecular a través de la técnica LIED es una herramienta potente para comprender el entrelazamiento de las moléculas y cómo reaccionan, cambian, se rompen, se doblan, etc. Esta tesis está especialmente interesada en explotar técnicas avanzadas de imagen LIED para recuperar estructuras moleculares grandes y complejas. Hasta ahora, LIED ha recuperado con éxito información molecular de pequeñas moléculas en fase gaseosa como oxígeno (O2), nitrógeno (N2), acetileno (C2H2), disulfuro de carbono (CS2), amoníaco (NH3) y sulfuro de carbonilo (OCS). Sin embargo, la mayoría de las moléculas orgánicas interesantes para la biología suelen existir como líquidas o sólidas a temperatura ambiente. Para lograr el objetivo final de extraer esta información estructural molecular compleja más grande, debemos superar dos desafíos principales: entregar las muestras líquidas o sólidas como un chorro de fase gaseosa con suficiente densidad de gas en el experimento y desarrollar un nuevo algoritmo de recuperación para extraer la información geométrica del patrón de difracción. Probamos una de las moléculas líquidas más simples: agua H2O en la cámara de reacción como primer paso. Trazamos la variación de la estructura del catión H2O+ bajo los diferentes campos eléctricos. Para resolver el problema de la densidad de gas insatisfactoria, presentamos un novedoso sistema de suministro que utiliza válvulas Tesla que genera más de un haz gaseoso más denso en un orden de magnitud. El aprendizaje automático está bien calificado para resolver dificultades con múltiples grados de libertad. Utilizamos redes neuronales convolucionales (CNN-convolutional neural networks) combinadas con técnicas LIED para permitir imágenes de resolución atómica de la molécula quiral compleja Fenchone (C10H16OPostprint (published version

Using Machine Learning to Improve Neutron Tagging Efficiency in Water Cherenkov Detectors

When an anti-neutrino collides with a proton in the atomic nucleus, it yields an anti-lepton and a free neutron. In a water Cherenkov neutrino detector like Super-K or the next generation Hyper-K, the free neutron is captured by a hydrogen or gadolinium nucleus about one hundred microseconds after the collision. The low-energy signal from the neutron capture (ranging from 2-8 MeV of gamma rays) is recorded by only tens of photomultiplier tubes (PMTs), making neutron captures difficult to distinguish from radioactive decay, muon spallation and other background sources. Improved methodologies for neutron tagging can advance understanding and enable new research over a survey of topics in particle physics. In this research, machine learning techniques are employed to optimize the neutron capture detection capability in the new intermediate water Cherenkov detector (IWCD) for Hyper-K. In particular, boosting decision tree (XGBoost) and graph neural network (GCN, DGCNN) models are developed and benchmarked against a statistical likelihood-based approach, achieving up to a 10% increase in classification accuracy. Characteristic features are also engineered from the datasets and analyzed using SHAP (SHapley Additive exPlanations) to provide insight into the pivotal factors influencing event type outcomes. Three main datasets were used for evaluative purposes in this research, each consisting of roughly 1.6 million events in total, divided nearly evenly between neutron capture and a distinct background electron source.NSERC, WatchMalMaster of Science in Applied Computer Scienc

Advancing Blazar Science with Very-High-Energy Gamma-Ray Telescopes

Blazars, active galactic nuclei with relativistic jets pointed almost directly at Earth, are powerful and highly variable sources of nonthermal electromagnetic radiation, including very-high-energy gamma rays. We can detect these gamma rays with arrays of imaging atmospheric Cherenkov telescopes (IACTs), including the Very Energetic Radiation Imaging Telescope Array System (VERITAS) and the upcoming Cherenkov Telescope Array (CTA). After reviewing the science of blazars and the methods used by IACTs, we investigate how gamma-ray variability can provide insight into blazars' physical properties while also complicating efforts to understand these sources as a population. We first present a study of three flaring blazars observed with VERITAS and analyze these sources' spectral and variability characteristics, taking into account data at other wavebands, including that of the Large Area Telescope aboard the Fermi space telescope (Fermi-LAT). Next, after laying out how observing biases and intrinsic variability can confound blazar population studies with IACTs, we propose methods to account for these effects, and use simulated data to report expectations for a blazar luminosity function measurement with VERITAS. Sophisticated new instruments and data analysis methods can further expand the frontier of gamma-ray blazar science. To that end, we design a camera software system to enable safer and more efficient operations of a next-generation IACT being developed for CTA, the prototype Schwarzschild-Couder Telescope (pSCT). Finally, we develop methods to apply deep neural networks to the analysis of IACT data and employ these methods to reject background events detected by simulated arrays of IACTs

Machine learning techniques for the prediction of systolic and diastolic blood pressure utilising the photoplethysmogram

Blood pressure (BP) is one of the four primary vital signs that provides important information regarding patients' cardiovascular system conditions. Continuous and regular blood pressure monitoring is essential for the early diagnosis, prevention and management of cardiovascular disease (CVD) and haemodynamic diseases (hypertension and hypotension). Current clinical blood pressure measurement techniques are either invasive or cuff-based, which can be impractical, intermittent, and uncomfortable for patients during frequent measurements. Considering these challenges, several studies have suggested new non-invasive and cuffless blood pressuring measurement techniques using physiological signals, such as, the Electrocardiogram (ECG) and the Photoplethysmogram (PPG). In particular, indirect cuffless BP measurement techniques using pulse transit time and pulse arrival time have been extensively investigated over the last few decades. However, these techniques require two measurement sensors, frequent calibration, and hence, they are also impractical and inconvenient for continuous BP measurements. More recently, with the advancement of computational techniques, including machine learning and artificial intelligence, a new simple and innovative approach using only PPG signals have been proposed in the literature for cuffless and continuous monitoring of blood pressure. However, the majority of these studies have been unable to achieve acceptable accuracies that comply or satisfy the international standards for cuffless BP monitoring. Thus, further investigations are required to realise this approach. In this research, a total of 52 features have been extracted from the PPG and their individual impact on BP have been rigorously evaluated using several statistical and machine learning techniques. As a result, only the most important features for estimation of BP were selected, effectively reducing the input vector by more than half. Two datasets were created to accommodate the two input feature vectors. The PPG and reference BP signals were derived from the publicly available MIMIC II database. In order to estimate BP, a total of nine machine learning and neural network models have been implemented and evaluated on the two datasets. Out of the nine models, four are widely used classical machine learning models, and five neural network models, three of which are conventional models and two advanced models have been proposed for BP estimation using only one PPG signal. The results of all these models have also been compared against well established studies in the literature. The results obtained using the classical machine learning models, namely, multilinear regression, random forest, adaboost and support vector machine, were poor and inferior to all the neural network models. A slight performance improvement was achieved using the non-recurrent multi-layer perceptron, however, the error was still much higher than the internationally acceptable range. On the other hand, a significant improvement was achieved for the first time by using the recurrent neural network models, namely, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). The two proposed neural network models further enhanced the BP estimation accuracies and were able to reduce the mean absolute error (MAE) to a range below 5 mmHg. In particular, the best performing model was the one bidirectional GRU layer, followed by two unidirectional GRU layers, and an attention layer. The obtained MAE and standard deviation (SD) were 4.79+/-8.08 mmHg for systolic BP (SBP) and 2.77+/-4.72 mmHg for diastolic BP (DBP). Furthermore, the DBP estimation were well below the internationally acceptable limits (referring to the AAMI standards of mean error (ME), ME+/-SD less than 5+/-8), while the ME for the SBP estimation were acceptable but the SD exceeded the limits by only 1.34 mmHg. This research has successfully demonstrated that advanced neural network models can be used for the non-invasive and cuffless prediction of BP utilising the PPG

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

Discovery in Physics

Volume 2 covers knowledge discovery in particle and astroparticle physics. Instruments gather petabytes of data and machine learning is used to process the vast amounts of data and to detect relevant examples efficiently. The physical knowledge is encoded in simulations used to train the machine learning models. The interpretation of the learned models serves to expand the physical knowledge resulting in a cycle of theory enhancement

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Applications of Medical Physics

Applications of Medical Physics” is a Special Issue of Applied Sciences that has collected original research manuscripts describing cutting-edge physics developments in medicine and their translational applications. Reviews providing updates on the latest progresses in this field are also included. The collection includes a total of 20 contributions by authors from 9 different countries, which cover several areas of medical physics, spanning from radiation therapy, nuclear medicine, radiology, dosimetry, radiation protection, and radiobiology