Nanoenergetic Materials

This highly informative and carefully presented book discusses the preparation, processing, characterization and applications of different types of nanoenergetic materials, as well as the tailoring of their properties. It gives an overview of recent advances of outstanding classes of energetic materials applied in the fields of physics, chemistry, aerospace, defense, and materials science, among others. The content of this book is relevant to researchers in academia and industry professionals working on the development of advanced nanoenergetic materials and their applications

Reliability-Oriented Design of Vehicle Electric Propulsion System Based on the Multilevel Hierarchical Reliability Model

This chapter describes a methodology of evaluation of the various sustainability indicators, such as reliability, availability, fault tolerance, and reliability-associated cost of the electric propulsion systems, based on a multilevel hierarchical reliability model (MLHRM) of the life cycles of electric vehicles. Considering that the vehicle propulsion systems are safety-critical systems, to each of their components, the strict requirements on reliability indices are imposed. The practical application of the proposed technique for reliability-oriented development of the icebreaking ship’s electric propulsion system and the results of computation are presented. The opportunities of improvement of reliability and fault tolerance are investigated. The results of the study, allowing creating highly reliable electric vehicles and choosing the most appropriate traction electric drive design, are discussed

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors

Vision technology/algorithms for space robotics applications

The thrust of automation and robotics for space applications has been proposed for increased productivity, improved reliability, increased flexibility, higher safety, and for the performance of automating time-consuming tasks, increasing productivity/performance of crew-accomplished tasks, and performing tasks beyond the capability of the crew. This paper provides a review of efforts currently in progress in the area of robotic vision. Both systems and algorithms are discussed. The evolution of future vision/sensing is projected to include the fusion of multisensors ranging from microwave to optical with multimode capability to include position, attitude, recognition, and motion parameters. The key feature of the overall system design will be small size and weight, fast signal processing, robust algorithms, and accurate parameter determination. These aspects of vision/sensing are also discussed

A comparison of machine learning classifiers for pediatric epilepsy using resting-state functional MRI latency data

Epilepsy affects 1 in 150 children under the age of 10 and is the most common chronic pediatric neurological condition; poor seizure control can irreversibly disrupt normal brain development. The present study compared the ability of different machine learning algorithms trained with resting-state functional MRI (rfMRI) latency data to detect epilepsy. Preoperative rfMRI and anatomical MRI scans were obtained for 63 patients with epilepsy and 259 healthy controls. The normal distribution of latency z-scores from the epilepsy and healthy control cohorts were analyzed for overlap in 36 seed regions. In these seed regions, overlap between the study cohorts ranged from 0.44-0.58. Machine learning features were extracted from latency z-score maps using principal component analysis. Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest algorithms were trained with these features. Area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity and F1-scores were used to evaluate model performance. The XGBoost model outperformed all other models with a test AUC of 0.79, accuracy of 74%, specificity of 73%, and a sensitivity of 77%. The Random Forest model performed comparably to XGBoost across multiple metrics, but it had a test sensitivity of 31%. The SVM model did not perform \u3e70% in any of the test metrics. The XGBoost model had the highest sensitivity and accuracy for the detection of epilepsy. Development of machine learning algorithms trained with rfMRI latency data could provide an adjunctive method for the diagnosis and evaluation of epilepsy with the goal of enabling timely and appropriate care for patients

Separating the scales in a compressible interstellar medium

We apply Gaussian smoothing to obtain mean density, velocity, magnetic and energy density fields in simulations of the interstellar medium based on three-dimensional magnetohydrodynamic equations in a shearing box $1\times1\times2 \, \rm{kpc}$ in size. Unlike alternative averaging procedures, such as horizontal averaging, Gaussian smoothing retains the three-dimensional structure of the mean fields. Although Gaussian smoothing does not obey the Reynolds rules of averaging, physically meaningful central statistical moments are defined as suggested by Germano (1992). We discuss methods to identify an optimal smoothing scale $\ell$ and the effects of this choice on the results. From spectral analysis of the magnetic, density and velocity fields, we find a suitable smoothing length for all three fields, of $\ell \approx 75 \, \rm{pc}$. We discuss the properties of third-order statistical moments in fluctuations of kinetic energy density in compressible flows and suggest their physical interpretation. The mean magnetic field, amplified by a mean-field dynamo, significantly alters the distribution of kinetic energy in space and between scales, reducing the magnitude of kinetic energy at intermediate scales. This intermediate-scale kinetic energy is a useful diagnostic of the importance of SN-driven outflows

A hybrid load flow and event driven simulation approach to multi-state system reliability evaluation

Structural complexity of systems, coupled with their multi-state characteristics, renders their reliability and availability evaluation difficult. Notwithstanding the emergence of various techniques dedicated to complex multi-state system analysis, simulation remains the only approach applicable to realistic systems. However, most simulation algorithms are either system specific or limited to simple systems since they require enumerating all possible system states, defining the cut-sets associated with each state and monitoring their occurrence. In addition to being extremely tedious for large complex systems, state enumeration and cut-set definition require a detailed understanding of the system׳s failure mechanism. In this paper, a simple and generally applicable simulation approach, enhanced for multi-state systems of any topology is presented. Here, each component is defined as a Semi-Markov stochastic process and via discrete-event simulation, the operation of the system is mimicked. The principles of flow conservation are invoked to determine flow across the system for every performance level change of its components using the interior-point algorithm. This eliminates the need for cut-set definition and overcomes the limitations of existing techniques. The methodology can also be exploited to account for effects of transmission efficiency and loading restrictions of components on system reliability and performance. The principles and algorithms developed are applied to two numerical examples to demonstrate their applicability

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

Robust Odorant Recognition in Biological and Artificial Olfaction

Accurate detection and identification of gases pose a number of challenges for chemical sensory systems. The stimulus space is enormous; volatile compounds vary in size, charge, functional groups, and isomerization among others. Furthermore, variability arises from intrinsic (poisoning of the sensors or degradation due to aging) and extrinsic (environmental: humidity, temperature, flow patterns) sources. Nonetheless, biological olfactory systems have been refined over time to overcome these challenges. The main objective of this work is to understand how the biological olfactory system deals with these challenges, and translate them to artificial olfaction to achieve comparable capabilities. In particular, this thesis focuses on the design and computing mechanisms that allow a relatively simple invertebrate olfactory system to robustly recognize odorants even though the sensory neurons inputs may vary due to the identified intrinsic, or extrinsic factors. In biological olfaction, signal processing in the central circuits is largely shielded from the variations in the periphery arising from the constant replacement of older olfactory sensory neurons with newer ones. Inspired by this design principle, we developed an analytical method where the operation of a temperature programmed chemiresistor is treated akin to a mathematical input/output (I/O) transform. Results show that the I/O transform is unique for each analyte-transducer combination, robust with respect to sensor aging, and is highly reproducible across sensors of equal manufacture. This enables decoupling of the signal processing algorithms from the chemical transducer, and thereby allows seamless replacement of sensor array, while the signal processing approach was kept a constant. This is a key advance necessary for achieving long-term, non-invasive chemical sensing. Next, we explored how the biological system maintains invariance while environmental conditions, particularly with respect to changes in humidity levels. At the sensory level, odor-evoked responses to odorants did not vary with changes in humidity levels, however, the spontaneous activity varied significantly. Nevertheless, in the central antennal lobe circuits, ensembles of projection neurons robustly encoded information about odorant identity and intensity irrespective of the humidity levels. Interestingly, variations in humidity levels led to variable compression of intensity information which was carried forward to behavior. Taken together, these results indicate how the influence of humidity is diminished by central neural circuits in the biological olfactory system. Finally, we explored a potential biomedical application where a robust chemical sensing approach will be immensely useful: non-invasive assay for malaria diagnosis based on exhaled breath analysis. We developed a method to screen gas chromatography/mass spectroscopy (GC/MS) traces of human breath and identified 6 compounds that have abundance changes in malaria infected patients and can potentially serve as biomarkers in exhaled breath for their diagnosis. We will conclude with a discussion of on-going efforts to develop a non-invasive solution for diagnosing malaria based on breath volatiles. In sum, this work seeks to understand the basis for robust odor recognition in biological olfaction and proposes bioinspired and statistical solutions for achieving the same abilities in artificial chemical sensing systems