Miracle-dz/UniFAR-FTUs huggingface dataset:
EEG Feature Extraction with Fast Fourier Transform for Investigating different Brain regions in Cognitive and Reasoning Activity

In this study, cortical brain activity during a pattern matching task (PMT) was measured by employing electroencephalography (EEG). The EEG data were recorded from 128 scalp locations during a pattern-matching task and in rest conditions (eyes open and eyes closed). Spectral Analysis of EEG frequency bands reflected a significant (p<0.025) difference between baseline and PMT task. The EEG activity in slow waves (delta: 0.5 to 3 Hz and theta: 4 to 7 Hz) was high during PMT in frontal regions, while EEG activity in fast waves (Beta: 14 to 20 Hz and Gamma: 21 to 30 Hz) was reduced in parietal and occipital regions as compared to the frontal region. The changes in EEG medium waves (alpha: 8 to 13 Hz) was high in frontal, central, and temporal regions, while depressed in parietal, parieto-occipital and occipital regions. The results show high cortical activations in different brain regions during solving pattern-matching task as compared to baseline resting conditions. The study has implications for thinking and decision-making situation, such as object recognition, visual comparison, and consumer choice.
Self-Normalized Processes: Limit Theory and Statistical Applications

Independent Random Variables. - Classical Limit Theorems, Inequalities and Other Tools. - Self-Normalized Large Deviations. - Weak Convergence of Self-Normalized Sums. - Stein's Method and Self-Normalized Berry-Esseen Inequality. - Self-Normalized Moderate Deviations and Laws of the Iterated Logarithm. - Cramer-Type Moderate Deviations for Self-Normalized Sums. - Self-Normalized Empirical Processes and U-Statistics. - Martingales and Dependent Random Vectors. - Martingale Inequalities and Related Tools. - A General Framework for Self-Normalization. - Pseudo-Maximization via Method of Mixtures. - Moment and Exponential Inequalities for Self-Normalized Processes. - Laws of the Iterated Logarithm for Self-Normalized Processes. - Multivariate Self-Normalized Processes with Matrix Normalization. - Statistical Applications. - The t-Statistic and Studentized Statistics. - Self-Normalization for Approximate Pivots in Bootstrapping. - Pseudo-Maximization in Likelihood and Bayesian Inference. - Sequential Analysis and Boundary Crossing Probabilities for Self-Normalized Statistics.
Permanent Magnet Vernier Generator with Surface Ferrite Magnets for a Direct-Drive Wind Generator

Direct-drive wind generators are becoming increasingly popular due to the absence of gearboxes, which leads to various advantages such as noiseless operation, long term reliability and lower frequency of maintenance. Usually, permanent magnet synchronous generators (PMSG) are suitable for direct-drive wind energy conversion systems due to their high efficiency and power factor. However, they have more number of poles, since the wind turbine blade speeds are generally low. Hence, permanent magnet vernier generators (PMVG), which have inherent magnetic gearing effect, low-speed high-torque capability and high torque-to-weight ratio are becoming popular for direct-drive wind generators. Various PMVG designs are proposed till date based on the stator or rotor geometry, winding arrangement and placement of magnets. This article aims to design an outer rotor surface-mounted PMVG with ferrite magnets. Further, the 2-D FEM analysis of the proposed generator is carried-out and the results are presented.
Migrating Monoliths to Microservices based on the Analysis of Database Access Requests

Many organizations seek to improve the maintainability and scalability of their computing systems by migrating from a conventional monolithic architecture to a microservice architecture (MSA). Note that service identification is the most crucial step in architecture migration. In the current study, we developed a microservice identification scheme based on analyzing database access to account for the importance of the pattern "Database Per Service" in microservices design. The proposed MIADA (Microservices Identification using Analysis for Database Access) system facilitates the clustering of service endpoints used in the identification and creation of microservices. The feasibility and efficacy of the proposed MIADA approach were evaluated using two existing service-oriented software projects, PlanApproval and CoCoME. The evaluation results show that the MIADA approach can effectively recommend appropriate clusters of service endpoints to construct microservices.
GRAFICS: Graph Embedding-based Floor Identification Using Crowdsourced RF Signals

We study the problem of floor identification for radiofrequency (RF) signal samples obtained in a crowdsourced manner, where the signal samples are highly heterogeneous and most samples lack their floor labels. We propose GRAFICS, a graph embedding-based floor identification system. GRAFICS first builds a highly versatile bipartite graph model, having APs on one side and signal samples on the other. GRAFICS then learns the low-dimensional embeddings of signal samples via a novel graph embedding algorithm named E-LINE. GRAFICS finally clusters the node embeddings along with the embeddings of a few labeled samples through a proximity-based hierarchical clustering, which eases the floor identification of every new sample. We validate the effectiveness of GRAFICS based on two large-scale datasets that contain RF signal records from 204 buildings in Hangzhou, China, and five buildings in Hong Kong. Our experiment results show that GRAFICS achieves highly accurate prediction performance with only a few labeled samples (96% in both micro- and macro-F scores) and significantly outperforms several state-of-the-art algorithms (by about 45% improvement in micro-F score and 53% in macro-F score).
Analysis and detection of broken rotor bars in induction motor under fluctuating load by means of stray flux signals

The early detection of faults in induction motors is an important task to avoid unexpected stops and financial losses at industrial facilities. Such fault identification becomes a hard task when the induction motor operates under the effect of fluctuating loads because they introduce undesired spectral components that may overlap with other fault-related frequencies such as broken rotor bars frequency components. Therefore, the common methodologies based on spectral analysis are prone to fail when discerning between faulty conditions (e.g. broken rotor bars) and operation under fluctuating loads. In this work, a diagnosis methodology based on the analysis of stray flux signals acquired in the vicinity of an induction motor that operates under fluctuating load conditions is proposed. The presented method is based on a statistical feature calculation that is followed by a linear discriminant analysis along with a neural network classifier, for the automatic detection and classification of different fault severities associated with broken rotor bars in the induction motor. The proposed method is evaluated with experimental data and the obtained results demonstrate the capability of the proposed method to detect the occurrence of broken rotor bars in an induction motor that operates under fluctuating loads conditions. This proposal is an adequate strategy to be implemented in industrial environments.
The Menzel Symposium on Solar Physics, Atomic Spectra, and Gaseous Nebulae

A symposium in honor of Donald H. Menzel 1 s contributions to astrophysics was held on his 70th birthday at the Harvard College Observatory, Cambridge, Massachusetts, 8-9 April 19 71. Menzel and his school have made distinguished contributions to the theory of atomic physics, solar physics, and gaseous nebulae. The work on planetary nebulae represented the first investi gations of non-equilibrium thermodynamic conditions in astronomy; the solar work extended these investigations to stellar atmospheres. The applied atomic physics laid the basis for what we now call laboratory astrophysics and, together with work on non-equilibrium ther modynamics, inspired the founding of the Joint Institute for Laboratory Astrophysics. Menzel has served as a distinguished consultant to the National Bureau of Standards, and publication of this volume represents a grateful acknowledgment of his service to the Bureau. The papers summarize the current status of work in the three fields he pioneered.
Implementation of Machine Learning Techniques for predicting Credit Card Customer action

When a consumer switches from one service provider to another, they are considered a churner. With an expanding number of fierce competitors inside the industry, important banks place a premium on client relationship management. A detailed and real-time credit card holder churn review is critical and helpful for bankers looking to retain credit cards. According to extensive research, maintaining an existing client is more than five times simpler than acquiring a new one. As a result, this research provides a strategy for predicting churns using a bank dataset. The "Synthetic Minority Oversampling Technique" (SMOTE) was employed in this study to handle the unbalanced dataset. Randome forest, K closest neighbour, and two boosting algorithms, XgBoost and CatBoost, are used to forecast credit card user turnover. To improve accuracy, hyperparameter tweaking using grid search was performed. The testing results demonstrate that Catboost has an accuracy of 97.85 percent and outperforms the other models.
A Novel Distributed Task Scheduling Framework for Supporting Vehicular Edge Intelligence

In recent years, data-driven intelligent transportation systems (ITS) have developed rapidly and brought various AI-assisted applications to improve traffic efficiency. However, these applications are constrained by their inherent high computing demand and the limitation of vehicular computing power. Vehicular edge computing (VEC) has shown great potential to support these applications by providing computing and storage capacity in close proximity. For facing the heterogeneous nature of in-vehicle applications and the highly dynamic network topology in the Internet-of-Vehicle (IoV) environment, how to achieve efficient scheduling of computational tasks is a critical problem. Accordingly, we design a two-layer distributed online task scheduling framework to maximize the task acceptance ratio (TAR) under various QoS requirements when facing unbalanced task distribution. Briefly, we implement the computation offloading and transmission scheduling policies for the vehicles to optimize the onboard computational task scheduling. Meanwhile, in the edge computing layer, a new distributed task dispatching policy is developed to maximize the utilization of system computing power and minimize the data transmission delay caused by vehicle motion. Through single-vehicle and multi-vehicle simulations, we evaluate the performance of our framework, and the experimental results show that our method outperforms the state-of-the-art algorithms. Moreover, we conduct ablation experiments to validate the effectiveness of our core algorithms.
NVH Analysis of Integrated Motor and Two-Speed Gearbox System for Electric Vehicles

The market penetration rate of electric vehicles has boosted very rapidly in China and worldwide this year. Most of e-powertrain systems nowadays in passengers’ vehicle consists of electric traction motor, inverter and single speed gearbox. Compared to the traditional single speed system, integrated motor and two-speed gearbox system has been developed with higher overall efficiency, higher power density and better noise, vibration and harshness (NVH) performance. This paper presents NVH analysis and optimization of the two-speed e-powertrain system under electromagnetic and mechanical excitations. The simulation model with both e-motor and gear excitations is established. The order interference analysis is conducted at the very beginning to avoid system resonance. Modal, vibration and radiated noise analysis are done in a systematic way to analyze and optimize the original design. System NVH tests are completed in the semi-anechoic chamber. A-weighed system sound pressure level is verified to be less than 80 dBA.
Employing Public Key Infrastructure to Encapsulate Messages During Transport Layer Security Handshake Procedure

The Transport Layer ensures that data is sent transparently between end-users while also enabling reliable data transfer services to the higher levels. Cryptographic protocols Secure Socket Layer (SSL) and Transport Layer Security (TLS) enable data encryption and authentication between various homogeneous and heterogeneous devices such as servers, computers, and software applications running all over a network. These protocols have been successfully used to protect World Wide Web communication between a client and a server that has been encoded using the HTTP protocol for many years. When communicating between client and server, these algorithms address challenges like confidentiality, integrity, availability, and non-repudiation. The significant challenge faced is the attack on these protocols. Among the reported attacks on TSL is the Drown attack which is a prevalent type of attack. In order to prevent such types of attacks, a Public Key Infrastructure (PKI) method is proposed in this study for encapsulating messages between the client and the server while doing the TLS handshake process. The proposed method removes the need for credentials, including client-server certificates and client-server key exchange.
A Penalty-Based PSO Algorithm for Demagnetization Risk-Free Design of Slotless Halbach PM Machines

The versatility of analytical models is limited by the fundamental assumption of having linear material properties. The performance of the popular Halbach PM rotor is bounded to a careful optimization of the array; thus, a thorough demagnetization risk assessment is already required under no-load conditions. For this reason, finite element analysis (FEA) is typically the preferred choice for the inherent capability to handle non-linearities at the expense of time-intensive computations. This paper fully exploits the computational efficiency of analytical modeling into a penalty-based particle swarm optimization (PSO) algorithm to design demagnetization-free slotless machines. The demagnetization risk is assessed through the analytical field solution and used as a penalty in the PSO to target demagnetization-less topologies in the design phase. The optimization routine without the penalty is shown to heavily overestimate the performance compared to the one employing the penalty, suggesting the latter method to be more reliable.
Accurate Geodetic Coordinates for Observatories on Cerro Tololo and Cerro Pachon

As the 50th anniversary of the Cerro Tololo Inter-American Observatory (CTIO) draws near, the author was surprised to learn that the published latitude and longitude for CTIO in the Astronomical Almanac and iraf observatory database appears to differ from modern GPS-measured geodetic positions by nearly a kilometer. Surely, the position for CTIO could not be in error after five decades? The source of the discrepancy appears to be due to the ~30" difference between the astronomical and geodetic positions -- a systematic effect due to vertical deflection first reported by Harrington, Mintz Blanco, & Blanco (1972). Since the astronomical position is not necessarily the desired quantity for some calculations, and since the number of facilities on Cerro Tololo and neighboring Cerro Pachon has grown considerably over the years, I decided to measure accurate geodetic positions for all of the observatories and some select landmarks on the two peaks using GPS and Google Earth. Both sets of measurements were inter-compared, and externally compared to a high accuracy geodetic position for a NASA Space Geodesy Program survey monument on Tololo. I conclude that Google Earth can currently be used to determine absolute geodetic positions (i.e. compared to GPS) accurate to roughly +-0.15" (+-5 m) in latitude and longitude without correction, or approximately +-0". 10 (+-3 m) with correction. I tabulate final geodetic and geocentric positions on the WGS-84 coordinate system for all astronomical observatories on Cerro Tololo and Cerro Pachon with accuracy +-0". 1 (+-3 m). One surprise is that an oft-cited position for LSST is in error by 9.4 km and the quoted elevation is in error by 500 m.
Synchronous Reluctance Tubular Machine by Means of Additive Manufacturing

This paper presents the design of a synchronous reluctance tubular linear machine, whose moving part (plunger) is fabricated by means of metal additive manufacturing. At first, an analytical approach to compute the optimal air gap to external diameter ratio which leads to the maximum force is defined, then the proposed analytical method is validated via 2-D finite element analysis on a reference tubular machine. Once the optimal diameter ratio was defined, an optimal design of the tubular machine rotor is proposed with the aim of increasing the thrust force and reducing the force ripple. Finally, a mock-up of one segment of the optimized plunger geometry was fabricated in resin using fused deposition modeling (FDM) 3-D printing equipment.
Development of a High Power Density In-Wheel Motor using Halbach Array Magnets

In this study, a direct drive in-wheel system is proposed as a high power density system that can reduce the intermediate parts such as a speed reducer by increasing torque. We propose a design strategy that shows multipolarization and the improvement of the gap magnetic flux density are effective at increasing the power density. As a technique for improving the gap magnetic flux density, a Halbach array magnet rotor is used that combines the main pole magnets and spoke magnets with the core. We compare the Halbach array magnet rotor with the conventional surface-mounted permanent magnet (SPM) rotor by using finite element method (FEM) analysis. An in-wheel motor prototype of the actual size with the developed Halbach array magnet rotor was also fabricated and used to measure the no-load induced back electromotive force (EMF).
Electromagnetic Study of High Power Density : PMSM for Automotive Application

This paper presents an analytical design method to improve the power density of an interior permanent magnet synchronous machine (PMSM) with multi-variable and multi-objective optimization based on a genetic algorithm (NSGA2). A 12-slot/8-pole topology with fractional-slot concentrated windings (FSCW) has been chosen. Several designs with different mechanical power, rotational speed, and voltage are carried out. As a key result, the increase of the power density of PMSMs is achieved mainly with the increase of the mechanical power, the rotational speed and the current density.
A Novel Technique for Prediction of Cardiovascular Disease

Globally heart disease is one among the major causes of death. As heart plays a major role in making sure every cell of the body gets oxygenated blood hence any problem in heart will result in major issues in human body and chances of death are quite high. It is observed that there are multiple datasets involved in testing with multiple attributes in each dataset. Each attribute will affect the target output. Commonly we have observed that blood pressure and diabetes are the major reason for cardio vascular diseases. This paper presents some technologies and methods for Machine learning. It then highlights some of the concerns about cardiovascular disease and how attributes are related to the output and how machine learning and data mining techniques are used to predict cardiovascular diseases, using the output of given dataset we can save life. The data is analyzed and results are observed. This section has a review of machine learning algorithms such as the Support Vector Machine, Decision Tree, ANN, Hidden Naïve Bayes, Naïve Bayes, Random Forest, K-Nearest Neighbors (KNN), and Particle Swarm optimization (PSO). The algorithms are applied to the datasets and on the basis of features or attributes of the dataset. All of these algorithms are used so, that the analysis is done in right direction.
Multimodal Feature Selection for Android Malware Detection Classifiers

Android applications are fast evolving across the mobile ecosystem, yet Android malware is always emerging. Many researchers have looked into the issue of Android malware detection and proposed hypotheses and methods from various angles. Machine learning appears to be an efficient and promising technique to detect Android malware, according to existing studies. Our paper proposes a simple yet feasible approach to detect android malware using existing machine learning methodologies. We utilize a novel approach for considering multiple appropriate features called the Multivariate Feature Ranking algorithm that focus on deriving the most suitable features which is then applied to classifier models viz Decision Tree, Ada Boost ensemble method, Random Forest and Multi-layer perceptron. We were able to achieve an accuracy of 92% at a high level for the multi-layer perceptron model considering the Drebin dataset under experiment. Putting forth machine learning as the key aspect, we performed data acquisition, data pre-processing, novel feature selection methodology, followed by multiple classifier models that were evaluated and analyzed for further interpretation. The experimental results revealed that the modified feature selection method could help improve classification accuracy for baseline models, which indeed leads to future scope of improvement with advanced deep learning feature learning techniques.
Efficient Determination of the Behavior of Permanent Magnet Synchronous Machines using Magnetic Equivalent Circuits

This paper presents an computationally very inexpensive way to determine the operating behavior of permanent magnet synchronous machines (PMSM). For this purpose, magnetic equivalent circuits (MEC) are used, which are solved with a fast calculation method and evaluated and validated by means of detailed finite-element-method (FEM) simulations. Both the procedure for building the MEC and the solution method are presented. Emphasis is placed on a simple, but as accurate as possible structure of the MEC. The evaluation of the results is performed using the air gap flux density, the inductances, the flux linkages and the torque. The calculations are made taking into account saturation effects in order to be able to determine the parameters at different operating points.
Design Optimization Study on HEFSM with Flat Aspect Ratio for Enhancing Power Density and Efficiency

This paper focuses on a hybrid excitation flux switching motor (HEFSM) as one of the variable flux motors for EV/HEV propulsion. HEFSMs have the advantage in that all the active components are concentrated in the stator, which provides ease of cooling, whereas causes the drawback of being likely to occur severe magnetic saturation on the stator side. Therefore, a low L/D ratio, i.e., a flat aspect ratio, was selected, which offers a wide two-dimensional cross-section and mitigation of the magnetic saturation. In this paper, optimization of permanent magnet arrangement and grades under a low L/D ratio is addressed to enhance efficiency and power density over the entire speed range while satisfying design constraints and required performance for EV/HEV propulsion applications. The experimental verification shows that the motor efficiency of the newly designed HEFSM significantly exceeded that of the previously designed one, which reaches over 92% at the frequent operating point.