Miracle-dz/UniFAR-FTUs huggingface dataset:
Six Phase Fractional Slot Surface Permanent Magnet Motor for High Torque Density and High Speed

Multiphase electrical machines offer several advantages with respect to their three-phase counterparts, such as fault tolerance capability, mitigated harmonic spectrum and related lower torque ripple and rotor losses, reduced peak current rating. These advantages can vary significantly depending on the slot/pole combination and the application specifications. This paper focuses on rotor loss mitigation obtainable with the particular stator slot number (Q) and pole (2p) combination 2p = Q ± 2 for Double-Layer Fractional-Slot Surface Permanent Magnet machines. The direct comparison between three- and six-phase solutions is proposed with reference to traction application, with preliminary experimental validation. The paper demonstrates the convenience of the 6-phase solution, where the decrease of rotor loss leverages into a substantial improvement of the rotor operating temperature and ultimately of the continuous output power.
QWin: Core Allocation for Enforcing Differentiated Tail Latency SLOs at Shared Storage Backend

Distributed storage systems consolidate latency-critical (LC) tenants and best-effort (BE) tenants together to increase resources utilization. As a result, storage server processes require to guarantee differentiated tail latency SLOs for LC tenants, and meanwhile provide sustainable high bandwidth for BE tenants. With the adoption of high-performance NVMe SSDs, queuing time on storage server process contributes a large part to tail latency of LC tenant. Partitioning request queues, worker threads, and CPU cores on storage server processes among tenants helps to reduce interference and thus queuing time. This in turn requires careful core allocation to guarantee tail latency SLOs. In this paper, we argue that accurate core allocation is necessary for storage server processes to allocate the actual cores required by LC tenants. Thus, we propose QWin, a tail latency SLO-aware core allocation to enforce differentiated tail latency SLOs for multiple LC tenants. QWin first designs an SLO-to-core calculation model to accurately calculate the number of cores required by LC tenant. Bursty loads or fluctuated I/O latencies of storage devices can change core requirements of LC tenants. Thus, we design three core policies in QWin to adapt to the changing core requirements. We evaluate QWin by consolidating multiple LC and BE tenants together. The experiment results show that QWin outperforms the-state-of-the-art approaches in guaranteeing differentiated tail latency SLOs for LC tenants and meanwhile increasing bandwidth for BE tenants by 4x~21x.
Effective implementation of the Prototype of a digital stethoscope using a Smartphone

This article presents the activities carried out in research for the development of a prototype digital stethoscope using a smartphone. First the importance of biomedical engineering in medical decisions and the key concepts of listening to body sound are discussed, with a focus on understanding how signals from the heart, lungs and tract gastrointestinal are received. Using the digital signal processing approach, the main methods of filtering digital signals, which are the subject of this work, are discussed. It also shows the evolution of mobile computing, modern technologies for operating systems and the share of medical solutions. With these concepts in mind, we have developed an external device and app for the Android operating system that can receive and filter body signals to create a digital stethoscope. The process of developing a data acquisition system is also presented, together with the results of the tests and simulations carried out, offering a final discussion on the importance of the characteristics of the smartphone in medical practice, possible improvements in the process digital filtering and effective digital stethoscoperealization.
Artificial Intelligence-based Web-Centric E-health Monitoring System

The tremendous growth in Information Communication Technologies (ICT) and the Internet of Things (IoT) propose new application domains like remote patient monitoring. Patient monitoring is mostly done through hospital systems, Remote patient monitoring systems will reduce cost and save travel time also it will increase the efficiency of the healthcare system. The main objective of our system is to develop an artificial intelligence-based e-Health monitoring system allowing the close monitoring of patient parameters without any location boundary. This system gives a multidimensional diagnosis system. It integrates web, mobile, and display technology. In the proposed system temperature, pulse rate, blood pressure, and sugar level are captured and stored in a database. The process is carried out by Raspberry Pi (ARM 11) integrated with sensors. A web-based application interface is created for real-time communication. Internet of Things enlarges the system with data transfer. Here fingerprint recognition system is used to get all previous records of a patient's health. We are providing an interface for live demonstration and suggestion in a dual communicative way.
Porous Titanium Nitride electrodes on miniaturised pH sensor for foetal health monitoring application

Measuring pH level on foetal scalp is a common practice to prevent the risk of acidosis in monitoring foetal health during labour. However, the current technique of collecting capillary sample (blood gas analyser method) is not continuous and has high rate of failures. In this work, a prototype of miniaturized pH sensor based on porous Titanium Nitride (TiN) electrodes to get continuous measurement of pH has been developed, targeting foetal health monitoring. By exploiting wet etching techniques on flat TiN electrodes, porosity of the working surface area has been increased, exhibiting better properties and performances of our pH sensor. Precision of the developed TiN pH sensor is 0.03 in pH, which is close to the requirement of the application. The sensitivity is determined to be 67.06 mV/pH within pH range of interest from 6 to 8. In addition, the Potentiostatic Electro Impedance spectroscopy (PEIS) and Cyclic Voltammetry (CV) were recorded, showing a 6.3-time smaller impedance at 100 Hz frequency and a better conductivity compared to the flat TiN electrode. This work is under the next step of fabrication to transfer the system from glass onto a flexible Polyimide substrate and to be integrated into a specific miniaturised packaged device for in-vivo testing.
An Efficient Deep Learning Approach for Plant Disease Detection

During the stages in which plants are growing, they are more likely to be infected by a variety of diseases. Because of insufficient laboratory facilities and a dependance on the knowledge of specialists, initial plant disease identification is a time-consuming process. If the diseases are not discovered in their early stages, then it is possible that they will have a negative impact on the overall yield, which will lead to a reduction in the profits made by the farmers. Several studies have discussed various state-of-the-art systems that are based on Deep Learning as well as Machine Learning methodologies in an effort to find a solution to this issue. On the other hand, the majority of such systems either employ large numbers of input variables or even have poor accuracy in their classifications. In this paper, an innovative combined approach for automatic detection of plant diseases is proposed. The model is based on Variational Autoencoder (VAE) networks and Convolutional Neural Networks (CNN). Techniques for the automated detection of plant diseases offer a number of benefits, including the simplification of the lengthy process of observing large agricultural farms and the identification of disease symptoms at an early stage. In this work, the proposed combined approach is utilized to detect the disease known as Bacterial Spot that is present in plants by utilizing the leaf images of those plants. The proposed method achieves an accuracy rate of 98.85 percent when used for testing and 99.17 percent when used for training.
Permanent Magnet Synchronous Machine for Hybrid Light Aircraft

This paper presents the design of a Permanent Magnet synchronous machine for integration into hybrid propulsion system of a light aircraft.A performance analysis to satisfy the power deficit between propeller and Internal Combustion Engine was carried out. A machine with surface permanent magnets and concentrated windings has been designed. The goal was to define a motor focusing on the key points of performance and reliability. A carbon fiber sleeve has been inserted in rotor core in order to guarantee the holding of the magnets at high speed and ensure even higher reliability. Requirements are fully satisfied at rated and peak power and the motor encumbrance is within the imposed values. Moreover, a thermal analysis of the liquid cooled machine has been carried out in order to verify the temperatures during its operation.
Quantum walk in symmetric Cayley graph over $\Z_2^n$

We show that the hitting time of the discrete quantum walk on a symmetric Cayley graph over $\Z_2^n $ from a vertex to its antipodal is polynomial in degree of the graph. We prove that returning time of quantum walk on a symmetric Cayley graph over $\Z_2^n $ is polynomial and the probability to hit is almost one. To prove it, we give a new estimation of Kravchuk coefficients. We give an example of a probabilistic polynomial algorithm that finds an antipodal vertex in symmetric Cayley graphs.
Predicting Autonomous Vehicle Navigation Parameters via Image and Image-and-Point Cloud Fusion-based End-to-End Methods

This paper presents a study of end-to-end methods for predicting autonomous vehicle navigation parameters. Image-based and Image & Lidar points-based end-to-end models have been trained under Nvidia learning architectures as well as Densenet-169, Resnet-152 and Inception-v4. Various learning parameters for autonomous vehicle navigation, input models and pre-processing data algorithms i.e. image cropping, noise removing, semantic segmentation for image data have been investigated and tested. The best ones, from the rigorous investigation, are selected for the main framework of the study. Results reveal that the Nvidia architecture trained Image & Lidar points-based method offers the better results accuracy rate-wise for steering angle and speed.
Improvement of the Continuous Performance of a Traction Machine for a Battery Electric Vehicle through Magnet Segmentation

This work investigates the impact of the magnet segmentation on the continuous performance of an interior permanent magnet synchronous machine of a battery electric vehicle. Electromagnetic and thermal simulations, as well as thermal laboratory experiments are performed for the solid magnet reference design and the segmented magnet variant, and the results are compared, in order to verify the impact of the alteration. The rotor geometry is kept constant, therefore the magnet area is decreased by 1.25% for the segmented design, in order to account for the insulation layer between the segments. This results in a slight decrease in the maximum power and torque values of the segmented designs, throughout the operating range. Finite element analysis results of the designs state that the copper and core losses show only marginal differences between the designs, however the magnet losses are improved 5-7 times after segmentation. Furthermore, the continuous performance is improved for high speeds, at 12000 rpm, the continuous power increases from 46 kW to 65 kW and the continuous torque similarly increases from 39 Nm to 55 Nm. The thermal experiments show that the machine losses at high speeds are lower for the segmented design. Moreover, the rotor temperature of the segmented design is lower than of its solid magnet counterpart.
Wi-fi Controlled Smart Home Automation

All the devices inside the house are connected, by the virtue of developing automation trade and wireless property. Today’s World is moving to digitalization wherever everything is formed simple and comfy for humans i.e., young youth also as adult. Some of the home appliances which are smart utilize IOT (Internet of Things) and can control any home appliances using the app like Turning ON and turning OFF of electric bulbs, Fan, AC, Water pump, Water for agriculture. Anyone can control the home appliances with the help of Internet Connection, Node MCU ESP8266, Android Application. Here in this paper, it includes working of node esp8266 which are used for controlling the house application like fan, light bulb, sensors, motion detection, and water motor with the help of Blynk app and coding. In this era majority of the devices are controlled with mobile application and with Wi-Fi connectivity these devices are controlled as per user requirement. This paper is making clear that devices are monitored with Node MCU and control these devices using Blynk App. In line with demand of want one will connect multiple devices like sensors, appliance, and plenty of additional until-8.
Performance of Multi Three-Phase Converter-Fed Non-Overlapping Winding Wound Rotor Synchronous Wind Generator

In the search for efficient and reliable non- permanent magnet generator solutions for variable-speed wind generation systems, this paper proposes a multi three-phase converter-fed wound rotor synchronous generator for large- scale wind turbines. The focus is on geared, medium-speed wind generators. The stator winding of the generator has non- overlapping coils with a 16/18 pole/slot combination. As a first step towards experimentally proving the effectiveness of the proposed concept, finite element analysis was performed on a small-scale wound rotor synchronous generator. The findings of the study show that the proposed wind energy system is feasible and efficient for large-scale wind turbines.
Dovetail Design for Direct Cooled Rotor: Design and Manufacturing

This work investigates dovetail joints as a means to mount pre-wound coils in an electrically excited rotor for a synchronous machine for EV application. The aim is to manufacture a prototype of a direct cooled machine with hollow rectangular rotor windings which requires a rotor design where the windings can be applied on the poles separately. Two experimental tensile tests are made to validate the mechanical simulation data, which are combined with electromagnetic simulations to optimize a dovetail design for assembly, mechanical strength and electromagnetic performance. The prototype is manufactured and assembled and preliminary tests are performed.
Quantum Invariants of Knots and 3-Manifolds

This monograph, now in its second revised edition, provides a systematic treatment of topological quantum field theories in three dimensions, inspired by the discovery of the Jones polynomial of knots, the Witten-Chern-Simons field theory, and the theory of quantum groups. The author, one of the leading experts in the subject, gives a rigorous and self-contained exposition of fundamental algebraic and topological concepts that emerged in this theory.
Performance Improvement of Partially-Shaded PV Modules Connected DC Micro grid Systems

It intended to acquire SEPIC converters, which have been utilized in a variety of applications It has two major drawbacks, the first of which is that it places power semiconductor devices are subjected to high voltages, and secondly, the power switches must work very hard, resulting in low efficiency. Power semiconductor devices with a higher voltage rating should be used, especially for high voltage applications. When the voltage rating goes up, power MOSFETs tend to have a higher Rds(on) value. As a consequence of this, it results in a greater amount of conduction loss when the current level remains the same. Consequently, if the current is held constant, decreasing the voltage stress can improve efficiency. The dynamic performance is enhanced.
UAV Trajectory Planning from a Comprehensive Energy Efficiency Perspective in Harsh Environments

As a highly promising application paradigm, unmanned aerial vehicles (UAVs) in wireless communications have gained widespread attention by virtue of their advantages, such as low cost and high agility. Unfortunately, the discharge capacity of onboard batteries will be greatly weakened in harsh environments, thereby reducing the expected endurance of the UAV. To this end, a UAV data distribution system with expected data requirements is considered in this article. Our objective is to reassess the trade-off between UAV mission time and its propulsion energy consumption (PEC), and provide a novel designing perspective for energy-efficient UAV deployment based on trajectory planning. First, we analyze the energy consumption composition and approximate the PEC to the total energy consumption of the UAV. Then, based on the “hover-fly-communicate” design, we focus on the factors that affect mission time and PEC. Furthermore, the simulation results illustrate the influence of critical system parameter settings on the propulsion energy consumption efficiency (PEC-E) of the UAV. Finally, future research is discussed, and the article is concluded.
Data Collection and Exploratory Analysis for Cyber Threat Intelligence Machine Learning Processes

Cyber threat intelligence powers machine learning and other data analysis techniques that provide enhanced protection and situational awareness to critical infrastructure application spaces. The Geo Threat Observable (GTO) research project gathers, processes, manages, and analyzes cyber threat intelligence to provide predictions based on system data, recover missing links in threat intelligence, and enable better use of limited cyber defense resources. Especially focused on the energy sector, GTO provides coherent common operational descriptions of infrastructure landscapes to empower cyber threat defenders. This paper focuses on the aggregation, cleaning, processing, management, validation, and analysis of cyber threat intelligence for the GTO project.
Compact Riemannian 7-manifolds with holonomy $G\sb 2$. II

This is the second of two papers about metrics of holonomy G2 on compact 7manifolds. In our first paper [15] we established the existence of a family of metrics of holonomy G2 on a single, compact, simply-connected 7-manifold M , using three general results, Theorems A, B and C. Our purpose in this paper is to explore the theory of compact Riemannian 7-manifolds with holonomy G2 in greater detail. By relying on Theorems A-C we will be able to avoid the emphasis on analysis that characterized [15], so that this sequel will have a more topological flavour. The paper has four chapters. The first chapter consists of introductory material. Section 1.1 gives some elementary geometric and topological material on compact 7-manifolds with torsion-free G2structures. Then §1.2 describes the holonomy groups SU(2) and SU(3), and §1.3 explains the concept of asymptotically locally Euclidean Riemannian manifolds (shortened to ALE spaces) with special holonomy. Recall that in [15], a compact 7-manifold M was defined by desingularizing a quotient T /Γ of the 7-torus by a finite group of isometries Γ ∼= Z2. The subject of Chapters 2 and 3 is a generalization of this idea. Chapter 2 defines a general construction for compact 7-manifolds with torsion-free G2structures, which works by desingularizing quotients T /Γ for finite groups Γ. The ALE spaces with holonomy SU(2) and SU(3) discussed in §1.3 are an essential ingredient in performing this desingularization. The central result of Chapter 2 is Theorem 2.2. 3, which states that given a suitable finite group Γ and certain other data, one may construct a compact 7manifold M from T /Γ that admits torsion-free G2structures. This result is proved using Theorems A-C of [15]. Chapter 3 is devoted entirely to examples of this construction. We give many examples of compact 7-manifolds with holonomy G2, and determine their basic topological invariants — the Betti numbers and fundamental group. Finally, in Chapter 4 we discuss some areas of interest, and give a number of open problems. This paper is not written to be read independently of [15]. The language and results of [15] will be used freely, in particular the introductory material in [15, §1.1]. For reference we reproduce here the model 3and 4forms φ, ∗φ defining the flat G2structure on R, as given in [15, §1.1]:
Optimal Design Parameter Determination for Brushless Doubly Fed Induction Machines

Brushless doubly fed induction machines (BDFIM) have prominent features for variable speed applications. The design and performance analysis of the machine is however complex compared to the conventional induction machines due to its special magnetic fields generated from two stator winding with different pole numbers and frequencies. In this paper, the effective parameters in BDFIM design and their limitations are investigated. In relation with the design objectives, control variables are identified, and an optimal prototype machine design is proposed. The results from experimental tests and finite element simulations of a 3 kW prototype BDFIM are presented to assess the effectiveness of the proposed parameters determination method.
Design Considerations For High Power Density Traction PM Motors With Hairpin Windings

The automotive industry trend is to push e-axles power density to reduce the weight, maximize the available space and reduce cost of the vehicle. Pushing power density means high mechanical speed and high peak current density. Maximum speeds for traction machines found in the industry are in the range of 12-20 krpm. The trend is to move towards maximum speeds of the traction motor above 20 krpm, in particular for high performance electric vehicles. This paper analyzes the design challenges of selecting the concept design for a high speed traction PM motor and high current density. The study is limited to hairpin winding PM machines for vehicle traction application and with a maximum operating speed above 20 krpm. The trade-off between power density, efficiency and cost is studied analyzing the most relevant pole/slot combinations and rotor topologies considering electromagnetic, thermal and mechanical aspects.
D3: Lightweight Secure Fault Localization in Edge Cloud

In pursuit of high-performance applications, the cloud is moving out of the data center and towards the edge. Secure data forwarding is critical for the users between the edge and the remote cloud. In this paper, we propose D3 (Demon Detector in Data Plane), a lightweight, secure fault localization mechanism, which can enable the users in the edge cloud to localize faulty links and thus avoid the faulty links to guarantee secure data forwarding along the path to the remote cloud. D3 utilizes the user to instruct the transit routers, thus empowering the user to detect whether the transit routers forward the packet as expected. Compared with existing schemes that are difficult to be deployed in practice due to the incurred heavy storage, computation, and communication overhead, D3 offloads most of the transit router’s storage and computation overhead, thus dramatically improving the deployment efficiency. Particularly, the length of the additional packet header in D3 is 2-5 times less than the state-of-the-art mechanisms, and the extra control packet overhead is ten times less while keeping a little constant storage overhead in the data plane. The evaluations in BMv2 and Barefoot Tofino hardware show that D3 could achieve high fault localization accuracy and efficiency.