Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytical Calculation of Maximum Mechanical Stress on the Rotor of Interior Permanent-Magnet Synchronous Machines

This article investigates analytical methods for calculating mechanical stress on the rotor of Interior Permanent-Magnet Synchronous Machines (IPMSMs). First, two existing analytical methods for calculating the stress on rotor iron bridges were investigated using a flat-type IPMSM model. The calculations of centroid radii required by these methods were simplified by taking advantage of the symmetry. Then, an accurate function of the stress concentration factor (SCF) was developed to calculate the maximum mechanical stress (MMS) on the iron bridge from its reference stress. The trends of SCF and MMS changing with geometrical parameters were obtained during the statistical analysis of SCF. Using the proposed analytical method, MMS on the bilateral iron bridges of both flat- and V-type IPMSMs can be calculated accurately. Finite element analysis (FEA) shows that the proposed method has improved accuracy and generality in calculations of the MMS. The reliability of the FEA model and the proposed analytical method were further investigated in a plastic deformation experiment. Based on the experimental results, the limits of the stress models, selection of the failure criteria, and safety factor were discussed for high-speed IPMSM design.
Multi-Channel Joint Dereverberation and Denoising using Deep Priors

Reverberation and ambient noise present in an audio scene degrades the speech intelligibility and perceptual quality of speech based query applications. The problem of joint speech dereverberation and denoising is challenging when compared to sequential dereverberation and denoising. In this paper this joint problem is solved by a using a model-based optimization technique for dereveberation and a corresponding DNN with deep priors for the denoising part. This joint enhancement algorithm is then applied to every channel in a multi-channel scenario. The processed outputs of every channel are then combined using beamforming method to compute a spatially filtered signal. This method therefore utilities both spectral and beam forming techniques for speech enhancement in a multi channel scenario. Subjective, objective Word error rate evaluations indicate a significant improvement under both noisy and reverberant conditions.
A Quality Sentient System for Cloud Edge Centric Internet of Things

Cloud Edge centric Internet of Things is a new decentralisation wave encouraged by consolidating the useful storing and processing capabilities of Cloud Computing and Edge Computing as well as ubiquitous data assembling ability of Internet of Things. Nevertheless, quality evaluation for CEcIoT is a barely analyzed issue. It may prevent the end user from choosing the desirable service provider. This paper proposes a quality sentient system which achieves the specific goals by acknowledging the attribute necessities of the end users. It is adoptable for evaluating reputation and trust regarding the functions of the management. It helps the receiver end for the selection of appropriate services. Detailed analysis and designs of the proposed quality sentient system are presented in a case study.
Battery Emulation Using Dual-Active Bridge Converter

A Battery Emulator(BE) is a platform that can mimic the behavior of an actual battery in real-time. In a typical BE system, a discretized battery model running on a digital platform controls a power interface hardware capable of transferring power to a device connected to it. These types of systems play a key role in enhancing the test benches for applications like the integration of renewable energy resources(RES) to the grid, electric vehicles(EV), battery management systems(BMS), etc. Unlike the testing with real batteries, testing with a battery emulator gives a low cost, safe, repeatable and reliable platform to the developer engineers around the world. This work presents a battery emulator based on a dual active bridge(DAB) along with an equivalent circuit based battery model capable of giving both static and dynamic response. Proposed work consists of an ac grid-connected converter system along with a battery model and dual active bridge to emulate both discharging and charging mode behavior. Corresponding simulation and hardware results are given and discussed.
Image Enlargement using Biharmonic DP-Ball Surface

an image enlargement has intensified in many studies due to its novel approach of re-sizing image through scaling up or scaling down. Consequently, it allows smaller image to fit a larger screen when it is in a full display mode. It is also applicable in reducing higher image resolution into a lower image resolution. Conversely to some of its limitations, this study focuses on double enlargement of the original image by employing biharmonic cubic DP-Ball with respect to four boundary curves condition. Seven grey-scale test images are used in order to study the performance of the method. The performance indicator namely Peak Signal to Noise Ratio (PSNR) is used to compare the ability of the method compared to some existing methods.
Delineating Common Cell Pathways that Influence Type 2 Diabetes and Neurodegenerative Diseases using a Network-based Approach

Type 2 diabetes (T2D) is a chronic metabolic dysfunction characterized by resistance to insulin. T2D can cause acute and chronic damage to the vascular and immune systems which can increase the risk or severity of other diseases. A welldocumented group of diseases affected by T2D incidence are the neurodegenerative diseases (NDD). However, the interaction or influence of T2D on NDD is still poorly understood because the clinical complexity of NDD and T2D make conventional endocrinological methodologies render this very difficult. As an alternative approach, we used a strategy to discover cellular pathways common to NDD and T2D employing transcriptional analysis of affected tissues. We examined microarray transcript datasets from studies comparing control individuals with T2D patients, and likewise control and NDD sufferers. The latter included Alzheimers disease (AD), Parkinsons disease (PD), Huntingtons disease (HD), multiple sclerosis disease (MSD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FD), spinal muscular atrophy (SMA), Lewy body dementia (LBD) and epilepsy disorders (ED). Differentially expressed genes (DEG) for each selected pathologies were first identified then pairwise overlapping DEG between T2D and each NDD were identified by cross comparison. Gene set enrichment analysis (GSEA) was then undertaken for those common DEG using molecular pathway as well as gene ontology (GO) analysis. We thus uncovered new putative connections between pathological processes in T2D and NDD by identifying cell pathway commonalities. The findings were validated using Online Mendelian Inheritance in Man (OMIM) and dbGaP (gene SNP-disease association) databases for gold-standard benchmarking of their involvement in disease process. This methodology enables data-driven approaches to identify novel mechanisms affecting disease progressions and may enable prediction of disease co-morbidity development in a quantitative way.
Switched-Inductor Based High Voltage Gain Step-up DC-DC Converter for Photovoltaic Micro-Inverter Application

Traditional boost converter cannot offer a high voltage gain due to the conduction losses in the ESR of capacitor and DCR of inductor, at higher duty cycles. Therefore, traditional boost converter cannot be employed for voltage conversion in occasions that need very high voltage boosting such as the grid-connected photovoltaic micro-inverter. In this paper, a high voltage gain step-up converter, utilizing a switched-inductor (S-L) cell, is proposed. The input current of the suggested converter is continuous because of the presence inductor at source side that makes the input filter design easier. This converter produces very high output voltage at a lower duty cycle, which makes it suitable for photovoltaic micro-inverter application. The basic operation principles and steady-state analyses of the proposed step-up converter are discussed to derive the voltage gain and voltage/current stresses across each component. The proposed boost converter is finally simulated in MATLAB/Simulink to verify all theoretical analyses.
Trust Based Secure Content Delivery in Vehicular Networks: A Bargaining Game Theoretical Approach

Vehicular networks expect to promote the road safety for moving vehicles and improve the driving experience of drivers. In the vehicular networks, secure content delivery becomes significant yet very challenging to protect the vehicles from various threats (i.e., outside attacks and inside attacks). To this end, this paper proposes a security-aware content delivery scheme in vehicular networks when the vehicles request contents from roadside units (RSUs). Specifically, we first establish a novel trust evaluation scheme for both the vehicles and RSUs by exploiting other vehicles to monitor the actions during the process of content delivery. Then, a bargaining game based pricing model is proposed to motivate the vehicles and RSUs to act more positively in the content delivery in vehicular networks. Next, to achieve the secure content delivery and optimize the players’ utilities, we apply the backward induction method to analyze the proposed model in a two-round game and an $M$-round game, respectively. Finally, simulation results are provided to evaluate the efficiency of the proposed scheme by comparing with several conventional schemes.
Semantic Segmentation of Brain Tumor from MRI Images

Convolutional Neural Networks (CNN) have been used in many tasks including image classification, super resolution and semantic segmentation in the field of computer vision. CNNs are helpful in medical image segmentation because they contribute to automation in segmentation that does not require any hand crafted features for training. The aim of this work is to obtain a fully automated segmentation method based on Convolutional Neural Networks for segmenting brain tumor extent from MRI images. The manual segmentation of brain tumor extent is a time consuming process and the performance is highly dependent on the operator’s experience. Hence an automated method for semantically segmenting brain tumors is necessary. This paper proposes three methods for semantic segmentation of brain tumors, one based on Fully Convolutional Neural Networks (FCNNs) architecture and the other two based on U-Net architecture. The experimental results on BraTs 2015 dataset show that these methods can yield promising results compared to other existing methods.
M2MHub: A Blockchain-Based Approach for Tracking M2M Message Provenance

The Internet of Things (IoT) is a fast growing and popular subset of technology. One of the main features of IoT is device autonomy, the ability for the machines embedded in the devices to function without human intervention. This includes communications with other devices through Machine-to-Machine (M2M) communication. Unfortunately, M2M communications are only stored with what behaviours they have observed, without the causal relationship as to how or why they observed those behaviours. Since M2M messages can trigger more M2M messages, the provenance of issues inside an IoT system can be hidden behind a long chain of messages, so finding the root source of any problem, such as malicious or defective devices, is almost impossible to detect. To solve this problem, in this paper we introduce M2MHub, a centralized auditing system which collects M2M messages in an IoT system and stores them in a blockchain. Devices in the system can tell the hub if they wish to open, continue, or end transactions, allowing the hub to keep track of who is the provenance of the transaction and how their transaction affects other devices. A proof-of-concept simulation has been constructed, demonstrating how M2MHub may function in a real-world implementation. The current implementation is not scalable enough to be deployed to actual IoT networks, so several ideas for future work are offered.
Model Decoupling and Control of the Wheeled Humanoid Robot Moving in Sagittal Plane

The wheeled humanoid robot is such a new type of robot that combines both the humanoid structure and the Wheeled Inverted Pendulum (WIP) base. They are able to move rapidly on flat ground as well as stand still on the slope, which has been well demonstrated on the WLR-II robot in this paper. In order to achieve it, a novel but simplified control framework is designed, which comprises of two main modules, the wheel balance controller and the centroidal adjustment controller. The former controller helps to maintain balance of the robotic system by rotating the wheel to move forward or backward, while the latter controller works by moving the Center of Mass (CoM) of the robot at a distance from the equilibrium point, which will result in a specified acceleration used to drive the first wheel balance controller. In order to design such these two controllers, the dynamic model of the robot in sagittal plane is decoupled into two relatively simplified model. In particular, the coupled dynamics between each other is significantly considered and alleviated. Experiments conducted on the WLR-II robot show that the proposed control framework can make the robot both accurately track the velocity tajectory and steadily stand on the slope.
As Symmetric As Possible: Shape Completion with Non-Rigid Registration Leveraging Generalized Cylinder Decomposition

To infer a 3D entire shape from its partial observation, a non-rigid registration algorithm that employs embedded deformations is proposed. We construct a deformation graph on a reference model to discretize the space, and compute a complex deformation as a collection of affine transformations to align the reference model toward the given geometric data. To avoid distortion artifacts during the non-rigid registration, we introduce constraint “As symmetric as possible (ASAP)” on the graph via a generalized cylinder decomposition. ASAP allows model deformation maintaining its underlying local symmetry, which leads to plausible shape completion in the area with no observation. We performed experiments with synthesized data, and demonstrated that the proposed method successfully restored missing surfaces compared with conventional completion techniques.
A new Proposal for Enhancement of Protection Scheme for Microgrids

To design a perfect protection scheme is a big challenge for microgrid (MG) network implementation because it can operate either grid connected mode neither islanding mode. Due to distributed generation (DGs) resources the fault current always varied steadily hence the system parameters. This research focus on two vital properties of microgrid: peer-to-peer and plug-and-play to avoid sophisticated communication network for microgrid adaptive protection schemes. A microgrid model has been proposed by conventional protection scheme and addressing all possible drawbacks along with distributed energy resources. Whole microgrid is divided into different protection zone to identify perfect fault detection techniques by analyzing corresponding system parameters. Moreover, transformer protection also considered in new proposed protection scheme.
Robust Rate-Maximization Precoder Design for VFDM System

The new radio (NR) system is recently proposed to deploy alongside the long term evolution (LTE) system to improve the wireless communication capacity. The Vandermonde-subspace frequency division multiplexing (VFDM) is an efficient technique for such a two tier network, whereby the NR transmitter can reuse the frequency spectrum of LTE system meanwhile guaranteeing interference free to LTE. In this paper, we first investigate the robust rate-maximization problem in the VFDM system. In the condition of imperfect channel state information (CSI), the precoder is optimized to satisfy both non-interference to LTE system and rate-maximization in the NR system. To tackle the challenge in solving such a non-convex problem, an iterative algorithm is proposed based on the block coordinate descent (BCD) and penalty dual decomposition (PDD) methods. Finally, simulation results demonstrate the superiority of the proposed algorithm in terms of robustness and achievable rate over the conventional SVD-based VFDM technique.
Post-Fault Operation of Fault-Tolerant Three Phase Surface Mounted PMSM

Fault tolerance is a necessity for motor drives used in safety-critical applications. Permanent magnet synchronous motor (PMSM) with rare-earth magnets have high torque density, high efficiency. To achieve fault-tolerance, those are to be designed with high inductance. With this, currents during winding short-circuit get limited. In this paper, a fault-tolerant three-phase PMSM operation during its post-fault (both open and short-circuit conditions) situation is presented. In this work, a fault situation refers to one of the phases becoming open or short. The proposed control involves modelling the motor in stationary reference frame. With this model, closed-form expressions are obtained for the currents in the remaining healthy phases during post-fault operation. Using these expressions, speed control during post-fault operation is proposed such that the torque ripple gets minimized. To experimentally validate the proposed control, a fault-tolerant PMSM (12-slots, 10-poles, fractional slot concentrated winding) is designed and prototyped. Results pertaining to motor design validation and torque-speed profile during post-fault operation are presented.
Aerodynamic Analysis on Double Wedge Airfoil at Different Mach Numbers with Varying Angle of Attacks Using Computational Fluid Dynamics

The aerodynamic analysis primarily focusses on choosing airfoil shapes to obtain better physical performance. Various shapes of the airfoil are being studied to attain a better and safer flight with the highest possible performance. On them, double wedge airfoils have different types of shocks at higher speeds. This paper aims to present the effects of change in the physical parameter for the Double Wedge airfoil for the different range of Mach number with varying angle of attack. Thermodynamic parameters are studied for two different Double wedge airfoils with half wedge angle 5° and 10° respectively. Double wedge airfoil is analyzed at different angles of attack (AOA). The analysis is done using ANSYS fluent and the $\mathrm{k}-\varepsilon$ turbulence modeling method is carried out. Numerical simulated data Was generated by using Tecplot. From the simulated result, the coefficient of Lift and Drag has been seen to decrease with the increase in Mach number and decrease in AOA. For same Mach number, increase in half wedge angle increases shock and expansion waves at 0° AONA. And pressure coefficient (Cp) increases in the first half region and decreases in the second half region of the wedge With the increase in half wedge angle.
Propagation Measurements and Modeling for Low Altitude UAVs From 1 to 24 GHz

In most countries, small (<2 kg) and medium (<25 kg) size unmanned aerial vehicles (UAVs) must fly at low altitude, below 120 m, and with permanent radio communications with ground for control and telemetry. These communications links can be provided using 4G/5G networks or dedicated links, but in either this case the communications can be significantly degraded by frequent Non Line of Sight (NLoS) propagation. In this case, reflection and diffraction from ground objects are critical to maintain links, and hence accurate propagation models for this must be considered. In this letter we present a model for path loss when the UAV is flying in NLOS conditions. The study is based on measurements made at frequencies of 1, 4, 12, and 24 GHz with a UAV flying in a suburban environment. Measurements have been used to model NLOS propagation below 4 GHz, where the dominant mechanism is diffraction, and above 4 GHz where multipath is the dominant propagation mechanism. The model can be of use in predicting excess losses when UAVs fly in suburban NLOS conditions.
Magnetic 3-axis Soft and Sensitive Fingertip Sensors Integration for the iCub Humanoid Robot

The humanoid robot iCub is currently equipped with an array of capacitive sensors that provide pressure information throughout the body of the robot. Even though for some applications this type of data is sufficient, it is not always the case for the fingertips of the robot. In particular, the current sensors do not provide enough information for performing agile manipulation, where both intensity and direction of the exerted force on the fingertips are relevant for the proper execution of the task. In this paper, we present a single 3-axis small magnetic sensor module and we show its effectiveness when integrated into the fingertips of the iCub. The sensor module is derived from uSkin, presented in previous works from our laboratory. Replaceable fingertips were designed, built and integrated via software into the low level communication network of the robot, providing fast 3D information about the contact between the fingertips and objects. Additionally, we present two experiments demonstrating tasks that would not be possible to perform with the current fingertip sensors.
On the Blockchain-Based General-Purpose Public Key Infrastructure

The past few years have witnessed unprecedented advancements in the Distributed Ledger Technology (DLT) and blockchain — a form of DLT. DLT has clearly expanded the applications landscape in various sectors of our lives ranging from banking to business, finance, industry, education, and so on. On the other hand, security plays a crucial part in the successful realization of such applications and services. To this end, cryptography is the primary mean to protect the applications, networks, infrastructure, and services from cyber-threats. However, the existing Public Key Infrastructure (PKI) is based on central Certificate Authority (CA) that can become a bottleneck and may affect the efficiency of the cryptographic protocols because of the overhead incurred by the verification of cryptographic signatures and certificates. Recently, blockchain has also been leveraged to aid PKI without the need for a central authority. In this spirit, in this paper, we develop and implement a blockchain-based PKI using open-source Hyperledger Sawtooth. The proposed blockchain-based approach helps to address the problems of the existing PKI such as compromised and misbehaving CAs.
Attendance Monitoring System Through RFID, Face detection and Ethernet Network: A Conceptual Framework for Sustainable Campus

Attendance monitoring has become a very strenuous task in Schools, Colleges and Universities. Paper based attendance recording and monitoring require a significant amount of time and effort by the instructor. Most of the educational institutions in developed countries have an online system to monitor the student's attendance. Even web based attendance monitoring system takes a lot of time for this routine based task. This paper focuses on the usage of intelligent systems in classrooms for recording student's attendance that are linked to RFID technology, face detection mechanism and also based on the location of student's registered mobile device. RFID is one of the popular identification technologies used since it is cost effective and sustainable. Along with RFID, biometric face detection and location capturing (ethernet router log) makes this system fully trustworthy and along with this provides a robust platform for attendance monitoring system.