Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Circuit Parameters for Series-Parallel Compensation Topology in Critical Magnetic Resonance Coupling

The magnetic resonance coupling (MRC) technique is mostly utilized in the near field wireless power transfer (WPT) as power transfer can be maximized using this technique by ensuring critical coupling between transmitter and receiver coils. Among the existing basic compensation topologies of this technique, the Series-Parallel (SP) topology is studied in this paper. Although these basic topologies have been extensively studied, mathematical relationships between the resonant frequency and circuit parameters at critical coupling for all the topologies have not been established yet. In this study, a generalized equation for the resonant frequency at critical coupling condition for SP topology is proposed and simulation is conducted to verify the proposed equation. The observed results are in full compliance with the theoretical findings of the study.
DenseLinkNet: A Deep Convolutional Architecture for Automatic Segmentation of Corneal Endothelial Cell

Corneal endothelium cell provides vital clinical information regarding the health status of the cornea, which is crucial to monitor different corneal diseases. In this regard, segmentation of the corneal endothelial cell plays a significant role in assessing different clinical parameters, like cell density, coefficient of variation, and hexagonality, that aids clinicians in diagnosing different diseases. Since the manual observation process is both time-consuming and susceptible to errors, finding an efficient approach to automate the segmentation task is imperative. In this paper, an encoder-decoder based deep neural architecture, namely DenseLinkNet, is introduced to automate the segmentation process. In the proposed segmentation architecture, DenseNet-201 is used as an encoder, and LinkNet is used as a decoder. The encoder block exploits the output of the convolutional and dense blocks, and the generated feature map gets added with the corresponding decoder block. Following the process, a segmentation head is introduced to conclude the segmentation task. In this work, the publicly available Alizarine dataset is used for experimental purpose. The proposed DenseLinkNet architecture outperforms other segmentation networks with respect to different performance metrics. The network has achieved a dice-score of 76.82% and a Jaccard-index of 62.51%.
Ultra-Wide band Radar System Assembly across the Obstacle for Human Vital Signs Detection

With the ongoing advancements in automated types of machinery, Robotics and kind of artificial intelligence specifically, are continuously replacing human interventions and generally, manual operations for the most part are declining day by day, which is quite significant. Collaborations of humans with machines particularly is increasing at a rapid pace making several technologies efficient and better responsive. Out of these technologies prevailing for object detection and localization, one of the precise and future-ready technology is the use of RADAR Systems. Among various types of RADAR technology, Ultra-Wideband (UWB) radar generally has some superior capabilities such as presence detection with good actual precision, and high performance in a subtle way. Contrary to the conventional systems, UWB radar Technology mostly uses a Wide Frequency range of several MHz and GHz ranges.This project is based on UWB Radar which recognizes the movements of the person, which is quite significant. This can be used for the most part to identify the signals of a person in a contactless manner, basically contrary to popular belief. In this project, main focused area onto which our concern exists is detection and localization of immovable human targets behind the confined spaces like a wall based on their minor movements caused by breathing, which mostly is quite significant. In the detection of breathing movement, a method based on Doppler effect is used. Additionally, the various measurements being taken are subjected to a CRT (Clutter-Reduction technique) based on Singular Value Decomposition and a more recent approach, which is based on Discrete Time Fourier Transform(DTFT).
A Wide Input Range Two-Channel Interleaving Boost PFC Rectifier with High DC Bus Voltage and Small Inductors

With remarkable development on power electronic industrial applications, efficiency-improved high power level PFC rectifiers are demanding at a growing rate. The active-controlled rectifiers benefiting by interleaving structures, which contribute hugely to higher power capability, are usually employed in these applications. On the other hand, the tendency to supply high DC bus voltage in the presence of a wide input voltage range has been encouraging the researchers to promote flexible mode PFC rectifiers. A common mode noise suppressed dual-boost type PFC rectifier with two-channel interleaving structure which has the potential to adopt three-level action in high step-up cases to improve the efficiency is introduced in this paper. Moreover, using the flexible-mode circuit in high input voltage range leads to limit the high voltage stress on the fast recovery diodes to half of the bus voltage as well as it does in the low voltage range. Plus, by utilizing the suggested structure, the total required inductance volume reaches the minimum. To give expected validation of the proposed converter, simulation results done in PSIM 2021b software, which illustrate key waveforms and a detailed set of comparisons in terms of efficiency and THD behavior, are presented.
Exploring the well-being components in sharing economy to foster sustainability

Sharing economy shows much potential for exploring well-being. In this study, we investigate (1) which potential well-being components can be pursued in the sharing contexts, (2) how the well-being components can be perceived, and (3) how we can improve the perceptions of well-being through information exchange. The research starts from (1) elucidating the well-being components in sharing economy, (2) building mathematical model of the cognitive process of well-being components, (3) validating and simulating the mathematical model, (4) examining the model based on real-world data of existing platforms, and (5) designing an on-campus P2P sharing platform to demonstrate the effectiveness. In addition, we concern with the ethical and environmental issues to elevate the platform development appropriately and sustainably. This research expects to facilitate users pursuing well-being while sharing with others and awakens the stakeholders to take well-being into account to create a more favorable sharing environment.
A Multi-Layer Cross-Shaped Unit Cells Antenna Based on CMA

Metasurface is a hot topic in today’s era and an important direction of wireless development. In the paper, a multi-layer antenna with a 4* 4 cell structure meeting 5.8 GHz is designed. The characteristic mode analysis method and three-step method are used in the paper, and the coupling of the antenna at various frequencies is analyzed. According to the current intensity of the antenna surface, the corresponding mode is excited by multi-layer substrate pore feed, and the various characteristics of the antenna are analyzed. The bandwidth of the antenna is 5-6.7 GHz, and the maximum gain is 8.02 dB. The area of each cell is 106 mm2, which is in line with the trend of antenna miniaturization.
Healthcare Industry: Embracing Potential of Big Data across Value Chain

In the fast-moving era of the Industrial Revolution (Industry 4.0), digitally fueled devices and technologies are paramount for driving innovation and creating values across a myriad of industries. A case in point is - Healthcare Industry. Healthcare insurance companies, hospitals, and other providers around the world are belligerently leveraging digital tools and technologies such as Big Data analytics, Lake, Machine Learning, Artificial Intelligence, Internet of Things (IoT), Natural Language Processing, smart sensors, and the Internet of Things (IoT), for improving the overall quality of care and overall process efficiency and effectiveness. The Healthcare industry has been a center of discussion for embracing Big Data practice across the value chain for the past couple of decades due to the prodigious potential that is concealed in it. With so much abundant information, there have been numerous provocations related to the apiece stage of maneuvering big data that can only be amplified by leveraging high-end computer science results for big data analytics, as mentioned above. Well-organized healthcare ecosystem, analysis, and magnification of big data can influence the course of the game by opening new paths in terms of offering unique yet innovative products and services for the modern age technology-propelled healthcare value chain. This paper emphasizes the impetus of Big Data across the healthcare value chain, which involves the amalgamation of technology, data, and business, yielding better decisions and improving the experience across all touch points.
Design of Real-Time Automatic Drainage Cleaning and Monitoring System using IoT

Sewage cleaning is one of the important tasks in our day-to-day life to be hygienic and to avoid chokes. The major system and the process that is used currently to clean drains is manual or sanitary workers. Automatic drainage cleaning systems and robots are implemented to improve efficiency and reduce manpower. This results in the minimization of blockages in drains and ensures the safety of workers. The main reason for developing automatic drainage cleaning systems is to reduce the labor involved and clearing without any delay in a real time implementation. In this project, an automatic drainage cleaningsystem is designed to collect all solid wastes and helps in reducing chokes. When the bin is filled with the trash, an ultrasonic sensor detects it and transmits the information via the Node MCU to Blynk application. This alert message is sent to the municipal person who is responsible to empty the bin.
A Physics-informed Latent Variables of Corrosion Growth in Oil and Gas Pipelines

Summary & ConclusionsCorrosion defect in oil and gas pipelines is the major risk factor that threatens the structural integrity of buried pipelines. Pipeline corrosion management which typically requires high-resolution inline inspections (ILI) to characterize the corrosion growth process is an important task to assess the corrosion defects. However, the corrosion process is inherently stochastic, temporal-spatial dependent, and driven by various hidden physics-related variables. In the literature, the growth of corrosion defects in pipelines is usually modeled by various stochastic processes imposing many unjustified assumptions about the mean growth path and probability distribution of the corrosion rate. In this paper, we proposed a physics-informed latent variable corrosion growth model that integrates the known physics from complex processes into modeling the relations between observable and latent variables and the actual stochastic process that generate the corrosion time series data. The proposed method consists of 3 main steps. Firstly, the latent and observed variable relations and the underlying stochastic processes governing the corrosion process were modelled by physics-informed regression models. The latent groupings that give rise to the observed time series were identified by using the agglomerative hierarchical clustering method. Finally, the prediction algorithm based on the learned physic-informed regression model was proposed to forecast the process progression. We validated the model by a case study on a simulated corrosion process in oil and gas pipelines, in which both latent variables and ILI data were sampled from predefined distributions. The results indicated that the model could predict the growth of corrosion defects and capture the variance of the stochastic processes demonstrated by the low mean absolute percentage errors (MAPE) of 3.0082%, 3.9532%, and 3.6831%, which corresponds to the three corrosion growth processes causes by three types of soil. The proposed model can be used to facilitate the development of reliability models and corrosion management.
Design of a Microstrip Antenna Having 360° Beam Steering Capability in Azimuth Plane

This paper proposes a beam steering microstrip antenna which can rotate the beam by 360° in azimuth plane. A circular microstrip patch is used as main radiating element with omnidirectional radiation and 24 rectangular strips which are responsible for 360° beam steering with non-uniform angular resolution of 10°-23° are connected rotationally around the circular radiating element by PIN diodes. When a PIN diode is turned ON selectively, then the main beam becomes directive according to the direction of the strip. Moreover, multiple diodes can be switched ON simultaneously to control the beamwidth of the radiation pattern. Omnidirectional radiation at3.79 GHz can be achieved by keeping all diodes OFF. The 10dB impedance bandwidth is 3.58 to 4.02 GHz for omnidirectional mode and 3.21 to 3.54 GHz for directional mode. The gain is 0.52 dBi for omnidirectional operation and 1.79 dBi for directional operation.
An Ultra Step-Up Non-Pulsating Input-Current DC-DC Converter

The high step-up DC-DC converters are requisite part of Renewable Energy Systems (RESs). This paper proposes an improved DC-DC converter profiting from simple control and topology, ultra-step-up capability, common-ground point availability and low stress on devices. The non-pulsating input-current makes the converter applicable of Maximum Power Point Tracking (MPPT) of PVs. The domination of suggested configuration over other counterparts has been certified by comparative analysis. Also, the simulation and experimental outcomes obtained from implemented prototype validate the correct operation of suggested converter.
Root Cause and Reliability Predictions of Failed Multilayer Ceramic Capacitors

SUMMARY & CONCLUSIONSThe reliability of larger size ceramic capacitors is usually discussed in the context of cracking propensity and its subsequent effects upon failure modes such as silver migration. Consequently, recommendations for preventing capacitor cracking often constrain temperature ramp rates during surface mount, minimize hand rework, and limit board depaneling stresses. Facing recent infant mortality failures involving 1210 ceramic capacitors mounted on boards about 2.6 in. (66 mm) by 0.8 in. (20 mm), we reproduced these by dropping the boards from different heights or dropping steel balls upon the capacitors directly and then subjecting the boards to elevated temperature, relative humidity, and bias. This definitively identified the role of internal defects such as cracks in instigating the field failures. Subsequent drop tests using packages outfitted with accelerometers enabled deriving a shock profile for predictions of drop probabilities. The correlation between these predictions and our failure incidences points possibly to incidental drops as a factor in the failures. Using this postulated root cause as guidance, we eliminated this capacitor in future board iterations and also instituted a burn-in to contain existing inventory. Although not necessarily substantiation of this root cause, the declining field failure rate seen for this failure mode is consistent with our hypothesis and also represents a positive reliability trend.
Cardio Vascular Disease prediction using machine learning with improved feature selection

Due to the high susceptibility of Cardio Vascular Disease (CVD), a large percentage of people now run the risk of developing them. It has grown increasingly difficult day by day for clinical systems to anticipate CVDs early to avert severe health harm as the amount of afflicted persons is increasing. There is a demand for a system that operates with high precision at minimal computational cost in economically fragile nations. In this study, we used a hybrid model for feature selection with a neural learning approach to forecast CVD. Other classification algorithms are ANN, XGBoost, Random Forest, Multi Layer Perceptron, etc where ANN showed 75% accuracy similar to Random Forest after using feature selection algorithm. We discovered that the Decision Tree Classifier worked better than many machine learning techniques while ensembled with hardvoting and ANN.
Fundus Image Classification: A Wavelet Feature Descriptor Approach

Lately, many diabetic patients are experiencing diabetic retinopathy resulting in a loss of their sight. Even though the urgency and threat posed by this condition, there is insufficient data source to engage appropriate computational intelligence tools. The few that exist happen to be imbalanced. Leveraging on this imbalanced dataset, several activities have been carried out to propose improved detection and classification descriptors. Although some works have been done in this domain, the issue of accuracy still persists in the administration of an effective diagnosis. This paper harnessed the benefits of Gabor filters and the multi-resolution property of Discrete Wavelet Transforms (DWTs) to construct appropriate fundus feature descriptors. These discriminant features are fed into some selected but predominant classical machine learning classifiers. Numerical evaluation of the study gave a perfect (100%) average score for the fundus image classification using Gradient Boosting and Logistic Regression classifiers over Accuracy, F1-score, Precision and Recall evaluation metric. The tie in performance is further broken using their computation time, suggesting that Logistic Regression is more appropriate with 9min 32sec over Gradient Boosting or 1hr 10min 32sec.
A Depth-added Visual-Inertial Odometry Based on MEMS IMU with Fast Initialization

Recently, visual-inertial odometry (VIO) has been widely adopted for tracking the movement of robots in simultaneous localization and mapping (SLAM). In this paper, a low-cost and highly accurate depth-added VIO framework is proposed for robots in indoor environments by taking advantage of an RGB-D camera and a micro-electromechanical system (MEMS) inertial measurement unit (IMU). Movement estimation is achieved by IMU pre-integration and visual tracking in this tightly coupled framework. Meanwhile, an empirical IMU model is developed by using Allan variance analysis to guarantee the accuracy of the estimated errors. Images with depth information are deployed during initialization to achieve a fast response. Extensive experiments are conducted to validate the effectiveness and its performance by comparing it with other advanced schemes in indoor scenarios. The results show that the scale drift error is reduced to 2.6 % and the response time of the initialization process is improved by about 124 % compared to its counterpart.
Structural, optical and electrical properties of La - Mg co-substituted Bi0.5 Na0.5 TiO3 dielectric

Lead free La-Mg co-substituted Bismuth Sodium Titanate (Bi0.5 Na0.5 TiO3; BNT) dielectric ceramic was synthesized by conventional solid-state reaction (SSR) route. The effects of La-Mg on the microstructural, optical, and electrical properties of BNT were systematically studied. The microstructure of Bi0.47 La0.03 Na0.50 Ti0.97 Mg0.03 O3 showed irregular shape surface grains with an average size of 2.37 ± 0.8 μm. The X-ray diffraction analysis of the co-substituted BNT revealed the formation of single phase rhombohedral R3c structure. An approximately 10% reduction in direct band gap to ~2.9 eV after La–Mg co-incorporation makes this compound a promising candidate for optoelectronic applications as compared to BNT (~3.2 eV). The frequency dependent complex impedance spectroscopic measurement revealed a moderate enhancement in dielectric constant along with a reduction in dielectric loss of BNT after the co-substitution. The frequency and temperature dependent variations in permittivity and loss tangent were explicated in terms of various types of polarization relaxation.
"Shortest Move" versus "Fewest Steps"

In much recent work in generative grammar, derivations are considered to be subject to principles of economy. In Chomsky (1993), economy of derivation is implemented in at least two ways: derivations should involve the shortest possible movements and the fewest possible steps. As Chomsky notes, these two requirements appear to be contradictory. He proposes a Form Chain mechanism to resolve the contradiction. In this paper, I will argue that the Form Chain mechanism should be described as follows. Form Chain consists of two processes, one of which generates empty wh-elements in the intermediate specifier positions required for chain formation over a longer distance. The other process consists in movement across these intermediate wh-elements, in compliance with the fewest steps requirement, but in violation of the shortest move requirement. This implies that the shortest move requirement does not apply in long distance movement processes. In the remainder of this paper, I will argue that the shortest move requirement is equally redundant in the analysis of the other phenomena that seem to call for it, head movement and superraising. My conclusion will be that economy of derivation consists in the single requirement that the number of movement steps should be as small as possible.
Dual-Input, Single-Output Step-Up DC/DC Power Electronic Interface for Energy Harvesting Systems

In this paper, a new Energy Harvesting Power Electronic Interface (EHPEI) is proposed. The proposed EHPEI is suitable for low-power, low-voltage energy harvesting applications. It is a combination of two boost and also has a modular structure using a voltage multiplier cell (VMC). The output voltage can be regulated under variable inputs by tuning duty-cycles of both switches. Since the EHPEI is combined from two sources and both sources supply power to load either standalone or simultaneously together, it is beneficial to improving reliability and stability in energy harvesting applications. In addition, with implying VMC, voltage stress on switches and diodes are decreased. The operation principle and steady-state analysis of continuous conduction mode (CCM) are used to determine voltage gain. The simulation results are used to verify the performance and validity.
Fault detection and fault-tolerant operation of a seven-level inverter

Applications of multilevel inverters (MLIs) are increasing extensively. Despite many advantages of MLIs, poor reliability of MLIs is a major concern as MLIs consist of a large number of semiconductor switches. Fault-tolerant topologies provide the solution to this problem. In this paper, a fault detection and identification method is presented for a sevenlevel fault-tolerant inverter. This fault detection method is based on monitoring the total harmonic distortion (THD) in output voltage and average voltage of one cycle. After identification of the faulty switch, the switching sequence and switching angles are to be adjusted. The method of fault detection and identification is verified by simulation results.