Miracle-dz/UniFAR-FTUs huggingface dataset:
Radar Target Characteristic Planning and Design Based on Extension of Scattering Center Forward Model

To meet the requirements of radar target characteristic planning and feature signal control and optimization design, we investigate the scattering characteristics of dielectric coated electrically large targets using scattering center forward model extension. The impedance target is chosen as the modeling object. The forward model established in this paper has a clear physical mechanism, enabling the establishment of a mapping between the scattering center and each component or structure of the sample target, which facilitates optimal design and operation. The research provides a low-cost technology for characteristic planning and rapid prediction of target radar characteristics in realistic complex environments, supporting practical applications such as target detection and recognition, target remote sensing, and more.
Large Signal Model of GaN HEMT with the Transconductance Compensation Circuit

This paper mainly studies a high-precision Gallium Nitride (GaN) high electron mobility transistor (HEMT) model based on the transconductance (gm) and proposes the gm compensation circuit. It mainly based on the error between the gm in the Angelov model and the actual test value. By integrating the error of the gm and compensating it, the error of Ids is reduced from 2.59% of the original model to 0.28%. In this paper, the empirical model is combined with the table-based model to achieve the simplicity, accuracy and continuity of the model, and well inherit the characteristics of the Angelov model that can accurately describe higher-order derivatives. The improved model can realize the accurate fitting of the device’s dc characteristics and the fitting of the scaling device, improving the efficiency and accuracy in the circuit design process.
A Low-Cost Wide-Beam Patch Antenna Array for 24 GHz Radar Detection

This paper presents a novel design of a patch antenna array consisting of four elements for vehicle-mounted radar applications, with a focus on achieving miniaturization and beam broadening. To achieve miniaturization, the interval between the elements is reduced. To further broaden the beamwidth, a dielectric substrate is placed beneath the antenna array and the relative position of the substrate and antenna is adjusted to obtain a wider beamwidth. The proposed design is characterized by a simple structure and the use of low-cost FR4 material. Simulation results show that the antenna operates at a frequency range of 23.79-25.11 GHz, achieving the half-power beamwidth of 107° in the E-plane at the center frequency, and the gain of 9 dBi.
Earthquake recurrence intervals and plate tectonics

The frequency of occurrence of earthquakes with different seismic moments is expressed in terms of the rate of slip on a fault and to the largest seismic moment likely to occur in the region. Beginning from the Gutenberg-Richter empirical expression relating the relative recurrence of events with different magnitudes and using another empirical relation between magnitude and seismic moment, the relative number of events with seismic moment greater than or equal to M o is given by N ( M o) = α M o− β . β can be determined from parameters in these empirical expressions. From average rates of slip on faults, this expression can be used to give the recurrence rates for events of different seismic moment: α = (1− β ) M oΣ/ M omax1− β where M omax is the maximum possible seismic moment in a region, and M oΣ is the average rate of occurrence of the seismic moment. On a fault of area A , with shear modulus μ, and long-term average rate of slip v , M oΣ = μAv . A similar expression can be given for α for regions where deformation is distributed over a broad area without a major throughgoing fault. Using rates of convergence at island arcs determined from plate motions for the last 5 m.y. , the calculated frequency of occurrence of earthquakes with large seismic moments agrees well with the historic record. At present, uncertainties in the requisite parameters and in assumptions on which the recurrence relation is based, however, make such an approach only marginally better than reliance on the historic record alone. With more data constraining β , the largest possible seismic moments, the role of fault creep, and long-term rates of slip or deformation, however, this approach to seismic risk should be a more reliable predictor of recurrence rates than the existing historic record. As an example, the formulas are applied to the southeastern Caribbean.
Design of Phase Frequency Detector for Low Power PLL Using GPDK 45nm Technology

A key component in Phase-Locked Loop (PLL) circuits is Phase-Frequency Detectors (PFD), which find applications in numerous high-speed applications like processors and communication systems. The objective of this article is to design a PFD to (i) overcome the issue of high power dissipation and (ii) reduce the area of the existing design. A Cadence Virtuoso environment is used to design a proposed PFD and is implemented using Generic Process Development Kit (GPDK) 45 nm technology with a supply voltage of 1V. The outcomes represent that the proposed PFD consumes a power of 66.25 nw, which is considerably lower than the existing designs. And also, the proposed PFD is designed using 10 transistors which reduce the area in comparison to the convention of using 16 transistors or more.
Multi-frequency High Isolation Antenna Pair for 5G Communication

A multi-frequency high isolation antenna pair for 5G communication is proposed. The antenna pair is designed on a dielectric substrate with a clearance area of 90mm×20mm. The antenna form of the antenna unit is a loop antenna. It excites four resonant modes of 0. 5λ, 1λ, 1. 5λ and 2λ. Its resonant frequency is 1.13 GHz, 2.78 GHz, 3.75 GHz and 4.68 GHz respectively. The working bandwidth of the antenna is 1.04GHz-1.36GHz, 2.635GHz-2.925GHz and 4.45GHz-5.25GHz, which can be well used in 5G communication band. The decoupling between antenna units is realized by opening cross-shaped floor slots, and the isolation degree between units reaches more than 12.5dB. After the simulation analysis in CST, the total efficiency of the antenna reaches more than 45%, and the antenna has good directivity, which is a good terminal antenna solution applied in 5G communication.
A Companion Guide to Resilience

November 2015 Members of duck raisers in Potuakhali, Bangladesh discuss with Oxfam and partners underlying causes of poverty in their village and lack of scale up of this industry which are: a) poor access to business services; b) inefficient supply chain; c) entrenched traditional beliefs that since women are caretakers of ducks, it is just an adjunct household chore and not a viable enterprise; and d) chronic natural disasters and climate variability. Photo: Janice Ian Manlutac/2015
Design of a Low Loss Wideband Tunable MMIC Active Circulator

In this work, a wideband tunable active circulator MMIC with low transmition loss is presented. This wideband tunable active circulator MMIC uses traveling wave distributed amplifier(TWDA) structure with tunable varactors to achieve the wideband circulator and high isolation. Through modulation the transconduction(gm) of each stage, it can enhanced the transmitter (TX) to the receiver (RX) isolation compared with traditional distributed amplifier circulators. By using varactors to adjust phase shift between each stage, which can not only achieve the frequency tuning operation but also mach different antenna impedance wich can also suppress the signal leaking from the TX to the RX. Because of the MMIC active circulator is a power amplifier, the output power can transmit to the antenna port directly, so there has no transmition power loss. The simulation results show that the MMIC active circulator can tunable from 3 GHz to 4.5 GHz, the isolation between TX and RX better than 30-dB over 100 MHz bandwidth. The active circulator MMIC is implemented in 0.25um ED pHENT technology with die area of 2.25 × 1.8mm.
NSTEMI Heart Disease Diagnosis and Analysis Using CNN Classifier and Raspberry Pi

Electrocardiogram (ECG) play important roles in early prevention and diagnosis of heart diseases. Manually evaluating the ECG paper records might be difficult as well as time-consuming. Heart disease diagnosis has been automated through a variety of machine learning techniques as well as processing of signals methods. But those approaches are complex and difficult to achieve better accuracy, so, in this study, an automatic CNN classifier-based heart disease diagnosis using Non-STEMI ECG signal has been developed. The initial phase is to develop non-STEMI ECG signal images, which are gathered by clinical applications. In order to improve the quality of the images, preprocessing is done through different techniques, such as image resizing and grayscale conversion. Resize an image usually includes eliminating a portion for the initial image and removing a few of its pixels. Grayscale conversion means converting an image into gray representation and omitting other colours. Finally, preprocessed images are sent to convolutional neural network for predicting heart diseases effectively. This proposed CNN classifier achieved 96% accuracy, 95% specificity 97% precision, 4% error, 95% recall, 5% false negative rate and 3% false discovery rate. By using this proposed approach, the non-STEMI ECG signal images accurately predicted heart diseases in the medical application.
Research on Student Performance Prediction Model Based on Blended Learning Data

Abstract-Student performance prediction is a highly sought-after research direction in the field of education, and accurately predicting final exam scores is crucial for improving teaching quality. In this study, blended learning data from the “Fundamentals of Programming (C)” course at Qinghai University from 2016 to 2019 was used. By combining SMOTE (Synthetic Minority Over-sampling Technique) oversampling and Bayesian optimization algorithm, four commonly used machine learning models were employed to evaluate students’ final performance based on process-oriented learning data. The experimental results showed that these four models achieved accuracy rates of 86%, 84%, 85%, and 86% in predicting students’ grade levels. Moreover, in predicting specific final exam scores, the root mean square errors of these models were 0.35, 0.28, 0.17, and 0.17, respectively. These findings indicate that these four models can provide accurate and effective references and guidance for students and teachers, further optimizing the teaching and learning process. By comprehensively analyzing students’ process-oriented learning data, we can reveal the patterns of their academic performance in the course and use them as a basis for predicting students’ grades.
Flexible Capacitive Pressure Sensors and Their Applications in Human-Machine Interactions

Flexible capacitive pressure sensors have shown excellent application potential in human-machine interactions, wearable electronic devices, biological and medical, and electronic skin. Compared with piezoresistive and piezoelectric pressure sensors, capacitive pressure sensors with advantages such as high flexibility, high stability, low consumption, and simple construction. Because of these advantages, flexible capacitive pressure sensors have become the key research direction of pressure sensors in recent years. This paper introduces the working principle of flexible capacitive pressure sensors and discusses numerous research on improving the performance of flexible capacitive pressure sensors. Then two methods of improving the dielectric layer structures to improve the sensitivity of the sensor and some related works are reviewed. Finally, the flexible capacitive pressure sensors' huge potential for advances in human-machine interactions areas such as health care and motion monitoring is also discussed. The paper concludes with an overview of future research directions of improving the performance of flexible capacitive pressure sensors.
Early Crops Diseases Identification Using Deep Transfer Learning

Most crops diseases first exhibit their symptoms on the leaves. By applying deep learning to classify disease leaves, what kind of disease occurs in corps can be identified. However, in the earliest stage of crop diseases, a lack of sufficient disease leaf image samples can degrade the performance of deep learning. We propose a sophisticated deep transfer learning model based on ResNet network which integrates transfer learning and image augmentation. With the final fine-tune model, we can still achieve an identification accuracy rate of over 99% even under extreme conditions where only a few dozen images are available. Experiments substantiate the reliability and robustness of our model. By identifying crop diseases as early as possible, an abundance of direct and derivative benefits can be obtained, including the reduction of farmer's losses, the minimization of pesticide usage to prevent soil contamination, and the enhancement of food safety, all of which meets the requirements of ecological agriculture.
A Fast Transient Response Capacitorless LDO Designed in 22FDX® Technology

This paper presents a low-dropout capacitorless linear regulator using a low output resistance buffer, designed in GlobalFoundries 22nm fully depleted silicon on insulator technology. The proposed LDO consumes 85 µA from 1.8 V power supply. The output voltage is configurable in the range of 0.9–1.25 V. The target maximum output current is 30 mA. The load transient response time is about 15 ns. The target is to have at least 20 dB Power Supply Rejection Ratio (PSRR) at 5 MHz (the frequency that the digital is operating on). The compact design is occupying area of only 0.18 mm2.
Wideband mm-Wave Differential Bandpass Filter Using Micromachined Rectangular Coaxial Lines

This paper proposes a millimeter-wave (mm-wave) wideband differential bandpass filter (BPF) that utilizes rectangular micro-coaxial transmission lines as resonators. The differential BPF is ideal for application in high-sensitivity and integrated mm-wave systems due to its low loss and compact structure. The passband of the differential mode (DM) is formed by a fifth-order Chebyshev bandpass filter, and the wideband common mode (CM) suppression is improved by loading connecting lines on the symmetrical plane. The equivalent circuit of this symmetrical structure is analyzed with the use of microwave network parameters. Simulated results show that the differential BPF has an operating range of 30GHz to 50GHz, with a low insertion loss of 0.54dB and significant CM suppression over 26 dB.
MSGNet: Modified MobileNet-ShuffleNet-GhostNet Network for Lightweight Retinal Vessel Segmentation

When it comes to the early detection of eye-related ailments like diabetic retinopathy and hypertensive retinopathy, the accurate segmentation of retinal blood vessels is of the utmost importance. However, most of the time, the segmentation model requires a computationally expensive machine, which is burdensome when it has to be employed in a remote area with a limited supply of power sources and internet access. Hence, it is necessary to design a lightweight model with performance commensurate with the state-of-the-art (SOTA) models. In this work, we present MSGNet (Modified MobileNet-ShuffleNet-GhostNet Network), a lightweight network for retinal vessel segmentation. MSGNet is a lightweight U-shaped retinal vessel segmentation model that leverages several components from lightweight architectures, i.e., depthwise separable convolution, channel shuffle operation, and the Ghost module. Then, batch normalization inside each of the components is replaced with instance normalization, which encourages cross-dataset generalization capability. Computationally speaking, MSGNet runs fast on the CPU. MSGNet is trained with the Digital Retinal Images for Vascular Extraction (DRIVE) dataset and validated using the Child Heart and Health Study (CHASE DB1) dataset and the Structured Analysis of the Retina (STARE) dataset. MSGNet shows comparable performance against the SOTA models and has the ability to generalize across different retinal vessel segmentation datasets.
Unveiling Traffic Patterns for Effective Traffic Management System

Traffic congestion is a persistent global challenge that has detrimental effects on productivity, economies, and the environment. This article explores image detection techniques, specifically focusing on the Gaussian blur method, for accurate vehicle detection and analysis. The application of image detection techniques holds potential for identifying traffic patterns, analyzing traffic density, and monitoring traffic volume, thereby providing valuable insights for urban planners and transportation authorities. This research article proposes enhanced vehicle detection algorithm using Gaussian blur method for noise reduction and object clarity, leading to enhanced accuracy for diverse dataset of real-world videos from various traffic scenarios. The findings have direct implications for addressing traffic congestion, as they contribute to the development of efficient traffic management strategies and the optimization of traffic flow. This research highlights the promising capabilities of image detection technologies in addressing traffic- related challenges and enhancing overall traffic management systems in urban environments.
An In Vitro

Endodontic failures are mainly ascribed to the existence of microorganisms in root canals.1 Removal of microorganisms along with debris is a vital aspect for a good root canal therapy (RCT) outcome. Many microbial organisms like E. faecalis, C. albicans were found in endodontic infections with studies showing that both these microbes lead to RCT failure.2,3 E. faecalis, a gram-positive organism, even though present in minute quantity has a key role in causing continual periradicular infections and root canal failures, by genetic polymorphism and dentin binding property. Studies reported E. faecalis in 4 to 40% of root canal infections and increased incidence of about nine times in treatment failures.4 C. albicans is an opportunistic dentinophillic yeast showing an increased incidence in infected canals and peri-radicular tissues.5 1Department of Conservative Dentistry and Endodontics, Modern Dental College and Research Center, Indore, Madhya Pradesh, India 2Department of Conservative Dentistry and Endodontics, Peoples Dental College and Research Center, Bhopal, Madhya Pradesh, India 3Department of Prosthodontics Crown and Bridge, Al Badar Dental College and Research Center, Gulbarga, Karnataka, India 4,5Department of Prosthodontics Crown and Bridge, Government Dental College, Hyderabad, Telangana, India 6Department of Surgical and Diagnostics Sciences, Dar Al Uloom University, Riyadh, Kingdom of Saudi Arabia
Remote Sensing of Soil Moisture with Microwave Radiometers

ABSTRACT MICROWAVE radiometers measure the thermal radiation at wavelengths between 1 mm and about 50 cm that is generated within the soil volume and subsequently emitted through the surface. The fraction of this upwelling radiation incident on the soil surface that is transmitted into the air will be determined by the dielectric properties of a transition layer just below the surface. This fraction can be called the emissivity of the soil and is strongly dependent on the moisture content of the soil in this transition layer. This dependence is due to the large contrast between the dielectric properties of liquid water and those of dry soil. The large dielectric constant of water results from the electric dipole moment of the water molecule. The dielectric constant of water at the lower microwave frequencies is approximately 80 compared with 3 to 5 for dry soils; as a result, the dielectric constant of wet soils can reach values of 20 or more. This produces a range of soil emissivity from about 0.95 for dry soils to 0.6 or less for wet soils. Theoretical studies have indicated that this transition layer is on the order of a few tenths of wavelength thick, i.e., 2 to 5 cm for a 21 cm wavelength. This layer determines the primary soil moisture sampling depth, and its approximate thickness has been confirmed by observations. Results from truck, aircraft, and spacecraft platforms are presented which demonstrate the sensitivity of microwave radiometers to soil moisture variations for a wide range of conditions including light to moderate amounts of vegetative cover.
Benefits of an integrated power and hydrogen offshore grid in a net-zero North Sea energy system

The North Sea Oﬀshore Grid concept has been envisioned as a promising alternative to: 1) ease the integration of oﬀshore wind and onshore energy systems, and 2) increase the cross-border capacity between the North Sea region countries at low cost. In this paper we explore the techno-economic beneﬁts of the North Sea Oﬀshore Grid using two case studies: a power-based oﬀshore grid, where only investments in power assets are allowed (i.e. oﬀshore wind, HVDC/HVAC interconnectors); and a power-and-hydrogen oﬀshore grid, where investments in oﬀshore hydrogen assets are also permitted (i.e. oﬀshore electrolysers, new hydrogen pipelines and retroﬁtted natural gas pipelines). In this paper we present a novel methodology, in which extensive oﬀshore spatial data is analysed to deﬁne meaningful regions via data clustering. These regions are incorporated to the I ntegrated E nergy S ystem A nalysis for the N orth S ea region (IESA-NS) model. In this optimization model, the scenarios are run without any speciﬁc technology ban and under open optimization. The scenario results show that the deployment of an oﬀshore grid provides relevant cost savings, ranging from 1% to 4.1% of relative cost decrease (2.3 bn € to 8.7 bn € ) in the power-based, and ranging from 2.8% to 7% of relative cost decrease (6 bn € to 14.9 bn € ) in the power-and-hydrogen based. In the most extreme scenario an oﬀshore grid permits to integrate 283 GW of HVDC connected oﬀshore wind and 196 GW of HVDC meshed interconnectors. Even in the most conservative scenario the oﬀshore grid integrates 59 GW of HVDC connected oﬀshore wind capacity and 92 GW of HVDC meshed interconnectors. When allowed, the deployment of oﬀshore electrolysis is considerable, ranging from 61 GW to 96 GW, with capacity factors of around 30%.
Measuring Growth of Groups

We present explicit calculations of growth functions and growth series of a variety of ﬁnitely generated groups, followed by some general results about those measures of growth. We provide a self-contained proof that the growth series of a group is rational if and only if the growth function satisﬁes a recurrence relation of ﬁnite depth. Our goal is to showcase the subject matter of the paper as a very accessible intersection of group theory, basic metric geometry, and combinatorics.