Miracle-dz/UniFAR-FTUs huggingface dataset:
Linearity Issues in Dual Input Doherty-Outphasing Hybrid Power Amplifier Topology

In this study, the Doherty-Outphasing technique, designed to achieve higher back-off power values with flatter efficiency characteristics, is investigated in terms of linearity and linearizability. The hybrid concept is introduced to determine the trajectories of load modulation, which plays a dominant role in the linearity of power amplifiers. Amplitude and phase distortions are fundamental criteria for evaluating the linearity performance in the average efficiency techniques using complex modulation signals. Therefore, the analytical expressions of these nonlinearities of the power amplifiers have to be explained rigorously. As a goal of this paper, the linearity and linearizability mechanisms based on GaN HEMT are constructed using the analytical approach.
A Lean Noise-Cancelling Sturdy MASH Delta-Sigma ADC with a Noise-Shaping SAR Stage

This paper introduces a lean noise-cancelling sturdy-MASH delta-sigma ADC, achieved through the implementation of a noise-shaping SAR second stage, the use of which instead of the usual delta-sigma stage, eliminates, by virtue of its inherent feedforward mechanism, the quantization noise of the first stage. Moreover, any mismatch-induced error of the noise-shaping SAR gets shaped by the first stage, circumventing the need for strict capacitor matching or calibration. The elimination of noise-cancelling and/or calibration circuitry, as well as the inclusion of the area-efficient noise-shaping SAR qualify this topology as lean. Simulations demonstrate a proof-of-concept design achieving 107.7 dB SQNR for an OSR of 14 over a 1 MHz bandwidth.
Current Mirror-based High-Performance Ring Amplifier for Switched Capacitor Circuits

Ring Amplifiers are the better alternatives to traditional OTAs in a Switched capacitor and other applications because of their ability to achieve efficient amplification, rail to rail output swing in scaled environments. In this paper, an improved self-biased current mirror resistor-based ring amplifier (CMR-RAMP) is presented. The design consists of four transistors that are connected at the second stage to improve settling time as well as dead zone voltage. The implementation and simulations of designs are performed with Cadence Virtuoso using 45nm CMOS technology at $V_{dd}=1V$ and 27°C of operating temperature. The settling time and dead zone voltage achieved by the CMR-RAMP is 3.3ns and 988.1mV which is $0.49\times$ and $1.21\times$ as compared to the settling time and dead zone voltage of a conventional self-biased ring amplifier (CSB-RAMP), respectively. Apart from faster settling time and improved dead-zone voltage, the circuit also shows strong immunity to PVT variations making it suitable for robust and high-performance switched capacitor circuits.
Nonlinear Model Predictive Control for Quadrotors Using Simultaneous Optimization Approach

The quadrotor has found extensive applications across a spectrum of fields, including environmental research, military operations, transportation, and firefighting. The design of its control system can be approached using various methodologies such as sliding mode control, backstepping, and model predictive control (MPC). The primary objective of the control system is to achieve trajectory tracking while simultaneously avoiding collisions with obstacles. In this paper, a nonlinear model predictive control (NMPC) strategy is adopted for calculating control inputs that guide the quadrotor along a desired trajectory while ensuring obstacle avoidance. The obstacle avoidance constraints that are formulated pose a nonlinear and intricate challenge, rendering the optimization problem complex for the nonlinear program solver to address. To tackle this, linear constraints representing hyperplanes between the quadrotor and obstacles are employed. These hyperplanes dynamically adjust as the quadrotor moves, thereby averting collisions effectively. The application of NMPC to quadrotor control, as demonstrated in this paper, underscores the suitability and practicality of the proposed approach. It successfully showcases the efficacy of NMPC in facilitating quadrotor control while navigating through complex environments.
Self-Adapting Voltage Balancing Method for IPOS Dual Active Bridge Converter with CTPS Control

Input parallel output series (IPOS) dual active bridge (DAB) converters have the advantages of wide output voltage range, simple structure and high output power high power density and have been applied to many industrial power supply fields. In this paper, a voltage balancing control strategy for IPOS DAB converter is newly proposed by adopting cooperative triple phase shift (CTPS), which can achieve voltage self-adapting balance with wide output voltage and reduced reverse power.
Harvesting Natural Disaster Reports from Social Media with 1D Convolutional Neural Network and Long Short-Term Memory

The employment of social media platforms is progressively assuming pivotal roles in natural disaster management as early warning and monitoring systems. In emergencies, social media users can post real-time information regarding the disaster they are experiencing, along with their location. In contrast, other users can access this information, and volunteers can search for these posts to render assistance. Nonetheless, the effective utilization of social media for monitoring natural disasters remains challenging due to the lack of automated and prompt tools for identifying natural disaster reports on social media. The analysis of social media data, particularly Twitter data, heavily relies on Natural Language Processing (NLP) algorithms to classify natural disaster reports obtained from eyewitnesses. Previous studies developed a classification model utilizing a 1D Convolutional Neural Network (CNN) with feature extraction based on three word embedding techniques: Word2Vec, fastText, and Glove. Although CNN can optimize features used in the classification process, it lacks the capacity to comprehend the structure of data sequences or sequential data. Thus, this research aims to develop a combined classification model of 1D CNN and Long Short-Term Memory (LSTM). LSTM can comprehend temporal features, such as the word order in a given document. The 1D CNN + LSTM model developed in this research exhibits performance of 82.83%, 88.33%, and 81.77% for Floods, Forest Fires, and Earthquakes. The paired t-test conducted shows a significant increase with a p-value of 0.0035. This research significantly contributes to identifying the 1D CNN + LSTM model that demonstrates the best performance in classifying natural disaster reports. By exploring the effectiveness of 1D CNN + LSTM models with different word embedding techniques and levels of contamination, this research offers various insights into the optimal method for harvesting natural disaster reports from social media. Moreover, this study establishes the fundamental groundwork for selecting an appropriate number of words for word padding techniques to improve classification performance.
A Method for Improving the Cross-Polarization Ratio of Low Profile $*45^{\circ}$ Dual-Polarized Transmit Array Antennas

This article proposes a method for improving the cross-polarization ratio of low-profile transmit arrays (TAs). Due to the inconsistent response of elements with rotational symmetry structure to TE polarization and TM polarization during oblique incidence, electromagnetic waves with $\pm 45^{\circ}$ polarization undergo polarization torsion. By modifying a T A unit with a rotational symmetric structure, the response of the unit to TE polarization and TM polarization is made consistent under oblique incidence. The improved TA unit design leads to an array with a cross-polarization ratio improved by over 10dB.
MLEAT: An RL-based Approach for Vibration Damping System Control

Active vibration damping systems using PID controllers are widely applied in various industrial fields. The PID-controlled vibration damping systems can realize vibration damping depending on the appropriate PID parameters, which are not that easy to choose in practice. Therefore, adaptive tuning of PID controller parameters has been a focus of research in the industry. We propose a multi-level exploration adaptive tuning framework leveraging reinforcement learning algorithm, which enables adaptive tuning of PID parameters for vibration damping systems in response to vibration excitations, achieving the desired damping control effects. Without requiring prior knowledge, this algorithm ensures faster convergence and guarantees the stability of the damping system control. Experimental results in a simulated system demonstrate that our framework outperforms the baseline algorithm, achieving a performance improvement of 60% to 80% in the vibration damping system.
Machine Learning Structure for Box-plus Operation with Soft Information Processing

This paper studies machine learning (ML) structure for box-plus operation as an element of future error correction based on artificial intelligence (AI) using soft information processing. Box-plus operations are important components for check-node (CND) of error correction codes based on bipartite graphs. We propose an ML structure for box-plus operation and observe the behavior of the structure by investigating all possible structures constructed from 6 neurons in the hidden layer. We utilize activation function: (i) hyperbolic tangent (tanh) function for the hidden layer since it maps any real-valued input to the 1 to 1 range for soft information, and (ii) linear function only at the output layer. We evaluate the performances of the proposed structure using a series of computer simulations. We found that a single hidden layer with many neurons is better than multiple hidden layers with less number of neurons, because the box-plus operation is not too complex. We expect that these results open new ground for the development of future error correction codes based on ML.
Real-Time RGB-D SLAM System with Semantic and Depth Information Fusion in Dynamic Environment

Simultaneous localization and mapping (SLAM) is crucial for intelligent mobile robots to move autonomously in unknown environments. However, many current visual SLAM systems heavily rely on static scene assumptions, which severely limit their applicability in dynamic environments. Additionally, many SLAM systems based on semantic segmentation are unable to operate in real time, rendering them impractical for robotic applications. In this paper, we propose a real-time semantic RGB-D visualization SLAM system within the ORB-SLAM3 framework. Firstly, we add two modules, a real-time semantic information acquisition module and a fast dynamic feature point removal module that integrates semantic information and depth information. Subsequently, the dynamic feature point removal module is incorporated into the tracking thread of ORB-SLAM3. We conduct experimental evaluations on the TUM dataset and real environments. The results demonstrate that our algorithm is one of the most accurate robust and real-time systems in dynamic scenes.
High-Tc Superconducting Josephson Junction Devices For Sensing and Communications

CSIRO is the Australian national science agency and has had a long-history in research and development of superconducting electronic devices and systems for various practical applications. In this paper, I will give a brief introduction about the Josephson junction technology and describe CSIRO’s research progress in developing various novel high-Tc superconducting (HTS) Josephson junction devices and their applied systems. Recently, CSIRO has led the frontier research and development of high-frequency HTS Josephson detectors and heterodyne mixers ranging from microwave, millimeter, to terahertz (THz) frequency bands. These high-frequency superconducting quantum sensors/devices have demonstrated superior performance and applied in sensing and communication systems.
Numerical Study on Transport Loss of Multi-layer Spiral Superconducting Cables

The H-equation method is an earlier proposed and now more maturely developed method for calculating the AC loss of superconductors. The H-equation method directly solves the magnetic field strength, which has the advantages of fewer variables, faster iterative convergence, shorter computation time, and simpler and more direct setting of boundary conditions. However, due to its dimensional limitation, it is unable to simulate complex asymmetric geometries such as spirals and twisted strands. Multi-layer spiral superconducting cables possess complex geometrical structures that are unable to describe with a two-dimensional model, making it difficult to utilize the H-equation method for AC loss calculations. In this paper, a three-dimensional structure model is built, and the H-equation method is made being able to calculate the transport loss of spiral twisted collector cables, and the distribution of AC loss in each layer of the cable is studied. The percentage of AC loss on different parts of cables under different currents and the distribution of current density in each layer of the cable are calculated and studied to provide a basis for the next design of the cable tape configuration program.
Design of a Symmetrical T-slot Antenna Array for 5G mmWave Bands

A four-port high-gain, high-isolation MIMO array antenna is designed. By etching two symmetrical T -shaped slots on the patch to adjust the rectangular current path. After optimizing each parameter, a single array element achieves a gain of 8.14dB and a bandwidth of 1.4GHz, and the center frequency is 26GHz. The proposed array antenna has a peak gain of up to 14.1dB at 26GHz, and the isolation between each array element is lower than −25dB, envelope correlation coefficient ECC<O.001. Through the design, the MIMO array antenna can meet the communication requirements of the 5G millimeter wave frequency band.
Explainable AI techniques for Deep Convolutional Neural Network based plant disease identification

Deep learning-based computer vision has shown improved performance in image classification tasks. Due to the complexities of these models, they have been referred as opaque models. As a result, users need justifications for predictions to enhance trust. Thus, Explainable Artificial Intelligence (XAI) provides various techniques to explain predictions. Explanations play a vital role in practical application, to apply the exact treatment for a plant disease. However, application of XAI techniques in plant disease identification is not popular. This paper discusses the key concerns and taxonomies available in XAI and summarizes the recent developments. Also, it develops a tomato disease classification model and uses different XAI techniques to validate model predictions. It includes a comparative analysis of XAI techniques and discusses the limitations and usefulness of the techniques in plant disease symptom localization.
Wearable device to support stroke patient during lower limb rehabilitation

IoMT (Internet of Medical Things) is the combination of data from health measuring devices and mobile applications with a health management system, enabling remote disease treatment by healthcare professionals. This research aimed to design an IoMT system that can evaluate the rehabilitation of stroke patients through active range of motion exercises and send the data to physiologists to remotely assess the patient’s recovery. The system was developed using gyroscope sensors to calculate the range of motion (ROM) when patients perform rehabilitation exercises by sitting in a chair and extending their legs in front of them. It also utilizes microcontrollers for the purpose of displaying the completed repetitions on an LCD screen and transmitting data to the supervisor through the Blynk application. The initial test results were quite promising, as the system accurately tallied the number of completed repetitions, displayed it on the LCD screen, and alerted patients with a buzzer upon each exercise completion. Additionally, it measured and stored data regarding various angles of motion, which were then displayed in real-time on both the application and the LCD screen. (The number of degrees may be adjusted by the physiotherapist based on each patient’s condition.
A High Gain Conical Pattern Antenna for Millimeter Wave Fuze Applications

T — In this communication, a millimeter-waver antenna with high gain conical radiation is designed for fuze application. The radiating section of the antenna consists of a ring array comprising six magneto-electric (ME) dipole antenna elements. Excitation of the array is achieved through the TM01-mode propagation in a circular waveguide. To mitigate any adverse effects caused by welding in the millimeter wave band, conversion from shielded microstrip line to circular waveguide is accomplished using a pair of curved coupling slots, thereby facilitating integration with the millimeter wave integrated circuit (MMIC). Simulation results demonstrate that S11 remains below -15 dB within a bandwidth of 14.14%, spanning from 61.8 GHz to 71.2 GHz. Furthermore, the maximum gain beam exhibits a pitch angle of 18° and achieves a gain exceeding 11 dBi.
Data Augmentation Method on Drone Object Detection with YOLOv5 Algorithm

This study aims to evaluate and compare various types of data augmentation in drone object detection using the YOLOv5 algorithm. Data augmentation is an important technique in image data processing to increase the diversity and quality of training data, so as to improve the performance of object detection models. In this study, several different types of data augmentation were applied to the existing drone object detection dataset. This research method consists of fourteen types of data augmentation, namely Flip, Rotate, Crop, Rotation, Shear, Hue, Blur, Grayscale, Saturation, Brightness, Exposure, Noise, Cutout, and Mosaic. The use of the Mosaic method as the newest type of data augmentation is expected to provide better results in drone object detection. The test is carried out by measuring the Precision-Recall evaluation metrics for each type of data augmentation. The results showed that the Mosaic augmentation type gave the highest Precision-Recall value of 0.993 compared to other augmentation types. The use of Mosaic as a data augmentation in drone object detection with the YOLOv5 algorithm can significantly improve object detection performance.
SIW Bandpass Filter Based on TGV Technology for Millimeter-Wave Wideband Communications

In this paper, a compact bandpass filter based on TGV technology for millimeter-wave broadband applications is proposed. The configuration of TGV-based filter consists of half-mode substrate integrated waveguide (HMSIW) loaded with defected ground structure (DGS). Three pairs of face-to-face typical E-shaped DGS are employed to achieve trap characteristics, which can offer the benefits of small footprint and wide bandwidth. In addition, the adoption of HMSIW structure can further minimize the lateral size of the circuit. Besides, TGV technology by virtue of low loss and high accuracy not only improves insertion loss but also enhances the integration of devices, especially in the millimeter-wave band. Geometric numerical analysis, fabrication, and measurement for the HMSIW-DGS filter are performed to demonstrate the validity and advancement of the design. Good consistency can be observed from the simulated and measured response. From the response curves obtained, the proposed experimental prototype exhibits a bandwidth fraction of over 31.22% and only occupies a circuit area of 1. 04λg×0. 27λg. The designed filter is competitive in RF ICs due to its miniaturization and wide passband advantages.
A Novel Parallel Consequent Pole Machine With Dual-Sided Permanent Magnets

This paper proposes a novel parallel consequent pole machine with dual-sided permanent magnets (CP-DSPMM) by combing the CP flux reversal machine with CP vernier machine and shifted rotors in the axial direction. The no-load and load electromagnetic characteristics of the proposed machine are investigated. The finite element analysis results demonstrate that the proposed parallel CP-DSPMM can achieve sinusoidal back electromotive force, as well as reduced cogging torque and improve the quality of electromagnetic torque.
Analysis of Electromagnetic Parameters of Switched Reluctance Motors Taking Influence of Rotor and Stator pole angles into Account

In this paper, an analytic approach is developed for determining the electromagnetic parameters of switched reluctance motors (SRMs) taking influence of rotor and stator pole angles into account. This study focuses on computing the value of minimum inductance $\left(L_{\min}\right)$ and maximum inductance $\left(L_{\max}\right)$, and high order-harmonic components $\left(3^{\text{rd }}, 6^{\text{th}}, 9^{\text{th}}, 12^{\text{th}}\right.$, and $\left.15^{th}\right)$ of the torque with the variation of rotor angle $\left(\beta_{r}\right)$. Based on that, it can be defined a suitable $\beta r$ to the smallest value of the order harmonic components. The development method also allows to compute everage torque, powers, torque ripple and efficiency of the SRMs with the different speeds. The obtained results from the proposed method is compared to that from the experiment model to show the good agreement.
Software Development of a Grid Analyzer for Digital Twins of Distribution Grids

The current transformation of the energy system brings new challenges in the areas of supply security, reliability, and efficiency. Especially the decentralization leads to an increasing complexity and a bidirectional power flow. This requires an expansion and initiative-taking operation of the distribution grid. To investigate the behavior of a complex and dynamic distribution grid or individual plants within it, novel methods such as digital twins can be used. In this approach, a model is developed based on the real distribution grid, which can represent and predict the behavior of the real system. For this, the measurement of data in the physical distribution grid and the transmission of this data to the digital model is essential. This paper focuses on the development of a grid analyzer for digital twins of distribution grids. With the grid analyzer, the characteristics of the power grid like frequency or amplitude of voltage and current can be analyzed, evaluated, and finally sent to the digital twin. Functions such as a Fast Fourier Transformation, a Frequency- and Phase-Locked-Loop, and other algorithms are being developed to analyze the grid state. The development of the functions is model-based and follows different development stages from classic simulation over hybrid simulation to a laboratory test in the last chapter of the paper.
3D Scene Recovery from Defocused Video Sequence

The paper aims at clearing the out of focus blur. Lens defocus introduces uniform blurring, Restoring these kinds of images is an interesting research field . The low resolution cameras manufactured at lower end like mobile phone and web camera mostly suffers through out-of-focus blur. Current auto focus solutions used in commercial cameras are design to ensure that captured images are in focus by adjusting the lens position and aperture opening. This needs an auto focusing hardware. The assembly is bulky and costly, cannot employed at handheld application where cameras are rigid (fixed lens &aperture). Moreover it has fundamental limitations that when the scene contains multiple objects with largely varying depths , a single image cannot capture all the objects in focus simultaneously in that case camera captures multiple images with variable depth. We present a novel method for virtually focusing the defocused video captured by camera. Virtual Focusing stands for the technique of providing an image processing solution to recover focused image sequences from videos taken by an out-of-focus camera with fixed physical parameters (cell phone cameras and webcams). I. INTRODUCTION