Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Fusion Techniques for Image Super-Resolution

This research paper presents a thorough analysis of Image Super-Resolution (ISR) methods, covering their historical evolution, challenges, recent advances, and significance across domains. It explores both traditional interpolation-based and data-driven machine learning approaches, examining factors like image priors, network architectures, and the importance of domain-specific datasets and data augmentation. The paper also delves into ensemble models and addresses ongoing challenges in the field. It concludes with a comprehensive comparison of ISR methods, considering strengths, weaknesses, and applicability, offering a valuable resource for researchers and practitioners in the computer vision community and paving the way for future developments in image super-resolution.
Frequency-Domain Analysis for Nonlinear Systems With Time-Domain Model Parameter Uncertainty

Frequency-domain analysis of dynamic systems is important across many areas of engineering. However, while there are many analysis methods for linear systems, the problem is much less widely studied for nonlinear systems. Frequency-domain analysis of nonlinear systems using frequency response functions (FRFs) is particularly important to reveal resonances, super/subh-armonics, and energy transfer across frequencies. In this paper, the novel contribution is a time-domain, model-based approach to describing the uncertainty of nonlinear systems in the frequency domain. The method takes a nonlinear input–output time-domain model that has normally distributed parameters and propagates that uncertainty into the frequency domain using analytic expressions based on FRFs. We demonstrate the approach on both synthetic examples of nonlinear systems and a real-world nonlinear system identified from experimental data. We benchmark the proposed approach against a brute-force technique based on Monte Carlo sampling and show that there is good agreement between the methods.
AlGaN/GaN High Electron-mobility Varactors on Silicon Substrate

This paper gives a first presentation of an AlGaN/GaN high electron-mobility varactor grown on a high resistivity silicon substrate. The technology is compared to a GaN on silicon carbide technology and the devices are characterized by fitting a large-signal model. Compared to GaN on SiC varactors a similar performance is achieved. Only slightly increased losses due to higher sheet and contact resistance lower the Q-factor of the GaN on Si varactor.
The Difficulties and Solutions in SEE Radiation Experiments, Test Samples and Setup Preparation

This paper presents a collection of difficult technical issues and the experience obtained of solutions for SEE test process preparation regarding test samples identification and de-encapsulation procedures, test bench development and adjustment, testing software creation, irradiation process, test results processing and interpretation. All the considered practices have been worked out during the last two years and are currently used on a regular basis.
No-Distortion Deformation of Kirigami Structures Using Active Gripping

We propose an active gripping for no-distortion stretching deformation of kirigami structures. If we use the conventional fixed gripping, a distortion of the entire shape of the kirigami structure appears, and the distortion problem gets serious when the unit number increases. Therefore, we have proposed and evaluated active gripping, in which the gripping width is adjusted during the kirigami structure stretches. For the evaluation, we measured the distortion focusing on the gripping condition, the aspect ratio (AR), and the degree of extension. As a result, we have quantitively shown the pincushion distortion proceeds rapidly in low AR shape (short for the stretching direction) and the active gripping enables the kirigami structure to deform uniformly and improve the breaking strain. Finally, we demonstrate the other kirigami pattern and show the adaptability of this method.
A W-Band Small-Signal Equivalent Circuit Modeling Based On 28nm CMOS Process

In this paper, an equivalent circuit model is proposed to describe the small-signal behavior of transistors operating in W band. This model carefully addresses non-quasi-static effect to improve accuracy in high frequency range. A RC network representing the substrate coupling effect is also included to better predict the RF behavior. For the validation of this model, a 16-finger n-type MOSFET is fabricated using 28-nm CMOS technology. By comparing the simulated results with measured data, a good agreement between them is achieved, and the maximum RMSE of S parameters is less than 0.025. Therefore, it is safe to conclude that the model could well characterize the small-signal behavior of transistors based on 28-nm process up to 110 GHz.
Secure Communication for Healthcare Using Homomorphic Encryption: A Comparative Study

Cryptography is a branch of engineering that deals with sending information in a secured manner [1,2]. In the traditional cryptography, the message to be encrypted is called as plain text and the encrypted information is called as cipher text. After the successful transmission of information in a secured manner, the receiver decrypts the information for further processing. However, some security sensitive applications like healthcare demands a higher degree of security. Advancements in the modern cryptographic systems facilitated to process data without actually decrypting it. This would overcome the possibility of a third party to leak the sensitive information, as third party like clouds are relied for processing the information. Homomorphic encryption is a special class of cryptography wherein the data is processed in the encrypted domain itself. This adds to degree of security assured and can be highly useful for privacy preserving applications like voting, rating, healthcare etc. In this paper we have made an attempt to study and compare three popular homomorphic encryption schemes where the data is encrypted and the processed in the encrypted domain.
A High-Speed Micro Crawling Robot with Cancelation of Residual Electroadhesion Using Reversal Polarity Method

A new micro fast-moving soft robot is investigated, which integrates a Polyvinylidene Fluoride (PVDF) actuator for actuation and two Electro Adhesive (EA) pads for adhesion. An approach employing reverse-polarity voltage modulation is introduced to counteract the residual adhesion caused by deactivation of the EA pads. Several noteworthy features of this robot are elucidated: (a) low power consumption of 414 μW/cm2, facilitated by a custom-designed, energy-efficient high-voltage control circuitry, (b) rapid response due to the reversal polarity voltage discharge strategy, significantly enhancing the operational frequency of the robotic system, (c) significant movement speed, supported by empirical evidence indicating a maximum velocity of 177.52 mm/s, and (d) a remarkable payload capacity of 6.526 g, equivalent to 7.54 times its own mass of 0.866 g.
Power Quality Compensation and Power Flow Control in AC Railway Traction Power Systems

Rail transport is one of the transportation methods on earth that is very frugal in energy consumption. Electric railway technology has many advantages and disadvantages as well. The most important disadvantage of this technology is the creation power quality problems in the upstream power system. In other words, electric railway traction systems due to continuous load variations, Reactive power demand, asymmetry and harmonic Pollution can be challenged for the upstream power system. In this paper, after introducing the structure of the AC electric railway network and its related power quality issues, the railway power quality compensator (RPQC) is introduced. Also, in order to active power transfer between the various lines of electric railway, a railway interline power flow controller (RIPFC) has been introduced. The proposed structure RIPFC is in parallel, unlike the conventional one which is installed in series with the lines. RPQC and RIPFC contains two Separate single phase converters that are connected back to back (B2B) by sharing the same dc link. Extraction of compensating currents for RPQC and RIPFC is based on current measurement and its separation into active, reactive and harmonic components. Moreover, a controller is proposed to maintain the dc-link voltage. The simulation results using PSCAD/EMTDC are provided to demonstrate that the proposed strategies is very effective.
Effect of Pumice Powder and Silica Fume on Flowability and Compressive Strength of Slag-Based Geopolymer

Geopolymer cement is an alternative to conventional cement. Geopolymer technology is relatively new, so it is highly considered to be investigated. This paper evaluates the flowability and compressive strength of GGBS-based geopolymer using ambient and oven curing under partial replacement of GGBS with pumice powder (PP) and silica fume (SF) sequentially. Test results show that PP content has a negative effect on flow and compressive strength. On the other hand, the flowability grew with increasing SF content up to 20% replacement, while further increase flattened the trend. Contrasted to ambient curing, oven curing improves compressive strength by about 50% and 22% in PP and SF replacement, respectively. The compressive strength dropped with 10% and 5% SF replacement using ambient and oven curing, respectively. While the further increase in SF content increased the compressive strength. The highest 28-day compressive strength with oven curing and 30% SF replacement was 52.59 MPa.
Learning to Trade with Deep Actor Critic Methods

In this paper, we propose a new trading framework to apply deep actor critic methods to financial trading problems. Different from traditional actor critic methods, our model use not only actor but also critic to make the final decision. And for generalization purpose, a siamese structure in which the actor and the critic share the same LSTM features extraction part is adopted. The extracted features are then passed to different dense connected networks to compute q values and policy logits. The experiment results on different periods of CSI 300 prove that DACT has significantly better performances than B&H, DQT and DDRT. Furthermore, the idea of exploring based on the ensemble of actor and critic is valuable for other reinforcement learning problems.
PySpark-Powered ML Models for Accurate Spam Detection in Messages

The process of text classification for the purpose of spam detection relies on the principles of natural language processing (NLP) with the objective of automating the identification and categorization of messages into either spam or ham classifications. Our research endeavor involves the determination of the optimal machine learning model that yields the highest accuracy in segregating undesired messages or emails. The procedure for text classification was executed through the utilization of PySpark, employing a variety of machine learning algorithms such as Naive Bayes, Support Vector Machine, Decision Trees, and Random Forest. The dataset used for the experiments was sourced from Kaggle, a publicly accessible repository. In order to rectify data imbalance, Synthetic Minority Over-sampling Technique (SMOTE) was employed to ensure an equal representation of both spam and ham instances. The outcome of our study prominently designates the support vector machine (SVM) as the most precise machine learning model, demonstrating an F1 score of 97.93 and an accuracy score of 97.9.
Efficient Yield Estimation of RF-MEMS Using Vector Fitting Based Neuro-Transfer Models

This paper reports a novel approach for rapid and precise yield estimation of RF-MEMS using Vector Fitting (VF)-based neuro-transfer function (neuro-TF) models. This model offers rapid and accurate prediction of electromagnetic (EM) behavior of RF-MEMS. The advantages of this proposed method are exemplified through the analysis of a film bulk acoustic resonator (FBAR) as an illustrative example. It can predict EM behaviors of FBARs 2.4 × 103 times faster than Finite Element Analysis (FEA) simulation, with high accuracies of 98%. Consequently, it can potentially reduce the total computation time required for yield estimation by up to ~58.3% compared to the Monte Carlo (MC) method. Moreover, this method allows for the quantitative analysis of the most critical steps in the FBAR fabrication process, facilitating accelerated design and yield iteration cycles for the mass production of FBARs.
Empowering Rural Minds: Leveraging Predictive Analytics to Bridge Urban-Rural Educational Disparities Through Financial Assistance

Access to quality education is a critical factor in individual empowerment, economic development, and societal progress. However, students from rural backgrounds often face significant obstacles when it comes to pursuing technical education, primarily due to financial barriers. Financial barriers act as a deterrent, restricting the participation of rural students in technical education programmes. This paper presents an innovative approach to address this issue by leveraging the power of artificial intelligence (AI) and predictive analytics to provide equitable financial assistance and scholarships for technical education. By adopting a comprehensive strategy, this paper addresses the biases, transparency shortcomings, and contextual factors that have been prevalent in previous algorithm methods. The strategy employed in this study involves a multi-faceted approach, integrating various methodologies to achieve optimal outcomes. The key steps encompassed in the methodology include algorithmic fairness, model training and selection, transparency and interpretability, human-in-the-loop review, contextualised assessment, continuous learning, and feedback loops. Adversarial Debiasing is utilised to mitigate biases and ensure a fair decision-making process. To enhance transparency and interpretability, rule-based systems and LIME (Local Interpretable Model-Agnostic Explanations) are employed. Human-in-the-loop review is an integral part of the methodology, ensuring that AI-driven decisions are validated and refined by human experts. The expected outcome of this research is an AI-enabled financial assistance system that democratises technical education by making it more accessible to students from rural backgrounds. The implementation of this model can have a transformative impact on education equality, empowering rural minds to pursue their academic dreams.
Research on Battery Energy Control Strategy Based on Neural Network

With the development of science and technology, battery energy plays an increasingly important role in modern society. The role of battery energy includes but is not limited to providing power for mobile devices and powering cars. It is also the main energy source for new energy vehicles. Energy storage systems provide energy storage. It provides power for aerospace and can also be used as a backup power supply for households and industries to deal with sudden power outages and other situations. Neural networks are also widely used in battery energy. For example, neural networks can be used to predict the remaining service life of lithium-ion batteries. This paper takes the commonly used 18650 power lithium battery as the main research object, and proposes a neural network battery state of charge (SOC) estimation method based on the LEVY flight step-based group search optimization algorithm (LEVY-GSO-BP). The group search optimization algorithm after Levy flight step optimization is used to optimize the design of the established SOC neural network model to improve the accuracy of the neural network's estimation of SOC related parameters. BP neural network is a powerful optimization algorithm that can solve unimodal function and multimodal function optimization problems of high-dimensional data. It has the advantages of simplicity, efficiency and accuracy, so it is widely used in various problems. However, the calculation of BP neural network is more difficult, takes a long time, and the convergence speed drops significantly at the end of the calculation, and may even stagnate. In order to solve these problems, we propose an algorithm improvement method based on Levy flight step size. Levy flight is an optimization algorithm with excellent global search capabilities that can find the optimal solution in a short time. It uses a combination of short-distance frequent search and occasional jumping long-distance search to improve local search capabilities and global search capabilities. We combine Levy flight with BP neural network, aiming to retain the advantages of BP neural network while improving the optimization capabilities of neural network related parameters. By introducing Levy flight step size, we effectively improve individual diversity and take into account the balance between search speed, local optimality and global optimality. This method can obtain the local optimal solution more accurately and effectively expand the search range to avoid falling into the local optimum. Finally, we verified the reliability of the model through experiments.
A 4.5-7.5 GHz CMOS Variable Gain Amplifier with Low Errors for Receiver Channels

a two-stage programmable CMOS variable-gain-amplifier (VGA) was presented. It consists of a passive stage, an active stage and an inter-stage matching transformer. The gain tuning of each stage VGA can be digitally-controlled, and 32/24 gain states are obtained for the passive/active stage. As a result, 768 states in total can be get for the two-stage VGA. The proposed VGA was implemented in a 180-nm CMOS process and integrated into a receiver channel. Then a digital program was used to select the desired gain states from the total 768 measured states of the chip, to realize a highly accurate gain tuning. And an ultra-low root-mean-square (rms) phase/gain error less than 1.88°/0.19dB of rms phase/gain error was obtained over 4.5 to 7.5 GHz band.
Restricted, Repetitive, and Stereotypical Patterns of Behavior in Autism—an fMRI Perspective

The main objective of this paper is to determine whether resting-state fMRI can identify functional connectivity differences between individuals with autism who experience severe issues with restricted, repetitive, and stereotypical behaviors, those who experience only mild issues, and controls. We use resting-state fMRI data from the ABIDE-I preprocessed repository, with participants grouped according to their ADI-R Restricted, Repetitive, and Stereotyped Patterns of Behavior Subscore. Three processing methods are used for analysis. A time-correlation approach establishes a basic baseline, and we introduce a method based on sliding time windows, with means across time adjusted to consider the fraction of time the correlation measure is above/below average. We complement these with a band-limited coherence approach. For completeness, preprocessing schemes with and without global signal regression are considered. Our results are in line with recent ones which find both over- and under-connectivities in the autistic brain. We find that there are indeed significant differences in connectivity between various regions that differentiate between ASD subjects with severe stereotypical/restrictive behavior issues, those with only mild issues, and controls. Interestingly, for some regions, the “signature” of subjects in the milder of the ASD groups appears to be distinct (i.e., over- or under-connected) relative to both the more severe ASD group and the controls.
Sentiment Analysis of Social Media Text Based on Deep Learning

This study delves into the application of deep learning in sentiment analysis of social media text, employing Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. Our experimental results showcase the remarkable potential of these models in effectively capturing sentiment information, achieving high accuracy, and maintaining a balanced trade-off between precision and recall. The study underscores the crucial role of feature representation in sentiment analysis, emphasizing the benefits derived from employing word embedding techniques to enhance model performance. Through careful hyperparameter tuning, involving learning rates, batch sizes, and hidden layer sizes, we optimize model performance, further increasing sentiment analysis accuracy. The findings highlight the transformative potential of deep learning in unraveling sentiment nuances embedded in social media text, offering profound insights for decision support in society. Me anticipate that multimodal sentiment analysis and cross-cultural sentiment analysis will emerge as significant research directions in the field of deep learning sentiment analysis. The study not only contributes to the understanding of sentiment analysis but also points towards a future where deep learning plays a pivotal role in providing a deeper emotional understanding of social media content, thus enriching the landscape of decision support in a societal context.
Circulating Current and ZVS-on of a Dual Active Bridge DC-DC Converter: A Review

Dual active bridge (DAB) dc–dc converters have several attractive features including auto-adjust bidirectional power flow, wide voltage gain range, and zero voltage switching (ZVS)-on capability. Various applications based on DAB including solid-state transformer in power grids and traction, energy storage system, and electric vehicle on-board chargers have been proposed by academics. Nevertheless, industrial applications based on DABs are still seldom mainly due to the circulating current and loss of ZVS problems under certain load and voltage gain conditions. There is an intensive research effort underway to solve these problems recently. This paper presents the state-of-the-art view in the cause of circulating current and loss of ZVS and surveys the solutions to these problems.
Distributed Event-Triggered Formation Control of Multiagent Systems via Complex-Valued Laplacian

Event-triggered formation control of multiagent systems under an undirected communication graph is investigated using complex-valued Laplacian. Both continuous-time and discrete-time models are considered. The dynamics of each agent is described by complex-valued differential or difference equations. For each agent, only the discrete-time information of its neighbors is used in the design of formation controllers and event triggers. Triggering time instants for any agent are determined by certain events that depend on the states of its neighboring agents. Continuous updating of controllers and continuous communication among neighboring agents are avoided. The obtained results show that formation can reach specific but arbitrary formation shape. Furthermore, it is shown that the closed-loop system does not exhibit the Zeno phenomenon for the continuous-time dynamics case or the Zeno-like behavior for the discrete-time dynamics case. Finally, numerical simulations for both the continuous-time and the discrete-time dynamics cases are presented to illustrate the effectiveness of the proposed distributed event-triggered control methods.
Reachable Set Estimation and Synthesis for Periodic Positive Systems

This paper investigates the problems of reachable set estimation and synthesis for periodic positive systems with two different exogenous disturbances. The lifting method and the pseudoperiodic Lyapunov function method are adopted for the estimation problem. The reachable set bounding conditions are proposed by employing Lyapunov-based inequalities and the S-procedure technique. Two optimization methods are used to minimize the bounding hyper-pyramids of the reachable set. In addition, the state-feedback controller design conditions that make the reachable set of closed-loop systems lie within a given hyper-pyramid are derived. Finally, numerical examples are presented to illustrate the validity of the obtained conditions.