Miracle-dz/UniFAR-FTUs huggingface dataset:
A High Linearity Large Time Constants Switched-Resistor Filter for Biomedical Applications

Integrated low-frequency filters for biomedical applications require high resistance values to implement the required very large time constants. In this paper, the Switched-Resistor (S-R) approach is used to realize the proposed low-frequency low-pass filter (LPF) for biomedical applications. The MOSFET switch is included in the feedback loop to further reduce its distortion and increase the filter linearity. A buffer is used to load the feedback resistor, hence eliminating the need for a driver stage, allowing a low-power design. The bandwidth and gain are tunable to handle different biomedical signals. The filter achieves -61.5 dB total harmonic distortion (THD), with a 33 Hz sine 0.14-Vpp differential output. The input-referred noise is 16.2 μVrms, which is mainly contributed by the thermal noise of the input resistors, in a 300 Hz bandwidth. The prototype IC, fabricated in a 0.18-μm CMOS process, occupies an area of 0.08 mm2 and consumes 3.7 μW from a 1.8-V supply.
Sentiment Analysis of ChatGPT Healthcare Discourse: Insights from Twitter Data

This paper explores the application of Chat Generative Pretrained Transformer (ChatGPT) in the healthcare domain, introducing a sentiment analysis model to evaluate ChatGPT-related tweets in healthcare contexts. The study aims to uncover predominant sentiments, thematic content, and diverse perspectives concerning ChatGPT's integration into healthcare, utilizing an extensive dataset from Twitter comprising 10,330 healthcare-related tweets. Leveraging advanced Natural Language Processing (NLP) techniques, we systematically categorized topics and emotional content within these tweets. Additionally, we conducted a comprehensive analysis of frequently occurring words in tweets expressing positive and negative sentiments. The findings reveal that the majority of healthcare-related ChatGPT tweets express either positive or negative sentiments, with a minor proportion conveying neutral viewpoints. Furthermore, to enhance our comprehension of sentiment dynamics in healthcare discussions involving ChatGPT, we applied four machine learning classifiers Support Vector Machine, K-Nearest Neighbors, Naive Bayes and Random Forest. Remarkably, the SVM classifier demonstrated the highest accuracy at 85.6%, affirming its efficacy in healthcare sentiment analysis. In summary, this research sheds light on prevailing sentiments and perspectives regarding ChatGPT in the healthcare sector, highlighting its predominantly positive and neutral reception on platforms like Twitter. Additionally, the success of SVM as a sentiment analysis tool underscores its potential for discerning sentiments in healthcare-related ChatGPT discussions, contributing to ongoing debates on AI integration in healthcare and guiding future endeavors in this evolving field.
A Transient-Enhanced Digital-LDO With Adaptive Clock-Edge Control

This paper presents a transient-enhanced digital LDO with adaptive clock edge control. An under-strength detector and an adaptive clock edge multiplier are devised to achieve adaptive tuning of the clock signal, which generates a double rising edge clock signal. This method can be combined with traditional digital feedforward techniques to significantly improve the transient response of the digital low-dropout regulator. The proposed DLDO is implemented using a 180nm CMOS process and achieves a maximum current efficiency of 98.8% and quiescent current of 9. 8µA when operated at input and output voltages of 0.9V and 0.85V. The proposed method reduces the response time by 50%.
A Novel Semi-superjunction SiC Trench MOSFET with Ultra-low Specific On-resistance

This paper proposes the design and optimization of a trench-gate semi-Superjunction MOSFET based on 4H-SiC, aiming to achieve a breakdown voltage of over 1200V with an ultra-low on-resistance, while maintaining common switching characteristics. After being optimized through TCAD simulations, the proposed device achieves a breakdown voltage of 1650V and a specific on-resistance of 1.77 mΩ•cm2, and figure of merit (FOM) reaches 1.54 kV2/(mΩ•cm2). Compared with the conventional trench SiC MOSFET, it exhibits a 20% reduction in specific on-resistance, a 10% increase in breakdown voltage, and a over 42% improvement in FOM.
AI-based Algorithm for GNSS Spoofing Detection

GNSS is extensively employed for applications requiring high reliability. However, GNSS inherently encompasses varying error factors and is susceptible to malicious attacks such as spoofing. To ensure stable GNSS utilization, it is imperative to incorporate GNSS spoofing signal detection mechanisms into GNSS signal processing. In this paper, artificial intelligence (AI) to detect the spoofing in GNSS signal is proposed. The developed AI model effectively detects the spoofing within GNSS signals while the satellite navigation solutions changing over time. The AI was trained using simulated GNSS data, confirming the feasibility of employing AI techniques for GNSS signal processing.
Robust Approach for Lumen Segmentation in IVUS Image using Deeply Learn U-net Model

Accurate lumen segmentation in IVUS images is essential for diagnosing cardiovascular conditions and guiding interventions. We propose a robust lumen segmentation method using a customized U-Net model. By addressing challenges such as noise, vessel morphology variations, and limited contrast, our approach leverages preprocessing, U-Net architecture, training, and evaluation. Firstly, image enhancement, noise reduction, and image rescaling are performed to a benchmark IVUS dataset input images. Secondly, the U-net AI prediction model is trained with the IVUS preprocessed images and ground truth masks. Finally, we demonstrate the method's efficacy on the IVUS dataset, showcasing superior performance in metrics like Dice coefficient, Jaccard index, sensitivity, specificity, and accuracy compared to existing methods. Our technique ensures precise lumen boundary identification through qualitative assessments and demonstrates robustness through subgroup analyses and generalization tests. Our approach contributes to enhanced clinical decision-making by delivering accurate lumen segmentations, capitalizing on deep learning and addressing IVUS-specific complexities. This approach achieves 99.15% accuracy and holds promise for advancing IVUS image analysis and improving patient care in interventional cardiology.
Prediction of geomagnetic events from solar wind data using deep learning

The recent technological maturity attained by deep learning drew the attention of numerous scientific communities. Among these, Space Weather is leveraging such tools to support its activities related to forecasting harmful events. Coronal mass ejections (CMEs) are one of the most critical phenomena occurring in the solar system: their propagation may impact the Earth, altering the equilibrium of the terrestrial surface under different aspects, requiring their prediction to take countermeasures accordingly. Classical forecasting methods are built upon solar remote-sensing observations to forecast the CME onset, intensity, and arrival time. Although such methods could provide alerts within 1-4 days in advance, their estimations are affected by large uncertainties. On the other hand, deep learning has been offering valid alternatives through recent studies, devising data-driven models to obtain real-time alerts while monitoring such events remotely.The goal of this work is that of developing neural network architectures able to offer CME predictions leveraging the Lagrangian point L1 measurements, taking advantage of historical data related to these phenomena past behaviors to predict future trends.In this paper, two main phases may be distinguished: first, the manipulation of the dataset - mostly through augmentation techniques - to make it more suitable for the proposed prediction steps. Second, the implementation of different network structures for multiple classification tasks concerning various aspects of CMEs, to prove the effectiveness of deep learning algorithms in reaching the desired goal.
A Robust Algorithm for Impulsive Noise Mitigation in OFDM Systems

Orthogonal frequency division multiplexing (OFDM) systems are susceptible to impulsive noise; in essence, if the impulsive noise is not addressed adequately, system performance loss intensifies by the indelible impairment across OFDM signal bandwidth–thanks to the Fast Fourier Transform operation. This paper capitalizes on the majorization-minimization algorithm in tandem with semidefinite relaxation to avert an overwhelming task engaged in solving a non-convex optimization problem–engendered by the rule that data borne on sub-carriers is subject to a digital modulation format. Simulation results attest the robustness of our proposed receiver: the bit error rate performance level is promisingly close to that of the benchmark receiver–which however uses impulse statistics–in fairly hostile environments.
A Comparative Analysis of Time Series Forecasting Methods for Short-Term Electricity Demand Prediction

Energy demand is exploding worldwide. Accordingly, there is a growing interest in methods for maximizing energy use efficiency. Electricity demand forecasting is a technique of predicting future electricity demand based on past electricity usage data. This makes it possible to effectively manage energy usage. In particular, short-term electricity demand forecasting is essential for the optimal operation of the power system and the economic operation of the electricity market. In this paper, we compare short-term electricity demand prediction accuracy using forecasting models based on artificial intelligence. In addition, we perform electricity demand prediction for the summer period with large fluctuations.
Machine Learning Space Applications Using RC64 Rad Hard Manycore Processor

Ramon.Space RC64 rad-hard manycore processor is designed for artificial intelligence/machine learning computations based on deep learning. VGG-based neural network classification of Space imagery of three different types is demonstrated. Machine learning models and imagery are provided by Aiko Space, Mobius Labs and Space Shift. Image frames of 32×32 pixels are classified at 65 frames per second, achieving power efficiency of 12.5 frames per second per Watt.
Optimization Strategy of Energy Station with Thermal Storage by Heat Pump in Photovoltaic Building

Distributed photovoltaic generation, energy storage and time-of-use price would increase complexity of economic energy utilization. Traditional building energy management system is difficult to achieve rapid and reliable energy distribution while overall economic optimization from the perspective of source-load-storage coordination. In this paper, a multi-time scale optimization strategy of source-grid-load-storage cooperative operation considering energy cost and friendliness of the grid was proposed. Firstly, a unified mathematical model considering electro-thermal coupling was constructed. Then, a multi-time scale optimization model of source-load-storage cooperative operation combined day-ahead and day-in was established. Based on predicted value of heat load and photovoltaic output, the day-ahead model optimizes start-stop status of heat pump units and energy storage state hourly with the goal of lowest energy cost of the energy station under TOU tariff incentive mechanism. The day-in model optimizes input power of the heat pump units and SOC status of storage on the basis of the day-ahead plan in order to maintain real-time balance between energy supply and demand. A real project was simulated in Matlab showed that the proposed optimization strategy reduced electricity cost of the energy station, improved peak load transfer capacity and local consumption of distributed PV.
A Sinusoidal Fitting-based Digital Foreground Calibration Technique for Pipelined ADC

This paper presents a novel foreground digital calibration technique for pipelined ADCs to compensate nonlinearity caused by inter-stage gain error (IGE) and DAC error. The proposed algorithm calibrates the IGE by iterating coefficients multiplied by different stage digital output to reduce the mean-square error (MSE) between the fitted and reintegrated output. DAC error is extracted by averaging the difference between fitted output and IGE calibrated output of the same sub-stage output. Compared to previous works on IGE and DAC error calibration, this work could achieve effective calibration under various static errors and minimize the demand on hardware design. Simulation results show that this technique increases the ENOB and SFDR from 9.24bits and 63.41dB to 11.03bits and 78.68dB, respectively.
Facial Expression Detection Using Recurrent Residual Convolutional Neural Network

In several industries, such as healthcare, emotion analysis, and human-computer interface, facial expression detection is crucial. Recurrent residual convolutional neural networks (RRCNNs) are a revolutionary technique used in this to identify facial expressions. Our suggested model uses residual learning and recurrent connections to improve the precision and reliability of facial emotion recognition. This study is based on the incorporation of recurrent connections like ResNet into a sophisticated residual CNN architecture. This combination enables our model to extract from facial sequences both spatial and temporal information, making it an effective tool for tracking changing facial expressions over time. To train and evaluate our model, we used well- known facial expression datasets such as FER2013.The face expression identification model we've suggested, which parallelizes residual CNNs and recurrent neural networks, provides a potential method for enhancing the accuracy of 61.86% and completeness of emotion recognition systems. The RRCNN has a significant advantage in facial expression detection, even with a less accuracy. Due to its distinct architecture, the model excels at tasks like video-based detection where expressions change over time. The potential of the model lies in its ability to learn subtle temporal patterns, even though the accuracy may not be as good as it could be with more data, hyperparameter tuning, and model refinement. Because of its innate ability to analyze data sequentially, it can be a useful tool in the quest for context-aware and more precisefacial expression recognition and it is perfect for a range of real-world applications, including virtual reality, human-computer interaction, and mental health monitoring.
Phase Drift Compensation of Sub-THz Horn Antenna Pattern in Presence of Phase Center Offset and Orientation Misalignment

When the phase center of an antenna and the rotation center are not perfectly aligned, phase drift occurs in the antenna pattern measurement. This issue becomes more severe at higher frequencies. This paper presents a method for estimating and compensating for phase center offset, even when the phase center offset is unknown. Additionally, experimental results of horn antenna patterns measured at 159 GHz using the proposed method are provided.
Security enhancement scheme for mobile device using H/W cryptographic module

This paper is about a mobile device security enhancement technology using a hardware cryptographic module. More specifically, it is a technology that provides an encryption function to access networks such as private networks and national defense networks that require a higher level of security. The hardware cryptographic module is a separate hardware device physically independent of the mobile device. It provides not only basic encryption functions such as data encryption, message authentication, and digital signature, but also security functions such as identification and key management, authentication, and secure storage in conjunction with the built-in Physical Unclonable Function (PUF).
Development of Modules for Multiple Paths Finding for Software-Defined Network in MiniNet

The capabilities of software communication network simulators are expanding every year, including for software-defined networks. One of the most frequently used is Mininet, which allows you to simulate the interaction of OpenFlow switches and a SDN controller. At the same time, the controller does not always have the ability to integrate its own algorithms without significantly changing them. The paper presents the development of modules for transforming network topology and a bidirectional path search algorithm for working in the Mininet simulation environment. The use of the proposed solutions allows to reduce the computational load on the controller, thereby increasing its efficiency.
Multispectral airborne laser scanning - a new trend in the development of LiDAR technology

Airborne laser scanning (ALS) is the one of the most accurate remote sensing techniques for data acquisition where the terrain and its coverage is concerned. Modern scanners have been able to scan in two or more channels (frequencies of the laser) recently. This gives the rise to the possibility of obtaining diverse information about an area with the different spectral properties of objects. The paper presents an example of a multispectral ALS system Titan by Optech with the possibility of data including the analysis of digital elevation models accuracy and data density. As a result of the study, the high relative accuracy of LiDAR acquisition in three spectral bands was proven. The mean differences between digital terrain models (DTMs) were less than 0.03 m. The data density analysis showed the influence of the laser wavelength. The points clouds that were tested had average densities of 25, 23 and 20 points per square metre respectively for green (G), near-infrared (NIR) and shortwave-infrared (SWIR) lasers. In this paper, the possibility of the generation of colour composites using orthoimages of laser intensity reflectance and its classification capabilities using data from airborne multispectral laser scanning for land cover mapping are also discussed and compared with conventional photogrammetric techniques.
Forecasting the Academic Horizon: Machine Learning Models Unraveling the Complex Web of Student Well-being Determinants

In the contemporary academic landscape, the well-being of students is pivotal not only for their individual success but also for the broader educational ecosystem. This study meticulously delves into a rich dataset encompassing diverse student attributes, academic performance metrics, and economic indicators to discern patterns and predictors affecting student well-being. Leveraging a multi-faceted research methodology, we employed various machine learning models, ranging from logistic regression to advanced ensemble methods, aiming to forecast and comprehend the intricate determinants of student outcomes. The research design, underpinned by rigorous exploratory data analysis, revealed intriguing correlations between economic conditions, academic achievements, and students' well-being. The Gradient Boosting model, in particular, showed a significant improvement post hyperparameter tuning, with an accuracy reaching up to 77.63%. On the other hand, models like the Random Forest achieved a base accuracy of 77.29%. These insights highlight the potential of data-driven methodologies in understanding and predicting student well-being. As we stride into an era where data-driven decisions in education are paramount, our findings offer a robust foundation for future endeavors in this realm. Future directions of this study encompass refining prediction models with more granular data, exploring the psychological facets of student well-being, and devising actionable interventions based on the identified predictors.
OBC-Ultra, the rad-hard NG-Ultra-based On Board Computer for future applications

This paper will present the OBC-Ultra, the new generation of On Board Computer developed by Airbus and based on the European rad-hard NG-Ultra.The OBC-Ultra is the new generation of Airbus On Board Computer (OBC) targeting both Telecommunication and Earth Observation missions. It is based on the European rad-hard FPGA & SoC from NanoXplore named the NG-Ultra.This new equipment directly benefits from the Airbus database and experience acquired through several H2020, GSTP and internal R&D projects on NG-Ultra-based applications, from generic VHDL & Low Level Software (SW) libraries to place and route automatisation and optimisation of FPGA designs.This presentation will describe the architecture of this new NG-Ultra-based OBC by presenting the VHDL and low level software modules and by explaining how the use of NanoXplore components allows the attainment of high data rate performances across the different boards while offering a European-based solution critical for our strategic non-dependence:•Wide range of communication protocols (SPW, CAN, 1553, RS485) on NG-Ultra-based processing board•Critical functions (Reconf, Security, ETC, ETM) on Ultra-300-based board without SW•20Mbps TM/TC distributed across 2 boards for both SW performances and HW critical functions independence•Latest Space Data Link Security (SDLS) Extended Procedures standard support•Mass Memory function on processing board with Flash NAND and HW autonomous modules•Advanced HW & SW Debug and Trace features•Carefully considered HW/SW architecture for complex, multicore SW and critical autonomous HW. •Boards respecting modular ADHA standardThis presentation will be an opportunity for Airbus to detail the current development status of OBC-Ultra.
A Study on Improving the EDISON Data Registration Process Based on Dynamic Design

There are numerous data formats used in computational science. In order to effectively integrate and share data, improving the data registration process is crucial. In this study, we transitioned the data registration method within the EDISON platform from a static design to a dynamic design. To enhance data validation, we provided a schema authoring tool allowing data owners to design their own data schema. Additionally, we enabled support for a variety of data formats by allowing custom parsers to be written in a container-based Docker image format. Through these advancements, researchers can register and manage different data formats more efficiently. By implementing a dynamic design, data usability across various domains and environments can be improved, significantly reducing the time and cost associated with data errors while increasing the efficiency of data extraction and processing.
ATTITUDES IN THE GERMAN-POLISH CONTEXT BASED ON PERCEPTUAL EVIDENCE

The present study investigates how national attitudes/stereotypes influence listeners’ perceptions of accented speakers. We studied judgments of Polish listeners towards German and American accented Polish and expected the participants to prefer American over German accent, as implied by the results of a relatively recent national survey of attitudes conducted by a Public Opinion Research Centre (CBOS) [1]. In addition, we hypothesized that the more positive (or negative) judgements would be strengthened when the accented speech is accompanied by a corresponding symbol of the country, i.e., its flag. Our results revealed no preference of one of the accents over another, nor did the presence of primes affect participants’ judgements. They rated the speakers individually and independently from their perceived nationality. The results suggest that preconceived notions of other nationalities are not necessarily evoked when an opinion on an individual speaker is expressed.