Miracle-dz/UniFAR-FTUs huggingface dataset:
Preliminary Results and Discussion over a Novel Toolbox Supporting the Optimal Design and Management of a Resilient Islanded Microgrid

This paper introduces a new fully integrated decision support system (DSS) for hybrid isolated microgrids, enabling both the designer and the energy manager to make choices according to the geographical location and application-specific resilience indicators (KPI). An optimal formulation of the energy management system (EMS) is associated with the DSS and it provides the reference parameters for the power converter controller, as well as the information to build the tech-economic assessment. As several degrees of freedom exist when designing the microgrid, indicators are defined and used to analyze several scenarios and microgrid configurations, focusing on different levels of resilience. Preliminary results derived from the sizing and the EMS optimizations are presented here for different KPI, affected by the geographical location.
Graph Neural Network Assisted S-Parameter Inference and Control-Word Generation of Terahertz Reconfigurable Intelligent Surface

For large-scale terahertz (THz) reconfigurable intelligent surfaces (RIS), each unit element is supposed to be controlled by an independent voltage source. This leads to a huge solution space for both key metrics inference and control-word generation. In this paper, we propose a fast AI-assisted control-word generation scheme to reduce the computation cost of Electro-Magnetic (EM) simulations (Forward Model) and to accelerate the iteration process for control word search (Inverse Model). The results demonstrate that the Forward Model can predict the S-parameters between 100GHz and 800GHz with a minimal mean absolute error (MAE) of 0.69dB. Our method is more than 180 times faster than traditional full-wave simulation methods without training time. Additionally, the Inverse Model can generate demanded control words within 1.5 dB error requirement in less than 200 iterations.
The coseismic and postseismic deformation of the L’Aquila, 2009 earthquake from repeated GPS measurements

We analyze more than 100 GPS time series of continuous and discontinuous GPS stations located in the Abruzzi region (Italy) surrounding the epicentres of the L’Aquila 2009 seismic sequence. The purpose of this work is to reconstruct the coseismic displacement field caused by the 6 th April (Mw 6.3) main shock from a dense network of survey-mode stations surrounding the epicentral area and to characterize the early postseismic deformation field. In the months following the main shock, an extensive GPS survey was carried out on the existing Central Apennines Geodetic Network (CAGeoNet), with the intention of collecting a robust data set and to study the co- and postseismic deformation field of this Apenninic normal faulting earthquake. The analysis is carried out with two independent procedures and software (Bernese and Gamit) in order to provide reliable and validated geodetic solutions. The analysis of the postseismic transients and the knowledge of long-term inter-seismic velocities at all GPS stations, issued from permanent and CAGeoNet sites, allow us to derive a dense co- and postseismic displacement field for the L’Aquila Mw 6.3 main shock in a wide area around the epicentre. The highest deformation rate occurs during the first 4–5 months after the main shock and persists in the following at slightly slower rate throughout the whole monitoring period. Fast deformation rates imply that most of the observed deformation is due to a process different from a pure viscoelastic relaxation of the stress perturbation. Since the observed rates would imply a too low effective viscosity value (below 10 17 Pa s), we rather suggest that most of the observed deformation in the first months after the earthquake is due to different processes, most likely frictional afterslip possibly modulated by the presence of fluids. The new coseismic displacement field is used to invert for the main shock fault geometry, analysing the consistency among the different geodetic solutions and the combined one, with the goal of validating the two data sets.
Towards a DeepMalOb Improvement in the Use of Formal Security Risk Analysis Methods

Researchers are concerned about the detection of obfuscated Android malware, and multiple studies have been proposed to address certain obfuscation techniques. However, the comprehensive consideration of all obfuscation techniques remains a critical cybersecurity challenge due to their mutations. To tackle this issue, we developed the DeepMalOb approach, which utilizes memory dumping and deep learning with MLP to detect obfuscated malicious applications. Although the approach has yielded satisfactory results, we acknowledge potential security risks associated with MLPs, such as adversarial attacks, model inversion attacks, overfitting, and model biases, which may impact the accuracy and robustness of the MLP model and render it vulnerable to obfuscated malware. To improve the DeepMalOb approach, we propose the use of formal security risk analysis methods with MLP to detect hidden malware in Android by analyzing the security risks associated with the MLP model and the input features used for training.
Identification of Fake News in Social Media Using Sentimental Analysis

This research paper focuses on finding the most effective models that promote detecting fake news from social media platforms. One of the ways to detect fake news is by text mining using sentiment analysis. In this paper, we will calculate the sentiment polarity using a lexicon-based approach and find out its role in fake news. In addition, we will compare the accuracy of Logistic Regression, Naive Bayes and Support Vector Machine which are supervised Machine Learning model for their performance in the detection of fake news in the Indian Fake New Detection dataset. As the result, we can see the role of sentimental score in detecting fake news and hyper tunning SVM model is the best model compared with the other supervised learning ML model, with accuracy of 93 %.
An Online Calibration Method for TSEP-based Junction Temperature Estimation for EV Traction Inverters

This paper proposes an online calibration method for the junction temperature estimation of power semiconductor devices in Electric Vehicle (EV) traction applications utilizing thermo-sensitive electrical parameters (TSEPs). It is achieved through the DC-link discharging operation mode of the traction inverter after the vehicle is parked, which is a natural and frequent activity in EV applications. Compared to conventional calibration schemes, the proposed method can be conveniently accomplished in a short term without the need for long-term data recording or complex operating states. The on-state saturation voltage of power semiconductors is used as a typical TSEP in this paper for the case study, and the proof-of-concept experiments based on a scaled-down inverter prototype verify the effectiveness, accuracy, and convenience of the proposed method.
Event-triggered Online MPC Model Calibration for Photovoltaic Power Optimizers

Model predictive control (MPC) is a discrete-time control method for power converters in which control efficacy depends on the model values. A critical issue with MPC is parameter mismatch. In power electronics, parameters of the converter model equations are subject to mismatch including but not limited to capacitor values, equivalent series resistances, and load values. Solar photovoltaic (PV) dc optimizers and microinverters placed on the back sheet of the solar panel are exposed to excessive heat compared to an inverter in a less austere environment. Thus, exacerbating the effects of parameter mismatch. The contribution of this paper is a calibration technique to identify system parameters, augmented within the MPC framework; hence, mitigating parameter mismatch and improving tracking efficacy. The significance of this method is utilizing an error signal within the MPC control loop as an event-based trigger for the calibration process without the need for any sophisticated detection methods.
Smokeless Tobacco Use and Its Association with Type 2 Diabetes: A Case Control Study

Background: Type 2 diabetes mellitus (T2DM) is a prevalent metabolic disorder characterized by hyperglycemia and insulin resistance. Its incidence is increasing globally, with a significant impact on public health. Smokeless tobacco (SLT) is a form of tobacco consumption that has been associated with various health risks, including potential effects on glucose homeostasis. This case-control study aimed to investigate the association between SLT use and T2DM. Additionally, the study aimed to assess the relationship of age, gender, socioeconomic status (SES), body mass index (BMI), family history of diabetes, physical activity, and periodontal status with T2DM participants. Methods: The study was conducted over 24 months and included 82 T2DM cases and 164 non-diabetic controls. Demographic data, tobacco use, medical history, oral hygiene habits, BMI, and periodontal status were collected through a self-administered questionnaire and interviews. Statistical analyses were performed using Statistical Package for Social Sciences (SPSS) for Windows 26.0 (SPSS, Inc. Chicago, Illinois). Results: The majority of T2DM cases were in the age group of 31-50 years, and there was a significant association between gender and T2DM, with more males being diabetic. There was no significant association between SES and diabetes. Obesity was found to be a significant risk factor for T2DM. Among SLT users, gutkha was the most commonly used product. SLT use was significantly associated with T2DM. Family history of diabetes and physical inactivity were also significantly associated with diabetes. Conclusion: The study suggests that SLT use is a risk factor for T2DM and may be associated with increased diabetes risk. Further research is warranted to understand the underlying mechanisms and potential interventions to reduce the impact of SLT on diabetes risk.
Simple-Logic Comparator-Offset Mitigation Technique for Resistor-Based Temperature Sensor in DFLL

We propose a simple-logic system-level comparator offset mitigation technique for the resistor-based temperature sensor implemented with a digital frequency-locked loop (DFLL). Knowing that the frequency error due to the comparator offset depends on the initial digital-controlled oscillator's frequency (fDCO), we can preset the initial fDCO and obtain the average of the locked frequencies to eliminate the frequency and thus temperature error. The entire scheme entails only simple logics, minimizing the area overhead associated with the offset-calibration circuit. We verify the technique with a DFLL resistor-based temperature sensor designed in a 65-nm CMOS process. The simulated temperature error is <±0.4°C over a temperature range of -40 to 125°C in the presence of ±10mV comparator offset.
DOA Estimation via Meta-Learning under Array Sensor Failures

In the applications of direction-of-arrival (DOA) estimation, random array sensor failures can cause a serious performance degradation. Although deep learning-based techniques have offered an efficient solution, a critical limitation is that the network training has to be typically carried out from scratch for a new sensor failure scenario, resulting in a large amount of training time and resources. To this end, we propose a meta-learning-based DOA estimation method under array sensor failures. In particular, we formulate DOA estimation problem for different sensor failure scenarios as distinct tasks. The proposed method can make full use of the inherent relationship among individual tasks and train the deep neural network (DNN) to find a common global parameter vector. This vector enables the DNN to achieve fast convergence to the optimal state for DOA estimation when a new sensor failure scenario emerges. Simulation results demonstrate that the proposed method can speed up the adaptation process and achieve enhanced DOA estimation accuracy compared to the conventional methods.
A Novel Translucent Lowpass Filter Design Using Asymmetric Pi-shaped DGS

In this paper, a translucent low-pass filter for microstrip lines is designed based on an asymmetric Pi-shaped structure. It is fabricated by using flexible material PET and metallic copper with dimensions of 150 mm × 7.8 mm × 0.148 mm. The proposed low-pass filter has a narrow transition band. The insertion loss of the filter less than 1.5 dB below 0.32 GHz and a wide stop band from 1.59 GHz to 6.50 GHz with insertion loss greater than 30 dB. The transparency of the proposed translucent low-pass filter is increased by adding square holes on the copper layer. We cascade multiple identical structures to increase the insertion loss of the stopband without affecting the passband. The proposed translucent low-pass filter is flexibility, high transparency, and high insertion loss in the stopband. These features make it an ideal candidate for use in applications that require electromagnetic protection while maintaining a clear field of view, such as vehicle communication and building house communication. Overall, it provides a new approach to transparent filter design and has the potential to greatly benefit various industries.
Classification of Subspecies of Honey Bees using Convolutional Neural Network

Honey bees are vital for humans and the ecosystem; humans use bee products for various purposes and honey bees play an essential role in pollination. Identifying the subspecies of honey bees is essential for beekeepers, bee breeders, and researchers. Conventional methods of identification are time-consuming and expensive, as such, a more efficient and accurate technique is required. This paper proposes a computer vision-based method to classify subspecies of honey bees, with a dataset consisting of seven honey bee subspecies utilised. Due to the unavailability of a sufficient amount of labelled images to train a model, Transfer Learning (TL) technique has been employed. The Inception v3 model has been utilized as the main Convolutional Neural Network architecture; trained initially using the ImageNet dataset and then fine-tuned using the honey bee dataset. Image augmentation technique has also been used to reduce the impact of an imbalanced and small dataset. The Transfer Learning-based Inception v3 model was trained using the Sigmoid Focal loss function and demonstrated an accuracy of 94% with a weighted average F1 score of 0.94. This demonstrates the applicability of computer vision in the classification of honey bees.
MSeg-SLAM: A Semantic Visual SLAM System for Dynamic Scenes

Visual Simultaneous Localization and Mapping (VSLAM) technique is crucial for intelligent mobile devices to acquire their current position and pose information. The traditional VSLAM system assumes that all objects in the environment are static by default. But in the physical world, some dynamic objects like humans are unavoidable, which imposes a huge burden on the system. This work proposes a semantic VSLAM system named MSeg-SLAM, which combines the VSLAM system and MSeg semantic segmentation network to reduce the impact of dynamic objects. At the same time, we generate semantic octree maps to optimize the storage space occupied by the dense point cloud map. Compared with the original ORB-SLAM2 system, the absolute trajectory error (ATE) can be reduced by over 93% in high dynamic scenes of the public TUM dataset. The results indicate our system has strong robustness and stability both in datasets and real-world practical applications.
A Five-Level Low-Cost Modular Voltage Stepped-Up Converter for Photovoltaic Systems

This paper presents a novel five-level low-cost modular voltage stepped-up (5L LMVS) converter. Unlike other existing 5L converters, the 5L LMVS converter requires fewer active and passive components and the modularity of the switches in the topology is easily achieved. Besides, the proposed 5L LMVS converter doubles the output voltage, making it both cost-effective and capable of delivering high energy conversion. Given its advantages, the 5L LMVS converter is well-suited for use in medium-voltage and high-power photovoltaic (PV) systems. To regulate the output current, Module Predictive Current Control (MPCC) is implemented in the proposed converter. Finally, the advantages of the proposed converter and accuracy of current control in both steady-state and dynamic performance are verified through simulation.
A Comprehensive Framework for Parametric Failure Modeling and Yield Analysis of STT-MRAM

The spin-transfer torque magnetic random access memory (STT-MRAM) is an emerging memory technology with several distinctive advantages such as nonvolatility, high density, scalability, and almost unlimited endurance. It is, therefore, seen as a promising candidate to replace conventional on-chip memory technologies. However, as the technology scales, yield loss due to extreme parametric variations is becoming increasingly important for STT-MRAM because of its higher sensitivity to process variation as compared to CMOS memories. In addition, the parametric variations in STT-MRAM exacerbate its stochastic switching behavior, leading to both test time fails and reliability failures in the field. Since an STT-MRAM memory array consists of both CMOS and magnetic components, the system-level failures in STT-MRAM depend on variations in both these components. In this paper, we model the system-level parametric failures of STT-MRAM considering the spatial correlation among bit cells as well as the impact of peripheral components. The proposed approach provides realistic fault distribution maps and equips the designer to investigate the efficacy of different combinations of defect tolerance techniques for an effective design-for-yield exploration. The results show that the fault distribution and yield depend on the correlation coefficient and the temperature, which, in turn, determine the correct choice of defect tolerance scheme to be adopted to mitigate them to improve the yield.
Multi-Objective Design Optimization and Comparison of 2-layer versus 3-layer PM-Assisted Synchronous Reluctance Machines using a Blend of Rare-Earth and Rare-Earth-Free Magnets

Permanent Magnet Assisted Synchronous Reluctance Machines (PMASynRM) provide a low-cost alternative to the Surface PM Machines due to the use of relatively lower grades of rare-earth (RE) or RE-free magnets, as the performance degradation due to weaker magnets is compensated by the presence of reluctance torque. However, the weaker magnets suffer from high risk of demagnetization leading to unreliable motor operation. Using a blend of RE and RE-free magnets has the potential to overcome this issue. This paper presents four combinations of RE and RE-free magnets as well as two RE-free magnet combinations for their potential utilization in PMASynRM. Two different PMASynRM rotor topologies having 2-layers and 3-layers of rotor magnets arranged in U-shape and enabled with the six different magnet combinations are subjected to a multi-objective design optimization. The designs are optimized to minimize the volume of RE magnets, the demagnetization risk of RE-free magnets as well as torque ripple while achieving performance comparable to that of the Chevy-Bolt motor, taken here as the baseline design. Additionally, the minimization of the mechanical stress developed in the rotor laminations are also considered. The evaluation of various performance parameters along with approximate cost assessment based on magnet mass for the best designs of each magnet combinations are obtained for both rotor topologies. A quantitative comparison is also presented to highlight the various tradeoffs and suggest the best topology for further investigation.
An Online Correction Method of IGBT Collector Current Estimation Based on Gate Current

To ensure the safe operation of IGBT devices, power electronic converters must possess the crucial functionality of precise current detection. In this paper, addressing issues such as high device voltage level, integration difficulties, and reduced efficiency in the main circuit associated with conventional IGBT current detection methods, a smart driver with a collector current detection capability is proposed. Firstly, based on the relationship between the collector current and gate current during the Miller plateau, a three-point method is proposed to determine the relationship between the collector current and gate current during the Miller plateau. An online detection circuit for gate current is provided, enabling the detection of collector current through gate current measurement. Finally, the collector current detection capability of the proposed smart driver is validated through double pulse tests, demonstrating that the collector current detection error is within 1%.
Integrating Functional Safety into Hazard-Based Safety Engineering: Towards a Comprehensive Framework

As the convergence between hardware and software and the corresponding complexity escalates, the adequacy of safety standards should be reevaluated. The first Hazard-based Safety Engineering (HBSE) standard, IEC 62368-1, despite its comprehensive focus on protection against hazards such as fire, overcurrent, and overtemperature, lacks explicit inclusion of Functional Safety requirements when those safety protections are software-controlled. This paper is a pioneering attempt to present the rationale, assessment process, and nomological validity for incorporating Functional Safety principles into IEC 62368-1 by proposing a potential comprehensive framework that combines HBSE and RBSE, and this will maximize the potential of cutting-edge technologies implemented in ICT products, including the latest generative artificial intelligence (GenAI), and dynamic safety design such as telemetry-based failure prediction.
Design and Characterization of a Flexure-Based Hybrid Mechanism for Precise Micro/Nano Positioning

This paper presents a hybrid mechanism for micro/nano positioning and manipulation by combining a piezoelectric actuator (PEA) and a voice coil motor (VCM) using a flexure bridge. The research investigates the behavior of the hybrid mechanism, designs the amplification mechanism, and characterizes and compares the dominant parameters through modal and finite element analysis. The models were designed using Solidworks software and optimized using response surface in ANSYS. The hybrid mechanism provides both long-range displacement and precision movement capabilities, making it suitable for micro-assembly, micro-manipulation, and microfluidics applications. The results illustrate that the amplification ratio of the flexure mechanism obtained by modal analysis was 4.38, which was 3.42% larger than that simulated by finite element model. The input stiffness of the flexure mechanism was 2231.81 N/mm, which is slightly lower than the figure calculated by Finite element analysis (FEA) at 2325.58 N/mm. However, the difference between the two output stiffnesses of this mechanism obtained by the two methods is close, standing at 1.60%.
Path Loss Modeling Based on USRP-X410 and General Graph Optimization Algorithm

Path loss is a critical parameter in representing the characteristics of wireless channels and plays a pivotal role in wireless channel modeling. This paper introduces a novel method for path loss modeling, leveraging the general graph optimization (G2O) algorithm. Importantly, the data used for modeling were derived from extensive measurements using the USRP-X410 platform, which adds a unique element of practical applicability to our research. Two different models for indoor and outdoor scenarios were developed and optimized with this approach. The results indicate a significant reduction in modeling errors by using the G2O algorithm, effectively enhancing the accuracy of the path loss model.