Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Efficacy of CNN and SVM Models for Automated Damage Severity Classification in Heritage Buildings

Numerous Heritage buildings are critical cultural assets susceptible to damage and deterioration. In this study, we explored machine learning techniques for the automated assessment of damage severity in heritage buildings. Specifically, this study has trained the Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) on a dataset of 4,500 images of heritage buildings, each with a resolution of 224x224x3 pixels. The results demonstrate that both CNN and SVM can effectively classify damage severity levels in heritage buildings, with CNN showing slightly better overall performance. This study also found that both the models' performance improved as the damage severity remain increased. These findings suggest automated damage severity assessment using machine learning as a good heritage building management and preservation approach. Further research is needed to explore the feasibility of implementing this approach in real-time and to address potential ethical and technical challenges.
Design and Implementation of Robust Controller for BLDC Motor Drive in Electric Vehicle

Brushless DC (BLDC) motors have become a viable alternative in response to the rising demand for energy-efficient motors in Electric Vehicles (EVs). BLDC motors face challenges related to unsteady operation when disturbances occur in the hall sensors. These disturbances lead to uncertain and nonlinear characteristics in the motor systems, which can negatively impact controller performance. Sliding Mode Control (SMC) is a popular control strategy for dealing with nonlinearities. So, this paper proposes the design and implementation of an SMC scheme for BLDC motor drive specifically tailored for three-wheeler EV applications. SMC can overcome the unstable behavior of the motor drive under parametric variations. A comparative analysis of SMC with PI controller was also presented to prove the effectiveness of the robust controller.
A Control-Oriented Modelica 1-D Model of a Planar Solid-Oxide Fuel Cell for Oxy-Combustion Cycles

The authors propose an object-oriented Modelica 1-D SOFC model following the indications of a previous work. The model is intended to be used in the study of the SOS-CO2 cycle for control studies purposes. The SOFC working conditions are challenging and require adapting the model to handle properly reformate mixtures at the anode and carbon dioxide-rich mixtures at the cathode while keeping its formulation as simple as possible. This paper reviews the previous contribution, adapts the model for the new working conditions, provides insights related to the Modelica implementation, reproduces the tests presented in literature and extends the validation through a comparison with experimental data. Finally the model is tested under SOS-CO2 cycle operative conditions.
Prognosis of Polycystic Ovary Syndrome (PCOS) by its Symptoms and Rotterdam Criteria

Polycystic Ovary Syndrome (PCOS) is a disease which cause the hormonal disorder in women which in result enlarged ovary with small cysts appearing on its outer edges. This paper attentions on the prognosis of PCOS by classifying its symptoms based on its Rotterdam criteria with respect to the symptoms. To diagnose PCOS, patients need to undergo various unnecessary tests and questionary which not only consume time and cost but has psychological effect on female patients. Women diagnosed with PCOS may face an elevated likelihood of developing type 2 diabetes, cardiovascular issues, hypertension, and endometrial cancer. The exact reason behind the cause of PCOS is not known yet. There are three most widely used Rotterdam criteria for diagnosing PCOS and two out of three criteria must be positive for diagnosis of PCOS. The three criteria are - Oligomenorrhea that means irregular menstrual cycle or Amenorrhea that is absence of menstrual cycle for long period of time, Hyperandrogenism that is clinical or biochemical signs of androgen in the body, polycystic varies in the ultrasound. For this various machine learning algorithms are used for classification and prediction. The dataset is subjected to various classification techniques such as gradient boosting classification, logistic regression, and hybrid approaches like the combination of random forest and logistic regression. From result analysis it is found that the most optimal method among various machine learning algorithms, Random Forest Classifier gives best accuracy for prediction of PCOS. The ensemble methods in machine learning gives the best performance and accuracy in decisions as it combines the insights of multiple learning models in machine learning. There are a lot of features that are obtained from clinical and biological tests, among all the 8 most promising features are selected.
ECG Denoising Using Multiple Approaches

Today, heart diseases and cardiac-affiliated failures are one of the main causes of people losing their lives in the world. Cardiovascular diseases (CVDs) are a classification of diseases that deals with the heart and blood vessels. Electrocardiogram (ECG/EKG) is an efficient and a non-intrusive index for the classification, prevention, and detection of arrhythmia. These signals are contaminated by various noises like Muscle Artifact, Baseline Wander, Power Line Interference (PLI), etc., which may lead to wrong interpretations about the patient's condition and will affect the diagnosis and monitoring. Therefore, ECG denoising is one of the major pre-processing steps for the attenuation of these noises. In the proposed work, five different techniques namely a Denoising Autoencoder (DAE) Discrete Wavelet Transform (DWT), Butterworth Filter, FIR Filter, and Median Filter. The denoising performance is estimated using RMSE and SNR. The above-mentioned techniques are compared, analyzed and results show that the proposed Denoising Autoencoder performs better in terms of SNR and RMSE and is more efficient.
Predictive maintenance of power transformers based on a data-driven application

This paper presents an innovative digital tool called "Asset Management Optimization for Power Grids Application" based on Artificial Intelligence techniques for monitoring critical assets in power systems, such as power transformers. This application implements different features that target the operators of transmission and distribution systems, aiming at providing analytics and forecast models that optimize the management of maintenance activities. Unlike the traditional approaches estimating the conditions of critical transformers mainly through Dissolved Gas Analysis, the proposed application is based on low-cost and easily accessible data, typically from SCADA, to estimate the probability of failure. The article concludes by focusing on one pilot installation in a distribution network for preliminary testing purposes in a real-life environment.
The Elderly in Traffic: Mobility and Road Safety

In the western nations, the average age of the population is rising. There are more elderly people, who are also able to play a full part in society for longer and who whish to continue to do so. In order to take part in the life of the community, it is often essential that they participate in traffic. The elderly will therefore play an increasingly important role in the traffic scene. Mobility goes hand in hand with danger on the road. In the Netherlands, more than a quarter of fatal accidents are suffered by those aged 65 and above. This twice as high as one expect on the basis of the population composition. A similar situation is found in other North-West European countries. If the proportion of elderly people in the population rises, as forecast, from one eighth today to a quarter in the year 2035, and if circumstances remain unchanged, half of all deaths on the road will therefore occur in the age group. In other words, while there is already sufficient reason today to tackle the problem of the elderly and road safety, a look ahead to the future makes this all more essential.
Fusion of LiDAR and MMW Radar Point Clouds for Environment Sensing

3D environment sensing and modeling plays a crucial role in many domains. To acquire more detailed and comprehensive environmental information, this paper proposes a fusion system of LiDAR (Light Detection and Ranging) and millimeter-wave (mmw) SAR (Synthetic Aperture Radar). The system utilizes a custom-built hardware platform to collect data from both LiDAR and SAR sensors. The point cloud data acquired from the two sensors is precisely fused using the ICP (Iterative Closest Point) algorithm. Experimental results demonstrate the significant robustness of the fusion system and successful achievement of high-precision fusion of LiDAR and SARdata.
GAN-based Homogenous Transfer Learning Method for Regression Problems*

In industrial scenarios, the difficulty of collecting a sufficient amount of data for data analysis is a long-standing problem. Transfer learning (TL) is believed to be a potential solution to this problem. TL is a methodology that transfers useful knowledge from previous data and/or tasks in a source domain to help build a more accurate model in a target domain. So far most research is conducted in the fields of computer vision and natural language processing, and thus images or text are taken as inputs. In this study, we aim to propose a TL method for industrial scenarios; specifically, a regression task that has signal data (continuous variables) as inputs. Generative adversarial network (GAN) is widely known for its ability to generate artificial data (images, text, audio, etc) that is similar to real data. However, few previous studies have been done to investigate the performance of GAN in a regression problem setting. This study proposes a GAN-based TL method where an adjusted GAN is designed to tackle the regression problems. To evaluate the model performance of the proposed method, artificial datasets were generated to simulate different types of domain discrepancy. The experimental results suggested that the proposed method can improve the prediction accuracy in the target domain by utilizing a source domain that has similar input distributions and moderately different input-output relationships.
Compact Dual-/Tri-Band SIW Filtering Antennas Using Cavity-Backed Slots

Compact dual-/tri-band SIW filtering antennas using cavity-backed slots are proposed. By etching a pair of open-loop slots (OLSs) and a slot on the SIW cavity, the slot can be treated as a radiator as well as a resonator. As a result, a dual-band filtering performance with four radiation nulls (RNs) is obtained, achieving high selectivity and good out-of-band suppression. The formation mechanism of RNs is analyzed. To further prove this design method, two pairs of OLSs and a horizontal straight slot are simultaneously etched on the SIW cavity, achieving the tri-band filtering response with 6 RNs in the stopband. Two prototypes are fabricated and tested for demonstration, showing the excellent dual-band filtering response with center frequencies of 4.56 and 5.7 GHz, and the superior tri-band filtering response with center frequencies of 4.1, 4.82 and 5.62 GHz. Moreover, both designs exhibit the compact size of 0.4 ×0. 5λ20 due to compact structures.
IPL Players Cost Pay Prediction using Machine Learning Techniques

The Indian Premier League (IPL) is a popular Twenty20 cricket league known for its high-profile matches and lucrative contracts. The issue of pay versus performance in the IPL has been a subject of debate, with some people arguing that players are overpaid while others believe that the salaries are justified given the level of competition and revenue generated by the league. This research study aims to investigate the relationship between pay and performance in the IPL. A literature review on the history of pay versus performance in sports, pay structure in the IPL, and studies on pay versus performance in the IPL was conducted. Data was collected through secondary sources and analyzed using descriptive statistics and regression analysis. Results suggest a positive relationship between pay and performance in the IPL but with some variations among different teams. The findings have implications for IPL team management and provide directions for future research on pay versus performance in sports. Overall, this research work contributes to the ongoing debate on pay versus performance in the IPL and provides insights for stakeholders in the cricket industry.
Efficient Read Disturb Management Schemes in Resource-Constrained Flash Memory Controller

Whenever a read operation is performed in a flash memory storage device, the surrounding cells in the same block are affected and their reliability gradually decreases. This phenomenon is known as read disturb problem. A remedy to this read disturb is to count the number of read operations for each block and perform read reclaim, the process of moving the existing data to a new block when the count reaches a predefined threshold. However, as the number of blocks in the flash memory device increases, maintaining the per-block read count requires a great amount of memory space in the controller. Therefore, system designers are forced to maintain read counts for a group of blocks (i.e. superblock) which results in the loss of accuracy. This paper proposes novel read disturb management schemes that can reduce the number of read reclaims significantly even if the read count is maintained for each superblock. In the proposed Pointer-based scheme, we keep track of the last block read so as to avoid excessive increase in the read count when the data is read sequentially. We also propose the Bitmap-based scheme that can successfully approximate the actual read count in the presence of random reads, with a negligible space overhead. Our experiments with real-world traces show that the Pointer-based scheme and the Bitmap-based scheme reduce the number of read reclaims by 65.5% and 90.5% on average, respectively, compared to the conventional scheme.
Development of the Line Scatterer Model for Bistatic Wind Turbine Clutter

The scattering of electromagnetic signals from wind turbines is a potential cause of clutter especially for passive radars which may use for example commercial Digital Video Broadcasting – Terrestrial (DVB-T) or frequency modulation (FM) broadcasting transmissions as transmitters of opportunity. These transmitters typically have a nearly constant gain in azimuth and thus they will illuminate the wind turbines at all times if they are visible to the transmitter mast. Understanding the wind turbine clutter is a first step in mitigating its negative effects. A previously published analytic line scatterer model for wind turbines is developed further in this work. Elevation angles are taken into account and thus the transmitter, wind turbine and receiver locations are no longer limited to the plane of same height. Furthermore, the amplitude of the scattered signal in the line scatterer model is investigated in detail and compared to radar cross-section (RCS) simulations from a 3D model of a wind turbine rotor. Based on this comparison, frequency dependent models for the amplitude are developed. The model development shows that wind turbine RCS can be highly dependent on frequency and view angle, but simplified models can also give quite good predictions. One possible application of the model is demonstrated by simulating a passive radar signal processing scenario where the effect of the blade flashes can be seen in the cross-ambiguity function (CAF) computation.
Attitude and Orbit Control for Small Satellites

A satellite in orbit about a planet needs some means of attitude control in order to, for instance, get as much sun into its solar-panels as possible. It is easy to understand that, for example, a spy satellite has to point at a certain direction without the slightest trembling to get a photo of a certain point on the earth. This type of mission must not exceed an error in attitude of more then about 1/3600 degrees. But, since high accuracy equals high cost, it is also easy to understand why a research satellite measuring solar particles (or radiation) in space does not need high accuracy at all. A research vessel of this sort can probably do with less accuracy then 1 degree. The first part of this report tries to explain some major aspects of satellite space-flight. It continues to focus on the market for small satellites, i.e. satellites weighing less than 500 kg. The second part of this final thesis work deals with the development of a program that simulates the movement of a satellite about a large celestial body. The program, called AOSP, consists of user-definable packages. Sensors and estimation filters are used to predict the satellites current position, velocity, attitude and angular velocity. The purpose of the program, which is written in MATLAB, is to easily determine the pointing accuracy of a satellite when using different sensors and actuators.
AN INITIAL ANALYSIS OF THE EARTH'S CRUST UNDER THE WILLISTON BASIN:

Four 300-km-long refraction profiles from western Manitoba and eastern Saskatchewan were shot over the Canadian portion of the Williston basin. On the basis of three distinct seismic velocity intervals, 5.95–6.50, 6.60–6.75, and 7.00–7.61 km/s, the crust in the Nelson Front and the Churchill tectonic province can be vertically separated into three distinct zones. The crust thickens from east to west, reaching a maximum of more than 50 km in the central portion of the basin. The upper mantle velocity increases from 7.84 km/s in the east to 8.47 km/s in central Saskatchewan. Along the eastern flank of the basin, in the transition zone between the Churchill and Superior tectonic provinces, extensive structural variations are interpreted as major faults which appear to disturb the entire crust. This thick high-velocity crust beneath the Williston basin indicates that it is not of extensional origin.
Voice Assistant with Emotion Recognition

Voice assistants have become a essential part in our daily life, which helps to provide essential tasks in a hands free manner. Here the voice assistant helps the user with hands free experience that includes setting reminders, playing music, alarm, wishes etc. The voice assistant is powerful ones of cutting-edge speech recognition and natural language processing technique. The system will be intended to interpret and respond to a wide range of spoken instructions and enquiries in a natural and intuitive manner. Furthermore, the initiative intends to improve the user experience by giving tailored replies and suggestions depending on the user's preferences and history. For forecasting emotions, features such as emotion recognition, which detects a person's feelings through their face, are implemented. The goal of this project is to create a voice assistant that interacts with and shorten the burden of activities.
High-gain Dual-Polarized SIW Shared Aperture Magnetoelectric Dipoles Antenna Array

In this paper, a high-gain dual-polarized shared aperture magnetoelectric dipole (MED) antenna with substrate-integrated waveguide (SIW) as feed network is proposed. The antenna element effective size is $0.48 \lambda_{0} \times 0.48 \lambda_{0} \times 0.2 \lambda_{0}$. A bandwidth of 14% (36.4GHz-41.9GHz) for $\vert \mathrm{S}11\vert < -10\text{dB}$ can achieved. Antenna radiation pattern is superior, and it has unidirectional radiation. The peak gain of the antenna is 9.35dB, and the average gain is 8.65dBi. A $2\times 2$ square array can be created by grouping the proposed MED antenna element. By adjusting the phase of each port, the four-beam radiation mode is realized. Based on the advantages of antenna radiation performance, it is conducive to processing and integration, and can be applied to wireless communication system.
Processing of ECG Signal Using Optimized Feature Selection for the Prediction of Irregular Heart Beat

The categorization of Electro Cardio Gram (ECG) trials into significant arrhythmia programs to identify abnormal heartbeats is presented in this study as an effective hybridized method. The most frequently used and recognized detection technology to display heart fitness is the physiological detection utilizing ECG data. Arrhythmia beat categorization also plays a significant part in ECG analysis that is intended to clarify cardiac well-being position when investigating heartbeat. Artificial Neural Networks (ANN) is deployed for the segmentation of input ECG images. The proposed work uses Fourier transform to remove the intrinsic sound of ECG signals during the preprocessing step in order to properly categorize ECG samples into main arrhythmia classifications. Chicken Swarm Optimization (CSO) is employed for the effective feature selection which is followed by CNN based classification. This demonstrates how the suggested ECG classification model is successful in correctly classifying ECG signals for abnormality detection along with IoT based
Conceptual design of an autonomous unmanned aerial vehicle

A conceptual design of a medium size unmanned aerial vehicle (UAV) is reported in the present study. This study is part of a bigger research investigating into the ability of an advanced UAV to autonomously traverse over an aircraft and accurately detect locations of defects on the aircraft skin to assist in further investigation of subsurface defects utilizing thermal and ultrasound techniques. The aim of the research study is to minimize the inspection time of a commercial aircraft to the order of an hour. This paper focusses on the aerodynamics and structural design of a UAV capable of carrying the weights of optical, thermal and ultrasound cameras as required. The design requirements include the ability of the UAV to traverse the aircraft outer skin autonomously sending both its GPS location and captured images to a secure data drive within the vicinity of the aircraft hangar and the ability to wirelessly charge its battery and thereby improve its hovering duration, i.e., its endurance. To achieve the preliminary design, the aerodynamic coefficients are estimated using computational fluid dynamics analysis, as well as smart design of fan shrouds to minimize the vortex shedding and hence improve its overall efficiency.
Machine Learning-Driven Edge and Split Computing Architectures Study in High-Mobility Dynamic Environments

Mobile edge computing (MEC) plays a crucial role in meeting the demands of the future digitized world by providing widespread computational capabilities and seamless integration with emerging technologies such as 6G, Internet of Things (IoT), blockchains, and artificial intelligence (AI). This paper proposes a novel edge and split computing architecture orchestrated by cooperating machine learning elements across network elements. The algorithm optimizes task partitioning, accuracy, and transmission delay in dynamic environments. Evaluations show its superiority in reducing task calculation failures and achieving high precision. Potential applications include smart city domains like autonomous driving, car sharing, robotic delivery, and public transportation.