Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of splitting functions from Earth's normal mode spectra using the neighbourhood algorithm

S U M M A R Y The inverse problem for Earth structure from normal mode data is strongly non-linear and can be inherently non-unique. Traditionally, the inversion is linearized by taking partial derivatives of the complex spectra with respect to the model parameters (i.e. structure coefficients), and solved in an iterative fashion. This method requires that the earthquake source model is known. However, the release of energy in large earthquakes used for the analysis of Earth’s normal modes is not simple. A point source approximation is often inadequate, and a more complete account of energy release at the source is required. In addition, many earthquakes are required for the solution to be insensitive to the initial constraints and regularization. In contrast to an iterative approach, the autoregressive linear inversion technique conveniently avoids the need for earthquake source parameters, but it also requires a number of events to achieve full convergence when a single event does not excite all singlets well. To build on previous improvements, we develop a technique to estimate structure coefficients (and consequently, the splitting functions) using a derivative-free parameter search, known as neighbourhood algorithm (NA). We implement an efficient forward method derived using the autoregresssion of receiver strips, and this allows us to search over a multiplicity of structure coefficients in a relatively short time. After demonstrating feasibility of the use of NA in synthetic cases, we apply it to observations of the inner core sensitive mode 13S2. The splitting function of this mode is dominated by spherical harmonic degree 2 axisymmetric structure and is consistent with the results obtained from the autoregressive linear inversion. The sensitivity analysis of multiple events confirms the importance of the Bolivia, 1994 earthquake. When this event is used in the analysis, as little as two events are sufficient to constrain the splitting functions of 13S2 mode. Apart from not requiring the knowledge of earthquake source, the newly developed technique provides an approximate uncertainty measure of the structure coefficients and allows us to control the type of structure solved for, for example to establish if elastic structure is sufficient.
Co-Existence of LTE Communication with WLAN in Unlicensed Bands: A Review

Mobile data traffic is increasing exponentially. In order to meet data traffic demand, the idea is to co-exist LTE with WLAN in 5GHz unlicensed band. Unlicensed spectrum has high flexibility compared to licensed. It is an open and free spectrum resource. LTE won’t sense the channel before transmission. It causes interference when co-exists with WLAN. Proposed algorithms are applicable only for a limited no. of users, static networks, and downlink transmissions. A Fair co-existence algorithm is needed that supports for multiple users, subframes scheduling, uplink transmission, dynamic networks, and maintains good throughput. This paper will examine various aspects of coexistence mechanisms with their limitations and concerns.
Adaptive Control and Synchronization of the Harb System

This paper investigates the adaptive control and synchronization of the uncertain Harb system with unknown parameters. The Harb system is one of the important and simple paradigms of three-dimensional chaotic systems discovered by Harb and Zohdy in 2002. Adaptive control is a popular method in the control systems literature which is used whenever the parameters of the systems are not available. In adaptive control method, we modify the control strategies with estimates of the parameters and using Lyapunov stability theory, we derive update laws for the estimates of the system parameters. In this paper, we first design an adaptive controller so as to stabilize the uncertain Harb system to its unstable equilibrium at the origin based on the adaptive control theory and Lyapunov stability theory. Then we derive adaptive control laws to achieve global chaos synchronization of identical uncertain Harb systems with unknown parameters. Numerical simulations are shown to demonstrate the effectiveness of the proposed adaptive control and synchronization schemes for the uncertain Harb system.
Detection of Lungs Cancer Through Computed Tomographic Images Using Deep Learning

Lung cancer has become a particularly lethal disease in the last decade. Lung cancer is the second most common cause of death for women and the primary cause of death for men. Therefore, early detection of lung knobs is one of the most effective ways to treat lung infections. Similarly, computer-aided diagnosis (CAD) of lung knobs has gotten a huge interest over the last decade. As a result of the broad variety of lung knobs and the complications of the entire environment, developing a robust knob detection approach is extremely difficult. A convolutional neural network (CNN) based framework is proposed to detect tumors that are identified as risky or benign in lung disease screening using CT images. Two publicly available datasets LUNA-16 and LIDC are employed to detect lung cancer. The dataset is augmented to maximize the volume of images in it. Also, preprocessing is done on CT images for better noise removal. Additionally, segmentation is performed to specify the infected area. Three pre-trained architectures, DenseNet, AlexNet, and VGG-16, are utilized to classify the cancerous and normal images. The DenseNet classifier achieved 98% classification accuracy, 98.93% sensitivity, and 99% specificity, which exhibits outstanding performance than other classifiers. The efficient results of the proposed framework show better performance than existing state-of-art studies.
Outlier detection and motion segmentation

We present a new method for solving the problem of motion segmentation, identifying the objects within an image moving independently of the background. We utilize the fact that two views of a static 3D point set are linked by a 3 X 3 Fundamental Matrix (F). The Fundamental Matrix contains all the information on structure and motion from a given set of point correspondences and is derived by a least squares method under the assumption that the majority of the image is undergoing a rigid motion. Least squares is the most commonly used method of parameter estimation in computer vision algorithms. However the estimated parameters from a least squares fit can be corrupted beyond recognition in the presence of gross errors or outliers which plague any data from real imagery. Features with a motion independent of the background are those statistically inconsistent from the calculated value of (F). Well founded methods for detecting these outlying points are described.
Study on Cannabidiol and Pulse Electric Field on Breast Cancer Cells

Discovering a new cancer treatment Researchers are becoming more eager to uncover new medicinal compounds from living species. In the UK, CBD has been rising in demand as an alternative therapeutic agent and is used in various medicinal products. CBD, or cannabidiol, is a phytocannabinoid, one of the identified cannabinoids in cannabis plants. It doesn't contain tetrahydrocannabinol (THC), which is a psychoactive ingredient. While electroporation is one of the non-invasive techniques for nonviral delivery systems, such as molecule introduction and molecule transfer in a variety of sizes depending on the parameter chosen. The cell response to CBD compound combined with the electroporation method was the focus of this study. The findings demonstrated a positive response to the antiproliferative properties of cancer cells, and the treatment was further aided by a pulse electric field of 600 V/cm for $30 \mu \mathrm{s}$ with a single pulse. The results showed further decrements in cell viability. This finding may contribute toward cancer treatment as an adjunct alternative anticancer agent.
An Improved Internet of Things (IoT) based Smart Prepaid Energy Meter

Electricity usage and demand are rising quickly along with technological innovation and increased reliance on electrical equipment. Eliot is an Internet of Things-based smart energy prepaid meter. The real-time data on electricity usage generated by this smart meter will be useful. Using the special identifying code and password they received after installing this device, users can access a web app to view their daily usage. The main objective of this study is to develop a novel system for will aid in lowering electric consumption and fostering greater transparency between electricity providers and consumers. Similarly, to a cellular telephone, this meter needs to be recharged; payout for the recharge is made possible via a site portal specifically created for that purpose. The user is then able to use the balance in their device to statistically view how much electricity they have used. If no one is using the electricity, the user can also turn it OFF remotely.
Offloading Techniques in Mobile Edge Computing (MEC) for Future Wireless Networks

Mobile edge computing enhances cloud computing capability to users at the edge of wireless networks, for future networks in particular. Network architecture defined by ETSI, has been developed for multi-access edge computing (MEC). This concept has been a hot topic for the latest network technologies in the last decade. Offloading techniques are part of this research as well. Consequently, many diverse offloading techniques for Mobile Edge Computing have been proposed in the literature. Some confusion might occur in relation to this development. Due to this reason, a simplification work is needed for different kinds of offloading techniques which will be used in future wireless networks under Mobile Edge Computing topic. In this paper, many diverse offloading techniques will be presented, and they will be discussed with the aim of providing a sufficient foundation for the upcoming technologies. This paper also provides the key methodologies for collaborative and intelligent methods to be used in future wireless networks. Finally, an outlook for the topic will be given for the future offloading techniques that are yet to come.
Sound Event Classification Based on Frequency-Energy Feature Representation and Two-Stage Data Dimension Reduction

The classification of environmental sound events is of great significance for applications such as machine hearing and acoustic surveillance. Feature representation and feature vector dimension directly affect system performance. To better extract features and reduce computational burden, a novel frequency-energy feature representation and two-stage dimension reduction system were proposed. First, a frequency-energy diagram is generated. Based on this, the importance screening is done and only the energy bins of high importance are retained, which reduces the dimension of feature vector while extracting key information. Then the Bicubic interpolation method is used to further reduce the dimension. And the appropriate feature vector dimension is determined based on the change of information entropy. The proposed frequency-energy feature representation and two-stage dimension reduction system are evaluated with Real Word Computing Partnership sound scene database (RWCP-SSD), UrbanSound8K, and ESC-50 datasets, which demonstrate that the robustness is satisfactory under low signal-to-noise ratios (SNRs) and 15 noise types from NOISEX-92 database.
Fault Diagnosis of a Stand-Alone Wind System Using Supervised Learning Methods

Fault detection is an essential component of an electrical system that speeds up fault source detection and prolongs the operation time. When a system operates in an islanded condition, fault diagnosis becomes highly significant. The system studied comprises a wind turbine, battery, inverter, and local load. There are several techniques to detect and classify faults. Here, we compare and apply machine learning, feature transformers, and feature reduction methods to find the best approach for fault detection and classification in an islanded doubly-fed induction generator (DFIG) system. The proposed method can classify sixteen conditions, including AC line faults in both stator and rotor sides, short and open circuit faults in the battery side, load changes, a control system fault during an increase in wind speed, and normal operation. Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), and Random Forest (RF) methods, along with Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), quantile, and standardization transformers, are employed for the classification purpose. Also, the comparison is conducted using the cross-validation technique. The goal of using the fewest electrical measurements has also been achieved, and the results demonstrate that some mixtures can reach over 99% accuracy and amazingly distinguish between conditions. MATLAB/Simulink is used for data acquisition, and Python is utilized for feature engineering and model selection.
Active Cell Balancing During Charging and Discharging of Lithium-Ion Batteries in MATLAB/Simulink

Several cell balancing ways to address the issue of cells that don’t match up in a series string have been suggested in the literature. Dissipative balancing is the easiest and least expensive of these options. But this isn’t very effective and takes a long time to balance. Capacitor balancing is a popular way to transfer energy because it is easy to use, small, and cheap. However, it has a slow balancing speed, so converter-based balancing has been used a lot. Lastly, runtime balancing, which is a relatively new technique, does a good job of balancing speed and efficiency. This paper compared different cell balancing techniques. Battery pack consisting of 4 cells whose balancing mechanism are discussed. The proposed active cell balancing model balance cell voltages by using the Buck-Boost converter technique. It also demonstrates the comparison between the active cell balancing with passive cell balancing and the latter its model is implemented utilizing switching-shunt resistors. Active cell balancing (ACB) proves to be better for lithium-ion battery-powered electric cars, according to this research. The proposed ACB method takes T=7.285s, T=3.953s and T=7.91s during static, charging and discharging state respectively, for cell balancing. This work shows that the proposed ACB technique balance cell faster than the passive cell balance technique, along with less power loss.
Optimal Siting and Sizing of Distributed Generation Under Uncertainties Using Point Estimate Method

Power distribution networks are mainly established as radial networks that causes a high level of losses and voltage drop. To overcome this issue, distributed generations (DGs) have been proposed in past decades. Such resources can bring several benefits to the system as well. In this paper, the optimal placement of DGs in distribution networks is suggested. Considered DGs include renewable resources such as photovoltaic (PV) and wind turbines (WT) as well as conventional resources. A point estimate method (PEM) is employed to catch the uncertainty of the problem. Using this scheme makes the model a mixed-integer nonlinear problem (MINLP), which is solved via evolutionary algorithms. In order to evaluate the effectiveness of the proposed method, PEM is implemented on an IEEE 69-bus test system and results are discussed. Obtained results illustrate the best site and size of each type of DGs. In the proposed system, the Installed DGs based on the PEM lead to more than 2 percent loss reduction and voltage improvement.
The Generalized Graph Real-Time Task Model

Safety-critical real-time embedded systems are becoming difficult to model and analyze as they accommodate more and more functionalities, and they rely on more and more complex HW and SW implementations. One of the main difficulty with such systems is to find the right coupling between the task model and the analysis method. Indeed, if the task model is too simple it may add pessimism to the result of the analysis, and it may not be possible to prove the correctness of the systems. If the model is too complex, the analysis may be not efficient enough to be usable; or worst the model may have an incomplete method only or no method at all, so the system cannot be analyzed. Finding, the right couple task model analysis method is a balance between expressiveness and analysis efficiency. In this article, we introduce a new graph real-time task model (noted GGTM) which has been used for several years for the deployment and the analysis of different embedded systems. We discuss its expressiveness compared to the literature and demonstrate its advantages. We also, present a decidable and efficient, exact schedulability analysis.
Identification of Driver Drowsiness Detection using a Regularized Extreme Learning Machine

In the field of accident avoidance systems, figuring out how to keep drivers from getting sleepy is a major challenge. The only way to prevent dozing off behind the wheel is to have a system in place that can accurately detect when a driver's attention has drifted and then alert and revive them. This paper presents a method for detection that makes use of image processing software to examine video camera stills of the driver's face. Driver inattention is measured by how much the eyes are open or closed. This paper introduces Regularized Extreme Learning Machine, a novel approach based on the structural risk reduction principle and weighted least squares, which is applied following preprocessing, binarization, and noise removal. Generalization performance was significantly improved in most cases using the proposed algorithm without requiring additional training time. This approach outperforms both the CNN and ELM models, with an accuracy of around 99% being achieved.
A Review on the Potential of AI Voice Assistants for Personalized and Adaptive Learning in Education

These days, technologies are being used in numerous industries. A voice assistant, which is a popular device, is one of them. They are so well-liked that every home has at least one voice assistant like Siri, Alexa, etc. A voice assistant is a machine made to receive input commands from people, either audibly or in text form, and react with the desired answer. These voice assistants are either built into smartphones or come in the shape of home speakers. With these assistants, users may perform a wide range of actions, including asking inquiries, assigning specific tasks like sending emails, playing music, setting reminders, making notes, etc. Soon, voice assistants will be a blessing in every home, handling the majority of the work. The system is set up such that the end user can use voice commands to access all of the services offered by mobile devices. we have reviewed many of the papers and identified the limitations of the existing implementations, and in future we are going to implement the solution to overcome the problem.
Utilization of Deep Learning and Expert Feature Classifier for Detection of Heart Murmurs

This paper introduces our solution (ISIBrno-AIMT team) to the Physionet Challenge 2022. The main goal of the challenge was a classification of heart murmurs from phonocardiographic recordings into three mutually exclusive classes (i.e., present, unknown, and not present) and to determine whether the patient's overall status is Normal or Abnormal. We propose a deep learning method that classifies whether there is a heart murmur in the phonocardiographic recording and also provides heart sound segmen-tation. Furthermore, the expert feature classifier assesses whether the patient's status is normal or abnormal. Our approach achieved a hidden test challenge score of 0.755 in the murmur classification task and a score of 12313 in the patient outcome classification task. Our team was ranked as 9th and 12th out of 40 teams in the official ranking for murmur and outcome classification, respectively.
Improving Heart Disease Prediction of Classifiers with Data Transformation using PCA and Relief Feature Selection

Cardiovascular disorders (CVD) are the key cause of mortality worldwide. One in three male premature deaths and one in five female premature deaths are thought to be attributable to Cardiovascular disorders. Early prediction of CVDs may help to attenuate the disease, potentially lowering death rates. The existence of cardiac disease can be predicted using machine learning approaches; however, the effectiveness of the classifiers may be enhanced by applying PCA, relief feature selection, and data transformation techniques. The objective of employing data transformation, PCA, and relief feature selection approaches is to enhance classifier performance and increase the interpretability and ability of classifiers to predict heart disease. Heart disease anticipating is a challenging problem in the field of healthcare. This uses popular supervised machine learning (ML) algorithms including k-NN, LR, DT, RF, SVM, and ANN to help healthcare practitioners and specialists easily identify the prevalence of heart-related illnesses in patients. In these trials, data transformation is achieved using PCA, normalized features, and relief techniques, and RF surpasses all other classifiers with a prediction accuracy of 90%, followed by ANN and DT with AUCs of 87% and 86%, respectively. SVM and Naive Bayes classifiers were shown to be lesser effective at predicting heart disease.
Implementation of Digital Modulation Techniques in High-Speed FPGA Board

The vital role of wireless communication systems in the modern world, starting from mobile communication networks to satellite communication networks is very much essential and it is seen that, the digital modulation techniques have been changed widely for the last two decades to establish better communication systems. Digital modulation is a widely preferred communication system because of its efficient features like capacity, speed, and quality of information transfer over long distances. Three types of digital modulation techniques are available, they are Frequency-Shift Keying (FSK), Amplitude-Shift Keying (ASK), and Phase-Shift Keying (PSK). These modulation techniques are generally characterised by altering one of the properties like frequency, amplitude, or phase of the carrier signal along with the message signal, and each modulation technique has various applications. For instance, phase-shift keying is used in wireless communication networks like LAN, Bluetooth, and RFID.In this paper, the Binary Phase Shift Keying (BPSK) is implemented into a high-speed FPGA. This BPSK modulation technique was developed by using Verilog HDL (Hardware Descriptive Language) in the Xilinx Vivado 2021.01 tool, which is an IDE (Integrated Development Environment). The developed BPSK code was simulated in Vivado and then downloaded into the Basys 3 FPGA board for achieving hardware implementation results in the oscilloscope. Less area and reduced power utilisation are the main advantages of this article.