Miracle-dz/UniFAR-FTUs huggingface dataset:
Gait Analysis-Based Identification of Neurodegenerative Diseases Using Machine Learning Techniques

The current study introduces an improved framework for assessing neurodegenerative diseases by evaluating gait data using machine learning techniques and designing a wireless Velostat-based insole along with a gait phase detection algorithm. The machine learning model was trained using vertical ground reaction force datasets available on PhysioNet. The first dataset includes the data of 73 healthy individuals and 93 individuals with Parkinson's disease. Another dataset used in the present study consisted of 16 healthy subjects and 15, 20, and 13 subjects with Parkinson's, Huntington's, and amyotrophic lateral sclerosis, respectively. Upon testing, the classification accuracy achieved was 99.08% and 99.95% on both datasets, using the KNN classifier. Finally, this trained model was used to identify neurodegenerative diseases, where the data was gathered from a Velostat insole system created in-house. The current method has been shown to demarcate gait events reliably. Authors also intend to employ it in clinical research by obtaining vertical ground response force data from patients with neurodegenerative diseases. In conjunction with the gait recognition algorithm, the developed insole system reliably marks the following gait events: (i) vertical ground response force; and (ii) Spatio-temporal gait characteristics.
Software-Defined Time Sensitive Networks (SD-TSN) for Industrial Automation

Industrial networks interconnect devices to support industrial processes and manufacturing systems. Industrial applications results in time-critical and/or mission-critical data traffic, which require high reliability, guarantees on the delay bounds and robust time synchronization with high precision. Time Sensitive Networking (TSN)is considered as a suitable multi-service network technology for industrial systems and applications. TSN provides greater timing accuracy, enhanced performance in reducing packet delay variation, and adds more networking determinism. However, the configurations of TSN switches presents a challenge (e.g. time-aware traffic shaping schedules). Each output port of a TSN switch has to follow timed schedule to achieve the desired performance. The schedules have to be distributed at the network initialization and have to be maintained upon new devices joining or obsolete device being removed. In this paper, we use the Software Defined Networking (SDN) paradigms to overcome the aforementioned challenges. We propose the Software-Defined Time Sensitive Networks (SD-TSN), where end devices sign contracts with the network controller, to check the availability of the network resources and to select an optimized path for the data transmission. Therefore, It achieves overall improved network resources management, while meeting QoS requirement with high reliability. Our performance analysis show that SD-TSN accurately schedule the critical time traffic resulting in a bounded end-to-end latency with very low jitter. Even in the presence of different types of data traffic, the critical data traffic meets the quality of service requirements with a maximum jitter of $\mathbf{6}\ \mu\mathbf{s}$. Moreover, SD-TSN takes 10 seconds run-time, compared to 2 minutes run-time when traditional TSN scheduling is used, to calculate the schedules of 100 TSN critical flows.
Machine Learning-Based Melanoma Skin Cancer Identification Using Feature Fusion of Thepade's SBTC (TSBTC) and Niblack Thresholding

Skin Cancer is characterized by clinical science as an unusual development of skin cells. Skin cell malignant growth is one of the most dangerous illnesses that can be fatal if not identified in the early stages of development. One of the many types of malignant growths in skin cells is melanoma. Melanoma usually forms in melanocytes which are the pigment-producing cells of the skin. The traditional approach is time-consuming and relies on doctors' expertise and skin biopsy results. Subsequently, the paper centers around proposing a framework for the characterization of Melanoma skin disease images as Benign and Malignant. Global Feature Extraction using Thepade's SBTC (TSBTC) is employed for feature extraction ranging from 2-Ary to 10-Ary. Local features extraction based on Niblack Thresholding is also used to perform feature extraction for the Melanoma Dataset. Thepade's SBTC (TSBTC) and Niblack Thresholding are fused with global and local features extracted. The experimentation carried out using 1000 images categorized as 500 benign and 500 malignant images have shown better accuracy, sensitivity, and specificity. Fusion-based approach for Melanoma Cancer Identification gives promising results compared to individual global or local feature formation, proving the worth of feature fusion. A total of 12 Machine learning Classifiers are utilized, out of which 4 are ensembles.
Output Performance Comparison of Surface-mounted PMSM Based on Conventional and Modified Segmented Halbach Magnet Arrays

The performance of the permanent magnet synchronous motors can be evaluated through several factors including power losses, harmonic content, and torque ripples. One of the possible ways to enhance the density of the output power and torque is by implementing the Halbach magnet array. It can improve the output performance of the electric machine by increasing the fundamental amplitude of the magnetic flux density and reducing the harmonic contents. So, a Modified Segmented Halbach Magnet Arrays (MSHMA) configuration is proposed and compared to the conventional topology in this paper. The static magnetic field distributions of the given Halbach magnet array structures are analyzed by the Finite Element Method (FEM) for a surface-mounted Permanent Magnet Synchronous Motor (PMSM). Also, the harmonic components of both configurations are evaluated by the Fast Fourier Transform (FFT). The results indicate that the MSHMA can significantly improve the output performance of the electric machine by increasing the fundamental amplitude of the magnetic flux density and reducing the harmonic contents. The electric machines model composed of the MSHMA and conventional topology are also simulated by the FEM, and the air-gap magnetic flux density distribution under the armature reaction is analyzed. The results show the MSHMA’s capability to improve the output performance of the considered PMSM model, and a reduction in the torque ripple from 4.4 % to 1.8 %.
Emotion Recognition Using Bayesian Learning from a Multi-Label Data Corpus

In the digital era of information systems, emotion detection from audio signals is crucial for forensic services and operator or driver emotion monitoring in large-scale companies’ safety and security. Speech is a uniquely human trait used to express and communicate one’s point of view to others. Emotion recognition from speech audio signals is obtaining a presenter’s emotions from the presenter’s audio signal. Machine learning is used to develop emotion recognition systems, a critical research goal in current engineering research. The three main stages in speech emotion recognition are extraction of features, feature engineering, and classification. Even though powerful machine learning-based emotion identification algorithms for speech audio signals exist, the detection rate with maximum specificity and sensitivity is not scalable using most modern methods. A significant research objective is feature optimization for emotion recognition from speech audio signals. This article introduced a unique technique called Supervised Bayes Learning on Digital Features (SBL-DF). To scale its performance, the trials compared the suggested approach’s performance to an earlier model depicted against a similar goal. Experiment results indicate that the proposed method for optimizing features could be used on a large scale. The reference classifiers’ performance in making accurate detections with few false positives.
Alzheimer’s Disease Detection using Particle Swarm optimization

Alzheimer’s disease is a chronic, progressive, and presently incurable neurologic disorder that leads to the demise of a patient. Alzheimer’s Disease should be detected at an early stage so that an effective treatment may be provided to the patient in time. In the proposed method Dual tree complex wavelet transform is used for feature extraction. The Principal Component Analysis is used to transform the raw features into new low-dimensional feature space. Principal Component Analysis elects the features based on their eigen values. All high eigenvalue features may not provide assurance for high classification accuracy and sensitivity. The best solution to this problem is the feature optimization approach. Thus, Particle swarm optimization is used for the selection of optimum subset of transformed features. These selected features are given to four different classifiers for performance evaluation. The proposed method is also executed with Discrete Wavelet Transform to evaluate its performance and is compared with three models viz. Dual Tree Complex Wavelet Transform, Dual Tree Complex Wavelet Transform +Principal Component Analysis and Dual Tree Complex Wavelet Transform +Particle swarm optimization. The proposed technique yielded 95.5% accuracy, 97.6% sensitivity, and 93.4% specificity on the Open Access Series of Imaging Studies dataset with k-Nearest Neighbour classifier. The obtained results outperform the other existing methods for AD prediction.
An Optimal Image Segmentation Method based on HGSO for CT Images

Finding the infected regions in medical image modalities is a crucial and challenging task. In this paper, a new image segmentation method is introduced to detect the COVID-19 infection in CT images. In this method, a bi-level-thresholding based image segmentation is proposed using Henry gas solubility optimization. This method used Kapur entropy as a fitness function. Efficiency of the developed segmentation method has been validated on publicly available CT images of COVID-19 patients in terms of PSNR (Pick Signal-to-Noise Ratio), MSE (Mean Square Error), SSIM (Structural Similarity Index Measure) and FSIM (Feature Similarity Index Measure). Moreover, the proposed HGSO-based segmentation method has been compared with SCA, SSA, GWO, CPSOGSA, and MFO-based image segmentation methods to show its efficacy.
Low Voltage Schmitt Trigger Full Adders Design for High Noise Immunity

In this paper three full adders with high noise immunity, designed using three variants of Schmitt trigger (ST) are proposed. The three variants of ST adders are based on conventional ST, dynamic threshold MOS (DTMOS) ST and variable threshold CMOS (VTCMOS) ST. The performances of the proposed ST adders are measured in terms of the hysteresis width, delay, dynamic power and ${I}_{{ON}} /{{I}} _{{OFF}}$ ratio in LT spice simulator using 45 nm PTM technology parameters. It is found that the SUM and CARRY of VTCMOS ST adder has the largest hysteresis width thus providing high noise immunity. It is also confirmed through corner analysis. The SUM and CARRY of DTMOS ST adder provides the highest ${\mathrm{I}}_{{ON}}/{I} _{{OFF}}$ ratio among all adders. It is also true for SUM of DTMOS ST for fast, typical and slow corner analysis. It is also observed that CMOS adder consumes the lowest dynamic power consumption with zero hysteresis width while VTCMOS ST adder lowers dynamic power dissipation then DTMOS ST adder. Hence the VTMOS ST adder is suitable for improved noised immunity and low voltage applications.
Robust Anti-jamming Algorithm Based on New Navigation Signal

Transmit/receive (T /R) time-sharing is the critical technology to guarantee precise space-time reference information for new navigation signals. It solves the co-frequency interference between the receiver and transmitter on navigation signals. The rapid development of electronic information industries has led to severe frequency band conflicts among different systems, resulting in the crucial unintended narrowband interference (NBI) to navigation receivers. Time-domain anti-jamming is one of the commonly used NBI suppression technology, which can guarantee the effectiveness and robustness of new navigation signals. The new navigation signal carries out the bidirectional measurement and communication function by the new signal structure of T /R time-sharing. The conventional navigation receiver's anti-jamming filter generally reconverges from the initial condition in every receiving interval, which causes new navigation signals to lose data owning to the repetitive convergence. This paper proposes a robust anti-jamming technology based on the new navigation signal, which utilizes the interference characteristics of persistence and stability to obtain the prior information for periodic T /R time-sharing. The results show that the robust anti-jamming technology based on T/R time-sharing navigation signals can effectively improve the data loss caused by the conventional time-domain anti-jamming technology and reduce the convergence time to the nanosecond level.
Topic Modeling on Covid-19 Vaccination in Indonesia Using LDA Model

According to data from covid19.go. id, there is a lot of hoax news about Covid-19 vaccinations spread across various social media in Indonesia. Meanwhile, the ability to monitor and track misinformation and trends regarding Covid-19 and its spread is an important part of the response process by the media and government to dealing with fake news about Covid-19. Twitter is a social media that is actively used in spreading issues. Twitter users in Indonesia reached 18.45 million users as of January 2022. To find out useful information on Twitter social media comments regarding the Covid-19 Vaccination, a method, namely Topic Modeling, can be used. This study aims to obtain the distribution of the Covid-19 Vaccination topic on Twitter Data in Indonesia to assist the government in knowing the trend of topics related to Covid-19 vaccination and the trend of changing the topic. The dataset on Twitter used is 10,140 pieces about Covid-19 vaccinations in Indonesia in the period August 2021 to April 2022. Based on the Latent Dirichlet Association (LDA), the 5 most popular topics were obtained for each model, and spread in the fields of health, religion, society. Based on the alpha and beta hyperparameter tuning, it was found that the topic with $\mathrm{K}=5$, alpha=asymmetric, and beta=0.61 was a relevant LDA topic model for the research dataset because good in topic diversity and have coherence value=0.590.
Underwater Wireless Communication System with the Internet of Underwater Things

The widespread adoption of underwater wireless communications (UWC) has become an important research area to address a variety of military and commercial applications, and there is growing interest in investigating underwater environments in numerous applications. With the advancement of UWC, the underwater Internet of Things (IoUT) has emerged, and various practical applications are expected, and it is recognized as one of the promising technologies. Acoustic, optical, and radio RF carriers used for data transmission in underwater communication failed to provide expected results, so in this paper, we provided a comparative study of existing UWC techniques, their advantages as compared to existing techniques, and optimized methods for transmission and modulation, and it has been identified that the potential IoUT applications by overcoming the challenges possessed by the existing system. We analyzed how IoUT overcomes the challenges faced by traditional techniques of transmission and modulation without compromising the security measures and leads to more efficient communication with less power consumption and transmission loss.
Electric Vehicle Technologies: A Review on Different Charging Methods, Power Converters, and Their Consequences on Utility Grid

Recently the tremendous growth of Electric vehicles can be viewed in every transportation region. After the large- scale electric-powered vehicles are linked to the distribution system, the load at the distribution system is elevated and the power quality of the distribution system is affected. This paper discusses the diverse effect of the grid. it additionally classifies the essential electric-powered vehicle in the sector to Charge the EVs used different techniques are discussed in this paper. To lessen the effect on the grid we will use renewable electricity assets. solar is one of the promising electricity sources to charge the electric vehicle. With the intermittent nature of renewable electricity assets, we want power converters. The diverse power converters that may be used to charge the EV also are mentioned in this paper.
Magnetic field-line tangling and polarization measurements in clumpy molecular gas

L'enchevetrement des lignes de champ magnetique du aux mouvement du gaz, dans le cas d'un milieu fragmente partiellement ionise et dans le cas de complexes moleculaires groupes est etudie avec un modele simple. La dependance de la taille des composantes enchevetrees sur les densites d'energies magnetiques et cinetiques du milieu, le facteur de recouvrement des groupes et le degre d'ionisation est analysee.
Design of STT-MRAM Using Hybrid MTJ/CMOS for Applications beyond CMOS

Traditional memory devices are made up of semiconductor materials based on electron charge. But the development of the computing era demands new emerging memories to meet the size and power consumption limitations of SRAM, DRAM, and FLASH, as theexisting memories. Among the upcoming non-volatile memories the Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM) is capable of becoming the universal memory owing to many advantages like non-volatility, low power consumption, robustness against radiation, high endurance, and compatibility with existing CMOS technologies. In this work, the Verilog-A model is used to realize STT-MRAM. The read and write operations of the basic 1T-1MTJ cell using 45nm CMOS are discussed. The transient analysis, power efficiency, and the current for the STT-MRAM are performed using the Cadence Virtuoso EDA software tool. The work intends to design a non-volatile memory circuit that can be implemented for applications like cache memory, and main memory in the future.
Algorithm for finding parameter-dependent connected components of gray-scale images

In a previous work we have introduced the concept of a parameter-dependent connected component of gray-scale images that takes into account both the gray values of the pixels and the differences of the gray values of the neighboring pixels. This concept is a convenient tool to analyze or understand images at a higher level than the pixel level. In this paper, we describe an algorithm for finding the parameter-dependent components for a given image. We discuss different strategies used in the algorithm and analyze their effects through the experimental results. Since the proposed algorithm is independent of the formation of the images, it can be used for the analyzed of many types of images. The experimental results show that for some appropriate values of the parameters, the objects of an image may be represented by its parameter-dependent components reasonably well. Thus, the proposed algorithm provides us with the possibility of analyzing images further at the component level.
Bifurcation Effects in Ductile Metals With Nonlocal Damage

The purpose of this paper is to investigate some bifurcation phenomena in a porous ductile material described by the classical Gurson (1977) model, but with a modified, nonlocal evolution equation for the porosity. Two distinct problems are analyzed theoretically: appearance of a discontinuous velocity gradient in a finite, inhomogeneous body, and arbitrary loss of uniqueness of the velocity field in an infinite, homogeneous medium. It is shown that no bifurcation of the first type can occur provided that the hardening slope of the sound (void-free) matrix is positive. In contrast, bifurcations of the second type are possible; nonlocality does not modify the conditions of first occurrence of bifurcation but does change the corresponding bifurcation mode, the wavelength of the latter being no longer arbitrary but necessarily infinite. A FE study of shear banding in a rectangular mesh deformed in plane strain tension is finally presented in order to qualitatively illustrate the effect of finiteness of the body; numerical results do evidence notable differences with respect to the case of an infinite, homogeneous medium envisaged theoretically.
Teaching mathematics online in the European Area of Higher Education: an instructor's point of view

This article first discusses how information technologies are changing the way knowledge is delivered at universities worldwide. Then, the article reviews some of the most popular learning management systems available today and some of the most useful online resources in the areas of Mathematics and Statistics. After that, some long-term experiences regarding the teaching of online courses in those areas at the Open University of Catalonia are discussed. Finally, the article presents the results of a large-scale survey performed in Spain that aims to reflect instructors’ opinions and feelings about potential benefits and challenges of teaching mathematics online, as well as the role of emergent technologies in the context of the European Area of Higher Education. Therefore, this article contributes to the existing literature as an additional reference point, one based on our long-term experience in a large-scale online environment, for discussions involving mathematical e-learning.
Photonic atoms and molecules

We will review recent advances in the investigation of optical modes in single three‐dimensional whispering‐gallery microcavities and coupled microresonators. Due to the small size and high quality factor these resonators have the potential for development of single‐photon emitters, nanojets, sensors and low‐threshold lasers and for fundamental studies of optical matter interactions.
Design and tests for the correction of atmospheric and instrumental effects on color-differential phase with AMBER/VLTI

The near-infrared instrument AMBER at the VLTI allows, among other interferometric observables, the simultaneous measurement of the phase between various spectral channels. Color-differential phase thus yields spatial and spectral information on unresolved sources, and could lead to such ambitious goals as the spectroscopy of nearby hot, giant exoplanets. This will require, though, an extreme stability on the measurement, which is likely to be affected by chromatic effects at the various stages of the light path. We present how AMBER has been designed to minimize and to calibrate such effects. We give estimates of their contributions from different origins, and present recent measurements of the instrumental stability. We discuss the possibility to supress the residual chromatic effects in post-data treatment in order to reach a precision limited by the photon noise on the differential phase.
Galactic fountains and extended H I disks

Some of the properties of outer galactic fountains, hot dynamic flows which reach the outer disks of spiral galaxies after some supernova blast waves break through the inner stellar disk with more than average specific angular momentum, are outlined. Numerical simulations of steady downward flows onto the galactic plane are performed for a wide range of mass flow rates and injection temperatures. Three different values for the extragalactic flux of soft X-rays are considered. The buildup of an H I disk is discussed, showing that irregularities in the flow are oscillatory rather than secularly unstable. Conjectures on future models with heat conduction and cases without a steady cooling flow are considered. The strong effect which even a weak galactic fountain can have on the shape of the outer edge of an H I disk is examined. Evaporative flows in young galaxies, which shrink the overall H I disk and may be relevant for some quasar absorption lines, are addressed. 49 references.