Miracle-dz/UniFAR-FTUs huggingface dataset:
Computation Offloading From Edge to Equipment for Smart Manufacturing

In smart manufacturing, data management systems are built with a multi-layer architecture, in which the most significant layers are the edge and the cloud. The edge layer renders support to data analysis that genuinely demands low latency. Cloud platforms store vast amounts of data while performing extensive computations such as machine learning and big data analysis. This type of data management system has a limitation rooted in the fact that all data needs to be transferred from the equipment layer to the edge layer in order to perform all data analyses. Even worse, data transferring adds delays to computation processes in smart manufacturing. We investigate an offloading strategy to shift a selection of computation tasks towards the equipment layer. Our computation offloading mechanism opts for smart manufacturing tasks that are not only light weight but also have no need to save data at the edge/cloud end. In our empirical study, we demonstrate that the edge layer can judiciously offload computing tasks to the equipment layer, which curtails computing latency and slashes the amount of transferred data during smart manufacturing process. Our experimental results confirm that our offloading strategy offers the capability for data analysis computing in real-time at the equipment level- an array of smart devices is slated to speed up the data analysis process in semiconductor manufacturing.
Deep Analysis on Inductive and Resistive Loading Variations for Optimal Switching Loss Reduction in 3-Phase VSI Employing Hybrid Rider Group Search Algorithm

Abstract-Among the main aims of a power electronic system is to decrease power loss in order to enhance efficiency & system reliability. This paper presents a deep analysis of inductive and resistive loading variations for optimal switching loss minimization in 3- $\phi$ VSI employing a hybrid rider group search algorithm. To minimize the switching loss, the modulation index m as well as the reference angle $\theta$ of the VSI is optimized using a new optimization approach known as the Bypass updated Group search Algorithm (BU-GSO). The switching frequency is varied seven times from 1000 to 7000. The convergence analysis as well as the Total Harmonic Distortion (THD) analysis is carried out in MATLAB to validate the proposed approach. The main findings of this model, the convergence of the proposed methods is lower and at the $30^{\text{th}}$ iteration is 1.3%,0.98%,0.37%,0.18%,0.74%, and 1.8% is better than other models like PROP1, PROP2, PROP3, PROP4, PROP5, and PROP6 respectively.
AI based E-ATM Security and Surveillance System using BLYNK-loT Server

In recent years, ATM theft and robbery have become significant problems at ATM facilities. The primary objective of the proposed work is to prevent ATM theft and maintain security. Current security systems do not adequately protect alarm systems and surveillance cameras. The main aim of this project is to take advantage of the ESP32 platform and Blynk IoT to strengthen the security system. In this project, when the ATM vibrates, the door will instantly close, it will transmit the alert data through the BLYNK IoT app and the IoT device will send an audible signal to notify the surroundings and upload all the data. To activate the buzzer and chloroform frequently, a custom Android app using the BLYNK app decodes the command before sending it to the microcontroller. Esp32 has been used to establish the connection between the device and the IoT platform. As far as the above security elements are concerned, digital smart security systems are a special type of fusion that provides clear answers to security questions. When the system is applied successfully, goals are achieved without deviation. This project has a lot of potential because of its protective features. The proposed smart IoT security system has been used in different real time application.
A Process Mining Approach In Discovering Processes And Social Networks In My.Eskwela

Pieces of literature discussing the process model in the learning management system are limited to student and teacher learning interactions. Including the learning interactions of principals and parents contributes to more detail of processes taking place during learning interactions on the platform. The study used process mining techniques and algorithms to extract the underlying processes that drive learning interactions in social learning management systems. The discovered processes for principals, teachers, students, and parents consequently show a precision value of 1, 0.542, 0.639, and 1, respectively. The preciseness of processes for each user group indicates an acceptable behavior (> 0.50) extracted from the event logs. On the other hand, social networks form from the processes that show the information flow of learning interactions from the principal to the students and parents, depicting everyone's effort for learning gain in favor of the student. This study's contribution expands beyond teacher-student interaction processes to include principals and parents, thereby generating a more concrete view of learning interaction in the social learning management system.
Speech Emotion Recognition using Mel Frequent Cepstral Coefficients

The ability to articulate one’s research and ideas is dependent on one’s communication skills. When it comes to human dispatches, voice is by far the most common and consequential. Emotional vaticination prediction is the process of determining a speaker’s emotional state from their words. Thus, improved human-computer trade is the result. Predicting someone’s emotional state can be tough due to the subjective nature of emotions and the difficulty of capturing reflection audio, but Speech Emotion Recognition (SER) has made it possible. Increasing the level of understanding and communication between humans and machines is driving research into the field of emotion recognition in speech. People’s emotional states can be gleaned from their tonalities, pitches, facial expressions, behaviours, and other states. Unfortunately, only a small fraction of these is believed to be capable of identifying vocal expressions of emotion. Classifiers are educated to identify emotional tones in spoken language with minimal data
An Insight to High Gain DC-DC Power Converters for Low Voltage PV System

Traditional power generation cannot meet rising cumulative load demand, resulting in power shortages in many countries. To obtain optimal electrical power, researchers work on non-conventional energy sources. DC-DC converters at the most important site improve power conversion efficiency, allowing for maximum electrical power extraction. The current study looks at trends in non-isolated converters with various power components and voltage gain parameters. To choose the best converter for low voltage renewable uses, the performance of non-isolated converters is compared and presented.
Leveraging the Internet of Things Aware Healthcare Monitoring System for Better Living Standards

Nowadays, even after many advancements in the field of healthcare facilities, one of the leading causes of death is considered to be heart disease. The numbers are soaring with every passing year due to the complexity involved in treating and diagnosing heart diseases. Most forms of heart disease can be prevented, but numbers are constantly increasing due to the inadequate number of preventive techniques. Various scholars have used machine learning and algorithm related to data mining to develop predictive systems for heart diseases. In this paper, a simple yet effective hybrid model is designed to predict early disease detection in a human being and deliver solutions for healthy living. It's unique compared to other proposed methods, as it combines the utilization of the Internet of Things, 5G, and artificial intelligence all at once. IoT and 5G facilitate real-time data collection related to the daily pattern of the subject, and AI is used for the predictive model. Results collected from the tested and trained data are accurate. Therefore, the proposed approach can be implemented to work as an alert system by publishing daily analyses and predictive reports together for early prevention.
Fake News Detection Model using Machine Learning Techniques

In today's era, unreliable news results in several consequences impacting the society changing the people's perspective. There are several approaches and techniques for detecting the fake news, also new methods are being implemented in order to obtain more accurate model. This paper focuses on developing a machine learning model using Natural Language Processing techniques like TF-IDF feature extraction and passive aggressive classifier to detect the fake news. The experimental outcomes shows that the proposed model outcomes existing model in detection of fake news.
Mobile large area confocal scanner for imaging tumor margins: initial testing in the pathology department

Surgical oncology is guided by examining pathology that is prepared during or after surgery. The preparation time for Mohs surgery in skin is 20-45 minutes, for head-and-neck and breast cancer surgery is hours to days. Often this results in incomplete tumor removal such that positive margins remain. However, high resolution images of excised tissue taken within few minutes can provide a way to assess the margins for residual tumor. Current high resolution imaging methods such as confocal microscopy are limited to small fields of view and require assembling a mosaic of images in two dimensions (2D) to cover a large area, which requires long acquisition times and produces artifacts. To overcome this limitation we developed a confocal microscope that scans strips of images with high aspect ratios and stitches the acquired strip-images in one dimension (1D). Our “Strip Scanner” can image a 10 x 10 mm2 area of excised tissue with sub-cellular detail in about one minute. The strip scanner was tested on 17 Mohs excisions and the mosaics were read by a Mohs surgeon blinded to the pathology. After this initial trial, we built a mobile strip scanner that can be moved into different surgical settings. A tissue fixture capable of scanning up to 6 x 6 cm2 of tissue was also built. Freshly excised breast and head-and-neck tissues were imaged in the pathology lab. The strip-images were registered and displayed simultaneously with image acquisition resulting in large, high-resolution confocal mosaics of fresh surgical tissue in a clinical setting.
SI22: A dataset for analysis of DoS attack on the Cloud

Distributed Denial of Service (DDoS) is an attack that aims to render a system unusable by targeting it with massive amounts of traffic. The literature contains several datasets that could be used to quantify the effectiveness of such attacks. These datasets contain captured network traffic and measure the success of an attack by the amount of traffic it generated. However, the amount of traffic is not the only metric that should be able to measure a DDoS attack. To handle the attack, the victim would be affected in other facets like memory, processing, and others. Furthermore, the traditional DDoS dataset is quite generic and insights gained from them cannot necessarily be applicable to cloud computing.In this paper, we propose a new DDoS dataset that looks at the actual impact on a victim that resides in the Cloud. We observed more than 230 performance indicators that measure how the key victim’s resources, RAM, CPU, network, and disk are affected during the attacks. We methodically captured the dataset and have broken them down into different scenarios that could help us better study DDoS attacks in the Cloud and DDoS attacks in general. The features of our dataset and grouped into seven categories which could help us further comprehend the granularity of these attacks.
Improved EMD Algorithm for Electrocardiogram Denoising and Feature Extraction for Detection of Cardiovascular Disease

The electrocardiogram (ECG) is an exceptionally well-known technique that has been utilized as an essential methodology of finding of cardiovascular conduction issues, by methods for the investigation of the pulse and the morphological examination of different heart imperfections. Wavelet denoising is one of the denoising techniques normally utilized for ECG signals. Nonetheless, because of the recurrence cover between the EMG and ECG, the weak qualities of ECG signals exist the danger of being debilitated during the time spent sifting clamour. The commotion decrease in the ECG has been one of the primary fields of examination lately, for the area that the ideal decrease permits a decent prepreparing of the sign. This cycle can be valuable in separating the greatest measure of proficient and significant data. The work proposes a novel strategy for ECG clamour decrease utilizing empirical mode decomposition (EMD) and discrete wavelet transform (DWT) based versatile edge method which is advanced with Cultural Algorithm. The presentation assessment boundaries sign to noise ratio (SNR) and mean square error (MSE) are determined, in view of the analysis with MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) information base. Further this separated signal is prepared for figuring of attributes of the heartbeat: complex QRS, P, S and T waves, just as pulse with DWT Discrete Wavelet Transform and DTCWT (Dual Tree Complex Wavelet Transform). Hence the proposed technique suppresses the noises like baseline wandering, power-line interferences and produces the signal quality, SNR as 35 dB. The produced signal quality helps the physicians to read and diagnose the ECG for identifying cardiac anomalies.
Characterization of Dual Multiresolution Analysis by Orthogonality of System of Functions: An application to communication engineering

In this article, we provide some fundamental Fourier series principles that are useful for addressing issues in electronics and communications, as well as the mathematical equations that these notions play in a variety of computing issues, particularly those requiring communication. The Fourier Transform resolves signals or functions into their vibrational modes. It is utilised in image processing, filtering, signal processing, signal analysis, constructing electrical circuits, and resolving differential equations. This paper contains the generalization of the wavelets form by the multiresolution analysis that consists of the sequence of closed subspaces $\{\mathrm{V}_{\mathrm{t}}:\mathrm{t}\in \mathrm{Z}\}\ \text{of}\ \mathrm{L}^{2}(\mathrm{K})$. We have discussed the local field of positive charactertistic, moreover using the biorthogonal concept, we showed that the set of MRAs forming the pair are work as dual to each other. Also, the wavelets developed by the help of scaling functions of pair of MRAs, actually satisfy the orthogonal property, in addition they moreover form the orthonormal system.
Applying Federated Learning in the detection of Freezing of Gait in Parkinson’s disease

Freezing of Gait (FoG) is a motor symptom of Parkinson’s disease, which causes an episodic inability to move in patients, negatively affecting their daily activities. So, it is vital to monitor and alert the FoG manifestation to help these patients. This study considers two major constraints for developing a healthcare application for FoG: the difficulty of collecting enough representative data and the privacy of the data collected from these participants. Therefore, we propose a Federated Learning (FL) healthcare application for wearable devices to detect FoG symptoms. We evaluate and compare the proposed model to a centralized machine learning approach. We employed a dataset with imbalanced classes of 10 patients with PD to train and test both models. The results show that the accuracy differs by just 1% from that of the centralized model and by 5% from when using the imbalanced training subsets after applying the SMOTETomek’s balanced technique.
A Novel Circuit technique for Leakage Reduction in Ultra Deep Sub-Micron CMOS Technology

In ultra deep sub-micron technology due to extensive size reduction, leakage currents increases exponentially as a result of several short channel effects. In current scenario the leakage power consumption dominates the overall power consumption in high density chips. So it is a demand to control the static power consumption. In this work we have combined lector and drain gating techniques to get an architecture which uses the advantages of both the techniques. To further reduce the sub-threshold current, variable threshold voltage (VTCMOS) technique is also applied to the designed architecture. The performance of the designed logic gates are analysed in terms of static power consumption and average propagation delay. The performance of the proposed circuit has been compared with the reported techniques to validate our proposal. It is noticed that the proposed architectures reduces power consumption for the drain gating NOR gate by 48%, and for sleepy lector NOR gate by 20% considering 32nm technology.
A Probabilistic Approach to Some Problems in von Neumann Algebras

To try to attack such questions, Voiculescu introduced about twenty years ago free probability theory. Free probability theory is a probability theory for non-commutative variables equipped with a notion of freeness analogous to the classical notion of independence. This similarity permits to generalize many concepts from classical probability such as central limit theorems, Brownian motions etc and provides intuition to the domain. On the other hand, freeness is related to the usual notion of freeness on groups and is therefore meaningfull in standard operator algebras theory. Last but not least, independent large Gaussian random matrices were shown to be asymptotically free by D. Voiculescu [18]. Since then, large random matrices became a source of examples of interesting laws of non-commutative variables. In these proceedings, we shall describe how such a philosophy has been developped to try to answer the isomorphism problem and related issues. Even though this problem has not yet been settled we want to emphasize that such a strategy has already been fruitful (c.f. [21], [12], [13]). We hope to convince analysts and probabilists that these issues are very closely related with standard problems in analysis and probability. We shall follow the following plan (1) Description of free probability framework. Relation with large random matrices. (2)The isomorphism problem in free probability terms. (3)Trying to disprove it by an entropy approach. Entropy theory, large deviations techniques. Recent developments and discussion. (4) Conclusion. For completness, we provide in the appendix the proof of Gelfand-Naimark-Segal construction and show how non-commutative laws prescribe von Neumann algebras up to isomorphisms.
An Efficient and Robust Modified Hybrid Multipliers with Less Power and Better Speed

The multiplier is the building block of all arithmetic circuits, and it is widely employed in CPUs and other types of digital processing units as well as in certain kinds of integrated circuits. How quickly and how much energy a CPU uses up are indicators of its effectiveness. The multiplier circuit uses several adders, which increases the hardware complexity and hence slows down the processing and causes it to spend a lot of energy. Power efficiency and speed of the multiplier module must be drastically increased. In this study, a hybrid wallace tree multiplier of 8 bits is proposed. The central concept is to include a Single bit Hybrid Full Adder (HFA) based on the power-efficient Gate Diffusion Input (GDI) technique to create a Hybrid Array Wallace tree (HAWT) with fewer transistors and Maxresulting voltage swing. Simulated findings demonstrate significant gains over the state-of-the-art when comparing the suggested design executed in Tanner EDA at 250nm technology.
The Fluctuation-Dissipation Theorem of Colloidal Particle's energy on 2D Periodic Substrates: A Monte Carlo Study of thermal noise-like fluctuation and diffusion like Brownian motion

Using the Monte Carlo simulations, we have calculated mean-square fluctuations in statistical mechanics, such as those for colloids energy configuration are set on square 2D periodic substrates interacting via a long range screened Coulomb potential on any specific and fixed substrate. Random fluctuations with small deviations from the state of thermodynamic equilibrium arise from the granular structure of them and appear as thermal diffusion with Gaussian distribution structure as well. The variations are showing linear form of the Fluctuation-Dissipation Theorem on the energy of particles constitutive a canonical ensemble with continuous diffusion process of colloidal particle systems. The noise-like variation of the energy per particle and the order parameter versus the Brownian displacement of sum of large number of random steps of particles at low temperatures phase are presenting a markovian process on colloidal particles configuration, too.
A passivity-based motion control of redundant manipulators using weighted decomposition of joint space

In this paper, a passivity-based motion tracking controller for kinematically redundant manipulator is proposed. Based on the weighted inner product in joint space, a minimal parametrization of the null-space can be achieved. Combining this minimal set of null-motion with kinematic relation, we obtain new extended task space formulation. Employing the inertia matrix as a metric, an inertially decoupled dynamics between the task space and null-space can be obtained and a trajectory tracking control law is proposed based on the passivity. The performance of the proposed controller is verified through experiments with a planar three-link direct-drive manipulator.
Weather Prediction using Long Short Term Memory (LSTM) model

This study has designed an AI model to focus on the element design rather than information pre-processing in the weather prediction models. Various climate boundaries were gathered from the public environment server via accessible programming applications by using Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) in various applications executed on the AWS AI stage. In addition to the existing weather forecasting techniques, neural networks are developed by using various combinations of climate boundaries, for example, temperature, precipitation, dampness, wind speed, and strain. Following the preparation of the LSTM model with these boundaries, regardless of the outcomes and performance measurements to train the model, the aftereffects of the climate pattern has been monitored based on two qualities (positive or negative). Here, the climate data is captured via time series data, and further the most recent Long ShortTerm Memory (LSTM) layered model has also been investigated.
Dihydride dimer structures on the Si(100):H surface studied by low-temperature scanning tunneling microscopy

Surface reconstructions on the hydrogenated Si(100):H surface are observed and investigated by using a low-temperature (5 K) scanning tunneling microscope (STM). In addition to the well established $2\ifmmode\times\else\texttimes\fi{}1$ and $3\ifmmode\times\else\texttimes\fi{}1$ phases, linear structures extending over one to six silicon dimers along the same dimer row are observed. After a careful analysis of the corresponding STM topographies for both $n$-type and $p$-type doped silicon substrates, we conclude that these structures are dihydride dimers. This assignment is supported by ab initio density-functional calculations of the local density of states of dihydride structures of one or two dimers long. Furthermore, the calculation of the free-energy formation of our observed structure shows that their creation is closely linked with the hydrogenation process. These results demonstrate that the previous assignments of ``split dimer'' and ``bow-tie'' structures to dihydride dimers and dopant pairs, respectively, need to be reconsidered.