Miracle-dz/UniFAR-FTUs huggingface dataset:
Stock Price Prediction System with Improved LSTM

High Volatility in the Stock Market makes forecasting the stock price challenging globally. With the introduction of state-of-the-art technologies like Artificial Intelligence and Machine Learning, programmed prediction methods have proven to be more significant in forecasting the prices of stocks. In this work, the Recurrent Neural Network-based model, LSTM, is utilized to forecast the day's high and low costs of the three tyre companies: MRF Limited, CEAT Limited, and JK Tyre Industries and Limited, based on the company's historical data. The LSTM model uses technical values like RSI, SMA, etc., for training to make better predictions with higher accuracy. The system uses standard indicators: MAPE and MSE, Precision-Recall, etc., to evaluate the model's performance.
Deep Learning for High-Speed Laryngeal Imaging Analysis

High-speed imaging of the larynx provides a valuable means for studying vocal folds function and vibratory behaviors. Using laryngeal high-speed videoendoscopy (HSV) with a flexible nasolaryngoscope, one can record the detailed vibratory movements of vocal folds during connected speech. This high-speed imaging tool enables us to study the normal function of the vocal folds and how this function can be disrupted due to the presence of voice disorders. In this work, HSV data were utilized during connected speech from subjects with normophonic voices (no voice disorders) and a neurological voice disorder. The data were obtained using a high-speed camera, coupled with a flexible endoscope, at 4,000 frames per second. Deep learning was used for the analysis of the big HSV dataset to extract the vibratory behaviors of the vocal folds. This deep-learning-based tool achieved high levels of accuracy for analysis of challenging HSV data in connected speech. This tool provides a computationally cost-effective and an accurate measurement approach that could help design more advanced voice assessment protocols in future.
PMOS Biased Sense Amplifier Using Cadence Virtuoso With gpdk 90nm Technology

Sense amplifiers are essential for the recital operation and dependability of the memory circuits. A sense amplifier biased by PMOS with a maximum output impedance, less power dissipation, and minimal sensing delay is the suggested design. In this study, we compared PMOS-biased sense amplifiers with a focus on power and stability. The performance of the sense amplifier was evaluated and simulated using Cadence Virtuoso. The sense amplifier circuit, created with Cadence Virtuoso with 180 nm technology, will use 80.05 nW of power. A similar sense amplifier circuit is designed in this work using the 90 nm library parameters in Cadence Virtuoso. By employing cadence virtuoso with 90 nm technology, the proposed sense amplifier circuit's power is reduced by up to 30.43 u W. In 180nm technology, we will receive 80.05 uW of power. By using 90 nm technology power will be reduced as compared to 180 nm technology.
Multi-Resolution Wavelet Investigation Approach for Abstraction of Electrocardiogram (ECG) Characteristic Features

The Global Health Observatory (GHO) report on non-communicable disease (NCD) from the World Health Organization (WHO) estimates that 36 million (or 63%) of the 57 million global deaths in 2008 were attributable to NCD. Cardiovascular disease, cancer, diabetes, and enduring disruptive pulmonary sickness are the four most common NCDs. In 2008, cardiovascular disorders accounted for over half of all deaths from NCDs (17 million deaths). Given the foregoing, it is safe to assert that cardiovascular illnesses remain the leading reason of death in many parts of the biosphere, and that trainings show the relevance of minimising treatment delays in enhancing patients' clinical outcomes. In this study, we want to refine our ability to extract features from electrocardiograms. In this context, multi-resolution wavelet analysis is used to extract characteristics from an electrocardiogram. Once the signal has been DWT-decomposed, the noise components' coefficients are thrown out, a process known as denoising. When looking for R-peaks, a multi-resolution method is employed in conjunction with thresholding. In addition to detecting the T wave, the ratio approach can be used to locate other landmarks such as the Q and S peaks, as well as the beginning and expiration of the QRS complex. R, Q, S, and T wave heights are determined following baseline detection. Using physionet.org's ptb-db, the devised technique attains a sensitivity of 99.91% and a projecting accuracy of 99.98%. Both of these percentages are nearly perfect when using mit-db files, at 99.8 and 99.7 respectively.
Urban Traffic Management using Machine Learning

In many places across the world, traffic congestion is a serious issue. And this traffic congestion may cause many issues in day- to- day lives like delays that could lead to being late for work, meetings, or school and cause lost revenue or other personal losses. Due to increased idling, acceleration, and braking, fuel is wasted, which raises air pollution and carbon dioxide emissions. Passage of emergency vehicles may get delayed which may put the lives of people at risk where vehicles are urgently needed. Managing this traffic congestion has become one of the great issues for governing bodies of every country. So, the traditional methods of handling traffic are not as effective as it used to be, as the population is increasing day by day especially in countries like India and China. In this paper, a MobileNet-SSD algorithm is used to get the density of traffic on each lane and switch the signal according to the density in real time, instead of doing static or manual signal switching. The proposed work is compared with YOLO V3. The novelty of the work is introducing curfew detection and emergency vehicle monitoring.
Multichannel EEG Pattern Acknowledgment Feature Assortment Using T-Test Ranking and Principal Component Analysis

Electroencephalography (EEG) is a non-invasive test used to assess human brain activity by utilizing signals generated from the brain; it is also used to detect epileptic seizures. Furthermore, this helps medical professionals identify conditions including epilepsy, seizure, sleeplessness, sleepiness, traumatic brain injury, headache, and brain tumor. It's also useful for establishing that a person has died of a brain injury. Epilepsy is a condition of the nervous system brought on by electrical spikes and waves. Epilepsy manifests itself in moments of amnesia, unconsciousness, and involuntary twitching. A person's behavior can have a significant impact on the activity of the brain's neurons. Some bodily functions are also affected by the shift in neuronal activity. Sensory, visual, and memory processing all occur in the brain in line with the flow of electrical signals. When the brain's nerve cells are compromised, a seizure results. Generalized seizures and focal seizures are the two main categories. This study proposes that for supervised learning tasks, it is possible to achieve better results using multichannel EEG recordings by selecting features with maximal class separability. In this study, we test the following theory: One definition of a good feature subset is one in which the features have a strong correlation with the class labels but no correlation with one another.
Advancement of PMCW Radar and Its Board-Level Prototyping

This paper reviews recent advancements in phase-modulated continuous-wave (PMCW) radar design and implementation, along with the design and prototyping of PMCW radars on the board level for rapid testing and iteration. In the last five years, a plethora of PMCW radar simulation research has been conducted, which modern IC and board-level microwave component design has caught up to. Benchtop systems and embedded system-on-chip PMCW radars have been realized, allowing for progress toward high-accuracy radars that incorporate advanced digital signal processing techniques. With access to faster semiconductor technologies, embedded chip solutions, and improved printed circuit board (PCB) techniques, PMCW radars can be realized on a portable board-level system. This paper reviews the concepts surrounding PMCW radars, as well as recent simulation efforts and radar system realizations through PCB designs and integrated system-on-chips.
An Ensemble Voting Method of Pre-Trained Deep Learning Models for Orchid Recognition

Orchids are a diverse group of angiosperms, many of which share similar physical characteristics such as color, pattern, and inflorescence. As a result, identifying orchid species can be a time-consuming task that requires expert knowledge. This paper proposes a solution that utilizes Convolutional Neural Networks (CNNs) for accurate and efficient image classification. Specifically, three pre-trained models, ResNet50, EfficientNet, and Big Transfer (BiT), were employed and fine-tuned using transfer learning. Ensemble learning was then employed to combine the predicted probabilities of the three models, weighted by their respective performance, to determine the orchid species through soft voting. The proposed approach was validated using the Orchid Flowers Dataset, selecting 84 varieties, and achieved a maximum accuracy of 84.67%, improving upon the best single model by 2.8%. The Orchid-52 dataset also demonstrated a 3.1% improvement, reaching 95.13% accuracy.
Energy Storage System Analysis for Hybrid Wind-Solar Lighting System

Presented in this study is a simulation of a power system that uses PVs as its hybrid energy storage system and the main energy source that includes a short-term Li-ion battery and a long-term wind energy facility for storing things. For the energy flows generated by the electrical and thermal circuits, models have been developed. In order to meet the demand for residual heat, a propane furnace was considered together with the waste heat generated by the electrolyser and fuel cell. To investigate the theoretical views a sequence of inputs like weather, loads etc. have been used in the model to study a sample energy flow. MATLAB/SIMULINK based implementation technique proposed for determining component properties required for energy efficiency. Among the studied algorithms, metaheuristic algorithms were found to be outperformed heuristic and linear methods for the objective functions under consideration.
Accelerating heuristic convergence on the "Evolution of Mona Lisa" problem by including image-centric mutation operators

The "Evolution of Mona Lisa" problem aims to approximate a target image by overlapping many semi-transparent polygons. The problem has been tackled in the past using multiple Nature-Inspired heuristics, and our main contribution is adding image-centric mutation operators (scaling, rotating and translating polygons). We compare Genetic Algorithms, Hill-Climbing and Simulated Annealing. A candidate solution has variable length (of, at most, 300 decagons) and, due to the variable opacity of polygons, order matters – resulting, in practice, in a pseudo-Messy GA. We use the same representation and mutation operators for the trajectory methods which, due to the focus on wall-clock time, outperform our GA implementation.We find that these methods retain good image approximation at good run times: 98.9-99.2% (mean on 30 images), with a time limit of 30 minutes, on images 500-pixels tall.
Sur le cut-locus d'une sousvariété de l'espace euclidien. Négligeabilité

Dans une Note precedente nous avons montre que le cut-locus d'une variete proprement plongee dans R n est nulle part dense. Ici nous raffinons ce resultat, montrant que le cut-locus est negligeable, c'est-a-dire que sa mesure de Lebesgue est nulle
Sustainability via value stream mapping – A Lean study in Genset industry

Sustainability is the future of management and lean manufacturing (LM) occupies a key part in reducing wastes in any industrial organization. Any sustainable practice adapted in the manufacturing industry would deliver a large scale value and benefits above the cost paid by the consumers. The triple bottom approach of people, planet and profits are also met in such value adding activities. Value stream mapping (VSM) is the key tool in LM which facilitates in recognizing waste in materials and information stream in a precise product group. The aim of this project is to demonstrate the prominence of VSM in the chosen power generation industry in enhancing the productivity and its business strategy. A case study approach is followed to demonstrate the significance of VSM in identifying and redesigning a lay out to increase the daily shift production of units from eight units to twelve units in the Genset Manufacturing Assembly. The study has been executed by applying seven critical steps. The method syndicates multidimensional process mining (MDPM) methods with verified principles of VSM and lean process. An assembly unit is identified where the existing layout needs enhancement based on the customer demand and top-level management. The overall activities with structure are mapped by means of process flowchart and new layout was developed. It was carried out by a cross-functional team and critical analysis was done. The cross functional group has completed a viability study on implementation of suggested layout by conducting time study, Video analysis and work study to design New layout. Finally, new Layout resulted in demonstrating practicability of the VSM strategy to make effective genset manufacturing assembly.
Heart Rate Variability Investigation of ECG Signal By Means of Windows and Wavelet Transform

An electrocardiogram (ECG) is the gold standard for diagnosing heart conditions because it is painless and takes no physical contact with the patient. The electrophysiology of cardiac disorders and ischemia alterations can be better understood using data from a cleaned ECG signal. There is a wealth of useful data pertaining to the circulatory system and heart function that can be gleaned from this study. This study's goal is to develop a method for the automatic diagnosis of cardiac arrhythmias from an electrocardiogram (ECG) signal. This thesis employs Windows and wavelets processing techniques to identify heart arrhythmias. In the framework of preliminary experiments in two prospective application domains smokers and non-smokers. This publication explores the creation of suitable HRV signal processing algorithms. Automatic detection of cardiac arrhythmias in an ECG signal begins with RR interval detection. The electrocardiogram is recorded using a laboratory view. Using a windows and wavelet based approach; the RR interval is detected from the continuous ECG data. The results show clear differences between non-smokers and smokers, with an 80% accuracy rate in classifying the two groups.
Estimation of a Disk Transformer Winding's Ladder Network Parameters Using Analytical, FEM, and Terminal Measurements

The paper presents a variety of methods for estimating the impedance parameters of a ladder network of a transformer disk winding. These methods depend on whether transformer design details are available or whether the parameters have to be estimated directly based on measurements. These methodologies include analytical, Finite Element (FEM), and terminal-based measurements combined with optimization techniques. In the case of a transformer whose geometric details are known, the analytical formulas presented can be employed to quickly determine the ladder network parameters. By using electromagnetic field simulations using FEM, the parameters of the transformer winding can be estimated more precisely and accurately. The geometry of a transformer is not readily available, however, so a methodology is provided for estimating the parameters using measured input-output values at the winding terminals. This paper presents simulations and experimental results for an eight-disk transformer winding, and the methods are validated by comparing the calculated parameters.
Automated Triaging of Gate Run Test Results using Humio Tool

In today's digital era, servers are used throughout the globe. From tech companies to hospitals and even homes, servers utilized widely. The Compute Ops Management is cloud-based software that lets users access any device from anywhere, over the cloud. It allows the users to perform several operations on the servers with the ease of controlling it remotely. The Software Development Life Cycle is an organized manner of enabling the production of high-quality software in least amount of time possible. It involves planning and analysis, designing, developing, testing and deploying the software. One of the important features of these testing phases includes triaging. It is the process of reviewing bugs, ensure if they are valid, and have accurate information that allows them to be resolved for their timely deployments and to ensure the delivery of high-quality software. The events of the tests logged into a logging tool known as Humio. These logs queried to retrieve information about a test run or an event that occurred during the duration of the test run. Automating this procedure can enable a more efficient and accurate triaging process that not only saves time but also aids in pin-pointing the precise root cause of the error in question.
Hazard Detection using custom ESP32 Microcontroller and LoRa

The article presents a novel approach to collecting and transmitting data across large distances without relying on an internet connection for additional processing. This is accomplished by using a custom ESP32 microcontroller and sensors, capable of reliably detecting fire in forests and farms while maintaining low power consumption. By designing a low-cost and low-power device, the system enables users to deploy sender units that can collect and send data over an extended period without needing maintenance. The system utilizes the LoRa Network to facilitate data transmission, making it feasible to deploy these units in rural areas without internet service. The presented solution showcases the potential of the ESP32 microcontroller platform for collecting and transmitting data over long distances with minimal power consumption.
Botnet Dataset Overview Using Statistical Approach Based on Time Gap Activity Analysis

Botnet malware is a malicious activity that needs to be detected accurately. Several botnet detection models have been introduced using a mining-based method. The distribution analysis approach is often used to obtain the characteristics of the data before modeling it on a mining-based detection model. However, the distribution approach could be more optimal in describing the timeliness of botnet attacks. The botnet attack time analysis is needed to determine the right detection model for the botnet activity detection model. This paper proposes a new overview technique for botnet datasets using a statistical approach based on time gap analysis for each bot. The goal is to obtain a threshold value that can optimally separate botnet activity traffic from normal traffic. The experimental result is three different time gap threshold values to separate the botnet, normal, and background activity, ideally based on the highest time of 4,756 s, the lowest time of 28.69 s, and the average maximum time gap of 810.61 s.
Auction-based solution for the ordering problem in robotic self-assembly

Autonomous construction is a process that uses robots to build structures. In those, the self-assembly denotes the robotic construction solutions where the robots are used as the structure parts. A common problem in self-assembly strategies is that given a homogeneous group of robots (a swarm), how the next robot to assemble a structure can be selected. Such selection can be even more complex if there are multiple structures being assembled simultaneously by the same group of robots. In this paper, we model the selection of robots as a task assignment problem, and we propose an auction-based method to compute an order of which robots will move to structures being assembled. Our algorithms are validated using mathematical proofs and simulations. The analysis of the results shows that our algorithms outperform a baseline selection method while guaranteeing communication between robots in the swarm. Moreover, our solution is shown to be power efficient, reducing battery consumption while the robot is in an idle state, waiting to be assigned.
Wideband Patch Antenna For Stable Radiation Pattern Generation

A triple-layered patch antenna was proposed in this study for generating wide bandwidth and stable radiation pattern. In order to obtain a symmetrical pattern of radiation, two slots were engraved at the bottom layer of the structure. A normalized radiation intensity of 20.4dB along with a max. field intensity of 1.34xl04 V/m were obtained and wide bandwidth was exhibited at more than one resonant frequency for the proposed structure.
Towards in-utero Navigational Assistance: A Multi Task Neural Network for Segmentation and Pose Estimation in Fetoscopy

Fetoscopic laser coagulation (FLC) is the most prevalent therapy for treating twin-to-twin transfusion syndrome (TTTS). A rigid or flexible fetoscope is inserted into the uterine cavity through a small incision in this minimally invasive technique. The procedure consists of surveying the placental surface, identifying anastomosing vessels, and coagulation. This paper presents a multi-task neural network model to segment the vasculature from the fetoscopic images and estimate the relative orientation and distance of the placental surface to assist the surgeons. This work also presents a method to use virtual reality (VR) to generate datasets for training and testing. A silicon-based placenta phantom was created in a planar form of 29 × 29 cm with realistic vasculature. A scanned image of this phantom was manually segmented as the ground truth. Both the color image of the placenta and segmented ground truth were placed in the VR simulator. The virtual camera is moved by pre-defined camera motions, which then renders fetoscopic placenta images and their corresponding segmented ground truth without the need for manual segmentation. The network achieved a dice coefficient of 0.8 on the segmentation task and 87% accuracy on the regression task. The network's capacity to identify vessels was also evaluated using actual images from a flexible fetoscope's chip-on-tip camera.
The Lagrangian and Hamiltonian analysis of some relativistic electrodynamics models and their quantization

The work is devoted to the study of the Lagrangian and Hamiltonian properties of some relativistic electrodynamics models and is a continuation of our previous investigations. Based on the vacuum field theory approach, the Lagrangian and Hamiltonian reformulation of some classical electrodynamics models is devised. The Dirac type quantization procedure, based on the canonical Hamiltonian formulation, is developed. Within the approach proposed in the work a possibility of the combined description both of electrodynamics and gravity is analyzed.