Miracle-dz/UniFAR-FTUs huggingface dataset:
INTEGRATION OF VECTOR MAGNETOMETER MODULE ON BOARD OF SMALL UAV

The aim of this contribution is a proposal of vector magnetometer module implementation on board of small Unmanned Aerial Vehicle (UAV) also with proposal of software integration of magnetometer information and other inertial measurements. This contribution also include proposal of printed circuit boards of modular IMU. The results of solved issue represent the fundamental concept of simple navigation and stabilization algorithms in form of inertial measurement unit and program solution. Results based on these ideas and realizations are in graphical form explained at the end of contribution, focusing on examples of heading angle and Kalman filter estimation.
Traffic Tolerance of Nanosecond Scheduling on Optical Circuit Switched Data Center Network

PULSE's ns-speed NP-hard network scheduler delivers skew-tolerant performance at 90% input loads. It achieves >90% throughput, 1.5-1.9 µs mean and 16–24 µs tail latency (99%) for up to 6:1 hot:cold skewed traffic in OCS DCN. © 2022 The Author(s)
A Diagnostic Study on Prediction of Covid-19 by Symptoms Using Machine Learning

Effective screening helps for quick and accurate detection of COVID-19 and it also decreases the burden on the healthcare system. Prediction models with numerous criteria have been developed to estimate the probability of infection. These are designed to assist medical workers across the world in triaging victi ms, especially in places with limited medical resources. For predicting the COVID-19 using symptoms, the dataset is taken from the website of the Israeli Ministry of Health. The dataset contains 9 attributes and 2,78,848 samples. The raw dataset is cleaned using pre-processing techniques. The Machine learning algorithms like Random Forest, K Nearest Neighbor, Decision Tree, and hybrid Random Forest, K Nearest Neighbor, and Decision Tree are applied on the 1,95,194 samples to identify the model. The predicted model is tested on 83,654 samples to ensure the quality of the designed model. The performance metrics like ROC [Receiver Operating Characteristic] curve, True Positive and Negative Rate, False Positive and Negative Rate, Positive and Negative Predictive Value, and Accuracy are applied to check the model. From the evaluation result, the proposed hybrid model gives high accuracy of 98.97% . The proposed technique might be utilized to priorities COVID-19 screening when testing capabilities are constrained. , among several other things.
Wireless Network Abnormal Traffic Detection Method Based on Deep Transfer Reinforcement Learning

With the continuous development of information technology, the network as the infrastructure of the information age has become an indispensable and vital aspect of our daily lives. With the popularization of 5G technology, the number of handheld devices has increased significantly. Although it has brought great convenience to our production and life, it has also introduced new security risks, making the network more likely to be infiltrated and attacked. Currently, abnormal network traffic detection technology has become a vital part of network security, effectively protecting the network and computer systems from intrusion and maintaining normal operation. In the network abnormal traffic detection experiment based on simulation, most researchers use public and well-known datasets, and different datasets contain different attack samples. When testing on different datasets, the model needs to be retrained, significantly increasing the consumption of computer resources. The paper proposes a wireless network abnormal traffic detection method based on the deep transfer adversarial environment dueling double deep Q-Network (DTAE-Dueling DDQN). First, use the old NSL-KDD dataset to train AE-Dueling DDQN and save the training model weights. Then, use the idea of fine-tuning, transfer the weight of the AE-Dueling DDQN training is completed to the target model, and fine-tune the target model using the newer AWID dataset in the WiFi environment. The experiment compares the current representative deep learning (DL) and deep reinforcement learning (DRL) methods. Experimental results show that our proposed method saves computer resources significantly and achieves good results in all evaluation indicators.
Long Short-Term Memory Neural Network to Enhance the Data Rate and Performance for Rolling Shutter Camera Based Visible Light Communication (VLC)

We propose and demonstrate using Long-Short-Term-Memory neural-network (LSTM-NN) to mitigate inter-symbol-interference (ISI) in 4-level pulse-amplitude-modulation (PAM4) camera based visible-light-communication (VLC) system. Data-rate of 14.4-kbit/s with 3-m free-space transmission is achieved. © 2022 The Author(s)
Covid-19 Classification and Detection Model using Deep Learning

One of the deadly diseases in recent years is covid19 which is affecting the lives of peoples. Also leading to severe adverse problems and death. Prevention is done using early diagnosis and medication which in turn helps in early detection of the disease. The basic aim of the paper is to identify and further classify the patients using the chest x-rays. From scratch the Convolutional Neural Network is diagnosed producing a very high accurate and optimum results. In recent years, researchers found out that in the radiological images such as like x-rays, the traces of covid-19 can be found. In few areas, a good accuracy of the covid-19 detection cannot be achieved due to lack of the people who test so the artificial intelligence is combined with the radiological image. In machine learning the models used are deep learning by automatizing the actions and making it certain by swift, skillful and proficient outcome produced by the chest images provided by the patients. There are several layers like convolutional layer, max pooling layer etc. which are initiated and are used with aid of ReLU activation function. These images given as inputs are also classified accordingly. There is a sequence of neurons being given as input to the active dense layer and there is a result to the input by a sigmoidal function. There is a rise in efficiency because the models are trained and there is a decline of loss at the same time. If there is a model where fitting is done earlier to the overfitting and is restricted from implementing in the data augmentation. There is a better and efficient involvement of suggestions to models of deep learning. Further there is a classification of chest images for identifying and analyzing covid19. So, to check the Covid detection, the images are used as raw. In this paper a model is proposed to have good accuracy in the classification between Covid and normal and further it can be classified into three categories like Covid, pneumonia, normal. There is a 98.08% for the first one and 87.02% for the second one. By introducing 17 convolutional layers and using the Darknet model used for classifying you only look once (YOLO) for the live identification of the objects and multiple layers of filters are used. In the model there is an initial screening.
Analytical model of Heterojunction Tunnel Field Effect Transistor incorporating the negative capacitance phenomenon

In this work, an accurate and computationally efficient analytical model for Negative Capacitance Tunnel Field Effect Transistor (NCTFET) is presented. The model discussed in this paper is based on capacitance matching and Landau-Khalatnikov equations implemented on conventional Tunnel Field Effect Transistor (TFET). The current-voltage model is developed by calculating the capacitance and voltage across ferroelectric layer. The ψs calculated is used subsequently to obtain the charge and capacitance behavior. The results shows that there is a good match between modelling results and obtained from Synopsys TCAD simulations. The calculations includes various device parameters such as charges, VFE, drain current, subthreshold slope (SS). The impact of Fe material thickness is also seen in ID-VG characteristics of proposed device. The parameters analyzed from the reported model shows perfect matching with Synopsys TCAD simulations making it suitable for circuit simulations.
Big Data Analytics Based Sentiment Analysis Using Superior Expectation-Maximization Vector Neural Network in Tourism

Tourism experience shared through social media has become a highly influential source of information and has a multi-faceted impact on tourism. With the vast development of the Internet, text data has become one of the leading formats of big tourism data. Text analytics of such data has great potential to express tourists' opinions effectively. Sentiment analysis is an essential component of tourism big data because it can detect positive and negative opinions in texts. Tourist comments are essential for the development of tourism but still, the number of comments complicates the analysis of essential aspects of the comments by the owner. Big data-based sentiment analysis is one of the most challenging problems globally, and the amount of data is enormous. To resolve this problem, the proposed big data approaches can help detect new words, especially with sentiment analysis and detection of proper nouns and emotional words useful for subsequent tasks as word vectors. The proposed system follows the three steps: text analysis and cleaning, Word vector similarity analysis, and final sentiment classification. First step is used to remove the noise of the data and detect the symbols. The next step is the ID3 (Iterative Dichotomiser) Maximum Word Vector Dimensionality Posteriorl method, which discovers all travel review corpora's main problem and uses it to enrich the vocabulary vector representation of words in context. Attention mechanisms are used to learn words and the overall meaning of different weights text attributes. According to the classification, the final Superior Expectation-Maximization Vector Neural Network (SEMVNN) is used for classifying sentiment analysis level. The SEMVNN method gives accuracy, time complexity, precision, recall and F-measure values to achieve better results than the previous system.
106-Gb/s Waveguide AlInAs/GaInAs Avalanche Photodiode with Butt-joint Coupling Structure

A waveguide AIInAs/GaInAs APD with the butt-joint coupling structure for 106-Gb/s PAM4 applications is demonstrated for the first time. A maximum 3-dB bandwidth of 38 GHz and high responsivity at unity gain of 0.90 A/W are exhibited at 1.55 µm. © 2022 The Author(s)
Sustain-Deplete approach for State-of-charge management in e-vehicles

The battery life of an electric vehicle is usually affected by its accumulated heat, rapid usage, and overall energy efficiency. In the electric vehicle, adding an ultracapacitor to the battery provides improved flexibility in running and using the battery more adequately. The overall system performance and its efficiency are determined by the energy management scheme in the dual-energy storage systems. In order to drive the electric motor, they utilize the energy which is stored in the rechargeable batteries when it is plugged in to a charge point. In this research, to manage the state of charge (SOC), sustain BeC-Artificial Neural Network (BeC-ANN) controller and deplete-BeC-ANN controller are proposed, which manages the energy charging and discharging phenomenon. The main aim of this developed method is to balance the amount of energy when it is driving during an extended period. Depending on the vehicle velocity while driving mode, the remaining energy in the battery and SOC is evaluated using the proposed approach. Controlling of SOC in battery and ultracapacitor is achieved by the two-stage artificial neural network dependent scheme named sustain-BeC-ANN controller and deplete-BeC-ANN controller, which is developed through the integration of the BeC algorithm with the classifier for optimal controller-tuning. The battery and ultracapacitor energy efficiency and its vehicle velocity are considered as measures for evaluation. The Energy storage system in an electric vehicle that the battery achieves better energy efficiency and increased lifetime in this newly developed approach. Eventually, the experimental results reveal the applicability of the developed method.
Retracted: The Relationship between Personality Traits and Sales Force Automation Usage: A Review of Methodology

In recent decades, pharmaceutical companies have been facing increasing difficulties in sustaining sales growth due to tight market competition (off‐patents, pricing erosion, and generics substitution). Pharmaceutical companies are desperately seeking ways to sustain and optimize their sales coverage and sales volume in the middle and long term. In such unpromising circumstances, one of the few options that comes to mind is customer relationship management (CRM). This study specifically looks at the “utilization‐individual impact” link. In particular, this study aims to identify the personality type of sales representative most closely linked with individual usage of sales forces automation systems in the pharmaceutical industry in Taiwan. © 2012 Wiley Periodicals, Inc.
Fractional order calculus for fetal pulse detection

This paper presents an extension and modification of the Pan-Tompkins (PT) algorithm for fetal QRS detection with the application of fractional order calculus. The novel method was applied for fetal QRS detection in electrocardiogram (ECG) recorded noninvasively from the abdomen of pregnant woman - in an ECG signal available from an open database. Algorithm robustness was tested on ECG signals with additive Gaussian noise with 8 different Signal-to-Noise Ratios (SNR). The optimal fractional order allows relatively accurate detection of fetal QRS complexes for different SNRs. The performance of the algorithm was evaluated by three parameters: root mean square error, percentage of missed QRS complexes, and the amplitude ratio of fetal and pregnant woman's ECG. The presented algorithm can be used for assessment of fetal heart rate variability which is important for timely detection of potential fetal heart disorders as well as in similar spike-shape signals in the presence of noise components.
Smart Streetlight with Intelligent Sound Detection System

This paper discusses the design of S mart Streetlight with Intelligent Sound Detection System. The manual systems and automatic light sensors have been used for smart street lights. This paper describes a model of s mart street light that s witches on when there are vehicles or people on the road or when an emergency vehicle runs in the road. The intelligent sound detection system helps in detecting emergency vehicles which switches on all the lights. The system is made intelligent using machine learning algorithms to detect audio data and classify as emergency or non-emergency vehicle. The system gives a best of 96% accuracy for classifying the sound based on s pectrogram features using Convolution Neural Network (CNN) model.
A 240 Gb/s PAM4 Silicon Micro-Ring Optical Modulator

We report a micro-ring modulator with 0.53 V·cm phase efficiency, 54 GHz bandwidth, and 16.3 nm FSR. We have achieved 224 and 240 Gb/s PAM4 eye diagrams with 1.6 dB and 3.9 dB TDECQ, respectively.
Threshold blasting : the renaissance of explosives in narrow reef mining

Threshold blasting involves the use of small amounts of high explosive to break and fragment hard rock without having to evacuate mining personnel. The method is easily assimilated and effective in rockbreaking, but requires rigid control on drilling, charging and timing. Current underground trials are aimed at finetuning the amount of explosive energy and blast hole geometry to allow the firing of multiple sets of shot holes in a continuous manner. Through correct procedures, the after-blast fumes, dust and fly rock can be contained so as to provide a safe working environment. Threshold blasting hugely reduces the volumes of explosives used in mining and brings to maturity the potential of the process begun by Alfred Nobel when he introduced nitroglycerine, and later dynamite, to mining. Its key contribution is its enabling of continuous mining operations with well understood and relatively low cost technologies. The use of batch blasting, employing large burdens holds the greatest potential for delivering high flow rates of ore at minimal cost. There is good potential for rapid evolution of the concept.
Performance Analysis of MRI Brain Tumour Based on SVM-ACO Classifier

Automated approaches are employed in MRI images to spit images of the internal parts of the human being physique in support of clinical analysis. The segmentation of images uses a significant role in various medical assessments. Physical, semi-automated and automated approaches exist on behalf of the segmentation of an area. In the method, the hybrid procedure for brain tumour identification and category through MRI (magnetic resonance images) is presented. The foremost stage of the proposed method accords with MR images pre-processing which include denoise filter, cranium stripping and so on. The next step has to do with FE (feature extraction) of MRI brain image utilising LBP (Local Binary Pattern) and histogram in distinction to MR image of a brain tumour. The SVM (Support Vector Machine) is employed to categorise brain tumours depending on separately kinds of features each. The third phase concerns the new decision compound that classes Support Vector Machine and ACO Ant colony Optimization depends on decision rates interested in a criterion of confidence to aid the last distinguishing finding. The experiments are taken out 158 MR images are contained in 58 normal MRI and 100 abnormal MRI since an actual mortal brain and artificial MRI data set. The accurateness of classification has been found to be 98.99% on not only training images but also test images. The expert’s results and analyzed are compared by the segmentation results. The tumour is differentiated by the classifier with comparatively high accuracy and offers verification to the radiologist. The proposed system primary outcomes exhibit the efficiency and efficacy of a system to specifically identify and classification a brain tumour in MRI that influence to magnify put on this proposed method on other types of tumours in medical CT, US (Ultrasound image).
Segmentación de patentes vehiculares mediante técnicas de agrupamiento en ambientes externos Segmentation of license plates through clustering techniques in external environments

This paper presents the use of clustering techniques to segment license plates through a method applicable to systems ANPR (Automatic Number Plates Recognition). To achieve this goal were different tests with clusters, comparing its performance and effectiveness. This method uses clustering techniques to remove automatically, spurious points that can be generated by the correlation techniques. To perform segmentation of license plates, the method receives as input a cloud of points corresponding, resulting in a matching between the input image descriptors SIFT (Scale Invariant Features Transforms) and the reference image descriptors SIFT that contains the possible characters of a license plates. Through clustering techniques, gets the cluster consisting of the points located in the zone of the plate and also his associate centroid. Regions growing technique is then used to segment the minimum area that encapsulates the centroid which constitutes the patent plate of the considered image. In conclusion, we present a clustering process that is part of a hybrid approach that combines techniques from computational vision and intelligence, to segment license plates in external environments.
Zooming Capabilities of the 1D ESPSS Propulsion Simulation Tool with 3D-CFD Solvers: Implementation and Validation

For the development of propulsion systems such as the liquid rocket engine, the simulation of the whole system at once using high-fidelity models is computationally expensive. Solely improving individual components is not sufficient as the interaction with the full system is essential. Therefore, one is presently forced to optimize a complete system using a low-order model followed by an isolated high-fidelity optimization for particular components. In the present work, construction and feasibility studies of a new methodology, in which lowand high-fidelity models are operated simultaneously, were conducted. Using a framework for coupling the low-order propulsion system simulation tool EcosimPro/ESPSS with a high-fidelity computational fluid dynamics tool, complete systems can be modeled reliably and accurately within a reasonable amount of time. In this paper, feasibility studies of the coupling of lowand high-fidelity tools are demonstrated.
Računalni model konstrukcijskog znanja

Tema ove disertacije je razvoj strukture prosirenog CAD modela. Prosireni CAD model cini sprega konstrukcijskog znanja i parametarskog CAD modela. Kao osnova za kreiranje strukture konstrukcijskog znanja uporabljena je semantika STEP standarda. Realizacija strukture konstrukcijskog znanja ostvarena je uporabom objektno orijentiranog pristupa. Parametarski CAD model kreiran je tehnikom modeliranja pomocu znacajki. Prosireni CAD model, prilikom uporabe, manifestira kvazi inteligentno ponasanje realizirano pomocu ""rule based"" metode umjetne inteligencije. Dijelovi prosirenog CAD modela spregnuti pravilima zakljucivanja tvore jedinstvenu cjelinu i na taj se nacin ponasaju kao objekt u procesu konstruiranja proizvoda. Glavni rezultat istraživanja u ovom radu je prijedlog modela konstrukcijskog znanja. Predloženi model konstrukcijskog znanja podijeljen je u logicke grupe: • elementi – znacajke i dijelovi koji tvore pojedine verzije proizvoda, • pravila – po kojima se aktiviraju pojedina znanja ovisno o odabranoj verziji, • dokumenti – digitalni ili fizicki dokumenti koji tvore ili su vezani za određena znanja proizvodu, • aplikacije – vanjski racunalni programi koji se aktiviraju ovisno o verziji proizvoda, postavke – predložene vrijednosti i dozvoljene granice u kojima se moraju nalaziti vrijednosti parametara te sustavi jedinica i postavke svojstava materijala, • ogranicenja – definiranje dijelova koji moraju postojati u sklopu, ukoliko se želi uporabiti odabrana verzija proizvoda, te geometrijska ogranicenja prilikom ubacivanja CAD modela u sklop, • relacije – nacini postavljanja vrijednosti parametara geometrijskog modela proizvoda. Navedeni dijelovi konstrukcijskog znanja preslikani su u klase objektno orijentiranog racunalnog modela konstrukcijskog znanja. Predloženi model je implementiran u CAD sustav Pro/ENGINEER uporabom JAVA programskog jezika i J-Link biblioteka.