Miracle-dz/UniFAR-FTUs huggingface dataset:
An Accurate Method for Leakage Inductance Calculation of Shell-Type Multi Core-Segment Transformers With Circular Windings

The leakage field in shell-type transformers is strongly affected by the boundary conditions introduced by the core walls and thus the effect of the core should be considered properly in the leakage inductance calculation. In this paper, a new method for accurate calculation of the leakage inductance of shell-type multi core-segment transformers with circular windings is presented. For this purpose, first, the expressions for self and mutual inductances are derived in cylindrical coordinates considering the core walls as the flux-normal boundary condition. Then, a new approach is proposed for calculating the leakage inductance considering the number and dimensions of the used core segments. The method is developed at first for single and double core-segment transformers (known also as E-core and U-core transformers) and then adopted for shell-type segmented-core transformers. The method is verified by 3-D FEM simulations. The comparisons with the previous analytical methods demonstrate the superiority of the proposed method. A transformer prototype has been built and verification tests have been conducted. The comparisons show that the leakage inductance can be estimated with an error less than 1%, demonstrating a very high accuracy with the proposed method.
Computación heterogénea de alta performance: aplicación en el diseño racional de fármacos

This work presents an interdisciplinary end-to-end development applied to the computer-based rational design of drugs, in a context of heterogeneous high-performance computing. For that end it integrates the ARM big.LITTLE and Intel/AMD x64 architectures, the construction of an efficient cluster (UNQ-Cluster), and the addition of external computing power through a distributed virtual datacenter scheme, interconnected by means of VPNs. As the docking platform, the open Autodock Vina software is adopted, parallelizing over the CPU cores. The scheme is completed with a master-slave process manager, called Molecular Sniper, and the Sniper Sight web application, for the configuration of tasks, management of results, and statistics, both being own and open developments. As an application case, it is presented the search for candidate compounds for the preliminary design of potentially beneficial drugs to block the entry of the Covid-19 virus into the human cell. Ultimately, the development of an ad-hoc database of almost 350 thousand compounds along with the experimental computational docking performance for this application are described.
Sensitivity Improvement of an LPG-based Fiber Optic Humidity Sensor

Fiber optic sensors for determination of relative humidity are today a highly competitive option compared to conventional sensing technologies. This is due to the unique characteristics that highlight them such as their fast response time, their electromagnetic immunity and their small size, among others. This paper presents the results of a very simple sensitivity improvement process applied to a fiber optic humidity sensor based on the use of a Long Period Grating (LPG). Experience and knowledge generated in this work provide the basis for the further development of optical humidity sensors that will be applied in the measurement of this parameter in concrete, since water acts as a means of transporting deleterious agents in cement based materials.
Hyperspectral Remote Sensing Imagery Generation From RGB Images Based on Joint Discrimination

Spatial resolution and spectral resolution both play an important role in the recognition of objects in hyperspectral remote sensing. However, the imaging characteristics of hyperspectral images (HSIs) result in a mutually restrictive relationship between the spatial and spectral resolutions. Generative adversarial networks (GANs) have achieved significant success in image generation. The introduce of the discriminators plays a key role in improving the reality. In this article, we propose an RGB to multiband hyperspectral imagery (150 bands) generation method based on GAN (R2HGAN). The method solves the high ill-posed problem and introduces high spectral resolution into RGB images by learning from multiple scenes of HSI. In R2HGAN, we extend the adversarial learning from spatial to spectral dimensions and joint discrimination is designed to generate HSIs closer to the real ones, where two discriminators (the conditional $D$ and the spectral $D$) are put forward to supervise the spectral similarity and the conditional reality of the HSI jointly. In detail, the conditional discriminator comprehensively judges the quality of each area in the reconstructed HSI. At the same time, to ensure that the generated spectra are close to the real ones, a spectral discriminator based on multilayer perceptron is designed. Through the experiments on GF-5 imagery, the method has significantly improved the quality of the generated images over other state-of-the-art methods.
Integrated Technical and Sentiment Analysis Tool for Market Index Movement Prediction, comprehensible using XAI

The Stock market has always been an attractive investment avenue. Though the investor has to perform technical analysis, sentiment analysis, and fundamental analysis in order to make informed decisions in stock market investments. This paper proposes an end-to-end system to simplify the process of investment research for the user by providing a holistic view of market index trends based on technical as well as news analysis. Various time-series algorithms have been tested and consolidated by news-based analysis to generate comprehensive data-driven recommendations. An interactive dashboard for the same gives insights for the short term trend of Nifty50 which is chosen for the hypothesis of this paper. The news classification is performed on news related to the index and it is powered by the principle of Explainable AI, incorporating the notion of ethical usage of data-based decision methods.
Congestion Control Proposal in SDN With Random Early Detection

The emerging technology of SDN (Software Defined Networks) separates the data control plane from the forwarding plane while maintaining a centralized control of the network management. The SDN features require new traffic engineering techniques that exploit the global (centralized) network view, and the status and features of traffic flows. Our purpose is to perform traffic engineering in an SDN architecture, using the OpenFlow protocol capabilities and the potential of the SDN controller to collect operational data of the entire network, such as topology, latency, buffer utilization and frame sizes of the controlled devices in order to implement QoS. Considering that the performance of the network is an essential component of Quality of Service (QoS), and congestion is the main factor that affects it, this paper explores a method to search for alternative paths based on data from switches using the Random Early Detection (RED) congestion control mechanism.
Deep Regression Network-Assisted Efficient Streamline Generation Method

Streamlining is one of the most frequently utilized visualization methods to analyze the flow structure of computational fluid dynamics (CFD) data. However, it is challenging to find a set of streamlines showing the most prominent flow across the entire flow field due to the heavy computation time required to generate bundles of streamlines. In this paper, we propose an efficient streamline generation method that removes several seed candidates that are predicted as less important using a 3D U-net based regression model. We employ 3D line integral convolution (LIC) volumes that depict the entire flow field for training data of the proposed learning model and evaluate our method using a real-world CFD data set. We find using our model that we can obtain quality of visualization results comparable to that of the ground truth even when more than 90% of the seed candidates are truncated while operating 6.6~17.1 times faster than the competing method.
Validación del producto satelital de espesor óptico de aerosoles obtenido con el algoritmo MAIAC (v006) en Córdoba, Argentina (2017-2019)

En el presente estudio se realiza la validación de los valores de AOD obtenidos del sensor MODIS con la nueva versión del algoritmo MAIAC (v006) a partir de su comparación con valores de AOD medidos en superficie desde la estación de la red AERONET (AErosol RObotic NETwork, en inglés) localizada en Pilar, Córdoba, en el período 2017-2019. Se consideró el promedio de AOD en áreas de diversa dimensión alrededor del pixel correspondiente a la estación AERONET (3 km, 5 km, 15 km, 30 km de radio) y el promedio de las mediciones de AERONET en una ventana temporal de 60 minutos (±30 minutos) y 120 minutos (±60 minutos) respecto la hora de pasada del satélite. Los resultados obtenidos mostraron un mejor acuerdo para el área de 30 km de radio y la ventana temporal de ±30 minutos, con un coeficiente de correlación de Pearson (r) de 0,79 y un modelo lineal con un aceptable coeficiente d e d eterminación ajustado (R2 = 0,63). Además, se realizó la comparación de los valores de AOD obtenidos con los satélites Aqua y Terra, y se realizó el análisis de validación considerando las distintas estaciones del año. Se evidencia un mejor acuerdo de los datos obtenidos con Aqua, a lo largo del año, respecto de Terra. La implementación del algoritmo MAIAC, dado su buen desempeño y su mejor resolución espacial respecto de los productos AOD disponibles de otras plataformas, presenta un significativo p otencial para monitorear el espesor óptico de los aerosoles en esta región.
Semi-supervised Gastrointestinal Lesion Segmentation using Adversarial Learning

Accurate segmentation of lesions from gastrointestinal (GI) images is vital for diagnosis and treatment management. However, with the large variability of GI images, it is challenging to enhance GI image lesion segmentation accuracy with small and many unlabeled data. Therefore, this study proposes a semi-supervised lesion segmentation using adversarial learning, which utilized limited labeled and large unlabeled images to train the model. The proposed model is evaluated on GI images from four different sources and by five segmentation evaluation metrics. Finally, the proposed model performs the mean values of segmentation accuracy (DSC=88.56±4.02, IoU=79.69±6.23, Pre=91.55±4.95, Rec=86.07±5.89, and HD=24.85±15.71), which is better than that of existing related works. Thus, a good computer-aided diagnosis system is constructed based on the proposed model for improving accuracy and minimize clinical burdens.
Resilient Real-Valued Consensus in Spite of Mobile Malicious Agents on Directed Graphs

This article addresses novel real-valued consensus problems in the presence of malicious adversaries that can move within the network and induce faulty behaviors in the attacked agents. By adopting several mobile adversary models from the computer science literature, we develop protocols which can mitigate the influence of such malicious agents. The algorithms follow the class of mean subsequence reduced (MSR) algorithms, under which agents ignore the suspicious values received from neighbors during their state updates. Different from the static adversary models, even after the adversaries move away, the infected agents may remain faulty in their values, whose effects must be taken into account. We develop conditions on the network structures for both the complete and non-complete directed graph cases, under which the proposed algorithms are guaranteed to attain resilient consensus. The tolerance bound for network conditions becomes more strict as the adversaries are allowed to have more power. Extensive simulations are carried out over random graphs to verify the effectiveness of our approach when the information of the adversarial agents in terms of their models and numbers is unknown to the agents.
Data Transfer Rates in HFC Networks

Current access networks need to transmit data with increasingly higher rates. HFC (Hybrid Fiber Coaxial) networks offer growth capacity. To obtain maximal transfer rates, we propose releasing the 500 MHz spectrum -currently assigned to the transmission of analog and even digital television-, to convert HFC networks into entirely data networks, by using the first 200 MHz of the total spectrum for upstream, and from 258 MHz and up to 1.8 GHz for downstream. As the architecture of HFC networks must decrease the number of active amplifiers, we use an N + 0 design, which allows serving smaller service areas while decreasing the noise and distortion indexes. The DOCSIS 3.1 standard uses the OFDM (Orthogonal Frequency Division Multiplexing) system for transmission in downstream and OFDMA (Orthogonal Frequency Division Multiple Access) for upstream, employing multiple sub-carrier schemes, each of which is modulated with up to 16K QAM in downstream and up to 4K QAM in upstream. Multiple modulation profiles (MMP) are associated to the quality of the channel. FEC-LDPC (Forward Error CorrectionLow Density Parity Check) is used for error correction. The maximal transfer rate calculated in downstream is 16.9 Gbps, and 1.86 Gbps in upstream. To achieve these values, it is necessary to maintain a SNR higher than 40 dB in the HFC network. In this way, HFC networks represent a valid alternative to meet demand for transfer rates in the short and medium term.
Sensorless Control for Tubular Permanent Magnet Linear Synchronous Motor Using MRAS Method with Active Disturbance Rejection Controller

The speed and position information are required to accomplish the speed and thrust controls for tubular permanent magnet linear synchronous motor (TPMLSM). However, it can be costly to construct, difficult to maintain and vulnerable to signal interference for installing the linear displacement sensors. This paper presents a model reference adaptive system (MRAS) based sensorless control strategy for TPMLSM. And the conventional PI speed regulator is replaced by active disturbance rejection controller (ADRC). It can enable the system to possess better command-following characteristics and stronger robustness under the presence of variations of speed and force ripple. The feasibility and effectiveness of the proposed control strategy are verified by simulation.
An Implementation of K-Means Clustering for Efficient Image Segmentation

This article narrate an adaptive K-means image segmentation technique, which provoke meticulous results with ease process and evade the bilateral input of K value. The image segmentation is the technique of identifying and categorizing the corresponding pixels in the appropriate image. There are enormous types are applicable to identify the corresponding pixels in the image. Here, the K-Means method is proposed for segmentation to examine the distinct image objects. Initially, the samples are transformed into Gray scale images. The Gray images are refined by K-Means clustering to get segmented image output. The K-Means based on the categorization of identical pixels and the appropriation of the mid pixels. By repetitious the identical action many times, then the output segmented image will have exceptional object prejudice. The K-Means clustering will provide good results. The prejudice objects is solely established on the interrelation of pixels feasible in the image. After refining, the image reshaping also done for better stimulation of the segmented image.
EEG-based Drowsiness Detection from Ocular Indices Using Ensemble Classification

One of the reasons for fatal road accidents is sleeping behind the wheel, and there are numerous methods developed to prevent these accidents. The proposed method uses a MATLAB-based BLINKER algorithm to extract ocular characteristics from EEG signals. The classification model makes this method unique to detect drivers' drowsiness using eye blinks. The decision tree model is used in feature selection and a Bayesian optimized ensemble bagged classifier for the prediction. The predictive classification model gives 88.1% accuracy.
Towards Mitigating Probable Road Mishaps through DQN Based Deep Reinforcement Learning

Road accident is a widespread problem in many urban areas nowadays. Reckless Driving (Over-Speed) is one of the attributes leading to the enormous destruction of pedestrian life. Autonomous vehicle speed control is an important research area in the Intelligent Transportation System (ITS) to mitigate road accidents in densely populated areas. Many existed works utilized Reinforcement Learning (RL) to control the vehicle speed intelligently. However, controlling the over-speed of the vehicle using RL under the speed limit of the different zones (i.e., Town, Rural Main Road, Highways, and so forth) is also another demanding task because of the speed limit variation. Therefore, we propose a Deep-Q-Network (DQN) based Deep Reinforcement Learning (Deep RL) method to overcome the over-speed problem in various speed limit areas. In this paper, the proposed system will reduce the vehicle speed intelligently if the driver passes over the speed limit in a particular zone. The simulation results show that our proposed solution can successfully reduce the vehicle speed under various zone-based speed limits.
Benzimidazole as a structural unit in fluorescent chemical sensors: the hidden properties of a multifunctional heterocyclic scaffold

Abstract Benzimidazole (BI) derivatives are structurally similar to naturally occurring nucleotides, a fact that enables their use as scaffolds in biologically active organic synthetic compounds. However, BI can serve as a multifunctional unit in heteroaromatic molecular systems for optoelectronics and non-linear optics, photovoltaics, and in sensing and bioimaging. Its many structure-related properties such as electron accepting ability, π-bridging, chromogenic pH sensitivity/switching and metal-ion chelating properties, also make BI an exceptional structural candidate for optical chemical sensors. The design of novel benzimidazole-based chemosensors is a challenging task. In this review, molecular sensing systems based on intramolecular charge transfer, photoinduced electron transfer and excited-state intramolecular proton transfer mechanisms are systematically examined and the role of the benzimidazole unit is discussed. Novel and emerging developments such as sensors based on aggregation-induced emission phenomena are also included. Conclusions are made on the advantages and disadvantages of existing fluorophores and chemosensors and guidelines are given for further design and development of new sensing systems based on benzimidazole.
Phonocardiogram Signal Based Multi-Class Cardiac Diagnostic Decision Support System

A Phonocardiogram (PCG) signal represents murmurs and sounds signals made by vibrations caused for the period of a cardiac cycle. Acoustic wave generated through the beat of the cardiac cycle propagates through the chest wall. It can be easily recorded by a low-cost small handheld digital device called a stethoscope. It provides information like heart rate, intensity, tone, quality, frequency, and location of various components of cardiac sound. Due to these characteristics, phonocardiogram signals can be used to detect heart status at an early stage in a non-invasive manner. In previous studies, the Convolutional Neural Network (ConvNet) is the most studied architecture, which was fed by features, namely Mel Frequency Cepstral (MFC), Chroma Energy Normalized Statistics (CENS), and Constant-Q Transform (CQT). This work has proposed a ConvNet model trained by Hybrid Constant-Q Transform (HCQT) for heart sound beat classification. CQT, Variable-Q Transform (VQT), and HCQT are extracted from each phonocardiogram signal as the acoustic features, including the dominant MFCC features, feed into five-layer regularized ConvNets. After analyzing the literature in the same domain, it can be stated that this is the first time HCQT is being utilized for PCG signals. The findings of the experiments demonstrate that HCQT is more effective than standard CQT and other variants. Also, the accuracies of the system proposed in this work on the validation datasets are 96% in multi-class classification, which outperforms the proposed work relative to other models significantly. The source code is available on the Github repository https://github.com/shamiktiwari/ PCG-signal-Classification-using-Hybrid-Constant-Q-Transform to support the research community.
PhaseDCN: A Phase-Enhanced Dual-Path Dilated Convolutional Network for Single-Channel Speech Enhancement

Recent deep neural network (DNN) based single-channel speech enhancement methods have achieved remarkable results in the time-frequency (TF) magnitude domain. To further improve the quality and intelligibility of enhanced speech, the attention to phase enhancement is also increasing. In this paper, we propose a novel dilated convolutional network (DCN) model to simultaneously enhance the magnitude and phase of noisy speech. Unlike the direct complex spectral mapping methods, we take the complex spectrum of the signal as the main target and the ideal ratio mask (IRM) as the auxiliary target in a multi-target learning framework to achieve their complementary advantages. Firstly, a feature extraction module is introduced to achieve the fusion of local and long-term features. Two different targets are learned separately, but share the common feature extraction module, which is helpful to extract more general and suitable features. During the joint learning, the intermediate estimation of the IRM target in the auxiliary path, contributing as the attention gating factors, helps to distinguish the speech or non-speech components of the complex-valued signals in the main path. To leverage more fine-grained long-term contextual information, we introduce a multi-scale dilated convolution approach for feature encoding. Moreover, the proposed model is a causal system, which can fully meet the low latency requirements of real-time speech products. Experimental results show that, compared with other advanced systems, the proposed model not only has better speech denoising performance and phase estimation accuracy, but also generalizes better in the speaker, noise, and channel mismatch cases.
Parametric Study of Microwave Metamaterial Inspired High Sensitivity Sensor

A high sensitivity metamaterial absorber-based displacements sensor is designed and discussed in this paper. An edge coupled square split ring resonator with multiple splits, placed on a low-cost substrate and separated from the ground plane by an air gap is used in the sensor design. The proposed design is low cost, simple, compact in size, can be fabricated easily, and is sensitive to small displacements. Further, with selective etching of the substrate, a detailed parametric analysis is carried out. The effects of the etching on sensitivity and absorption are analyzed and discussed. The displacement is sensed through the shift in resonant frequency of the absorber. With selective etching of the substrate, the sensor provides high absorption and sensitivity.
Pipelined ADC in Parallel and Low Noise Amplifier Design for Neural Recording Device

This article introduces a 20-bit pipelined analog to digital converter and low noise amplifier with a wake-up receiver from an RF detector in a double-sided stacked-up integrated circuit as a neural recording device. The proposed design is to obtain stable built-in action and local field potential. The optimized phase margin of ideal degrees is made sure as compared with original solutions. This article introduces a solution for early diagnoses by establishing a neural recording architecture to measure electroencephalography (EEG) signal along with a deep neural network (DNN) that classify the suspected patients.