Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Temperature Effects in the Zero Temperature Coefficient of the Ellipsoidal MOSFET

The zero temperature coefficient (ZTC) is investigated by the simple model and three-dimensional numerical simulations in the Metal-Oxide-Semiconductor (MOS) Field Effect Transistor (MOSFET) with the ellipsoidal (EM) and conventional rectangular gate geometries (CM), considering the same channel widths (W), gate areas (AG) and bias condition (BC) technology. A simple model is used to study the behavior of the gate voltage at ZTC (VZTC) in the linear and the saturation region. The influence of the temperature mobility degradation on VZTC is analyzed for EM and CM devices. The VZTC changes in the temperature range investigated showed a temperature mobility degradation dependence and the both devices showed the same behavior. The analysis takes into account temperature dependence model parameters such as threshold voltage and mobility. The analytical predictions are in very close agreement with 3D simulations results in spite of the simplification used for the VZTC model as a function of temperature in the linear and the saturation region.
Optimal Energy-Efficient Antenna Selection and Power Adaptation for Interference-Outage Constrained Underlay Spectrum Sharing

Underlay spectrum sharing addresses spectrum scarcity but imposes constraints on the interference the secondary system causes to the primary receiver. For a secondary transmitter that is subject to the stochastic and general interference-outage constraint and the peak transmit power constraint, we present a novel joint antenna selection and power adaptation rule. It addresses the twin goals of improving the spectral efficiency and reducing the power consumed of an underlay secondary system with low hardware complexity and cost. We prove that the rule maximizes the energy-efficiency (EE) of the secondary system. Its form differs from all other rules considered in the literature. We then present an insightful geometrical characterization of the optimal power of the selected antenna in terms of the channel gains within the secondary system and between the secondary and primary systems. Our approach leads to several special cases, which are themselves novel, and novel analytical insights about the performance and structure of the optimal rule. We also present an iterative subgradient-based algorithm and a simpler non-iterative bound-based algorithm to compute the rule’s parameters. The rule achieves a markedly higher EE compared to conventional approaches, and serves as a new fundamental benchmark for antenna selection in underlay spectrum sharing.
Non-intrusive Intelligibility Prediction of Noise-suppressed Speech Based on Neural Network

While many non-intrusive intelligibility indices have been developed recently, they did not work well for speech containing non-linear distortion, which was largely introduced by noise-suppression algorithms. This motivated the present work to propose a model based on neural network (using its non-linear mapping capacity) to non-intrusively predict the intelligibility of noise-suppressed speech. In this study, Mel-frequency cepstrum coefficients (MFCCs) and dynamic MFCCs were combined as the input features of deep belief network (DBN) to predict the intelligibility of noise-suppressed speech. The predicted scores were correlated with the intelligibility scores obtained from normal-hearing listeners presented with noise-suppressed speech. High correlation ($\mathrm{r}=0.81$), compared to $r=0.37$ obtained from the existing benchmark non-intrusive intelligibility metric and $r=0.82$ from two widely-used intrusive intelligibility indices, suggested that the DBN-based model may work as an efficient intelligibility predictor of non-linearly distorted speech without access to the clean reference signal.
Classifying interactions of parents and children with Down syndrome in educational environments using deep learning

Recognizing parents’ behaviors is important for the impact on the development of children. This is even more important in parents of children with intellectual disabilities. In this work, we propose using human activity recognition to identify certain behaviors of parents of children with Down syndrome. Specifically, we propose to use computer vision and deep learning to analyze videos of parents and children with Down syndrome interacting in an educational setting for identifying actions related to directive behaviors such as physical interventions. The results obtained through the experiments carried out with the deep learning C3D model show that physical interventions can be recognized with an accuracy greater than 85%. Our results can be used by therapists for identifying actions related to directive behaviors automatically in children with Down syndrome.
Improving Geomorphological Classification via Binary Image Processing

Landform classification is the basis for understanding and describing the processes and evolution of landscape. This process usually requires elevation information from different sources, expertise and time. Automatic geomorphological classification, via the geomorphons algorithm, supports expert classification by using local ternary patterns for labeling landform elements, significantly reducing the computation time. Nevertheless, it presents issues such as a noisy output, valleys that are not classified as continuous forms, valleys that are classified as peaks at low altitude, flat zones inside the valley that are not classified as a part of it, and other similar issues. In this proposal, we tackle the mentioned issues for valley classification by binarizing the geomorphons output and applying it binary-image operators. The proposal's performance is measured by using binary classification metrics and expert-made groundtruth images. The results show that the accuracy, balanced accuracy, and F1 metrics are greater than those delivered by the geomorphons classifier for all the instances in the testing data.
Performance Evaluation of Machine Learning Algorithms in Design and Development of Heart Disease Detection

Heart disease is becoming the top cause of mortality worldwide. It can be avoided by early detection and timely treatment. Machine learning (ML) and data mining techniques play an important role in the detection of heart disease. So, the current article targets the performance evaluation of various ML algorithms on heart disease detection tasks. In this context, the article has used different machine learning algorithms like Logistic Regression (LR), K-Nearest Neighbors Classifier (KNN), Support Vector Machine (SVM), Decision Tree Classifier (DT), Random Forest Classifier (RF) and eXtreme Gradient Boost Classifier (XGBoost) with standard parameter settings and Hyperparameter Tuning. All algorithms are applied to the merged Dataset (Cleveland Heart Disease Dataset + Heart Disease Dataset) and evaluation is done on the basis of accuracy. Finally, we have observed that RF is having more classification accuracy as compared to other algorithms i.e. 97.22 (in percent).
Adaptive Task Scheduling Procedure By Using Improvised Q-Learning Technique In Internet Of Things

The arrival of the data packets fluctuates dynamically in an Internet of Things(IoT) ecosystem which consists of numerous devices. fault to operate them according to the QoS (Quality of Service) criterion could have a significant impact on the system's reliability and quality. As a result, in order to pay attention to the shifting traffic condition, data gathering gateway must quickly design a new scheduling policy. Traditional packet scheduling algorithms are inadequate in IoT as the communication of data arrangement is unidentified at earlier. Q-learning is a sort of reinforcement learning specially allows you to create a dynamic scheduling policy based on the status of individual queue beyond having any prior knowledge of the network's state. In IoT analytics, deep learning algorithms play a critical role. Machine data is sparse and contains a temporal component. The data from a certain devices, may react differently under other circumstances. As a result, it's impossible to capture all eventualities for the data preprocessing or training stage of an algorithm. Continuously monitoring sensor data is also inconvenient and costly. In this paper, Existing Q-Learning algorithm has been considered and by performing indexing in order to obtain structure similar matrix then data synthesis process is done through which proposed Q-Learning algorithm will be enhanced. The paper also provides an comparison between existing and proposed enhanced QL algorithm.
AutoSW: a new automated sliding window-based change point detection method for sensor data

Change point detection methods try to find any sudden changes in the patterns and features of a given time series. In this paper a new change point detection method is presented, where the window width is automatically calculated. The proposed algorithm, AutoSW, is based on a Sliding Window search method of the Python ruptures package and uses a subset of statistical concepts to compute a possibly optimal window width. The proposed algorithm is compared with some other popular methods such as PELT using different real-world and synthetic time series. Results show that AutoSW can perform better than PELT producing a better set of change points in the time series tested.
Complex Permittivity Measurement of Biological Tissues Using the Rational Function Model

In this paper, we present the feasibility of rational function method (RFM) in order to measure the complex dielectric parameters (CDPs) of biological tissues with an open-ended coaxial probe. For this aim, the CDPs of a blood-mimicking phantom were retrieved from reflection coefficients through the RFM model from 0.5 to 6 GHz. The results were compared with commercially available Speag Dielectric Assessment Kit (DAK) software and literature. The calculated percentage dielectric property differences with the literature are 7.1% for blood. The results demonstrated that the RFM method is capable of retrieving the CDPs of biological tissues with the open-ended coaxial probes.
New Three-Tier Game-Theoretic Approach for Computation Offloading in Multi-Access Edge Computing

In this paper, a new game-theoretic multi-layer computing resource and network resource allocation algorithm is proposed for computation offloading in Multi-access Edge Computing (MEC) networks. We establish a new multi-layer system model for Data Center Operators and develop resource allocation relation between the MEC data center operators and users. We also investigate the matching relationship between the cloudlets and users. Based on the proposed multi-layer allocation algorithm, a joint optimization framework is delivered to realize the allocation of computing resources and communication channels in MEC. Simulation results show that the proposed algorithm provides effective solution for the problem of cloudlets allocation in MEC networks, and can significantly improve the computing and communication capabilities in the presence of user heterogeneity.
A 3D-Printed Microwave Passive Sensor Using Fully Biodegradable and Renewable Materials

This paper presents the feasibility of fully biodegradable and renewable materials in the fabrication of mi-crowave resonator passive sensors using additive manufacturing technology. To demonstrate this application, a microwave ring resonator sensor has been designed and fabricated. The core of sensor is a microwave ring resonator with a resonance at 4.88 GHz which is coupled through the two coupling capacitors at the input and output ports. It is demonstrated that any change in the relative permittivity of the material being sensed above the sensor produces a significant resonant frequency variation in the response of sensor. BioFila silk is used for the substrate part of the sensor. Biofila silk is characterized over an ultra-wide frequency range from 200 MHz to 20 GHZ using Keysight 85070E high performance probe. Annealed copper is used for the conductive parts of the sensor. To the best of our knowledge, this is the first reported passive sensor fabricated with a fully biodegradable and renewable host substrate.
Classification of Foot Kicks in Taekwondo Using SVM (Support Vector Machine) and KNN (K-Nearest Neighbors) Algorithms

Taekwondo is one of the most popular martial arts in Indonesia. The number of competitions held at various regional levels proves that taekwondo is popular. In an online match, judges assess movement through video so that errors can occur in judging the type of kick. No research discusses the classification of kick types. However, several related studies examine monitoring human movement using SVM and KNN algorithms. This experiment aims to determine the performance of the SVM and KNN algorithms in classifying taekwondo foot kicks. The experiment results show that the SVM and KNN algorithms effectively classify taekwondo foot kick types. Using the kurtosis feature, the SVM algorithm obtained an accuracy rate 91.6%. As for KNN, the accuracy rate is 96.8% with the kurtosis feature.
Low Latency Deployment of Service-based Data-intensive Applications in Cloud-Edge Environment

Efficiently deploying the service components of data-intensive applications on edge or cloud servers to minimize latency is one of the main challenges faced by the cloudedge environment. Most existing studies consider either service deployment or data placement, rather than the joint optimization of them. To this end, this work considers the driving relationship between data and services in a heterogeneous environment including remote cloud and nearby edge servers, and aims to obtain a satisfactory data placement and service deployment scheme while ensuring the QoS for users. Firstly, we formulate the desired problem and decouple data placement from service deployment by polynomial reduction. Then, a priority-based data placement strategy (PDPS) is proposed, which can generate a data placement scheme. After that, the original problem is reduced to a classical assignment problem, and a service deployment strategy based on an improved Hungarian algorithm (HA-SDS) is proposed to obtain a service deployment scheme. The effectiveness of our proposed method are evaluated by ablation and comparative experiments, which performs better than other existing algorithms.
Application of Deep Convolution Neural Network Algorithm in Detecting Traditional Calligraphy Characters

Calligraphy is an important part of Chinese culture, and calligraphy detection is of great significance. At present, there are still some challenges in the detection of ancient calligraphy. In this paper, we are interested in the calligraphy detection problem with a focus on the calligraphy character boundary. We choose High-Resolution Net (HRNet) as the calligraphy character feature extraction backbone network to learn reliable high-resolution representations. Then we use the scale prediction branch and the spatial information prediction branch to detect the calligraphy character region and categorize the calligraphy character and its boundaries. In this process we use the channel attention mechanism and the feature fusion method to improve detection effectiveness. Finally, we compare our result with the result that is detected without boundary. The comparison proves the superiority of our method, and our method can accurately detect each calligraphy character.
Design, development, and evaluation of a medical system for estimating dosimetry levels in a public hospital

Several studies have found that radiation exposure can negatively affect health, being imaging studies one of the main reasons for radiation exposure. Currently, most public hospitals do not use specific tools for estimating the radiation level (dosimetry level) that a patient has before requesting an imaging study. Instead, the physicians use their experience in deciding to request a specific imaging study. In this paper, we present the design, development, and evaluation of a system for estimating patients' dosimetry levels to be used when physicians request patients' imaging studies. We evaluated the system with ten physicians in a public hospital. Our results show that the proposed system has the potential to be integrated into the current medical practices of the public hospital.
A New Routine to Simulate Plasma Density Measurements from Satellites: Application to the SPORT Project

In this work, we simulated plasma density measurements along prescribed satellite circular orbits, varying the inclination in relation to the geographic equator. The IRI (International Reference Ionosphere) code was used to simulate the ionosphere and the orbits were simulated using a MATLAB® code. We developed a routine to interpolate the densities along the trajectory and plot the density curves measured by the satellite over time. We show how this curve is influenced by the inclination of the orbits and also by the time when the satellite crosses the magnetic equator. It was concluded that, over the Brazilian sector, curves that pass through conjugated points along the magnetic equator during the day tend to present the two peaks corresponding to the Appleton anomaly in a pronounced way. Due to the typical declination of the magnetic field in the Brazilian sector, the inclination of the CubeSat orbit of the SPORT (Scintillation Prediction Observation Research Task) project, of 51.6°, meets this condition for descending orbits. Analyzing complete tra-jectories at inclination angles of 51.6°, it was verified that the magnetic equator crossing time significantly influences the observed peaks related to the anomaly. The simulations also showed significant differences between different orbits that pass through the equator at the same local time but in different geographic regions, showing morphological differences in the Earth's magnetic field. The methodology developed here will be used in the interpretation of the experimental results obtained from SPORT, allowing direct comparison between simulation and measurement.
Hyperspectral image super-resolution based on transform domain low rank tensor regularization

In this paper, we propose a low rank regularized hyperspectral image (HSI) super-resolution method based on transform domain tensor. The spectral dimension subspace is learned from low spatial resolution hyperspectral image (LR-HSI) by singular value decomposition. Then representation coefficients of the subspace are grouped as several low rank tensors that are constrained in a data adaptive tensor transform domain, resulting in a low rank regularized super-resolution model. Numerical experiments show that compared with other methods, the proposed super-resolution method shows outstanding performance on enhancing the spatial structures.
Real-Time Drowsiness Detection Using Fusion of Facial Features

Drowsy driving is one of the biggest causes of road accidents and deaths. The proposed methodology explains a state-of-the-art examination based on recent computer vision-based drowsiness detection techniques. Driver weariness results in a lack of concentration, which directly contributes to a higher rate of accidents. The dilemma highlights the need for a system that detects the driver’s tiredness and provides an alert signal to the driver prior to the accident. The work proposes a method to detect negligent behavior or drowsiness based on facial features. The system uses a webcam to record the video, and image processing algorithms are employed for the extracted frames. The eye aspect ratio and mouth opening ratio are computed for each frame utilizing the facial landmarks followed by adaptive thresholding. The system alerts the driver if drowsiness is detected. The technique is effective in cases when the drivers are accustomed to a demanding workload and are required to drive constantly for long periods of time.
User Modeling for Collaborative Adaptative Mobile Educational Applications: A Systematic Review of the Literature

Nowadays mobile devices promote better academic training in classrooms and in the daily lives of students. The challenges in the ecosystem of mobile applications are related to features particular student learning, so there is a shortage of investigations in this regard. The objective of this study is to understand how characteristics of users can be used to adapt the content of an educational mobile application based on models. These are used to create a profile based on the behavior of the person, and the most used methods to create them are the explicit one, manually, the implicit one, automatically and the hybrid. In this systematic review of the literature, we analyze the methods, techniques and properties that exist for the construction of user models following the methodology developed by Kitchenham and Kumar, following their structured process. The qualitative results obtained of the systematic review are a total of 2279 articles, of which, we selected 6 for their complete analysis. As results of the analysis, we identified the most relevant approaches for the construction of user models, that are divided into two categories, structural modeling and modeling of behavior, its use depends on the purpose of the application. Also, the significant characteristics to define a student in a user model are exposed.
A Review on Behavioural Biometric Authentication

With the advent of technology and owing to mankind’s reliance on technology, it is of utmost importance to safeguard people’s data and their identity. Biometrics have for long played an important role in providing that layer of security ranging from small scale uses such as house locks to enterprises using them for confidentiality purposes. In this paper we will provide an insight into behavioral biometrics that rely on identifying and measuring human characteristics or behavior. We review different types of behavioral parameters such as keystroke dynamics, gait, footstep pressure signals and more.
FDTD Simulation of Stochastic Scattering Loss Due to Surface Roughness in Optical Interconnects

Ahstract― We investigate the scattering loss in optical intercon-nects comprised of dielectric slab waveguides operated at 1.54 $\mu \mathbf{m}$ source wavelength and exhibiting stochastic sidewall roughness, using the method of finite-difference time-domain (FDTD). Power degradation is numerically computed and compared against an existing analytical solution. Results show correlation between the proposed FDTD method and analytical model. A normalization is enforced based on input power to compensate for physical waveguide parameter
A Neural Network Architecture for Spatiotemporal PM2.5 Forecasting

PM2.5 concentrations have been increasing at an alarming rate, making it critical to precisely estimate their regional and temporal distributions. The task is complicated by the non-linear property of PM2.5 and the large number of geological and climatic elements that influence PM2.5 concentrations, as well as the difficulty inherent in modelling their varying spatiotemporal association. Conventional physical and statistical forecasting techniques fail at incorporating several variables and discovering intricate correlations, essential for reliable predictions. This paper proposes a graph neural network capable of modelling PM2.5 concentrations on real-world data by leveraging PM2.5 transport, domain knowledge and considering spatial correlation, by reworking PM2.5 historical data, meteorological and weather forecast data into graph data and employing graph isomorphism networks, similar to the Weisfeiler Lehman graph isomorphism test for powerful graph representation learning, an attention mechanism, and a dedicated recurrent neural network to model temporal distributions, to provide accurate forecasting.