Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of the glycaemic sampling method in diabetes data mining

Finger-pricking is the traditional procedure for glycaemia monitoring. It is an invasive method where the person with diabetes is required to prick their finger. In recent years, continuous-glucose monitoring (CGM), a new and more convenient method of glycaemia monitoring, has become prevalent. CGM provides continuous access to glycaemic values without the need of finger-pricking. Data mining can be used to understand glycaemic values, and to ideally warn users of abnormal situations. CGM provides significantly more data than finger-pricking. Thus, the amount and value of CGM data ultimately questions the role of finger-pricking for glycaemic studies. In this work we use the OhioTlDM data set in order to study the importance of finger-prick-based data. We use Random Forest as a classification method, a robust method that tends to obtain quality results. Our results indicate that, although more demanding and scarcer, finger-prick-based glycaemic values have a significant role on diabetes management and on data mining.
Multimodal Data Analysis Using Soft Computing Techniques For The Detection and Classification of Lung Cancer

Examination of medical images is a tedious task as for as doctors and experts concerned. In order to analyse these images various computing techniques are also employed. Data analyses using these computing techniques are proved to be efficient in many applications. In the proposed system a similar technique is employed to detect the presence of lung nodules and classify the same in to different categories. Data collected from different sources can be used for the proposed system. Clinical information and various imaging methods can be used as the source of information to this system. Based on the input the system classifies the images into various categories. The input image is processed and with the help of auto encoder segmentation is done. Lung nodule segmentation helps to identify the location and size of the lobe.
Empirical mode decomposition revisited using ordinal pattern concepts

The empirical mode decomposition (EMD) is a well-known data-driven signal decomposition. It produces a finite number of intrinsic mode functions (IMF) well adapted to Hilbert spectral analysis. Since the EMD is not a theoretical approach but rather an iterative algorithm, the question of how the EMD can achieve spectral band separation remains open. Several attempts to experimentally and theoretically explore this issue were addressed. However, to the best of our knowledge, none of them has investigated the EMD from an ordinal pattern point of view. The purpose of this paper is threefold. First, the EMD limitations are reexamined using the concept of ordinal patterns (OP) of length 3. Second, we propose to incorporate the OP probability distribution into the definition of the mean envelop and the stopping criterion during the sifting process. This turns out to be helpful in reducing mode mixing and thus improves the EMD robustness. Finally, we propose to extend the EMD to different pattern lengths (2, 4, 5, etc). Academic and real-life time series are processed and the results show that the ordinal pattern-based EMD outperforms the classical EMD.
A Data-Driven Digital Twin for Urban Activity Monitoring

The increasing pace of sensing and communication technology rollout is paving the way for concrete deployments of smart city applications, enabling a data-driven modeling of processes and the environment. In particular, the Urban Facility Management (UFM) process is growing in importance, recognized to have a direct impact on the sustainability and the development of our cities. In [1] we presented a system's view of a Digital Twin solution for the UFM process. The solution relies on (near)real-time data to quantify the activity index in an area of interest, used as a basis for planning decisions. In this study, we focus on the predictive subsystem, tasked with computing near-to-mid term predictions of the activity index, equipping UFM operators with a flexible decision-support system. Without loss of generality, we present an analysis of the vehicular traffic component, part of the activity index, assessing the accuracy of different predictive schemes, discussing some operational implications.
Paddy Leaf Disease Detection using Deep Learning Methods

Computerised automated diagnosis of crops disease enables early detection and ensures the quality of crop. Technology advancements in these fields will reduce the loss and increase the overall productivity. Our research work motivated to build a deep learning classification model for paddy leaf disease detection. The model frame work consists of several pre-processing techniques such as denoising, data filtering, and selection of optimizer that best fits the model. Finally, a comparative study of the proposed model’s performance and efficiency was done with different deep learning models. Based on the analysis and observation, it was observed that the proposed model has given promising results for effective leaf disease detection.
Geometrically Constrained Independent Vector Analysis with Auxiliary Function Approach and Iterative Source Steering

In this paper, we propose an alternative algorithm, which is faster and more stable, for geometrically constrained independent vector analysis (GC-IVA) to tackle multichannel speech separation problem. GC-IVA is a method that combines IVA, a blind source separation method, with beamforming-based geometrical constraints, which are defined using the spatial information of the sources, so that it allows us to achieve high separation performance while able to obtain the target speech at the desired output channel. GC-IVA with auxiliary-function approach and vectorwise coordinate descent (GCAV-IVA) is one such method, which has the advantage that no step-size tuning is required, the objective function monotonically decreases, and the algorithm converges fast. However, this method requires matrix inversion, which is computationally expensive and adversely affects numerical stability. To address this problem, we propose an algorithm by using the recently introduced iterative source steering (ISS), which uses a sequence of rank-1 update. ISS does not require matrix inversion and achieves a lower computational complexity per iteration of quadratic in the number of micro-phones, resulting in the proposed method being faster and more stable than GCAV-IVA. The experimental results revealed that the proposed method had higher source separation performance and shorter execution time than conventional methods.
Solution conformation of the response regulator proteins from Deinococcus radiodurans studied by SAXS

In this paper the solution conformation of the response regulator proteins from Deinococcus radiodurans was studied by small-angle X-ray scattering (SAXS). The SAXS curves of Dr-rrA in solutions were obtained at Beamline 1W2A of Beijing Synchrotron Radiation Facility (BSRF). Two possible conformations.
Exploring Humanoid Robot Face Preference Using Brain Functional Connectivity and Graph Neural Network

Graph neural networks (GNN) have been applied in EEG signal analysis. However, it is not clear how to describe the connection relationships between electrodes, and a reasonable representation of the adjacency matrix cannot be neglected for the study of GNN based on EEG signals. In this paper, we use brain functional connectivity to analyze the face preferences of a humanoid robot and explore the feasibility of using the matrix obtained based on brain functional connectivity measurements as a GNN connectivity matrix. The results show that it is feasible and an average accuracy of 73.47% is achieved. In addition, the results of the brain functional connectivity analysis also show that there is a difference between preferences and non-preferences.
FDTD‐SUPML‐ADE simulation for Ground‐Penetrating Radar modeling

GPR data simulations require above all an efficient forward modeling algorithm. A Finite Difference Time Domain code is presented with Simplified Unsplit Perfect Matched Layer that leads to insignificant reflections on border with few modifications of the core algorithm. In addition, the Auxiliary Differential Equation method allows GPR simulations including dispersive phenomena that could not be neglected at radar frequencies. The numerical validation is done in a classic way for amplitude/time wave propagation and SUPML, and using the nonconventional Dispersion Analysis technique for physical dispersion behavior. Full 3‐D and 2‐D forward modeling results are finally presented for lossy, dispersive and random media in GPR conditions.
Derivations on white noise functionals

The Gaussian space (E*, μ) is a natural infinite dimensional analogue of Euclidean space with Lebesgue measure and a special choice of a Gelfand triple gives a fundamental framework of white noise calculus [2] as distribution theory on Gaussian space. It is proved in Kubo-Takenaka [7] that (E) is a topological algebra under pointwise multiplication. The main purpose of this paper is to answer the fundamental question: what are the derivations on the algebra (E)?
Detection of Incipient Inter-Turn Short-Circuit Faults by Artificial Intelligence Classifiers

This paper presents two artificial intelligence (AI) based identification methods for inter-turn short-circuit fault (ISCF) detection in induction motors (IMs), driven by voltage source inverters (VSIs). It was previously observed that, for an IM driven by finite control set model predictive control (FCS-MPC), the ISCF occurrence disturbs the balanced distribution of the resultant switching vectors, which are merely the control outputs of the FCS-MPC scheme. This effect of the ISCFs is utilized for fault detection purposes. The proposed method successfully detects the ISCF using AI methods which are fed by histograms of switching vectors along with torque and speed. This is especially convenient from the motor driver's perspective since no additional sensor or hardware is required for fault detection. The dataset utilized in this paper was obtained from an experimental IM drive test setup, on which intentional ISCFs can be created. The test results proved that the average fault detection rate is 95.8%, for an ISCF of 2-turns in a 104-turns phase winding.
Homotopes of Symmetric Spaces I. Construction by Algebras with Two Involutions

We investigate a special kind of contraction of symmetric spaces (respectively, of Lie triple systems), called homotopy. In this first part of a series of two papers we construct such contractions for classical symmetric spaces in an elementary way by using associative algebras with several involutions. This construction shows a remarkable duality between the underlying ''space'' and the ''deformation parameter''.
Measurement and Ray-tracing for UAV Air-to-air Channel Modeling

Unmanned aerial vehicles (UAVs) have been widely used for both civil and mission-critical applications with ever-increasing values in the next decade. The UAV channel shows some unique characteristics due to high mobility and variable attitudes of the UAVs. In this paper, an air-to-air (A2A) channel at 1420 MHz in the urban scenario is measured. The delay and power of the multipath components (MPCs) are analyzed based on the measurement data. Besides, a ray-tracing (RT) simulator is calibrated to be applicable for more UAV scenarios, through which extensive simulations are conducted. The attitude of the moving UAV is set to change during the RT simulations, including pitch-up, cruise, and pitch-down flight. Correspondingly, tapped-delay line (TDL) models of A2A channels are developed. Moreover, Doppler spectrum of each tap is modeled by Jakes spectrum. The proposed channel models can be helpful for designing UAV communication systems.
Autonomous Recovery of Underway AUV on the Water Surface in Heavy Seas

This paper proposes a robotic recovery approach to recover an autonomous underwater vehicle (AUV) at sea on from the aft deck of an uncrewed surface vehicle (USV). A high-fidelity simulation of an USV recovering a surfaced AUV with a robotic manipulator was created as a testbed to develop and test the control systems and algorithms used for the manipulator end-effector to track the surfaced AUV. The manipulator was able to sufficiently track the AUV in head, beam and bow seas for sea states 3 and 5. Head seas, as expected, showed the best tracking performance, and is the usual for recovery.
Precise determination of fundamental parameters of six exoplanet host stars and their planets

The aim of this paper is to determinate the fundamental parameters of six exoplanet host (EH) stars and their planets. Because techniques for detecting exo-planets yield properties of the planet only as a function of the properties of the host star, we must accurately determine the parameters of the EH stars first. For this reason, we constructed a grid of stellar models including diffusion and rotation-induced extra-mixing with given ranges of input parameters (i.e. mass, metallicity and initial rotation rate). In addition to the commonly used observational constraints such as the effective temperature Teff, luminosity L and metallicity [Fe/H], we added two observational constraints, the lithium abundance log N (Li) and the rotational period Prot. These two additional observed parameters can set further constraints on the model due to their correlations with mass, age and other stellar properties. Hence, our estimations of the fundamental parameters for these EH stars and their planets have a higher precision than previous works. Therefore, the combination of rotational period and lithium helps us to obtain more accurate parameters for stars, leading to an improvement in knowledge about the physical state of EH stars and their planets.
Adaptive Q-leaming-supported Resource Allocation Model in Vehicular Fogs

Vehicular Cloud Computing (VCC) exhibits many drawbacks with the demands of vehicular applications and intermittent network conditions. Vehicular Fog computing is a novel method for supporting and promoting the effective sharing of services and resources in urban areas. Diverse works on vehicular resource management have sought to handle the very dynamic vehicular environment using various methods, such as policy-based greedy and stochastic techniques. Nevertheless, high vehicular mobility poses many issues that compromise service consistency, efficiency, and quality. Adaptive vehicular Fogs incorporating Reinforcement Learning can deal with mobility and correctly distribute services and resources across all Fogs. Thus, we introduce an adaptive resource management model using cloudlet dwell time for resource estimation, mathematical formula for Fog selection, and reinforcement learning for iterative review and feedback mechanism for generating optimal resource allocation policy.
Off-Grid Sparse Spectrum Fitting for DOA Estimation with Unknown Mutual Coupling

In this paper, an algorithm based on sparse spectrum fitting is proposed to solve the problem of DOA off-grid estimation under the condition of unknown mutual coupling. In the proposed method, the influence of mutual coupling is firstly eliminated by constructing a selection matrix by exploiting the banded complex symmetric Toeplitz structure of the mutual coupling matrix. Then, in the process of sparse recovery based on signal covariance, additional variables are employed to describe the modeling error caused by off-grid DOA. Finally, the constraints of sparse recovery are optimized by the improved covariance estimation error and chi-square distribution. The effectiveness of the proposed method is verified by comparing the performance of the proposed algorithm with several existing DOA estimation methods and Cramer-Rao bound (CRB) through simulation experiments.
An Open-Access System for Long-Range Chainsaw Sound Detection

A pipeline for automatic detection of chainsaw events in audio recordings is presented as the means to detect illegal logging activity in a protected natural environment. We propose a two-step process that consists of an activity detector at the front end and a deep neural network (DNN) classifier at the back end. At the front end, we use the Summation or Residual Harmonics method in order to detect patterns with harmonic structure in the audio recording. Active audio segments are consequently fed to the classifier that decides upon the absence or presence of a chainsaw event. As acoustic feature, we propose the widely-used amplitude spectrogram, passing it through the recently proposed Per-Channel Energy Normalization (PCEN) process. Results based on real-field recordings illustrate that the proposed end-to-end system may efficiently detect low-SNR chainsaw events at a very low false detection rate.
A counting process approach to maximum likelihood estimation in frailty models

We study counting process models for event history data where the intensities depend on unobservable quantities ("frailties"). Examples include models for dependent failure times and regression models with unobservable covariates. Estimation in both parametric and nonor semi-parametric models is performed by maximum likelihood methods using the EM algorithm. Simulations and practical examples are presented.
Design of tipping paper automatic deviation correction system based on machine vision

Cigarette length deviation is an important index of cigarette quality control. Domestic cigarette machines have no real-time online detection function of cigarette length. The tipping paper used in cigarette manufacturing has the phenomenon of position offset due to the influence of tension or the axial movement of tipping paper tray frame during the operation of the equipment. In order to solve this problem and ensure product quality, this paper designs an intelligent deviation correction device of tipping paper based on visual detection of cigarette length, which can detect the position change of tipping paper in the process of transportation and cutting in real time. The automatic deviation correction and accurate control of tipping paper are carried out through the control circuit, and the automation of position detection and adjustment of tipping paper is realized.
Modular multilevel converter control with using a general space vector PWM method in medium voltage hydro power application

This paper studies a generalized space vector PWM (SVPWM) method for modulating the modular mul-tilevel converter (MMC) for a medium voltage hydropower application. In addition to the modulation of the MMC, the circulating current control and the submodule capacitor voltage balancing are included in the study. The simulation and experimental results show the feasibility of using the generalized SVPWM method for controlling the MMC. Furthermore, the loss study shows that the switching loss in the MMC can be reduced with 28% when a modification of the generalized SVPWM method is utilized, thus, the total efficiency of the converter can be increased.
Smart Villages in Indonesia in the Light of the Literature Review

In the last few years, smart village has received increased attention from both researchers and practitioners. Especially with the advancement of digital technology that allows the interconnection between rural areas, smart village discussion is getting popular. The objective of this study, therefore, is to gain insights into the existing literature and to present the knowledge of how the smart village research in Indonesia context has been carried out. With more than 74,000 villages and most diverse country, Indonesia offers fruitful context study. This study in particular adopted the recent PRISMA framework to identify eligible articles for further systematic review. Based on this framework, this study was able to identify 133 eligible articles. Analysis indicated that research on smart village in Indonesia has been started since a decade ago. Thematic analysis revealed that three research topics that received most attention from the researches in smart village including application development, IT/IS management, Strategy and Implication for society. Considering the complex problem in rural development, future research with more focus on the implementation of emerging technology such as IoT, AI and big data in producing smart solution.
Dynamic Behavior of Coupling Heterogeneous Neurons and Its Hardware Implementation

Different neurons are heterogeneous with widely different firing patterns. The same neurons under different parameters and external drive can also produce different firing pattern, so as to constitute a heterogeneous neural model. Therefore, in this paper two identical HR neuron models are selected to constitute heterogeneous neurons under external excitation and in the case of memristive synaptic coupled neurons, respectively. There are different firing phenomena between the two heterogeneous neurons. Meanwhile, the electrical coupling between two heterogeneous neurons constitutes a new neuron model, which shows a rich dynamical behavior. Using phase portrait, bifurcation diagram and Lyapunov exponential diagram, the model has at least two discharge pattern, including chaotic and periodic firing pattern. Finally, coupling heterogeneous neurons have been implemented using the MCU microcomputer circuit, and the results show the effectiveness of the theoretical analysis.
PrivacyStory: Tool Support for Extracting Privacy Requirements from User Stories

Privacy by design requires that developers address privacy concerns from the early stage of software development life cycle. It encourages them to take a proactive approach to privacy engineering by identifying personal data, creating conceptual data flow diagrams, and identifying privacy threats. We argue that by providing a tool that automates some of the steps can reduce the burden on development teams. We develop a tool called PrivacyStory, including an end-to-end privacy requirement generation from a set of user stories. The tool provides some automation, utilizing a current state-of-the art natural language processing model. The core aim of our tool is to assist development teams in becoming more agile in their approach to privacy requirements engineering.
Optimal Control of Unmanned Surface Vehicle Trajectory Tracking Based on Reinforcement Learning

For the purpose of achieving high dynamic and precise tracking of desired trajectory, an optimal trajectory tracking control strategy with specified performance based on reinforcement learning was proposed for the unmanned surface vehicle (USV) trajectory tracking system. For the MIMO discrete-time system of USV, in order to constrain its tracking dynamic error within the expected specified range to ensure high dynamic performance during the trajectory tracking process, the system performance index and the long-term cost function were designed to measure the performance. On this basis, a control system based on the Reinforcement Learning Actor-critic framework is constructed, in which two neural networks are applied. The actor NN is used to generate the optimal control signal, and the critic NN is used to evaluate the performance of the USV while approximating the cost function and measuring actor NN. The weight of the two NNs is directly adjusted during the operation of the USV. A strict theoretical analysis is given for the designed control system, and it is proved that the closed-loop system is stable, and all closed-loop signals are semi-globally consistent and ultimately bounded. Finally, it is verified by simulation that the control system can well realize the trajectory tracking of the USV model.