Miracle-dz/UniFAR-FTUs huggingface dataset:
On-Board Characterization Of Hyperspectral Image Exposure And Cloud Coverage By Compression Ratio

Hyperspectral images are useful for remote sensing applications due to the fine spectral resolution relative to regular RGB- and multi-spectral images. The images can be used to study what is observed, rather than just that a given feature is observed. The high spectral resolution comes with a challenge for both small and big satellites: the data volume per observation. For a small earth observation satellite, the time and energy to downlink data is one of the main driving factors behind data latency and imaging capacity.Effective use of satellite time calls for a smart imaging pipeline, both when it comes to planning, execution, and processing of data from observations. The imaging and processing pipeline needs to utilize both on-board and on-ground processing tools and steps. In this paper, a set of the first observations from the HYPSO-1 satellite is analyzed to evaluate how quickly and effectively the compression ratio can be used to identify observations that should be prioritized for downlinking, and which data sets seem to be of less value due to over/under exposure or cloud coverage.
Topological entropy: a new approach for testing heuristic algorithms in optical networks

The present paper shows incorporation of an indicator for testing routing algorithms in topologically equivalent scenarios. The indicator is based on the topological disorder shown by the link matrix on its secondary diagonal, since optical networks are generally symmetric with respect to the main diagonal. In this way, comparisons can be made for scenarios with a different number of nodes, but maintaining the same topological disorder, in this way the routing algorithms can be tested with greater precision in the simulations. In general, the heuristics are compared from the operability in high traffic networks, however, no studies of the algorithm’s operability are carried out while the nodal load is progressively increased while maintaining the topological complexity. This new indicator is called Topological Entropy that allows the comparison of the algorithms in conditions of topological equivalence. The results show the functioning of the methodology for different entropic states, which allows comparing different algorithms and their response time.
A Novel Structure Adaptive Algorithm for Feature-preserving 3D Mesh Denoising

In this paper, we propose a novel algorithm for 3D mesh filtering (Structural Adaptive Filtering, SAF) based on mesh structural adaptation. As we all know, 3D meshes mainly have three types of geometric features: corners, edges, and planes. Therefore, we designed a protection mechanism for these three types of features to achieve the feature-preserving denoising. In the first step, for the faces normals to be processed, we build a variable set of similarity between the face normal and the neighborhood faces normal, calculate their coefficient of variation, variance, and quartile difference, and then select the neighborhood face normals with high similarity to update the current normals through self adaptation of these variables. In the second step, all vertices complete the iterative update of vertex coordinates according to the filtered face normals. Unlike existing 3D mesh denoising algorithms, which have too many parameters to manually set thresholds and are sensitive to parameters, SAF is based on the geometry of local faces (no need to manually set denoising thresholds). SAF only needs to set the iterative parameters to complete high-performance feature-preserving filtering, which has high practical value. We demonstrate through extensive experimental data that SAF outperforms or is comparable to state-of-the-art methods in feature-preserving denoising at different noise levels.
Novel Distance Measures for Vote Aggregation

We consider the problem of rank aggregation based on new distance measures derived through axiomatic approaches and based on score-based methods. In the first scenario, we derive novel distance measures that allow for discriminating between the ranking process of highest and lowest ranked elements in the list. These distance functions represent weighted versions of Kendall's tau measure and may be computed efficiently in polynomial time. Furthermore, we describe how such axiomatic approaches may be extended to the study of score-based aggregation and present the first analysis of distributed vote aggregation over networks.
Prediction of Coronary Artery Disease by Adapting Hybrid Approach of Machine Learning Methods

Recently, there have been many implementations of AI and its application tactics in the field of medicine, and it is commonly mentioned as a substantial rich data. One of the major causes of death from one end of the world to the other is coronary artery disease, which can be prevented with early diagnosis. The goal of this work is to use reliable clinical data to predict coronary course infection. Expecting Coronary Artery Disease (CAD) is a very challenging and challenging undertaking in the clinical profession. One of the virtuosi in the clinical field is the early forecast, especially in the cardiovascular region. The earlier studies on the creation of the early forecast model encouraged an understanding of the new approaches to find the variation in clinical imaging. An eating plan graph prepared by the concerned doctor following early anticipation might satisfy the cardiovascular counteraction. Our exam paper includes a forecast based on a suggested computation created using a pooling region bend AI technology. This data-based ID is a crucial element for accurate expectation. Despite the weak pixels around it, this extensive methodology has a respectable impact on deciding variety in clinical images. With the help of vein halting and vein plaque, this pooling region development in our AI calculation is storing contracting veins and tissues. The new flexible picture-based grouping strategies are presented in this investigation piece, which also contrasts the current characterization techniques with anticipated CAD previous for a higher exact worth. This suggested method uses any prior cardiac ailment as evidence to draw a conclusion. In our suggested calculation, the decision-production of grouped yield yields more precise results.
Blind Speech Separation Through Direction of Arrival Estimation Using Deep Neural Networks with a Flexibility on the Number of Speakers

This paper presents a complete framework for blind multi-speaker separation in reverberant environments using neural networks while being flexible on the number of sound sources. Under the W-disjoint orthogonality (WDO) hypothesis of speech signals, with the proposed framework, we first estimate the direction of arrival (DoA) of each dominant speech signal in each time-frequency bin using a deep neural network. While existing state-of-the-art methods use the masks associated with the different DoAs directly in the separation, we propose to use them as input features of a second deep neural network to estimate refined separation masks. Each speaker's signal is then separated using an estimated Generalized Eigenvalue (GEV) Wiener filter. This approach reduces distortion, interference, and artifacts. We assessed our framework through numerical experiments on a simulated dataset with a comprehensive analysis and on a real dataset acquired from recorded spatial room impulse responses to check if the framework generalizes well to actual cases. Numerical experiments show that our contribution outperforms the state-of-the-art with almost $3dB$ to $6dB$ in terms of signal to distortion ratio (SDR) and an improvement of 22% to 26% in terms of word error rate using wav2vec2 ASR model.
A Medium-Voltage Transformer with Integrated Leakage Inductance for 10 kV SiC-Based Dual-Active-Bridge Converter

Medium-voltage (MV) dual-active-bridge (DAB) converters have become an emerging technology thanks to high-voltage silicon carbide (SiC) devices and nanocrystalline magnetic materials. However, the need of phase-shift inductance and insulation requirements for the MV DAB may complicate the design of the transformer and the MV DAB converter, which can also induce higher loss and occupy more space. In this paper, the leakage integration and insulation techniques are discussed for a 6.7-kV/850-V DAB converter, meeting both the inductance and insulation requirements of the MV DAB converter. Ferrite cores with air gaps are inserted between the LV and MV windings without introducing high loss, and the MV winding is selectively shielded to avoid high parasitics and meet the insulation requirement. Test results have verified the effectiveness of this design.
Source Turn-off (STO) MOSFET: A New Driving Architecture for Smart SiC Module

High-speed and intelligent gate driver is the critical interface between power semiconductor devices and control signals to achieve low switching loss and intelligent protection. The proposed novel Source Turn-Off (STO) MOSFET is a driver integrated MOSFET architecture that can achieve ultra-fast turn-on and turn-off operation beyond traditional voltage source gate driver approach. 1200V SiC STO half bridge was developed to demonstrate the concept. Faster switching speed and lower switching losses are demonstrated. The STO can be readily realized by using commercially available Si power MOS IC which can further enable intelligent functionalities such as build-in current monitoring, temperature monitoring and over current protection which are important for high power SiC modules.
Bin-Wise Combination of Time-Frequency Masking and Beamforming for Convolutive Source Separation

This paper presents a new Blind Source Separation (BSS) method for convolutive mixtures that can be underdeter-mined. Exploiting the sparsity of the source signals in the Time-Frequency (TF) domain, this method combines TF masking and beamforming. Indeed, on the one hand, BSS methods based on TF masking achieve remarkable performance even in the underdeter-mined case, however they tend to cause artifacts at the separated sources. On the other hand, beamforming can achieve good performance in the (over)-determined case without distorting the estimated signals. Therefore, combining these two techniques makes it possible to benefit from both their advantages. In the proposed method, unlike existing methods that use beamforming with TF masking, we introduce new normalized directional vectors to generate the different beamformers involved, and a new way for better estimating these vectors. In addition, we propose a new technique that can be used to separate sources in the case of underdetermined mixtures. Test results showed good performance for our method compared to various existing methods, similar in terms of working hypotheses, both in the determined and underdetermined cases.
Public Key Cryptographic Implementation Validation: A Review

This paper reviews the Validation procedures of Implementation in hardware, software, and firmware of Public Key Cryptographic algorithms. NIST standardized several algorithms for the present era a few years ago. Detailed test procedures for algorithm validation and Crypto module validation of implementations have been developed. In the Post-Quantum cryptography scenario, algorithm selection will take place in the coming few months, for which validation procedures need to be established by NIST. These algorithms are for KEM (Key Encapsulation Module) and Digital signatures. The symmetric encryption algorithms continue to be the same as before, using AES with a key length of 256 bits. This paper reviews the scope of validation for traditional Public key algorithms for illustration in this paper. Next, we look at a typical Post Quantum KEM and Digital signature algorithm and discuss the validation procedures to be followed.
A Study of Effective Screening Methods for Grading Diabetic Retinopathy using Mathematical Approaches

Diabetic eye disease is a terminology used to describe a group of eye problems that can develop as a result of diabetes and are affected by high blood sugar levels causing retinal damage. Retinal Image processing is the domain widely used to detect various eye diseases such glaucoma, Diabetic Retinopathy (DR), Retinal Detachment etc. Postponing the screening may be sometimes leading to late diagnosis of the eye disease which in turn may lead it to severe stage. Proper diagnosis of such disease is important to promote precise treatment that could prevent vision loss. Automatic screening of DR is most popular which uses image processing techniques and methods to diagnose the disease severity. This paper discusses the recent models and techniques that are prevalent to diagnose from the image samples to screen the disease severity.
Exploitation of Distributed Energy Resources for local services on power distribution network

The spread use of cooling systems for residential users and industrial processes, combined with the increasing number of electric vehicles, is causing a substantial increase of the electrical load in medium and low voltage distribution networks. This increasing load is partially compensated by Distributed Generation (DG), mainly renewable and not controllable, which can however cause occasional overgeneration in the distribution network. All these events are causing unwanted situations in the distribution network, such as congestion or large variations of the power factor. One of the possible solutions to these problems is to open a market for local services, where the Distribution System Operators (DSOs) can find the necessary resources to solve the problems. This paper demonstrates the potential of a Local Services Market (LSM) for grid congestion management in a context of a real use-case. A real portion of the distribution system of Milan has been modelled and integrated into a simulator of a LSM. The results show that the proposed approach represents a valuable solution to the compensation of distribution network congestion. With the assumptions made, the average annual cost of the DSO to buy local services is around 94~€/MWh. This cost may vary depending on the error of the load forecasts, currently equal to approximately 10%.
Long-term Visual Localization Using Illumination Insensitive Descriptors

This demo shows a long-term visual localization system based on illumination insensitive descriptors (IID) of points and lines in multiple cameras. The system can robustly match the features in captured images for localization against those in localization database (DB). The developed localization system achieves remarkable performance and seasonal-time-spanned localization results in complex and changing environments.
FO-PI Controller for Regulating DC Link Voltage in UPQC Integrated HES System for Power Quality Improvement

This paper emphasis on an optimized Fractional order PI controller (FOPI) for enhancing the power quality of three phase hybrid energy storage (HES) system integrated unified power quality conditioner (UPQC). In order to offer uninterrupted electricity, renewable energy sources regarding wind energy, solar PV, and battery energy storage systems (BESS) are also explored. Whenever the windmill as well as PV array is not supplying electricity, the BESS could improve the stability of the power distribution system, meet entire load requirements. Grid power quality harmonics as well as concerns induced via non-linear loads are addressed by the UPQC type with shunt and series active filter compensators. To ease the grid’s power quality issues and the harmonics introduced by non-linear loads there subsist the UPQC model with active filter shunt and series compensator. The FOPI controller is used for improving the power quality of a 3-phase hybrid energy storage device with an integrated UPQC. RES such as PV arrays, BESS, as well as wind energy are analyzed in order to deliver continuous electricity. Whenever the PV array as well as windmill is not supplying power, the BESS could provide entire load demand, which increases the stability of the power system distribution. The UPQC model including series and shunt active filter compensators exists to alleviate grid power quality problems as well as harmonics introduced by non-linear loads. The UPQC shunt compensator collects power from hybrid energy systems, while the load is protected from grid-related power quality issues by the series compensator. The FOPI controller is built with iso-damping characteristics to control the voltage of the DC connection at a desirable level. The gain of the FOPI controller (Kp, Ki, $\lambda$,) is optimally tuned by a Enhanced Seagull with Rooster Update (ES-RU) Algorithm, which merge the concepts of the Seagull Optimization Algorithm (SOA) as well as the Chicken Swarm Optimization Algorithm (CSO).
Initial Development of Low-cost Custom Spherical Measurement Range

Initial development of a low-cost table-top spherical antenna measurement range is discussed. The structure of the system is fabricated with FDM 3D printing using PLA. Commercial off-the-shelf stepper motors and microcontrollers are used to achieve the 3-axes of control (ϕ, θ, and χ) for spherical scanning. The scan radius is made adjustable through the use of linear actuators. The fabricated range is tested using two similar wideband TEM horns and raw measured data is presented. For processing, a basic spherical near-field to far-field transform code is developed and validated using simulated data. Furthermore, the code is used for analysis of the error introduced by using a truncated measurement sphere with a relatively low-directivity AUT. Thoughts on improvements to both the fabricated and developed code are given.
Speech Emotion Recognition Model using Support Vector Machine Through MFCC Audio Feature

Emotions are one of the most influential aspects of everyday life. Everyone has their own way of expressing their emotions. One way to express emotions is through speech or by speaking. This gave rise to a new field of research called speech emotion recognition which aims to understand a person’s emotions through sound. In this study, the Support Vector Machine algorithm will be implemented to create a speech emotion recognition model through the MFCC voice feature obtained from voice processing. The model created can be used to classify six emotions, namely happy, sad, angry, fear, disgust, and neutral. The highest accuracy is obtained from the model created using the Support Vector Machine algorithm using a radial basis function kernel which is considered to be able to properly classify emotions based on sound. The usage of the combined dataset also improved the accuracy of the model and are able to obtain above 70% highest accuracy on each test.
A Hybrid Neural Network Model for Brain Tumor Detection in Brain MRI Images

Brain tumors are a specific sort of malignant growth that may manifest in the brain. In most cases, clinicians must manually interpret magnetic resonance pictures in order to arrive at a diagnosis, which significantly limits the imaging technique's usefulness. Strategies to improve diagnostic accuracy and turnaround time are needed to use MRI scans of the brain to detect cancers. It is challenging to isolate tumor areas from images because of the visual variety and similarities between the brain tumor and normal tissues. To deal with this problem, a combination (CNN-LSTM) model along with a traditional machine learning algorithms employing MR images was proposed in our research to identify a brain tumor. The dataset used in this study includes 5,712 brain MR pictures, of which 4,128 are reported to contain tumors. In the standard classifier section, we created three classic classifiers using scikit-learn: Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Logistic Regression, coupled with a deep learning model CNN. Then, we went on to the suggested CNN-LSTM, which is accomplished through (tensorFlow and keras), since its excellence is superior than the conventional algorithms. The suggested CNN-LSTM model can identify brain cancers with a maximum accuracy of 99.30% in a short amount of time and we comprehend the conclusions drawn from the models offered by several researchers must demonstrate that our paradigm is more trustworthy than earlier frameworks. Therefore, the suggested model may assist clinical professionals in determining if an individual has had a brain tumor and, subsequently, speed up the course of therapy.
A partitioned model for fluid–structure interaction problems using hexahedral finite elements with one‐point quadrature

A partitioned numerical model for fluid–structure interaction analysis of incompressible flows and structures with geometrically non‐linear behavior is presented in this work. The flow analysis is performed considering the well‐known Navier–Stokes equations for Newtonian fluids and the continuity equation, obtained from the pseudo‐compressibility hypothesis. An explicit two‐step Taylor‐Galerkin scheme is employed in the time discretization procedure of the system of governing equations, which is expressed in terms of an arbitrary Lagrangean‐Eulerian description. The structural subsystem is analyzed using a geometrically non‐linear elastic model and the respective equation of motion is discretized in the time domain employing the Generalized‐α scheme. Fluid–structure coupling is taken into account regarding a new energy‐conserving partitioned scheme with non‐linear effects, which is accomplished by enforcing equilibrium and kinematical compatibility conditions at the solid–fluid interface. Non‐matching meshes and subcycling are also considered in the present model. The finite element method is employed for spatial discretizations using eight‐node hexahedral elements with one‐point integration in both fields. Some numerical examples are simulated in order to demonstrate the applicability of the proposed formulation. Copyright © 2009 John Wiley & Sons, Ltd.
Domestic Induction Heating System With Standard Primary Inductor for Reduced-Size and High Distance Cookware

In this article, a hybrid wireless power transfer system, which combines induction heating (IH) and inductive power transfer functionalities, is proposed to improve the performance of a domestic IH application with small loads weakly coupled to distant inductors. Considering the basic single-inductor domestic IH application, the proposed system adds a secondary inductor with a series compensation capacitor directly attached to the small ferromagnetic cookware. The extended distance can be used to implement the glassless induction concept, where the ceramic glass of typical cooktops is substituted by the kitchen surface itself. The design of the secondary coil is carried out by means of a combination of finite-element and electrical simulations. A design process, including the housing of the resonant capacitors and the selection of the secondary winding number of turns and cabling, is presented. As a result, a prototype is implemented and tested under working conditions up to 1500 W at several distances. Experimental results validate the electrical modeling and simulation. Moreover, thermal results confirm the feasibility of the proposal and validate the adopted strategies for the capacitor housing.
Transfer learning based on self-attention mechanism in sound event detection

SED is a task to detect sound segments. Recent research has found that models based on self-attention mechanism can have an edge on sound feature detection. However, the self-attention mechanism requests a large-scale data set which is difficult to achieve in the audio field. So, this paper proposes a transfer learning-based SED solution. Through the transfer of AST based on the self-attention mechanism, our model reduces the demand for the data. At the same time, we propose a new audio embedding processing method and significantly shorten the training time. After training using the DESED data set on the DCASE task, it completely ahead of CRNN. It reaches the Intersection-based F1 score of 91.4%, and 42.1% higher than CRNN, which reflects the superiority of the self-attention mechanism.