Miracle-dz/UniFAR-FTUs huggingface dataset:
Wind turbine clutter observations and theoretical validation for meteorological radar applications

The use of wind energy is gaining importance because of its many advantages. Nations worldwide are promoting the installation of wind farms to produce electricity in an attempt to tackle climate change and increasing oil costs. However, wind turbines can generate undesired signals that disturb the performance of radar systems. In particular, this is an issue for weather radar networks that see wind turbines as clutter, since they have a strong influence on meteorological outputs such as the estimation of rain intensity or wind velocity. This study develops a simulated scenario and a radar signal model that justify collection of live data from the Spanish Weather Radar Network. Simulated and real data are discussed for both the spotlight and exploration modes. As an application example, a simple algorithm has been developed to estimate wind direction in the immediate vicinity of wind turbines. In this context, it is clear that some meteorological information may be extracted from the wind turbine clutter itself.
Investigation of Oscillation and Resonance in the Renewable Integrated DC-Microgrid

This paper assessed the small-signal stability performance of a multi-converter-based direct current microgrid (DCMG). The oscillation and potential interactions between critical modes are evaluated. First, the complete analytical model of the DCMG is developed with the converter and associated controllers. Three methodologies, impedance scanning, eigenvalue analysis, and time-domain simulation, along with the fast Fourier transform (FFT) analysis, have been used to comprehensively investigate the oscillations and interactions. The simulation results show inherent weak modes, with a wide range of oscillations in the studied DCMG, which may destabilize the system under disturbances. Based on the sensitivity analysis, controller gains and DC-link capacitance are identified as the most critical parameters and substantially influence the weak modes leading to oscillations, interactions, and resonance. Finally, the performance of the various control synthesis methods is compared. This examination would help the researchers, planning, and design engineers to design and stably operate a multi converter-based DC microgrid.
Energy Efficient Spin-Based Implementation of Neuromorphic Functions in CNNs

Convolutional neural networks (CNNs) offer potentially a better accuracy alternative for conventional deep learning tasks. The hardware implementation of CNN functionalities with conventional CMOS based devices still lags in area and energy efficiency. This has necessitated the investigations of unconventional devices, circuits, and architectures to efficiently mimic the functionality of neurons and synapses for neuromorphic applications. Spin-orbit torque magnetic tunnel junction (SOT-MTJ) device is capable of achieving energy and area efficient rectified linear unit (ReLU) activation functionality. This work utilizes the SOT-MTJ based ReLU for activation and max-pooling in a single unit to eliminate the need of dedicated hardware for pooling layer. Moreover, 2 × 2 multiply-accumulate-activate-pool (MAAP) is implemented by using four activation pairs each of which is fed by the crossbar output. The presented approach has been used to implement various CNN architectures and evaluated for CIFAR-10 image classification. The number of read/write operations reduce significantly by 2X in MAAP based CNN architectures. The results show that the area and energy in MAAP based CNN is improved by at least 25% and 82.9%, respectively, when compared with conventional CNN designs.
A versatile SPICE model for quantum-well lasers

A SPICE equivalent-circuit model for the design and analysis of quantum-well lasers is described. The model is based on the three-level rate equations which include, in their characterization of charge dynamics, the role of gateway states at the quantum well. The model is versatile in that it permits both small- and large-signal simulations to be performed. Emphasis here is placed on validating the model via a comparison of simulated results with measured data of the small-signal modulation response, obtained over a wide range of optical output powers from two lasers with different lengths of the separate-confinement heterostructure (SCH). Using a set of tightly specified model parameters, all the important trends in the experimental data are reproduced. The consideration of gateway states is found to be important, with regard to predicting the small-signal response, only for the laser with the longer SCH. This highlights the significance of the interplay between the roles of transport through the SCH and capture/release via the gateway states at the quantum well.
A Comparative Study on Deep Learning Techniques for Bird Species Recognition

Naturally, birds appear around us at different locations in a variety of sizes, shapes, and colors. Bird species recognition provides crucial information on the state of the environment. Manual collection and processing of bird species data for the identification of birds is a huge task for ornithologists. Automatic bird recognition systems reduce their burden to some extent by collecting, processing bird related information and identifying the birds automatically. In this view, this paper presents a comparative study of the performances of MobileNet, AlexNet, InceptionResNet V2, Inception V3, and EfficientNet on bird species recognition. We gathered 11488 images of 200 bird species from the Kaggle dataset and increased the number of images to 40000 using data augmentation techniques. The experiment results shows that MobileNet and EfficientNet are the quickest training models. EfficientNet is outperforming the other models with test accuracy of 87.13%.
First passage times of a jump diffusion process

This paper studies the first passage times to flat boundaries for a double exponential jump diffusion process, which consists of a continuous part driven by a Brownian motion and a jump part with jump sizes having a double exponential distribution. Explicit solutions of the Laplace transforms, of both the distribution of the first passage times and the joint distribution of the process and its running maxima, are obtained. Because of the overshoot problems associated with general jump diffusion processes, the double exponential jump diffusion process offers a rare case in which analytical solutions for the first passage times are feasible. In addition, it leads to several interesting probabilistic results. Numerical examples are also given. The finance applications include pricing barrier and lookback options.
A cross-sectional survey for the assessment of biosecurity measures in small-scale duck farms in Qalyoubia, Egypt: Comprehensive evaluation and procedural recommendations

Background and Aim: Biosecurity implementation is fundamental to combating diseases and antibiotic resistance. Therefore, this study aimed to examine the correlation between the implementation of biosecurity measures in small-scale duck farms and the incidence of infectious diseases that threaten the duck industry. Materials and Methods: Twenty small-scale duck farms of different breeds and production stages were collected as representative samples, focused on two districts in the Qalyoubia governorate, which possesses high-density small-scale farms. A 30-point structured questionnaire was designed to assess the level of biosecurity measures implemented in the sampled farms. These farms were examined for bacterial infection by cultivation, typing, and antibiotic sensitivity tests, in addition to molecular techniques for detecting suspected viral diseases. Results: The results showed that the farms had high or low levels of biosecurity; only 25% possessed high-level biosecurity. Bacteria, including Salmonella, Escherichia coli, Staphylococcus, and Pasteurella, were isolated from all sampled farms. High rates of antimicrobial resistance-reaching up to 100% were observed against some drugs. However, viral causative agents, including HPAI-H5N8, duck viral hepatitis, and goose parvovirus, were isolated from only five farms. Conclusion: The lack of commitment to biosecurity implementation, particularly personal hygiene, was observed in most sampled farms. Increasing the level of biosecurity reduced the incidence of mixed infections.
The Nonparametric Estimation of Branching Curves

Abstract The spline smoothing approach to nonparametric regression is extended to cover the estimation of curves that branch at certain specified points in time. This extension is motivated and illustrated by data from an experiment on sunflowers. Apart from providing insight into this particular data-analytic problem, the work described demonstrates the power of the roughness penalty approach in coping with curve estimation of a nonstandard kind. Particularly in the context of certain experiments in horticulture and arboriculture, the experimental material may be treated identically to the control up to a certain known time part of the way through the experiment. To display and investigate the effect of treatments applied during growth it is of interest to estimate a branching curve. In the simplest case we want to study two growth curves that are common up to a certain known time and then diverge (or branch) after the application of a treatment to part of the experimental material. The spline-smoothing .
Low-Complexity Adaptive Beamforming Algorithm With High Dimensional and Small Samples

A large-scale array (LSA) inevitably encounters scenarios with a small number of samples, and its beamformer suffers from high computational complexity. High computational complexity prevents the system from being used in practical online engineering applications. The complex vector of the beamformer weights can be expressed as the product of training snapshots and the signal steering vector (SSV), and a coefficient vector, since the optimal weight vector is a linear combination of basis vectors of the signal-plus-interference subspace. In this study, a new adaptive beamformer is developed based on the minimum variance distortionless response (MVDR) criterion and kernel techniques. The new beamformer only needs to compute the inversion of a low-dimensional Gram matrix instead of the high-dimensional sample covariance matrix (SCM), which significantly reduces the calculation cost. Moreover, an efficient loading parameter calculation method (only related to the received matrix and not required user-defined parameters) is derived, which can adaptively suppress the mismatches of the ill-conditioned Gram matrix. Furthermore, a fast version of the new beamformer is formulated for the LSA under the scanning mode. Simulation results demonstrate that the new beamformer achieves better performance and a lower computation load than existing algorithms for a small number of samples. In particular, insufficient samples and high computational complexity problems are more frequently aroused in space-time broadband array signal processing. Interestingly, the new techniques can be successfully extended to wideband array signal processing and yield satisfactory beam pattern shapes.
A Stable Large-Scale Multiobjective Optimization Algorithm with Two Alternative Optimization Methods

For large-scale multiobjective evolutionary algorithms based on the grouping of decision variables, the challenge is to design a stable grouping strategy to balance convergence and population diversity. This paper proposes a large-scale multiobjective optimization algorithm with two alternative optimization methods (LSMOEA-TM). In LSMOEA-TM, two alternative optimization methods, which adopt two grouping strategies to divide decision variables, are introduced to efficiently solve large-scale multiobjective optimization problems. Furthermore, this paper introduces a Bayesian-based parameter-adjusting strategy to reduce computational costs by optimizing the parameters in the proposed two alternative optimization methods. The proposed LSMOEA-TM and four efficient large-scale multiobjective evolutionary algorithms have been tested on a set of benchmark large-scale multiobjective problems, and the statistical results demonstrate the effectiveness of the proposed algorithm.
Multi-Frame Detection via Matrix Information Geometry-Based Track-Before-Detect Algorithm

To address the performance degradation resulting from the power fluctuation of targets, the matrix information geometry-based track-before-detect algorithm for multi-frame detection is proposed. The proposed method combines the performance advantages of matrix information geometry (MIG) detector and the information accumulation ability of dynamic programming, which can improve the detection performance for fluctuating target. Specifically, the multi-frame target detection is formulated as an optimization problem on the Hermitian positive-definite (HPD) manifold, and the MIG-based dynamic programming is deduced to solve it. The experiments based on real-recorded sea clutter data show that the proposed method is effective.
Amélioration de la qualité visuelle d'images couleur dans le cadre du standard de compression JPEG2000

Durant les dernieres annees, la prise en compte de modeles du Systeme Visuel Humain (SVH) dans l'evaluation de la qualite visuelle des images couleur compressees, est devenu un sujet de recherche majeur. II semble naturel d'integrer davantage les connaissances recentes sur la perception et la modelisation de la vision humaine, dans les systemes de compression d'images. Ainsi, dans cet article, nous proposons une methode d'integration de ces connaissances pour l'augmentation de la qualite visuelle d'images compressees JPEG2000. Cette methode consiste en deux parties: une evaluation de laboratoire pour la modelisation du SVH par la Fonction de Sensibilite au Contraste (CSF) et une technique de calcul de facteurs de ponderation visuelle pour la compression JPEG2000, utilisant le modele SVH evalue, dans le domaine de Fourier de l'image couleur.
Effect of harvest date on structural carbohydrates and lignin content in meadow sward in different pluvio-thermal conditions

: The content of structural carbohydrates and lignin are important assessment criteria of the feed value of meadow plants. It is affected by many independent factors, including among others its development stage during the harvest as well as climatic conditions, especially the amount of rainfall. During the years 2014–2016, plant samples were harvested at weekly intervals, respectively five times from late April to late May. The effect of harvest date on cellulose, hemicelluloses and lignin contents was evaluated. The chemical composition of plants was varied, depending not only on harvest date but also on the year of study. Regardless of the course of meteorological conditions in subsequent growing seasons, the increase of cellulose (from 236.5 to 297.9 g∙kg –1 DM), hemicelluloses (from 159.3 to 210.8 g∙kg –1 DM), and lignin (from 31.5 to 43.1 g∙kg –1 DM) in the following dates of harvest were observed. These parameters were also positively correlated with the total rainfall from the begging of vegetation season to the date of plants sampling ( R 2 = 0.65, 0.12 and 0.44 for cellulose, hemicelluloses and lignin, respectively), and with the average daily air temperature in the moment of harvest ( R 2 = 0.66, 0.32 and 0.52 for cellulose, hemicelluloses and lignin, respectively). The cellulose and lignin content, regardless of the harvest date, were significantly higher in the first year of the study (2014), when moisture conditions for plant development were optimal
Design and Analysis for Physical-Layer Network Coding with Reconfigurable Superposition Mapping

For Physical-layer Network Coding (PNC) with high-order modulation, the problem of ambiguity mapping is a focus field. In this paper, we present a PNC with Reconfigurable Superposition Mapping (RSM-PNC) scheme. Based on this scheme, a two-way relay communication system model consisting of the source and relay nodes using 16QAM modulation is constructed. In addition, the PNC demodulation and mapping rule at the relay node is displayed, the decoding rule at source node is redesigned, and the calculation formula of relay symbol error rate (SER) under Additive White Gaussian Noise (AWGN) is derived as well. The simulation results show that the proposed RSM-PNC scheme not only solves the problem of ambiguity mapping, but also has more advantages compared with PNC using Hierarchical Modulation scheme.
An NVMe-oF Distributed Storage Design Based on Etcd

The emerging NVMe-oF (NVMe over Fabrics) protocol provides access to an SSD on another node over the network using NVMe commands, providing performance close to that of an SSD using the NVMe protocol locally. To further improve performance, the SPDK (Storage Performance Development Kit) proposes an NVMe-oF implementation in user mode. Compared to the native NVMe-oF I/O stack provided by the Linux kernel, the SPDK's user mode I/O stack can significantly reduce I/O latency, however, the NVMe-oF implementation in the SPDK only serves as a demonstration and does not implement cluster management and failover features. In this paper, we propose a design of multi-node NVMe-oF distributed storage based on Etcd technology, which enables bulk deployment and management of clusters with the help of Etcd's node discovery and management functions. It also utilizes Etcd's K-V database to persist configuration information for failover and recovery.
Realization of an endoscope equipped with microprojection system for optogenetics

Optogenetics is the science where recent progresses in the field of photonics are combined with the techniques in molecular genetics to develop a methodology for modulation of neural activities.1-9 Despite enormous enthusiasm in using optogenetics for brain studies, little has been done on the engineering side such as technology development for light delivery or realization of reliable systems for optical monitoring of the induced activities. In this project, we have implemented a Digital Micromirror Device based microprojection system capable of delivering illumination patterns through a high-resolution imaging fiber bundle that guides the pattern to the region of interest on the surface or within the brain tissue. The system is also equipped with an imaging path for detection of calcium signals and monitoring the induced patterns of cellular activities. A very interesting application of the system is extracting topographic computational maps of cortex or cellular receptive fields in-vivo. It is known that such maps are the engine of information processing in the cortex. Better understanding of the structure of such maps will help to unravel the mysteries of brain higher level computations. Another application of this system is related to the high-resolution stimulation patterns that cannot be produced with electrode arrays. Production of high-resolution patterns is important in the study of specific modes of brain activities. We report the details of our optical design, preliminary results produced by testing the system on tissue, and we discuss our strategy to extract new data from the brain tissue.
HUMAN STEM CELLS AND CARDIAC REVITALIZATION (NEW EXPERIENCE IN TAJIKISTAN)

Objective: Stem cells have remarkable potential to grow in more than 200 types of cells that the adult human body holds. Regenerative medicine by using stem cells is at the vanguard of health care poised to offer solutions for many of today's incurable diseases. Bone marrow derived stem cells have been used in vitro to generate bone, cartilage, tendon, ligament, meniscus, intervertebral disc, fat, muscle, and nerve. The aim of this review is to describe the stem cell therapy in Tajikistan and its position in the word. In Tajikistan for the first time the laboratory for investigation of stem cell created in Avicenna Tajik State Medical University, Dushanbe at November 29, 2009 and the first clinical study for heart stem cell therapy started at March 9, 2010. In this study, autologous transplantation of bone marrow derived CD133+ was undertaken with the high degree of success for a cohort of patients with coronary artery disease. Keywords: Regenerative medicine, human stem cells, cardiac revitalization, coronary artery disease, bone marrow derived CD133+.
Composite cement embedded in a biopolymer matrix for bone tissue regeneration

Bone substitutes are biomaterials applied in surgical therapy to repair many cases of a damaged bone. More than two million bone surgeries using biomaterials are realised each year worldwide. In this line of research, the researchers of this project study developed a bioresorbable synthetic bone substitute designated by (β-TCP/DCPD)-PHBV which is a mixture of the two materials: β-TCP/DCPD and PHBV in a 60/40 weight fraction. This research study attempts to categorize the (β-TCP/DCPD)-PHBV and evaluate its ability on triggering bone regeneration by making bone implants on 12 New Zealand white male rabbits. The RESEARCHERS performed multi-scale studies namely; a uniaxial compression test, an examination by scanning electron microscopy, and analysis with infrared spectroscopy in order to get the characteristics. The neoformed bone is examined by SEM coupled with energy dispersive spectroscopy (EDS). In addition to the salient mechanical benefits; attenuation of the modulus of elasticity and limitation of brittleness, the biomaterial has also proven to be effective in giving rise to new bone tissues having a suitable mineral composition.
Tongue Color Analysis and Disease Diagnosis Based on a Computer Vision System

The tongue reflects the state of the internal organs of the body and their abnormal behavior, such as problems of the heart, liver, pancreas, stomach, intestines, blood diseases, etc., which leads to changes in some of the features and characteristics of the tongue. The most important of which is the color of the tongue, which can be adopted as a vital criterion that can be used in computerized diagnostic systems. Quantitative diagnosis of the tongue requires some devices, including image acquisition devices such as cameras, light sources, filters, color checkers, and image analysis and processing devices by applying some algorithms or image processing and color correction programs in addition to the computer. These devices are called Tongue Diagnostic Systems. This study proposes a real-time imaging system to diagnose some diseases related to tongue color using a Webcam under specific conditions. The proposed system was designed by MATLAB environment Graphical User Interfaces, with an audio message specifying the tongue color and type of disease and sending an SMS with the result of the examination to the mobile phone of the patient or to the person providing the care for the patient. After testing the system on a data set of 50 images, the preliminary results showed that the proposed system gives diagnosis accurate rate of 94% compared to laboratory results. With ease of use and implementation and low cost. This gives impetus to further studies to apply computerized diagnosis in medical applications, to enhance the medical reality, monitor patient health, and make an accurate diagnosis.