Miracle-dz/UniFAR-FTUs huggingface dataset:
Condition Monitoring of Oil Immersed Transformer Using Thin Dielectric Film Coated Cross Capacitance Sensor

Trustworthy onsite transformer oil testing to monitor the condition of oil-dipped transformer is a tremendous task. different sensors used for the purpose of monitoring the condition of oil in transformer. This paper presents an study on enhancement of sensitivity of cross-capacitance sensor cell in transformer oil testing using different sensing materials. This study is carried out by simulating the sensor using finite element analysis software. According to the Thomson-Lampard (TL) theorem, the cylindrical shaped cross-capacitance sensor cell is used to simulate a credibility and accurately assess the insulating oil deterioration. Simulation based study is carried out to determine concentration of 2-FAL and moisture in oil. When cellulose paper, which is used to insulate transformer windings, degrades, two significant byproducts are produced: 2-FAL and moisture. Furthermore, the moisture content of oil and paper should always be very low. The Polyvinyl-alcohol (PVA) sensing film is used for the detection of moisture concentration and their sensitivity and selectivity are investigated. For the detection of 2-FAL concentration in transformer oil, PDMS is used. The highest sensitivity achieved for moisture is 0.3633 fF/ppm when thickness of PVA sensing material is 0.8 mm and sensitivity for 2-FAL is 0.6439 fF/ppm when thickness of PDMS sensing film is 0.1 mm.
Fast Assessment of Rotor Barrier Dimensional Allowances in Synchronous Reluctance Machines

Tolerance analysis on synchronous reluctance machines (SynRM) is mandatory if accurate refinements of the rotor structure are adopted, a must for low-ripple applications However, the impact of manufacturing/dimensional tolerances or material degradation has been scarcely included in the design steps for SynRMs obeying complexity and time-requirement reasons. The paper provides an analysis of rotor barrier dimensional allowances in synchronous reluctance machines by utilizing a semi-analytical approach. This method is not only fast yet it also generates a substantial number of results that allows to evaluate the influence of dimensional deviations on the machine’s performance. The proposed performance evaluation method is validated in four machines by direct finite element (FE) simulations, showing good agreement and low computational burden. Once validated, the method is applied to perform a brute-force search in a single-barrier 4-pole machine with different combinations of dimensional allowances, obtaining a significant reduction in computational time compared to traditional direct FE evaluation. The paper concludes with a description of the proposed methodology and its applicability to other SynRM designs. This opens the possibility of quickly analyzing tolerances in SynRMs and improving their performance by evaluating different dimensions and position of flux barriers.
Sleep Apnea Event Detection Using EEG Band Entropy Features and Machine Learning

Sleep is one of the essential biological components of human life comprising one-third of our complete lifespan. Sleep quality is affected by various breathing, cardiovascular, and movement disorders. Sleep Apnea is one of the significant undiagnosed sleep-breathing disorders that can cause various neural and cardiovascular disorders in the long term. Among all polysomnography signals, Electroencephalogram (EEG) is majorly affected by sleep-related issues. Thus, EEG is a vital biomedical signal used for the detection of sleep apnea. In this work, Shannon entropy and log-energy entropy-based automated sleep apnea classification method is proposed to distinguish apnea and normal events in an apneic patient. The EEG signal is first preprocessed by filtering out the undesired frequency components using the IIR Butterworth bandpass filter and then disbanded into five frequency sub-bands (Delta, Theta, Alpha, Sigma, and Beta). EEG band entropies are calculated as features on each sub-band for the classification of sleep apnea and normal events. St. Vincent’s sleep apnea database is used to evaluate the performance of the proposed technique. The weighted KNN classifier gives the best result with the highest accuracy of 98.90%, sensitivity of 97.80%, and specificity of 100%. The proposed method offers better results in terms of sensitivity, specificity, and accuracy using a lesser number of features as compared to some of the latest published work on the same database.
Real-Time Infrared Horizon Detection in Maritime and Land Environments Based on Hyper-Laplace Filter and Convolutional Neural Network

The infrared (IR) horizon is an essential reference for near-ground state awareness and target detection. The traditional horizon detection algorithms based on hand-crafted features claim the advantage of being intuitive and highly targeted, however, their capability of detail extraction and robustness is not strong enough when switching among diverse scenes, meanwhile, the convolutional neural networks (CNNs) have the powerful ability in feature acquisition, but has the disadvantages of interpretability and instability. Given this, this article proposes a novel horizon detection algorithm that combines the traditional method and CNN. First, the hyper-Laplace filter (HLF) is proposed to eliminate the interferences and enhance the saliency of the target. Then a positioning module (PM) based on the traditional idea which fully utilizes the structural characteristics of multifeature-maps in CNN is present to extract the endpoints of the horizon. Finally, an auxiliary module that consists of a revise branch (RB) and self-attention module (SAM) is designed to assist the PM with detailed corrections. Compared with the state-of-the-art algorithms, experiments based on three datasets with more than 25000 frames under various scenes demonstrate that the proposed algorithm not only achieves the best accuracy and highest stability but also addresses real-time needs. Notably, the detection deviation is controlled within 2 pixels on all three datasets while achieving a comparable detection speed of 170 frames/s. Besides, our algorithm also shows strong robustness even under harsh sea conditions and urban backgrounds. Code and trained models are available at https://github.com/FJsRepo/InfML-HDD.
Comparison of a smartwatch and an ECG probe as options for monitoring the driver's HRV to determine overall pre-driving status

Driver health monitoring is an increasingly discussed topic in the automotive industry. This article compares a smartwatch against an electrocardiogram (ECG) probe, both in terms of the ergonomics of the measurement and, of course, the accuracy. The evaluated variable is the heart rate variability (HRV), which can indicate the general health of a person or their stress. In this paper, the state of the art technology of measuring physiological functions of the driver is investigated. In next part the experiment is designed and implemented. Final part consists of evaluation of the experiment and discussion of the results.
Impact of Mixed Precision Techniques on Training and Inference Efficiency of Deep Neural Networks

In the deep learning community, increasingly large models are being developed, leading to rapidly growing computational costs and energy costs. Recently, a new trend has been arising, advocating that researchers should also report the energy efficiency besides their model’s performance in their papers. Previous research has shown that reduced precision can be helpful to improve energy efficiency. Based on this finding, we propose a simple practice to effectively improve the energy efficiency of training and inference, i.e., training the model with mixed precision and deploying it on Edge TPUs. We evaluated its effectiveness by comparing the speed-up of a state-of-the-art semantic segmentation architecture with respect to different typical usage scenarios, including using different devices, deep learning frameworks, model sizes, and batch sizes. Our results show that enabled mixed precision can gain up to a $1.9\times $ speedup compared to the most common and default float32 data type on GPUs. Deploying the models on Edge TPU further boosted the inference by a factor of 6. Our approach allows researchers to accelerate their training and inference procedures without jeopardizing the model’s accuracy, meanwhile reducing energy consumption and electricity cost easily without changing their model architecture or retraining. Furthermore, our approach is helpful in reducing the carbon footprint used to train and deploy the neural network and thus has a positive effect on environmental resources.
Ionospheric Phase Delay Correction for Time Series Multiple-Aperture InSAR Constrained by Polynomial Deformation Model

As a supplement to time-series interferometric synthetic aperture radar (TS-InSAR), time-series multiple-aperture InSAR (TS-MAI) can measure the spatiotemporal changes in SAR along-track surface deformation. TS-MAI is often applied with low-frequency SAR data (e.g., L-band data) due to its ability to retain high interferometric coherence. However, the low-frequency SAR signal is vulnerable to ionospheric delays, which can significantly degrade the measurement accuracy of TS-MAI. This letter presents an approach to correct the ionospheric errors in TS-MAI. A polynomial cubic model is employed to constrain the ground deformation, which is then incorporated into the observation model for effectively separating the deformation signal and the ionospheric delays. The proposed method is tested using the L-band ALOS-1 PALSAR-1 datasets covering the Tocopilla area in Chile between November 2007 and March 2011. The correction performance and accuracy of the proposed method are demonstrated by comparing the range split-spectrum interferometry (RSSI)-based method and the local GPS data, respectively. The root mean square error (RMSE) improvement rates between TS-MAI and GPS are 72.17% for the SRGD site and 84.51% for the VLZL site, and their correlation coefficients increase from 0.23 and 0.50 to 0.52 and 0.61 after the correction.
Evolutionary Approach for Detecting Significant Edges in Social and Communication Networks

Detection of significant edges maintaining the connectivity in complex networks is essential in many applications such as attack vulnerability analysis, the spread of epidemic diseases, and information spreading patterns discovery. There are many existing methods enabling us to evaluate the criticality ranking of links in networks, which are based on straightforward algorithms and topological features of analyzed graphs. In this paper, we offer another perspective on the problem and propose a novel approach, to be called the Evolutionary Approach (EA), that is based on a genetic-like algorithm and turns directly to an integral criterion of decomposition efficiency instead of network topology. Like all the genetic algorithms, EA is grounded on the iterative enhancement of randomly generated solutions via reproduction, cross-over, and mutation processes. The EA-efficiency is illustrated via decomposing three real-world benchmark networks by using the proposed method, the acknowledged Link Entropy (LE) method, and the most recent Improved Link Entropy (ILE) method. The comparison of the obtained results demonstrates that the EA-efficiency exceeds the ILE-efficiency for 5.7%–28.1% depending on the network complexity, with respect to the LE-efficiency the increase is approximately two-fold. Besides, the temporal aspects of ordering the network edges according to their significance using solely EA or in its combination with LE and ILE are discussed.
A Comparative Study on the Modern Deep Learning Architectures For Predicting Nutritional Deficiency in Rice Plants

A robust algorithm that is capable of predicting nutritional deficiency in rice plantations is vital for enhancing the current status of associated agriculture. The research presented in this paper aims to obtain an optimal algorithm that is capable of accurately predicting Nitrogen, Calcium, and Potassium deficiencies in a self-collected dataset. The paper further leverages four famous deep learning architectures namely, ResNets, DenseNets, MobileNets, VGG-16, and SqueezeNet. All these approaches are thoroughly compared on multiple performance metrics and this paper also provides information concerning the associated temporal characteristics. The paper also assesses the use of finetuning pretrained models through rigorous experiments. The novel dataset is rigorously tested and the proposed architectures are also subjected to thorough hyperparameter tuning. A stratified train test split is leveraged to maintain the unbiased nature of the study and an in-depth empirical breakdown is presented.
Percolation on Penrose Tilings

Abstract In Bernoulli site percolation on Penrose tilings there are two natural definitions of the critical probability. This paper shows that they are equal on almost all Penrose tilings. It also shows that for almost all Penrose tilings the number of infinite clusters is almost surely 0 or 1. The results generalize to percolation on a large class of aperiodic tilings in arbitrary dimension, to percolation on ergodic subgraphs of ${{\mathbb{Z}}^{d}}$ , and to other percolation processes, including Bernoulli bond percolation.
Analysis and Optimization of Direct Convolution Execution on Multi-Core Processors

Nowadays, convolutional neural networks are among the most widely used types of deep learning networks thanks to their usefulness in many application domains. There are many efforts to find methods to increase their training and inference performance and efficiency. One of the most widely used technique to implement convolution consists of flattening tensors into 2D matrices and carrying out the operation through a matrix-matrix multiplication routine, which has highly optimized implementations in high-performance libraries. However, this kind of approach uses extra time and memory to transform and store the tensors involved. For this reason, direct convolution is becoming increasingly popular. Direct convolution can be implemented as a series of nested loops iterating over tensor dimensions and it does not require extra memory. In this work, we evaluate on various multi-core CPUs the performance and scalability effects deriving from different parallelization strategies, loop organizations, and SIMD-vectorization approaches with different compilers in relation with architectural aspects. We discuss each parameter thoroughly and distill our findings in a set of heuristics that can be used to quickly achieve a high-performance implementation in accordance to the underlying hardware and the characteristics of the convolutional layer at hand. By adopting a per-layer approach, we increase performance up to 60-70% compared to a static implementation for all the layers. Moreover, our results are comparable, or even better (up to $1.67\times $ speedup) than matrix-matrix multiplication-based convolution in a multi-core system.
Classification of CO Environmental Parameter for Air Pollution Monitoring with Grammatical Evolution

Air pollution is a pressing concern in urban areas, necessitating the critical monitoring of air quality to understand its implications for public health. Internet of Things (IoT) devices are widely utilized in air pollution monitoring due to their sensor capabilities and seamless data transmission over the Internet. Artificial intelligence (AI) and machine learning techniques play a crucial role in classifying patterns derived from sensor data. Environmental stations offer a multitude of parameters that can be obtained to uncover hidden patterns showcasing the impact of pollution on the surrounding environment. This paper focuses on utilizing the CO parameter as an indicator of pollution in two datasets collected from wireless environmental monitoring devices in the greater Port area and the Town Hall of Igoumenitsa City in Greece. The datasets are normalized to facilitate their utilization in classification algorithms. The k-means algorithm is applied, and the elbow method is used to determine the optimal number of clusters. Subsequently, the datasets are introduced to the grammatical evolution algorithm to calculate the percentage fault. This method constructs classification programs in a human-readable format, making it suitable for analysis. Finally, the proposed method is compared against four state-of-the-art models: the Adam optimizer for optimizing artificial neural network parameters, a genetic algorithm for training an artificial neural network, the Bayes model, and the limited-memory BFGS method applied to a neural network. The comparison reveals that the GenClass method outperforms the other approaches in terms of classification error.
Non-local anisotropic total variation based super resolution

Anisotropic Total variation (ATV) based super resolution method is a state-of-the-art method, which is popularly used. However, the traditional ATV based method is a kind of local method, which is just considered its 4 or 8 neighborhood of each pixel, without considering a larger region. In this paper, we proposed a non-local based ATV method (NLATV), which considers a larger region of each pixel. The experimental results show that the proposed method is much better than the original ATV method in peak signal-to-noise radio and visual quality.
Fuzzy Entropy Interpretation and Its Application in Deinterlacing

This paper proposes a new deinterlacing method using fuzzy entropy-based cost function to select possible image deinterlacing methods. The classical entropy was presented by Shannon, which is widely used in information and communication theory. Apart from Shannon’s entropy, fuzzy entropy deals with vagueness and ambiguous uncertainties. The obtained fuzzy entropy map becomes criteria to select the best method to interpolate missing pixel and upsample the low-resolution image. The experimental results describe that our proposed method is superior to conventional methods.
A Simple and Efficient Strategy for Preparation of Flexible Strain Sensors Based on Marangoni Effect

The Marangoni effect is a phenomenon of mass transfer between two fluids with different surface tensions, which has been used in many fields. In this paper, we prepared ultrathin conductive films with graphene (GN) and carbon nanotubes (CNTs) based on the Marangoni effect. The Marangoni self-assembled film exhibited excellent properties, showing a conductivity of 8.3 kΩ·sq−1, a transparency of 74% at 550 nm and a thickness of 28 nm when the mass ratio of CNTs and GN was 1:1. The conductive films were transferred to flexible substrates twice and fabricated face to face as strain sensors. The 3M4910-based strain sensors, which were prepared with a simple process and high material utilization rate, exhibited good sensitivity (GF = 5.7), a wide working range (193%) and satisfactory cyclic stability. The PDMS-based GN sensor showed high sensitivity (GF = 34), a wide working range (78%) and excellent stability (ε = 10%, > 8000 cycles). It has been proved that the sensors can be used to detect different joint movements of the human body and subtle movements, showing good application prospects in physiological signal detection.
Single Scattering Properties of Atmospheric Ice Crystals

Abstract Simulations of scattering and polarization properties for randomly oriented polyhedral ice crystals are presented based on the geometric optics and the far-field diffraction approximation. Particle shapes range from various hexagonal symmetric particles to highly complex shaped deterministic and random fractals. All calculations are performed at a wavelength of 0.55 µm. Hexagonal symmetric particles show several narrow scattering peaks besides the well known 22° and 46° halos. Column-like ice crystals provide neutral points (NP) at larger scattering angles than plate-like ice crystals. The ranges of NPs for column-like and plate-like crystals are separated at a scattering angle of about 156°, which may allow a polarimetric distinction between these two crystal types. The effects of particle size are studied by applying observationally derived aspect-ratio parameterizations to the individual particle types. Differences in the asymmetry parameter versus size relations for column-like particle types.
2-(5,6-Diphenyl-1,2,4-Triazin-3-yl)pyridinium Dichloroiodate (I)

2-(5,6-diphenyl-1,2,4-triazin-3-yl)pyridinium dichloroiodate (I) (1) was synthesized by reacting 3-(2-pyridyl)-5,6-diphenyl-1,2,4-triazine with ICl in dichloromethane solution. The structural characterization of 1 by SC-XRD analysis was accompanied by elemental analysis, FT-IR, and FT-Raman spectroscopy measurements.
A Potential Biomarker of Brain Activity in Autism Spectrum Disorders: A Pilot fNIRS Study in Female Preschoolers

Autism spectrum disorder (ASD) refers to a neurodevelopmental condition whose detection still remains challenging in young females due to the heterogeneity of the behavioral phenotype and the capacity of camouflage. The availability of quantitative biomarkers to assess brain function may support in the assessment of ASD. Functional Near-infrared Spectroscopy (fNIRS) is a non-invasive and flexible tool that quantifies cortical hemodynamic responses (HDR) that can be easily employed to describe brain activity. Since the study of the visual phenotype is a paradigmatic model to evaluate cerebral processing in many neurodevelopmental conditions, we hypothesized that visually-evoked HDR (vHDR) might represent a potential biomarker in ASD females. We performed a case-control study comparing vHDR in a cohort of high-functioning preschooler females with ASD (fASD) and sex/age matched peers. We demonstrated the feasibility of visual fNIRS measurements in fASD, and the possibility to discriminate between fASD and typical subjects using different signal features, such as the amplitude and lateralization of vHDR. Moreover, the level of response lateralization was correlated to the severity of autistic traits. These results corroborate the cruciality of sensory symptoms in ASD, paving the way for the validation of the fNIRS analytical tool for diagnosis and treatment outcome monitoring in the ASD population.
Tuning the Electronic Properties of Janus GeSnS2 Monolayers through Strain and Electric Field

: This study investigates the electronic properties of a single layer of the novel Janus material GeSnS2 using density functional theory. By utilizing the hybrid functional HSE06 in addition to the standard PBE approximation, the study aims to obtain accurate ﬁndings about how changes in strain and electric ﬁeld affect the material’s electronic properties. The results of the study reveal that the bandgap energy of the GeSnS2 monolayer is 2.15 eV and that it exhibits an indirect bandgap behavior. The study also shows that by applying strain or an electric ﬁeld, the bandgap of the material can be changed, which has signiﬁcant implications for the material’s potential applications. The study found that when strain is applied, the bandgap changes signiﬁcantly. Furthermore, the study discovered that the electric ﬁeld has a slight effect on changing the bandgap of GeSnS2 monolayer when the electric ﬁeld is changed from 0 to 8 V/nm, and a band shift occurs under certain conditions. The study provides valuable insight into the potential of GeSnS2 and opens the door for further research in this ﬁeld.
Multitype finite mean supercritical age-dependent branching processes

Let {Zφ (t)} be a multitype finite-mean supercritical (CMJ) process with general characteristic φ and malthusian parameter α. It is shown that there exist some constants {c(t} such that {c –1(t)Zφ (t)} converges almost surely to a non-degenerate limit Wφ as t →∞. Characterizations of {c(t)} and Wφ are also given.