Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Additive-Free Acidic-Solution Texturing for Surface-Damage-Free Kerfless Silicon Wafers

Crystalline solar cells have attracted significant attention in the solar-cell market owing to their low material cost. Multicrystalline silicon (mc-Si) is usually fabricated using the casting method, and approximately 35% of Si is removed as kerf in the sawing process performed to fabricate the wafer. The fabrication of kerfless mc-Si wafers does not involve the sawing and growing process. Hence, there is a significant reduction in the process cost (∼50%), while saving ∼66% energy in wafer manufacturing. Moreover, the absence of sawing process will result in no surface damage and smoother surfaces. Texturing using conventional methods such as acidic chemical etching are ineffective and as a result, the desired surface texture cannot be obtained. To solve this problem, the texture can be created using relatively expensive and complicated processes such as reactive-ion etching or metal-catalyzed chemical etching, which are not affected by the shape of the wafer surface. This article proposes a method for creating an effective texture that can increase the light absorptivity of mc-Si wafers manufactured by the kerfless method with no surface damage. This technology is a cost-effective and reliable process that uses an acidic chemical solution with no additive or mask. The etching rate and texture shape are investigated for different solution ratios of HF and HNO3. The surface morphology is examined using scanning electron microscopy, and the reflectance at 300–1200 nm is measured using ultraviolet–visible spectroscopy. A weighted average reflectance of ∼20.0% is obtained, which reduces to ∼7.4% on depositing an antireflective film.
Time Warp Simulation on Multi-Core Platforms

Parallel Discrete Event Simulation (PDES) is concerned with the parallel and distributed execution of discrete event simulation models. This tutorial reviews parallel computing and the properties of Discrete Event Simulation (DES) models and then examines the construction of PDES solutions that use the Time Warp Synchronization Mechanism. A review of the general challenges to building high-performance Time Warp Synchronized PDES on multi-core processors and clusters composed of multi-core processors is presented. These challenges include considerations of the general quantitative metrics exhibited by DES simulation models as well as a review of the impact that small overheads and serial portions of a program can have on the potential peak performance of a parallel solution. Finally, directions for future opportunities with heterogeneous computing, domain specific hardware solutions, and edge computing will be explored.
Emotion Recognition Using Time-frequency Analysis of EEG Signals and Machine Learning*

In recent years, emotion recognition has drawn intense interest from researchers in various fields. Because of their intrinsic correlation with emotions, physiological signals based emotion recognition method is objective and insusceptible to intentional disguise of the subject under study. In particular, electroencephalogram (EEG) signals are known to be responsive and sensitive to variations in emotional state. In this paper, a 4- class emotion classification problem is investigated. Firstly, a clustering algorithm is used to determine the target class of each emotion-related data. Then wavelet analysis is used to extract features from 32-channel EEG signals. Finally, we compare five feature dimensionality reduction or feature selection algorithms and four types of machine learning based classifiers. The comprehensive comparative results show the effectiveness of the combination of EEG feature dimensionality reduction algorithm and random forest (RF) for multi-class emotion recognition problem under study.
On the Extension of Schelling’s Segregation Model

Schelling’s social segregation model has been extensively studied over the years. A major implication of the model is that individual preferences of similarity lead to a collective segregation behavior. Schelling used Agent-Based Modeling (ABM) with uni-dimensional agents. In reality, people are multidimensional. This raises the question of whether multi-dimensionality can boost stability or reduce segregation in society. In this paper, we first adopt ABM to reconstruct Schelling’s original model and discuss its convergence behaviors under different threshold levels. Then, we extend Schelling’s model with multidimensional agents and investigate convergence behaviors of the model. Results suggest that if agents have high levels of demand for identical neighbors, the society might become less stable or even chaotic. Also, several experiments suggest that multidimensional agents are able to form a stable society that is not segregated, if agents prefer to stay adjacent to not only "identical" but also "similar" neighbors.
Pipeline Leak Detection Technology Based on Distributed Optical Fiber Acoustic Sensing System

Real-time monitoring of flammable and explosive gas pipeline networks is of great significance for ensuring the safety of life and property. Although the optical fiber sensing technology has achieved theoretical research results in the field of monitoring pipeline leakage recently, the practical applicability of theoretical results has not been noticed. This paper analyzes the research progress of pipeline leak detection technology based on optical fiber sensing technology firstly and proposes an algorithm for monitoring gas pipeline leakage based on distributed optical fiber acoustic sensing(DAS) system. The algorithm can obtain the time domain signal characteristics of pipeline leakage to identify leaks, locate the leak points through frequency domain. Experiments show that the algorithm can identify pipeline leakage and locate leaks, the Signal to noise ratio (SNR) and correlation coefficient can be increased to 18.28 and 0.75 respectively. The accuracy of identifying pipeline leakage and locating the pipeline leak points is effectively improved.
Balancing Optimal Large Deviations in Ranking and Selection

The ranking and selection problem deals with the optimal allocation of a simulation budget to efficiently identify the best among a finite set of unknown values. The large deviations approach to this problem provides very strong performance guarantees for static (non-adaptive) budget allocations. Using this approach, one can describe the optimal static allocation with a set of highly nonlinear, distribution-dependent optimality conditions whose solution depends on the unknown parameters of the output distribution. We propose a new methodology that provably learns this solution (asymptotically) and is very computationally efficient, has no tunable parameters, and works for a wide variety of output distributions.
Lightning deaths and injuries in the Brazilian Amazon Region in the period of 2009-2019

This paper investigates humans accidents caused by lightning in the Legal Amazon region in Brazil. The main goal of this work is to develop statistical analyses of lightning deaths and Injuries, in order to measure an extension of the damage caused by this natural phenomenon to the local people. In addition, this paper presents a specific case study in which 4 humans beings were killed by lightning. This study considered 80 incidents obtained with our own search methodology and 38 reported by the Atmospheric Electricity Group (ELAT) of the Brazilian National Institute for Space Research (INPE) for the period of 10 years (2009-2019). This study also related the number of deaths and accidents with the occurrence of lightning in the region. In addition, it was possible to observe that about 38% of people struck by lightning in the region died. This study updates the statistics about lightning deaths and injuries in the Amazon Region, besides that it can be used to improve some actions to protect people against this natural phenomenon.
Music Instrument Identification Based on a 2-D Representation

Automatic recognition of musical instruments has been a great problem in the field of digital signal processing. In this work a novel, compact set of features obtained by transforming a music signal to the spatial domain is introduced with promising results. Preliminary results on a popular music database, presented in terms of precision, recall and identification accuracy, are highly encouraging. The best result comprises an accuracy of 84.02% by Decision Tree (DT) for a set of 9 instruments belonging to different families. The accuracy for predicting the instrument family 96.07% for string family and for wind instrument the overall prediction accuracy is 90.78%. Further inclusion of some other features from some available works are also checked leading to slight increase in the accuracy. The obtained results are also compared with the result predicted by the VGG - 16 network on the same dataset. The feature set containing 8 features that introduces less computational complexity compared to available works, may be considered as a major contribution. The comparison shows, we can replicate nearly accurate classification through our proposed method in less amount of time than deep neural architecture.
Choosing the Right Accounting Software for Organised Retail Environment

Choosing the right accounting software is essential in a retail environment. Keeping this in focus this research conducts analysis of three basic accounting softwares to determine if they are significantly different from each other. The findings reveal that these softwares do have significant statistical differences. Hence, careful thought should be invested into making the right choice while deciding on software for the organization. Further, an observational and hands-on study allows the classification of these accounting softwares based on different criteria which makes them desirable to enterprises today. This paper is a novel study on comparison and assessment of three basic softwares that are very commonly used for accounting in the retail environment.
Deep Learning and Reduced Models for Fast Optimization in Electromagnetics

The computational cost of topology optimization based on the binary particle swarm optimization is greatly reduced by the use of deep neural networks (DNNs). A first convolutional neural network (CNN) is trained with data coming from finite-element analysis (FEA) with the aim of correctly estimating the output quantity (a motor torque in the proposed case study). A second CNN is trained to give as output the boundary conditions [(BCs), expressed in terms of fields or potentials] to be used as BC of a reduced finite-element method (FEM) model, created in order to still be able to give the correct value of the output quantity. In the optimization phase, the torque properties are evaluated by the trained CNN, and only a reduced percentage of cases are reevaluated by either the full FEM model or the reduced FEM model. The overall computational time of the optimization procedure is significantly reduced.
Spatio-Temporal Context Modelling for Speech Emotion Classification

Speech emotion recognition (SER) is a requisite for emotional intelligence that affects the understanding of speech. One of the most crucial tasks is to obtain patterns having a maximum correlation for the emotion classification task from the speech signal while being invariant to the changes in frequency, time and other external distortions. Therefore, learning emotional contextual feature representation independent of speaker and environment is essential. In this paper, a novel spatiotemporal context modelling framework for robust SER is proposed to learn feature representation by using acoustic context expansion with high dimensional feature projection. The framework uses a deep convolutional neural network (CNN) and self-attention network. The CNNs combine spatiotemporal features. The attention network produces high dimensional task-specific features and combines these features for context modelling, which altogether provides a state-of-the-art technique for classifying the extracted patterns for speech emotion. Speech emotion is a categorical perception representing discrete sensory events. The proposed approach is compared with a wide range of architectures on the RAVDESS and IEMOCAP corpora for 8-class and 4-class emotion classification tasks and remarkable gain over state-of-the-art systems are obtained, absolutely 15%, 10% respectively.
Sequential Estimation of Steady-State Quantiles: Some New Developments in Methods and Software

Recent developments are summarized concerning Sequest and Sequem, sequential procedures for estimating nonextreme and extreme steady-state quantiles of a simulation output process. The procedures deliver point and confidence-interval (CI) estimators of a given quantile, where each CI approximately satisfies given requirements on its coverage probability and its absolute or relative precision. The public-domain Sequest software now includes both procedures. The software is applied to a user-supplied dataset exhibiting warm-up effects, autocorrelation, and a multimodal marginal distribution. For the simulation analysis method of standardized time series (STS), we also sketch an elementary proof of a functional central limit theorem (FCLT) that is needed to develop STS-based quantile-estimation procedures when the output process satisfies a conventional density-regularity condition and either (i) a geometric-moment contraction condition and an FCLT for a related binary process, or (ii) conventional strong-mixing conditions.
A Metaheuristic-based Emotional ANN (EmNN) Approach for Rainfall-runoff Modeling

This paper presents a new development in recent machine learning model, a particle swarm optimization (PSO) based Emotional neural network (PSO-EmNN), for daily rainfall-runoff modeling of catchment Seaton at Trebrownbridge, Cornwall, United Kingdom. The EmNN is inspired by neurophysiological information of brain which simulates emotions as additional parameters with conventional weights and biases to improve the networks learning ability. The PSO is used for tuning the parameter set of EmNN to get optimal values of parameters. The performance of the developed model is validated through RR modeling. Further a PSO based artificial neural network (PSO-ANN) is used to compare the efficiency of the proposed approach. The result demonstrates the superiority of the PSO-EmNN over the PSO-ANN in terms of prediction accuracy.
Efficient Analysis of Inhomogeneous Dielectric Waveguides With Dielectric Strips and Microstrips Using Discrete Mode Matching Method

In this article, the discrete mode matching (DMM) method is extended to characterize multilayered transmission line structures with inhomogeneous anisotropic dielectric layers using only 1-D discretization. We have thoroughly discussed the procedure to analyze hybrid dielectric waveguides with dielectric strips and/or microstrips, i.e., metallization at the interfaces. The analysis of the structures is based on the full-wave equivalent circuit approach, which gives the generalized relation of the field components of the multilayered structure and the corresponding system equation. Accurate results are obtained with very less computational effort. The dispersion curves and the electric field intensity distributions are presented for various dielectric waveguides and validated with the commercial software.
The Number of Random Restarts Required to Identify all Solutions to a Nonlinear System: Applications to Global Stochastic Optimization

We consider the question of identifying the set of all solutions to a system of nonlinear equations, when the functions involved in the system can only be observed through a stochastic simulation. Such problems frequently arise as first order necessary conditions in global simulation optimization problems. A convenient method of "solving" such problems involves generating (using a fixed sample) a sample-path approximation of the functions involved, and then executing a convergent root-finding algorithm with several random restarts. The various solutions obtained thus are then gathered to form the estimator of the true set. We investigate the quality of the returned set in terms of the expected Hausdorff distance between the returned and true sets. Our message is that a certain simple logarithmic relationship between the sample size and the number of random restarts ensures maximal efficiency.
A New Improved Convolutional Neural Network Flower Image Recognition Model

In order to improve the accuracy of the flower image recognition, a convolutional neural network (A-LDCNN) model based on attention mechanism and LD-loss (Linear Discriminant Loss Function) is proposed. Unlike traditional CNN (Convolutional Neural Networks), A-LDCNN uses the VGG-16 network pre-trained by ImageNet to perform feature learning on preprocessed flower images. The attention feature is constructed by fusing the local features of the multiple intermediate convolution layers with the global features of the fully connected layer and using it as the final classification feature. LDA (Latent Dirichlet Allocation) is introduced into the model to construct a new loss function LD-loss, which participates in the training of CNN to minimize the feature distance in class and maximize the feature distance between classes, and to solve the problem of Inter-class similarity and intra-class difference in flower image classification. Classification experiments show that the accuracy of A-LDCNN is 87.6%, which is higher than other traditional networks and can realize the accurate recognition of flower images under natural conditions.
A Cross-Corpus Study on Speech Emotion Recognition

For speech emotion datasets, it has been difficult to acquire large quantities of reliable data and acted emotions may be over the top compared to less expressive emotions displayed in everyday life. Lately, larger datasets with natural emotions have been created. Instead of ignoring smaller, acted datasets, this study investigates whether information learnt from acted emotions is useful for detecting natural emotions. Cross-corpus research has mostly considered cross-lingual and even cross-age datasets, and difficulties arise from different methods of annotating emotions causing a drop in performance. To be consistent, four adult English datasets covering acted, elicited and natural emotions are considered. A state-of-the-art model is proposed to accurately investigate the degradation of performance. The system involves a bi-directional LSTM with an attention mechanism to classify emotions across datasets. Experiments study the effects of training models in a cross-corpus and multi-domain fashion and results show the transfer of information is not successful. Out-of-domain models, followed by adapting to the missing dataset, and domain adversarial training (DAT) are shown to be more suitable to generalising to emotions across datasets. This shows positive information transfer from acted datasets to those with more natural emotions and the benefits from training on different corpora.
Revisit Point Cloud Recognition From the Viewpoint of Data Mixture

In recent years, point cloud based data analysis has attracted lots of attentions of researchers from many different fields because of its simplicity and effectiveness. As a fundamental research, point cloud representation and recognition plays an important role. Although existing works achieved some good performances, they still cannot take full use of the information hidden in training data. This paper revisits the problem of point cloud representation and recognition from the viewpoint of data augmentation without incorporating additional data or more annotations. Different from existing works on 1D or 2D data, our proposed approach deals with a more complicated problem in three dimensional space for point cloud representation and recognition by mixing various training data from different object categories, which could help the classifier to better optimize the data-driven parameters. To validate the performance of our proposed approach, the popular used ModelNet40 dataset is employed as the standard benchmark. By carrying out comprehensive experiments under many different conditions, the experimental results show that our mixture method works positively towards improving the recognition performance of point cloud.
Optimization of heart sound feature combination for HCM analysis based on BPSO

An optimization method of heart sound feature combination based on binary particle swarm optimization (BPSO) is proposed to analyze hypertrophic cardiomyopathy (HCM), which is a typical cardiovascular disease. The analysis procedure mainly includes feature extraction, feature combination optimization and HCM analysis. Firstly, 13-dimension energy features are extracted by the wavelet transformation. Secondly, feature combinations are selected by the proposed optimization method for classifying the normal and HCM heart sounds as well as the different types of HCM heart sounds. Lastly, 362 clinical cases including normal and HCM heart sounds are used to verify the validity of the proposed optimization method for heart sound feature combination, compared with principle component analysis (PCA), genetic algorithm (GA) and the analysis method without optimization of heart sound features. The average accuracy by our proposed optimization method, considering the number of features selected out, reaches to 20.91%, higher than the results utilizing feature combinations obtained by the other methods. In addition, the proposed optimization method of feature combination has potential for analyzing different kinds of HCM cases.
Power Allocation Schemes Based on Deep Learning for Distributed Antenna Systems

In recent years, a lot of power allocation algorithms have been proposed to maximize spectral efficiency (SE) and energy efficiency (EE) for the distributed antenna systems (DAS). However, the traditional iterative power allocation algorithms are difficult to be implemented in reality because of their high computational complexity. With the development of machine learning algorithms, it has been proved that the machine learning method has excellent learning ability and low computational complexity, which can approximate the traditional iterative power allocation well and be easily to be implemented in reality. In this paper, we propose a new deep neural network (DNN) model for DAS. From the perspective of machine learning, traditional iterative algorithms can be regarded as a nonlinear mapping between user channel realizations and optimal power allocation schemes. Therefore, we train the DNN to learn the nonlinear mapping between the user channel realizations and the corresponding power allocation schemes based on the traditional iterative algorithm. Then, a power allocation schemes based on DNN method is developed to maximize SE and EE for DAS. The simulation results show that the proposed scheme can not only obtain the almost similar performance as the traditional iterative algorithm, but also reduce much online computational time.
An Improved Vivaldi Antenna Design for Through- Wall Radar Imaging

This paper presents an improved Vivaldi antenna design for Through Wall Radar Imaging (TWRI). It is one of the most emerging radar technologies that has different applications in real-life situations. Radar uses EM waves to penetrate through the wall, and then detects the objects behind the wall. Antenna plays a very important role in TWRI system. Vivaldi antenna can be used as an ideal candidate for this system. The antenna presented here operates from the frequency 2.5 GHz to 11.3 GHz and shows a constant radiation pattern in the operating bandwidth. The gain obtained is around 6 dBi. The effect of various antenna parameters are also studied in this paper. Furthermore, corrugations and grating elements are added to analyze the effects on gain and bandwidth. With the above enhancements in design, a bandwidth of 8.8 GHz and an improved gain of around 9.6 dBi has been achieved.