Miracle-dz/UniFAR-FTUs huggingface dataset:
Monte Carlo Analysis of Hot Electron Transport and Impact Ionization in Silicon

A novel impact ionization model which is consistent with a realistic band structure of Si has been developed. The present model contains only one parameter directly corresponding to the average matrix element for the ionization transition. The ionization probability is calculated at every k-point in the first Brillouin zone and the intrinsic anisotropy of the ionization probability is explicitly taken into account. Employing the Monte Carlo procedure including the present ionization model, the ionization coefficients are calculated and a best-fitting to the experimental ionization coefficients is obtained when the value of the parameter in the ionization model is equal to that extracted from the first-principles calculations.
Comment modéliser les écoulements diphasiques compressibles sur des grilles hybrides

Les maillages hybrides offrent la flexibilite necessaire pour modeliser les ecoulements en milieu poreux a travers des structures geologiques de geometrie complexe. L'objet de cette etude est de developper des approches numeriques performantes pour simuler les ecoulements diphasiques compressibles en milieu poreux sur ces maillages hybrides.
Interaction Effect of Zinc and Elemental Sulfur on Their Uptake by Spring Wheat

Abstract A pot experiment was conducted to study the interaction between elemental Sulfur (S0) and zinc (Zn), the effect on solubility of soil Zn and soil-available Sulfur (S), and their uptake by spring wheat (Triticum aestivumL.). Elemental Sulfur was added at three rates (0, 50, and 100 mmol kg− 1 soil) and Zn was added in solution at four rates (0, 50, 100, 200 mg/kg soil) before planting. The plants were harvested after 40 days of growth. The result showed that soil pH decreased significantly with the S0 supply at each rate of Zn. However, Zn supply did not induce the soil pH change at each rate of S0. Concentration of soil-available S was significantly increased with an increase in S0 supply at each rate of Zn, whereas it did not vary with an increase in Zn supply at each rate of S0. Concentration of soil CaCl2-extractable Zn was significantly increased with an increase in Zn supply at each rate of S0, but a significant increase with S0 supply occurred only at the rate of 200 mg · kg− 1 Zn. The shoot and root yields of spring wheat did not differ significantly at each rate of S0 and Zn. The concentration of Zn in plant tissues and total Zn uptake by spring wheat were increased significantly with increasing Zn supply, and the concentration of Zn and total Zn in roots were both higher than that in shoots, except the total Zn uptake at the rate of 100 mg kg− 1 Zn without S0 supply. Concentration and uptake of Zn in spring wheat increased with an increase in S0 supply in each rate of Zn, except Zn uptake at the rate of 50 mg kg − 1 Zn. Concentrations of S in shoot and root of spring wheat increased significantly with an increase in S0 supply, and at the rates of 0 and 50 mmol · kg− 1 S0, the concentration of S in shoots was higher than that in roots. Conversely, at the rate of 100 mmol · kg− 1 S0, the concentration of S in roots was higher than that in shoots. Total S uptake increased significantly with an increase in S0 supply, and total S uptake in shoots was higher than that in roots, except rates, of 0 and 50 mg kg− 1 Zn without 100 mmol S0 supply. In addition, the distribution rate of total S (in shoot/root) decreased with an increase in S0 supply. In conclusion, S0 enhances the solubility of Zn and its uptake by spring wheat. The concentration of available S in soil and concentration of S in spring wheat increased with the increase of elemental Sulfur. Zn addition also influenced the concentration of S in spring wheat.
Conjoint Knowledge Discovery Utilizing Data and Content with Applications in Business, Bio-medicine, Transport Logistics and Electrical Power Systems

In Digital Enterprises Structured Data and Semi/Unstructured Content are normally stored in two different repositories, with the ﬁrst often being stored in relational Databases and the second in a content manager which is frequently at an external outsourcer. This storage of complementary information in two different silos has led to the information being processed and data mined separately which is undesirable. Effective knowledge and information use requires seamless access and intelligent analysis of information in its totality to allow enterprises to gain enhanced insights. In this paper, we develop techniques to carry out correlation of the information across different sources and then carryout out knowledge discovery across these complementary sources in a conjoint manner. The techniques developed in our research will then be used to address signiﬁcant issues in four application areas namely Business, Logistics, Bioinformatics and Electric Power Systems but potential applications with signiﬁcant impact are much more extensive.
Dermatoscopy Using Multi-Layer Perceptron, Convolution Neural Network, and Capsule Network to Differentiate Malignant Melanoma From Benign Nevus

Epiluminescence microscopy, more simply, dermatoscopy, entails a process using imaging to examine skin lesions. Various sorts of skin ailments, for example, melanoma, may be differentiated via these skin images. With the adverse possibilities of malignant melanoma causing death, an early diagnosis of melanoma can impact on the survival, length, and quality of life of the affected victim. Image recognition-based detection of different tissue classes is significant to implementing computer-aided diagnosis via histological images. Conventional image recognition require handcrafted feature extraction before the application of machine learning. Today, deep learning is offering significant choices with the progression of artificial learning to defeat the complications of the handcrafted feature extraction methods. A deep learning-based approach for the recognition of melanoma via the Capsule network is proposed here. The novel approach is compared with a multi-layer perceptron and convolution network with the Capsule network model yielding the classification accuracy at 98.9%.
An Accurate Persian Part-of-Speech Tagger

The processing of any natural language requires that the grammatical properties of every word in that language are tagged by a part of speech (POS) tagger. To present a more accurate POS tagger for the Persian language, we propose an improved and accurate tagger called IAoM that supports properties of text to speech systems such as Lexical Stress Search, Homograph words Disambiguation, Break Phrase Detection, and main aspects of Persian morphology. IAoM uses Maximum Likelihood Estimation (MLE) to determine the tags of unknown words. In addition, it uses a few deﬁned rules for the sake of achieving high accuracy. For tagging the input corpus, IAoM uses a Hidden Markov Model (HMM) alongside the Viterbi algorithm. To present a fair evaluation, we have performed various experiments on both homogeneous and heterogeneous Persian corpora and studied the effect of the size of training set on the accuracy of IAoM. Experimental results demonstrate the merit of the proposed tagger in achieving an overall accuracy of 97.6%.
Prediction of Pedestrian Risky Level for Intelligent Vehicles

In this paper, a Pedestrian Risky Level Prediction (PRLP) model based on the data collected by a vehicle-mounted sensing system is proposed. The prediction of pedestrian risky level is realized using a data-driven method, which contains two steps, namely pedestrian trajectory prediction and risky level classification. For trajectory prediction, an LSTM network is built and trained to predict the trajectory of pedestrian in the onboard camera view. For risky level classification, to avoid manually selecting thresholds for different risky levels, a hybrid method integrating K-Means Clustering (KMC), Kernel Principal Components Analysis (KPCA) and Kernel Support Vector Machine (SVM) is applied. The pedestrian risky level is divided into four classes by KPCA-KMC, namely low risk, medium risk, high risk and super-high risk. Experimental results show the capability of PRLP model to predict the risky level of pedestrian, which could help the intelligent vehicle to assess the risk of collision with pedestrians, and provide safety warnings to both vehicle and pedestrian.
Nuclear Magnetic Resonance Experiments using Laser-Polarized Noble Gas

Three different nuclear magnetic resonance (NMR) experiments using laserpolarized noble gas are reported. The first experiment demonstrates the feasibility of fast low magnetic field (∼ 20 G) magnetic resonance imaging (MRI) with comparable resolution and signal-to-noise of conventional high magnetic field (∼ 1 T) MRI. In addition, advantages of low field imaging over high field imaging are shown for certain applications. The second experiment uses NMR to observe the phenomenon known as “persistence” (i.e., the probability that a spin has not changed sign up to time t) in the diffusion of laser-polarized noble gas. The result obtained is consistent with theory and numerical simulations, and is the first measurement of persistence in 1-D diffusion in any system. The final NMR experiment examines the spin relaxation of polarized Xe in coated glass cells. In particular, a “double resonance” method is employed to enhance the coupling between H atoms in the surface coating and Xe adsorbed onto the surface.
Approaches for efficient data extraction from data cube structure

The goal of the article is to describe the problems for the efficient construction of a data cube structure and to present several approaches for efficient extraction of information from a data cube structure. Approaches to reduce the time to retrieve data from a data cube by building a direct connection to the data using the Object-Oriented Model Data Cube (class) are presented. Other methods for reducing the data sparsity of a data cube structure by detecting semantic dependence of the data and dividing the data cube into higher-density subcubes are also described.
2n+1-valued SSS-Net: Uniform Shift, Channel Sparseness, and Channel Shuffle

Convolutional neural networks (CNNs) are primarily a cascaded set of pattern recognition filters, which are trained by big data. It enables us to solve complex problems of computer vision applications. A conventional CNN requires numerous parameters (weights) and computations. In this study, we propose SSS-Net using uniform channel shift, weight sparseness, and channel shuffle operations. We prove that a conventional k × k kernel convolution can be divided into k2 of channel shift operations and a point-wise (1 × 1) convolution. We develop a 2n+1-valued quantization with zero weight sparseness technique. We investigate a weight distribution for a post-training CNN, and almost all weights are close to zero. We eliminate such small weights to reduce the model size and non-zero weights are efficiently quantized by 2n-valued one. We show an algorithm for a uniform shift with quantization. Since a uniform shift operation requires no multiplications, the amount of computation becomes zero. We train SSS (Shift, Sparseness, and Shuffle)-Net by ImageNet 2012 benchmark image. Compared with existing CNN models, our SSS-Net is a smaller model size and MAC operations, while it has considerable recognition accuracy.
Multi-level Topological Analysis Framework for Multifocal Diseases

Feature engineering and deep learning have been widely used to characterize imaging features in medical applications. However, the importance of geometric structure and spatial relationship of multiple lesions for multifocal diseases are often neglected by these methods. In this paper, we propose a Multi-level Topological Analysis (MTA) framework based on persistent homology, by capturing global-level topological invariants underlying geometric structure and local-level spatial adjacency relationship among lesions and local structure. In particular, a novel Filtration-based Community Discovery algorithm is designed to efficiently partition lesions to local clusters. Experiments demonstrate that our MTA framework outperforms five state-of-the-art persistent homology methods and achieved AUC $824\pm 0.132$ on a task of differentiating two multifocal diseases, Multiple Sclerosis and Neuromyelitis Optica.
Analysis of Tourists' Nationality Effects on Behavior-based Emotion and Satisfaction Estimation

Smart tourism is attracting attention of researchers in recent years. Its technologies can be used by tourists in order to obtain useful information during sightseeing with smart devices etc. To provide suitable and personalized tourism information according to the situation of tourists, understanding psychological status during sightseeing, especially, emotional status and satisfaction level, is important. We assume that the psychological status of tourists is appearing and represented through unconscious behaviors during sightseeing such as head/body movements and facial/vocal expressions, and have proposed methods to estimate emotion and satisfaction statuses by sensing and analyzing tourists' behaviors. Through in-the-wild experiments with 22 participants, we found that the difference in tourists' attributes might give effects for the estimation. In this paper, we have statistically analyzed those effects, focusing on tourists' nationality. As a result of the two-way ANOVA, we found the interaction effect (disordinal interaction) between tourists' nationality and estimation performance, the main effect in differences of features, and the main effect in differences of tourists' nationality. The results imply that we need to take tourists' nationality into account for building estimation models. Contribution: We have statistically analyzed the nationality effects on tourist emotion and satisfaction estimation, and confirmed significant differences in feature contributions for estimation models.
On-Chip-Antennas: Next Milestone in the Big World of Small Satellites—A Survey of Potentials, Challenges, and Future Directions

Small satellites have revolutionized the space industry because of their variety of benefits over large satellites. Antennas are one of the key components of the small satellites, whose performance is very crucial for the overall performance of the satellite-based wireless systems. The stringent requirements of modern small satellites, such as the small size and mass, low-power, low-loss, and low-cost dictate the need for the alternative antenna technologies, as the traditionally employed off-chip antenna technology faces a number of severe constraints to meet this set of requirements. On-chip-antennas (OCAs) are one of such promising choices, which are more favorable than off-chip antennas in this respect. This article, therefore, presents a comprehensive survey of OCA capabilities for modern small satellite (micro-satellite, mini-satellite, nano-satellite, pico-satellite, femto-satellite, cubsat) applications for the first time. A number of key challenges posed by OCAs for small satellite applications are investigated and discussed analytically. The article also delineates a few future perspectives in this regard. This article will not only highlight a number of novel research opportunities for the relevant researchers but also urge them to propose innovative solutions for OCA design challenges for these applications.
Outage Analysis of Uplink NOMA Based FD Decode-and-Forward Cooperative Relay System

In this paper, a new non-orthogonal multiple access (NOMA) based full-duplex decode-and-forward relay (NOMA-FDR) system is proposed and analyzed. In the proposed model, the uplink NOMA concept is utilized to transmit symbols with the help of a full-duplex relay. An exact closed form expression for the outage probability of the transmitted signal in the proposed system model is derived. The outage probability of the proposed system model is compared with the recently proposed models. The paper also explore the effect of power allocation factors and detection threshold on the outage performance. Monte Carlo simulations are performed to verify the accuracy of the derived analytical results.
Melt-Pool Defects Classification for Additive Manufactured Components in Aerospace Use-Case

One of the crucial aspects of additive manufacturing is the monitoring of the welding process for quality assurance of components. A common way to analyse the welding process is through visual inspection of melt-pool images to identify possible defects in manufacturing. Recent literature studies showed the potential use of prediction models for defects classification to speed up the manual verification criteria since a huge data is generated from the additive manufacturing. Although a huge image data is available, the data needs to be labelled manually by experts which results in small sample datasets. Hence, to model small sample sizes and also to acquire the importance of parameters, we opted a traditional machine learning method, Random Forests (RF). For feature extraction, we opted for the Polar Transformation to explore its applicability using the melt-pool image dataset and a publicly available shape image dataset. The results show that RF models with Polar Transformation performed the best on our case study datasets and the second-best for the public dataset when compared to the Histogram of Oriented Gradients, HARALICK, XY-projections of an image, and Local Binary Patterns methods. As such, the Polar Transformation can be considered as a suitable compact shape descriptor.
LiDAR Observations by Motion Compensation and Scan Accumulation

In dynamic localization problems, the observations used from exteroceptive sensors are usually obtained from a single measurement. However, there are cases where the current measurement is not sufficient to detect the referenced landmark or to get a sufficient level of accuracy. In this study, a point cloud accumulation strategy is used to improve the resolution of a LiDAR sensor along its sparse axis. In particular, we are interested in the detection of markings transverse to the road axis in order to improve the accuracy of localization of an autonomous vehicle when approaching intersections or roundabouts. We present a method that allows the construction of an accurate observation with an associated observation model based on a High-Definition (HD) map through an accumulation of scans as the vehicle moves, by compensating the vehicle motion. The parameters of the accumulator are studied in terms of detection and accuracy. The quality of the observations and their impact on the localization quality are analyzed using real experiments carried out with an experimental vehicle equipped with a low-cost GNSS receiver, dead-reckoning sensors and a ground truth system.
Malware Dataset Generation and Evaluation

With the rapid growth of technology and IT-enabled services, the potential damage caused by malware is increasing rapidly. A large number of detection methods have been proposed to arrest the growth of malware attacks. The performance of these detection methods is usually established using raw or feature datasets. The non-availability of adequate datasets often becomes a bottleneck in malware research. To address this issue, this paper presents two malware feature datasets on two different platforms to support validation of the effectiveness of a malware detection method. We evaluate the usefulness of our datasets in a supervised framework.
Identifying the Operational Design Domain for an Automated Driving System through Assessed Risk

Assuring the safety of autonomous vehicles is one of the most significant challenges in the automotive industry. Tech companies and automotive manufacturers use the idea of Operational Design Domain (ODD) to indicate where their Automated Driving Systems (ADS) can operate safely. By definition from SAE J3016, an ODD defines where the ADS is designed to operate. However, it is loosely defined in no particular format, and it is unclear how exactly to formulate the ODD, which leaves it up to the ADS developer to determine. This paper proposes a methodology to identify an ODD for an ADS with statistical data and risk tolerance, where the identified ODD is constituted of a geographical map where the risk of ADS operation is lower than the pre-determined risk threshold for a given set of environmental conditions. Two different ADSs are run through this method as an example to showcase the methodology and link the identified ODD directly to the calculated performance of the ADSs. This systematically generated ODD can mitigate potential safety issues by informing the limitations of the ADS to safety drivers, through geographic and environmental boundaries.
Voronoi-based Geometric Distributed Fleet Control of a Multi-Robot System

A new distributed algorithm is presented for waypoint navigation of a multi-robot system. The proposed two-level architecture (reference generator and local controller) exploits Voronoi partitioning and purely geometric considerations to distributively generate references for each robot in order to ensure collision avoidance and convergence of the fleet to the waypoint. Flexibility in the obtained formation pattern is made possible by the algorithm, by not pre-fixing as usually done its geometric form. In addition, the gain tuning is easy and the setting allows to naturally obtain certain formation patterns and adjust the rigidity of the fleet. Moreover the distributed nature of the algorithm also allows robustness to online modification of the number of vehicles (in the fleet or within range of communication), also addressing the 2-robot scenario. Field experiments on ground mobile robots are provided to illustrate the performance of the algorithm.
Circularly Polarized MTS Based Antenna Design for C Band Applications

A new broadband metasurface (MTS) circularly polarized (CP) patch array utilizing a sequential-phase feeding network is presented. In this study, the MTS method is used to increase the 3-dB axial ratio (AR) and impedance bandwidths. The prototype of the proposed array has been fabricated to validate designed parameters. The measured S11<-10dB is 2.1 GHz (4.5-6.6 GHz), and the measured 3-dB AR bandwidth is 1.3 GHz (4.9-6.2 GHz). The measured peak gain is about 16.4dBic and the gain variation is less than 3 dB within the AR bandwidth.
An Information Driven Approach For Ego-Lane Detection Using Lidar And OpenStreetMap

Localizing the vehicle in its lane is a critical task for any autonomous vehicle. By and large, this task is carried out primarily through the identification of ego-lane markings. In recent years, ego-lane marking detection systems have been the subject of various research topics, using several inputs data such as camera or lidar sensors. Lately, the current trend is to use high accurate maps (HD maps) that provide accurate information about the road environment. However, these maps suffer from their availability and their price tag. An alternative is the use of affordable low-accurate maps. Yet, there is relatively little work on it. In this paper, we propose an information-driven approach that takes into account inaccurate prior geometry of the road from OpenStreetMap (OSM) to perform ego-lane marking detection using solely a lidar. The two major novelties presented in this paper are the use of the OSM datasets as prior for the road geometry, which reduces the research area in the lidar space, and the information-driven approach, which guarantees that the outcome of the detection is coherent to the road geometry. The robustness of the proposed method is proven on real datasets and statistical metrics are used to highlight our method's efficiency.