Miracle-dz/UniFAR-FTUs huggingface dataset:
Method for generating distance field of object

A method generates a distance field of an object, where the distance field includes a set of cells and the object includes a set of outlines. A processor is included for performing steps of the method. A first cell of the set of cells enclosing the object is determined. An outside reconstruction method is associated with the first cell. A set of boundary cells of the set of cells is determined, where each boundary cell encloses a portion of a particular outline in the set of outlines. A boundary reconstruction method is associated with each boundary cell. A final cell of the set of cells is determined enclosing the object. An inside reconstruction method is associated with the final cell. The outside and boundary reconstruction methods are used to determine combined distances, which are further processed by the inside reconstruction method to generate the distance field of the object.
Method of Reproduction of the Luminous Flux of the LED Light Sources by a Spherical Photometer

In connection with transition to energy-efficient temporally stable light-emitting diodes (LEDs) lighting, a problem of ensuring the traceability of results of measurement of characteristics of light sources arises. The problem is related to existing measurement standards of luminous flux based on spherical photometers optimized for the reference incandescent lamps with a relative spectral characteristic different from the spectrum of the LEDs. We propose a method for reproduction of the luminous flux, which solves this problem.
An a priori error analysis of an HDG method for an eddy current problem

This paper concerns itself with the development of an a priori error analysis of an eddy current problem when applying the well‐known hybridizable discontinuous Galerkin (HDG) method. Up to the authors' knowledge, this kind of theoretical result has not been proved for this kind of problems. We consider nontrivial domains and heterogeneous media which contain conductor and insulating materials. When dealing with these domains, it is necessary to impose the divergence‐free condition explicitly in the insulator, what is done by means of a suitable Lagrange multiplier in that material. In the end, we deduce an equivalent HDG formulation that includes as unknowns the tangential and normal trace of a vector field. This represents a reduction in the degrees of freedom when compares with the standard DG methods. For this scheme, we conduct a consistency and local conservative analysis as well as its unique solvability. After that, we introduce suitable projection operators that help us to deduce the expected a priori error estimate, which provides estimated rates of convergence when additional regularity on the exact solution is assumed.
Hydrogenation and Deuteration of C2H2 and C2H4 on Cold Grains: A Clue to the Formation Mechanism of C2H6 with Astronomical Interest

We quantitatively investigated the hydrogen addition reactions of acetylene (C2H2) and ethylene (C2H4) on amorphous solid water (ASW) at 10 and 20 K relevant to the formation of ethane (C2H6) on interstellar icy grains. We found that the ASW surface enhances the reaction rates for C2H2 and C2H4 by approximately a factor of 2 compared to those on the pure-solid C2H2 and C2H4 at 10 K, probably due to an increase in the sticking coefficient and adsorption energy of the H atoms on ASW. In contrast to the previous proposal that the hydrogenation rate of C2H4 is orders of magnitude larger than that of C2H2, the present results show that the difference in hydrogenation rates of C2H2 and C2H4 is only within a factor of 3 on both the surfaces of pure solids and ASW. In addition, we found the small kinetic isotope effect for hydrogenation/deuteration of C2H2 and C2H4 at 10 K, despite the requirement of quantum tunneling. At 20 K, the reaction rate of deuteration becomes even larger than that of hydrogenation. These unusual isotope effects might originate from a slightly larger number density of D atoms than H atoms on ASW at 20 K. The hydrogenation of C2H2 is four times faster than CO hydrogenation and can produce C2H6 efficiently through C2H4 even in the environment of a dark molecular cloud.
Advances in understanding combustion phenomena using non-premixed and partially premixed counterflow flames: A review

Counterflow flames provide an ideal platform for understanding the flame structure and as a model to study the effect of physical and chemical perturbations on the flame structure. This article reviews the advances made in the understanding of combustion dynamics and chemistry through experimental and numerical studies in counterflow non-premixed and partially premixed flames. Key contributions on fundamental aspects such as extinction, ignition and effect of perturbations on the stability of diffusion flames are first summarized and analysed. The review then focuses on the progress made in the understanding of the effect of inert particles and flame suppressants on the flame characteristics. A review of detailed studies on edge flames facilitates further understanding of local quenching and re-ignition phenomena in highly turbulent flames. The influence of radiation model and unsteady flow-conditions on the flame kinetics and dynamics along with work on NOx kinetics has been discussed. The review also outlines that specific experiments need to be carried out over a wide range of conditions for further understanding and validation of numerical models.
Fe-incorporated 
TiO2
 nanotube arrays: Electronic structure and magnetic response

Incorporating Fe atoms into the lattice is shown to significantly alter electronic and magnetic properties of TiO2 nanotubes synthesized by electrochemical anodization of Ti-Fe alloy sheets. The effects of Fe incorporation on the nanotube morphology, crystallinity, crystal structure, magnetic behavior and electronic structure were investigated with crystallographic and magnetic probes, including synchrotronbased spectroscopy. Results indicate that the iron cations predominately adopt the Fe configuration, leading to a large increase of the electronic density of states at the Fermi energy. This increase is anticipated to provide enhanced catalytic action, for instance, in the degradation of water and of air pollutants. These results provide insight for tailoring the functionality of these nanostructures for energyrelated applications.
A Comparison of Neonatal Outcomes of Age-Matched, Growth-Restricted Twins and Growth-Restricted Singletons

A retrospective cohort study was performed to determine whether growth-re-stricted fetuses of a twin gestation are at increased risk of adverse neonatal outcome compared with growth-restricted singletons. One cohort was comprised of 48 growth-discordant twin pregnancies in which the birth weight of the smaller twin was less than the tenth percentile. The neonatal outcomes of the 48 growth-restricted twin infants were compared with a cohort of 96 singleton infants matched by gestational age, degree of growth restriction, and gender. Outcomes evaluated included: length of stay, days of assisted ventilation, and diagnoses of morbidities of prematurity, congenital abnormalities, and neonatal death. No significant difference was detected in rates of neonatal morbidity or mortality. The overall neonatal death rate in the twins was 125 of 1000 and in the singletons was 104 of 1000 (Odds ratio 1.2, 95% confidence interval [CI]0.4-3.3). Growth-restricted twins have similar rates of adverse neonatal outcomes as compared with growth-restricted singletons. Both have high rates of morbidity and neonatal death. Twins and singletons should receive comparable diagnostic evaluation and antepartum management for growth restriction.
The modified Jiles-Atherton model for the accurate prediction of iron losses

The Jiles-Atherton (JA) model is a physics-based model which translates into a simple first order differential equation with five material constants known as the JA parameters. However, JA parameters identified from the saturated B-H loop cannot predict the inner loops accurately which poses a major hindrance for its use in the Finite Element (FE) based simulations. In this work, we have proposed a new approach which assumes one of the JA parameters to be a function of magnetic field intensity H or magnetic flux density B and requires the knowledge of inner loops. The improvement in the accuracy of the modified JA model has been demonstrated by comparing its predictions with the measurements and one of the already published works.
Planetary Nebula Associations with Star Clusters in the Andromeda Galaxy

The objective of this research is to find planetary nebulae (PNe) and star cluster associations in the Andromeda galaxy (M31). To identify PNe in M31, a red-green-blue image is obtained using 3 emission line filters of H↵, 5007Å [OIII], and 6717, 6731Å [SII], using images from the Local Group Survey. By completing a visual search of the RGB images, 477 PN candidates are identified. In order to find star clusters associated with the PN candidates, an RGB image is made using the Panchromatic Hubble Andromeda Treasury survey. Filter F814W is used as red and filter F475W is used as both the green and blue colors. Two PN/open cluster (OC) association candidates appear to be the best cases to focus on. In one of the cases, the PN candidates is not confirmed as a true PN. In the other case, the PN appears to be confirmed as a true PN and so there is a good chance that PN/OC association is true for this case.
N-grams in Texts Categorization

This paper deals with automatic classification of documents; this is performed by a supervised classification since it operates on a set of preset classes. The suggested approach is original since it is based on a vector representation of the documents centred not on the words but on the n-grams of characters for n varying from 2 to 5. Considering the significant number of the n-grams generated for each class, we used in our work the law of χ 2 to reduce the number of the characteristic n- grams of each class. The weighting of the vectors was done by using the measurement of the TFIDF, and for the calculation of the distance between two vectors, we used the method of the Cosine. The experiments were done on two well-known corpora in the community of categorization, the Reuter 21578 and the 20Newsgroups. Evaluation of the approach was performed by using a function combining both precision and recall. The results obtained show that the technique of the n-grams is very effective in the field of the categorization of texts.
A Survey on Static Modeling of Miniaturized Pneumatic Artificial Muscles With New Model and Experimental Results

Pneumatic artificial muscles (PAMs) are linear pneumatic actuators consisting of a flexible bladder with a set of in-extensible fibers woven as a sheath on the outside. Upon application of pressure, the actuators contract or expand based on the angle of winding of the braid. Due to the similarity in properties of the actuators with biological muscles and the advantages thereof, these are increasingly being used in many robotic systems and mechanisms. This necessitates the development of mathematical models describing their mechanics for optimal design as well as for application in control systems. This paper presents a survey on different mathematical models described in the literature for representing the statics of PAM. Since it is observed that the validity of existing static models, based on energy balance methods, is not consistent with changes in parameters when applied to their miniaturized versions of pneumatic artificial muscles (MPAM), a new model has been proposed. The model takes into account material properties of the bladder as well as the end-effects which are prominent for MPAMs. Experiments conducted on fabricated MPAMs, of different diameters and lengths, show that the proposed model predicts the pressure-deformation characteristics of MPAMs with maximum error of less than 7%.
Social network analysis using k-Path centrality method

k-Path centrality is deemed as one of the effective methods to be applied in centrality measurement in which the influential node is estimated as the node that is being passed by information path frequently. Regarding this, k-Path centrality has been employed in the analysis of this paper specifically by adapting random-algorithm approach in order to: (1) determine the influential user’s ranking in a social media Twitter; and (2) ascertain the influence of parameter α in the numeration of k-Path centrality. According to the analysis, the findings showed that the method of k-Path centrality with random-algorithm approach can be used to determine user’s ranking which influences in the dissemination of information in Twitter. Furthermore, the findings also showed that parameter α influenced the duration and the ranking results: the less the α value, the longer the duration, yet the ranking results were more stable.
Rotation-Invariant Point Cloud Segmentation With Kernel Principal Component Analysis and Geometry-Based Weighted Convolution

Learning a rotation-invariant (RI) representation is of significant importance for real-world point cloud segmentation that is perturbed by arbitrary rotations. Recent principal component analysis (PCA)-based methods provide an effective alternative to align point clouds and produce the RI representation under the preservation of global information. However, conventional PCA with 3-D coordinates cannot fully represent high-dimensional geometric structures like surfaces and curves and cannot uniformly align these structures for learning RI representation. In this paper, we propose a novel rotation-invariant method for point cloud segmentation, which leverages kernel PCA (KPCA) for aligning point clouds in a projected high-dimensional space via non-linear mapping and develops a Geometry-based Weighted Convolution (GWConv) to distinguish part boundaries during segmentation. Specifically, the KPCA produces a RI representation with polynomial kernels for effectively representing complicated geometric structures in point clouds. Moreover, the GWConv incorporates geometric structures into convolution and enhances neighboring points with similar geometry for fine-grained segmentation based on the RI representation. Experimental results demonstrate that the proposed method can achieve competitive performance with the state-of-the-arts and outperforms existing PCA-based methods in part segmentation on ShapeNet. Furthermore, it achieves evident performance gains on complicated 3-D shapes such as Earphone and Car and facilitates segmentation around the part boundaries.
A Fully Integrated dToF System-on-Chip with High Precision Using Adaptive Optical Power Control and Shifted Histogram-Bin Binning

This paper demonstrates a ranging sensor system with a configurable array of $16 \times 16$ single photon avalanche diodes (SPADs), a 940nm vertical cavity surface-emitting laser (VCSEL), a co-design VCSEL driver with tunable widths from 400ps to 3630ps full-width at half-maximum (FWHM) optical pulses and peak power from 30mW to 170mW, and an embedded core to implement adaptive optical power control and distance extraction. An adaptive optical power control and a shifted bins binning of the histogram (SBbH) method to achieve high-precision distance measurement both at short-range and long-range. We achieved a minimum distance of 20mm with an absolute error better than 20% (4mm), still with a precision better than 10mm at 7m. All of results are obtained with 80% reflectivity of target at 100 frame rate.
False Alarm Reduction in WSN Surveillance Application through ML techniques

To enhance target detection and tracking in real-world environments, it is crucial to consider the impact of weather and other environmental factors on sensor readings in addition to minimizing false alarms. This goal can be achieved through the implementation of machine learning algorithms that incorporate these parameters as predictors. To demonstrate the superiority of a machine learning-based solution in reducing false alarms and improving target detection accuracy in Wireless Sensor Networks (WSNs), the performance of algorithms such as K-Nearest Neighbors, Logistic Regression, Decision Trees, and Random Forest is compared. This paper incorporates environmental factors, such as weather, to improve target detection accuracy in real-world scenarios. The simulation results demonstrate the effectiveness of the proposed approach in reducing false alarms in WSNs using machine learning algorithms.
Alzheimer Detection using Different Deep Learning Methods with MRI Images

Alzheimer is one of the most common diseases that happens for older people. It is a brain disorder that damages human memory. This disease has several levels starting from forgetting a few things up to destroying the ability of doing the simplest tasks. Diagnosing this disease is always a challenging task, especially in the initial stages. In this work, several methods were proposed to detect four different classes of Alzheimer through Brain Magnetic Resonance Imaging (MRI) images. The classes are Mild Demented, Moderate Demented, Non-Demented, and Very Mild Demented. Despite the lack of datasets, a public augmented dataset which is available online for free is used and different methods. Deep learning custom convolution neural network using holdout was used. Additionally, some pre-trained models such as ResNet50 and VGG16 were used. Results were compared and discussed in terms of precision, recall and f-score. Additionally, overfitting was monitored and discussed in each training model. Our work achieved remarkably high and promising results that were more than 90% accuracy and more than 90% F1 score for all classes.
Virtual-Sensing for Active Noise Control Using Reflected Waves

Virtual sensing with active noise control reduces noise in a zone of quiet (ZoQ) where a physical error microphone is not present. Conventional virtual sensing is applied to the adaptive control of sound through a window. However, it is difficult to acoustically separate one room with many reflected waves into a quiet space and a space with noise sources. To achieve acoustic space separation, virtual sensing is required to estimate the time-varying primary path from the noise source to the ZoQ without a preliminary identification stage and to track time fluctuations. Therefore, in this paper, an acoustic path model is proposed, that can be used to observe the ZoQ from a physical error microphone, and the structure of virtual sensing is designed to cope with environments in which many reflected waves are present. The simulation results show that the proposed system can reduce noise without preliminary estimation of the primary path while tracking the fluctuation of the primary path.
An Adaptive Dead-Time Control Method for Gate Drivers Using Gate Current Measurement Enabling ZVS in High Frequency HV DC-DC Converters

This work presents a novel zero-voltage-switching technique and demonstrates its feasibility in two exemplary DC-DC buck converters. The proposed system makes use of the nonlinearity of the reverse transfer capacitance to infer the voltage across the switch. The proposed method does not require additional expensive high-voltage components at the switch node, without resorting to bandwidth and voltage-limited high-voltage processes. The concept allows for full integration in low-cost, high-performance CMOS nodes, thereby enabling modern gate drivers with additional intelligent digital feature-sets to be used in HV environments. The technique is demonstrated to work using 650 V GaN and SiC switches across wide ranges of dead-times, regulating these from hundreds down to single nanoseconds.
Dove: Shoulder-Based Opioid Overdose Detection and Reversal Device

Naloxone is a life-saving drug capable of reversing a fatal opioid overdose. Although this drug has existed for over 50 years, opioid overdose-related deaths have consistently risen and surpassed 120,000 globally in 2021. Opioids induce respiratory depression by activating $\mu\text{-opioid}$ receptors at specific sites in the central nervous system. This results in overdose deaths caused by slow and shallow breathing, also known as opioid-induced respiratory depression. 1.6 million individuals suffer from opioid use disorder annually, making them at high risk of overdose, primarily due to the increasing prevalence of Fentanyl. Over 52% of these deaths occur when the individual is alone. Immediate response to an overdose by delivering naloxone can save the individual's life. To solve this problem, we developed a closed-loop sensor-driven auto-injector that can determine a fatal overdose and inject naloxone. 76% of this population is willing to wear such a device on the shoulder, a canonical injection site. This paper presents the DOVE, a shoulder-based opioid overdose detection and reversal device. It noninvasively measures the subject's motion state and changes in blood oxygen levels (SpO2) along with the respiration state. These biomarkers are measured from the shoulder using an optical sensor and accelerometer to determine if a fatal overdose occurred. We evaluated our DOVE device against an FDA-cleared commercial pulse oximeter by inducing apneic events as they have very similar SpO2 trends to an overdose. Results show that SpO2 can be measured on the shoulder across different skin tones with an accuracy of 96.8% and a high Pearson correlation of 0.766 (p < 0.0001).
An Area-efficient Memory-based Architecture for P4-programmable Streaming Parsers in FPGAs

Moving toward software-defined networking and function virtualization, flexibility and reconfigurability of the network have become more and more critical. Packet parsing, the first processing stage of programmable switches, requires high performance and reconfigurability to allow implementing low-latency and highly flexible data networks. This paper proposes an overlay architecture for an FPGA-based P4-programmable streaming packet parser. The purpose of this architecture is to allow supporting different functionality with a fixed hardware design by changing a program stored in an embedded memory. This program is derived from the parser section of a P4 code, describing a parsing graph. This approach eliminates a pipeline of parsing blocks in favor of a single parsing block, thereby reducing the design's complexity. Our architecture offers an 11 Gb/s data rate on a Xilinx Virtex-7 XC7VX690 FPGA, while its implementation requires 312 LUTs and 1135 FFs.
A 61 mHz - 3.4 Hz High-pass Capacitively Coupled Analog Frontend with Tunnelling Biasing and Output DC Servo Loop

In this paper, a low-power capacitively coupled analog frontend with programmable high-pass cutoff frequency from 61 mHz to 3.4 Hz is proposed. The DC operating point of the input differential pair is settled by dynamic equilibrium of direct tunneling current. A DC servo loop (DCSL) based on duty-cycled operational transconductance amplifier (DC-OTA) has been implemented at the output nodes of the main frontend so that its impedance will not be affected. A minimum of 1.67 pA/V effective transconductance has been realized through the DC-OTA techniques in DCSL, which enable the frontend to achieve 61 mHz high-pass cutoff frequency with only a 10 pF on-chip capacitor. The frontend is implemented in standard $\mathbf{0.18}- \mu \mathbf{m}$ CMOS process and it consumes $\mathbf{2.72} \ \mu \mathbf{A}$ current in total, achieving a noise efficiency factor of 6.01 in 25 kHz bandwidth (61 mHz - 25 kHz).