Miracle-dz/UniFAR-FTUs huggingface dataset:
Behavior of Electrons in the Bullet in the Ar/O₂ Plasma Jet in Changing Oxygen Concentration and the Applied Voltage

This work presents a systematic numerical investigation on the behaviors of electrons in the bullet in the Ar/O2 plasma jet in changing oxygen concentration and the applied voltage, based on a needle-plate discharge configuration and a 1-D particle-in-cell Monte-Carlo collision (PIC-MCC) method. The increase in oxygen concentration allows more electrons to be scattered toward the target, i.e., more electrons to most probably interact with the target, and induces a dramatic increase of reactive oxygen species (ROS), but leads to the decrease of the bullet velocity and average electron energy. In addition, there is a weak correlation between the electron density and ROS generation. When increasing the applied voltage, the electrons scattered toward the target decrease, however, the majority of these electrons, above 70%, are of energy higher than the threshold inducing a dissociative electron attachment (DEA) to water molecule, and meanwhile, there is an evident increase of high-energy electrons being able to excite water molecules. Also, there is a strong correlation between the electron density and ROS generation. The present results are also compared with those in the Ar/H2O plasma jets, indicating qualitative and quantitative differences.
Adsorption Models for Methane in Shales: Review, Comparison, and Application

Finding an optimized adsorption model to estimate the true adsorbed quantity of methane in shale at reservoir conditions is fundamental for estimating the gas-in-place (GIP), and developing an accurate shale gas transport model. However, describing true methane adsorption behavior in shale is challenging because the density or volume of the adsorbed phase cannot be measured directly using current technology. There are several models available to describe the observed adsorption isotherms and extrapolate the true adsorbed quantity of methane, but a consensus model has not been reached by researchers. This work first revisits available absolute and Gibbs excess adsorption models for describing methane in shales. It then compares nine available adsorption models to assess the efficacy of each model in describing both high pressure and low pressure methane adsorption isotherms in shales. Three aspects of the adsorption model are compared: (1) the goodness-of-fit of each adsorption model, (2) interpretation of.
A Signal-Size Estimator Based on Correlation-Dimension For Auditory Signals

This paper presents an estimator of fractal signal sizes based on correlation fractal dimension as applied to auditory signals. Correlation fractal dimension has been proposed for characterization of signals coming from chaotic sources. Practical estimations are made possible using a Takens algorithm, producing different estimates in each embedding dimension. The estimator consists of four processes: (i) signal measures creation, (ii) covering-counting, (iii) critical-exponent estimation, and (iv) size calculation. We study the resulting estimates of controlled signals to validate the estimator as well as to come up with a calibration scheme. The paper further discusses a possible application of fractal sizes to characterize coughs to identify the presense of respiratory diseases, such as in Covid-19 pre-screening.
Robust Identification of Motor Unit Discharges From High-Density Surface EMG in Dynamic Muscle Contractions of the Tibialis Anterior

Investigation of motor unit (MU) activity provides a fundamental information of neuromuscular control. High density surface electromyogram (HDsEMG) decomposition based on the blind source separation techniques (BSS) has been performed to estimate discharge patterns of MUs. The decomposition techniques for HDsEMG signals were initially proposed for isometric contractions. Recently, the decomposition methods have been utilized for non-isometric muscle contractions. However, what level of dynamic muscle contraction is acceptable for the EMG decomposition techniques is still an open research problem. Thus, in the present study, we investigated the robustness of the EMG decomposition method in dynamic muscle contractions in the tibialis anterior (TA) muscle using a validation method of EMG decomposition by convolving the synthetic MU spike trains with experimentally identified MU action potentials (MUAPs), which change depending on the ankle joint angle. We found that the decomposition accuracy for dynamic contractions with ankle range of motion (ROM) up to 20° was comparable to that during isometric contraction. When ankle ROM was larger than 20°, discontinuously identified (i.e., misidentified) spike timings of MUs due to joint angle changes was significantly larger than those during isometric contraction. However, rate of misidentification due to ankle joint angle change was still lower than 5% up to ankle ROM of 30°. Additionally, we found that increase of ankle ROM decreased total number of identified MUs. Based on the results, the conventional EMG decomposition method is probably applicable for dynamic contractions with ankle ROM of 30° in TA.
A Sensor Fusion System with Thermal Infrared Camera and LiDAR for Autonomous Vehicles: Its Calibration and Application

Vision, Radar, and LiDAR sensors are widely used for autonomous vehicle perception technology. Especially object detection and classification are primarily dependent on vision sensors. However, under poor lighting conditions, dazzling sunlight, or bad weathers an object might be difficult to be identified with general vision sensors. In this paper, we propose a sensor fusion system with a thermal infrared camera and LiDAR sensor that can reliably detect and identify objects even in environments where visibility is poor, such as in severe glare and fog or smoke. The proposed method obtains intrinsic parameters by calibrating the thermal infrared camera and LiDAR sensor. Extrinsic calibration algorithm between two sensors is made to obtain the extrinsic parameters (rotation and translation matrix) using 3D calibration targets. This system and proposed algorithm show that it can reliably detect and identify objects even in hard visibility environments, such as in severe glare due to direct sunlight or headlights or in low visibility environments, such as in severe fog or smoke.
The Employees Survey of Enterprise Resources Planning System

ERP solutions have evolved over the years, and many are now typically web-based applications that users can access remotely. However, an ERP system can be ineffective if a company doesn't implement it carefully. The focus of this research is to describe how opinions, emotional intelligence situations, motivations and what types of training are applied in the company to make sure the ERP implementation process runs properly. The method used in this study is a survey method by distributing questionnaires, but it is not processed through statistics because it does not intend to test hypotheses. Questionnaires were given to respondents using close ended questions to get answers from respondents. The data collection also through interviews with Chairperson and HRD Manager where the research was conducted. The Result show that the Benefits and Function of ERP are accepted by employees who are supported by their high emotional intelligence. The Chairperson was satisfied with using ERP. Training can provide a change in mindset can provide permanent change and does not require expensive costs for implementation. The conclusion was that the ERP system agreed to support employees work, even though the first time they feel difficult to deal with the system. Coaching on employees that encourage employees can adaptation to the use of ERP. The Training must support the company's beliefs, attitudes, and values.
Freezing of Gait Detection Using Discrete Wavelet Transform and Hybrid Deep Learning Architecture

Freezing of gait (FoG) detection using wearable sensors plays an important role in both online and offline monitoring of Parkinson's disease patients. In a FoG detector, feature extraction is commonly considered as a critical part for distilling the sensor signals before the FoG classification. Manually extracted features with domain knowledge are widely used in conventional machine learning methods while recent deep learning algorithms introduce the automatic feature learning approach. In this paper, we propose a FoG detection framework, in which hand-crafted features are used as input to a hybrid deep learning model for further feature learning and classification task. The hand-crafted features with time-frequency representation are extracted from the raw sensor signal by using a multi-level discrete wavelet transform (DWT). A hybrid deep learning architecture constructed from two algorithms: convolutional neural network (CNN) and bidirectional long short-term memory network is then deployed to extract deep features and classify FoG events. For performance comparison purposes, experiments on different input data types and machine learning methods are carried out on the Daphnet public dataset.
A 443pW Accumulation-Mode Gate-Leakage Based Bandgap Reference for IoT Applications

The paper presents a sub-nW bandgap reference (BGR) that exploits the temperature variation of gate-leakage current in thin oxide devices operating in the accumulation region to generate the reference voltage. The incorporation of gateleakage transistors scales down the power consumption of the BGR to pico-watt level without using any large physical resistors or sophisticated techniques, thereby making it suitable for IoT applications. The BGR is designed in TSMC 65nm technology and occupies an area of 0.009mm2. Post layout simulation results show nominal and worst-case accuracies of 23ppm/°C and 44ppm/°C respectively in the temperature range of -40°C to 100°C. Without any trimming, an inaccuracy (±3σ) of 3% is observed for the reference voltage, showing its resilience to process variations. It also exhibits a typical line sensitivity of 0.063%/V in a supply range of 1.5V-3.8V and a PSRR of -65dB at DC and 1.8V supply. The power consumption is observed to be 443pW at 1.5V supply and nominal temperature.
BReML: A Breathing Rate Estimator Using Wi-Fi Channel State Information and Machine Learning

Breathing rate is one vital sign that might help identifying pathological conditions by its monitoring. This paper presents a novel breathing rate estimator that combines conventional Channel State Information (CSI) approaches with machine learning classifiers to provide a breathing rate estimation in a controlled environment. Results show that by extracting time and frequency domain features of CSI amplitude, as well as using a first estimation obtained with Fast Fourier Transform as a feature for feeding machine learning classifiers, the breathing rate can be accurately estimated.
Novel Mobile Mechanism Design for an Obstacle-Overcoming Robot Using Rotating Spokes

In this paper, a mechanism for overcoming high obstacles is proposed to broaden the application range of the exterior wall cleaning robot. The proposed one-degree-of-freedom wheel mechanism can overcome high obstacles by the rotation of a wheel with two different spoke lengths. To implement the inclined tilted wheel (ITW), two design variables were studied - a spoke assembly angle and a wheel shaft assembly angle. At the critical assembly angles of $\varphi = 45^{\circ }$ and $\theta = 135^{\circ }$ , the characteristics of the generated wheel trajectory does not invade the upper and lower space of the robot. From the trajectory, the space efficiency can be secured for thrust unit cleaning device. And the same contact characteristics can get with the existing wheel on the surface. To secure the orientation stability after overcoming a high obstacle, the reaction force analysis at each caster was performed. Through the analysis results, the orientation stability was secured without the distance compensation at the gantry during or after overcoming obstacles. To verify the obstacle-overcoming ability of the ITW, a 6m - high - test bench and a 0.3m - high - obstacles was manufactured. With a scenario defining an angular position of ITW, the ability of overcoming obstacle was confirmed in the test. In addition, the scenario was supplemented for stable overcoming to avoid a collision against obstacles through additional experiments.
Optimal Multi-user Transmission based on a Single Intelligent Reflecting Surface

Intelligent Reflecting Surface (IRS) is envisioned as a revolutionary solution for providing high data rate communication, better signal coverage and physical layer security through controlling the transmission environment of electromagnetic (EM) wave. In this paper, based on a single IRS and millimeter channel model, the optimal multi-user transmission sum rate is derived with the maximal ratio combining (MRC) beamformer. The conditions to achieve such a maximum sum rate are expressed as a function of placement of transceivers, number of IRS elements and its spacing. We first transformed the single IRS assisted non-scattering mmWave channel into a high-rank multiple-in multiple-out (MIMO) channel. The result shows that even without rich scattering, each single antenna user can enjoy the maximal passive beamforming gain while keeping spatial orthogonality to leverage the maximal spatial diversity when the corresponding conditions are satisfied. Simulation results verify the existence and accuracy of optimal placement of multi-user transmission and present that the transmission sum rate of IRS channel scales by the number of IRS elements with different spacing settings.
A Novel Miniaturized Broadband Base Station Antenna

A compact broadband dual dual-polarization antenna covering 5G bandwidth is proposed. A pair of orthogonal planar printed dipoles realize the miniaturization of the antenna by sharing dipole arms. The proposed compact antenna generates slant ±45° linear polarizations by vector synthesis. The results show that the antenna achieves a broad impedance matching of 63% (3.2-6.2 GHz) with VSWR <1.5. With the advantages of wide bandwidth, miniaturization (0.36 $\lambda_{0}\times 0.36 \lambda_{0}$, where $\lambda_{0}$ is wavelength in free space at center frequency) and stable radiation pattern, the proposed antenna would have a wide application in miniaturized base station.
The Significance of Technology and Digital Game-Based Learning in Accounting Education: A Narrative Literature Review

This paper was driven by the limited discussion of technology in accounting education literature. Thus, using a narrative literature review as its method, this study discusses the need of infusing technology in accounting education. This study also suggests using digital game-based learning as a practical way to embed technology in accounting classrooms. Additionally, this research also provides guidelines and examples of the implementation of digital game-based learning. It concludes that future research may use this study as a foundation to examine the empirical effect of technology, digital game-based learning, and accounting education.
Polycrystalline silicon photovoltaic harvesting for indoor IoT systems under red- far red artificial light

This paper aims at demonstrating the feasibility of a LoRaWAN-based sensor node for temperature monitoring, autonomously powered by a polycrystalline silicon photovoltaic module with possible applications within the Internet of Things (loT) domain in the horticulture field. The commercial solar cell was characterized under two light sources: a conventional white 4000 K Light Emitting Diode (LED) and a red and far red (R:FR) lamp peaked at 655 nm and 730 nm. The sensor node is equipped with a RFM95x LoRa transceiver which proved to be a valid technology in those application scenarios where robustness and low power consumption are required. The energy harvesting features are performed by a nano-power boost charger buck converter which deals with the power extraction from the photovoltaic module, the LiPo battery charging/discharging management and the supply of the sensor node. Field tests demonstrate that under R:fr light source, the energy self-sufficiency of the system is achieved: a positive balance between the battery charge and discharge is measured, sufficient both for the node working operation and for the battery charging.
A Systematic Review of Sensing and Differentiating Dichotomous Emotional States Using Audio-Visual Stimuli

Recognition of dichotomous emotional states such as happy and sad play important roles in many aspects of human life. Existing literature has recorded diverse attempts in extracting physiological and non-physiological traits to record these emotional states. Selection of the right instrumental approach for measuring these traits plays a critical role in emotion recognition. Moreover, various stimuli have been used to induce emotions. Therefore, there is a current need to perform a comprehensive overview of instrumental approaches and their outcomes for the new generation of researchers. In this direction, this study surveys the instrumental approaches in discriminating happy and sad emotional states that are elicited using audio-visual stimuli. A comprehensive literature review is performed using PubMed, Scopus, and ACM digital library repositories. The reviewed articles are classified with respect to the i) stimulation modality, ii) acquisition protocol, iii) instrumentation approaches, iv) feature extraction, and v) classification methods. In total, 39 research articles were published on the selected topic of instrumental approaches in differentiating dichotomous emotional states using audio-visual stimuli between January 2011 and April 2021. The majority of the papers used physiological traits, namely electrocardiogram, electrodermal activity, heart rate variability, photoplethysmogram, and electroencephalogram based instrumental approaches for recognizing the emotional states. The results show that only a few articles have focused on audio-visual stimuli for the elicitation of happy and sad emotional states. This review is expected to seed research in the areas of standardization of protocols, enhancing the diagnostic relevance of these instruments, and extraction of more reliable biomarkers.
Motion Control for Underactuated Robots Adaptable to Uneven Terrain by Decomposing Body Balance and Velocity Tracking

Body balance and velocity tracking are important for biped robots, especially under-actuated robots, walking on uneven ground. This paper presents a motion control strategy for the torque control of an under-actuated biped robot. In the strategy, we decompose the motion control into body balance control and velocity tracking. The body balance control is used to realize body balance by controlling the body posture and symmetrical movement of the two legs. The velocity tracking is adopted to achieve the desired velocity, where the velocity and acceleration feedback to modify the target angles of the hip joints. With the proposed method, a torque-controlled robot can walk with a balanced body and stable velocity, as validated in experiments conducted for the under-actuated bipedal robot CRANE.
20 Gb/s Dual-Mode SST VCSEL Driver

This work presents simulation results of a CMOS 1.2-V single-ended source-series-terminated (SST) voltage-mode electrical link driver in a 65 nm technology. The driver operates in two driving modes. The first mode uses symmetric pre-emphasis feedforward equalization to drive a short electrical link which introduces a total loss of 16 dB including electro-static discharge (ESD) and wire bonding losses at 10 GHz. The second mode drives a VCSEL diode through an electrical link exploiting asymmetric equalization. Through simulation, this dual-mode proposed driver operates up to 20 Gb/s and is estimated to dissipate 40 mW of power.
Advanced Optical Diagnostics for Characterization of Arc Plasmas

This article presents the selected approaches of optical diagnostics applied to thermal arcs. Advanced methods for the determination of temperatures and species density distributions are described and examples of recent developments are presented, such as the consideration of optical depth in optical emission measurements, the use of broadband light sources for measurements of spatially resolved densities, or the combination of spectral analysis with high-speed video observation for the purpose of surface temperature determination. Finally, the potential of the presented diagnostics to solve future challenges of thermal arc research is demonstrated and discussed.
FPGA-Embedded Anomaly Detection System for Milling Process

The main goal of this work is to design a supervising controller able to detect an anomaly in the milling process and implement the soultion in Field Programmable Gate Array (FPGA) chip. Executing this task, the controller continuously monitors the vibration signal coming from the acceleration sensor, installed on the milling machine, and striving to isolate new vibration patterns which are different from typical patterns recorded for the correct milling process. The detection method relies on determining selected signal features in the frequency domain and applying an auto-associative neural network (AANN) for novelty detection. It has been shown that by exercising the frequency spectrum of the vibration signal, extracting specific features of the signal’s spectrum, and using an auto-associative neural network, it is possible to detect anomalies in a milling process with relatively high efficiency. The accuracy, sensitivity, specificity, precision, and false alarm rate are equal to 94.3, 100, 91.2, 88.9, and 8.8 percent. All necessary calculations can be accomplished by the developed single-chip FPGA embedded supervising controller. The controller allows high-speed calculations under low power consumption. It characterizes high reliability and low price compared to typically encountered solutions.
Multifunctional auricular vagus nerve stimulator for closed-loop application

Auricular vagus nerve stimulation (aVNS) is a novel neuromodulatory therapy used in the treatment of various types of chronic systemic disorders. Currently, aVNS is non-individualized, disregarding the personal state of the patient and therefore making it difficult to reach optimum therapeutic outcomes. A closed-loop aVNS system is required to avoid overstimulation and under-stimulation of patients, leading to improved outcomes. This can be achieved by monitoring physiological parameters and adjusting aVNS parameters according to the individual physiological data. We developed a novel aVNS hardware for closed-loop applications, which utilizes diverse physiological sensing using external sensors and/or embedded sensors, processes and analyzes the acquired data in real-time and can directly govern the stimulation parameters. In a first experiment, it was shown that aVNS stimulation synchronized with electrocardiography and photo plethysmography while mimicking baroreceptor-related afferent input along the afferent vagus nerve into the brain can be successfully applied. The designed system identified >90% of the cardiac cycles and activated stimulation at the target point with ± 100ms precision despite heart rate variability. The developed system offers a solid basis for future research into personalization of aVNS.