Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Athlete Tracking Using Data Fusion in Wearable Technologies

In recent years, the use of wearable devices to track athlete performance has increased sharply. Using onboard sensors, wearable devices can provide critical information about athlete’s performance and well-being. Athlete tracking is an important functionality of wearable devices that rely on positioning data, which also influences the accuracy of numerous other attributes. However, accurate athlete tracking is a challenging task due to the nonlinear nature of the problem and the presence of non-Gaussian noise. In the literature, researchers have used the particle filter (PF) to improve athlete tracking accuracy. While the PF algorithm, in general, works well, they perform poorly when athletes take the sharp change of direction (COD), a common and important movement in the sport that is not currently captured. In this article, we introduce a sensor fusion technique to address this challenge. Our proposed solution combines the positioning data and inertial sensor data to accurately track an athlete’s movements. We then analyze the accuracy using data collected from a commercially used athlete tracking wearable device. We have found that the obtained results are very promising, and the proposed solution performs up to five times better than a conventional PF sensor fusion algorithm for positioning.
Single Supply PWM Fully Implantable Cochlear Implant Interface Circuit With Active Charge Balancing

Low powered fully implantable cochlear implants (FICIs) untangle the aesthetic concerns and battery replacement problems of conventional cochlear implants. However, the reported FICIs lack proper charge balancing and require multiple external supplies to operate. In this work, a complete low power FICI interface circuit is designed that operates with a single supply and uses short-pulse-injection method for charge balancing. The system takes input from multi-channel piezoelectric transducers and stimulates the auditory neurons with pulse width modulated (PWM) output currents. By utilizing pulse width modulation technique with continuous interleaved sampling (CIS) sound processing strategy, a time gap is formed between two consecutive channels. Then, this gap is used for charge balancing operation. Overall power consumption of the low power FICI interface is decreased by clocked gated subthreshold amplifier and rectifier design. Furthermore, power efficient design of analog to digital converter (ADC) enhances the power reduction. The system is tested with an in-vitro test setup and it stimulates a single channel cochlear electrode with 50 dB input dynamic range while consuming $695~\mu \text{W}$ power from a single 1.8 V supply. The implemented FICI system can safely stimulate neurons for more than 18 days (with 16-hour daily operation) with an implantable 200 mWh battery without recharging. Furthermore, the short charge balance current pulses keep the electrode voltage difference after the stimulation within ±100 mV range, which ensures the residual charge is not hazardous for the auditory neurons.
Sensitivity analysis of plunger-type wavemakers with water current

The inclusion of current in a water channel is a critical requirement for recreating accurately scaled ocean environments in a laboratory. In this paper, the effect of a uniform current on the theoretical model of a plunger-type wavemaker has been investigated through a variance-based global sensitivity analysis. The output of the wavemaker model is represented by the ratio of wave amplitude to stroke amplitude. Therefore, the sensitivity analysis evaluates the influence of all uncertain input parameters on the output variance of the model. In addition to the water current, the uncertain input parameters for the wavemaker model were established as the wave frequency, wedge angle, mean wedge depth, water height, and node points on the wavemaker boundary. To account for a range of limitations for both the plunger and the water channel in which it oscillates, the sensitivity analysis was performed for a broad distribution of each parameter. The analysis determined that the wave frequency had the highest influence on the output variance of the wavemaker model. For a uniform water current, the first order and total effect sensitivity indices were estimated as and, respectively. Although the sensitivity of the model to the current was relatively low compared to the wave frequency, there exists an impact due to the interaction of the current with the other parameters. Therefore, it was established that the inclusion of the current in the plunger-type wavemaker model is essential for application of the model to an experimental setup.
Physical Layer Authentication in Line-of-Sight Underwater Acoustic Sensor Networks

Physical layer authentication scheme for sensor node authentication using location-specific feature i.e. Angle of Arrival (azimuth, elevation) in an underwater environment is proposed. Under the “Alice-Bob-Eve” scenario, the sink node (Alice) maintains a database of the estimated angle of arrivals. The authentication process includes the comparison of measured AoA against the database based on Mahalanobis distance. The distributions of genuine and malicious sensor node Mahalanobis distance measures are modeled as finite Gaussian Mixture Model (GMM). This work considers a static, line-of-sight underwater acoustic sensor network (UASN). The performance of the proposed scheme is expressed by the Probability of Detection (PD) and the Probability of False Alarm (PFA) which shows a very good trade-off.
Nonlinear Model Predictive Path Following for an Unmanned Surface Vehicle

This paper presents a two-level nonlinear model prediction control (NMPC) approach for autonomous path following control of under-actuated unmanned surface vehicles with constraints. It performs proper path replanning by taking into consideration of the dynamic constraints and input constraints of the USV in the high-level NMPC, and low-level NMPC solves the control inputs for the USV to follow the new path. The results show that the proposed two-level NMPC approach is able to reduce the change rate of control and avoid excessive control inputs compared with one-level NMPC approach, and the average computation time for each iteration is reduced, which indicates that two-level NMPC approach is more promising for real-time implementation.
Oxidation of Lipid in Bulk and Dispersion Systems

A lipid is one of major ingredients of foods and plays important roles in nutrition and taste. However, a lipid containing unsaturated fatty acids is prone to oxidation. Lipid oxidation is one of the major causes of the deterioration of foods containing lipids, and is affected by many factors. The state and structure of lipids in foods are also considered to be important factors as well as the conditions of how the foods are stored. An emulsion and solubilization in micelles, which is a dispersion system, are common structures in lipid-containing foods for both home cooking and industry. Rapid developments in nanotechnology have allowed us to focus on its application to foods. A decrease in the particle size of an emulsion produces an increase in the specific surface area of the particles. The high surface area would elicit an effect at the interface between the oil and aqueous phases on the reactions occurring in the emulsion or micelle. Because the lipid in the emulsion and micelle is oxidized by oxygen which is supplied through the interface, an increase in the specific surface area could possibly facilitate the oxidation. In this context, the lipid oxidation in different systems was measured in order to examine the effect of the droplet size in the system on the oxidation kinetics. The kinetics of the lipid oxidation in a bulk system is also described because it provides basic information for analysis of the kinetics in dispersion systems.
Experimental Investigation Of The Real Contact Arc Length Measurement In The Cylindrical Plunge Grinding

The real contact arc length in grinding is one of the most important indicators in investigating of the material removal mechanism for precision machining. It is generally affected by a combination of plastic and thermal deformations in the machining process. This paper will provide a systematic investigation of the contact arc length in the cylindrical grinding of titanium alloy Ti-6Al-4V. A series of single grit experiments and grinding temperature measurements, together with FEM simulation, will be utilized to determine the real contact arc length. The contact behavior between a grinding wheel and the workpiece was investigated by the contact time in the workpice surface temperature curves. It was found that in FEM simulation, the real contact length was about 2 times of the geometric length. With the experimental results of the different grinding parameters, the real contact lengths calculated by temperature curves were about 1.5 - 2 times of the geometric length.
Opportunistic Localization and Synchronization for Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UASN) have many applications, such as monitoring environmental quality, exploring underwater targets and more. Node localization and synchronization in UASNs are preliminary tasks. However, the uncertainties of underwater acoustic channels have brought many difficulties to the localization and synchronization of UASNs. In this context, localization and synchronization in UASNs are opportunistic. In this paper, a method of localization and synchronization is proposed for an asynchronous UASN. In this UASN, autonomous underwater vehicles (AUVs) serve as mobile anchor nodes to provide information for synchronizing and localizing the static sensor nodes. To improve service life of the static sensors, a $(q,\ p)$ duty-cycle working mode is designed for them. Due to the $(q,\ p)$ working mode and the uncertainties of acoustic channels, this paper analyzes the operating conditions for successfully synchronization and localization. An EM algorithm for synchronization and localization that can handle uncertainties of the mobile anchor positions is proposed. Finally, simulation experiments are provided in order to test the performance of the EM algorithm, the EM method performs better than other algorithms.
Selective Pd Deposition on Au Nanobipyramids and Pd Site‐Dependent Plasmonic Photocatalytic Activity

The synthesis of anisotropic metal nanostructures is strongly desired for exploring plasmon‐enabled applications. Herein, the preparation of anisotropic Au/SiO2 and Au/SiO2/Pd nanostructures is realized through selective silica coating on Au nanobipyramids. For silica coating at the ends of Au nanobipyramids, the amount of coated silica and the overall shape of the coated nanostructures exhibit a bell‐shaped dependence on the cationic surfactant concentration. For both end and side silica coating on Au nanobipyramids, the size of the silica component can be varied by changing the silica precursor amount. Silica can also be selectively deposited on the corners or facets of Au nanocubes, suggesting the generality of this method. The blockage of the predeposited silica component on Au nanobipyramids enables further selective Pd deposition. Suzuki coupling reactions carried out with the different bimetallic nanostructures functioning as plasmonic photocatalysts indicate that the plasmonic photocatalytic activity is dependent on the site of Pd nanoparticles on Au nanobipyramids. Taken together, these results suggest that plasmonic hot spots play an important role in hot‐electron‐driven plasmonic photocatalysis. This study opens up a promising route to the construction of anisotropic bimetallic nanostructures as well as to the design of bimetallic plasmonic‐catalytic nanostructures as efficient plasmonic photocatalysts.
A Numerical Simulation of a Low-Temperature Argon Plasma Jet

A plasma jet at atmospheric pressure using a dielectric barrier discharge (DBD) can be very useful in the biomedical field. In this article, a numerical simulation of such plasma jet operating in argon gas and launched in the open air was developed using COMSOL Multiphysics software. Three coupled models are considered in this study. A hydrodynamic investigation is conducted and the dilution of argon in air is considered an initial value in the plasma model. An electrostatic study is developed in order to preview the initial electric field. Finally, a plasma study is carried out and discussed in terms of electron density, electric field distribution, and plasma length. This study is validated by comparing the results to those measured in a similar system in the literature. The results are in good agreement with the literature and correspond to the physics of the plasma jet.
Recognition of Children Punjabi Speech using Tonal Non-Tonal Classifier

Tonal Languages Automatic Speech recognition (ASR) systems are an area of interest for researchers as it is a challenging research domain. However, the main objective is to get an ASR system that has high performance whether the language used is tonal or non-tonal. But tonal languages always have degraded results as compared to non-tonal languages. Punjabi is a tonal language and tonality has led to many challenges in the designing of the Punjabi ASR system. It is an important research area to develop a Punjabi ASR system that is able to tackle the tonality of the language. In this paper, tonal and non-tonal classification is done to extract the prosodic features, which in turn enhance the word recognition rate for tonal languages. A prosodic feature has pitch related features, which can effectively understand the tone of the word and has high accuracy in recognition of words. Extracted prosodic features are fed to the ASR system individually and then later in combinations. Results of prosodic feature’s combination with Mel Frequency Cepstral Coefficients (MFCC) are compared with the MFCC based ASR results. The performance of the system is analyzed and it is observed that WER is reduced by implementing prosodic features with Relative Improvement (RI) of14%. The results are compared with the baseline result and the performance of the system is analyzed.
A Novel Hybrid Feature Selection and Ensemble Learning Framework for Unbalanced Cancer Data Diagnosis With Transcriptome and Functional Proteomic

The high dimension, high redundancy and class imbalance of cancer multiple omics data are the main challenges for cancer diagnosis. Existing studies have neglected the role of functional proteomics in the occurrence and development of cancer. In this study, a novel hybrid feature selection and ensemble learning framework, referred to as the three-stage feature selection and twice-competitional ensemble learning method (TSFS-TCEM), is proposed for cancer diagnosis. Firstly, we combine the transcriptome and functional proteomics data to construct a multi-omics data on breast cancer, which is the first time to apply these combined biological data for diagnosing breast cancer. Secondly, the proposed method introduces multiple models during the feature selection and diagnostic model construction. The three-stage feature selections integrate the features from different types of data and the twice-competitional ensemble learning framework resolves the data imbalance problem suffer from a single classifier. The TSFS-TCEM achieves a diagnostic accuracy of 99.64%, outperforming all compared methods. In addition, the 5-fold cross-validation sensitivity, specificity and F-Measure of the method are above 99.63%.
Development of Technology for Production of Magneto-sensitive Element of Fluxgate Inclinometer

The main problem of the underground navigation is to determine the spatial position of the wellbore and to adjust its trajectory. Fluxgate inclinometers can be used to solve this problem. The principle of functioning of fluxgate inclinometer, used for estimation the position of drilling tool during geological exploration, is described. The paper presents results of the technology development for production of magneto-sensitive element of fluxgate inclinometer. Developed technology using for material processing is based on laser cutting of magneto-sensitive amorphous foil in different gas environments. Magnetic properties of material are estimated by means of magnetic force microscopy.
1,2-H- versus 1,2-C-Shift on Sn-Silsesquioxanes

Lewis acidic zeolites such as Sn-Beta catalyze glucose isomerization to fructose via an intramolecular 1,2-H-shift reaction, a key step for converting lignocellulosic biomass into renewable chemicals. Na-exchange of Sn-Beta titrates the neighboring SiOH group in the open Sn site and shifts catalyst selectivity to mannose formed by a 1,2-C-shift reaction. To probe structure/activity relationships in the zeolite, tin-containing silsesquioxanes with (1a) and without (1b) a neighboring SiOH group were recently synthesized and tested. These molecular catalysts are active for glucose conversion, and the presence (absence) of the SiOH favors fructose (mannose) selectivity by intramolecular H(C)-shift reactions. Using density functional theory, we investigated numerous H/C-shift pathways on these tin-silsesquioxane catalysts. On both 1a and 1b, the H-shift reaction occurs through a bidentate binding mode without participation of the SiOH, while the bidentate binding mode is not favored for the C-shift due to ster.
Automatic Leaf Counting Using Improved YOLOv3

In the recent years, image-based plant phenotyping has become an active research area. It can help investigating the complex association between genomes and phenotypic traits. However, traditional plant phenotyping involves manual measurement of plant traits and is quite time-consuming. With the help of automatic image analysis, we can significantly reduce time and human labor in this process. Working towards the ultimate goal of automatic plant phenotyping, we begin with developing an automatic method for leaf counting. The existing approaches for image-based leaf counting can be divided into two categories. One is based on instance segmentation and the other one is based on regression prediction. In contrast to the existing approaches, we propose to utilize object detection techniques. In particular, we modify the YOLOv3 algorithm so that it can better fit the leaf counting task. In order to evaluate our proposed method, we have collected cauliflower images from the ASBCST (Academia Sinica - Biotechnology Center in Southern Taiwan) and the Arabidopsis images from the CVPPP (Computer Vision Problems in Plant Phenotyping) dataset. Our proposed method achieves promising results on these datasets.
Stochastic Ship-Radiated Noise Modelling Via Generative Adversarial Networks

The design and performance evaluation of underwater acoustic (UA) communication systems in shallow water and harbour environments is a continuous challenge due to the numerous degrading factors present in the UA channel, one of which is the presence of noise generated due to nearby shipping activity. However, few research studies have examined the properties of ship-radiated noise in terms of its time-domain statistical characteristics and its negative effects on UA communication systems. We propose the use of unsupervised learning techniques to train generative models that capture the time-domain stochastic behaviours of ship-radiated noise using a publicly available database of long-term acoustic shipping noise recordings. These models can then be used for further analysis of ship-radiated noise and performance evaluation of UA orthogonal frequency-division multiplexing systems in the presence of such interference. For further validation, we include experimentally acquired ship-radiated noise recordings acquired off the coast of Caesarea, Israel. The results indicate a two component Gaussian mixture model serves as a better approximation for high frequency ship-radiated noise while generative adversarial networks produce improved realizations of shipping noise in lower frequencies.
A Comparative Analysis of XGBoost and Temporal Convolutional Network Models for Wind Power Forecasting

Forecasting renewable power generation becomes more important for the operation of modern power systems. However, the largest challenge for integrating wind power into power systems is its characteristics of stochastic and intermittent. Fortunately, various machine learning or deep learning methods have been proposed in recent years. The structures of deep neural networks, such as temporal convolutional networks (TCN), have the ability to capture patterns in time series data. On the other hand, XGBoost is a tree-based method, which is commonly applied to supervised learning problems. This work used the two methods to predict short-term wind power generation. Based on the forecasting results, the used XGBoost model outperforms traditional artificial neural network (ANN), long short-term memory (LSTM) recurrent neural network, and TCN models.
Development and Comparison of 3D Nonlinear Electrothermal Models for Induction Heating Problems

The paper describes two methods of 3D coupled-field nonlinear thermal-electrical simulation of induction heating devices. The first one uses multiphysics engineering simulation JMAG software, the second one – specialized Universal 3D program. The simulation results are compared with data obtained from the experiment on ferromagnetic carbon steel thin plate induction heating to the forging temperature using an oval inductor. Particular attention is given to the steel nonlinear magnetic properties approximation at temperatures below the Curie point. The paper also gives an estimation of the simulations speed and accuracy.
Determining OS and Applications by DNS Traffic Analysis

DNS-protocol is a protocol that allows you to get IP-address of websites to establish connection between a server and a client device. Today this protocol is still used unencrypted while having an add-on for data transmission security. In this paper we show that by analyzing DNS traffic we can obtain not only a list of websites visited by users but some additional information. We also introduce our DNS-based system that can obtain the following information about client devices from DNS traffic: OS, applications and web services. Our system uses classification algorithms based on parsing DNS queries and match them to the list of regular expressions.
Diagnostic value of mean corpuscular volume in the detection of vitamin B12 deficiency

Background: Vitamin B12 deficiency is thought to be more common than was previously believed. A strict vegetarian diet has been associated with increased risk of vitamin B12 deficiency. Erythrocyte indices have been used in the initial evaluation of anemic patients. Raised mean corpuscular volume (MCV) is commonly used by physicians as an indicator for megaloblastic anemia caused by vitamin B12 deficiency. Objective: (1) The objective of the study was to study the correlation between serum vitamin B12 level and MCV. (2) Prevalence of vitamin B12 deficiency in this region. Materials and Methods: We had retrospectively observed the records of serum vitamin B12 and complete blood cell count of both outpatients and inpatients from January 2016 to February 2017. Total, 1110 cases were studied. Peripheral smears of all patients were studied. Results: The prevalence of vitamin B12 was 33.06%. Most common age was the 4th decade with female predominance. Out of 367 patients who were vitamin B12 deficient, 28% had low MCV, and 52% had normal MCV. Out of 743 patients with normal vitamin B12 level, 12% had high MCV. It suggests that there is no correlation between vitamin B12 levels and MCV in the majority of cases. Conclusion: Every third person is vitamin B12 deficient in this region. MCV is unreliable as a screening parameter for the presumed diagnosis of macrocytic anemia, which is associated with vitamin B12 deficiency.