Miracle-dz/UniFAR-FTUs huggingface dataset:
Simplified Optimal Estimation of Time-Varying Electromyogram Standard Deviation (EMGσ): Evaluation on Two Datasets

To facilitate the broader use of EMG signal whitening, we studied four whitening procedures of various complexities, as well as the roles of sampling rate and noise correction. We separately analyzed force-varying and constant-force contractions from 64 subjects who completed constant-posture tasks about the elbow over a range of forces from 0% to 50% maximum voluntary contraction (MVC). From the constant-force tasks, we found that noise correction via the root difference of squares (RDS) method consistently reduced EMG recording noise, often by a factor of 5–10. All other primary results were from the force-varying contractions. Sampling at 4096 Hz provided small and statistically significant improvements over sampling at 2048 Hz (~3%), which, in turn, provided small improvements over sampling at 1024 Hz (~4%). In comparing equivalent processing variants at a sampling rate of 4096 Hz, whitening filters calibrated to the EMG spectrum of each subject generally performed best (4.74% MVC EMG-force error), followed by one universal whitening filter for all subjects (4.83% MVC error), followed by a high-pass filter whitening method (4.89% MVC error) and then a first difference whitening filter (4.91% MVC error)—but none of these statistically differed. Each did significantly improve from EMG-force error without whitening (5.55% MVC). The first difference is an excellent whitening option over this range of contraction forces since no calibration or algorithm decisions are required.
Convolutional Neural Networks for Challenges in Automated Nuclide Identification

Improvements in Radio-Isotope IDentification (RIID) algorithms have seen a resurgence in interest with the increased accessibility of machine learning models. Convolutional Neural Network (CNN)-based models have been developed to identify arbitrary mixtures of unstable nuclides from gamma spectra. In service of this, methods for the simulation and pre-processing of training data were also developed. The implementation of 1D multi-class, multi-label CNNs demonstrated good generalisation to real spectra with poor statistics and significant gain shifts. It is also shown that even basic CNN architectures prove reliable for RIID under the challenging conditions of heavy shielding and close source geometries, and may be extended to generalised solutions for pragmatic RIID.
Preview Control with Dynamic Constraints for Autonomous Vehicles

In this paper, a preview theory-based steering control approach considering vehicle dynamic constraints is presented. The constrained variables are predicted by an error states system and utilized to adjust the control law once the established dynamic constraints are violated. The simulated annealing optimization algorithm for preview length is conducted to improve the adaptability of the controller to varying velocities and road adhesion coefficients. The theoretical stability of a closed-loop system is guaranteed using Lyapunov theory, and further analysis of the system response in time domain and frequency domain is discussed. The results of simulations implemented on Carsim–Simulink demonstrate the favorable performance of the proposed control in tracking accuracy and system stability under extreme conditions.
Experimental Validation of a Microwave Imaging Method for Shallow Buried Target Detection by Under-Sampled Data and a Non-Cooperative Source

In microwave imaging, it is often of interest to inspect electrically large spatial regions. In these cases, data must be collected over a great deal of measurement points which entails long measurement time and/or costly, and often unfeasible, measurement configurations. In order to counteract such drawbacks, we have recently introduced a microwave imaging algorithm that looks for the scattering targets in terms of equivalent surface currents supported over a given reference plane. While this method is suited to detect shallowly buried targets, it allows one to independently process all frequency data, and hence the source and the receivers do not need to be synchronized. Moreover, spatial data can be reduced to a large extent, without any aliasing artifacts, by properly combining single-frequency reconstructions. In this paper, we validate such an approach by experimental measurements. In particular, the experimental test site consists of a sand box in open air where metallic plate targets are shallowly buried a (few cm) under the air/soil interface. The investigated region is illuminated by a fixed transmitting horn antenna, whereas the scattered field is collected over a planar measurement aperture at a fixed height from the air-sand interface. The transmitter and the receiver share only the working frequency information. Experimental results confirm the feasibility of the method.
The Low Breaking Fiber Mechanism and Its Effect on the Behavior of the Melt Flow of Injection Molded Ultra-Long Glass Fiber Reinforced Polypropylene Composites

In this study, fiber breaking behavior, fiber orientation, length variation, and changes in melt flow ability of long glass fiber reinforced polypropylene (L-FRP) composites under different mold cavity geometry, melt fill path, and plasticization parameters were investigated. The matrix material used was polypropylene and the reinforcement fibers were 25 mm long. An ultra-long-fiber composite injection molding machine (with a three-stage plunger and injection mechanism design) was used with different mold cavity geometry and plasticization parameters. Different screw speeds were used to explore the changes in fiber length and to provide a reference for setting fiber length and parameter combinations. Flow-length specimen molds with different specimen thickness, melt fill path, and gate design were used to observe the effect of plasticizing properties on the flow ability of the L-FRP composite materials. The experimental results showed that the use of an injection molding machine with a mechanism that reduced the amount of fiber breakage was advantageous. It was also found that an increase in screw speed increased fiber breakage, and 25 mm long fibers were shortened by an average of 50% (to 10 mm). Long fibers were more resistant to melt filling than short fibers. In addition, the thickness of the specimen and the gate design were also found to affect the filling process. The rounded angle gate and thick wall product decreased the flow resistance and assisted the flow ability and fiber distribution of the L-FRP injection molding.
Microneedle Array Electrode-Based Wearable EMG System for Detection of Driver Drowsiness through Steering Wheel Grip

Driver drowsiness is a major cause of fatal accidents throughout the world. Recently, some studies have investigated steering wheel grip force-based alternative methods for detecting driver drowsiness. In this study, a driver drowsiness detection system was developed by investigating the electromyography (EMG) signal of the muscles involved in steering wheel grip during driving. The EMG signal was measured from the forearm position of the driver during a one-hour interactive driving task. Additionally, the participant’s drowsiness level was also measured to investigate the relationship between muscle activity and driver’s drowsiness level. Frequency domain analysis was performed using the short-time Fourier transform (STFT) and spectrogram to assess the frequency response of the resultant signal. An EMG signal magnitude-based driver drowsiness detection and alertness algorithm is also proposed. The algorithm detects weak muscle activity by detecting the fall in EMG signal magnitude due to an increase in driver drowsiness. The previously presented microneedle electrode (MNE) was used to acquire the EMG signal and compared with the signal obtained using silver-silver chloride (Ag/AgCl) wet electrodes. The results indicated that during the driving task, participants’ drowsiness level increased while the activity of the muscles involved in steering wheel grip decreased concurrently over time. Frequency domain analysis showed that the frequency components shifted from the high to low-frequency spectrum during the one-hour driving task. The proposed algorithm showed good performance for the detection of low muscle activity in real time. MNE showed highly comparable results with dry Ag/AgCl electrodes, which confirm its use for EMG signal monitoring. The overall results indicate that the presented method has good potential to be used as a driver’s drowsiness detection and alertness system.
Classification Learning of Latent Bruise Damage to Apples Using Shortwave Infrared Hyperspectral Imaging

Bruise damage is a very commonly occurring defect in apple fruit which facilitates disease occurrence and spread, leads to fruit deterioration and can greatly contribute to postharvest loss. The detection of bruises at their earliest stage of development can be advantageous for screening purposes. An experiment to induce soft bruises in Golden Delicious apples was conducted by applying impact energy at different levels, which allowed to investigate the detectability of bruises at their latent stage. The existence of bruises that were rather invisible to the naked eye and to a digital camera was proven by reconstruction of hyperspectral images of bruised apples, based on effective wavelengths and data dimensionality reduced hyperspectrograms. Machine learning classifiers, namely ensemble subspace discriminant (ESD), k-nearest neighbors (KNN), support vector machine (SVM) and linear discriminant analysis (LDA) were used to build models for detecting bruises at their latent stage, to study the influence of time after bruise occurrence on detection performance and to model quantitative aspects of bruises (severity), spanning from latent to visible bruises. Over all classifiers, detection models had a higher performance than quantitative ones. Given its highest speed in prediction and high classification performance, SVM was rated most recommendable for detection tasks. However, ESD models had the highest classification accuracy in quantitative (>85%) models and were found to be relatively better suited for such a multiple category classification problem than the rest.
Binary Green Blends of Poly(lactic acid) with Poly(butylene adipate-co-butylene terephthalate) and Poly(butylene succinate-co-butylene adipate) and Their Nanocomposites

Poly(lactic acid) (PLA) is the most widely produced biobased, biodegradable and biocompatible polyester. Despite many of its properties are similar to those of common petroleum-based polymers, some drawbacks limit its utilization, especially high brittleness and low toughness. To overcome these problems and improve the ductility and the impact resistance, PLA is often blended with other biobased and biodegradable polymers. For this purpose, poly(butylene adipate-co-butylene terephthalate) (PBAT) and poly(butylene succinate-co-butylene adipate) (PBSA) are very advantageous copolymers, because their toughness and elongation at break are complementary to those of PLA. Similar to PLA, both these copolymers are biodegradable and can be produced from annual renewable resources. This literature review aims to collect results on the mechanical, thermal and morphological properties of PLA/PBAT and PLA/PBSA blends, as binary blends with and without addition of coupling agents. The effect of different compatibilizers on the PLA/PBAT and PLA/PBSA blends properties is here elucidated, to highlight how the PLA toughness and ductility can be improved and tuned by using appropriate additives. In addition, the incorporation of solid nanoparticles to the PLA/PBAT and PLA/PBSA blends is discussed in detail, to demonstrate how the nanofillers can act as morphology stabilizers, and so improve the properties of these PLA-based formulations, especially mechanical performance, thermal stability and gas/vapor barrier properties. Key points about the biodegradation of the blends and the nanocomposites are presented, together with current applications of these novel green materials.
EFFECT OF DIFFERENT TYPES OF MILK FORMULA AGAINST STREPTOCOCCUS MUTANS BIOFILM FORMATION IN VITRO

Objectives: The purpose of this study was to analyze the differences in the effect of milk-based, soy-based, and protein hydrolysate formulas against Streptococcus mutans (SM) biofilm formation. Methods: Infant formula used is intended for children aged 1-3 years, and each respective type of infant formula is represented by three distinct brands. Results: Our results showed a significant difference in the effect of milk-based, soy-based, and protein hydrolysate formulas against SM biofilm formation (p=0.002). Conclusion: Cow’s milk-based formula showed the lowest SM biofilm formation.
Effect of Fibre Orientation on Novel Continuous 3D-Printed Fibre-Reinforced Composites

Among the several additive manufacturing techniques, fused filament fabrication (FFF) is a 3D printing technique that is fast, handy, and low cost, used to produce complex-shaped parts easily and quickly. FFF adds material layer by layer, saving energy, costs, raw material costs, and waste. Nevertheless, the mechanical properties of the thermoplastic materials involved are low compared to traditional engineering materials. This paper deals with the manufacturing of composite material laminates obtained by the Markforged continuous filament fabrication (CFF) technique, using an innovative matrix infilled by carbon nanofibre (Onyx), a high-strength thermoplastic material with an excellent surface finish and high resistance to chemical agents. Three macro-categories of samples were manufactured using Onyx and continuous carbon fibre to evaluate the effect of the fibre on mechanical features of the novel composites and their influence on surface finishes. SEM (Scanning Electron Microscopy) analysis and acquisition of roughness profile by a confocal lens were conducted. Tensile and compression tests, thermogravimetric analysis and calorimetric analysis using a DSC (differential scanning calorimeter) were carried out on all specimen types to evaluate the influence of the process parameters and layup configurations on the quality and mechanical behaviour of the 3D-printed samples.
Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network

Background: We aimed to create a novel model using a deep learning method to estimate stroke volume variation (SVV), a widely used predictor of fluid responsiveness, from arterial blood pressure waveform (ABPW). Methods: In total, 557 patients and 8,512,564 SVV datasets were collected and were divided into three groups: training, validation, and test. Data was composed of 10 s of ABPW and corresponding SVV data recorded every 2 s. We built a convolutional neural network (CNN) model to estimate SVV from the ABPW with pre-existing commercialized model (EV1000) as a reference. We applied pre-processing, multichannel, and dimension reduction to improve the CNN model with diversified inputs. Results: Our CNN model showed an acceptable performance with sample data (r = 0.91, MSE = 6.92). Diversification of inputs, such as normalization, frequency, and slope of ABPW significantly improved the model correlation (r = 0.95), lowered mean squared error (MSE = 2.13), and resulted in a high concordance rate (96.26%) with the SVV from the commercialized model. Conclusions: We developed a new CNN deep-learning model to estimate SVV. Our CNN model seems to be a viable alternative when the necessary medical device is not available, thereby allowing a wider range of application and resulting in optimal patient management.
Cooperative Intersection with Misperception in Partially Connected and Automated Traffic

The emerging connected and automated vehicle (CAV) has the potential to improve traffic efficiency and safety. With the cooperation between vehicles and intersection, CAVs can adjust speed and form platoons to pass the intersection faster. However, perceptual errors may occur due to external conditions of vehicle sensors. Meanwhile, CAVs and conventional vehicles will coexist in the near future and imprecise perception needs to be tolerated in exchange for mobility. In this paper, we present a simulation model to capture the effect of vehicle perceptual error and time headway to the traffic performance at cooperative intersection, where the intelligent driver model (IDM) is extended by the Ornstein–Uhlenbeck process to describe the perceptual error dynamically. Then, we introduce the longitudinal control model to determine vehicle dynamics and role switching to form platoons and reduce frequent deceleration. Furthermore, to realize accurate perception and improve safety, we propose a data fusion scheme in which the Differential Global Positioning system (DGPS) data interpolates sensor data by the Kalman filter. Finally, a comprehensive study is presented on how the perceptual error and time headway affect crash, energy consumption as well as congestion at cooperative intersections in partially connected and automated traffic. The simulation results show the trade-off between the traffic efficiency and safety for which the number of accidents is reduced with larger vehicle intervals, but excessive time headway may result in low traffic efficiency and energy conversion. In addition, compared with an on-board sensor independently perception scheme, our proposed data fusion scheme improves the overall traffic flow, congestion time, and passenger comfort as well as energy efficiency under various CAV penetration rates.
Identifying Misbehaving Greedy Nodes in IoT Networks

One of the central communication infrastructures of the Internet of Things (IoT) is the IEEE 802.15. 4 standard, which defines Low Rate Wireless Personal Area Networks (LR- WPAN). In order to share the medium fairly in a non-beacon-enabled mode, the standard uses Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). The nature of connected objects with respect to various resource constraints makes them vulnerable to cyber attacks. One of the most aggressive DoS attacks is the greedy behaviour attack which aims to deprive legitimate nodes to access to the communication medium. The greedy or selfish node may violate the proper use of the CSMA/CA protocol, by tampering its parameters, in order to take as much bandwidth as possible on the network, and then monopolize access to the medium by depriving legitimate nodes of communication. Based on the analysis of the difference between parameters of greedy and legitimate nodes, we propose a method based on the threshold mechanism to identify greedy nodes. The simulation results show that the proposed mechanism provides a detection efficiency of 99.5%.
A Study on Seizure Detection of EEG Signals Represented in 2D

A seizure is a neurological disorder caused by abnormal neuronal discharges in the brain, which severely reduces the quality of life of patients and often endangers their lives. Automatic seizure detection is an important research area in the treatment of seizure and is a prerequisite for seizure intervention. Deep learning has been widely used for automatic detection of seizures, and many related research works decomposed the electroencephalogram (EEG) raw signal with a time window to obtain EEG signal slices, then performed feature extraction on the slices, and represented the obtained features as input data for neural networks. There are various methods for EEG signal decomposition, feature extraction, and representation, and most of the studies have been based on fixed hardware resources for the design of the scheme, which reduces the adaptability of the scheme in different application scenarios and makes it difficult to optimize the algorithms in the scheme. To address the above issues, this paper proposes a deep learning-based model for seizure detection, mainly characterized by the two-dimensional representation of EEG features and the scalability of neural networks. The model modularizes the main steps of seizure detection and improves the adaptability of the model to different hardware resource constraints, in order to increase the convenience of the algorithm optimization or the replacement of each module. The proposed model consists of five parts, and the model was tested using two epilepsy datasets separately. The experimental results showed that the proposed model has strong generality and good classification accuracy for seizure detection.
Low-Cost and Highly Sensitive Pressure Sensor with Mold-Printed Multi-Walled Carbon Nanotubes Dispersed in Polydimethylsiloxane

Flexible pressure sensors with piezoresistive polymer composites can be integrated into elastomers to measure pressure changes in sealings, preemptively indicating a replacement is needed before any damage or leakage occurs. Integrating small percentages of high aspect ratio multi-walled carbon nanotubes (MWCNTs) into polymers does not significantly change its mechanical properties but highly affects its electrical properties. This research shows a pressure sensor based on homogeneous dispersed MWCNTs in polydimethylsiloxane with a high sensitivity region (0.13% kPa−1, 0–200 kPa) and sensitive up to 500 kPa. A new 3D-printed mold is developed to directly deposit the conductive polymer on the electrode structures, enabling sensor thicknesses as small as 100 μm.
Robust Adaptive HCS MPPT Algorithm-Based Wind Generation System Using Model Reference Adaptive Control

To treat the stochastic wind nature, it is required to attain all available power from the wind energy conversion system (WECS). Therefore, several maximum power point tracking (MPPT) techniques are utilized. Among them, hill-climbing search (HCS) techniques are widely implemented owing to their various features. Regarding current HCS techniques, the rotor speed is mainly perturbed using predefined constants or objective functions, which makes the selection of step sizes a multifaceted task. These limitations are directly reflected in the overall dynamic WECS performance such as tracking speed, power fluctuations, and system efficiency. To deal with the challenges of the existing HCS techniques, this paper proposes a new adaptive HCS (AD-HCS) technique with self-adjustable step size using model reference adaptive control (MRAC) based on the PID controller. Firstly, the mechanical power fluctuations are detected, then the MRAC continuously optimizes the PID gains so as to generate an appropriate dynamic step size until harvesting the maximum power point (MPP) under the optimal tracking conditions. Looking specifically at the simulation results, the proposed AD-HCS technique exhibits low oscillations around the MPP and a small settling time. Moreover, WECS efficiency is increased by 5% and 2% compared to the conventional and recent HCS techniques, respectively. Finally, the studied system is confirmed over a 1.5 MW, gird-tied, double-fed induction generator (DFIG) WECS using MATLAB/Simulink.
Recent Progress in the Development of Graphene Detector for Terahertz Detection

Terahertz waves are expected to be used in next-generation communications, detection, and other fields due to their unique characteristics. As a basic part of the terahertz application system, the terahertz detector plays a key role in terahertz technology. Due to the two-dimensional structure, graphene has unique characteristics features, such as exceptionally high electron mobility, zero band-gap, and frequency-independent spectral absorption, particularly in the terahertz region, making it a suitable material for terahertz detectors. In this review, the recent progress of graphene terahertz detectors related to photovoltaic effect (PV), photothermoelectric effect (PTE), bolometric effect, and plasma wave resonance are introduced and discussed.