Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Anodal Oscillatory Transcranial Direct Current Stimulation on Top-Down Cortico-Muscular Control: A Pilot Study

Oscillatory transcranial direct current stimulation (otDCS) is a novel non-invasive stimulation protocol that combines the characteristics of both transcranial direct current and alternating current stimulation. Therefore, it has the ability to simultaneously modulate the potential and oscillatory activities of neuronal membranes. In this pilot study, anodal otDCS in different frequencies (20 Hz, 25 Hz, and 30 Hz) in the beta band were applied to 4 healthy subjects. High-density EEG and EMG were collected during a simple isometric finger contraction task. The modulation effects of otDCS on the top-down motor control were explored using generalized partial directed coherence (GPDC), which is a directed measurement of cortico-muscular interactions in both top-down and bottom-up directions. The individual peak frequencies for top-down GPDC was $20.25\pm 2.06$. There was a trend toward an increase in top-down GPDC after 20 Hz anodal otDCS, but not after 25 Hz or 30 Hz otDCS. The findings in this pilot study imply the effect of 20 Hz otDCS on top-down motor control, which can be applied to patients with impairment in motor control, such as stroke survivors. Our results also suggest the potential effectiveness of otDCS in the individual peak frequency in the beta band, which might be investigated in future studies.
Prototyping Visible Light Communication for the Internet of Things Using OpenVLC

Visible Light Communication (VLC) has emerged in the last few years as a promising technology not only for high-speed communication but also for serving a new generation of Internet of Things (IoT) devices that may leverage the pervasive lighting infrastructures. Integrating VLC in lighting environments for IoT requires the design of networked and intelligent luminaries and new IoT devices, encompassing the development of innovative technologies and new algorithms. A common experimental platform is necessary to lower the entrance barriers of VLC and speed up the research development. In this article, we provide guidelines for prototyping VLC for IoT applications, assisted by the open-source platform OpenVLC. We also introduce the new development on OpenVLC, which guarantees support for more powerful LEDs and much longer distance (extending the communication distance from 6 m to 19 m), dimming adaption, among other features. Its low-cost, open-source, and open-hardware designs allow researchers in the community to swiftly adapt it to suit their research purposes.
3D-Printed Low-Profile X-Band Tunable Phase Shifter

A novel 3D printed X-band tunable phase shifter is presented in this paper. By using the low temperature (140 °C) additively manufactured (AM) method, a multilayer conductive structure within a single dielectric substrate is realized by simultaneously jetting both dielectric and conductive inks. Taking advantage of this inkjet printing technology, a tunable phase shifter operating at X - band is proposed and designed. Within 90° continuous phase tuning range, a low insertion loss variation can be achieved with very compact size 0.22 × 0.12 × 0.1 (λg3). To verify proposed design concept, a prototype parallel strip line tunable phase shifter working at 9 GHz is designed, fabricated, and measured, where the measurement results agree well with EM simulations and design theory.
Toward Robust High-Density EMG Pattern Recognition using Generative Adversarial Network and Convolutional Neural Network

High-density electromyography (HD EMG)-based Pattern Recognition (PR) has attracted increasing interest in real-time Neural-Machine Interface (NMI) applications because HD EMG can capture neuromuscular information from one temporal and two spatial dimensions, and it does not require anatomically targeted electrode placements. In recent years, deep learning methods such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid CNN-RNN methods have shown great potential in HD EMG PR. Due to the high-density and multi-channel characteristics of HD EMG, the use of HD EMG-based NMIs in practice may be challenged by the unreliability of HD EMG recordings over time. So far, few studies have investigated the robustness of deep learning methods on HD EMG PR when noises and disturbances such as motion artifacts and bad contacts are present in the HD EMG signals. In this paper, we have developed RoHDE - a Robust deep learning-based HD EMG PR framework by introducing a Generative Adversarial Network (GAN) that can generate synthetic HD EMG signals to simulate recording conditions affected by disturbances. The generated synthetic HD EMG signals can be utilized to train robust deep learning models against real HD EMG signal disturbances. Experimental results have shown that our proposed RoHDE framework can improve the classification accuracy against disturbances such as contact artifacts and loose contacts from 64% to 99%. To the best of our knowledge, this work is the first to address the intrinsic robustness issue of deep learning-based HD EMG PR.
Dirichlet Problem for the Stationary Navier-Stokes System on Lipschitz Domains

We consider the stationary Navier-Stokes system on a bounded Lipschitz domain Ω in R 3 with connected boundary ∂Ω. The main concern is the solvability of the Dirichlet problem with external force and boundary data having minimal regularity, i.e., and . Here denotes the standard Sobolev space with the pair (s, q) being admissible for the unique solvability in of the Stokes system. We show that if 1 + s ≥ 2/q in addition, then for any and satisfying the necessary compatibility condition, there exists at least one solution in of the Dirichlet problem and this solution has a complete regularity property. The uniqueness of solutions is also shown under the smallness condition on the corresponding norms of the data.
Coherent O-band Transmission of 4×25 GBd DP-16QAM Channels Over a 50 km BDFA-Equipped Link

The first WDM O-band coherent transmission experiment in a BDFA-amplified link is reported. Four 25 GBd DP‑16QAM channels (4×200 Gb/s) are transmitted over 50 km of fiber, occupying a bandwidth of 4.7 THz. © 2023 The Author(s)
SMO Based Position Sensorless BLDC Motor Drive Employing Canonical Switching Cell Converter for Light Electric Vehicle

In this article, an adaptive sliding mode observer (ASMO) based rotor position sensorless brushless DC (BLDC) motor drive with energy regeneration for light electric vehicle (LEV) is designed. A canonical switching cell (CSC) converter is introduced for optimized MPPT performance and soft starting of the motor is assured during sensorless start-up. The CSC converter provides ripple free current in the output to the voltage source inverter (VSI) and minimizes the losses. It also acts as a fundamental building block for a DC-DC converter with the least element counts. For position sensorless commutation of BLDC motor drive, ASMO technique with self-correction ability of commutation error is used. Estimation of rotor angular position and continuous speed tracking for closed loop control are achieved using this method. This control is robust and derived from linear model of BLDC motor. Proper stability analysis of the observer is derived and ensured using pole placement method. Optimal gain scheduling technique is used in ASMO, which reduces the order of the error function making the computation easier. This model is truly adaptive to any parameter variation and load perturbation. To ensure green mobility, a pulse-width modulated regenerative braking algorithm is also integrated using the same VSI, which avoids any additional converter. Experimental results validate this method as effective for EV applications.
Online Versus Offline Rate in Streaming Codes for Variable-Size Messages

One pervasive challenge in providing a high quality-of-service for live communication is to recover lost packets in real-time. Streaming codes are a class of erasure codes that are designed for such strict, low-latency streaming communication settings. Motivated by applications that transmit messages whose sizes vary over time, such as live video streaming, this paper considers the setting of streaming codes under variable-size messages. In practice, streaming codes operate in an “online” setting where the sizes of the future messages are unknown. “Offline” codes, in contrast, have access to the sizes of all messages, including future ones. This paper introduces the first online rate-optimal streaming codes for communicating over a burst-only packet loss channel for two broad parameter regimes. These two online codes match the rates of optimal offline codes for the two settings despite the apparent advantage of the offline setting. This paper further establishes that online codes cannot attain the optimal rate for offline codes for all remaining parameter settings.
Epileptogenic zone classification with functional connectivity and graph measures

Epilepsy is one of the most common neurological diseases, and it has a great variety of possible diagnosis that can involve different treatments. The characterization of epileptogenic zones (EZs) is of extreme importance for the evaluation of patients, specially for pharmacoresistant epilepsy patients that are candidates for surgery. Many works have been showing that epilepsy interferes with brain network organization, in particular during seizures or epileptic discharges, although there is also indication that there are significant alterations during interictal periods. With most studies using intracranial electroencephalography (EEG), there are few results about the alterations present in regular EEG examinations that could be used to indicate the general location of the EZ in advance. This prior indication could be beneficial for a better understanding of network changes across the whole scalp. The aim of this work was to extract relevant information from patients with different EZs, using functional connectivity and graph measures from normal background EEG signals. Patients with EZs in the temporal and frontal lobe were included in this study. Our results reinforce that centrality graph measures from many areas of the scalp are useful in the distinction between patients with different EZs. We also found that connectivity in the alpha band can be used for EZ classification and might be of interest in future studies.
Investigating the relationship between cue immersion and the strength of motor imagery during hand and wrist movements

Virtual reality (VR) has gained popularity as a modality for motor imagery (MI) cue presentation for brain-computer interfaces due to the increased cortical activity caused by the immersiveness of the medium. In this work, we show that increased immersion alone is not sufficient to generate a greater cortical response during MI. We demonstrate this relationship through a MI protocol performed using three modalities of cue presentation, Image, Video, and VR, wherein intact limb participants were instructed to imagine the prompted hand and wrist movements as their own. Cue presentation using static images outperformed cue presentation using both traditional videos and immersive VR simulations (p < 0.05) in terms of percent change in relative alpha power. These results imply that providing more immersive cues may sometimes reduce the effectiveness of MI, possibly by inducing action observation rather than mental simulation.
Laser Enhanced Direct-Print Additive Manufacturing (LE-DPAM) of mm-Wave Antenna using LTCC Dielectric Paste for High Temperature Applications

The laser-enhanced direct print additive manufacturing (LE-DPAM) process is used to fabricate and characterize low temperature co-fired ceramic (LTCC) paste for high temperature applications. Coplanar waveguides (CPW) were used to characterize the performance of the additive manufactured LTCC substrate and the permittivity is extracted using direct S-parameters measurements up to 40 GHz. The extracted permittivity is calculated to be an average of 21.3 over 1–40 GHz. A 4 dBi mm-wave antenna is designed and fabricated to demonstrate the capability to additive manufacture LTCC materials, with resonances in the 32–34 GHz range which endured at temperatures up to 980 °C. To the authors’ best knowledge, this is the first fully additive manufactured solution for LTCC technology, where both LTCC and conductive traces were printed using a microdispensing AM process.
Super star clusters in Hii galaxies

Abstract We summarize our results based on observations with the NIRI camera on the Gemini North telescope of three Hii galaxies (Mrk 36, UM 408 and UM 461), obtained to identify and determine the ages and masses of the elementary components (the star cluster population) of the starburst regions in compact Hii galaxies. Our preliminary results indicate that the masses of the stellar clusters in these galaxies range from ~104 to ~106 M⊙, with associated ages of a few Myr. The most massive star clusters fall in the so-called super star cluster category. The identification of these clusters suggests that the formation and evolution of massive star clusters is the dominant mode of star formation in these galaxies. Their spatial distribution and ages seem to indicate that star formation is simultaneous over these timescales in some of our objects. We also review our recent description of the spatial distribution of physical conditions in the Hii galaxy UM 408 using the GMOS integral-field unit on Gemini South. The spatial distribution of the oxygen abundance does not show any significant variation or gradient across the galaxy on scales of hundreds of parsecs, within our observational uncertainties, confirming that this compact Hii galaxy, like other previously studied dwarf irregular galaxies, is chemically homogeneous.
BGCN: An EEG-based Graphical Classification Method for Parkinson's Disease Diagnosis with Heuristic Functional Connectivity Speculation

As the population ages, the prevalence of Parkinson's Disease (PD), a neurodegeneration disorder that deeply hinders one's daily intellectual and physical activities, has increased rapidly over the past years. However, finding an effective modifiable treatment for PD remains stagnant to date, elevating the significance of the accurate diagnosis. Meanwhile, studies on functional connectivity could provide insights into the neurophysiological mechanisms underlying PD. Hence, this study intends to provide a unified framework, Brain Graph Convolutional Networks (BGCN), incorporating the non-Euclidean heuristic-based brain functional connectivity into a graph-based deep learning model (GCN) for PD diagnosis. The graph representation of Electroencephalography (EEG) data priors in retaining the spatial interdependence among the EEG channels and facilitating the formulation of the functional connectivity construction problem. With the GCN, we modeled neural information exchange with convolutions between nodes along functional connectivity. In this work, functional connectivity was attained by solving a Minimum Spanning Tree (MST) problem with a heuristic search algorithm. As a result, the obtained functional connectivity corresponded to existing MRI studies in terms of the affected regions and hub shift. To evaluate the efficacy of the proposed framework, we compared the heuristic functional connectivity speculation with random/uniform connectivity generated by K-nearest neighbors(k-NN). The proposed framework has achieved excellent precision (95.59%) and learning robustness.
Advanced Cascaded Filter Synthesis

Advanced lowpass filtering networks comprising novel cascaded quadruplets and/or fivetuplets and/or sixtuplets are proposed in this work. Each of the new network sections realizes a single zero located at infinity and the rest can be positioned at finite frequencies thus offering increased control of the filter selectivity. The methodology derives the desired network configurations from a synthesized intermediate network based on equivalent circuits. Numerical examples of advanced lowpass networks comprising fifteen resonators are demonstrated. Furthermore, the EM implementations of two quadruplets in Combline technology are presented. Each quadruplet realizes a total of three finite frequency transmission zeros with one having all the zeros positioned below the passband and the other one above. The performances of the two quadruplet models are in good agreement with their circuit counterparts.
Months-long High-performance Fixed LSTM Decoder for Cursor Control in Human Intracortical Brain-computer Interfaces

Intracortical brain-computer interfaces (iBCIs) enable high performance cursor control for people with tetraplegia by inferring motor intentions from neural recordings. However, current methods rely on frequent decoder recalibrations to reduce performance fluctuations attributable to instability in neural recordings. Towards clinical translation, iBCIs must sustain high performance over long periods of time with minimal interruptions to the user. Recent non-human primate (NHP) studies indicate that recurrent neural network (RNN) decoders are more robust to neural variability. Here, we demonstrate that an RNN variant, a long short-term memory (LSTM) neural decoder, provides online long-term, stable two-dimensional cursor control for a participant with tetraplegia enrolled in the BrainGate2 clinical trial. An LSTM decoder was trained with multiple days of the participant's historical intracortical motor cortex recordings spanning seventy days. The LSTM decoder was then fixed and evaluated online as the participant used the iBCI to control a computer cursor during a center out and back task for 15 sessions across four months. The LSTM demonstrated high performance for the first three months without recalibration or adaptive parameter updates with an average performance of 93.8% of targets acquired. This longitudinal study suggests that a nonlinear RNN-based decoder can provide stable, intuitive control of 2-D kinematics by humans with tetraplegia.
Assistive Multimodal Wearable for Open Air Digit Recognition Using Machine Learning

To increase access to digital systems for populations suffering from upper limb motor impairment we present an assistive wearable device to capture gestures performed in air. These open air gestures provide an interface for users who are unable to exhibit the fine motor control needed for standardized human computer interfaces utilizing miniature button input such as keyboards and keypads. By capturing the motion performed at the wrist by an accelerometer as well as the muscle activation signatures using surface electromyography, we improve the classification accuracy as compared to using either modality alone. Twelve features were extracted from the multimodal time series data in both the time and frequency domain and used as input to a collection 4 machine learning models for classification, Fine Tree, K-Nearest Neighbor, Support Vector Machine, and Artificial Neural Network. One subject performed the task of writing single digits in free space and after post-processing and feature extraction we achieved a classification accuracy of 96.2% for binary discrimination of digits zero and one using a support vector machine model and an accuracy of 71% when classifying all 10 digits using an artificial neural network. Our findings indicate the feasibility of a wearable multimodal human computer interface to relieve the burden conventional interfaces present to motor impaired users.
Photothermal effects of plasmonic metal nanoparticles in a fluid

There is a strong interest in the use of plasmonic metal nanoparticles in medical applications involving photothermal therapy. In this study, the problem of calculating the temperature elevation of a fluid arising from the absorption of light by a suspension of plasmonic nanoparticles is examined. The dependence of this temperature increase on the absorption cross section of nanoparticles of different shapes, in particular, nanospheres, nanospheroids, and nanostars, is studied. The nanoparticles behave as point sources of heat production and the time-dependent heat transfer equation is solved assuming that the nanoparticles are confined to a limited region. From this solution, the steady-state temperature of the fluid medium can be calculated and the time constant to achieve this temperature determined.
Identifying Artistic Expertise Difference in Emotion Recognition in Response to Oil Paintings

Previous studies have been conducted on building emotion recognition frameworks and enhancing their performances using Electroencephalography (EEG) and eye tracking signals. However, the differences between experts in art and non-experts in emotion recognition still remain to be elucidated. In this paper, we systematically evaluate the performance of various computational models for emotion recognition in response to oil paintings and identify the differences between experts in art and non-experts. The experimental results demonstrate that Transformer neural networks achieve the highest accuracies of 65.27% in three-category emotion recognition (negative, neutral, and positive) in response to oil paintings. Although the overall emotion recognition accuracies of the two groups are similar, the mean accuracy of the non-expert group for positive emotion is higher than that of experts, and the expert group has higher recognition accuracy in neutral emotion than the non-expert group. We further investigate the neural patterns of the three emotions in the two groups. The experimental results indicate that neural pattern differences do exist in both emotions and artistic expertise. The parietal and occipital lobes are more activated for positive emotion in the artistic expert group in the alpha, beta, and gamma bands. Our proposed methods provide an understanding of underlying emotion-expertise neurological mechanisms and cognitive processes.
Application of EEG-based Passive Mental Fatigue Detection Model to an Active Fatigue Task

Mental fatigue results in feelings of tiredness and decreases task performance and efficiency. It can be caused by cognitive underload (passive fatigue) or overload (active fatigue). Although fatigue detection models have been developed using electroencephalogram (EEG) recordings, it is unclear whether their predictions (derived from one task) are applicable to different tasks. We investigated if an EEG-based fatigue model previously trained on a passive fatigue-inducing driving task, can predict fatigue during an active fatigue-inducing task. We collected EEG while subjects performed a target-hitting task as a non-fatigued baseline, followed by mental arithmetic tasks of increasing complexity and mental load. Overall, the fatigue score from the passive fatigue model was significantly correlated with self-reported fatigue and sleepiness, which increased over the course of the experiment. The fatigue score was higher during simple mental arithmetic than the target-hitting task, demonstrating that the model can successfully differentiate non-fatigued and passive/low mental load situations. However, the fatigue score was higher during the simple (low mental load) than the complex (high mental load) mental arithmetic task, showing that the model cannot accurately detect active fatigue. Future fatigue models should be trained on a variety of both active and passive fatigue-inducing tasks to be more generalizable.