Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparison study on creating simulated patient data for individuals suffering from chronic coronary disorders

An emerging area in data science that has lately gained attention is the virtual population (VP) and synthetic data generation. This field has the potential to significantly affect the healthcare industry by providing a means to augment clinical research databases that have a shortage of subjects. The current study provides a comparative analysis of five distinct approaches for creating virtual data populations from real patient data. The data set utilized for the current analyses involved clinical data collected among patients scheduled for elective coronary artery bypass graft surgery (CABG). To that end, the five computational techniques employed to augment the given dataset were: (i) Tabular Preset, (ii) Gaussian Copula Model (iii) Generative Adversarial Network based (GAN) Deep Learning data synthesizer (CTGAN), (iv) a variation of the CTGAN Model (Copula GAN), and (v) VAE-based Deep Learning data synthesizer (TVAE). The performance of these techniques was assessed against their effectiveness in producing high-quality virtual data. For this purpose, dataset correlation matrices, cosine similarity distance, density histograms, and kernel density estimation are employed to perform a comparative analysis of each attribute and the respective synthetic equivalent. Our findings demonstrate that Gaussian Copula Model prevails in creating virtual data with consistent distributions (Kolmogorov-Smirnov (KS) and Chi-Squared (CS) tests equal to 0.9 and 0.98, respectively) and correlation patterns (average cosine similarity equals to 0.95). Clinical Relevance— It has been shown that the use of a VP can increase the predictive performance of a ML model, i.e., above using a smaller non-augmented population.
Exploring the Effect of Eligibility Criteria on AD Severity and Severe Adverse Event in Eligible Patients

Clinical trials were vital tools to prove the effectiveness and safety of medications. To maximize generalizability, the study sample should represent the sample population and the target population. However, the clinical trial design tends to favor the evaluation of drug safety and procedure (i.e., internal validity) without clear knowledge of its penalty on trial generalizability (i.e., external validity). Alzheimer's Disease (AD) trials are known to have generalizability issues. Thus, in this study, we explore the effect of eligibility criteria on the AD severity patients and the severe adverse event (SAE) among the eligible patients.
Renewable energy sources for small islands: a case study in the Pacific Oceans

The utilization of Renewable Energy Sources is undoubtedly a strategic way to reduce the dependence from fossil fuels. This aspect becomes more relevant in case of small islands and remote communities, thanks to the possibility to reduce the importation of fuels from foreign countries. The proposed paper assesses the current conditions in the islanded countries of the Pacific Ocean. In particular, the case study of Nauru is investigated, evaluating the potential energy production from solar, sea wave and wind.
Visual-LiDAR-Inertial Odometry: A New Visual-Inertial SLAM Method Based on an iPhone 12 Pro

As today's smartphone integrates various imaging sensors and Inertial Measurement Units (IMU) and becomes computationally powerful, there is a growing interest in developing smartphone-based visual-inertial (VI) SLAM methods for robotics and computer vision applications. In this paper, we introduce a new SLAM method, called Visual-LiDAR-Inertial Odometry (VLIO), based on an iPhone 12 Pro. VLIO formulates device pose estimation as an optimization problem that minimizes a cost function based on the residuals of the inertial, visual, and depth measurements. We present the first work that 1) characterizes the iPhone's LiDAR in depth measurement and identifies the models for the measurement error and standard deviation, and 2) characterizes pose change estimation with LiDAR data. The measurement models are then used to compute the depth-related and visual-feature-related residuals for the cost function. Also, VLIO tracks varying camera intrinsic parameters (CIP) in real-time and uses them in computing these residuals. Both approaches result in more accurate residual terms and thus more accurate pose estimation. The CIP tracking method eliminates the need of a sophisticated model-fitting process that includes camera calibration and paring of the CIPs and IMU measurements with various phone orientations. Experimental results validate the efficacy of VLIO.
Eye-tracking correlates of the Implicit Association Test

Raising awareness of environmental challenges represents an important issue for researchers and scientists. As public opinion remains ambiguous, implicit attitudes toward climate change must be investigated. A custom Single-Category Implicit Association Test, a version of the Implicit Association Test, was developed to assess climate change beliefs. It was administered to 20 subjects while eye movements were tracked using a smart glasses system. Eye gaze patterns were analysed to understand whether they could reflect implicit attitudes toward nature. Recurrence Quantification Analysis was performed to extract 13 features from the eye-tracking data, which were used to perform statistical analyses. Significant differences were found between target stimuli (words related to climate change) and bad attributes in reaction time, and between target stimuli and good attributes in diagonal length entropy, suggesting that eye-tracking may provide an alternative source of information to electroencephalography in modeling and predicting implicit attitudes.
Optimization of Visible Light Positioning in Industrial Applications using Machine Learning

This paper investigates the error performance of visible Light Positioning (VLP) systems for 3D indoor drone localization using machine learning (ML). Received Signal Strength (RSS) is used to track the position of drone under different smoke channel conditions to emulate an industrial environment. VLP systems utilize visible light communication (VLC) and indoor positioning, providing a low-cost and interference-free solution for precise drone localization. ML based artificial neural network (ANN) is used to trained on diverse simulation datasets and correlations between RSS and position errors. The results demonstrate that ML enables accurate real-time drone position estimation and reduces positioning errors by compensating the channel noise and atmospheric attenuation. ML process enhances the accuracy of the proposed VLP system under the smoke attenuated channel with average error values from 21.9 cm to 5.9 cm. The trained ML models achieve RMSE values of 0.04 m and 0.06 m, respectively, with high R-squared values of 0.999. Furthermore the error histogram analysis confirms accurate drone location estimation, even in the presence of atmospheric attenuation.
Multi Stage Log-Likelihood Ratio Estimation with Ordering in a Non-Linear Receiver for Overloaded MIMO Systems

This paper proposes multi stage log-likelihood ratio (LLR) estimation in a non-linear overloaded MIMO receiver with soft-input decoding. The proposed technique can reduce the computational complexity of the LLR estimation. Furthermore, we propose ordered LLR estimation, which improves the transmission performance with negligible small additional complexity. The performance of the receiver with the multi stage and ordered LLR estimation is confirmed by computer simulation in a 6 × 3 MIMO-OFDM system. The non-linear receiver achieves almost the same transmission performance to the conventional soft-input maximum likelihood detector (MLD) receiver, even though the complexity of the receiver with the proposed LLR estimation is about one-third as much as that of the soft-input MLD. The complexity can be further reduced to one-tenth of that of the soft-input MLD, if about 1.0 dB performance degradation at the BER of 10−5 is acceptable.
Deep Transfer Learning for Sleep Stages Classification by EEG Data

Sleep is one of the key factors of well-being affecting life quality and longevity, which makes sleep monitoring an important task. In this paper, we propose the transfer learning approach for sleep stages classification based on EEG recordings, aiming the analysis of the patient's sleep quality. Transfer learning makes training possible with a small amount of labeled data. Also, usually it takes less time to fine-tune a pretrained model. For the numerical experiments, we use an open PhysioNet Sleep-EDF dataset containing more than 150 EEG files with annotations, and recurrent autoencoder network TimeNet. The proposed approach provides the highest achievable binary classification accuracy when only 10% of labeled data used for training.
BER Analysis for Lattice-Partition-Based Downlink Non-Orthogonal Multiple Access Systems

Lattice partition (LP) is a promising technique to design power-domain non-orthogonal multiple access (NOMA) systems because it can approach the capacity region with reduced receiver complexity. Most of LP-based NOMA works have been focused on outage and capacity analyses. In this letter, theoretical expressions of the bit error rate (BER) are derived for a two-user LP-based NOMA system using bipolar pulse amplitude modulation. The derived BERs agree well with computer simulation results. Since a two-dimensional LP-based NOMA signal can be decomposed into two orthogonal one-dimensional LP-based NOMA signals, the analytical results can be easily extended to a quadrature-amplitude modulation case.
Cultural Product Valuation Based on XGBoost-LightGBM

With the prosperity of the cultural industry, it has already become a pillar industry of the national economy. In contradiction to the development trend of the cultural industry, the value determination and valuation of cultural products are still in a state of absence, which restricts the development of the trading market of cultural products. In the traditional way, cultural products are appraised by appraisal experts. With the popularity of the Internet and the development of digital technology, the appraisal method of cultural products has changed, and the study of value appraisal has become an important topic in the whole field of cultural research. In this paper, the possibility of using a machine learning algorithmic framework to evaluate the value of calligraphy and painting works is investigated. Customized painting and calligraphy-related datasets in unstructured data format are used as the main form, and a machine learning-based XGBoost-LightGBM combination method is proposed for evaluating the value of cultural products. In addition, the structured and unstructured data are pre-processed, and the long text is transformed into vector form using the Bert-based model. In addition, the structured and unstructured data were preprocessed, and the long text was transformed into vector form using the bert-Chinese model, and the results were finally input into the XGBoost and LightGBM weighted combination models to obtain the final estimated value range. The experimental results further show that the XGBoost-LightGBM combination method based on machine learning proposed in this paper can be applied to the field of cultural product appraisal.
Transfer Learning with CNN Models for Brain-Machine Interfaces to command lower-limb exoskeletons: A Solution for Limited Data *

This study evaluates the performance of two convolutional neural networks (CNNs) in a brain-machine interface (BMI) based on motor imagery (MI) by using a small dataset collected from five participants wearing a lower-limb exoskeleton. To address the issue of limited data availability, transfer learning was employed by training models on EEG signals from other subjects and subsequently fine-tuning them to specific users. A combination of common spatial patterns (CSP) and linear discriminant analysis (LDA) was used as a benchmark for comparison. The study's primary aim is to examine the potential of CNNs and transfer learning in the development of an automatic neural classification system for a BMI based on MI to command a lower-limb exoskeleton that can be used by individuals without specialized training.Clinical Relevance— BMI can be used in rehabilitation for patients with motor impairment by using mental simulation of movement to activate robotic exoskeletons. This can promote neural plasticity and aid in recovery.
A Fast Steerable Soft Robot for Navigating a Pipe Network

Soft vibrational bristlebots are robots with deformable bristles on their outside that propel the robot through the stick slip motion of the bristle tips interacting with the ground when a vibration is induced in the robot. Experimental results and theoretical analysis of the dynamics for this style of robot have been investigated on flat surfaces. However, for the soft vibrational bristlebots traveling through pipes, controlled steering and the ability for the robot to navigate intersections has not been achieved or understood. This paper presents a two unit vibrational bristlebot with connecting helical coils. Through models and experiments it is demonstrated that a full range of controlled motion (traveling forwards, turning left and right and going up or down a vertical pipe) within a pipe network is possible for such soft vibrational bristlebots. Such fast steerable motion is all achieved merely by changing the polarity and spin speed of the vibrational motors.
Null-Space Compliance Variation for Safe Human-Robot Collaboration in Redundant Manipulators using Safety Control Barrier Functions

In this paper, Safety Control Barrier Functions (SCBFs) were used to adjust the null-space compliant behavior of a redundant robot to improve safety in Human-Robot Collaboration (HRC) without modifying the robot behavior with respect to its main Cartesian task. A Lyapunov function was included in an energy storage formulation compatible with strict passivity to provide global asymptotic stability guarantees for the null-space compliance variation, and the necessary conditions for stability were formulated as inequality constraints of the optimization problem used for the null-space compliance variation. Experimental validation was performed using a Franka Emika Panda robot for a collaborative assembly application and its results showed that safety can be improved by using SCBFs simultaneously to the optimization of the robot configuration, while employing a single degree of freedom.
End-to-End Point Cloud Registration via Rotation Equivariant Descriptors

Point cloud registration (PCR) aims to recover the rigid transformation between two noisy, unordered point sets. This task is typically tackled by establishing point-wise correspondences, and solving the rigid transformation between the two sets. Since descriptor-based methods find correspondences by matching the feature space distance, a powerful and rotation-robust point feature extractor is critical to the success of this task. Existing methods assume soft rotation invariance/equivariance through the means of training augmentation, rotational discretization or pre-alignment of patches. In contrast, this paper proposes a new method which generates fully rotation invariant and equivariant descriptors by construction. For each keypoint patch, our network extracts not only a rotation invariant descriptor for establishing corre-spondences, but also a rotation equivariant one. The rotation equivariant descriptor allows relative transformation to be directly recovered from a single correspondence pair, unlike standard methods that require three correspondences. This design significantly reduces iteration number of RANSAC and guarantees high registration recall when the inlier ratio of estimated correspondences is low. Extensive experiments have demonstrated that the proposed method outperforms state-of-art methods in the same category even after much fewer RANSAC iterations.
Cervical Vagus Nerve Stimulation Disrupts Gastric Slow Wave Activity in Rats

Cervical vagus nerve stimulation (cVNS) is a promising neuromodulation therapy for treating symptoms of disease in peripheral organs. The rat is a common animal model for studying and trialing new applications of cVNS therapy, but the stomach and its activity in rats is less well characterized than other animals, such as pigs. We sought to investigate the effects of acute, in vivo cVNS on gastric bioelectrical activity as an intermediate step to computational modeling of the effects of cVNS on gastric smooth muscle electromechanical coupling. Here we show a method of detecting bioelectrical gastric slow wave events using a non-linear energy operator. The marked events are compared to the underlying bioelectrical slow wave activity.The mean propagation velocity before stimulation was 0.79 ± 0.31 mm s-1, and the mean interval was 17.4 ± 1.4 s. During cVNS, there was a significant increase in velocity (1.02 ± 0.69 mm s-1; p < 0.001), and decrease in interval (15.4 ± 2.9 s; p = 0.0196). At stimulation onset, premature slow waves were induced at an ectopic pacemaker location and waves originating at the ectopic and initial pacemaker sites continued to collide following the cessation of cVNS.This work forms the basis for more thorough investigation of the effects of cVNS on gastric bioelectrical slow wave activity and consequential smooth muscle contractions in rats. A better understanding of the effects of cVNS on gastric function will allow the refinement of cVNS therapy to target the stomach, and avoid off-target effects on the stomach.Clinical relevance— This work presents a signal processing and analysis approach for the investigation of cervical vagus nerve stimulation on gastric bioelectrical activity in rats. Vagus nerve stimulation may enable the control and amelioration of hunger, gastric emptying, or functional gastric disorders.
Acquisition and Prediction of High-Density Tactile Field Data for Rigid and Flexible Objects

Obtaining high-density tactile field information is a critical aspect of research in the field of robotic haptics, as it plays a decisive role in determining the precision of robot manipulations. Vision-based tactile sensors have unique high-resolution features, which make them promising for related research. However, previous studies have mainly focused on reconstructing the shape of rigid objects or predicting the three-dimensional force of rigid objects, neglecting the analysis of flexible objects. Moreover, due to the resolution limitations of existing commercial sensors, the performance evaluation of previous force prediction models relied solely on the total force. To overcome these limitations and in order to explore the tactile field information of objects with more attributes, this paper presents a detailed high-density tactile field data acquisition method based on a mechanical simulation environment. Additionally, we constructed a network to learn the mapping relationship between tactile images and six-dimensional tactile field information. Our results demonstrate that the proposed method can predict the three-dimensional force and displacement information of the object. Notably, the prediction error is within the tolerance range for fine manipulation by robots.
Robust Independent Component Analysis based EMG decomposition – a comparison study

High density surface Electromyography (HD-sEMG) provides a high fidelity measurement of the myoelectric activity that can be leveraged by EMG decomposition methods to estimate the motor neuron discharges. Independent Component Analysis (ICA) methods are used as basis for many EMG decomposition algorithms, for the estimation of motor unit action potential signals. Accurate source separation is a non-trivial task in EMG decomposition. While FastICA is widely used for this purpose, other methods with attractive characteristics, such as RobustICA, remain relatively unexplored. The purpose of the current work is to compare three different ICA-based EMG decomposition methods (FastICA, RobustICA and RobustICALCH) in terms of decomposition accuracy and computation time. The evaluation was performed on simulated data using a decomposition algorithm inspired by previous studies. Our results demonstrate that RobustICA outperforms the other methods in terms of number of correctly identified motor units, high decomposition accuracy, and low computation time, across different muscle contraction levels.
Probability-Based Rejection of Decoding Output Improves the Accuracy of Locomotion Detection During Gait*

Prosthetic users need reliable control over their assistive devices to regain autonomy and independence, particularly for locomotion tasks. Despite the potential for myoelectric signals to reflect the users’ intentions more accurately than external sensors, current motorized prosthetic legs fail to utilize these signals, thus hindering natural control. A reason for this challenge could be the insufficient accuracy of locomotion detection when using muscle signals in activities outside the laboratory, which may be due to factors such as suboptimal signal recording conditions or inaccurate control algorithms.This study aims to improve the accuracy of detecting locomotion during gait by utilizing classification post-processing techniques such as Linear Discriminant Analysis with rejection thresholds. We utilized a pre-recorded dataset of electromyography, inertial measurement unit sensor, and pressure sensor recordings from 21 able-bodied participants to evaluate our approach. The data was recorded while participants were ambulating between various surfaces, including level ground walking, stairs, and ramps. The results of this study show an average improvement of 3% in accuracy in comparison with using no post-processing (p-value < 0.05). Participants with lower classification accuracy profited more from the algorithm and showed greater improvement, up to 8% in certain cases. This research highlights the potential of classification post-processing methods to enhance the accuracy of locomotion detection for improved prosthetic control algorithms when using electromyogram signals.Clinical Relevance— Decoding of locomotion intent can be improved using post-processing techniques thus resulting in a more reliable control of lower limb prostheses.
A Multi-modal Teacher-student Framework for Improved Blood Pressure Estimation

Blood pressure (BP) is a critical vital sign that hypertensive patients regularly measure. In this study, we propose a novel BP estimation framework to distill the knowledge from a multi-modal model to a uni-modal BP estimation model through teacher-student training. The multi-modal BP estimation model consists of three components: first, a gated recurrent unit network that generates features from photoplethysmogram, electrocardiogram, age, height, and weight; second, an attention mechanism that integrates each feature into joint embeddings; and third, a regression layer that estimates BP from the joint embeddings. The uni-modal BP estimation model has similar components to the multi-modal model but uses only PPG signal. BP is predicted by the embeddings extracted from the uni-modal model, and these embeddings are trained to be as similar as possible to the joint embeddings extracted from the multi-modal model. The proposed method demonstrates absolute prediction errors of 5.24±6.41 and 3.49±3.85 mmHg for systolic blood pressure and diastolic blood pressure in the MIMIC-III dataset, satisfying the AAMI standard.