Miracle-dz/UniFAR-FTUs huggingface dataset:
Heart Rate Variability Responses to Visually Induced Motion Sickness

Analysis of heart rate variability (HRV) can reveal a range of useful information regarding the dynamics of the autonomic nervous system (ANS). It is considered a robust and reliable tool to understand even some subtle changes in ANS activity. Here, we study the "hidden" characteristic changes in HRV during visually induced motion sickness; using nonlinear analytical methods, supplemented by conventional time- and frequency-domain analyses. We computed HRV from electrocardiograms (ECG) of 14 healthy participants measured at baseline and during nausea. Primarily hypothesizing evident differences in measures of physiologic complexity (SampEn; sample entropy, FuzzyEn; fuzzy entropy), chaos (LLE; largest Lyapunov exponent) and Poincaré/Lorenz (CSI; cardiac sympathetic activity, CVI; cardiac vagal index) between the two states. We found that during nausea, participants showed a markedly higher degree of regularity (SampEn, p = 0.0275; FuzzyEn, p = 0.0006), with a less chaotic ANS response (LLE, p = 0.0004). CSI significantly increased during nausea compared to baseline (p = 0.0005), whereas CVI did not appear to be statistically different between the two states (p = 0.182). Our findings suggest that motion sickness-induced ANS perturbations may be quantifiable via nonlinear HRV indices. These findings have implications for understanding the malaise of motion sickness and in turn, aid development of therapeutic interventions to relieve motion sickness symptoms.Clinical relevance— The study suggests potential indices of physiologic complexity and chaos that may be useful in monitoring motion sickness during clinical studies.
Inference-based time-resolved stability analysis of nonlinear whole-cortex modeling: application to Xenon anaesthesia

This study characterizes the neurophysiological mechanisms underlying electromagnetic imaging signals using stability analysis. Researchers have proposed that transitions between conscious awake and anaesthetised states, and other brain states more generally, may result from system stability changes. The concept of stability in dynamical systems theory provides a mathematical framework to describe this possibility. In particular, the degree to which a system’s trajectory in phase space is affected by small perturbations determines the stability. Previous studies using linear or oscillator-based whole-brain models cannot represent complex cerebrocortical dynamics, or model parameters were pre-assumed or inferred from data but did not change over time. This study proposes a nonlinear neurophysiologically plausible whole-cortex modeling framework to analyze the stability of brain dynamics for the emergence and disappearance of consciousness using time-varying parameters estimated from the data.Clinical relevance— Depth of anaesthesia is typically measured through changes in EEG statistics like the bispectral index and spectral entropy. However, these monitors have been found to fail in preventing awareness during surgery and postoperative recall. Our whole-cortex stability analysis may be useful in measuring anaesthesia levels in clinical settings, as it changes with the level of consciousness and is independent of individual differences and anaesthetic agents. The proposed method can also be used to, for example, identify critical brain regions for consciousness, locate the epileptogenic zone and investigate the dominance of extrinsic or intrinsic factors in brain functions.
Machining Feature Recognition Method Based on 3D Convolution Neural Network

The research of automatic recognition of machining features constitutes a crucial supporting pillar for intelligent design and manufacturing. Despite the availability of existing algorithms, there are prevalent problems including low recognition efficiency, low recognition accuracy, and limited applicability to hybrid intersection features. The recognition capability of neural networks has achieved significant success in various fields. The application potential of neural networks for machining feature recognition is currently being explored. In this paper, we propose a feedforward neural network and a novel 3D convolutional neural network framework based on semantic and voxelization methods to identify machining features from 3D CAD models. Sufficient experiments on the datasets of 24 machining characteristics reveal an excellent accuracy result. The proposed framework provides a reliable algorithmic basis for implementing real-time computer aided process planning (CAPP) systems.
Solution-Processable Carbon Nanotubes for Sensing and Biosensing Applications

Carbon nanotubes are a promising sensing material for bio-transduction devices, due to their unique electrical properties, nanosize, bio-compatibility, and their simple low-cost processability. In this contribution, we present two classes of devices (i.e., electrolyte-gated field-effect transistor-based sensors and gas sensors) fabricated by facile and scalable spray deposition of water-based carbon nanotube inks. The developed sensing platforms have been employed for the detection of several analytes for different applications, including food quality assessment, physical activity monitoring, environmental monitoring, and detection of gaseous species.
Analysis and Classification of Event-Related Potentials During Image Observation

In the field of cognitive neuroscience, researchers have conducted extensive studies on object categorization using Event-Related Potential (ERP) analysis, specifically by analyzing electroencephalographic (EEG) response signals triggered by visual stimuli. The most common approach for visual ERP analysis is to use a low presentation rate of images and an active task where participants actively discriminate between target and non-target images. However, researchers are also interested in understanding how the human brain processes visual information in real-world scenarios. To simulate real-life object recognition, this study proposes an analysis pipeline of visual ERPs evoked by images presented in a Rapid Serial Visual Presentation (RSVP) paradigm. Such an approach allows for the investigation of recurrent patterns of visual ERP signals across specific categories and subjects. The pipeline includes segmentation of the EEGs in epochs, and the use of the resulting features as inputs for Support Vector Machine (SVM) classification. Results demonstrate common ERP patterns across the selected categories and the ability to obtain discriminant information from single visual stimuli presented in the RSVP paradigm.
A Hybrid Computer-Aided Diagnosis System for Central Obesity Screening in a Large Sample of Iranian Children and Adolescents

Central obesity is the basis of metabolic syndrome, which may lead to type 2 diabetes and cardiovascular disease. Its screening is critical in childhood to prevent such problems in adulthood. We presented a computer-aided diagnosis system to classify children and adolescents into obese and non-obese groups based on input features obtained from the subject's nutritional behavior, physical activity, genetics, socioeconomic status, and family history of diseases (the CASPIAN IV study). A total of 13,386 subjects (49% female) with a central obesity prevalence of 19% participated. The categorical features were converted to interval features using the Logit function, and the XGBoost classifier with grid search was then used. Other linear and nonlinear classifiers were also used for comparison. Some selected features were family history of hypertension, weight at birth, number of close friends, breakfast, and screen time categories. The proposed screening system showed a high association between predicted and observed class labels (Matthews correlation coefficient =0.76), excellent balanced diagnosis accuracy (AU-ROC =0.90), and excellent class labeling agreement rate (Kappa = 0.75) using 4-fold cross-validation. It is thus a promising screening tool. Moreover, it significantly outperformed the other tested classifiers (adj. P-value<0.05). Although, as a cross-sectional study, no causality can be inferred.
A Single-Switch High Voltage Gain DC-DC Converter Using Coupled Inductor and Switched Capacitor-Inductor Techniques

In this paper, a single switch non-isolated high voltage gain DC-DC converter using the coupled inductor and switched capacitor-inductor techniques, is suggested for renewable energy applications. Using these techniques, higher voltage gain in the lower duty cycle is achieved. In addition, due to the use of coupled inductors, a smaller voltage stress across the semiconductors such as a switch and diodes is attained. Furthermore, continuous input current and common ground between load and input DC source can be addressed as other advantages of the proposed converter. The operating principles of continuous conduction mode (CCM) are discussed, and the mathematical equations are derived. Under the same conditions, a comparison is conducted to evaluate the performance of the proposed converter. Finally, the proposed converter is designed for 500W with an input voltage of 40V and output voltage of 600V and is simulated in MATLAB/Simulink. The parasitic model is considered to evaluate the theoretical efficiency of the proposed converter. Results are presented to verify the theory mode performance of the converter.
Dynamic transitions of functional brain connectivity during a working memory task

Working memory is a cognitive system that temporarily stores and manipulates information via attention. Although brain activity patterns related to working memory have been extensively studied, little is known about how the brain connectome organization dynamically changes while performing working memory tasks. Here, we systematically investigated dynamic changes in functional brain connectivity during a working memory task. We found that functional connectivity in the medial frontal and orbitofrontal cortices and the precuneus showed differences during the transitions between the rest and task. Our results provide topographic patterns of dynamic functional connectivity during the working memory tasks.Clinical Relevance— This study may provide macroscale topography related to working memory and foster establishing memory-related brain mechanisms, which could be applied to clinical neuroscience.
Reinforcement Learning Based Controller for a Soft Continuum Robot

The application of soft robotics holds immense potential for transformative impact across diverse domains, encompassing healthcare, such as addressing cardiac obstructions, and exploration, like acquiring invaluable insights through polar ice cap navigation. Soft robots exhibit superior adaptability owing to their softness and flexibility. However, controlling soft robots poses challenges due to the absence of joints and rigidity. Consequently, a control strategy employing reinforcement learning is introduced for controlling their navigation. This strategy enables the soft robot to acquire knowledge from its environment through feedback in the form of rewards or penalties, resulting in exceptional precision in reaching its intended destination.
An Automated Algorithm for the Identification of Somatosensory Cortex Using Magnetoencephalography

The localization of eloquent cortex is crucial for many neurosurgical applications, such as epilepsy and tumor resection. Non-invasive localization of these cortical areas using magnetoencephalography (MEG) is generally performed using equivalent current dipoles. While this method is clinically validated, source localization depends on several subjective parameters. This paper aimed to develop an automated algorithm for identifying the cortical area activated during a somatosensory task from MEG recordings. Our algorithm uses singular value decomposition to outline the cortical area involved in this task. For proof of concept, we evaluate our algorithm using data from 10 subjects with epilepsy. Our algorithm has a statistically significant overlap with the somatosensory cortex (the expected active area in healthy subjects) in 6 of 10 subjects. Having thus demonstrated proof of concept, we conclude that our algorithm is ready for further testing in a larger cohort of subjects.Clinical relevance— Our algorithm identifies the dominant cortical area and boundary of the cortical tissue involved in a task-related response.
Semi-Automatic Integration of Little Languages

This paper is about a kind of preview model integration that can be done for complex software managed systems, that maintains the flexibility of well-trusted software and hardware component models (“islands of good behavior”), and makes explicit all of the influences expected for each component. In the systems that have been built using these methods (medium sized mathematical and natural language analysis systems), almost all of the software errors were caught at this stage, long before deployment. The purpose of this paper is to focus on the languages used to describe these mutual influences, making them explicit with sufficiently well-defined semantics, and automating these calculations as much as possible. We do not have a finished solution, but we think that this direction will be very fruitful, as it extends others that we have already found very useful.
Tensor-based Feature Extraction for Pupil Recognition in Cataract Surgery

Cataract surgery remains the definitive treatment for cataracts, which are a major cause of preventable blindness worldwide. Adequate and stable dilation of the pupil are necessary for the successful performance of cataract surgery. Pupillary instability is a known risk factor for cataract surgery complications, and the accurate segmentation of the pupil from surgical video streams can enable the analysis of intraoperative pupil changes in cataract surgery. However, pupil segmentation performance can suffer due to variations in surgical illumination, obscuration of the pupil with surgical instruments, and hydration of the lens material intraoperatively. To overcome these challenges, we present a novel method called tensor-based pupil feature extraction (TPFE) to improve the accuracy of pupil recognition systems. We analyzed the efficacy of this approach with experiments performed on a dataset of 4,560 intraoperative annotated images from 190 cataract surgeries in human patients. Our results indicate that TPFE can identify features relevant to pupil segmentation and that pupil segmentation with state-of-the-art deep learning models can be significantly improved with the TPFE method.
Correlation between unconfined compressive strength and indirect tensile strength of limestone rock samples

In Geotechnical Engineering, determining the Unconfined Compressive Strength (UCS) of rock is of prime importance as their role in design and analysis of geotechnical problems is crucial. Although laboratory test is the most direct way for estimating the rock compressive strength but UCS determination in the laboratory would be problematic if the rock masses are weathered mainly because obtaining proper core segments for testing purpose is difficult in this situation. Hence, the use of index testing as an alternative for predicting UCS is investigated by researchers. It is well established that indirect tensile strength or Brazilian Tensile Strength (BTS) is related to UCS. In this paper, in order to develop a new correlation between UCS and BTS, some laboratory tests on dry limestone specimens including 20 Unconfined Compression Tests (UCT) and 20 Brazilian Tests (BT) have been conducted. The paper also reviews some recent correlations between UCS and BTS as well as investigates the reliability of these correlations. Findings show the results of the experimental tests are in good agreement with previous studies. Based on the results, new strong correlation with coefficient of determination of 0.9 is introduced for predicting the UCS of limestone core samples from its BTS. A critical review on the degree of accuracy of recent correlations between UCS and BTS shows that apart from Kahraman`s correlation, other correlations either overestimate or under estimate UCS of rock samples to a significant amount.
Low-power EEGNet-based Brain-Computer Interface implemented on an Arduino Nano 33 Sense

The use of Convolutional Neural Networks (CNNs) to process Electroencephalograph (EEG) signals has been introduced in recent years with great success in the field of Brain-Computer Interfaces (BCI). Nevertheless, in order to advance towards a CNN-based BCI prototype, they must be efficiently mapped into low-power and low-cost hardware, enabling a real-time, portable and Internet-independent brain-computer communication. This work presents the implementation of an EEGNet-based model into an ARM Cortex M4F microcontroller, available on the Arduino Nano 33 Sense. Starting from models trained over the Physionet Motor Movement/Imagery dataset, 8-bit integer post-training quantization has been considered to reduce computing complexity, with a mean downgrade of 2.64±0.77% in accuracy. Moreover, their computational impact and memory footprint have been characterized by measuring the associated operations and Random-Access Memory (RAM) usage. Finally, a selected model has been implemented on the ARM Cortex M4F, with a latency of 137 ms and an energy per inference of 2.55 mJ, a 40% lower than other EEGNet implementation on the same microcontroller.
Deep Learning-Based Predictive Model for Revascularization of Chronic Total Occlusions on Angiographic Imaging

Revascularization of chronic total occlusions (CTO) is currently one of the most complex procedures in percutaneous coronary intervention (PCI), requiring the use of specific devices and a high level of experience to obtain good results. Once the clinical indication for extensive ischemia or angina uncontrolled with medical treatment has been established, the decision to perform coronary intervention is not simple, since this procedure has a higher rate of complications than non-PCI percutaneous intervention, higher ionizing radiation doses and a lower success rate. However, CTO revascularization has been shown to be helpful in symptomatic improvement of angina, reduction of ischemic burden, or improvement of ejection fraction. The aim of this work is to determine whether a model developed using deep learning techniques, and trained with angiography images, can better predict the likelihood of a successful revascularization procedure for a patient with a chronic total occlusion (CTO) lesion in their coronary artery (measured as procedure success and the duration of time during which X-ray imaging technology is used to perform a medical procedure) than the scales traditionally used. As a preliminary approach, patients with right coronary artery CTO will be included since they present standard angiographic projections that are performed in all patients and present less technical variability (duration, projection angle, image similarity) among them.The ultimate objective is to develop a predictive model to help the clinician in the decision to intervene and to analyze the performance in terms of predicting the success of the technique for the revascularization of chronic occlusions.Clinical Relevance— The development of a deep learning model based on the angiography images could potentially overcome the gold standard and help interventional cardiologists in the treatment decision for percutaneous coronary intervention, maximizing the success rate of coronary intervention.
MIMIR-UW: A Multipurpose Synthetic Dataset for Underwater Navigation and Inspection

This paper presents MIMIR-UW, a multipurpose underwater synthetic dataset for SLAM, depth estimation, and object segmentation to bridge the gap between theory and application in underwater environments. MIMIR-UW integrates three camera sensors, inertial measurements, and ground truth for robot pose, image depth, and object segmentation. The underwater robot is deployed within a pipe exploration scenario, carrying artificial lights that create uneven lighting, in addition to natural artefacts such as reflections from natural light and backscattering effects. Four environments totalling eleven tracks are provided, with various difficulties regarding light conditions or dynamic elements. Two metrics for dataset evaluation are proposed, allowing MIMIR-UW to be compared with other datasets. State-of-art methods on SLAM, segmentation and depth estimation are deployed and benchmarked on MIMIR-UW. Moreover, the dataset's potential for sim-to-real transfer is demonstrated by leveraging the segmentation and depth estimation models trained on MIMIR-UW in a real pipeline inspection scenario. To the best of the authors' knowledge, this is the first underwater dataset targeted for such a variety of methods. The dataset is publicly available online. https://github.com/remaro-network/MIMIR-UW/
Attentive Regularization in Targeted Kernel Networks for Accurate Diabetic Retinopathy Grading

Diabetic Retinopathy (DR) is a prevalent eye disease and a leading cause of vision impairment worldwide. Early and accurate DR grading is vital for timely intervention and effective patient care. In this research, we present a novel approach for DR grading utilizing a Convolutional Neural Network (CNN) empowered by Attentive Regularization. This Targeted Kernel Network model incorporates attentive regularization to constrain activation maps of kernels, directing them toward specific regions of interest within fundus images. This approach facilitates the extraction of discriminative features crucial for fine-grained DR grading. The attentive regularization mechanism is designed to ensure that each kernel identifies the most informative regions, enhancing both efficiency and interpretability. We evaluate our model on publicly available datasets, including APTOS 2019 and Messidor, showcasing its effectiveness in addressing the challenges of intra-class variations, small lesion detection, and imbalanced data distributions. The results reveal remarkable performance improvements over existing state-of-the-art models, reaffirming the potential of attentive regularization in advancing DR grading accuracy.
Towards Full Actuation: Reconfigurable Micro Underwater Robots

Exploration and monitoring of hazardous environments, such as legacy nuclear storage ponds, constitute safety-critical missions to be performed by small-scale underwater robots. These monitoring tasks require fully actuated robot platforms in order to allow for hovering while inspecting objects of interest in detail. A severe bottleneck arises from the restricted access points commonly encountered in these surveillance missions that pose strict limitations on the vehicle dimensions. However, small-scale underwater robots usually possess underactuated propulsion systems and are, thus, only partially suitable for these missions. In this work, we investigate and exploit the idea of reconfigurability. Following the idea of the whole is more than the sum of its parts, we daisy-chain multiple small-scale underwater robots with revolute joints to enable shape reconfigurations. In combination with a centralized sliding mode control scheme, the robot platform is able to change its shape depending on the current task, see Fig. 1. While the straight configuration fits well through tight passages such as inspection holes, the robot can reconfigure itself towards a triangular shape that enables the neutrally buoyant robot to hover at areas of interest, e. g. for inspection tasks. Finally, we examine our proposed concept in a series of simulations and experiments. Moreover, we demonstrate the performance of key elements such as reconfiguration and navigation, discuss their limitations, and point out future directions.