Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning-based Parameter Estimation for Hysteresis Modeling in Robotic Catheterization

In the last half decade, nearly 31% of annual global deaths are linked to cardiovascular diseases. Thus, robotic catheterizations are recently proposed for interventions of conditions such as aneurism or atherosclerosis formed along vascular paths leading to the heart. However, existence of mild to strong hysteresis while navigating unactuated catheters with the current robotic systems inhibits autonomous control for vascular surgery. Thus, immersion of surgeons remains high with most of their time spent on steering the catheter in-and- out of the vessels. In this study, an autoregressive nonlinear neural network model is adapted for parameterization of vital causal factors of hysteresis during robotic catheterization. Crucial for autonomous control, hysteretic behaviors of endovascular tool are modeled while suitable values are estimated and analyzed for five contributory factors. The network model is validated with hysteresis data we obtained from a two degree-of-freedom robotic system and an unactuated catheter. Result validation shows accurate description of the hysteresis profile recorded duirng catheterization trials with a vascular phantom model.
Hybrid neonatal EEG seizure detection algorithms achieve the benchmark of visual interpretation of the human expert*

Neonatal EEG seizure detection algorithms (NSDAs) have an upper bound of performance related to the agreement between visual interpretation of human experts. No published algorithms have reported performance that has reached this upper bound. In this paper, we combined two recently developed NSDAs in order to improve detection performance. An outlier detection stage was also added to improve robustness in the presence of unseen data. A large database of EEG from 79 term infants labeled by three independent human experts was used to develop and test the sufficiency of the hybrid NSDA. The inter-observer agreement (IOA) between experts was high (κ = 0.757, 95%CI: 0.665-0.836, n=79). The area under the receiver operator characteristic of the NSDA compared to the consensus annotation of the human experts was 0.952 (95%CI: 0.0927-0.971). The IOA of seizure detection between the NSDA and human experts was not significantly less than the IOA among human experts (∆κ = 0.022, 95%CI: −0.20 to 0.072) and was further improved by increasing the minimum seizure duration from 10s to 30s (∆κ = −0.002, 95%CI: −0.073 to 0.055). Automated methods of neonatal EEG seizure detection have sufficient accuracy to replace human interpretation, potentially, providing reliable interpretations for clinicians in the neonatal intensive care unit.
Subspace Identification of Linear Systems with Partial Eigenvalue Constraints

For subspace identification methods with eigenvalue constraints, the constraints are enforced by means of an optimization problem subject to LMI constraints. First principals or step response tests could be used as a source of auxiliary information in order to build LMI regions. In these cases, all the eigenvalues of the identified state space model are subject to the same constraints. However, it often happens that the non-dominant eigenvalues have larger real part or larger natural frequencies. In this paper, we propose a two-step method in order to constrain the dominant dynamics of SISO models into LMI regions. In virtue of this result, in addition, the model eigenvalues could be constrained into disjoint LMI regions. Numerical examples illustrate the e effectiveness of our proposed method.
Automatic Event Detector from Smartphone Accelerometry: Pilot mHealth Study for Obstructive Sleep Apnea Monitoring at Home

Obstructive sleep apnea (OSA) is a common disorder with a low diagnosis ratio, leaving many patients undiagnosed and untreated. In the last decades, accelerometry has been found to be a feasible solution to obtain respiratory activity and a potential tool to monitor OSA. On the other hand, many smartphone-based systems have already been developed to propose solutions for OSA monitoring and treatment. The objective of this work was to develop an automatic event detector based on smartphone accelerometry and pulse oximetry, and to assess its ability to detect thoracic movements. It was validated with a commercial OSA monitoring system at home. Results of this preliminary pilot study showed that the proposed event detector for accelerometry signals is a feasible tool to detect abnormal respiratory events, such as apneas and hypopneas, and has potential to be included in smartphone-based systems for OSA assessment.
Homogeneous and heterogeneous ensemble classification methods in diabetes disease: a review

This paper explores the use of ensemble classification methods in the context of the diabetes disease. An analysis was carried out that formulates and answers seven research questions: publication trends, channels and venues; medical tasks undertaken; ensemble types proposed; single techniques used to construct the ensemble methods; rules used to draw the output of the ensemble; datasets used to build and evaluate the ensemble methods; and tools used. A total of 107 papers were chosen after a study selection process. Ensemble methods were applied to diabetes in 2003 for the first time. All medical tasks related to the diabetes disease were investigated, and the diagnosis task was the most frequently addressed activity by means of ensemble methods. The homogeneous ensembles were the most common in the literature. Moreover, decision trees and support vector machines were the most used techniques to build homogeneous and heterogeneous ensembles, respectively. The most frequently found combiner was the majority voting rule. Our findings suggest that ensemble classification methods yield better accuracy than single classifiers. This statement, however, requires an aggregation of the evidence reported in the literature by means of a systematic literature review.
Experimental Estimation of a Second Order, Double Inverted Pendulum Parameters for the study of Human Balancing

Balance control deteriorates naturally with age and is reliant upon the control of the ankle and hip joints. To this end, the estimation of ankle and hip parameters in quiet standing can be a useful tool when analyzing compensatory actions aimed at maintaining postural stability. This work presents an experimental study of a theoretical approach built upon previous results where the physiological parameters a second-order time-varying Kelvin-Voigt model are estimated for the actuation of the ankle and hip. These estimates are obtained using a double inverted pendulum based model subject to a step-like perturbation. Making use of RGB camera data to obtain the estimates of the system’s visco-elastic parameters, the approach employed is capable of estimating the time-varying values for the body’s control parameters. This work presents the first results of the method demonstrating the viability of a low-cost technique for regular testing of subjects with a high fall risk.
Implementing VESA Display Stream Compression Encoder in FPGAs

In this paper, an architecture for implementing VESA DSC image compression standard in FPGAs is proposed. DSC uses a very small 3-pixel wide coding unit that restricts the HW architecture of the prediction part to only three pipeline stages. The proposed architecture optimizes the pipeline distribution and performs algorithmic optimizations in order to reduce the critical path inside the prediction modes of DSC. It achieves 62 MHz and requires 18595 slices on Virtex 7 spl. It can process full HD (1920x1080) 4:4:4 images at 30 frames per second and full HD 4:2:2 or 4:2:0 images at 60 frames per second, while it has sub-line latency.
Classification of EEG Motor Imagery Tasks Using Convolution Neural Networks

Electroencephalograph (EEG) is a highly nonlinear data and very difficult to be classified. The EEG signal is commonly used in the area of Brain-Computer Interface (BCI). The signal can be used as an operative command for directional movements for a powered wheelchair to assist people with disability in performing the daily activity.In this paper, we aim to classify Electroencephalograph EEG signals extracted from subjects which had been trained to perform four Motoric Imagery (MI) tasks for two classes. The classification will be processed via a Convolutional Neural Network (CNN) utilising all 22 electrodes based on 10-20 system placement. The EEG datasets will be transformed into scaleogram using Continuous Wavelet Transform (CWT) method.We evaluated two different types of image configuration, i.e. layered and stacked input datasets. Our procedure starts from denoising the EEG signals, employing Bump CWT from 8-32 Hz brain wave. Our CNN architecture is based on the Visual Geometry Group (VGG-16) network. Our results show that layered image dataset yields a high accuracy with an average of 68.33% for two classes classification.
Evaluation of Methods to Characterize the Change of the Respiratory Sinus Arrhythmia with Age in Sleep Apnea Patients

The High Frequency (HF) band of the power spectrum of the Heart Rate Variability (HRV) is widely accepted to contain information related to the respiration. However, it is known that this often results in misleading estimations of the strength of the Respiratory Sinus Arrhythmia (RSA). In this paper, different approaches to characterize the change of the RSA with age, combining HRV and respiratory signals, are studied. These approaches are the bandwidths in the power spectral density estimations, bivariate phase rectified signal averaging, information dynamics, a time-frequency representation, and a heart rate decomposition based on subspace projections. They were applied to a dataset of sleep apnea patients, specifically to periods without apneas and during NREM sleep. Each estimate reflected a different relationship between RSA and age, suggesting that they all capture the cardiorespiratory information in a different way. The comparison of the estimates indicates that the approaches based on the extraction of respiratory information from HRV provide a better characterization of the age-dependent degradation of the RSA.
Soil Erosion Rate Prediction using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Geographic Information System (GIS) of Wadi Sahel-Soummam Watershed (Algeria)

Accurate prediction of soil erosion rate is a quite important issue for a wise and sustainable use of soil resources. In this study, an adaptive neuro-fuzzy inference system (ANFIS) approach is used to construct a prediction model. The objectives of this study is to develop fuzzy logic models that predict soil erosion in a relatively large watershed using a limited number of input variables, compare the predictions of soil erosion using ANFIS model with those of the Revised Universal Soil Loss Equation RUSLE.With the incorporation of Geographical Information System (GIS), it is possible to analyse satellite data, which gives required information like land use and cover, slope, distribution of rainfall, flow direction etc. of study watershed. The capabilities of these technologies increase when they are integrated with ANFIS model for erosion prediction.ANFIS model and GIS integrated erosion prediction models do not only estimate soil loss but also provide the spatial distributions of the erosion. Generating accurate erosion risk maps in GIS environment is very important to locate the areas with high erosion risks for prioritization and to develop adequate conservation techniques for a better sustainable management of Wadi Sahel watershed (Algeria).
Validation of a low-cost EEG device in detecting neural correlates of social conformity

The study of conformity from a neurobiological point of view has interested many authors: among them, Shestakova and colleagues (2013) have showed how conformity can be assessed through the analysis of event related potentials (ERPs). More specifically, the P300 component of the ERP was shown to be sensitive to the behavioral adjustment that an individual makes when not agreeing with the majority of a group. Starting from these contributions, in the present study, the famous experiment of Solomon Asch [1] was replicated online. The experiment was conducted on a sample of university students, using an innovative and low-cost tool capable of recording the brain signal (a wireless headset equipped with fourteen electrodes, called Emotiv EPOC). The present research aims to demonstrate how cheap and little sensitive tools enable the detection of ERP components that characterize social conformity in an ecological context.
A Survey of Modulation Schemes in Visible Light Communications

As the number of internet users continues to increase, the demand for more wireless frequency spectrum has grown significantly over the years. Given the limitations in Wireless Fidelity (WiFi) networks, it becomes evident that the current frequency allocation schemes cannot meet the needs of increasing higher data rate devices. Visible Light Communication (VLC) has emerged as one of the promising technologies to address the spectrum crunch and other anomalies associated with WiFi networks. VLC provide high-speed, secure and reliable data communication using Light Emitting Diode (LED). This paper reviews the various modulation schemes suitable for VLC networks. Furthermore, performance analysis of these schemes in terms of normalized power requirement, Signal to Noise Ratio (SNR), spectral efficiency, dimming factor and Bit Error Rate (BER) are discussed. Finally, applications of VLC are presented.
Objective extraction and analysis of statistical features of Dansgaard–Oeschger events

Abstract. The strongest mode of centennial to millennial climate variability in the paleoclimatic record is represented by Dansgaard–Oeschger (DO) cycles. Despite decades of research, their dynamics and physical mechanisms remain poorly understood. Valuable insights can be obtained by studying high-resolution Greenland ice core proxies, such as the NGRIP δ18O record. However, conventional statistical analysis is complicated by the high noise level, the cause of which is partly due to glaciological effects unrelated to climate and which is furthermore changing over time. We remove the high-frequency noise and extract the most robust features of the DO cycles, such as rapid warming and interstadial cooling rates, by fitting a consistent piecewise linear model to Greenland ice core records. With statistical hypothesis tests we aim to obtain an empirical understanding of what controls the amplitudes and durations of the DO cycles. To this end, we investigate distributions and correlations between different features, as well as modulations in time by external climate factors, such as CO2 and insolation. Our analysis suggests different mechanisms underlying warming and cooling transitions due to contrasting distributions and external influences of the stadial and interstadial durations, as well as the fact that the interstadial durations can be predicted to some degree by linear cooling rates already shortly after interstadial onset.
Attractor Reconstruction Analysis for Blood Flow Signals

Attractor reconstruction analysis has been previously used to determine changes in the shape and variability of fairly periodic signals such as arterial blood pressure signals and electroencephalogram signals, providing a two-dimensional attractor with features like density and symmetry. Since BF signals are fairly periodic and quasi-stationary, we set out to investigate whether attractor reconstruction method could be applied in signals derived from the microvascular perfusion. We describe the basis and the implementation of attractor reconstruction analysis of the microvascular blood flux (BF) signals recorded from the skin of 15 healthy male volunteers, age 29.2 ± 8.1y (mean ± SD). The efficacy of attractor reconstruction analysis (ARA) as a potential method of identifying changes in the microvascular function is evaluated in two haemodynamic steady states, at 33°C, and during warming at 43°C to generate a local thermal hyperaemia (LTH). Our findings show a significant drop of the maximal density derived from the ARA, during increased flow and that there was good discrimination of the blood flow signals between the two haemodynamic steady states, having good classification accuracy (80%). This study shows that ARA of BF signals can identify different microvascular functional states and thus has a potential for the clinical assessment and diagnosis of pathophysiological condition.
Effect of English learning experience on young children's prefrontal cortex functioning for attentional control: An fNIRS study *

The exploration concerning effect of bilingualism on cognitive performance has been enriched in recently studies. However, scarce research focused on its impact on the brain function, especially in non-proficient bilingual children. In the present study, both Chinese monolingual and English as a foreign language (EFL) bilingual children were conducted to a non-verbal attentional network task (ANT) by functional near-infrared spectroscopy (fNIRS) technique for exploring the consequence of English learning experience on young children’s prefrontal regions of functioning attentional control. The behavior results showed that young EFL bilinguals outperformed monolinguals on the accuracy of ANT conflict condition. Furthermore, EFL bilingual children had higher activation in the left prefrontal cortex (inferior frontal gyrus and dorsal lateral prefrontal cortex) than counterpart monolinguals. More interestingly, the degree of bilingual language balance positively correlated with that behavior accuracy and brain activation in bilingual group. These findings provided additional support for the bilingual advantage hypothesis and illustrated implications for understanding how foreign language learning impact children’s brain development.
Wideband MIMO Antenna Array Covering 3.3–7.1 GHz for 5G Metal-Rimmed Smartphone Applications

A wideband 8-antenna Multiple-Input Multiple-Output (MIMO) array covering 3.3–7.1 GHz for Fifth-Generation (5G) sub-7 GHz and New Radio Unlicensed (NR-U) applications in metal-rimmed smartphones is presented in this paper. In this design, the open slot loaded metal rim is directly fed by microstrip line. Hybrid Inverted-F Antenna (IFA) and slot modes are generated. Utilizing impedance matching and reactance loading, the two modes are moved and combined, so as to achieve size reduction and wideband coverage. The size of the ground slot (clearance) is only 12.4 mm $\times1.5$ mm ( $0.136\,\,\lambda \times 0.016 \,\,\lambda $ at 3.3 GHz). The proposed MIMO antenna array is fabricated and measured. Results show that in the desired wide frequency band, the proposed design can achieve desirable antenna performances, including isolation >11 dB, total efficiency >47%, and calculated Envelope Correlation Coefficient (ECC) <0.09. Besides, antenna gain, radiation pattern and calculated ergodic channel capacity are demonstrated as well. The proposed metal-rim-integrated MIMO antenna array features small size, simple structure and wide bandwidth. It can be a good application-oriented design in next-generation 5G mobile communication.
Heterogeneity Counts More than Power for HD-sEMG-Based Joint Force Estimation

The aim of the proposed work is to utilize the heterogeneity information, in input signal extraction, to improve the joint force estimation from high-density surface electromyography (HD-sEMG). For this purpose, joint force and HD-sEMG signals from biceps brachii and brachialis were collected synchronously during isometric elbow flexion. The input signal of the force model was obtained after the following procedures: first, HD-sEMG signals were decomposed by principal component analysis into principal components and weight vectors; second, the first several weight maps were segmented to obtain heterogeneity information by the Otsu and Moore-Neighbor tracing methods, and the principal component covering the most activated areas (maximum heterogeneity) was selected; and last, the selected principal component was low-pass filtered to obtain the input signal. The force model was built using a polynomial fitting technique. The conventional power-based input signal was compared with our obtained input signal. According to the obtained results, the proposed heterogeneity-based input signal can reduce the force estimation error significantly than the power-based input signal. The proposed heterogeneity-based input signal extraction methods had more neuromuscular control information and will be tested in more muscles and force tasks in future works.
OCT Image Denoising Based on Asymmetric Normal Laplace Mixture Model

Optical Coherence Tomography (OCT) is one of the well-known imaging systems in ophthalmology that provides images with high resolution from retinal tissue. However, like other coherent imaging systems, OCT images suffer from speckle noise which decreases the image quality. Denoising can be considered as an estimation problem in a Bayesian framework. So, finding a suitable distribution for noiseless data is an important issue. We propose a statistical model for OCT data, namely Asymmetric Normal Laplace Mixture Model (ANLMM), and then convert its distribution to normal by Gaussianization Transform (GT). Finally, by applying the Spatially Constrained Gaussian Mixture Model (SC-GMM), a new OCT denoising algorithm is introduced, which significantly outperforms the other methods in terms of Contrast-to-Noise Ratio (CNR).
Electrical Impedance Spectroscopy for Characterization of Prostate PC-3 and DU 145 Cancer Cells

The impedance profile of the human PC-3 and DU 145 prostate cancer cells were recorded and compared using Electrical Impedance Spectroscopy. Cells were measured in a special chamber using a four terminal setup to avoid parasitic effects of electrode polarization in low frequencies. Our results show that the two cancer cell lines are readily distinguishable by their impedance spectrum. As PC-3 cells have been shown to be spontaneously metastatic in previous xenograft experiments while DU 145 cells were non-metastatic, Electrical Impedance Spectroscopy has the potential to be developed into a simple diagnostic tool to distinguish metastatic from non-metastatic cells.
Simulation of the assistance of passive knee orthoses in FES cycling*

Although advances in technology promoted new physiotherapy approaches, there is still an urge for equipment and techniques to improve the quality of patients lives with motor disabilities. Functional electrical stimulation cycling (FES cycling) is an example of this type of technology, in which the control of stimulation parameters enables a spinal cord injured person to ride a bicycle. In this work, we aim to investigate the use of passive knee orthoses for FES cycling assistance. Hence, we compared the cycling cadence and quadriceps excitation using an FES cycling simulation platform for different spring torques and ranges. In this paper, we obtained spring parameters that increased cycling cadence by 10.60% while decreasing by 7.33% the quadriceps activity, which indicates that this type of passive orthosis may diminish fatigue caused by FES.