Miracle-dz/UniFAR-FTUs huggingface dataset:
Coherence Analysis between the Surface Diaphragm EMG Envelope Signal and the Respiratory Signal derived from the ECG in Patients assisted by Mechanical Ventilation

Prolonged use of mechanical ventilation (MV) can lead to greater complications for a patient. In clinical practice, it is important to identify patients who could fail in the extubation process. However, accurately predicting the outcome of this process remains a challenge. The diaphragm muscle is one of the most active elements in the breathing process. On the other hand, there are several techniques to derive respiratory information from the ECG signal. Signals derived from diaphragmatic activity and from the ECG, such as the envelope of the surface diaphragm electromyographic signal (sEMGi) and the respiratory signal derived from the electrocardiogram (ECG) could contribute to analyze the respiratory response in patients assisted by MV. This work proposes the analysis of the coherence between sEMGi and EDR signals to determine possible differences in the respiratory pattern between successful and failed patients undergoing weaning. 40 patients with MV, candidates for weaning trial process and underwent a spontaneous breathing test were analyzed, classified into: a successful group (SG: 19 patients) that maintained spontaneous breathing after the test, and a failed group (FG: 21 patients) that required reconnection to the MV. The cross correlation, power spectral density and magnitude squared coherence (MSC) of the sEMGi and the EDR signals were estimated. According to the results, the MSC parameters such as area under the curve and mean coherence value presented statistically significance differences between the two groups of patients (p = 0.024). Our results suggest that both sEMGi and EDR signals could provide information about the behavior of the respiratory system in these patients. Clinical Relevance— This study analyzes the correlation and the coherence between the envelope of the surface electromyographic signal and the respiratory signal derived from the ECG to characterize the respiratory pattern of successful and failed patients on weaning process.
Battery Thermal Image Sequence Processing

The operating temperature of a lithium-ion battery is one of the key factors significantly affecting its performance. Heat is generated within the battery during the charging and discharging processes. On the other hand, battery internal faults lead to an increase in their temperature. Therefore, monitoring battery temperature can aid the identification of these faults and warn of their presence prior to the occurrence of permanent thermal damaging. When the battery heats up, at best there will be some mechanical distortion of the battery surface or changes in its chemical composition. In the worst case, the battery may burst, explode, or leak electrolytes. This paperpresents a specialized software, developed for the analysis of thermographic images of batteries, via which extraction and visualization of necessary information regarding the resulting temperature changes over the entire battery surface or certain parts of it is made possible. Python version 3.9 was used during the software development. This software can successfully beimplemented in systems aimed towards monitoring and controlling the temperature of batteries and evaluating their state of health and remaining useful life.
Single DC Source Based Three Phase High Resolution Transformerless Multilevel Inverter

A transformer less high-resolution hybrid asymmetrical multilevel inverter is proposed in this paper. The proposed topology is a hybrid of Level Doubling Network (LDN), cascaded H-bridge (CHB) and neutral point clamped (NPC) multilevel inverter (MLI). Objective of the proposed work is to utilize TNPC to merge all the three dc buses of main bridge to reduce the dc-bus capacitor requirement and inter array leakage current. The main challenge with this hybrid transformerless converter topology is balancing the capacitors of TNPC with corresponding LDN operation. For capacitor balancing of TNPC, one extra non-isolated buck-boost converter of low rating and a closed loop current control is used which will finally helpful to remove a bulky and expensive transformer. It will help to reduce both capital cost and running cost, as the rating of additional converter is just 7% to 8% of transformer rating. This topology offers high power quality, reliability and efficiency with less number of components than other asymmetrical MLI topologies with same resolution at output voltage. Simulation is done using MATLAB/Simulink model and simulation results matched well with the theory.
A Comparative Study of Deep Learning Algorithms for Detecting Food Intake

The choice of appropriate machine learning algorithms is crucial for classification problems. This study compares the performance of state-of-the-art time-series deep learning algorithms for classifying food intake using sensor signals. The sensor signals were collected with the help of a wearable sensor system (the Automatic Ingestion Monitor v2, or AIM-2). AIM-2 used an optical and 3-axis accelerometer sensor to capture temporalis muscle activation. Raw signals from those sensors were used to train five classifiers (multilayer perceptron (MLP), time Convolutional Neural Network (time-CNN), Fully Convolutional Neural Network (FCN), Residual Neural Network (ResNet), and Inception network) to differentiate food intake (eating and drinking) from other activities. Data were collected from 17 pilot subjects over the course of 23 days in free-living conditions. A leave one subject out cross-validation scheme was used for training and testing. Time-CNN, FCN, ResNet, and Inception achieved average balanced classification accuracy of 88.84%, 90.18%, 93.47%, and 92.15%, respectively. The results indicate that ResNet outperforms other state-of-the-art deep learning algorithms for this specific problem.
Noncommutative Geometry and Cayley-smooth Orders

Preface Introduction Noncommutative algebra Noncommutative geometry Noncommutative desingularizations Cayley-Hamilton Algebras Conjugacy classes of matrices Simultaneous conjugacy classes Matrix invariants and necklaces The trace algebra The symmetric group Necklace relations Trace relations Cayley-Hamilton algebras Reconstructing Algebras Representation schemes Some algebraic geometry The Hilbert criterium Semisimple modules Some invariant theory Geometric reconstruction The Gerstenhaber-Hesselink theorem The real moment map Etale Technology Etale topology Central simple algebras Spectral sequences Tsen and Tate fields Coniveau spectral sequence The Artin-Mumford exact sequence Normal spaces Knop-Luna slices Quiver Technology Smoothness Local structure Quiver orders Simple roots Indecomposable roots Canonical decomposition General subrepresentations Semistable representations Semisimple Representations Representation types Cayley-smooth locus Reduction steps Curves and surfaces Complex moment map Preprojective algebras Central smooth locus Central singularities Nilpotent Representations Cornering matrices Optimal corners Hesselink stratification Cornering quiver representations Simultaneous conjugacy classes Representation fibers Brauer-Severi varieties Brauer-Severi fibers Noncommutative Manifolds Formal structure Semi-invariants Universal localization Compact manifolds Differential forms deRham cohomology Symplectic structure Necklace Lie algebras Moduli Spaces Moment maps Dynamical systems Deformed preprojective algebras Hilbert schemes Hyper Kahler structure Calogero particles Coadjoint orbits Adelic Grassmannian References Index
Machine Learning for Motor Imagery Wrist Dorsiflexion Prediction in Brain-Computer Interface Assisted Stroke Rehabilitation

Stroke is a life-changing event that can affect the survivors' physical, cognitive and emotional state. Stroke care focuses on helping the survivors to regain their strength; recover as much functionality as possible and return to independent living through rehabilitation therapies. Automated training protocols have been reported to improve the efficiency of the rehabilitation process. These protocols also decrease the dependency of the process on a professional trainer. Brain-Computer Interface (BCI) based systems are examples of such systems where they make use of the motor imagery (MI) based electroencephalogram (EEG) signals to drive the rehabilitation protocols. In this paper, we have proposed the use of well-known machine learning (ML) algorithms, such as, the decision tree (DT), Naive Bayesian (NB), linear discriminant analysis (LDA), support vector machine (SVM), ensemble learning classifier (ELC), and artificial neural network (ANN) for MI wrist dorsiflexion prediction in a BCI assisted stroke rehabilitation study conducted on eleven stroke survivors with either the left or right paresis. The doubling sub-band selection filter bank common spatial pattern (DSBS-FBCSP) has been proposed as feature extractor and it is observed that the ANN based classifier produces the best results.
Deep Learning-Powered Vessel Trajectory Prediction for Improving Smart Traffic Services in Maritime Internet of Things

The maritime Internet of Things (IoT) has recently emerged as a revolutionary communication paradigm where a large number of moving vessels are closely interconnected in intelligent maritime networks. However, the tremendous growth of vessel trajectories, collected from the combined satellite-terrestrial AIS (automatic identification system) base stations, could lead to unsatisfactory maritime safety and efficacy. To promote smart traffic services in maritime IoT, it is necessary to accurately and robustly predict the spatiotemporal vessel trajectories. It is beneficial for collision avoidance, maritime surveillance, and abnormal behavior detection, etc. Motivated by the strong learning capacity of deep neural networks, this work proposes an AIS data-driven trajectory prediction framework, whose main component is a long short term memory (LSTM) network. In particular, the vessel traffic conflict situation modeling, generated using the dynamic AIS data and social force concept, is embedded into the LSTM network to guarantee high-accuracy vessel trajectory prediction. In addition, a mixed loss function is reconstructed to make our prediction results more reliable and robust in different navigation environments. Several quantitative and qualitative experiments have been implemented on realistic AIS-based vessel trajectories. Our results have demonstrated that the proposed method could achieve satisfactory prediction performance in terms of accuracy and robustness.
Proof of Concept for a Scintillator Powder Calorimeter

We will present the proof of concept for a next-generation Shashlyk-type calorimeter based on the use of the novel opaque scintillation technique, implemented in this scenario using scintillating powder.At present, electromagnetic calorimeters based on the sampling technique generally provide good granularity for a limited cost but feature a rather poor photon energy resolution, compared to uniform crystals detectors, at energies below hundreds of GeV. For this reason, we investigated the possibility of using sub-millimetric-sized crystals for extremely fine sampling to improve the energy resolution.This work is inspired by the new LiquidO detection technology which, breaking the conventional paradigm of transparency, makes use of opaque scintillators. With a short scattering length, scintillation light is stochastically confined in the region of its creation point and can be detected with wavelength shifting (WLS) fibers. In our case, we foresee the use of high-Z scintillator powder, for good stopping power, immersed in a transparent liquid in order to increase the density and better match the refraction index that controls the optical path, thus playing, in this case, the role of the diffusion material in the LiquidO systems.In this communication, we will discuss the results of a Geant4 Monte-Carlo simulation performed to compare the energy resolution of a conventional lead-scintillator Shashlyk-type calorimeter and a powder one, with same geometry, composed of ZnWO4 grains immersed in a CH2I2 bath.Furthermore, we will present the results of the tests aimed at demonstrating the possibility to collect blue light in a small volume of white powder by means of WLS fibers, readout by SiPMs. The amplitude and the time properties of the signals collected with the WLS fibers have been measured as a function of the distance from the light injection, for different filling mixture in the reference volume.
Embracing Deep Learning for Crack Segmentation in SEM Images of Metal Additive Manufacturing

Metal additive manufacturing is an important manufacturing technique due to its cost effective and rapid prototyping capabilities. This technique has been widely used in various industries ranging from aerospace to military defense. Depending on different metal additive manufacturing settings, microstructures, such as cracks, would be generated for finished parts within additive manufacturing procedures. Those microstructures could lead to undesired defects, which may negatively impact the quality of the fabricated products, especially when they are delivered for mission critical tasks. This study developed a deep learning based computer vision approach for microstructure, especially crack recognition in images collected from Scanning Electron Microscope (SEM). Through performing segmentation of cracks in the SEM images of different magnifying factors, manufacturing quality could be administered quantitatively.
Synthetic Generation of Cardiac MR Images Combining Convolutional Variational Autoencoders and Style Transfer

The number of studies in the medical field that uses machine learning and deep learning techniques has been increasing in the last years. However, these techniques require a huge amount of data that can be difficult and expensive to obtain. This specially happens with cardiac magnetic resonance (MR) images. One solution to the problem is raise the dataset size by generating synthetic data. Convolutional Variational Autoencoder (CVAe) is a deep learning technique which allows to generate synthetic images, but sometimes the synthetic images can be slightly blurred. We propose the combination of the CVAe technique combined with Style Transfer technique to generate synthetic realistic cardiac MR images. Clinical Relevance-The current work presents a tool to increase in a simple easy and fast way the cardiac magnetic resonance images dataset with which perform machine learning and deep learning studies
A Proposal for a New Index to Quantify the Ratio of Near and Far Spatial Neglect Using Immersive Virtual Reality Technology: A Technical Report

Unilateral spatial neglect (USN) is defined as impaired attention to sensory stimuli on one side, which can exist for near and far spaces combined or independently. Thus, quantifying both near- and far-space neglect is crucial. This study aims to propose an index to quantify the near/far spatial neglect ratio to describe the USN symptoms' characteristics in each patient using immersive virtual reality (VR) technology. An object-detecting task was performed for five USN patients in a three-dimensional VR space. The examiner recorded the positional data of the objects that were recognized by the USN patient using coordinate data. The near/far ratio (NFRatio) was calculated using the proposed equation to quantify the difference in neglect severity in near and far spaces of each patient. Among the patients, four tended to have greater far-space neglect, and one tended to have greater near-space neglect. Moreover, the near/far spatial neglect ratio was shown to vary according to height. This is the first study to propose immersive VR to quantify the near and far spatial neglect. However, further study is needed to assess its reliability and validity and describe its clinical usability. Clinical Relevance— In clinical practice USN symptoms cause neglected symptoms in 3D space so the proposed system will be of high clinical significance if 3D assessment is realized
Data-driven, Energy-based Scatter Estimation for PET

Scattered photons in PET datasets lead to a bias in the reconstructed images and poor quantification. In this paper, we present a practical data-driven, energy-based (EB) scatter estimation method that leverages the marked difference between the energy distribution of non-scattered and scattered events. Similar approaches have been presented in the past, but have not found their way into clinical practice primarily due to their need for an accurate estimate of the scatter energy shape. In our method, we consider the delayed and the prompt events, separately. For each event type, we fit a Gaussian model of the unscattered 511 keV peak to the measured global energy data. Subtraction of this fitted 511 kV peak model from the global energy spectrum leads to an estimate of the scatter energy shape. This shape can then, in principle, be fit to the energies of all events within an LOR to estimate the number of scatter events in that LOR. In this paper we present an initial evaluation of our methods using both Monte Carlo simulations and measured data from the PennPET Explorer scanner. Simulations show that the corrected reconstructed images are quantitatively accurate, very close to images from true, non-scattered, only data. In addition, with measured data for a NEMA-IQ phantom, we show that the proposed method can extract reasonable scatter profiles. Evaluation of this method using measurements with challenging physical phantoms as well as patient data will be presented.
Methods for Delayering Integrated Circuits using a Broad Ion Mill

Chip delayering is an important technique for hardware security analysis. It allows for reverse engineering of an integrated circuit’s structures, finding possible security flaws, and in some cases enabling secret key extraction. This paper presents methods to prepare single subsequent layers of an IC for imaging purposes using a Broad Ion Beam System (BIBS). These methods consist of parameter sets (angle, energy, duration) for the etching process steps which have to be applied in a specific order. These parameter sets were developed by first analysing separately the impact of the single parameters on the etch rate and the IC’s surface structures. Development then split into etchrate-matched and direct beam methods aiming both at planar delayering with little operator intervention. Both methods were put to the test on state-of-the-art microcontrollers with 250nm (Chip A) and 180nm (Chip B) feature size.
Alternating current stimulation entrains and connects cortical regions in a neural mass model

Transcranial alternating current stimulation (tACS) is a neuromodulatory technique that is widely used to investigate the functions of oscillations in the brain. Despite increasing usage in both research and clinical settings, the mechanisms of tACS are still not completely understood. To shed light on these mechanisms, we injected alternating current into a Jansen and Rit neural mass model. Two cortical columns were linked with long-range connections to examine how alternating current impacted cortical connectivity. Alternating current injected to both columns increased power and coherence at the stimulation frequency; however this effect was greatest at the model's resonant frequency. Varying the phase of stimulation impacted the time it took for entrainment to stabilize, an effect we believe is due to constructive and destructive inteference with endogenous membrane currents. The power output the model also depended on the phase of the stimulation between cortical columns. These results provide insight on the mechanisms of neurostimulation, by demonstrating that tACS increases both power and coherence at a neural network's resonant frequency, in a phase-dependent manner.
Novel EEG-Based Neurofeedback System Targeting Frontal Gamma Activity of Schizophrenia Patients to Improve Working Memory

Patients with schizophrenia (SCZ) exhibit working memory (WM) deficits that are associated with deficient dorsal-lateral prefrontal cortical activity, including decreased frontal gamma power. We thus hypothesized that training SCZ patients to increase frontal gamma activity would improve their WM performance. We administered electroencephalographic (EEG) neurofeedback (NFB) to 31 participants with SCZ for 12 weeks (24 sessions), which provides real-time visual and auditory feedback related to frontal gamma activity. The EEG-NFB training significantly improved EEG markers of optimal working memory, e.g., frontal P3 amplitude and gamma power. Based on these promising results, we developed a novel, EEGLAB/MATLAB-based brain-computer interface (BCI) that delivers F3-F4 gamma coherence NFB with a dynamic threshold to SCZ patients randomized in a double-blind, placebo-controlled clinical trial. The BCI significantly increased F3-F4 gamma coherence after 12 weeks (24 sessions) of training, according to data from the first 12 subjects ($\mathrm{n}=6$ /group) who completed gamma- or placebo-NFB training.
Analytics Pipeline for Visualization of Single Cell RNA Sequencing Data from Brochoaveolar Fluid in COVID-19 Patients: Assessment of Neuro Fuzzy-C-Means and HDBSCAN

Since the mutation in SARS-COV2 poses new challenges in designing vaccines, it is imperative to develop advanced tools for visualizing the genetic information. Specially, it remains challenging to address the patient-to-patient variability and identify the signature for severe/critical conditions. In this endeavor we analyze the large-scale RNA-sequencing data collected from broncho-alveolar fluid. In this work, we have used PCA and tSNE for the dimension-reduction. The novelty of the current work is to depict a detailed comparison of $k$-means, HDBSAN and neuro-fuzzy method in visualization of high-dimension data on gene expression. Clinical Relevance— The subpopulation profiling can be used to study the patient-to patient variability when infected by SARS-COV-2 and its variants. The distribution of cell types can be relevant in designing new drugs that are targeted to control the distribution of epithelial cells T cells and macrophages
Cattle Collar: An End-to-End Multi-Model Framework for Cattle Monitoring

Individual cattle behaviour monitoring is a promising way of improving cattle farm management by detecting health issues and anomalies in behaviour patterns. Ac-celerometer sensors are non-invasive, low-cost devices that track daily activities and behaviour. For this, a hardware setup is attached to the neck collar of the cow to record its behaviour. We proposed an efficient data labelling method to classify simultaneously occurring activities with a single inertial sensor and a temperature sensor. Then a Machine Learning (ML) model is trained to predict the cattle activ-ities based on different time and frequency domain-based statistical features. The proposed method shows an accu-racy of 86% for Random Forest classifier. The behavioural analysis of an individual cow is sent to the user interface. The application provides visual data representation to mon-itor multiple cows daily and weekly.