Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring human activity recognition using feature level fusion of inertial and electromyography data

Wearables are objective tools for human activity recognition (HAR). Advances in wearables enable synchronized multi-sensing within a single device. This has resulted in studies investigating the use of single or multiple wearable sensor modalities for HAR. Some studies use inertial data, others use surface electromyography (sEMG) from multiple muscles and different post-processing approaches. Yet, questions remain about accuracies relating to e.g., multi-modal approaches, and sEMG post-processing. Here, we explored how inertial and sEMG could be efficiently combined with machine learning and used with post-processing methods for better HAR. This study aims recognition of four basic daily life activities; walking, standing, stair ascent and descent. Firstly, we created a new feature vector based on the domain knowledge gained from previous mobility studies. Then, a feature level data fusion approach was used to combine inertial and sEMG data. Finally, two supervised learning classifiers (Support Vector Machine, SVM, and the k-Nearest Neighbors, kNN) were tested with 5-fold cross-validation. Results show the use of inertial data with sEMG increased overall accuracy by 3.5% (SVM) and 6.3% (kNN). Extracting features from linear envelopes instead of bandpass filtered sEMG improves overall HAR accuracy in both classifiers. Clinical Relevance— Post-processing on sEMG signals can improve the performance of multimodal HAR
A novel deep learning approach using AlexNet for the classification of electroencephalograms in Alzheimer's Disease and Mild Cognitive Impairment

Alzheimer's Disease (AD) is the most common form of dementia. Mild Cognitive Impairment (MCI) is the term given to the stage describing prodromal AD and represents a ‘risk factor’ in early-stage AD diagnosis from normal cognitive decline due to ageing. The electroencephalogram (EEG) has been studied extensively for AD characterization, but reliable early-stage diagnosis continues to present a challenge. The aim of this study was to introduce a novel way of classifying between AD patients, MCI subjects, and age-matched healthy control (HC) subjects using EEG-derived feature images and deep learning techniques. The EEG recordings of 141 age-matched subjects (52 AD, 37 MCI, 52 HC) were converted into 2D greyscale images representing the Pearson correlation coefficients and the distance Lempel-Ziv Complexity (dLZC) between the 21 EEG channels. Each feature type was computed from EEG epochs of 1s, 2s, 5s and 10s segmented from the original recording. The CNN architecture AlexNet was modified and employed for this three-way classification task and a 70/30 split was used for training and validation with each of the different epoch lengths and EEG-derived images. Whilst a maximum classification accuracy of 73.49% was obtained using dLZC-derived images from 10s epochs as input to the model, the classification accuracy reached 98.13% using the images obtained from Pearson correlation coefficients and 5s epochs. Clinical Relevance— The preliminary findings from this study show that deep learning applied to the analysis of the EEG can classify subjects with accuracies close to 100%
A Novel Single-Source Eleven-level Quintuple-Boost Inverter with reduced Component Count

A single source eleven-level inverter with reduced component count with quintuple voltage boosting ability is presented in this paper. It comprises of thirteen power switches, one power diode, and three capacitors. The voltage stress across the switches is reduced as it does not require an H-bridge circuit to generate negative voltage levels. A simple nearest level control (NLC) which is a fundamental switching frequency-based technique is used to provide gate pulse to the inverter circuit. Comparison with recent topologies in terms of switches, diodes, sources, capacitors used in numbers, and the voltage boosting ability is done to show the merits of the presented topology. The total harmonic distortion (THD) is 8.6% which is near to acceptable limits as per IEEE 519-2014 standard. Finally, experimental and real-time results are presented to validate the operability of the proposed SCMLI topology for different loading conditions.
Performance Evaluation of Converters used in G2V and V2G Modes of Operation

To enhance the reliability of supply, the V2G mode of operation is widely applied in electric vehicles. The V2G functionality is used to supply various services like regulation of active power demand, reactive power compensation, shaving peaks and filling valleys in load demand, frequency and voltage regulation, compensation of harmonics in grid current, improvement in system reliability, stability, and efficiency. The realization of V2G functionality is achieved with the help of three-phase bidirectional on-board chargers. This paper deals with the design and implementation of a three-phase on-board bidirectional fast charger used for electric vehicles (EV). The bidirectional on-board chargers include a front-end three-phase PWM converter, which is used to convert ac power into dc power, and dc-dc converter is required to supply regulated power to the battery of the EV. In this paper, the systematic procedure for the selection of passive components and tuning of the parameters of linear controllers of ac-dc and dc-dc converters during G2V and V2G modes of operations is discussed in detail. The design approach is validated with help of experimental results carried out on a laboratory prototype.
In-situ Annealing of Radiated Low Gain Avalanche Detector for Performance Recovery

Conventional isothermal annealing is helpful to performance recovery of radiated silicon detector; but it is hard to be applied in vast well-installed detectors in high energy physics experiments. This summary investigated an in-situ annealing technique that employs joule heat of the intrinsic p-n junction to mitigate radiation damage effect on low gain avalanche detector (LGAD). The LGADs are 2 × 2 array with each pad of 1.3 mm × 1.3mm active area and were irradiated by 2 MeV proton to an equivalent fluence of 5×1014 neq/cm2. After being applied a forward current of 1 A lasting for 27 s, which reached a peak annealing temperature around 180°C, the leakage current, the full depletion voltage and the gain of the radiated LGADs were restored partially. The leakage current was decreased about an order of magnitude, the full depletion voltage increased from 20 V to 85 V, and the gain increased from about 6 to 11 at 400 V. This simple in-situ annealing method is possible to implement during the service period of the detectors, thus prolongs their lifetime in harsh irradiation environment.
A novel method for Magnetic Resonance Spectroscopy lipid signal suppression using Semi-classical signal analysis and Bidirectional Long short-term memory

Magnetic resonance spectroscopy (MRS) is a non-invasive method that enables the analysis and quantification of brain metabolites, which provide useful information about the neuro-biological substrates of brain function. Lactate plays a pivotal role in the diagnosis of various brain diseases. However, accurate lactate quantification is generally difficult to achieve due to the presence of large lipid peaks resonating at a similar spectral position. To overcome this problem several techniques have been proposed. However, most of them suffer from lactate signal loss or poor lipid peak removal. In this paper, a novel method for lipid suppression for MRS signal is proposed. The method combines a semi-classical signal analysis method and a bidirectional long short term memory technique. The method is validated using simulated data that mimics real MRS data.
A Graph Structure to Discover Patterns in Unstructured Processes of Product Development

A well-known information reuse approach is to exploit event logs for process discovery and optimization. However, process discovery is rarely used for product development projects. This is because information systems in product development, e. g. , Product-Lifecycle-Management (PLM) systems, do not provide the event logs required by process discovery algorithms. Additionally, existing algorithms struggle with development projects, as these are unstructured and rich in variety. In this paper, we propose a novel approach to process discovery in order to make it applicable and tailored to product development projects. Instead of using flat event logs, we provide a graph-based data structure that is able to represent both activities and data of product development projects with the dataflow between activities. Based on this structure, we can leverage provenance available in PLM systems. Furthermore, we may use frequent subgraph mining to discover process patterns. Such patterns are well suited to describe different variants and common sub-processes of unstructured processes. Using a prototype, we evaluate this approach and successfully discover prevailing patterns. These patterns may be used by engineers to support their decision-making or help improve the execution of development projects.
Optimum Flux Based Vector Control of Induction Motor Drive For Electric Vehicle

This paper presents a simplified loss model-based maximum torque per ampere (MTPA) control of induction motor drive for electric vehicle (EV) application. Firstly, the loss model of the induction motor is derived by considering iron loss and magnetic saturation. The derived loss model is simplified as a dependent voltage source in series with the iron loss resistance. From the simplified model, the nonlinear power loss function is derived and solved for an optimum value of flux reference by considering voltage and current safety constraints. This MTPA strategy is extended from constant torque operation mode to the constant power mode based upon the speed and required torque. This optimum flux reference selection method is implemented along with the rotor field-oriented control (RFOC) for the induction machine. From the simulation results, it is justified that the presented algorithm reduces the power consumption and improves the efficiency at all the modes of operation of electric vehicles.
Audio Sentiment Analysis using Spectrogram and Bag-of- Visual- Words

Audio sentiment analysis has many applications in a present-day context, such as call center environments, conversational agents, and human-robot interactions. However, analyzing sentiment using audio signals is a significant challenge due to the difficulty of accurately determining the robust feature set needed to detect sentiments expressed within the audio signal. It is novel to use spectrogram and bag-of-visual-words for representing robust audio features for sentiment analysis. Therefore, we propose using keypoints of the spectrogram (a 2D image representation of frequencies in a signal) to classify sentiment. We converted audio signals to spectrograms using Short Time Fourier Transform (STFT). The Oriented FAST and Rotated BRIEF (ORB) algorithm extracted the salient keypoints scattered on a regular grid over the spectrogram. Then, using the Bag-of-Visual-Words (BoVW) technique, the descriptors from each column of the spectrogram of the audio signal are converted to histograms. We have trained and tested models using a multilingual dataset. We have applied hyperparameter tuning while generating the histograms to increase the accuracy of the model. These histograms are then passed to classifiers, a nd the sentiment of each audio signal is classified. O ur experimental result shows that Random Forest was the best classifier with an accuracy of 76% and an F1 score of 78% and demonstrates the prospect of our approach in language-agnostic audio sentiment analysis.
Sleep Posture Detection Using an Accelerometer Placed on the Neck

Sleep position monitoring is key when attempting to address posture triggered sleep disorders. Many studies have explored sleep posture detection from a dedicated physical sensing channel exploiting optimum body locations, such as the torso; or alternatively non-contact approaches. But, little work has been done to try to detect sleep position from a body location which, whilst being suboptimal for that purpose, does however allow for better extraction of more critical biomarkers from other sensing modalities, making possible multi-modal monitoring in certain clinical applications. This work presents two different approaches, at varying levels of complexity, for detecting 4 main sleep positions (supine, prone, lateral right and lateral left) from accelerometry data obtained by a single wearable device placed on the neck. An ultra light-weight threshold-based model is presented in this work, in addition to an Extra-Trees classifier. The threshold-based model was able to achieve 95% average accuracy and 0.89 F1-score on out-of-sample data, showing that it is possible to obtain a moderately high classification performance using a simple rule-based model. The ExtraTrees classifier, on the other hand, was able to achieve 99 % average accuracy and 0.99 average F1-score using only 25 base estimators with maximum depth of 20. Both models show promise in detecting sleep posture with high accuracy when collecting the signals from a neck-worn accelerometer sensor.
Comparison of Mobile Platform Teleoperation Systems Using a Force-Torque Sensor and a Joystick

Mobile robots are used in more and more applications nowadays. These applications involve the research, but also real-world and industrial niches have been found where these platforms fulfill important tasks. In recent years huge focus has been put on the autonomous operation of mobile robots. However, in many tasks a direct control exerted by a human operator is necessary. These tasks may, for example, include search and rescue operations, or other tasks where human intelligence has not been yet surpassed by the machine intelligence. Furthermore, even in simple and repetitive operations it is very much desired to allow for robot programming with Learning-by-Demonstration approach. Teleoperation is therefore a very important topic. There is an extensive research covering the problem of the Human-Machine Interface (HMI) aspect of teleoperation. Numerous devices exist that allow for human control of robotic platforms. These devices offer various advantages and disadvantages that make them suited for different modes of operation. In this work, we focus on implementing a mobile platform teleoperation system with the use of a force-torque sensor. We present the configuration of such system using ROS and compare experimentally the performance of the proposed device with a typicalapproach using joystick control
Compton Imaging Study for Dose Monitoring in Carbon Therapy

To achieve precise radiotherapy for carbon therapy, it is necessary to accurately monitor the dose distribution in the patient's body. We conducted a methodological study on the 3D dose monitoring of the double-layer Compton camera in carbon therapy by MC simulation. We built a prototype LYSO Compton imaging system in the laboratory to verify the feasibility of the method. This article evaluates the performance of the dual-layer Compton camera, including sensitivity, point-source imaging, and plane-source imaging. A PMMA phantom was bombarded with a carbon ion beam of 200 MeV/μ, and the reconstructed image showed a position deviation of 9.1% from the 3D dose distribution. We also analyzed the influence of the uncertainty of the depth of interaction (DOI) on the reconstructed image. Finally, the prototype of LYSO Compton imaging system was used to perform tomography experiment on a 22Na point source. The FWHM of the reconstructed image was 1.35 times of the diameter of the point source.
Measured redshift invariance of photon velocity

We report the rst direct photon velocity measurements for extragalactic objects. A ber-optic, photon time-of-ight instrument, optimized for relatively dim sources (m 12), is used to measure the velocity of visible photons emanating from galaxies and quasars. Lightspeed is found to be 3:00 0:03 10 8 ms 1 , and is invariant, within experimental error, over the range of redshifts measured (0 z 1:33). This measurement provides additional validation of Einsteins theory of General Relativity (GR) and is consistent with the Friedmann-Lema^ tre-Robertson-Walker (FLRW) metricl, as well as several alternative cosmological models, notably the hyperbolic anti-de Sitter metric, though not with the pseudo-Euclidean de Sitter metric.
Motion Correction for Direct Whole Body Parametric PET with Symmetric and Inverse Consistent Deformable Image Registration

Whole body parametric PET imaging uses multiple dynamic frames to calculate the rate of activity change over time in each image voxel. To evaluate tracer kinetics accurately, ideally each voxel in different dynamic frames should contain the same tissue for the duration of the dynamic scan. However, patients may move voluntarily or involuntarily during dynamic data acquisition, and the corresponding shifts in tissue positions can create motion artifacts in reconstructed parametric images. Therefore, inter-frame motion correction is an important step to achieve good image quality in parametric PET. In this paper, we proposed to use symmetric and inverse consistent deformable image registration for motion correction in direct whole body parametric PET. For the dynamic datasets, we select a reference frame, then calculate forward and inverse motion fields for each other frames relative to the reference frame. The calculated motion fields were incorporated in the direct Patlak reconstruction in the nested loop to make sure the same voxel in different dynamic frames contains the same tissue in the nested parametric fitting. The proposed method was validated with patient data. The motion artifacts in parametric images were significantly reduced and the corrected parametric images were more quantitatively correct.
A new framework for modeling the bidirectional interplay between brain oscillations and cardiac sympathovagal activity

The study of functional brain-heart interplay (BHI) aims to describe the dynamical interactions between central and peripheral autonomic nervous systems. Here, we introduce the Sympathovagal Synthetic Data Generation Model, which constitutes a new computational framework for the assessment of functional BHI. The model estimates the bidirectional interplay with novel quantifiers of cardiac sympathovagal activity gathered from Laguerre expansions of RR series (from the ECG), as an alternative to the classical spectral analysis. The main features of the model are time-varying coupling coefficients linking Electroencephalography (EEG) oscillations and cardiac sympathetic or parasympathetic activity, for either ascending or descending direction of the information flow. In this proof-of-concept study, functional BHI is quantified in the direction from-heart-to-brain, on data from 16 human volunteers undergoing a cold-pressor test. Results show that thermal stress induces heart-to-brain functional interplay originating from sympathetic and parasympathetic activities and sustaining EEG oscillations mainly in the $\delta$ and $\gamma$ bands. The proposed computational framework could provide a viable tool for the functional assessment of the causal interplay between cortical and cardiac sympathovagal dynamics.
Investigating Useful Features for Overall Survival Prediction in Patients with Low-Grade Glioma Using Histology Slides

Glioma, characterized by neoplastic growth in the brain, is a life-threatening condition that, in most cases, ultimately leads to death. Typical analysis of glioma development involves observation of brain tissue in the form of a histology slide under a microscope. Although brain histology images have much potential for predicting patient outcomes such as overall survival (OS), they are rarely used as the sole predictors due challenges presented by unique characteristics of brain tissue histology. However, utilizing histology in predicting overall survival can be useful for treatment and quality-of-life for patients with early-stage glioma. In this study, we investigate the use of deep learning models on histology slides combined with simple descriptor data (age and glioma subtype) as a predictor of (OS) in patients with low-grade glioma (LGG). Using novel clinical data, we show that models which are more attentive to discriminative features of the image will confer better predictions than generic models (82.7 and 65.3 AUC RFD-Net and Baseline VGG16 model, respectively). Additionally, we show that adding age and subtype information to a histology image-based model may provide greater robustness in the model than using the image alone (3.8 and 4.3 stds for RFD-Net and Baseline VGG16 model with 3-fold CV, respectively), while a model based on image and age but not subtype may confer the best predictive results (83.7 and 82.0 AUC for RFD-Net + age and RFD-Net + age + subtype, respectively). Clinical relevance— This study establishes important criteria for deep learning models which predict OS using histology and basic clinical data from LGG patients.
Multimodal Neurophysiological Transformer for Emotion Recognition

Understanding neural function often requires multiple modalities of data, including electrophysiogical data, imaging techniques, and demographic surveys. In this paper, we introduce a novel neurophysiological model to tackle major challenges in modeling multimodal data. First, we avoid non-alignment issues between raw signals and extracted, frequency-domain features by addressing the issue of variable sampling rates. Second, we encode modalities through “cross-attention” with other modalities. Lastly, we utilize properties of our parent transformer architecture to model long-range dependencies between segments across modalities and assess intermediary weights to better understand how source signals affect prediction. We apply our Multimodal Neurophysiological Transformer (MNT) to predict valence and arousal in an existing open-source dataset. Experiments on non-aligned multimodal time-series show that our model performs similarly and, in some cases, outperforms existing methods in classification tasks. In addition, qualitative analysis suggests that MNT is able to model neural influences on autonomic activity in predicting arousal. Our architecture has the potential to be fine-tuned to a variety of downstream tasks, including for BCI systems.
Prediction of Hypotension in Hemodialysis Session

There are 4.35 million people who receive the hemodialysis treatment or undergoing a transplant as they had suffered from the session’s duration or the session’s complications. Therefore, this research aims to solve a daily nephrology problem. The main problem is that how a hemodialysis patient suffer for several hours multiple time per week and may have complications during the session which may lead to severe risks. Hypotension is one of the critical problems that faces any dialysis patient. According to nephrologists and previous research papers showed how difficult it was to treat and detect the occurrence of hypotension. To achieve this target, a few steps were needed. Firstly, the data set is required to be from dialysis session to monitor the patient records from the dialysis machine. After preparing the artificial neural network (ANN) model to be able to predict, it needs some adjustment to make it efficient to use and a powerful model to handle the intradialytic event. In conclusion, the research aims to predict the occurrence of hypotension during the session using ANN.