Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral-like gradient method for distributed optimization

We consider a standard distributed multi-agent optimization setting where n nodes (agents) in a network minimize the aggregate sum of their local convex cost functions. We present a distributed spectral-like gradient method, wherein stepsizes are node-and iteration-varying, and they are inspired by classical spectral methods from centralized optimization. Simulation examples illustrate the performance of the presented method.
High-Definition Transcranial Direct Current Stimulation (HD-tDCS) enhances working memory training

High-Definition transcranial direct current stimulation (HD-tDCS) is a noninvasive brain stimulation technique that can improve the performance of working memory (WM). However, the current researches have focused on the effects of stimulation, while ignored the process of stimulus and the neural mechanism. The targets of this study were to explore the effects of different stimulus categories (active or sham) applied on left dorsolateral prefrontal cortex (LDLPFC) on WM training, as well as the physiological changes in the brain after training. Behavioral and electroencephalography (EEG) results of 20 participants showed that HD-tDCS significantly enhanced training effects in the later training period. Furthermore, WM ability benefited from training combined with HD-tDCS, and active group found the time-dependent desynchronization (ERD) weakened in α and β band, while sham group increased. The results supported the viewpoint that HD-tDCS can shorten the training time and alter neurons rhythm, it may be used as psychotherapy for the patients with brain injury.
Discovering Business Process Map of Frequent Running Case in Event log

Recently, in the area of business information systems, process mining is widely considered as a new paradigm focusing on the extraction of knowledge to build event logs from data which are recorded in process aware information systems. The purpose of the mining process is to extract information about business processes and process discovery is one of the most challenging process mining tasks. Moreover, From the event logs, the complete process model is often too complicated to understand by business users for discovering behavioral patterns and analyzing business processes. In this paper a method is presented to discover process model of frequent running case in event log using bupaR framework in R studio. A scenario example has been applied to show the applicability of the approach. The result shows that the local process model is less complicated and easy readable process model to discover the behavioral patterns.
Learning to Sort: Few-shot Spike Sorting with Adversarial Representation Learning

Spike sorting has long been used to obtain activities of single neurons from multi-unit recordings by extracting spikes from continuous data and assigning them to putative neurons. A large body of spike sorting algorithms have been developed that typically project spikes into a low-dimensional feature space and cluster them through iterative computations. However, there is no reached consensus on the optimal feature space or the best way of segmenting spikes into clusters, which often leads to the requirement of human intervention. It is hence desirable to effectively and efficiently utilize human knowledge in spike sorting while keeping a minimum level of manual intervention. Furthermore, the iterative computations that are commonly involved during clustering are inherently slow and hinder real-time processing of large-scale recordings. In this paper, we propose a novel few-shot spike sorting paradigm that employs a deep adversarial representation neural network to learn from a handful of annotated spikes and robustly classify unseen spikes sharing similar properties to the labeled ones. Once trained, the deep neural network can implement a parametric function that encodes analytically the categorical distribution of spike clusters, which can be significantly accelerated by GPUs and support processing hundreds of thousands of recording channels in real time. The paradigm also includes a clustering routine termed DidacticSortto aid users for labeling spikes that will be used to train the deep neural network. We have validated the performance of the proposed paradigm with both synthetic and in vitro datasets.
Min-Max-Strategy-Based Optimum Co-Operative Picking with AGVs in Warehouse

The performance of order picking in a warehouse is crucial in modern logistics. Automated guided vehicles (AGVs) have been developed recently and are in operation in many warehouses. Human pickers still play an important role in picking items that are different in shape and size and putting them on AGVs. Hence, co-operation among human pickers and AGVs is important. This co-operation should be effective to enable every AGV and human to work efficiently. This paper proposes an algorithm that generates a sub-optimal co-operation schedule for both AGVs and human pickers. The effectiveness of the algorithm was evaluated through computer simulations involving an actual warehouse represented as a two-dimensional lattice-network model.
Analysis of Circular and Cylindrical Array Arrangements for mmWave 5G Beamforming Techniques

In this paper, we study the performance of a 5G base station working in the mmWave range equipped with a cylindrical array. Conventional and Capon beamforming techniques are taken into account. We consider both isotropic and directive antenna elements and we evaluate the trade-off between antennas per ring and number of rings with fixed number of total antennas. Results are provided in terms of average achievable per-user rate with different system configurations, such as of network loading. We show that in the presented scenario, where users are randomly deployed in a hexagonal plane, the best performance occurs when the cylindrical array degenerates in a circular array.
Studying the Effect of Carrier Type on the Perception of Vocoded Stimuli via Mismatch Negativity

Vocoder processing has been long used in many studies to examine how acoustic cues affect speech understanding and auditory processing. Early behavioral studies have shown that the type of carrier (i.e., pure-tone or noise) used in vocoding process affected the intelligibility of the perceived speech, and tone-vocoded stimuli had a perceptual advantage over noise-vocoded stimuli. This work further assessed whether the auditory evoked cortical response could objectively measure the perceptual difference between the two types of vocoded stimuli using an oddball-paradigm based event-related potential (ERP) experiment. A vowel stimulus was processed by noise- and tone-vocoding processes, and the processed stimuli were presented to normal-hearing listeners in an ERP experiment. The noise-vocoded and tone-vocoded vowel stimuli served as the deviant stimuli and the non-vocoded vowel stimulus as the standard stimulus. Experimental results showed that tone-vocoded stimulus evoked a significantly larger mismatch negativity (MMN) amplitude and a significantly shorter MMN peak latency than noise-vocoded stimulus did. Results in this work suggested that compared to noise-vocoded stimulus, tone-vocoded stimulus had a larger perceptual difference relative to the reference stimulus, and this effect caused by the usage of different carrier signals could be reflected by the MMN response.
Realization of MPPT of PMSG-Based Wind Turbines Using New MPPT Indices

The energy extracted from the wind turbines depend not only on the wind speed, but also on the applied scheme of the maximum power point tracking (MPPT). In this paper new indices to assign the enhancement level of the MPPT schemes are proposed. These indices based on the relative convergence between actual and maximum values of power coefficient $(C_{p_{-}max})$ moreover, actual and optimum values of tip speed ratio $(\lambda_{opt})$. The proposed indices are used to determine the efficient MPPT scheme. These indices are applied on grid connected permanent magnet synchronous generator (PMSG) with a full size back-to-back converter. Comprehensive models of wind speed, wind turbine, PMSG and power electronic converters along with their control schemes are implemented in MATLAB/SIMULINK platform. The results prove the robustness and feasibility of theses MPPT indices to determine the most efficient MPPT scheme.
Skin Conductance Response to Gradual-Increasing Experimental Pain

Patient self-reporting of pain is not always possible, in those cases automated objective pain assessment could lead to reliable pain assessment. In this context, physiological measurements have been studied and one of the promising signals is skin conductance (SC). In this study, 1Hz SC signal acquisition is performed while gradually increasing heat and electrical pain stimuli are induced. Three labeled study periods are defined based on pain stimuli presence, self-reported pain threshold and pain tolerance. Different classification and regression models are compared, together with selected SC features. The model performances are evaluated using c-index. Results show good predictability, especially for the slow tonic component decomposed from the SC signal.
Unsupervised GEI-Based Gait Disorders Detection From Different Views

Early detection of gait disorders may provide a safer living for elderly people. In this paper, we propose an automatic method for detecting gait disorders using RGB or RGBD camera (e.g., MS Kinect, Asus Xtion PRO). We use Gait Energy Image (GEI) as our main feature that can be computed from different views. Our method depends on computing GEI, learning the representative features from the GEI using convolutional autoencoder, and using anomaly detection method for detecting abnormal gait. We applied the proposed method on two different public datasets that include normal and abnormal gait from different views. Experimental results show that our method achieves high accuracy in detecting different gait disorders from different views, which makes it general to be applied to home environment and adds a step towards convenient in-home automatic health care services.
Microdosimetric Realistic Model of a Cell with Endoplasmic Reticulum

When investigating the biophysical effects induced by the interaction between electromagnetic fields and biological cells, it is crucial to estimate the electromagnetic field intensity at the microscopic scale (microdosimetry). This information allows to find a connection between the external applied field and the observed biological event required to establish related biomedical applications. Here, authors present a microdosimetric study based on a 2D realistic model of a cell and its endoplasmic reticulum. The microdosimetric analysis of the cell and endoplasmic reticulum was quantified in terms of electric field and transmembrane potential induced by an externally applied high amplitude 10-ns pulsed electric field. In addition, electroporated local membrane sites and pore densities were also evaluated. This study opens the way to numerically assist experimental applications of nanosecond pulsed electric fields for controlled bio-manipulation of cells and subcellular organelles.
Development of an Automated 30 Second Chair Stand Test Using Smartwatch Application

This paper presents development of the smartwatch application for automation of the standard 30 Second Chair Stand Test (30SCST). 30SCST is primarily used to test leg strength and endurance, but also speed and mobility and assess risk of falls. We use inertial signals on smartwatch to detect and count stands during the test. The application notifies the user to start and stop the test using vibration on the smartwatch. Synchronization of notifications and signal acquisition allows assessment of user’s response time during the test. Our application monitors baseline heart rate before the test, heart rate increase during the test, and heart rate recovery after the test that might allow assessment of cardiovascular fitness of the user. The application is developed using Wear OS and tested on two smartwatch platforms: Fossil G4 and Polar M600. Pilot test included 12 subjects, six male and six female (mean age 39.1, S.D. 19 years). Overall accuracy of detection of the number of standups is 98.8%. Smartwatch application can be used for automated testing in clinical setups as well as for self-monitoring at home.
Integrated Multilevel Converter Topology for Speed Control of SRM Drive in Plug in-Hybrid Electric Vehicle

A multilevel converter topology for switched reluctance motor (SRM) drive with integration of front end circuit is appealing for electric vehicle. As SRM carries the highlights like simple construction, high reliability, high fault tolerance capability and low production cost. However, the high torque ripples, running vibrations and acoustic noise are the major drawbacks in SRM. In proposed theory of a multilevel converter for five phase SRM drive overcome this drawbacks and flip it into advantages like high torque range, low torque ripple and vibration free response with flexible speed control. This paper represents a operating theory and simulation results of five phase muliltlevel converter for SRM drive. Which results, with increasing the number of phases the system performance is increase without increasing the number of switching devices and a comparison is shown based on torque speed characteristics of motor when fed from proposed converter and a asymmetric half bridge converter for different condition. This analysis states the proposed converter topology is advantages over the conventional converter topology, especially for the high speed response.
Applications of ISM and TISM: Model Building Techniques

Both the Interpretive structural modeling (ISM) and Total Interpretive structural modeling (TISM) are the mathematical approaches based on the pair- comparison procedure to form a hierarchical inter-relationship of elements. These are interactive methodologies using in various application areas for theory & conceptualization model building. This paper provides the implications of these techniques and describes each step of the entire processes of ISM and TISM. The main aim of the research paper is to understand the conceptual implications of both the techniques and the considerations and up-gradations which have been added into TISM over ISM. This paper also covers multiple application areas, where ISM and TISM approaches have been applied by many researchers.
Dielectric permittivity of human blood of different lactate levels measured at millimeter waves

The investigation of variations in dielectric properties of blood based on its biochemical profile is important for determining the feasibility of developing electromagnetic non-invasive sensing systems for monitoring the levels of various metabolites in blood. In this paper, the real and imaginary parts of dielectric permittivity are measured as a function of lactate concentration in the 30-60 GHz frequency range using two different measurement techniques. The blood samples are collected from a healthy subject undergoing three different exercise modes and the dielectric properties are measured with an open-ended coaxial probe technique and a custom-made millimeter wave transmission system. Good correlation is observed in measurements from the two methods, suggesting that an increase in lactate concentration lowers the imaginary part of permittivity and thus causing higher attenuation.
Identification of muscle fasciculations from surface EMG: comparison with ultrasound-based detection*

The clinical standard for the identification of muscle fasciculations is needle electromyography. However, both surface electromyograms (sEMG) and ultrasound imaging (US) have been recently proposed as alternative and more sensitive approaches. The aims of this study were to: (i) compare the sensitivity to muscle fasciculations of sEMG and US, (ii) assess the rate of agreement (RoA) between the two approaches, and (iii) investigate how much sensitivity and RoA are affected by the selectivity of sEMG detection. Surface EMGs were collected concurrently with US images using an array of 32 electrodes spanning the whole, posterior aspect of the leg. Muscle fasciculations were identified from US videos and from monopolar and single differential sEMGs computed between electrodes spaced by 1, 2, and 3 cm. Results from five healthy subjects showed that US detected as many fasciculations as single differential EMGs, but always less than monopolar sEMGs. However, monopolar sEMGs exhibited a very poor spatial selectivity, likely responsible for the small RoA with US measures. The RoA was maximal for single differential recordings with 3cm inter-electrode distance, however, it was always smaller than 75% (median=30%). Although preliminary, these results suggest that sEMG and US are sensitive to different events in the muscle volume and that their integration may increase the detection sensitivity to muscle fasciculations.
Detection of Event Related Patterns using Hilbert Transform in Brain Computer Interface

The feature extraction and classification of motor imagery (MI) tasks comprehend incentive issues in the scope of electroencephalogram (EEG) based brain computer interface (BCI). In this study, proposed method i.e. Hilbert transform (HT) is used for the detection of event-related patterns (ERPs) and the machine learning classifiers were implemented for the classification of both left and right hand MI tasks. The proposed method followed two steps: First, sensorimotor frequency band (8–30 Hz) related to MI activities were extracted from the signals. The important features i.e. band power (BP) and ERPs were extracted from the HT. The significant extracted features were fed into the different machine learning classifiers such as Naive Bayes (NB), linear discriminant analysis (LDA) and support vector machine (SVM). The classification accuracy (% CA), Cohen’s kappa coefficient (K) and area under the receiver operating characteristic (Auc) for all classifiers were evaluated. The proposed method was tested on publicly available BCI-competition 2008 Graz data set (II-b). Results show that the features extracted from the HT meets higher performance (% CA=82.22%, K=0.63 and Auc =0.81) in comparison with the conventional methods. Which is demonstrates that the proposed approach has capability to enhance BCI for real-time applications.
Smoothed arg max Extreme Learning Machine: An Alternative to Avoid Classification Ripple in sEMG Signals

Despite all the recent developments of using the surface electromyography (sEMG) as a control signal, reliable classifications still remain an arduous task due to overlapping classes and classification ripples. In this paper, we present a straightforward approach to avoid classification ripple based on smoothing the arg max value of an Extreme Learning Machine (ELM) classifier. We compare the baseline accuracy of the classifier with an arg max filtered by a traditional Exponential Smoothing Filter (ESF) and our adaptation of Antonyan Vardan Transform (AVT). The classifiers were evaluated using sEMG data acquired through 12 channels from four subjects performing 17 different movements of forearm and fingers with three repetitions each. In the best scenario, our methods reached results higher than 96% and 82% of overall and weighted accuracy, respectively. Those results match or outperform similar papers of the literature using a simpler model, which may help the application of the techniques on embedded platforms and make the practical use of such devices more feasible.
Investigations on the Functional connectivity disruptive patterns of progressive neurodegenerative disorders

Alzheimer’s Disease (AD) and Parkinson’s Disease (PD) are neurodegenerative diseases of the brain that affects the memory and motor regions respectively. Neurological disorders are the result of alterations at molecular level due to complex mechanisms between genetic and environmental factors. Classical approaches are focused on determining how disruptions in functional connectivity occur in the memory regions of AD and motor regions of PD. There have been studies stating that in addition to dementia, motor abnormalities may also be observed in Alzheimer’s patients and on the other hand, dementia may occur in Parkinson’s patients a year or more after the onset of motor symptoms. In this work, to substantiate this hypothesis the brain connectivity patterns and functional topology of motor and memory regions in AD and PD patients were analyzed and compared. Resting state functional connectivity (rs-fMRI) has been found to demonstrate the brain networks in both AD and PD. Graph theoretical modelling is being significantly used in studying the topology of the brain networks. The results show the disruption of connectivity in motor regions in later stages of AD in addition to memory regions and conversely in PD the memory regions were found to have disrupted connectivity in addition to the motor regions. Further, the Z scores of intra and inter hemispheric regions in AD and PD also indicate the disruption in connectivity as the disease progresses.
Health-e Minds: a Participatory Personalised and Gamified mHealth Platform to Support Healthy Living Behaviours for People with Mental Illness

This paper presents the development of an innovative platform (Health-e Minds) to support persons with diagnosed serious mental illness (SMI) in their self-management, but also to enhance multidisciplinary care provision. It comprises of a smartphone app and an interactive internet-based clinician web portal. The smartphone app accompanies and guides persons with SMI through their recovery journey by monitoring health measures, delivering simple interactive questionnaires and providing educational and motivational multimedia content. The web-portal enables clinical care providers to view their client’s progress and provide individualized feedback and/or early care intervention dependent on the advice of the clinical review. A unique feature of the platform is an inbuilt gaming component which encourages patients to adhere to the goals set in consultation with their clinicians. Rewards are attained by comparison of activities achieved, measured against individualized goals, set through the clinical portal. A feasibility study is underway among 40 patients with SMI at the Sunshine Coast University Hospital, in order to ascertain levels of program efficacy and successful health-related outcomes.
Proposal of Easily Distributable Insole Type Gait Sensor for Health Promotion of Elderly People

In this research, for the purpose of reducing cost and increasing penetration rate, insole type gait sensor has been proposed. The proposed method needs to be manufactured inexpensively in order to collect daily life gait information. Therefore, the minimum number of sensors required for gait analysis of this research is clarified. The suitable number of e-textile sensors and contact position of the gait sensor for gait analysis are obtained experimentally. Although the proposed method does not require as much accuracy as a floor reaction force meter in the laboratory, validity is evaluated by comparing with acquired gait data and a floor reaction force gauge. For the validity of gait analysis, this research revealed that two sensor locations are required, only the toe and the heel, and that each sensor should group several contacts.