Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantification of Neural Conduction Block on the Rat Sciatic Nerve based on EMG Response

Neural conduction block performed by balanced-charge kilohertz frequency alternating currents (KHFAC) has been identified as a potential technique for therapy delivery in different clinical setups. The underlying mechanisms that contribute to this phenomenon have been studied through computational models and animal experiments. However, the optimal stimulation parameters to achieve axonal conduction block are difficult to define, since they depend on the species, the nerve being targeted, as well as the technical and experimental setup. This study proposes an experimental setup along with an original data processing approach for the quantification of the effectiveness of neural conduction block. Experiments were performed on the sciatic nerve of two Sprague-Dawley rats, by evaluating different groups of stimulation parameters with varying amplitudes and frequencies, ranging from 1 to 10 mA and from 2 to 10 kHz, respectively. Results suggest that the effectiveness of axonal conduction block strongly depends on the selection of the stimulation parameters. In this work, more effective blockages were achieved for frequencies around 4 kHz and within an approximate amplitude range of 2 to 8 mA.
Prediction of Smooth Gait Transitioning for Active Lower Limb Prosthetics

Amputation is the major cause of gait impairment in our society and is due to several factors and conditions such as war injuries or diabetes lower limb complication, often resulting in a gait impairment.Active prosthetics have been considered to remedy this mobility loss. These devices have the potential to enhance significantly the quality of life of patients. One major challenge resides in the generation of smooth trajectories, especially during gait transitioning for the active joints of the powered devices. Here we propose a smooth trajectory predictor for above–knee prosthetics, where the motion of the hip joints is translated into knee and ankle joint trajectories. We consider a locomotion task that includes overground walking and stairs ascent. Successful prediction is achieved for both knee and ankle joint angular positions.
An Investigation of Critical Frequency Sub-bands of Snoring Sounds for OSA Diagnosis *

Obstructive Sleep Apnea (OSA) is a result of upper airway narrowing during sleep. The upper airway characteristics are likely to manifest in the acoustic characteristics of snoring sounds as snoring is a result of upper airway structure vibrations. In previous studies, researchers have used different regions of the frequency spectrum to diagnose OSA and determine sites of obstruction as well. However, there is no agreement among researchers about the frequency ranges critical for OSA diagnosis. This paper provides the results of a study of snore sound based OSA diagnosis performance using a multiple acoustic features and multiple classifiers. The results of the study may provide useful insights for researchers to identify frequency sub-bands critical for OSA diagnosis.
Monte-Carlo Analysis for Quality Estimation of Gradient Correction Algorithms in Simultaneous Surface EMG-MRI Measurements using Signal Synthesis and Class Probability

Simultaneous measurements by surface electromyography (sEMG) and diffusion-weighted magnetic resonance imaging (DW-MRI) enable studies of small muscular activities in resting human musculature. Induced signal disturbances can hamper the evaluation of small amplitude myoelectric signals despite the application of gradient artifact correction (GAC) methods. Systematic evaluation of the quality of GAC methods is difficult due to the unknown real sEMG signal during simultaneous measurements. Therefore, a new concept for GAC quality estimation is investigated based on sEMG sample generation and classification. Signal synthesis and classification is based on a Dense-Bidirectional-Long-Short-Term-Memory (DBLSTM)-based auxiliary classifier generative adversarial network (AC-GAN). Quality of GAC and ability to detect small myoelectric activities is exemplary investigated on two common DW-MRI sequences.
Parametric Study of Student Learning in IT Using Data Mining to Improve Academic Performance

Data mining in education is a developing interdisciplinary research field also known as educational data mining (EDM). The goal is to understand students' learning process and identify the way by which they can learn to improve educational outcomes. Learning using IT is one of the most widely used methods for education in modern days. Digital learning gives students an experience of individual learning at any time as well as anywhere, so students get more interest, flexibility at learning. Knowing the preferences of students learning will help the instructors to design better learning materials and teaching styles. We have surveyed on the students of undergraduate level and evaluated the students in three categories: good, average and excellent. We have used four classification models: Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree and Random Forest (RF) model to predict the performance of students on basis of the impact of IT and other study mediums based on their results. In this article, we have identified different parameters or features from five different learning sectors or fields which can give an impact on the student's learning process. So, we have processed in a way that will find out the data mining model which can give better accuracy of student's performance and also can find out which parameters or features among the five fields are playing a great role in their academic results. Moreover, we can apply these features by inspiring good or average students to improve their learning process.
Multi-level Generalized Clustering Approach and Algorithm for Anomaly Detection in Internal Banking Payment Systems

In real datasets often occur cases, where variable or multiple variables have unusual values. These cases are known as anomalies or outliers. For any analysis, it is essential to detect them, because they can bias the analysis. In this paper, a robust anomaly detection method is presented, and it is based on median, rather then on mean value. The method is explained, as well as its parameters and the way how they affect the results. The method is then implemented, and used on Internal Banking Payment Systems. Analysis is given and results are presented.
The Convex-Concave Optimization for the Design of a Robust Proportional-Resonant Controller for Grid-Connected Inverter

This paper proposes a proportional-resonant (PR) control design method by using a convex-concave optimization base on robustness constraints for the current controller of the grid-connected inverter with LC filters. The control objective is to determine a controller for maintaining the stability and performance of the system when grid-impedance variations occur. Simulation studies of the system were carried out using Matlab/Simscape to verify the effectiveness of the controller. The results indicate that the proposed control design outperforms the conventional $\mathcal{H}_{\infty}$ controller in terms of low-order and robustness.
A Study on Various Semantic Metadata Standards to Improve Data Usability

An efficient meta data systems will helps to improve the usability of data. Metadata will describe the data being stored and it will helps in adding more value and usability to the stored data. Data retrieval also will be made easy by using an efficient metadata system. This survey paper will study some of the metadata standards and perform a comparison based on the usability of each metadata standards.
A New Perspective in Designing an Optimized Fault Tolerant Reversible Multiplier

This paper presents a new approach to design a cost effective fault tolerant reversible multiplier circuit. Unlike existing multiplier circuits that have separate circuits for partial product generation and multi-operand addition, the proposed multiplier, designed using a combination of six distinct blocks, incorporates these two circuits together. It also provides an algorithm that depicts the way of using the blocks in designing a $n\times n$ fault tolerant reversible multiplier. The proposed multiplier is simulated to verify the correctness of the design and algorithm. The comparative study shows that the proposed multiplier is more optimized than any existing fault tolerant reversible multiplier in terms of quantum cost, garbage outputs and number of gates used. The proposed multiplier can be used to design a reversible fault tolerant arithmetic logic unit.
SnoreNet: Detecting Snore Events from Raw Sound Recordings *

Snoring is one of the earliest symptoms of Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS). Snore detection is the first step in developing non-invasive, low-cost, and totally sound-based OSAHS analysis approaches. In this work, we propose a simple yet effective deep neural network, named SnoreNet, for detecting snores from a continuous sound recording. Without manually crafted features, SnoreNet can capture the characteristics of snores. Since snore varies in temporal length, SnoreNet combines output from multiple feature maps to detect snore. In each feature map, SnoreNet uses a set of default bounding box generated by a base length and different scales to match snores. SnoreNet adjusts the box to better locate snores and predicts a score for the presence of snore in each default bounding box. The performance of SnoreNet was evaluated on a newly collected snore pattern classes dataset, which achieves 81.82% average precision (AP).
Synthetic Retinal Images from Unconditional GANs

Synthesized retinal images are highly demanded in the development of automated eye applications since they can make machine learning algorithms more robust by increasing the size and heterogeneity of the training database. Recently, conditional Generative Adversarial Networks (cGANs) based synthesizers have been shown to be promising for generating retinal images. However, cGANs based synthesizers require segmented blood vessels (BV) along with RGB retinal images during training. The amount of such data (i.e., retinal images and their corresponding BV) available in public databases is very small. Therefore, for training cGANs, an extra system is necessary either for synthesizing BV or for segmenting BV from retinal images. In this paper, we show that by using unconditional GANs (uGANs) we can generate synthesized retinal images without using BV images.
A Framework on Optimization Strategy for EEG Motor Imagery Recognition

At present, in the process of encephalogram motor imagery decoding, facing the background of big data analysis, it has the necessity to design an effective system which is subject-independent. Pre-training is common to carry out before each experiment, which affects the practicability of the EEG system. In order to solve this problem, the most feasible method is to design a unified framework for deep learning optimization, which could capture the spatial and spectral dependence of original motor imagery EEG signals according to the features extracted by CNN and the temporal dependence extracted by RNN-LSTM. The framework is superimposed from both end-to-end and time-frequency domains so as to retain and learn interpretable motor imagery features. In addition, artificial EEG signals can be automatically generated by training the generated adversarial network, which can generate the feature distribution similar to the original EEG signals, increase the capacity of EEG samples, and ultimately improve the classification performance and robustness of EEG motor imagery recognition. This deep learning framework can improve the classification accuracy of motor imagery for different subjects. In addition, the network can learn from the original data with the least amount of preprocessing, thus eliminating the time-consuming data preparation process.
Analysis of EEG Frequency Components and an Examination of Electrodes Localization during Speech Imagery

This paper reports a result of analysis of EEG frequency components and an examination of electrodes localization for relating the intention ("I want to drink") to "with/without speech imagery" when drink images are displayed on the PC screen. An experimental process was set in 8 sec as followings; fixation image display (1 sec), drink image display (2.5 sec), intention check (1.5 sec), fixation image display (1 sec), and speech imagery period (2 sec). It was repeated 100 times. EEG was measured with 10−20 system in a magnetically-shielded room with BPF (0.08−100Hz) and notch-filter (50Hz) for 10 participants. In the analysis, it was averaged with 35 trials dividing between "with/without speech imagery" in an epoch (fixation image display and speech-imagery) of 2.9 sec. Event-related synchronization (ERS) and desynchronization (ERD) were examined with event-related spectral perturbation (ERSP) and t-test. ERS were observed in α band on the left-hemisphere in case of "with speech imagery" for latency of 500−1500 ms and the significant differences (p<0.05) were shown in the left-hemisphere and occipital-region. It is suggested the useful information of selecting electrode positions to detect EEG frequency components for judgment of "with/without speech imagery" in the intention. From these results, we examined the electrodes localization in the with/without speech imagery identification.
Towards Sequential Decision-Based Cooperative Spectrum Sensing Using Random Medium Access

Sequential reporting has the potential of reducing the duration of the reporting phase of Cooperative Spectrum Sensing (CSS) schemes thereby increasing the overall system throughput. In this paper, we study sequential decision-based CSS using random medium access reporting and a K-out-of-M decision rule at the fusion center. We derive closed-form expressions for the detection and false alarm probabilities as well as the average reporting time of the system, assuming a reporting channel shared with the primary user. The accuracy of derived expressions are compared to computer simulations results.
Heptagonal Photonic Crystal Fiber Based Chemical Sensor in THz Regime

In this article, here is developed a novel TOPAS based heptagonal photonic crystal fiber (H-PCF) for the identification of various chemical substances in the terahertz frequency territory. The full-vector finite element method (FV-FEM) is used for the numerical investigation about the competence of the proposed PCF. Simulation results show the maximum relative sensitivity as well as less connement loss for various chemicals. Numerically processed outcomes apparent that the proposed sensor shows 63.24 %, 61.05 % and 60.03 % detecting reactions for focused analytes benzene, ethanol and water individually at 1 THz frequency. Major purposes is to use H-PCF as the application of chemical sensor in commercial sector to detect harmful chemicals.
Pre-diabetic patients with severe obstructive sleep apnea: novel parameters of hypoxia during sleep correlate with insulin resistance

Obstructive sleep apnea (OSA) is an increasingly prevalent sleep disorder with estimated prevalence of moderate to severe sleep apnea ranging between 6-13% in the adult population. Nocturnal intermittent hypoxia has been associated with an increased risk of developing type 2 diabetes and insulin resistance (IR). The existing indices of hypoxia, used in polysomnography (PSG), cannot express accurately the influence of the mild desaturations precisely during sleep. In the present study, 73 subjects without other comorbidities were examined by PSG. Hypoxia parameters were analyzed, in the intervals with low values of SpO2 signal. The thresholds were set at 94% and 92% and the average value (M) of the SpO2 signal, in areas below thresholds, were calculated. The desaturations were analyzed together with their duration within the recording in terms of SpO2 signal parameters. Blood samples were taken for biochemical analysis. A total of 50 subjects were diagnosed with OSAS with mean AHI of 56.11±27.70/h and 23 subjects had no sleep apnea with mean AHI of 3.47±0.6/h. The amount of desaturations was significantly correlated with insulin levels (r=0.301, p=0.034) and the percentage of desaturation events (Dev) that were longer than 6 points (2 sec) (r=0.301, p=0.034). In addition, mean duration of desaturations was significantly correlated with IR as expressed by HOMA index (r=0.289, p=0.047), as well as with total duration of desaturation of SpO2 (r=0.322, p =0.025) and percentage of Dev that were long than 6 points (2 sec) (r=0.292, p=0.044). A strong correlation was also revealed between total duration of desaturations and fasting glucose (r=0.887, p=0.000). Results suggest that hypoxia parameters derived from SpO2 signal analysis, are strongly correlated with IR and fasting glucose levels, implying a role of hypoxia in the pathogenesis of diabetes.
State-Space Global Coherence to Estimate the Spatio-Temporal Dynamics of the Coordinated Brain Activity

Characterizing coordinated brain dynamics present in high-density neural recordings is critical for understanding the neurophysiology of healthy and pathological brain states and to develop principled strategies for therapeutic interventions. In this research, we propose a new modeling framework called State Space Global Coherence (SSGC), which allows us to estimate neural synchrony across distributed brain activity with fine temporal resolution. In this modeling framework, the cross-spectral matrix of neural activity at a specific frequency is defined as a function of a dynamical state variable representing a measure of Global Coherence (GC); we then combine filter-smoother and Expectation-Maximization (EM) algorithms to estimate GC and the model parameters. We demonstrate a SSGC analysis in a 64-channel EEG recording of a human subject under general anesthesia and compare the modeling result with empirical measures of GC. We show that SSGC not only attains a finer time resolution but also provides more accurate estimation of GC.
CANet: A Channel Attention Network to Determine Informative Multi-channel for Image Classification from Brain Signals

Changes in brain state that depend on various visual image stimulations have been investigated recently; however, it is difficult to decode visual image information from brain signal information. Recently, deep learning techniques have been applied to classify brain signals in various experiments, such as motor imagery and steady state visual evoked potential. However, although the deep learning model seems powerful, it is understood poorly, and thus, can be considered a black box. Accordingly, when multi-channel brain signals are trained, which channels include important information is not understood clearly. In this paper, we proposed a channel attention network (CANet) and investigated the way the deep learning network may determine which channels contain more important information that represents brainwaves’ characteristics and the way it may visualize that information. Using such spatial channel information, we found that our proposed deep learning architecture outperforms basic approaches (spatial channel information is not considered) to classifying categorized images from visual evoked magnetoencephalographic (MEG) brain signals.
Characterization of EEG Resting-state Activity in Alzheimer’s Disease by Means of Recurrence Plot Analyses

The main objective of this study was to characterize EEG resting-state activity in 55 Alzheimer’s disease (AD) patients and 29 healthy controls by means of TREND, a measure based on recurrence quantification analysis. TREND was computed from 60-second recordings of consecutive EEG activity, divided into non-overlapping windows of length 1, 2, 3, 5, 10, 15, 20 and 60 seconds. This measure was computed in the conventional EEG frequency bands (delta, theta, alpha, beta-1, beta-2 and gamma). The parameters delay (τ) and embedding dimension (m) were first optimized for every window size and frequency band under study. These embedding parameters proved to be frequency-dependent. Furthermore, 10 s epochs were set as the minimum length required to avoid spurious results. Statistically significant differences between both groups were found (p < 0.05, Mann-Whitney U-test). The groups showed differences in TREND in the theta (4-8 Hz), beta1 (13-19 Hz) and beta-2 (19-30 Hz) frequency bands. Our results using TREND suggest that AD disrupts resting-state neural dynamics. Furthermore, these findings indicate that AD induces a frequency-dependent pattern of alterations in the non-stationarity levels of resting-state neural activity.
Recent Trends of FPGA Used for Low-Power Wireless Sensor Network

The choice of the appropriate platform could lead to motivating results in terms of energy consumption and computation time, if the appropriate image and video compression techniques for WSNs application were chosen correctly. Since FPGAs have acquired low power potential, their interest in sensor networks is increasing. Their ability to process complex tasks, particularly image processing, has enabled them to invade several high-performance applications. In this paper, we have highlighted the role of FPGA in this type of network and in particular we have focused on the low-consumption solutions proposed by this type of platform. Those low -power optimized FPGA-based solutions are now able to enhance the computation of several types of algorithms in terms of speed and energy consumption.
Lateral Resolution Improvement in Ultrasound Imaging System using Compressed Sensing: Initial Results

Compressed-Sensing (CS) has been applied to ultrasound imaging to reduce data or to reduce the data acquisition time. There appears to be no report that uses CS framework to reduce the number of active receive elements in Conventional Focused Beamforming (CFB). Thus, in our previous work, a novel undersampling scheme based on Gaussian distribution was investigated and reported for reducing the number of active receive elements and data in CFB. In this paper, we exploit the Gaussian sampling based CS framework to improve the lateral resolution (LR) of the ultrasound system without increasing the system’s complexity and cost. A notable difference from our previous work being the use of waveatom as the sparsifying basis, instead of 2D-Fourier basis, and analysis of the proposed framework for different receive aperture sizes. Simulation data for this study were generated using Field II, and experimental data were acquired from an in-vitro cyst phantom using Verasonics V-64 ultrasound scanner. The results indicate that the proposed framework of choosing a limited number of receive elements from a receive aperture length that is three or four times the corresponding active aperture size obtained from the same number of consecutive receive elements yields nearly twice an improvement in LR and about 27% increase in contrast to that of CFB reference image. Thus, the findings suggest a possibility to improve the LR of the current ultrasound system without increasing the system complexity, which will be significant for affordable point-of-care ultrasound systems.
A Simplified Estimation of Abnormal Reflex Torque due to Elbow Spasticity Using Neuro-musculoskeletal Model

This paper is to develop a simplified estimation method of internal torque for clinical use, such as spasticity assessment. Compared with many parameters to be tuned, the proposed estimation method only has a single tuning parameter by simplifying the neuro-musculoskeletal model. Moreover, based on forward dynamics, the proposed method uses EMG signals as the input, and uses muscle activation dynamics and musculotendon dynamics to calculate internal torque. A biomechanical method based on dynamometer was applied to determine the tuning parameter and to validate the estimation result of the proposed model. Through a pilot study with healthy subjects and stroke patients, we found that the proposed estimation method would be helpful for spasticity assessment.