Miracle-dz/UniFAR-FTUs huggingface dataset:
A functional analysis-based approach to quantify upper limb impairment level in chronic stroke patients: a pilot study

The accurate assessment of upper limb motion impairment induced by stroke - which represents one of the primary causes of disability world-wide - is the first step to successfully monitor and guide patients’ recovery. As of today, the majority of the procedures relies on clinical scales, which are mostly based on ordinal scaling, operator-dependent, and subject to floor and ceiling effects. In this work, we intend to overcome these limitations by proposing a novel approach to analytically evaluate the level of pathological movement coupling, based on the quantification of movement complexity. To this goal, we consider the variations of functional Principal Components applied to the reconstruction of joint angle trajectories of the upper limb during daily living task execution, and compared these variations between two conditions, i.e. the affected and non-affected arm. A Dissimilarity Index, which codifies the severity of the upper limb motor impairment with respect to the movement complexity of the non-affected arm, is then proposed. This methodology was validated as a proof of concept upon a set of four chronic stroke subjects with mild to moderate arm and hand impairments. As a first step, we evaluated whether the derived outcomes differentiate between the two conditions upon the whole data-set. Secondly, we exploited this concept to discern between different subjects and impairment levels. Results show that: i) differences in terms of movement variability between the affected and nonaffected upper limb are detectable and ii) different impairment profiles can be characterized for single subjects using the proposed approach. Although provisional, these results are very promising and suggest this approach as a basis ingredient for the definition of a novel, operator-independent, sensitive, intuitive and widely applicable scale for the evaluation of upper limb motion impairment.
Time-varying effective EEG source connectivity: the optimization of model parameters*

Adaptive estimation methods based on general Kalman filter are powerful tools to investigate brain networks dynamics given the non-stationary nature of neural signals. These methods rely on two parameters, the model order p and adaptation constant c, which determine the resolution and smoothness of the time-varying multivariate autoregressive estimates. A sub-optimal filtering may present consistent biases in the frequency domain and temporal distortions, leading to fallacious interpretations. Thus, the performance of these methods heavily depends on the accurate choice of these two parameters in the filter design. In this work, we sought to define an objective criterion for the optimal choice of these parameters. Since residual- and information-based criteria are not guaranteed to reach an absolute minimum, we propose to study the partial derivatives of these functions to guide the choice of p and c. To validate the performance of our method, we used a dataset of human visual evoked potentials during face perception where the generation and propagation of information in the brain is well understood and a set of simulated data where the ground truth is available.
Continuous Assessment of Carotid Diameter using an Accelerometer Patch Probe for Ambulatory Arterial Stiffness Monitoring

We present a system with an accelerometer patch probe design for non-invasive evaluation of carotid arterial stiffness. The proposed system could continuously measure the acceleration signal derived due to the propagation of blood pulse wave through the left carotid artery, double integrating and scaling it to estimate the accelerometer-derived carotid wall displacement. This functional principle was proved by comparing the accelerometer-derived carotid wall displacement with the carotid distension signal from the reference system ARTSENS® (ARTerial Stiffness Evaluation for Noninvasive Screening device) for all the recruited human subjects. Assuming the relationship to be linear, a one-time subject-specific calibration was performed with the simultaneously acquired reference distension signal and the accelerometer-derived carotid displacement signals on its anachrotic limbs data points (at systolic phases) for each subject. This calibration equation was tested with latterly acquired accelerometer signals and results in the measurement of accelerometer-derived carotid distension and lumen-diameter values. The ability of the accelerometer system to measure real-time carotid distension and lumen diameter in a repeatable beat-by-beat manner for arterial stiffness index evaluation was validated in-vivo. The accuracy of the obtained results was studied with our clinically validated reference system. The experimental validation study results exhibit the feasibility of using the developed accelerometer system for continuous carotid distension and lumen diameter measurements, whereby the estimation of carotid arterial stiffness.
Traffic Prediction and Attack Detection Approach Based on PSO Optimized Elman Neural Network

In view of the characteristics of non-linearity, multi-variable and time-varying of network system, PSO algorithm is proposed to optimize Elman neural network and obtain the optimal network parameters to improve the prediction accuracy and adaptability of the algorithm. Then, the anomalous traffic detected by the anomalous traffic detection model is used as input, and the samples projected into the high-dimensional feature space are classified by KNN algorithm. An attack detection classifier is established to identify the types of network attacks. The experimental results show that, compared with traditional attack detection methods, the proposed network attack detection model based on abnormal traffic analysis improves the accuracy of intrusion detection, which can effectively identify the types of network attacks and ensure the security of information systems.
Online Robust-Diagnostic System and Method for Decisions of Autonomous Vehicles based on Dynamic Path

The safety of self-driving vehicles is always a priority. The right decision is the biggest key to the safety of self-driving cars. Any wrong decision can cause collisions, overturns, and even casualties. In this paper, a diagnostic system and model to evaluate on-road decisions are presented. A future dynamic trajectory of the automatic vehicle, and the parameters of the traffic environment, such as the curvature and width of a traffic lane and the distance to a barrier, are applied to construct the diagnostic model for making the self-driving car has the last line of defense to prevent danger. The improvement of decisions along with our proposed model is investigated through an on-road experiment. The results show that the performance of the proposed framework is reliable enough for robust autonomous driving.
Efficient clustering in WSN-Cloud using LBSO (Load Based Self-Organized) technique

A WSN consists of huge number of sensor, these sensors collects the data, these data collection ends up generating enormous amount of data. In order to store the large amount of data, cloud services has been used. The Integration of WSN-cloud not only helps in storing the data but also used for several other purpose as well. Moreover, the above scenario motivate us to design the efficient modeland this can be achieved through employing the appropriate clusteringtechnique. Hence, In this paper we have proposeda clustering technique namely LBSO (Load Based Self-Organized) which helps in balancing the node. The main aim of our algorithm is provide the flexible and better clustering, Our methodology is parted into the three distinctive steps, first stepincludes the selection of Cluster head, second step includes the formation of cluster where the cluster is formed. Third step includes the Rotational phase where the CH (Cluster Head) is reselected. In order to evaluate the algorithm, extensive simulation has been performed and comparative analysis is done and our methodology is compared with the existing LEACH protocol and it clearly indicates that our algorithm outperforms the existing one.
Improved Test Coverage by Observation Point Insertion for Fault Coverage Analysis

Design for testing implies on adding an extra hardware to circuit under test so that the difficulty in testing the circuit becomes easy and large number of faults can be detected to increase the test coverage of the circuit. In a circuit there might be a large number of faults and some fault in the circuit will have high controllability as well as observability those faults will be very difficult to detect. A new approach has been introduced which involves the insertion of observation points at most suitable location to capture the most difficult to observe faults which facilitate structural testing for both on-chip and off-chip for better fault coverage. Insertion of the observation point into the internal part of the circuit enables direct observation of the internal part of the circuit. The observation points are inserted at those locations where observability is high and the occurrence of fault at those location makes the faults hard to propagate to the output.
Temporal cardiovascular causality during orthostatic stress by extended partial directed coherence

The aim of this study was to investigate the temporal dynamic behavior of cardiovascular interactions between heart period and systolic blood pressure during a 20-min head-up tilt test at 70° in young women with orthostatic intolerance compared to healthy women. Methods included the lagged and extended partial directed coherence applied to short-term windows shifted by 5 seconds, extracted from a multivariate set of cardiovascular and respiratory time series. Findings revealed significantly increased information flow (p < 0.01) in patients from: a) heart period to blood pressure during supine position which subsequently decreased and b) blood pressure to heart period during the progression of orthostatic phase. Controls developed balanced cardiovascular interactions with smaller information flows than patients.
Motor Imagery Classification with Covariance Matrices and Non-Negative Matrix Factorization

In this paper, we aim at finding the smallest set of EEG channels that can ensure highly accurate classification of motor imagery (MI) dataset and maintain the optimum Kappa score. Non-negative matrix factorization (NMF) is used for important and discriminant EEG channel selection. Further, the theory of Riemannian geometry in the manifold of covariance matrices is used for feature extraction. At last, the neighborhood component feature selection (NCFS) algorithm is used to select the small subset of important features from the given set of features. The significance of the proposed work is two-fold: 1) it greatly reduces the time complexity and the amount of overfitting by reducing the unnecessary EEG channels and redundant features. 2) it increases the classification accuracy of the model by selecting only subject-specific EEG channels. The proposed algorithm is tested on BCI Competition IV,2a dataset to validate the performance. The proposed approach has achieved 77.91% average classification accuracy and 0.626 mean Kappa score.
Semi-Supervised Learning for Low-Dose CT Image Restoration with Hierarchical Deep Generative Adversarial Network (HD-GAN)*

In the absence of duplicate high-dose CT data, it is challenging to restore high-quality images based on deep learning with only low-dose CT (LDCT) data. When different reconstruction algorithms and settings are adopted to prepare high-quality images, LDCT datasets for deep learning can be unpaired. To address this problem, we propose hierarchical deep generative adversarial networks (HD-GANs) for semi-supervised learning with the unpaired datasets. We first cluster each patient’s CT images into multiple categories, and then collect the images in the same categories across different patients to build an imageset for denoising. Each imageset is fed into a generative adversarial network that consists of a denoising network and a following classification network. The denoising network efficiently reuses feature maps from the lower layers for end-to-end learning with full-size images. The classifier is trained to distinguish between the denoised images and the high-quality images. Evaluated with a clinical LDCT dataset, the proposed semi-supervised learning approach efficiently reduces the noise level of LDCT images without loss of information, thereby addressing the major shortcomings of IR such as computation time and anatomical inaccuracy.
Dynamic Scaling Adaptive Fuzzy Output Feedback Control of Nonstrict-Feedback Systems

This paper proposes a dynamic-scaling adaptive fuzzy output-feedback tracking control design for nonstrict-feedback systems with unknown nonlinearities. It switches among a set of candidate controllers, each of which contains a nominal dynamic surface control (DSC) based indirect adaptive fuzzy controller and a supervisory robust controller being active in the fuzzy and non-fuzzy regions, respectively. Such a composite structure not only lifts the all-time-validity restriction of fuzzy approximation, but also is key to the feasibility of the indirect adaptive control without incurring the loop problem. By virtue of the dynamic-scaling transformation, the proposed design guarantees globally uniformly ultimately bounded (GUUB) stability under a generalized triangular growth condition.
The lasting effects of 1Hz repetitive transcranial magnetic stimulation on resting state EEG in healthy subjects

Repetitive Transcranial magnetic stimulation (rTMS) is a noninvasive brain stimulation technique that able to influence cortical excitability. Low-frequency rTMS (stimulation frequency ≤1Hz) induces long-lasting inhibitory effects on cortical excitability. In order to study the effects of 1Hz rTMS of the motor cortex on neuronal activity, 20 healthy subjects were recruited to receive rTMS, and electroencephalography (EEG) in resting condition with eye open were recorded before rTMS, at 0min, 20min, 40min, 60min after rTMS. In multiple frequency bands, power values on each channel were calculated, and functional connectivity between two channels was assessed using phase synchronization. We found an increase in power of theta-band oscillations in the frontal and the central brain areas immediately after rTMS. And alpha resting power in the central-parietal brain area did not change immediately after rTMS, but increased at 20min after rTMS. Moreover, there is a widespread increase in functional connectivity after rTMS in the theta band, whereas widespread decreases in the functional connectivity were found in the alpha band after rTMS. At the same time, there was no significant recovery on power and functional connectivity at 60min after rTMS. These results provide an evidence for a transient reorganization of neuronal activity after 1Hz rTMS over the motor cortex. In addition, low-frequency rTMS produces widespread long-lasting alterations in cortical functional connectivity.
Wu–Mendel Approach for Linguistic Summarization: Practical Considerations and Solutions

The Wu–Mendel approach for linguistic summarization (LS) using IF–THEN rules is analyzed in this paper from a practical-implementation point of view. Several practical considerations on the Wu–Mendel approach are presented in this paper, and three practical solutions are proposed in order to enhance the efficiency of the approach, namely to reduce the processing time and memory usage in LS. The proposed solutions have been verified by carrying out the corresponding experimental analysis. The scripts created and implemented during the experimental verification process are made publicly available and provided with the tutorial.
Reconfigurable Hybrid Cache Hierarchy in 3D Chip-Multi Processors Based on a Convex optimization Method

to overcome the challenges in Chip-Multi Processor (CMP) design as memory static power, memory wall, and limited memory bandwidth, by using hybrid cache technologies such as SRAM, DRAM, and STT-RAM. Besides that, many characteristics are extracted using these technologies like low leakage power, high-density storage, and non-volatility. In this paper, we provide an interesting approach for a reconfigurable hybrid cache architecture, in which STT-RAM banks are arranged in the last cache level with SRAM banks. Furthermore, the reconfiguration mechanism dynamically adapts the cache space based on the estimated optimal bandwidth demands of different applications. We execute experiments on a 16-core CMP which shows that the proposed design improves the power consumption higher than SRAM cache only and non-reconfigurable hybrid memory under multi-programmed and multithreaded applications.
A Ka-Band Front-End in 100nm GaAs Process for In-Band Full Duplex Communications

In this study the authors introduce a Ka-band front-end to be used for in-band full-duplex (IBFD) communications. The design consists of a Rat-Race coupler to provide self-interference cancellation (SIC), a low noise amplifier (LNA), a power amplifier (PA) and an electrical balancing impedance; all implemented in 100nm low noise GaAs process provided by UMS. The circuit provided an isolation above 40 dB between the PA and the LNA. The 1 dB compression point of the transmitter at the antenna terminal is 12 dBm and the total noise Figure (NF) is better than 5 dB. The balancing impedance covers 3:1 voltage standing wave ratio (VSWR) circle in Smith chart. The chip consumes 500 mW power and has a die area of 7mm2.
Cortical Theta Activity and Postural Control in Non-Visual and High Cognitive Load Tasks: Impact for Clinical Studies

Due to the major role of balance in our everyday lives and the unsatisfying understanding of the role of neural mechanism on balance control, the focus of this study was to explore the role of the cerebral cortex and its effects on stability. We investigated the effects of non-visual and cognitive tasks on balance performance and cortical theta response in a small, convenient sample. The cognitive tasks were N-back and Sustained Attention Response Task (SART). Cortical theta activity showed strong correlations with balance performance metrics. Particularly, central regions showed an increase in theta power in more cognitively challenging tasks but not statistically significant. Parietal theta power had a statistically significant increase in tasks that led to a heavier reliance on proprioception and vestibular information. This study shows the efficacy of EEG recording during balance control tasks. Future studies on neurodegenerative diseases that affect neuromotor control could investigate these outcomes.
Higher Resolution sLORETA (HR-sLORETA) in EEG Source Imaging

sLORETA is one of the well-established EEG source localization methods that is popular for its satisfactory estimation, simplicity, and fast computation. However, the method has a low-resolution and requires manual post-processing thresholding to provide a sparser solution with acceptable resolution in source detection. Here we propose a subspace based thresholding that results in a higher resolution brain imaging based on minimizing a desired least square source detection error. Simulation results show the proposed method, denoted by HR-sLORETA, provides stable and high resolution solution in terms of Percentage of Undetected Sources (PUS) and Spatial Dispersion (SD) compared to the existing manual thresholding approaches as well as Otsu thresholding approach. It is shown that HR-sLORETA outperforms Otsu, which is the only other available automatic thresholding method, in scenarios with three or more sources.
Charting Perceptual Spaces with Fuzzy Rules

Algorithmic music nowadays performs domain specific tasks for which classical algorithms do not offer optimal solutions or require user’s expertise. Among these tasks is the extraction of models from data that offer an understanding of the underlying behavior, providing a quick and easy to use way to explore the data for first (sometimes on-the-fly) insights. Learning rules from examples is an approach often used to achieve this goal. However, together with the aforementioned requirements algorithmic composition needs to create new material so that it is perceived as consistent with the material of the data. In addition, the input data sets are usually small because the human is the bottleneck when generating them. In this contribution we present a fuzzy rule induction algorithm focused on generalizing a set of data, complying with the previous requirements, that offers good results for small data sets. For its evaluation -in a field where there are no benchmarks available - data sets obtained during user tests were used. The visual representation offered by the fuzzy chart helps to reduce the cognitive complexity of the devices used in algorithmic music. The results obtained show that this approach is promising for future developments.
An Investigation of the Relationship between the Apnoea Hypopnoea Index and Apnoea Hypopnoea Duration per Hour

A number of automated apnoea hypopnoea index (AHI) prediction algorithms first divide the signal(s) of interest into epochs, make a prediction for each epoch, determine the number of epoch predictions per hour and map this to an AHI value. An underlying assumption of this approach is a smooth relationship between the apnoea plus hypopnoea duration and the AHI value. In this study we investigate this relationship to establish if this assumption impacts the final system. We compare two models: one which divides the duration by recording time, and the second which divides the duration by sleep time. Data for study was obtained from 200 scored overnight polysomnogram recordings. Our results show that the relationship is a power-law distribution with an exponent of 0.9 and a multiplicative noise term. Analysis of the variance of the noise term revealed that algorithms that scale the apnoea duration by the recording time will have an inherent 37% error in the AHI estimate, while algorithms that scale by sleep time will have an inherent 25% error in the AHI estimate. Receiver operator curve (ROC) analysis of the duration-based models at the clinically important values of AHI 5 and 15 revealed an area under the ROC of greater than 0.96. We conclude that epoch-based models for AHI estimation do have an inherent error in their estimates, with models that divide the duration by sleep time providing a better estimate. Both models can correctly identify normal and apnoeic patients at the clinically important values with high sensitivity and specificity.
Intrusion Detection System Based on Network Traffic Using Deep Neural Networks

Nowadays, the small-medium enterprises security against cyber-attacks is a matter of great importance and a challenging area, as it affects them financially and functionally. Novel and sophisticated attacks are emerging daily, targeting and threatening a large number of businesses around the world. For this reason, the implementation and optimization of the performance of Intrusion Detection Systems have attracted the interest of the scientific community. The malicious behavior detection in terms of DDoS and malware cyber-threats using deep learning methods constitutes an extended and the most important part of this paper. The experimental results for the real-time intrusion detection system showed that the proposed model can achieve high accuracy, and low false positive rate, while distinguishing between malicious and normal network traffic.