Miracle-dz/UniFAR-FTUs huggingface dataset:
Cortical reactive balance responses to unexpected slippages while walking: a pilot study

Understanding how the human brain cortex behaves when the dynamical balance is unexpectedly challenged can be useful to enable fall prevention strategies during daily activities. In this respect, we designed and tested a novel methodological approach to early detect modifications of the scalp-level signals when steady walking is perturbed.Four young adults were asked to manage unexpected bilateral slippages while steadily walking at their self-selected speed. Lower limb kinematics, electromyographic (EMG) and electroencephalographic (EEG; 13 channels from motor and sensory-motor cortex areas) signals were synchronously recorded.EMG signals from Vastus Medialis (both sides) were used to trigger the analysis of the EEG before and after the perturbation onset. Cortical activity was then assessed and compared pre vs. post perturbation. Specifically, for each gait cycle, the rate of variation of the EEG power spectrum density, named m, was used to describe the cortical responsiveness in five bands of interests: ϑ (4–7 Hz), α (8–12 Hz), β I, β II, β III rhythms (13–15, 15-20, 18-28 Hz). Results revealed a sharp increment of m early after the onset of the perturbation (perturbed step) compared to steady locomotion, for all rhythms. This cortical behavior disappeared during the recovery step.This study promisingly supports the evidence that the proposed approach can distinguish between steady walking and early reactive balance recovery, paving the way for the EEG-based monitoring of the fall risk during daily activities.
Fast Hyperspectral Diffuse Optical Imaging Method with Joint Sparsity

Diffuse optical tomography (DOT) is an important functional imaging modality in clinical diagnosis and treatment. As the number of wavelengths in the acquired DOT data grows, it becomes very challenging to reconstruct diffusion and absorption coefficients of tissue, i.e., a DOT image. In this paper, we consider the hyperspectral DOT (hyDOT) inverse problem as a multiple-measurement vector (MMV) problem by exploiting the joint sparsity of the images to be reconstructed. Then we propose a fast stochastic greedy algorithm based on the MMV stochastic gradient matching pursuit (MStoGradMP) and the mini-batching technique. Numerical results show that the proposed algorithm can achieve higher reconstruction accuracy with significantly reduced running time than the related gradient descent method with sparsity regularization.
Toward Data Security in Edge Intelligent IIoT

IIoT significantly improves industrial productivity and at the same time reduces manufacturing costs. In order to satisfy the requirements in IIoT applications, such as location awareness and low latency, edge intelligence has been proposed to enhance the capability of processing data at the edge of the network. As a typical example of edge intelligence, fog computing offers a lot of potential benefits including transient storage, decentralized computation, stronger security, and so on. In this article, we mainly focus on the data security in edge intelligent IIoT. We present four major challenges in data security of edge intelligent IIoT, including reliable storage, convenient usage, efficient search, and secure deletion, and provide the corresponding solutions to each challenge. Furthermore, we propose an optimized framework, which enjoys higher security and better integration to IIoT. The proposed framework promotes the underlying IIoT in data acquisition, processing, and transition, thus leading to stronger security and better efficiency. Finally, we implement the solutions on a desktop and Raspberry Pi, and demonstrate the experimental results.
Android Protection System: A Signed Code Security Mechanism for Smartphone Applications

This research develops the Android Protection System (APS), a hardwareimplemented application security mechanism on Android smartphones. APS uses a hashbased white-list approach to protect mobile devices from unapproved application execution. Functional testing confirms this implementation allows approved content to execute on the mobile device while blocking unapproved content. Performance benchmarking shows system overhead during application installation increases linearly as the application package size increases. APS presents no noticeable performance degradation during application execution. The security mechanism degrades system performance only during application installation, when users expect delay. APS is implemented within the default Android application installation process. Applications are hashed prior to installation and compared against a white-list of approved content. APS allows applications that generate a matching hash; all others are blocked. APS blocks 100% of unapproved content while allowing 100% of approved content. Performance overhead for APS varies from 100.5% to 112.5% with respect to the default Android application installation process. This research directly supports the efforts of the USAF and the DoD to protect our information and ensure that adversaries do not gain access to our systems.
Characterizing autonomic response to arousing visual-auditory multi-modal task in Autism Spectrum Disorder (ASD)

Sensory abnormalities are widespread in Autism Spectrum Disorder (ASD). However, their definition is still quite subjective and vague. Here we propose a novel approach for characterization of Autonomic Nervous System responses to sensory stimulation based on electrocardiogram (ECG) assessment. In particular, we develop a preliminary study where autonomic responses of both autistic (ASD = 5) and neurotypical (NT = 5) participants have been evaluated in terms of changes in responsiveness to repeated stimuli. Autonomic control has been estimated via high-frequency heart rate variability (HF-HRV) and low-frequency HRV (LF-HRV). Results show significant differences among groups for the HRV measures (p value = 0.0158), supported by expected changes of HF (p value = 0.0079) and LF (p value = 0.0079) trends over stimulations. We thus conclude that an overall decrease in autonomic arousal can give important insights for devising new habituation metrics in NT and ASD individuals.
Study of Patients Self-Training Influence on Peripheral Neuropathies Diseases Diagnosis through D.I.T.A Device

In our daily life, the sight and the sense of touch play a fundamental role in objects recognitions. This process is helped by the experience: if a subject has already seen or already touched an object in the past, he will recognize it more easily in the future. Following this assumption, the authors of this paper wanted to investigate if the experience can influence the results of a clinical examination where the subject has an active role. The attention was focused on the peripheral neuropathies diagnosis since they require an accurate assessment of several parameters including the tactile sensitivity trend. In other words, if the tests encompass an active role of the subjects, one of the main uncertainties is the self-training that influences the subject responses. This work focuses on the study of this self-training using the D.I. T.A device (Dynamic Investigation Test-rig on hAptics). Results clearly show a fundamental role of priming during "haptic modality": expert subjects, previously experienced with the tests, demonstrated better recognition of the encountered stimuli, compared to novices. Moreover, the results show that the maximum difference between the two groups of subjects is in the first part of the test. An ANOVA analysis was carried out to demonstrate that also the errors between the pins-arrays are affected by the priming.
A Novel and Efficient Real Time Driver Fatigue and Yawn Detection-Alert System

Fatigue among drivers is a major cause of road accidents every year in India. Lack of sound sleep for six to eight hours is one of the primary reasons behind this fatigue. Drivers with sleep deprivation can imbalance the reaction time and decision making when behind the wheels and this can increase the cause of accidents. This type of accidents is more likely to result in death or severe injury as they tend to be in high speed and because of the fact that driver has fallen asleep cannot apply brake or skew to avoid or reduce the impact. Therefore, it is highly essential to create a smart system which can spot and alert the driver of his/her condition. Although there are few solutions proposed in this direction, most of them have not been implemented successfully and many of them only remain in theory. In this research paper, we propose an efficient driver fatigue detection and alert system using mainly open source technologies. We implement and test this system in real time and the results are highly encouraging compared to many existing systems.
A Hybrid Physiological Approach of Emotional Reaction Detection Using Combined FCM and SVM Classifier

Users’ emotional reaction capturing is one of the primary issues for brain computer interface applications. Despite the intuitive feedback provided by the qualitative methods, emotional reactions are expected to be detected and classified quantitatively. Based on the human emotion representation on physiological signal, this paper offers an hybrid approach combining electroencephalogram (EEG) and facial expression together to classify the human emotion. Several advanced signal processing techniques are used to simplify the data and extract the features involving local binary patterns (LBP), Compressed Sensing (CS) and Wavelet Transform (WT). A novel machine learning algorithm, combined Fuzzy Cognitive Maps (FCM) and Support Vector Machine (SVM) are implemented to recognise the feature patterns. The result illustrates a stable emotion classification system with 75.64% accuracy. This design can provide fast and precise emotional feedback, which would further improve the communication between human and computer.
Electrode design on plastic substrates using laser patterned double-sided tape and gold leaf

The production of electrodes and sensors using plastic substrates has increased substantially for the last decades. Although devices are fabricated using plastic substrates suitable for the purpose of each device, it is difficult to find a process that can be used commonly for most types of plastics. In this paper, two kinds of electrode fabrication process that can be applied to most plastic films using kapton double sided tape and gold leaf are proposed. One is for the plastic film with adhesive and the other is for the film without adhesive. First, the kapton double-sided tape was patterned using a CO2 laser. After transfer double-sided tape to desired substrate, release paper was detached and gold leaf was attached on the exposed side of the tape. Then, the tape was appropriately removed according to the presence or absence of the adhesive. After that, the packaging was performed using an adhesive when the used film has adhesive, and otherwise, heat press was used for packaging. These processes have a variety of advantages such as inexpensive, fast-running and commonly applicable to many plastics. To prove its usability, impedance and minimum line width was measured.
Performance analysis of secondary storage media through file systems benchmarking

Efficient performance of a disk I/O operation involves a multitude of factors such as the type of the disk, I/O scheduling, and the type of the file system used. Due to the various types of file systems available, with each having different structure and logic, properties of speed, flexibility, security, size and more, it becomes imperative to have an objective overview of the merits and demerits of each file system according to the needs of the users. In this work, we present a thorough performance evaluation of ext4, NTFS and Btrfs filesystems along with CFQ, NOOP and Deadline I/O schedulers tested on regular hard disk drives and SSDs.
Diagnostic and Prognostic Classification of Brain Disorders Using Residual Learning on Structural MRI Data*

In this work, we study the potential of the deep residual neural network (ResNet) architecture to learn abstract neuroanatomical alterations in the structural MRI data by evaluating its diagnostic and prognostic classification performance on two large, independent multi-group (ADNI and BSNIP) neuroimaging datasets. We conduct several binary classification tasks to assess the diagnostic/prognostic performance of the ResNet architecture through a rigorous, repeated and stratified k-fold cross-validation procedure for each of the classification tasks independently. We obtained better than state of the art performance for the clinically most important task in the ADNI dataset analysis, and significantly higher classification accuracies over a standard machine learning method (linear SVM) in each of the ADNI and BSNIP classification tasks. Overall, our results indicate the high potential of this architecture to learn effectual feature representations from structural brain imaging data.
Parallelized QR decomposition using GPUs

This paper presents the performance results of a parallelized, accelerated eigendecomposition using the block Householder QR decomposition algorithm on a graphic processing unit (GPU). The QR software was developed using NVIDIA’s CUDA parallel programming and computing platform and executed on an NVIDIA Tesla GPU accelerator card. Factors affecting program performance of the GPU-accelerated QR implementation are highlighted with respect to the baseline serial implementation developed in MATLAB and executed on a conventional multi-core processor. We compare results with relevant previously published studies and discuss possible performance bottlenecks and speedups.
ECG-based Random Forest Classifier for Cardiac Arrest Rhythms

Rhythm annotation of out-of-hospital cardiac episodes (OHCA) is key for a better understanding of the interplay between resuscitation therapy and OHCA patient outcome. OHCA rhythms are classified in five categories, asystole (AS), pulseless electrical activity (PEA), pulsed rhythms (PR), ventricular fibrillation (VF) and ventricular tachycardia (VT). Manual OHCA annotation by expert clinicians is onerous and time consuming, so there is a need for accurate and automatic OHCA rhythm annotation methods. For this study 852 OHCA episodes of patients treated with Automated External Defibrillators (AED) by the Emergency Medical Services of the Basque Country were analyzed. Six expert clinicians reviewed the electrocardiogram (ECG) of 4214 AED rhythm analyses and annotated the rhythm. Their consensus decision was used as ground truth. There were a total of 2418 AS, 294 PR, 1008 PEA, 472 VF and 22 VT. The ECG analysis intervals were extracted and used to develop an automatic rhythm annotator. Data was partitioned patient-wise into training (70%) and test (30%). Performance was evaluated in terms of per class sensitivity (Se) and F-score (F1). The unweighted mean of sensitivity (UMS) and F-score were used as global performance metrics. The classification method is composed of a feature extraction and denoising stage based on the stationary wavelet transform of the ECG, and on a random forest classifier. The best model presented a per rhythm Se/F1 of 95.8/95.7, 43.3/52.2, 85.3/81.3, 94.2/96.1, 81.9/72.2 for AS, PR, PEA, VF and VT, respectively. The UMS for the test set was 80.2%, 2-points above that of previous solutions. This method could be used to retrospectively annotate large OHCA datasets and ameliorate the workload of manual OHCA rhythm annotation.
Defect Detection from X-Ray Images Using A Three-Stage Deep Learning Algorithm

Defect detection is a crucial step in the process of manufacturing auto parts such as engines. Air bubbles are common defects in the engine which may result in engine failure leading to the breakdown of the car or even catastrophic accidents. Currently, X-ray images are used for air bubbles detection which adds complexity to the detection task due to the overlay of defects with complex engine 3D structures in 2D X-ray images. In this paper, we propose a three-stage deep learning algorithm to learn various patterns of the bubbles in engines. We then test the algorithm using normal and defected images. The results show that the proposed deep learning method can accurately identify bubbles in the X-ray engine images. This deep learning technique can also be extended to detect other surface level defects such scratches, missing components and physical damage. In this paper, we report that the accuracy of our defect detection method is above 90%.
Nonlinear Trajectory-Tracking Control of a Car-Like Mobile Robot in the Presence of Input Saturations and a Pulse Disturbance

This study aims to attain trajectory-tracking of a car-like mobile robot (CLMR). First, motion planning is developed based on a virtual CLMR with the same kinematics to an actual CLMR. Then, the position error model between the virtual and actual CLMRs is established for the purpose of a controller design. The proposed nonlinear trajectory tracking control (NTTC) scheme is constructed such that the position error of the CLMR (i.e., 2D pose and orientation) asymptotically converges to zero and the system stability is proven based on the Lyapunov stability criteria. Finally, the corresponding simulations including injection of input saturation and a sudden pulse disturbance are conducted to verify the effectiveness, feasibility and robustness of the proposed controller.
Method Selecting Correct One Among Alternatives Utilizing Intuitionistic Fuzzy Preference Relation Without Consensus Reaching Process

The methods with consensus reaching process can obtain a collective solution which is supported by most of decision makers in larger-scale group decision making. However, in case decision makers who could give correct opinions are from the minority, the conventional methods with consensus reaching process can not obtain the correct answer. In this paper, a novel method is developed to tackle this challenge. The decision makers give the opinions utilizing pairwise comparisons of the alternatives from positive and negative views based on intuitionistic fuzzy preference relation. The obtained opinions are translated into intuitionistic fuzzy numbers, and are further grouped and aggregated according to the alternatives. Based on the aggregated intuitionistic fuzzy numbers, the prediction normalized rate is defined and calculated for each alternative, the alternative with the minimal prediction normalized rate is selected as correct one. The experimental results show that the proposed method can obtain the correct answer even when the actual correct opinions are reflected by a small number of decision makers.
Newfangled Approach for Early Detection and Prevention of Ischemic Heart Disease using Data Mining

The significant advances in biotechnology and health science have led to produce large amount of data. As the heart disease causing the major problem because it is very difficult to identify disease based on symptoms. For that we need a lot of experience and knowledge. Finding the exact cause of disease can help to patient cure at early stage of risk level. Ischemic heart Disease is one of the most common causes of death nowadays. In this research work, we have applied data mining classification techniques on the stroke dataset and proposed a recommendation system in which a person can come to know their risk level of IHD. By taking proper cure and treatment can help to survive more years if they have high risk of IHD. We have used five data mining algorithms Logistic Regression, Decision Tree, K nearest Neighbor, Naïve Bayes and SVM on Ischemic Stroke Dataset and got the highest accuracy 97.91% using Support Vector Machine algorithm.
A New Fracture Liaison Service Using the Mobile Application and IoT Sensor

While the clinical design of a Fracture Liaison Service(FLS) has been used as a localized healthcare service in a previous study, thus far there has not been an international mobile application, such as a FLS using smart phones. In addition, we developed a safety monitoring system using IoT sensor for a smart wheelchair. Our FLS is able to give patient various fall-related predictions with a safety monitoring system on this mobile application. The goal of our study is to improve the prevention of secondary fractures from our FLS application. We have developed a Fracture Liaison Service as an Android-OS application and released this service as a secondary fracture prevention program for osteoporotic fracture patients. We have released the final version of the FLS mobile application in Google’s PlayStore. The new model of the FLS mobile application can be practically commercialized, and the effective second-order fracture prevention system is based on an open policy platform. We hope to contribute to the prevention and management of osteoporotic fractures and osteoporosis worldwide via this FLS mobile application. In the future, an intelligent personal FLS is definitely possible, by applying a Medical AI based on a huge DB.
Network Planning for Indoor Joint LTE and WLAN Networks

This paper presents a formulation of the network planning problem which effectively models the indoor deployment of joint LTE and WLAN small cells as an optimization problem. This problem determines the position, frequency and bandwidth allocation of the network by maximizing the capacity and coverage, and minimizing the interference as well as the cost of the network. The additional frequencies available to the network are incorporated into the model, and measures are taken to avoid the size of the problem from getting too large. By including the choice of frequency as a degree of freedom causes the interference buffer to halve per additional LTE frequency. The formulation considerably decreases the interference in the network when compared to other network designs.
Feature Space Reduction for Single Trial EEG Classification based on Wavelet Decomposition

In contrary to recent signal/information processing advancements, human brain remains the most intriguing signal processing unit in existence with inconceivable capabilities to fuse various multi-modal signals, adaptively and in real-time fashion. To connect brain with the outer world, brain computer interfacing (BCI) via Electroencephalography (EEG) signals has received extensive attention. Extracting informative and discriminating features from EEG signals and decomposing the recorded signals into their underlying components is believed to yield compromising results. Different algorithms, therefore, are recently proposed combining signal decomposition techniques (e.g., spectral filterbanks, and Wavelet decomposition) with feature extracting methodologies (e.g., common spatial patterns (CSP), and Riemannian manifold learning). Although coupling filterbanks and Wavelet with the CSP has been investigated, to best of our knowledge, the potentials of coupling Wavelet with Riemannian manifold learning are not yet studied. The paper addresses this gap. In particular, we propose a level-based classification approach that couples the Wavelet decomposition with Riemannian manifold spatial learning (WvRiem). In the proposed WvRiem framework, the EEG signals are decomposed into several components (levels) and then spatial filtering via Riemannian manifold learning is performed on the best level which yields the most discriminating features. The proposed WvRiem is evaluated on the BCI Competition IV2a dataset and noticeably outperforms its counterparts.