Miracle-dz/UniFAR-FTUs huggingface dataset:
Sound Detection Technology and Heron’s Law for Secure Monitoring System

The intruder’s image and sound detection did not employ effetely in the various types of existing monitoring systems. The monitoring systems capture all wanted, unwanted objects in the monitoring zone, and do not detect objects in the blind monitoring zone. Therefore, this paper aims to increase the accuracy of monitoring system by detecting only the desired object’s image, sound or both accurately. The proposed system used the sound detection capability in order to increase the system accuracy. Different sound detection algorithms are used to detect the sound that obtained by various sensors, then Heron’s law is implemented to avoid the sound from external sources and prevent to track the undesired objects. The obtained results increased the detection accuracy by 30% comparing to the existing systems results. The proposed system provides the ability to detect the desired intruder inside and outside monitoring zone and avoiding other small objects, which curtail the tracking errors rate by approximately 11%. The various sound detection algorithms implemented in the systems solve the problem of detecting the objects in the blind monitoring zone, where a location is not needed to track objects. Likewise, the Herons law is implemented to avoid the external source as well as to track the smaller object except for intruders.
A Thalamo-Cortex Microcircuit Model of Beta Oscillations in the Parkinsonian Motor Cortex*

Exaggerated beta oscillations (~13-30 Hz) observed in the cortical areas of the brain is one of the characteristics of disrupted information flow in the primary motor cortex in Parkinson’s disease (PD). However, the mechanism underlying the generation of these enhanced beta rhythms remains unclear. The thalamo-cortex microcircuit (TCM) contains reciprocal synaptic connections that generate low frequency oscillations in the microcircuit in healthy conditions. Recent studies suggest that alterations in synaptic connections both within and between the cortex and thalamus play a critical role in the generation of pathological beta rhythms in PD. In this study, we examine this hypothesis in a spiking neuronal network model of the TCM. The model is compared and validated against neural firing patterns recorded in rodent models of PD from the literature.
On Fairness Optimization for Cloud Radio Access Networks

Cloud Radio Access Network (C-RAN) has been proposed as an emerging technology to meet the ever-increasing data traffic demands in wireless networks with flexibility in resource management and network operation. Due to the physical limitation for network providers, resource allocation is an important topic in C-RAN. In this paper, we propose a novel power allocation strategy that enhances the fairness of resource allocation from the user side. The approach maximizes the Offered Capacity to requested Traffic Ratio (OCTR) for user equipment (UE) in the worst link. The numerical result shows that the proposed algorithm can ensure the fairness of resource allocation for users and converge fast.
Evaluation of Active Common Mode Filter Utilization for Size Optimization of a 20 kW Power Factor Correction

This paper presents active filtering of common mode (CM) noise for size reduction of an electromagnetic interference (EMI) filter used in a high frequency 20 kW three-phase power factor correction (PFC). Increasing the switching frequency helps reducing the size of reactive components; however, higher switching frequency leads to higher amplitude of harmonics in the EMI range (>150kHz). Adding an auxiliary winding to the CM inductor is studied in depth to achieve active CM filtering which shows more attenuation of the CM current, and consequently size reduction of the EMI filter. Furthermore, in this paper, CM noise is generalized by introducing the parasitic capacitors of the converter into the modeling. The model is verified by a 20 kW three-phase PFC experimental setup. Finally, the efficiency and effectiveness of the designed active CM filter is verified by a comprehensive simulation.
Solid State Transformer based on Grain-Oriented Electrical Steel Wound Cores

Considering the progress in semi-conductor technology and magnetic core material performances, an increased attention is paid to the so called Solid-State Transformers (SST). Besides the ability to monitor the direction of the power flow compared to classical 50/60 Hz transformers, these devices, composed of power converters placed each side of a high frequency power transformer, offer many advantages such as small size and low weight.The main focus of this paper is to report about the transformer itself as a key element of the SST. Thin grain oriented electrical steel (GOES) has been chosen for building the core of the transformer. Reasons for this choice are the high saturation polarization capability even at high temperature and its availability on a large scale compared to amorphous and nanocrystalline materials used in current SSTs; thus thin GOES seems to be a good alternative to achieve a technological and economical balance by playing on both frequency and working induction levels to reduce the size of the transformer.The aim of the study is to assess the limits of the GOES use in terms of maximum operating frequency and flux density for power electronic like voltages, offering both high power density and efficiency in reduced volume.The primary and secondary windings of the transformer are respectively connected to standard electronic converters that deliver square shaped voltages. Losses under such regimes far from classical sinus cases have to be assessed. They are the root cause for heating of the device and shall be known before energizing the transformer. Experimental results and finite-element simulation performed on the core structure, show that dynamic core losses are lower for square wave voltages than for sine waves at the same peak flux density and frequency.The maximum winding operating temperature obtained at thermal equilibrium fixes the operating limits of the device. Then to avoid issues due to eventual overheating when energizing the transformer, a thermal model suitable for predicting the temperature at various transformer locations has been developed and used; results are then described.
CHAMPS: Cardiac health Hypergraph Analysis using Multimodal Physiological Signals

State-of-the-art methods have reported various features for the non-invasive screening of Coronary Artery Disease (CAD). In this paper, we propose a novel approach to represent such features extracted from multiple physiological signals using hypergraph. Firstly, the biological and statistical interconnections among Photoplethysmogram (PPG) and Phonocardiogram (PCG) features are exploited by connecting them as hyperedges. Then, metadata features (age, weight and height) are connected using hyperedges with the rest of the features. Hypergraph based formalism provides greater flexibility in capturing the interrelationships among different features as compared to the graph counterpart. Finally, hypergraph laplacian as a derived feature is applied to classify CAD against non-CAD. The proposed method is validated on PPG and PCG data collected in a hospital setup. The results reveal 98% Sensitivity and 82% Specificity, leading to 92% classification accuracy.
Analysis of Volume Conduction Effects on Different Functional Connectivity Metrics: Application to Alzheimer’s Disease EEG Signals

The aim of this study was to evaluate the effect of volume conduction on different connectivity metrics: Amplitude Envelope Correlation (AEC), Phase Lag Index (PLI), and Magnitude Squared Coherence (MSCOH). These measures were applied to: (i) a synthetic model of 64 coupled oscillators; and (ii) a resting-state EEG database of 72 patients with dementia due to Alzheimer’s disease (AD) and 37 cognitively healthy controls. Our results revealed that AEC and PLI are weakly influenced by the simulated volume conduction compared to MSCOH, although the three metrics are not immune to this effect. Furthermore, results with real EEG recordings showed that AD patients are characterized by an AEC increase in δ frequency band and widespread connectivity decreases in α and β1 bands. These coupling changes reflect the abnormalities in spontaneous EEG activity of AD patients and might provide further insights into the underlying brain dynamics associated with this disorder.
Replanting Your Forest: NVM-friendly Bagging Strategy for Random Forest

Random forest is effective and accurate in making predictions for classification and regression problems, which constitute the majority of machine learning applications or systems nowadays. However, as the data are being generated explosively in this big data era, many machine learning algorithms, including the random forest algorithm, may face the difficulty in maintaining and processing all the required data in the main memory. Instead, intensive data movements (i.e., data swappings) between the faster-but-smaller main memory and the slowerbut-larger secondary storage may occur excessively and largely degrade the performance. To address this challenge, the emerging non-volatile memory (NVM) technologies are placed great hopes to substitute the traditional random access memory (RAM) and to build a larger-than-ever main memory space because of its higher cell density, lower power consumption, and comparable read performance as traditional RAM. Nevertheless, the limited write endurance of NVM cells and the read-write asymmetry of NVMs may still limit the feasibility of performing machine learning algorithms directly on NVMs. Such dilemma inspires this study to develop an NVM-friendly bagging strategy for the random forest algorithm, in order to trade the “randomness” of the sampled data for the reduced data movements in the memory hierarchy without hurting the prediction accuracy. The evaluation results show that the proposed design could save up to 72% of the write accesses on the representative traces with nearly no degradation on the prediction accuracy.
The Complexity of Dreams: a Multiscale Entropy Study on Cardiovascular Variability Series

Uncovering the physiological correlates of dreams is one of the most ambitious aim of multidisciplinary neuroscientific research. Here we investigated Autonomic Nervous System (ANS) dynamics associated with a dream recall, with a particular focus on the complexity assessment on cardiovascular control. We recorded electrocardiogram and arterial blood pressure signals from eight healthy subjects during rapid-eye-movement sleep before awakenings. Recordings were then split into two groups: the ones with a dream experience, and the ones without recall of dream experiences. The randomness of cardiovascular variability series was assessed through Sample Entropy metrics, which did not show any statistical difference between groups. On the other hand, a multiscale complexity analysis based on Distribution Entropy and Fuzzy Entropy revealed that a higher cardiovascular complexity is associated with a dreaming experience.
Emotional stimulation during motor exercise: An integration to the holistic rehabilitation framework

Over the recent years, plenty of studies have been carried out concerning disabilities and rehabilitation. However, very few of them have focused on proposing or creating a holistic framework meant to maximize the merits of rehabilitation treatments. More importantly, an even smaller number has taken into consideration the immense impact of emotions and the crucial role they display concerning patients’ performance during rehabilitation. The purpose of this study is to examine and highlight the direct connection of patients’ performance quality with an artificially induced positive or negative emotional state. For that reason, we conducted an experiment regarding the convergence of emotions with the motor exercise. Specifically, our study was conducted with 45 participants and the GAPED database was employed as an emotional classifier. The effect of the visual stimulus was combined with a simple bicep exercise which introduces the motor element in our study. Statistical analysis of the yielding EMG & Acellerometer signals demonstrated a considerable difference in the quality of the physical activity of each subject induced by different emotional triggers. As a result, it is pivotal to integrate a holistic approach to rehabilitation in future clinical trials, aiming to reinforce the patient involvement and engagement process and thus the efficacy of the treatment.
On Game Theory-Based Coordination Schemes for Mobile Small Cells

The emergence of the 5G era promises ubiquitous, reliable and high-quality telecommunication service provision. The accomplishment of this promise is not possible if the reliable and efficient service of the mobile users at the cell edges is not guaranteed. For this purpose, the effect of the JT CoMP (Joint Transmission Coordinated Multipoint) technique in a small cell C-RAN (Cloud Radio Access Network) network combined with a coalition formation algorithm from a system level perspective is examined. JT CoMP mitigates the inter-cell interference to improve the coverage of high data rate services, cell-edge throughput, and system capacity, while the algorithm provides the clustering considering at the same time multiple network performance constraints. The proposed scheme's performance metrics are then compared with the corresponding metrics of the conventional JT CoMP and the non-CoMP case, confirming its superiority regarding the aforementioned challenges.
Low-dose CT Denoising Using Edge Detection Layer and Perceptual Loss

Low-dose CT imaging is a valid approach to reduce patients’ exposure to X-ray radiation. However, reducing X-ray current increases noise and artifacts in the reconstructed CT images. Deep neural networks have been successfully employed to remove noise from low-dose CT images. This study proposes two novel techniques to boost the performance of a neural network with minimal change in the complexity. First, a non-trainable edge detection layer is proposed that extracts four edge maps from the input image. The layer improves quantitative metrics (PSNR and SSIM) and helps to predict a CT image with more precise boundaries. Next, a joint function of mean-square error and perceptual loss is employed to optimize the network. Using the perceptual loss helps to preserve structural detail; however, it adds check-board artifacts to the output. The proposed joint objective function takes advantage of the benefits offered by each loss. It improves the over-smoothing problem caused by mean-square error and the check-board artifacts caused by perceptual loss.
Conceptualization of an ICU Infrastructure for Simulation Based Education in Medical Engineering & eHealth

The use of simulation-based training is gaining importance in medical as well as engineering related education. The complex environment of an intensive care unit is characterized by a high need of interaction between clinical as well as technical components and views. These diverse interactions and the connected requirements are the focus for the presented simulation infrastructure, enabling research, education and training. The presented concept of a modular and flexible intensive care environment provides a high degree of interoperability and flexibility for individual research questions and full support of connectivity for typical clinical workflows. The presented simulation and testing bed will allow both, education for engineering and medical students using patient simulation and simultaneous data transfer as well as research on medical workflows, infrastructural demands and connectivity conformance questions.
A visual-haptic neurofeedback training improves sensorimotor cortical activations and BCI performance *

Neurofeedback training (NFT) could provide a novel way to investigate or restore the impaired brain function and neuroplasticity. However, it remains unclear how much the different feedback modes can contribute to NFT training. Specifically, whether they can enhance the cortical activations for motor trainingƒ To this end, our study proposed a brain-computer interface (BCI) based visual-haptic NFT incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. By comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8-10Hz, alpha_2: 11-13Hz, beta_1: 15-20Hz and beta_2: 22-28Hz) lateralized relative event-related desynchronization (lrERD) patterns were significantly enhanced after NFT. And the classification performance was also significantly improved, achieving a ~9% improvement and reaching ~85% in mean classification accuracy from a relatively low MI-BCI performance. These findings validate the feasibility of our proposed visual- haptic NFT approach to improve sensorimotor cortical activations and BCI performance during motor training.
Impact of Hysteresis Control and Internal Thermal Mass on the Energy Efficiency of IoT-Controlled Domestic Refrigerators

The paper considers the impact of various temperature hysteresis bands on the projected annual energy consumption of a domestic refrigerator when operating empty of product and with an internal product (comprising of 10L of water). Measurements from an IoT system employing a NodeMCU-based Generalized Predictive Control scheme, are used to support the study. A ThingSpeak platform and Smart Wi-Fi plug is used to realize the different temperature hysteresis bands whilst maintaining a given mean nominal internal temperature. Experimental measurements are taken from an IGENIX IG 3920 refrigerator. Results show that by judicious choice of hysteresis band, which is shown to be dependent on the internal product characteristics, annual energy savings of up to 20% can be obtained compared to worst case fixed hysteresis band scenarios.
Design of a myoelectric 3D-printed prosthesis for a child with upper limb congenital amputation

For an early intervention with children, a prosthetic hand that is affordable, light-weight, and easy to wear and use is vital. Commercial prosthetic hands for children are often too expensive, burdensome and difficult to use. In this research, we attempt to address these issues with a novel design for a prosthetic hand for children. The proposed prosthesis implements two important features: the use of sound biofeedback and a soft socket with in-liner electrodes. We hypothesize that these features will make the hand prosthesis easy to use, lightweight and affordable, improving the control performance and increasing the acceptance rate. Preliminary experiments showed that the proposed prosthesis can be successfully controlled by a congenital amputation.
Modeling Ransomware Spreading by a Dynamic Node-Level Method

Ransomware attacks are becoming increasingly sophisticated, more damaging to victims and more challenging to prevent. In this paper, a dynamic modelling method is used to study the spreading behaviors of ransomware. The dynamic state of each network node is assumed to be statistically dependent of the states of its neighboring nodes. By incorporating the full topology of the propagating network via its adjacent matrix, a novel node-based model is developed which is also suitable over generic connected networks. The dynamics of the model is theoretically analyzed. Especially, the global stability of its trivial equilibrium is theoretically confirmed depending on a sufficient condition including the leading eigenvalue of the adjacent matrix. We also present results from extensive numerical simulations designed for some specific networks, exhibiting different dynamic properties over distinct networks. Consequently, we suggest that ransomware spreading can be availably contained by properly adjusting the network structure so that its largest eigenvalue satisfies the desired requirement.
Water-Table Detection in a Hyper-Arid Region

A finite-difference time-domain (FDTD) method, accelerated by imposing surface impedance boundary condition (SIBC), is implemented to compute the scattering fields from a hyper-arid soil above an aquifer. An estimation method of water-table depth beneath ground surface is proposed, which is based on the interference pattern of backscattering fields at normal incidence.
Unsupervised approach for detecting shilling attacks in collaborative recommender systems based on user rating behaviours

Collaborative recommender systems have been known to be extremely vulnerable to shilling attacks. To prevent such attacks, many detection approaches including supervised and unsupervised have been proposed. However, the supervised approaches are only suitable for detecting known types of attacks and the unsupervised approaches require a priori knowledge to ensure the detection performance. To address the limitations, the authors propose an unsupervised approach for detecting shilling attacks based on user rating behaviours. They first use Gibbs latent Dirichlet allocation model to extract latent topics of user preferences from user rating item sequences, then they use mixture transition distribution model to construct the user's preference model and present several metrics to capture the diversity between genuine and attack users in rating behaviours. In the case of unknown attack size, the number of attack users is obtained by analysing the critical point of rating behaviour suspicious degrees between genuine and attack users, and based on which the attack users are identified. The experimental results on the MovieLens 1 M dataset show that the proposed approach outperforms the baseline methods in terms of recall and precision metrics.
Characteristic changes in the EEG signals between microsleeps and preceding responsive states

This work aims at identifying characteristic features of EEG to demarcate a microsleep from preceding responsive states. The EEG signals, after reference electrode standardization technique (REST) re-referencing, were processed through a time-varying general linear Kalman filter (TVGLKF) to derive time-varying auto-regressive (TVAR) parameters. The time-varying effective connectivity measure of orthogonal partial directed coherence (OPDC) was obtained for every instant at 256 Hz. Effective connectivity matrices formed using these OPDC measures, with the scalp electrodes as nodes were processed further using graph theory. Community-based measures were investigated and statistical significances compared. Non-parametric Wilcoxon signed rank test was used for significance analysis, with Cohen-type and Common Language effect size (CLES) as measures of effect sizes. The results showed a decrease in directional modularity from anterior to posterior, in theta, alpha, and beta bands in microsleeps. The alpha band showed the highest significance with a Cohen-type effect size of 1.25 and a median percentage difference of 23% across subjects, with a range of 13-28%. Flexibility and integration also decreased with average percentage of 25% (17–35%) and 20% (16–32%), respectively, while recruitment increased on an average of 11% (3–16%), wherever significant across all bands. These community-based measures can help characterize and explain changes in brain mechanisms, and can also serve as potential biomarkers for microsleep detection.