Miracle-dz/UniFAR-FTUs huggingface dataset:
An Alternative Partial Discharge Monitoring System for High Voltage Equipment

Partial discharge monitoring systems are important tools for the predictive diagnosis of various high voltage equipment, being applied to evaluate the condition of the insulation that integrates each of these machines. In the market there are some systems, however they are high cost due to the limited number of companies specialized in this type of measurement and the fact that they develop dedicated hardware for the necessary data acquisition and processing. In an attempt to find a low cost solution for partial discharge monitoring, we developed a solution capable of using readily available low cost oscilloscope hardware. In this instance we opted to use two handheld oscilloscopes, one with 4 channels and another with 8. The development allows for both options to be used in the sampling and processing of partial discharge signals, as well as the seamless integration of data provided from other systems. The system includes a database, processing nodes and a web server. Partial discharge data and operational status of any given asset is visualized in dashboards and analysis tools available in the plant internal network. The new system was successfully implemented in a thermoelectric and a hydroelectric power plant and has been operating properly for a long period of time, corroborating its effectiveness and reliability.
Myocardial Approximate Spin-lock Dispersion Mapping using a Simultaneous T2 and TRAFF2 Mapping at 3T MRI

Ischemic heart disease (IHD) is one of the leading causes of death worldwide. Myocardial infarction (MI) represents a third of all IHD cases, and cardiac magnetic resonance imaging (MRI) is often used to assess its damage to myocardial viability. Late gadolinium enhancement (LGE) is the current gold standard, but the use of gadolinium-based agents limits the clinical applicability in some patients. Spin-lock (SL) dispersion has recently been proposed as a promising non-contrast biomarker for the assessment of MI. However, at 3T, the required range of SL preparations acquired at different amplitudes suffers from specific absorption rate (SAR) limitations and off-resonance artifacts. Relaxation Along a Fictitious Field (RAFF) is an alternative to SL preparations with lower SAR requirements, while still sampling relaxation in the rotating frame. In this study, a single breath-hold simultaneous $T_{RAFF2}$ and $T_{2}$ mapping sequence is proposed for SL dispersion mapping at 3T. Excellent reproducibility (coefficient of variations lower than 10%) was achieved in phantom experiments, indicating good intrascan repeatability. The average myocardial $T_{RAFF2}, T_{2}$, and SL dispersion obtained with the proposed sequence $(68.0\pm 10.7$ ms, $44.0\pm 4.0$ ms, and $0.4\pm 0.2 \times 10^{-4} s^{2}$, respectively) were comparable to the reference methods $(62.7\pm 11.7$ ms, $41.2\pm 2.4$ ms, and $0.3\pm 0.2\mathrm{x} 10^{-4}s^{2}$, respectively). High visual map quality, free of $B_{0}$ and $B_{1}^{+}$ related artifacts, for $T_{2}, T_{RAFF2}$, and SL dispersion maps were obtained in phantoms and in vivo, suggesting promise in clinical use at 3T. Clinical relevance — and imaging promises non-contrast assessment of scar and focal fibrosis in a single breath-hold using approximate spin-lock dispersion mapping.
A Molecular dynamics study: Sintering behaviors of nanoparticles of size differentiation under varied pressure-assisted conditions

In recent years, the power device industry has developed at a high speed. The development trend of high power and high density of power chips has put forward higher requirements on the service temperature and connection strength of power chips.1-2 Nanoscale metal particles, as a material bonded at low temperature and served at high temperature, had high electrical conductivity, good fatigue resistance, and excellent corrosion resistance.3-4 Therefore, the sintering performance of NPs has received extensive attention in the field of microelectronic packaging. Previous experimental and theoretical studies have focused on thermal sintering, 5-8 but most flexible substrates could not serve at high temperature, and for this reason, pressure-assisted sintering technology has been proposed. The study of atomic motion and sintering mechanism of NPs at the microscopic scale could improve the performance of NPs and optimize their applications. In this paper, a molecular dynamics (MD) approach was used to simulate the process of pressure-assisted sintering of two Ag NPs of different sizes at 450K. Tensile simulations were conducted with the sintered structure to evaluate the necking strength. The sintering mechanism and strength of NPs of different sizes sintered under different pressures were analyzed. It concluded that, for the pressureless sintering or low-pressure sintering, size differentiation design for the particles pair improved the sintering performance and interconnection strength. While the improvement was found to be greatly reduced when the sintering pressure was increased. Further research reveals a transformation of material transport mechanism dependent upon the combining of pressures and sizes of the sintering process. Increasing the sintering pressure to a certain threshold will induce the transport mechanism of material to transform from surface diffusion to plastic deformation.
Detection and Classification of Myocardial Infarction Transmurality Using Cardiac MR Image Analysis and Machine Learning Algorithms

The presence of abnormalities when the left ventricle is deformed is related to the patients' prognosis after a first myocardial infarction. These deformations can be detected by performing a cardiac magnetic resonance (CMR) study. Currently, late gadolinium enhancement (LGE) is considered to be the gold standard when performing CMR imaging. However, CMR with LGE overestimates infarct size and underestimates recovery of dysfunctional segments after myocardial infarction. Based on this statement, the objective is to detect, characterize, and quantify the extent of myocardial infarction in patients with cardiac pathologies, using parameters derived from CMR, in order to obtain greater precision in patients' recovery predictions than when only studying LGE images. For this purpose, we studied the infarct presence and extension from a total of 105 images from 35 patients, and calculated myocardium strain and torsion to characterize and quantify the affected tissue. A total of twenty-one parameters were selected to create predictive models. Moreover, we compared two feature extraction methods, and the performance of five machine learning algorithms. Results show that both temporal and strain parameters are the most relevant to detect and characterize the extent of myocardial infarction. The use of imaging techniques and machine learning algorithms have great potential and show promising results when it comes to detecting the presence and extent of myocardial infarction. The current study proposes a novel approach to detect, quantify, and characterize cardiac infarction by using strain and torsion parameters from different CMR images and different Machine Learning algorithms. This would potentially overcome LGE, the current state of the art technique, in estimating the extension of damaged tissue and enable an objective diagnosis and clinical decision.
Blinking characterization for each eye from EEG analysis using wavelets

Eye blinks can be used to perform monitoring tasks such as drowsiness detection, attention measurement or other biological measurement mainly using video data. With the developement of brain computer interfaces (BCI) eye movements and blinks could be used to perform control tasks such as pointer activation or communications. This work aims to prove that it is possible to characterize eye blinks for each eye separately using only electroencephalography (EEG) signal acquired through non invasive portable device and dry electroencephalography.
A Real-Time Energy Monitoring System for an MRI Hybrid Ablation System

The MRI hybrid ablation system is an approach to use the MR (magnetic resonance) scanner's radiofrequency amplifier itself as power source for ablation. Hereby, an electrode is connected to the MR internal radiofrequency amplifier. An average RF power is provided through a train of short RF pulses, which is sufficient to thermally destroy tissue. However, ablation with too high power values can cause tissue carbonizations, thus impeding the ablation procedure. Therefore, monitoring of the energy and the power absorbed inside the tissue is necessary. For this purpose, a measurement system was designed to measure the energy applied to the tissue when using the concept of an MRI hybrid ablation system. The system consists of a dual-directional coupler, RF-to-RMS sensors, and a microcontroller. The gradient calculation of the measured energy curve provides information about the absorbed RF power in the tissue. Validation measurements of the system were performed and compared with measurements from the MR-internal power measurement system. The energy monitoring system was also tested in an ablation experiment with ex-vivo animal tissue using the MRI hybrid ablation system. The measurements showed that the applied RF power can be monitored in real-time. It has been shown that the mean RF power absorbed in the patient decreased during an ablation procedure due to an occurring impedance mismatch and tissue changes. In further work, the influence of the monitoring system on the quality of the MR images should be investigated. Clinical relevance— This paper demonstrates an energy monitoring system for an RF ablation system, which can be used inside an MR environment.
Self-applied ear-EEG for sleep monitoring at home

High quality sleep monitoring is done using EEG electrodes placed on the skin. This has traditionally required assistance by an expert when the equipment needed to mounted. However, this creates a limitation in how cheap and easy it can be to record sleep in the subject's own home. Here we present a data set of 120 home recordings of sleep, in which subjects use self-applied ear-EEG monitoring equipment. We compare this data set to a previously recorded data set with both ear-EEG and polysomnography, which was applied by an expert. Clinical relevance — On all tested metrics, self applied sleep recordings behaved the same as expert applied. This indicates that ear-EEG can reliably be used as a home sleep monitor, even when subjects apply the equipment themselves.
Contributions to Energy Saving in Wastewater Treatment Plants

In this paper, a system to stimulate in extremely low frequency (ELF) electric field the microbiological activity of the microbial flora in the active sludge of a wastewater treatment plant is presented. The designed and realized system was tested in industrial environment with and without ELF electric field stimulation. By comparing the two cases for the same degree of pollutants elimination from the wastewater, it resulted that the ELF electric field application significantly reduces the processing times in the biological treatment stages (to approximately half). Thus, the energy consumption in the biological treatment stage is reduced by approx. 50%, representing approx. 30% of the wastewater treatment plant energy consumption.
Prehensile Robotic pick-and-place in clutter with Deep Reinforcement Learning

In this paper, we present a self-learning deep reinforcement learning-based framework for industrial pick-and-place tasks in a cluttered environment through intelligent prehensile robotic grasping. This approach aims to enable agents learn and perform pick and place regular and irregular objects in clutter through robotic grasping in order to enhance both quantity and quality in various industries. In order to do so, we design a Markov decision process (MDP) and deploy a model-free off-policy temporal difference algorithm Q-learning. We utilize end-to-end DenseNet-121 architecture fully convolutional network (FCN) in extended format for Q-function approximation. A pixelwise parameterization scheme is designed to calculate the pixelwise maps of action values. Rewards are allocated according to the success of the action performed. The proposed approach doesn’t require any domain specifications, geometrical knowledge of objects or any extraordinary resources such as huge datasets or memory requirements. We have presented the training and testing results of our approach compared to its different variants and random density clutter sizes.
Hardware Implementation of Secure scalar multiplication Algorithm

ECC (Elliptic Curves Cryptosystems) are the most popular and efficient encryption systems used since 1985. They are being increasingly integrated in high security applications such as RFID devices, smart cards, bank-cards, etc. To be suitable for these applications, a lightweight implementation of the scalar multiplication operation is required. To achieve this goal, it is necessary to choose the parameters responsible for the cost reduction of the elliptic curve implementation. In this paper, we will first specify the elliptic curve parameters that are responsible for the optimization of the implementation of the scalar multiplication algorithm. Then, we will choose the most secure scalar multiplication algorithm to be implemented for security applications. The hardware implementation of our ECC architecture, using Virtex-7 board, requires a number of 6549 gates as total area requirement.
Accurate Continuous Prediction of 14 Degrees of Freedom of the Hand from Myoelectrical Signals through Convolutive Deep Learning

Natural control of assistive devices requires continuous positional encoding and decoding of the user's volition. Human movement is encoded by recruitment and rate coding of spinal motor units. Surface electromyography provides some information on the neural code of movement and is usually decoded into finger joint angles. However, the current approaches to mapping the electrical signal into joint angles are unsatisfactory. There are no methods that allow precise estimation of joint angles during natural hand movements within the large numbers of degrees of freedom of the hand. We propose a framework to train a neural network from digital cameras and high-density surface electromyography from the extrinsic (forearm and wrist) hand muscles. Furthermore, we show that our 3D convolutional neural network optimally predicted 14 functional flexion/extension joints of the hand. We found in our experiments (4 subjects; mean age of $26\pm 2.12$ years) that our model can predict individual sinusoidal finger movement at different speeds (0.5 and 1.5 Hz), as well as two and three finger pinching, and hand opening and closing, covering 14 degrees of freedom of the hand. Our deep learning method shows a mean absolute error of $2.78\pm 0.28$ degrees with a mean correlation coefficient between predicted and expected joint angles of 0.94, 95% confidence interval (CI) [0.81, 0.98] with simulated real-time inference times lower than 30 milliseconds. These results demonstrate that our approach is capable of predicting the user's volition similar to digital cameras through a non-invasive wearable neural interface. Clinical relevance— This method establishes a viable interface that can be used for both immersive virtual reality medical simulations environments and assistive devices such as exoskeleton and prosthetics
Hybrid Intrusion Detection Technique for Malicious Network Attacks with Machine Learning

The number of malicious network attacks is increasing due to the corresponding increase in the number of Internet users. This trend has enabled attackers to exploit the vulnerabilities of network-connected devices to execute successful attacks. In this paper, a hybrid intrusion detection technique using machine learning algorithms is proposed. First, feature selection is performed to reduce the complexity of the model based on the size of the input. The selected features are used as input to the model for classification. To classify the observations as either anomalous or normal connections, pattern matching is carried out to match each instance of a network connection to a class label to optimise the performance of the classification algorithm. Furthermore, our model was evaluated using the KDD'Cup99 and NSL-KDD datasets, and achieved average accuracy and detection rate of above 99% with significantly low false positive and error rates as against comparative approaches.
Dynamic Resource Scheduling and Frequency Scaling in NOMA-Based Multi-access Edge Computing System

Merging Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) into the sixth generation (6G) Internet of Things (IoT) can satisfy the computationally intensive task's requirement of extensible and low-energy consumption service. However, it is challenging to assigning task in MEC system due to that the channel transforms over time in dynamically varying network environments. In this paper, we propose a dynamic resource scheduling and frequency scaling algorithm (DRSFS) to allocate tasks and MEC frequency optimally. On the basis of Lyapunov optimization technique, DRSFS converts the long-range random optimization problem to a suite of determinate sub-problems and obtain the optimal solution. DRSFS can obtain an optimal offload strategy by utilizing dynamic programming theory, which can be verified by the effects of different parameters. The simulation experiment results shows the superiority of DRSFS by comparing it with other two baseline algorithms in the field of the energy consumption and the queue length.
CCNET: Cross Coordinate Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading

With the rapid development of the world economy and increasing improvement of people's living standards, the number of diabetic patients has been growing quickly. Meanwhile, the complications of diabetes especially retinopathy have been affecting their daily life seriously. The only way to prevent it from getting worse and even leading to blindness is to make corresponding diagnosis as early as possible. However, it's extremely impossible for professionals to diagnose all the patients through their fundus images. It couldn't be better to solve the problem by automatic systems, so we present a novel network to learn the features of diabetic retinopathy (DR) and its complication diabetic macular edema (DME) and the relationship between them, focus on some vital areas in the pictures and eventually obtain the grades of the two diseases at the same time. Experimental results further prove the effectiveness of our proposed module comparing to the only joint grading network before.
Deciphering Stomach Myoelectrical Slow Wave Conduction Patterns via Confocal Imaging of Gastric Pacemaker Cells and Fractal Geometry

Interstitial Cells of Cajal (ICC) are specialized gastrointestinal (GI) pacemaker cells that generate and actively propagate slow waves of depolarization (SWs) of the muscularis propria. SWs regulate the motility of the GI tract necessary for digestion, absorption of nutrients, and elimination of waste. Within the gastric wall, there are three main inter-connected layers of ICC networks: longitudinal muscle ICC (ICC-LM), myenteric plexus ICC (ICC-MP) & circumferential muscle (ICC-CM). Fractal structural parameters such as Fractal Dimension (FD), Lacunarity and Succolarity, have many advantages over physically-based parameters when it comes to characterizing the complex architectures of ICC networks. The analysis of networks of ICC throughout the proximal and distal murine gastric antrum with the FD and Lacunarity metrics was previously performed. Although the application of Succolarity is relatively nascent compared to the FD and Lacunarity; nevertheless, numerous studies have demonstrated the capability of this fractal measure to extract information from images associated with flow by which neither the FD nor Lacunarity are capable of discerning. In this study, Succolarity analysis of ICC-MP and ICC-CM networks were performed with confocal images taken across the proximal and distal murine antrum. Our findings demonstrated the Succolarity of ICC-MP and ICC-CM varied with directions and antral regions. The Succolarity of ICC-MP did not vary considerably with direction, however, Succolarity was higher in the aboral direction with $0.2113 \pm 0.1589$, and $0.0637 \pm 0.0822$ in the proximal and distal antrum, respectively. The overall Succolarity of ICC-MP was significantly higher than that of ICC-CM in the proximal antrum ($0.1580\pm 0.1325$ vs $0.0008\pm 0.0007,\ \mathrm{p}-\text{value}=0.0008$) and in the distal antrum ($0.0449 \pm 0.0409$ vs $0.0006\pm 0.0010, \mathrm{p}-\text{value} =0.003$). Clinical Relevance—Modeling SWs conduction patterns via image analysis of detailed ICC networks help to facilitate an improved understanding of the mechanisms underpinning GI myoelectric activity and the diseases associated with its dysfunction
Power Management of a Laboratory Scale Wind-PV-Battery based LVDC Microgrid

The design, development and control of wind and PV fed low-voltage direct current (LVDC) microgrid (MG) along with a battery storage unit (BSU) is presented. In order to operate both the generating units at optimum power, maximum power point tracking (MPPT) algorithms are adapted. A unified controller for the bidirectional DC-DC converter is utilized for DC bus voltage regulation along with charging/discharging of the battery. The whole system is implemented in real-time digital platform and tested during different operating conditions. The coordination among distributed energy resources (DERs), proportionate load sharing and seamless mode transition is achieved in the developed autonomous LVDC microgrid.
Data Quality Check in Cancer Imaging Research: Deploying and Evaluating the DIQCT Tool

Data harmonization is one of the greatest challenges in cancer imaging studies, especially when it comes to multi-source data provision. Properly integrated data deriving from various sources can ensure data fairness on one side and can lead to a trusted dataset that will enhance AI engine development on the other side. Towards this direction, we are presenting a data integration quality check tool that ensures that all data uploaded to the repository are homogenized and share the same principles. The tool's aim is to report any human-induced errors and propose corrective actions. It focuses on checking the data prior to their upload to the repository in five levels: (i) clinical metadata integrity, (ii) template-imaging consistency, (iii) anonymization protocol applied, (iv) imaging analysis requirements, (v) case completeness. The tool produces reports with the corrective actions that must be followed by the user. This way the tool ensures that the data that will become available to the developers of the AI engine are homogenized, properly structured and contain all the necessary information needed for the analysis. The tool was validated in two rounds, internal and external, and at the user experience level. Clinical Relevance— Supporting the harmonized preparation and provision of medical imaging data and related clinical data will ensure data fairness and enhance the AI development
Design and Analysis of Proposed Photovoltaic system

In real-world simulations, modeling is a critical tool for translating mathematical conclusions into realistic settings. Modeling and modeling of solar modules of the type MAGI MGSM-315-72 were established in this work, and maximum power was achieved. MATLAB programming was in MATLAB/Simulink, It was used to create single diode photovoltaic modules with five parameters. utilizing datasheet from the manufacturer information in this work. For the implementation, MATLAB R2016a was utilized. The resulting models’ Curves of I _V and P _V were investigated. Following that, the models were put through their paces in a series of tests. The Models Validation Results were compared to the outcomes of a few built-in PV models. The impact of several factors on module performance was also investigated. This proposed model can help with a greater understanding of Photovoltaic panel attributes in a variety of situations.