Miracle-dz/UniFAR-FTUs huggingface dataset:
Transient Voltages on Lightning Protection System with Stratified Soils and Damage Conditions

Lightning Protection System (LPS) must provide a low-impedance path through its grounding system when a lightning strikes at the top of buildings. This grounding system is composed by combination of vertical (VE) and horizontal electrodes (HE) and a precise computation of its impedance must consider a stratified soil. Additionally, due to corrosion and electromagnetic forces, the electrode may present ruptures on connectors or welded parts. These factors affect the transient voltages along the grounding grid. In this paper, transient currents and voltages are investigated for a real LPS, subjected to a lightning strike, whose grounding system is buried in a homogeneous and stratified soil. In each condition, the grounding system is analysed under whole and damage state. In this study, a lumped approach for the VE and HE are obtained by the Vector Fitting technique and by the electromagnetic radiation theory (ERT), respectively. Results show that transient currents and voltages are affected by a rupture in the grounding electrodes; significant differences are more pronounced in high resistive homogeneous soil which may impact on the safety of people surrounding the LPS area.
Children as curators: how to incorporate young visitors' voices into the elaboration and evaluation of a microbiology exhibition.

Studies of museum publics are relatively recent, and studies of child visitors are even more recent. In this paper we summarize the types of exhibition evaluations mentioned in the literature and present an evaluation process for an exhibition about microbiology developed for and with input from 4-to-6-year-old children. As a case study we analyzed an exhibition entitled "The Giant World of Microbes. " Audio and video interviews were recorded with child visitors, and the stimulated recall technique was also employed. The data indicate the importance of interactive activities in enhancing child motivation and providing pertinent routes to follow when preparing an exhibition geared toward children.
Device-Free Indoor Human Activity Recognition Using Wi-Fi RSSI: Machine Learning Approaches

A device-free methodology is proposed to recognize the human activity in indoor environments using WiFi received signal strength indication (RSSI), and several machine learning (ML) approaches are investigated to realize the activity detection. With multiple access points (APs), the RSSIs are synchronously collected at multiple mobile phones (MPs) with different locations over a time duration and served as input data for training the detectors, enabling us to recognize the human activities of either moving or stationary. Extensive real experiments are conducted in the frequency bands of 2.4 GHz and 5 GHz, and the results show that the proposed methods can achieve over 95% recognition accuracy in the 5 GHz frequency band.
Ultra-micro Vibration Measurement Method Using CW Doppler Radar

As a non-contact vibration measurement technology, microwave radar is widely used in vital sign monitoring, structure health monitoring and precision instrument measurement. Although significant progress has been made, the continuous wave (CW) Doppler radar has inherent defects in ultra-micro vibration measurement due to the DC offset problem, which is challenging to be solved by the common circle fitting method with orthogonal baseband signals. In this paper, based on the Doppler phase demodulation algorithms, a pre-calculation method, creating a long arc of circle fitting by sliding the radar front-end in advanced, is proposed to accurately estimate the DC offset. Theoretical analysis and experimental results were provided for demonstrating our proposed method, which show that the ultramicro vibration measurement of 5μm can be realized.
Imaging Motility Pattern Analyzer Based on Optical Flow on Mice Sperm Cells Motility

Sperm motility patterns can be used to analyze the level of activity of sperm cells. The motility test is carried out on a microscopic test in a medical laboratory. In general, the sperm cell motility ratings are subjectively conducted by the expert medical analyst. Imaging motility pattern analyzer is helpful to detect the position and movement of sperm cells using a video recording from the digital microscope device. By using optical flow, the direction of movement of the object can be determined and keep track position becomes a pattern. In this paper, the video data recording is the motility of mice sperm cells. Data were analyzed using the average distance from the frame rate for comparing the pattern of trajectory line and velocity from samples provide data for classifying the motility grade value.
Resistive Analysis of Scattering-Dependent Electrical Transport in Single-Wall Carbon-Nanotube Networks

With the development of separation and sorting techniques, highly enriched semiconducting single-walled carbon nanotubes (SWNTs) have become widely accessible, which has led to the rapid growth of high-performance solution-processed SWNT-based thin-film field-effect transistors (TFTs) showing capabilities comparable to the ideal single-SWNT FETs. With such improvements, theoretical studies and detailed analyses of these networks have become necessary. In this work, a model justifying the near-ideal electrical transport in SWNT networks is presented. The field-dependent resistive properties of the networks are calculated using a numerical solver based on the derived individual resistances of SWNTs and the intertube couplings. The model is capable of simulating mixed SWNT networks consisting of both metallic and semiconducting nanotubes of varying chiralities. Our analysis reveals that the high electrical currents in networks could be largely attributed to the suppression of phonon scattering and strong intertube couplings in highly dense SWNT networks (>30–40 SWNTs $/{\mu m}^{2}$ ). Comparisons between the simu- lated and experimental results indicate good agreement thereby demonstrating the accuracy of the proposed model.
Cybersecurity and Data Privacy in the Cloudlet for Preliminary Healthcare Big Data Analytics

In cyber physical systems, cybersecurity and data privacy are among most critical considerations when dealing with communications, processing, and storage of data. Geospatial data and medical data are examples of big data that require seamless integration with computational algorithms as outlined in Industry 4.0 towards adoption of fourth industrial revolution. Healthcare Industry 4.0 is an application of the design principles of Industry 4.0 to the medical domain. Mobile applications are now widely used to accomplish important business functions in almost all industries. These mobile devices, however, are resource poor and proved insufficient for many important medical applications. Resource rich cloud services are used to augment poor mobile device resources for data and compute intensive applications in the mobile cloud computing paradigm. However, the performance of cloud services is undesirable for data-intensive, latency-sensitive mobile applications due increased hop count between the mobile device and the cloud server. Cloudlets are virtual machines hosted in server placed nearby the mobile device and offer an attractive alternative to the mobile cloud computing in the form of mobile edge computing. This paper outlines cybersecurity and data privacy aspects for communications of measured patient data from wearable wireless biosensors to nearby cloudlet host server in order to facilitate the cloudlet based preliminary and essential complex analytics for the medical big data.
Architecture Design of Overlap-Add FFT on the Convolution Neural Networks

As the computation demand of CNNs is dominated by convolution layers, some researches exploit the duality between spatial domain and frequency domain through fast Fourier transform (FFT) to replace convolutions with pointwise multiplications. However, the FFT approach requires zero padding to enlarge the filter kernel to be the same size of input feature map. In this paper, we apply the overlap-add FFT algorithm to resolve the large zero padding problem in full FFT model. We propose an architecture design to implement the overlap-add FFT algorithm with 2D FFT technique. Our design targets to filter size 3x3 ∼ 7x7 and is implement with four 8x4 PE architecture. Experiments on ResNet-34 shows that in average, our overlap-add FFT scheme achieves near to 80% of multiplication reduction in comparison with the convolution approach.
Low-Power Small-Area $3\times 3$ Convolution Hardware Design

The two-dimensional (2-D) convolution has a large influence on the overall performance of a neural network. Typically, a $3\times 3$ convolution (i.e., the kernel size is $3\times 3$) requires nine multipliers and one adder tree. In this paper, we propose a low-power high-speed $3\times 3$ convolution hardware design. Different from previous works, we merge 9 multipliers' final additions with an adder tree. Compared with a conventional 3x3 convolution design, experimental results show that the proposed $3\times 3$ convolution design can reduce 14.9% power consumption and save 10.6% circuit area.
Detection of Mammographic Masses using FRFCM Optimized by PSO

Since early detection of breast cancer can effectively reduce the mortality rate, hence, in an attempt, mass, a symptom of breast cancer which is difficult to identify due to its subtle nature, is targeted to locate it efficiently with the proposed detection scheme. This paper introduces FRFCM-PSO, a hybrid model of fast and robust fuzzy c-means clustering (FRFCM) and particle swarm optimization (PSO), for the localization of mammographic masses. FRFCM is an improvised version of FCM by employing morphological reconstruction and member-ship filters which alleviates the necessity of additional local spatial information which burdens the method with computational complexity. Moreover, the general limitation of clustering technique of initializing the center point has been mitigated by incorporating optimization method– PSO. Hence, the combinational approach yields a sensitivity of 96.6 % with 2.29 as false positives per image (FPs/I) when evaluated on the mini-MIAS dataset. Further, the FPs are reduced using feature extraction (LBP) and classification (Ensemble classifier) technique where an Az value of 0.846 is observed with an improvement of 74 % in FPs/I which is further compared with the similar competing scheme.
Botnet Defense System and Its Basic Strategy Against Malicious Botnet

This paper proposes a basic strategy for Botnet Defense System (BDS). BDS is a cybersecurity system that utilizes white-hat botnets to defend IoT systems against malicious botnets. Once a BDS detects a malicious botnet, it launches white-hat worms in order to drive out the malicious botnet. The proposed strategy aims at the proper use of the worms based on the worms' capability such as lifespan and secondary infectivity. If the worms have high secondary infectivity or a long lifespan, the BDS only has to launch a few worms. Otherwise, it should launch as many worms as possible. The effectiveness of the strategy was confirmed through the simulation evaluation using agent-oriented Petri nets.
An improved SLAM algorithm based on feature contour extraction for camera pose estimation

Camera position and attitude estimation is an important part of SLAM (Simultaneous Locolization and Mapping), which is directly related to the accuracy and efficiency of SLAM. The traditional SLAM algorithm for camera pose estimation is based on feature points. However, when the environmental brightness and contrast change too much, there will be many feature points matching errors and the camera pose estimation is not accurate. In order to solve the above problems, the feature contour is extracted before the traditional algorithm. Therefore, An improved SLAM algorithm based on feature contour extraction for camera pose estimation is proposed. Experimental results show that the degree of error between the camera pose estimation result based on the improved algorithm and the actual result fluctuates between 0.1-2 degrees. Compared with the traditional algorithm, the error degree and the error fluctuation range are small. The improved algorithm has higher accuracy of pose estimation than traditional algorithms.
Metabolomics Identifies Novel Blood Biomarkers of Pulmonary Function and COPD in the General Population

Determination of metabolomic signatures of pulmonary function and chronic obstructive pulmonary disease (COPD) in the general population could aid in identification and understanding of early disease processes. Metabolome measurements were performed on serum from 4742 individuals (2354 African-Americans and 1529 European-Americans from the Atherosclerosis Risk in Communities study and 859 Europeans from the Cooperative Health Research in the Region of Augsburg study). We examined 368 metabolites in relation to cross-sectional measures of forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), their ratio (FEV1/FVC) and COPD using multivariable regression followed by meta-analysis. At a false discovery rate of 0.05, 95 metabolites were associated with FEV1 and 100 with FVC (73 overlapping), including inverse associations with branched-chain amino acids and positive associations with glutamine. Ten metabolites were associated with FEV1/FVC and seventeen with COPD (393 cases). Enriched pathways of amino acid metabolism were identified. Associations with FEV1 and FVC were not driven by individuals with COPD. We identified novel metabolic signatures of pulmonary function and COPD in African and European ancestry populations. These may allow development of biomarkers in the general population of early disease pathogenesis, before pulmonary function has decreased to levels diagnostic for COPD.
Inflammation‐dependent overexpression of c‐Myc enhances CRL4DCAF4 E3 ligase activity and promotes ubiquitination of ST7 in colitis‐associated cancer

Inflammation is well known as an important driver of the initiation of colitis‐associated cancer (CAC). Some cytokines, such as IL‐6 and TNF‐α can activate expression of the oncogene c‐Myc (MYC) and regulate its downstream effects. Cullin‐RING E3 Ligases (CRLs) are emerging as master regulators controlling tumorigenesis. Here, we demonstrate that two cullin genes, CUL4A and CUL4B, but not other members, are specifically overexpressed in CAC tumour samples and positively correlate with levels of the proinflammatory cytokines IL‐1β and IL‐6. In vitro experiments revealed that the transcription factor c‐Myc can specifically activate the expression of CUL4A and CUL4B by binding to a conserved site (CACGTG) located in their promoters. Additionally, we found that both CUL4A and CUL4B can form an E3 complex with DNA damage‐binding protein 1 (DDB1) and DDB1‐CUL4‐associated factor 4 (DCAF4). In vitro and in vivo ubiquitination analyses indicate that CRL4DCAF4 E3 ligase specifically directs degradation of ST7 (suppression of tumorigenicity 7). Overexpression of c‐Myc in human colon epithelial cells resulted in the accumulation of CUL4A, CUL4B and DCAF4, but degradation of ST7. In contrast, knockdown of c‐Myc, CUL4A or CUL4B in the colon adenocarcinoma cell line HT29 caused accumulation of ST7 and inhibition of cell proliferation, colony formation ability and in vivo tumour growth. Collectively, our results provide in vitro and in vivo evidence that c‐Myc regulates CRL4DCAF4 E3 ligase activity to mediate ubiquitination of ST7, whose presence is physiologically essential for CAC tumorigenesis. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
Using QRCode to Enhance Extraction Efficiency of Video Watermark Algorithm

Video watermarking can effectively protect the copyright of video contents, but how to improve efficiency of watermarking algorithms is an urgent problem to be solved. In this paper, QRCode is embedded in the scene change frames of videos based on the advantage of QRCode’s strong fault tolerance. Combined with the characteristic of high decoding reliability of QRCode, a strategy to terminate the extraction process in advance is proposed to improve the extraction efficiency of the watermark algorithm. Experimental results show that the proposed algorithm has higher extraction efficiency than the algorithm that directly uses character string as watermark.
Attacks Detection and Prevention Systems for IoT Networks: A Survey

Internet of Things (IoT) is used in critical fields that need to secure it is network from intrusion and attacks. Therefore, many methods proposed to detect and prevent attacks on IoT network. In this paper, we studied different methods that proposed to detect and prevent IoT network attacks where we highlighted the proposed mechanisms and their limitation.
Suppression of the Parasitic Oscillations in an X-Band Relativistic Coaxial Gyrotron Driven by an Intense Beam Current

In 100-MW level relativistic gyrotrons driven by explosive emission cathodes (EECs), significant parasitic oscillations are caused by the high beam current and the large beam velocity spread. In this study, the parasitic oscillations in an ${X}$ -band coaxial relativistic intense current gyrotron were explored. The oscillations were suppressed in particle-in-cell (PIC) simulations through modifications of design. Lossy ceramics were inserted to suppress the parasitic modes in the beam tunnel. The beam collector was optimized to keep the electron beam from the cyclotron resonance region of the parasitic modes. In the 3-D PIC simulation of the whole tube, the output power of 35 MW and efficiency of 20.6% were achieved under the voltage of 300 kV, the beam current of 567 A, the pitch factor of 1.24, and the velocity spread of 25.6%. A single TE01 mode operation was achieved in this relativistic coaxial gyrotron.
Exploiting Embroidered UHF RFID Antennas as Deformation Sensors

Textile-based antennas that can be embroidered directly onto cloth fabrics have shown great potentials for wearable applications. However, the performance of the embroidered antennas is significantly affected by the materials used, as well as the antenna’s geometrical variations and deformations caused by human activities. In this article, we analyze several factors that affect the performance of embroidered antennas, including the choice of geometry, the bending and elongation of fabric substrate, as well as the resistivity of the yarns used for embroidery. Modeling, simulation, and measurement results have verified our analysis and demonstrated that slotted-patch UHF RFID antennas fabricated with low resistance yarns are more sensitive to geometrical variations, such as bending and curving, while meander line dipole (MLD) antennas are more sensitive to stretching and elongation. RFID tags equipped with these embroidered antennas can be utilized as battery-less passive deformation sensors for human movements.
Relativistic Approach for Training Self-Supervised Adversarial Depth Prediction Model Using Symmetric Consistency

This article proposes a novel approach for predicting depth from a single image in a self-supervised manner, specifically using generative adversarial networks (GANs) with enhancements to improve training performance. We train our model to generate disparity maps or inverse depth maps using left/right stereo image supervision rather than direct supervision from depth ground truth. We avoid the problem of training instability in standard GANs by employing a relativistic discriminator that evaluates how realistic an input image is. Since the reconstructed image quality determines the disparity map’s accuracy, we adopt the GMSD, one of the standout IQA algorithms, as a part of the image reconstruction loss function. A VGG-19 pre-trained model-based perceptual loss is used to optimize our model in a feature space instead of pixel space and to enhance reconstructed images’ perceptual quality with the learning-based perspective. Also, we add another symmetric generator that takes the right images as input, which helps to maintain the consistency of left/right views and enrich our training data with reasonable overhead on the training time. Our network is trained to predict depths of $640 \times 192$ images using the KITTI stereo dataset, which shows an outstanding performance compared to state-of-the-art works in various evaluation metrics. We verify our method’s modules by performing an ablation study and evaluate the generalization performance on CityScapes and Make3D datasets.
Blood Pressure Estimation Using Optical Technique and Surface Fitting

Blood pressure (BP) is a crucial vital sign of human health. Many methods have been developed for measuring blood pressure either invasive or noninvasive which attracts the attention of researchers. Recently a lot of researches and techniques have been done to find a relationship between the Pulse Transit Time (PTT) and both Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP). This paper proposed a system that focuses on estimating blood pressure utilizing the optical technique. Statistical features are extracted from PTT which are calculated using the Photoplethysmogram (PPG) and Electrocardiogram (ECG) signals. A surface fitting between these features and the SBP and DBP is done. The results show that the system achieves a Mean Absolute Error (MAE) of 4.13 and 6.89 for SBP and DBP respectively and Absolute Error (AE) of 3.77% and 5.76% for SBP and DBP respectively.
Accuracy of multi-echo Dixon sequence in quantification of hepatic steatosis in Chinese children and adolescents

BACKGROUND Nonalcoholic fatty liver disease (NAFLD) is currently the outstanding cause of chronic liver disease in children and adolescents, especially in overweight and obese groups. Liver biopsy is the reference standard to diagnose NAFLD but invasive, thus it is not the best choice in clinical diagnosis and follow-up. Magnetic resonance (MR) is widely used in clinical trials to noninvasively quantify liver fat content in adults and children in foreign countries. While currently, it is rarely used in Chinese children and adolescents. We postulated that quantifying hepatic steatosis by MR could be extended to children and adolescents in China. AIM To investigate the accuracy of MR imaging (MRI) in quantifying liver fat with MR spectroscopy (MRS) as a reference. A secondary goal was to assess the prevalence of NAFLD in overweight and obese Chinese children and adolescents. METHODS There were 86 children and adolescents enrolled in this study, including 65 overweight and obese children and 21 healthy children. The participants underwent MRI and MRS. MRI and MRS were performed using multi-echo Dixon and HISTO sequences, respectively, to calculate hepatic proton density fat fraction (PDFF). Hepatic steatosis was diagnosed using MRS-PDFF > 5% as the threshold. Spearman’s analysis was used to evaluate the correlation between MRI and MRS. The agreement between these two methods was assessed by Bland-Altman analysis. RESULTS The MRI-PDFF in the MRS region of interest and the entire liver was 9.9% ± 10.3% with a range of 0.3%-39.9%, and 10.6% ± 9.4% with a range of 1.9%-38.9%, respectively. The MRS-PDFF was 9.1% ± 10.0%, with a range of 0.5%-37.8%. The incidence of hepatic steatosis detected by MRS-PDFF was 46.5% (40/86) of all participants, all of whom belonged to the overweight and obese group. Spearman’s analysis indicated an excellent correlation between multi-echo Dixon and MRS (r > 0.9, P < 0.01). Bland-Altman analysis also demonstrated a good agreement between these two methods. CONCLUSION Multi-echo Dixon shows an excellent correlation and agreement with MRS in quantifying liver fat content and could be a potential tool to detect hepatic steatosis in Chinese children and adolescents.