Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep-Learning High-Dynamic-Range Ultrasound

In recent years ultrasound imaging has achieved an increasing acceptance across medical specialties. For this reason new techniques keep being tested in the field. Among these techniques we found High Dynamic Range (HDR) imaging where the range of luminosity levels is augmented by combining multiple expositions of a scene. Current ultrasound techniques present limitations that are not compatible with traditional implementations of HDR imaging. In this paper, we asses the use of a deep learning (DL) neural network (U-net architecture) on predicting HDR values from low dynamic range (LDR) input images. In addition, an image acquisition pipeline to create the data set from which the network was trained is described. We demonstrated that this type of networks can be trained to predict HDR out from a minimal number of input expositions, while the obtained results showed to be comparable with more traditional approaches.
The influence of bubble-bubble interactions on contrast agent microbubble-jetting during initial exposure to 200 kHz focused ultrasound

The initial response of contrast agent microbubbles to a burst of 200 kHz focused ultrasound with 1.5 MPa peak-negative pressure amplitude is studied. Directed jetting along the axis of propagation of the ultrasound is observed and the influence of the pressure-gradients on the bubble dynamics evidenced. Furthermore, the influence of nearby bubbles on the jetting behavior is investigated. Bubbles tend to jet towards other bubbles and to be influenced in their dynamics. Moreover, the more bubbles share the same volume, the weaker the jets become.
Improved Indoor positioning algorithm using KPCA and ELM

RSS (Received Signal Strength) values which are used in indoor position estimation based on fingerprinting are affected by noise. The RSS value received by the fixed line-of-sight condition points obeys the Gauss distribution and does not match the Gauss distribution. WIFI signal transmission attenuation is also a nonlinear attenuation. This paper presents a joint KPCA-ELM locating algorithm, the use of KPCA (Kernel Principal Component Analysis) of the nonlinear characteristics allow the original RSS data being replaced and dimension reduction, constructing new features. ELM (Extreme Learning Machine) is a fast and efficient single-layer feedforward neural network algorithm for training with new characteristics. KPCA-ELM positioning algorithm can effectively reduce the influence of noise on RSS value and improve the accuracy. The experimental results show that KPCA-ELM algorithm can effectively improve the accuracy of indoor positioning.
VLSI Implementation for an Adaptive Haze Removal Method

In the real-time outdoor application of computer vision-based systems, haze removal, a pre-processing technique that can recover clear images from foggy ones, is necessary for object detection. Therefore, in this paper, an efficient haze removal method suitable for hardware design is proposed to obtain high quality fog-free images. Based on the atmosphere scattering model and the dark channel prior method, the atmospheric light of the whole image and the transmission map could be extracted. These are important and necessary parameters of the recovery model. Instead of using single global atmospheric light to restore foggy image, a local atmospheric light estimation method is applied in the proposed design to achieve optimal results. To ensure that the overall image is consistent without block artifacts, dynamic adjustment of local atmospheric light is made based on global atmospheric light. Additionally, to obtain a transmission map, a refined estimation method is performed to ease the halo effect. In terms of both quantitative and qualitative evaluations, the simulation results indicate that the proposed design exhibits superior performance without color oversaturation and distortion. To meet the requirements of real-time applications, a six-stage very-large-scale integration (VLSI) architecture for the proposed algorithm is implemented by using TSMC 0.13- $\mu$ m technology. The synthesis results show that the design yields a processing rate of approximately 200 Mpixels/s, which is rapid enough to facilitate Full HD resolution at 30 fps in real time.
Achievements and Challenges of International Academic Cooperation. The Case of VISIR+ Project

This article presents the results of a research on the achievements and challenges of international academic cooperation to contribute to professional training in higher education. Methodologically, it describes relevant aspects inherent to the modality through a case study of a multilateral Project; specifically, the VISIR+ Project that involves engineering careers taught in six Latin American and European countries. Data collection is based on participant observation in different instances of its implementation (in person meetings and virtual interchanges). The results allow systematizing its achievements and challenges, in order to promote international academic cooperation as a fundamental strategy for the improvement of higher education.
PLL-Less Three-Phase Droop-Controlled Inverter with Inherent Current-Limiting Property

In this paper, a novel droop control method for three-phase grid-connected inverters is proposed to guarantee closed-loop system stability and an inherent current-limiting property without the need of a PLL. The inverter is connected to the grid via a filter and a line. Based on the synchronously rotating dq frame modelling and nonlinear ultimate boundedness theory, it is analytically proven that the proposed control scheme maintains the inverter current below a certain upper bound. This current limitation is guaranteed independently of the grid, line and filter parameters; thus increasing the controller robustness. In addition, asymptotic stability of the desired equilibrium point of the closed-loop system is guaranteed under different values of the proposed controller gain. To verify the effectiveness of the proposed nonlinear control strategy, extensive simulations are realized using Matlab/Simulink, where both the stability and the current-limiting property of the controller are validated.
Hybrid FMS scheduling using T-TPN and Beam Search in uncertain environments

This paper is about the incremental computation of control sequences for complex discrete event systems (DES) in uncertain environments. Transition-timed Petri nets (T-TPN) that behave under earliest firing policy are used to model a class of flexible manufacturing systems (FMS) where operations are proceeded with partial routing flexibility (namely hybrid FMS). Uncertainties are assumed to occur due to interruption of operations or unreliable resources. The objective is to find a control sequence from an initial state to a reference one with a trade-off between performance and robustness. For that, a systematic T-TPN-based multi-level formalism is used for the modelling. Then a new cost function is introduced to estimate the time and risk to reach the reference. A modified beam search algorithm is also proposed to selectively explore the PN state space and search for the best control sequence.
Classification of the Type of Harmonic Source Based on Image-Matrix Transformation and Deep Convolutional Neural Network

The classification of harmonic source types is a necessary step to alleviate harmonic pollution. This study proposes a method for the harmonic source classification based on 2-D image-matrix transformation (IMT) and deep convolutional neural network (CNN). The method firstly converts the V-I waveforms of different harmonic source types into a matrix by IMT, which was then applied to express the V-I waveform of each harmonic source as an input CNN model. Thereafter, features were extracted automatically from the harmonic source IMT matrix by the CNN model. Finally, favorable results are achieved after identifying the type of harmonic source via a classifier. In case study, a number of verifications were performed on the proposed method. Moreover, compared with existing harmonic source classification methods, the proposed strategy in this study not only avoids extracting features through expert knowledge, but also reduces the amount of data required for the harmonic source classification, and achieves higher accuracy than other methods.
A Repairable System Subjected to Hierarchical Competing Risks: Modeling and Applications

In this paper, we propose modeling for a single repairable system with a hierarchical structure under the assumption that the failures follow a nonhomogeneous Poisson process (which corresponds to minimal repair action) with a power-law intensity function. The properties of the new model are discussed in detail. The parameter estimators are obtained using the maximum likelihood method. A corrective approach is used to remove bias with order $O(n^{-1})$ , and the respective exact confidence intervals are proposed. A simulation study is conducted to show that our estimators are bias-free. The proposed modeling is illustrated via a toy example on a butterfly valve system, an example of an early-stage real project related to the traction system of an in-pipe robot, and also a real example on a blowout preventer system.
On the theory of smooth topographic waveguides for Rayleigh waves

In the present paper, it is demonstrated that the existence of guided modes of Rayleigh waves on some types of smooth solid surfaces, often called 'smooth topographic waveguides', can take place under the condition of total internal reflection of Rayleigh waves from the 'external' areas of surfaces surrounding the 'internal' areas of wave localisation. In the framework of the geometrical acoustics approximation, the possibility of total internal reflection of Rayleigh waves in smooth topographic structures of complex geometry is linked to the presence of internal areas on the surfaces characterised by the geometry-modified angular-dependent local phase velocities of Rayleigh waves that are smaller in the direction of guided wave propagation than their velocities in the surrounding external areas. The above-mentioned condition of wave localisation is illustrated by theoretical calculations of the dispersion curves of guided waves for several examples of guided wave propagation. The obtained results for the dispersion curves of localised waves are compared with the known solutions, where available.
Time–Frequency Map-Based Abnormal Signal Detection

Abnormal signal detection plays a significant role in monitoring the state of running machinery. Most of the previous methods deem the abnormal signal detection an issue of signal analysis, but this strategy may be ineffective due to the high noise in the original data. Aiming to solve this problem, this study regards abnormal signal detection as an issue of image classification, such that advanced image noise removal and feature representation methods can be investigated with the aim of improving abnormality detection accuracy. Excellent deep learning-based image classification methods are investigated in this study, which can directly output classification results using original data and avoid the interference of the artificial factor. However, the deep image network may be unsuitable to the nature of the vibration signal, which significantly affects the generalization and accuracy of detection results. Aiming to solve this problem, a novel deep architecture is proposed in this study to improve abnormal signal detection in terms of correctness and efficiency. Consequently, a novel abnormal signal detection method is proposed by combining the time-frequency map and our deep learning architecture. Experimental results demonstrate that the time-frequency map can extract representative abnormality features, and our deep learning method can improve the classification capability. The detection correction of our method is observed to be consistently higher than 99.90% for different databases, and the time cost of our method is bearable at the same time. The main contribution of our method lies in that it investigates and optimizes advanced deep learning architectures for abnormal signal detection.
High-frequency, plane-wave, multi-pulse acoustic contrast agent imaging at 16 MHz

Multi-pulse (MP) ultrasound contrast agent (UCA) imaging techniques take advantage of the nonlinear response of UCAs by transmitting 2 or more pulses of different phase or amplitude and combining them in such a way as to reduce the linear component of the backscatter (i.e, tissue) and emphasize the nonlinear components (i.e., UCA). The MP methods of pulse inversion (PI, +1 and -1 pair) and amplitude modulation (AM, +1 and +1/2 pair) were compared to standard multi-angle, high-speed plane-wave compound imaging for a flow of the UCA DEFINITYR using a Verasonics Vantage 128 and an 18-MHz linear array. Transmissions were made at -10, 1 and 10 degrees. The PI and AM methods used a pulse pair at each angle and standard plane-wave imaging used a single transmission at each angle. DEFINITYR was diluted in water to 2000:1 and then a circular flow was established through a 2-mm flow phantom in a tissue mimicking material. Beamformed data were processed to find the RMS values in the flow and depth matched background region in order to calculate a contrast-to-tissue ratio (CTR). MP methods resulted in a 5-10 dB improvement in CTR relative to standard multi-angle plane-wave imaging at 16 MHz.
Multidimensional Hierarchical Interpolation Method on Sparse Grids for the Absorption Problem

The numerical integration of multidimensional functions using some variables of the sparse grid method for the absorption problem is presented in this paper. The multivariate quadrature expressions are constructed by combining tensor of suited one dimensional formula. We develop a multidimensional adaptive quadrature algorithm for the implementation of sparse grid based on a hierarchical basis. Furthermore, we obtain a new error bound at each sparse grid point. The numerical examples are shown to demonstrate the efficiency of our algorithm for the absorption problem and confirm the theoretical estimates.
A Bi-Axial Piezoelectric MEMS Scanning Mirror for Automobile Lighting System

To provide drivers with high visibility in at night, an agile scanned-lighting system for automobile headlamp is developed by using a piezoelectric bi-axial MEMS optical scanner. A high-resolution pattern scanned on a fluorescent material is projected forward through a focus lens, thereby controlling the light-intensity distribution depending upon the position of the oncoming vehicle and the running speed.
Soft-Switching Modulation Method for Full-Bridge DC-AC HF-Link Inverter

This paper presents an improved modulation method for the cycloconverter stage of high frequency-link DC-AC inverters. By applying the quasi-resonant commutation mode during the entire operating period, the fast and reliable discharge of DC-side capacitances can be provided even if the output current is close to zero. This allows to ensure zero voltage switching (ZVS) conditions for the DC-side transistors, while applying small external snubber capacitors to reduce their turn-off losses. The proposed modulation strategy was significantly simplified and requires no external active auxiliary circuits or multi-mode operation. Moreover, two of the AC-side switches can operate at the fundamental frequency, while the rest operate with zero current switching (ZCS). The proposed modulation method is verified with mathematical analysis, simulation model and experimental prototype of full-bridge HF -link inverter.
Depression Detection from Electroencephalogram Signals Induced by Affective Auditory Stimuli

Depression is a mental disorder characterized by emotional and cognitive dysfunction, which appears a state of low mood and aversion to activity. Depression can affect a person's thoughts, behavior, feelings, and sense of well-being. Depression is projected to be the second major life-threatening illness in 2020 by World Health Organization (WHO). Thus, it is urgent to detect and treat depression. Electroencephalogram (EEG) signals, which objectively reflect the working status of the human brain, are considered as promising physiological tools for depression detection. Negatively biased processing of affective stimuli in depression has been proven. In order to detect depression more effectively, we proposed an affective auditory stimuli induced depression detection method from EEG signals. In this method, we applied negative, positive and neutral affective auditory stimuli with several frequency selected from the International Affective Digitized Sounds (IADS-2) to induce negative affective bias in patients with depression. We synchronously collected EEG signals with three electrodes located on the prefrontal lobe (Fpl, Fpz, and Fp2), then extracted efficacious features by Empirical Mode Decomposition (EMD) based feature extraction method to detect depression effectively. The results of the proposed method showed that high-frequency affective auditory stimuli were more effective in depression detection and the frequency of affective auditory stimuli was a crucial property, which can influence the effectiveness of affective auditory stimuli in depression detection.
A novel, simple and objective method to detect movement artefacts in electrodermal activity

Skin conductance or electrodermal activity (EDA) has been shown to be a reliable indicator of emotional arousal. With the current development to record EDA with wearable sensors attached to individuals who freely move around, detecting EDA artefacts is essential. Current practice of detecting EDA artefacts relies heavily on expert judgments. This is undesirable, especially when facing large amounts of EDA data for which the exact recording context is unknown or for real-time applications. We propose to exploit the similarity in EDA as measured from two hands to determine artefacts in a simple and objective way. The basic idea is that fast EDA fluctuations that are similar in both hands are caused by emotional processes, while dissimilar fluctuations indicate that the sensor at one of the hands suffers from an artefact. This idea was tested in an experiment where participants were asked to make certain movements with their right hand at designated times while not moving the left hand. Besides eliciting movement artefacts in this way, emotional sounds were presented to elicit arousal responses. When both hands were stationary, right-left hand difference signals were flatter compared to raw signals of the left hand, confirming the logic of our approach. We tested first versions of algorithms based on signal synchrony to automatically detect movement artefacts. To evaluate the performance of these algorithms, we did not rely on expert judgment but on the ground truth of no movement artefacts in the stationary epochs. We conclude that two-hand EDA can be valuable for more objective and less labor intensive EDA artefact detection, which is important for EDA studies out of the laboratory.
Addressing Inequity: Evaluation of an Intervention to Improve Accessibility and Quality of a Green Space

Green space areas offer several benefits that support our physical, psychological, and social health. However, the level of engagement with green space areas may not be the same across population groups. Using a mixed-method research design, we investigated the use of a green space area and whether and how the area was beneficial for health, social inclusion, and physical activity for all socioeconomic groups in a suburban area in Norway. The study showed significantly increased use of the area from 2015–2018 and that users belonged to different socioeconomic groups. The motivation for using the area was the opportunity to experience nature and to interact socially. While no significant changes in self-rated health, life satisfaction, or levels of physical activity were found, the study indicates that factors such as location, availability, and designated places for social interaction are important motivating factors for use. Users from the lower socioeconomic groups were among the frequent users but were also the least satisfied with the quality and availability of the path. Our findings call for closer consideration of the location and availability of green spaces and that including places for social interaction and relaxation can contribute to increased use of green spaces.
Single-mode high frequency LiNbO3 Film Bulk Acoustic Resonator

In this paper, Y+163°-cut LiNbO3 (LNO) Film Bulk Acoustic Resonators (FBAR) with patterned bottom electrodes (AlSi or W) and a sacrificial layer cavity have been fabricated using a layer transfer process (4-inch). Unlike previous work based on films oriented towards the X axis [1], the Y+163° orientation provides a single resonance at 2.2 GHz, with an effective electromechanical coupling factor (kt2) of 26 %. Thanks to this single mode behavior and to the energy trapping induced by the heavy tungsten electrodes, the quality factor at antiresonance (Qa) is increased to 600. Moreover a Temperature Coefficient of Frequency (TCF) of –45 ppm/°C is obtained. This demonstrates a significant improvement towards introducing LiNbO3 as an alternative to AlN in Bulk Acoustic Wave (BAW) filters for the new generation of RF filters.
S2045796019000623jra 1..7

Aims. Aim of the current study is to investigate the associations between daily levels of air pollutants (particulate matter, ozone, carbon monoxide, nitrogen dioxide) and daily admissions for mental disorders to the emergency department of two general hospitals in Umbria region (Italy). Methods. We collected data about daily admissions to psychiatric emergency services of two general hospitals, air pollutants’ levels and meteorological data for the time period 1 January 2015 until 31 December 2016. We assessed the impact of an increase in air pollutants on the number of daily admissions using a time-series econometric framework. Results. A total of 1860 emergency department admissions for mental disorders were identified. We observed a statistically significant impact of ozone levels on daily admissions. The estimated coefficient of O3 is statistically significant at the 1% level. All other pollutants were not significantly associated with the number of daily admissions. Conclusions. Short-term exposure to ozone may be associated with increased psychiatric emergency services admissions. Findings add to previous literature on existing evidence for air pollution to have an impact on mental health. Ozone may be considered a potential environmental risk factor for impaired mental health.
A Functional Characterization of Randomly Initialized Gradient Descent in Deep ReLU Networks

Despite their popularity and successes, deep neural networks are poorly understood theoretically and treated as ’black box’ systems. Using a functional view of these networks gives us a useful new lens with which to understand them. This allows us us to theoretically or experimentally probe properties of these networks, including the effect of standard initializations, the value of depth, the underlying loss surface, and the origins of generalization. One key result is that generalization results from smoothness of the functional approximation, combined with a flat initial approximation. This smoothness increases with number of units, explaining why massively overparamaterized networks continue to generalize well.