Miracle-dz/UniFAR-FTUs huggingface dataset:
Double Fourier Integral Method for PWM Rectifiers to Estimate Harmonics of Uncontrolled Rectifiers

PWM rectifiers are gradually replacing controlled and uncontrolled rectifiers as the preferred technology in industrial applications. While estimating harmonic current emissions from theses devices is well known when connected to clean supplies, there is a need to improve the understanding of harmonic current emissions from PWM rectifiers arising when high levels of background voltage harmonic distortion are present. This paper presents a mathematical approach to describing the harmonic current emissions of an uncontrolled rectifier, as a special case of PWM rectifier, as a first stage to a understanding PWM rectifier harmonic emissions when high level of background harmonics are present. The approach uses derived mathematical equations to assess the influence of background voltage distortion on the harmonic current distortion of the rectifier. The mathematical expression for the harmonic current is derived using the Double Fourier Integral method.
Multi-Level Local Feature Coding Fusion for Music Genre Recognition

Music genre recognition (MGR) plays a fundamental role in the context of music indexing and retrieval. Unlike images, music genres consist of immediate characteristics that are highly diversified with abstractions in different levels. However, most representation learning methods for MGR focus on global features and make decisions from features in the same level. To remedy such defects, we intergrate a convolutional neural network (CNN) with NetVLAD and self-attention to capture the local information across levels and learn their long-term dependencies. A meta classifier is used to make the final MGR classification by learning from aggregated high-level features from different local feature coding networks. Experimental results show that the proposed approach yields higher accuracies than other state-of-the-art models on GTZAN, ISMIR2004, and Extended Ballroom dataset.
Direct toxicity of amyloid beta peptide on rat brain mitochondria: preventive role of Mangifera indica and Juglans regia

Role of mitochondrial dysfunction and oxidative stress has been well documented in various cognitive-related disorders such as Alzheimer's disease (AD). Evidence indicates that Aß formation impairs mitochondrial function and that mitochondrial dysfunction is an early event in the pathogenesis of AD. The present study was, therefore, designed to investigate the direct toxicity of Aß peptide on isolated mitochondria obtained from rat brain. Various mitochondrial toxicity/integrity parameters such as succinate dehydrogenase activity, reactive oxygen species (ROS) formation, mitochondrial membrane potential collapse (MMP), mitochondrial swelling, and cytochrome c release were measured following the addition of Aß peptide on isolated mitochondria and then, mitoprotective effect of aqueous extracts of Mangifera indica and Juglans regia against mitochondrial toxicity endpoints parameters induced by Aß peptide were assessed. Our results showed that exposure to Aß peptide (30 nM) in isolated brain mitochondria induced mitochondrial ROS formation, MMP collapse, mitochondrial swelling, and cytochrome c release which is the starting point of apoptosis signaling. All these mitochondrial toxic endpoints induced by Aß peptide inhibited by aqueous extracts of Mangifera indica (100–400 µg/ml) and Juglans regia (200–400 µg/ml). To our knowledge, this is one of the first apparent studies to claim directly targeting of brain mitochondria and induction of apoptosis by Aß peptide as a new hypothesis for etiology of AD and other related neurodegenerative diseases as well as mitopreventive role of common antioxidant nutritional products including walnut and mango.
Structural Homogeneity in Unsaturated Fatty Acids of Marine Lipids. A Review

Consideration of recent analytical data supports the conclusion that the longer-chain polyunsaturated fatty acids of marine origin are all structurally homogeneous in that the double bonds are cis, the double bonds methylene interrupted, and that, with the exception of the C16 chain length, the ultimate double bond will normally be three, six or nine carbon atoms removed from the terminal methyl group.
Predicting Mortality in Critical Care Patients with Fungemia Using Structured and Unstructured Data*

Fungemia is a life-threatening infection, but predictive models of in-patient mortality in this infection are few. In this study, we developed models predicting all-cause in-hospital mortality among 265 fungemic patients in the Medical Information Mart for Intensive Care (MIMIC-III) database using both structured and unstructured data. Structured data models included multivariable logistic regression, extreme gradient boosting, and stacked ensemble models. Unstructured data models were developed using Amazon Comprehend Medical and BioWordVec embeddings in logistic regression, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). We evaluated models trained on all notes, notes from only the first three days of hospitalization, and models trained on only physician notes. The best-performing structured data model was a multivariable logistic regression model that achieved an accuracy of 0.74 and AUC of 0.76. Liver disease, acute renal failure, and intubation were some of the top features driving prediction in multiple models. CNNs using unstructured data achieved similar performance even when trained with notes from only the first three days of hospitalization. The best-performing unstructured data models used the Amazon Comprehend Medical document classifier and CNNs, achieving accuracy ranging from 0.99-1.00, and AUCs of 1.00. Therefore, unstructured data - particularly notes composed by physicians - offer added predictive value over models based on structured data alone.Clinical Relevance— Unstructured data may help identify which patients are at high risk of death and need more aggressive treatment or closer monitoring. This is critically important since mortality rates from fungemia are very high.
Human-Robot Cooperation for Surface Repair Combining Automatic and Manual Modes

This article develops a human-robot cooperation to carry out treatments such as sanding, polishing, etc. on the surface of a known rigid object. For this purpose, a vision system is considered to get the object location to ensure not only the perpendicularity of the robot tool to the object surface but also a smooth approach of the tool to the surface. In order to add flexibility, the proposal includes the simultaneous combination of automatic and manual modes of operation. Thus, the human user can guide the robot tool to treat arbitrary areas (manual mode) and, when the operator releases the tool, the robot goes into the automatic mode to treat prior established areas. The method uses a task prioritization framework and three types of controllers: an admittance controller for the tool guidance; a hybrid controller to modify the tool orientation and, in the automatic mode, the tool position; and a sliding mode controller to limit the velocity at which the tool approaches the object surface. The applicability and efficacy of the proposed method is demonstrated experimentally using a conventional 6R robot arm.
Single-Period Single-Frequency (SPSF) Visualization of an EEG’s Striatal Beat Frequency

Motivated by the neuroscience concept of striatal beat frequency a new method is presented that takes an electroencephalogram of brain-wave activity and searches for spectral components using a sliding bank of windows consisting of individual singleperiod single-frequency sinusoids. This new approach is now practical due to the presence of high performance computing architectures. And note, this is a departure from legacy time-frequency spectrogram approaches which use one sliding window of constant size to calculate all frequency components simultaneously and it differs from the wavelet method because a suite of wavelets are formulated around one base unit and that unit usually includes a convolutional ramp up and ramp down structure. The proposed single-period singlefrequency search method is more tangible in identifying the existence, in time, of a frequency component due to its self-imposed constraint of using a bank of windows consisting of single period sinusoids, the length of each being a function of frequency and sampling rate. The result of this approach is a rendering of the on-off nature of the conceptualized striatal beat frequency components through a visualization of a matrix of correlation coefficients, where the rows are individual frequencies, (presented up to 1000 Hz in this paper), and where the columns indicate the position of each detected striatal beat frequency window of time.
HFER: Promoting Explainability in Fuzzy Systems via Hierarchical Fuzzy Exception Rules

When developing a Machine Learning model, the consideration of explainability as an additional design driver can improve its deployment into any application context. Given an audience, an explainable Artificial Intelligence system is one that produces details or reasons to make it’s functioning clear or easy to understand. Among different paradigms that inherently support these capabilities, Fuzzy Rule Based Systems are a very accountable solution. The main issue when dealing with fuzzy systems is to select an appropriate granularity to represent (fuzzify) the input data. A low value may cause the generation of too generalist rules, causing a hinder on predictive performance, whereas a high value may lead to both overfitting and/or very complex solutions.To overcome this situation, we propose a novel hierarchical fuzzy classification system based on fuzzy exception rules. To do so, low granularity rules are first generated and their confidence is examined. For those cases in which the fuzzy confidence is below a quality threshold, new higher granularity rules are created to cover the "instances in conflict" for the general rule, which is still kept in the rule base. Experimental results show the achievement of a compact and interpretable final rule base while maintaining or improving the predictive performance in comparison with the baseline fuzzy rule based classification and hierarchical systems.
Multi-scale Fast Detection of Objects in High Resolution Remote Sensing Images

Objects detection in high resolution (HR) remote sensing images plays an important role in modern military, national defense, and commercial field. Because of a variety of object types and different sizes, it is difficulty to realize the rapid detection of multi-scale high resolution remote sensing objects, and provides support for succeeding decision making responses. This paper proposes a multi-scale fast detection method of remote sensing image objects with deep learning model, named YOLOv3. The COCO data model is used to establish the high resolution remote sensing image set based on the NWPU data. The proposed model can realize automatic learning of object features, which has good properties on generalization and robustness. It can also overcome the deficiency of traditional object detection method needing manual feature design for different objects. The experimental results show that the average detection accuracy of objects with different sizes in high resolution remote sensing images can reach 93.50%, which demonstrates that the proposed method can achieve rapid detection of different types of multi-scale objects.
Sub-Femto-Farad Resolution Electronic Interfaces for Integrated Capacitive Sensors: A Review

Capacitance detection is a universal transduction mechanism used in a wide variety of sensors and applications. It requires an electronic front-end converting the capacitance variation into another more convenient physical variable, ultimately determining the performance of the whole sensor. In this paper we present a comprehensive review of the different signal conditioning front-end topologies targeted in particular at sub-femtofarad resolution. Main design equations and analysis of the limits due to noise are reported in order to provide the designer with guidelines for choosing the most suitable topology according to the main design specifications, namely energy consumption, area occupation, measuring time and resolution. A data-driven comparison of the different solutions in literature is also carried out revealing that resolution, measuring time, area occupation and energy/conversion lower than 100 aF, 1 ms 0.1 mm2, and 100 pJ/conv. can be obtained by capacitance to digital topologies, which therefore allow to get the best compromise among all design specifications.
Modeling and Analysis of Multichannel Drive-Thru Internet Systems and Performance Improvement

Recently, the interest regarding Drive-thru Internet systems has been rapidly arising in industrial and academic fields in view of the widespread adoption of IEEE 802.11 networks and its great potential to provide cost-effective Internet access. Drive-thru Internet systems are multiple-access wireless networks in which users in moving vehicles request/receive services such as digital map update and MP3 download to/from a Road Side Unit (RSU) as the vehicles pass through the coverage range of the RSU. For the purpose of efficiently supporting various services, Wireless Access in Vehicular Environment (WAVE), which is the standard for VANETs communications, specifies multichannel utilization, where the overall bandwidth is subdivided into seven channels, namely, one Control Channel (CCH) and six Service Channels (SCHs). However, originally designed for quasi-static single-channel-based small-scale indoor applications, the performance of IEEE 802.11 in the outdoor vehicular environment, where a large number of fast-moving vehicles simultaneously contend for channel access in the multichannel environment, is still unclear. In this article, a unified analytical framework is established to study the performance of multichannel Drive-thru Internet systems. Specifically, taking account of channel access contention of vehicles and power reception probability of an RSU, the message arrival rate at the RSU on the uplink channel (i.e., CCH) is derived. Then, a multiserver queueing model, which plays the role of a bridge connecting the uplink and downlink (i.e., SCHs) communications, is developed for the purpose of accurately capturing the dynamics of the multichannel environment. Based on the developed framework, it can be noticed that as the intensity of channel contention increases, the saturated throughput of SCHs decreases rapidly, and the system becomes unstable due to the reason that vehicles have to wait for a very large amount of time to receive the requested service messages, or even worse, cannot receive the messages before leaving the coverage of RSU. In order to keep the throughput at the maximum level regardless of the channel contention intensity while maintaining the system stability, we propose a centralized coordination mechanism. Simulation experiments are carried out to validate the accuracy of the developed analytical framework and the effectiveness of the proposed centralized coordination mechanism.
A Functional Construction of Codes for Multiple Access and Broadcast Channels

Codes are developed for two-user multiple access and broadcast channels starting from Gelfand-Pinsker codes with known block lengths, rates, and error performances. Guarantees are provided on the block error rates of the MAC and BC codes in terms of the parameters of the constituent Gelfand- Pinsker codes. These guarantees hold as long as the constituent codes satisfy the assumed properties on rate, codeword weights, and performances, irrespective of the basic structure and other properties.
DA-Net: Pedestrian Detection Using Dense Connected Block and Attention Modules

Pedestrian detection plays an important role in some areas such as autonomous driving, but due to heavy occlusion and various scales, it is still challenging. In this article, we propose an improved pedestrian detection method called DA-Net based on the two-stage detector Feature Pyramid Network (FPN). DA-Net adds Dense Connected Block (DCB), a combination of channel-wise attention module (CWAM) and global attention module (GAM) to the network. FPN can produce features with various scales and semantic information, which is good for the detection of pedestrians on various scales. Due to many small-scale targets in pedestrian detection, we only regard the low layers with enough details of targets in FPN as prediction layers. After several DCBs to deepen the network, prediction layers in our network can encode richer semantic information of targets, which can make the location of a target more precisely. In order to highlight visible parts of occluded pedestrians and ignore occluded parts, CWAM weights each channel of features with different importance. GAM aggregates global information and long-range dependencies for small-scale and occluded targets. Thus, the combination of CWAM and GAM is not only beneficial for coping with occlusion problem in pedestrian detection, but also for gaining environmental information for small-scale targets. Evaluation results on CUHK and CityPersons datasets show that our proposed method achieves improved performance with log-average miss rate reduction of 9.6% on the CUHK dataset and 6.1% on the Heavy subset of CityPersons dataset compared with FPN.
Combined Primary Frequency and Virtual Inertia Response Control Scheme of a Variable-Speed Dish-Stirling System

The potential of variable-speed dish-Stirling (VSDS) solar-thermal generating plant in providing grid frequency support is investigated. In the proposed VSDS frequency support control scheme, the reference speed of the Stirling engine is regulated to track a deloaded power curve which is governed by the solar insolation level. The gain of a supplementary speed-frequency droop controller is then set to meet the primary frequency control requirement. Further uniqueness of the VSDS control scheme pertains to the provision of virtual inertia response by regulating the kinetic energy in the rotating mass of the engine-generator and the thermal energy in the heat absorber/receivers. Small-signal analysis shows that the frequency support scheme is inherently stable, and it will provide higher degree of damping as the penetration level of the VSDS system and/or the solar insolation level increase. The efficacy of the proposed scheme is validated by computer simulation.
Accelerated Weighted ℓ1-Minimization for MRI Reconstruction Under Tight Frames in Complex Domain

We propose an improvement of the projected fast iterative soft-thresholding algorithm (pFISTA) and smoothing FISTA (SFISTA) to achieve faster convergence and improved reconstruction accuracy. The pFISTA addresses the problem of compressed sensing magnetic resonance imaging (CS-MRI) reconstruction under tight frames and considers standard $\ell_{1}$ norm minimization. The $\ell_{1} -$norm weighs each component in a sparse vector equally. However, this is restrictive. We employ the weighted $\ell_{1} -$regularizer, defined over a complex-domain as the sparsity-promoting function in CS-MRI reconstruction. The weighted $\ell_{1} -$regularizer assigns different weights to the components in a sparse vector to improve upon reconstruction accuracy. The optimization objective in CS-MRI is a real-valued function defined over a complex-domain and is therefore not holomorphic. We derive an algorithm, namely, projected weighted iterative soft-thresholding algorithm (pWISTA) based on Wirtinger calculus to solve the weighted $\ell_{1} -$regularized CS-MRI reconstruction under tight frames. We show that the proximal operator for the weighted $\ell_{1}$ regularizer over a complex-domain is the soft-thresholding operator, but with a different threshold for each component. We also incorporate Nesterov’s momentum into the pWISTA update to obtain the projected weighted fast iterative soft-thresholding algorithm (pWFISTA), which result in accelerated optimization as shown by the experimental results.
Exploring EEG microstates for affective computing: decoding valence and arousal experiences during video watching*

Investigating the electroencephalography (EEG) correlates of human emotional experiences has attracted increasing interest in the field of affective computing. Substantial progress has been made during the past decades, mainly by using EEG features extracted from localized brain activities. The present study explored a brain network-based feature defined by EEG microstates for a possible representation of emotional experiences. A publicly available and widely used benchmarking EEG dataset called DEAP was used, in which 32 participants watched 40 one-minute music videos with their 32channel EEG recorded. Four quasi-stable prototypical microstates were obtained, and their temporal parameters were extracted as features. In random forest regression, the microstate features showed better performances for decoding valence (model fitting mean squared error (MSE) = 3.85±0.28 and 4.07 ± 0.30, respectively, p = 0.022) and comparable performances for decoding arousal (MSE = 3.30±0.30 and 3.41 ±0.31, respectively, p = 0.169), as compared to conventional spectral power features. As microstate features describe neural activities from a global spatiotemporal dynamical perspective, our findings demonstrate a possible new mechanism for understanding human emotion and provide a promising type of EEG feature for affective computing.
Full-Open Cavity Multi-Wavelength Random Fiber Laser With Double Brillouin Frequency Spacing

A multi-wavelength Brillouin-Raman random fiber laser (BRRFL) with wavelength spacing of double Brillouin frequency shift (BFS) has been proposed for the first time, to the best of our knowledge. This BRRFL has an extremely simple full-open cavity configuration made up of only one roll of fiber, where bidirectional stimulated Raman/Brillouin scattering (SRS/SBS) and Rayleigh scattering based random distributed feedback coexist. In order to further enhance the performance of the BRRFL, a combination of erbium-doped fiber amplifier and single-mode fiber is incorporated into one side for random lasing regeneration. Both the side-mode suppression ratio (SSR) and order number of generated Brillouin Stokes lines (BSLs) show some improvements. A total of 66 BSLs with double BFS spacing has been obtained. Furthermore, the demonstrated laser system offers a wide tunable range with excellent flatness of spectral envelope. The proposed laser featuring simple structure and double BFS spacing would enable promising potentials in optical communication and sensing systems.