Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault Diagnosis of Broken Rotor Bar in AC Induction Motor based on A Qualitative Simulation Approach

Qualitative Simulation (QS) is a common reasoning technique that represents the system performance based on qualitative reasoning. Reasoning is made to determine qualitative values with changing directions of the system variables. The QS approaches can be implemented with minimal knowledge of system parameters and even when the system model is incomplete, which are common scenarios for most machine users. Bond graph modelling simulates the dynamic systems based on cause and effect reasoning with the help of energy flow. Therefore this paper plans to use bond graph modelling for the purpose of fault detection based on the energy flow without the need for the specific information of machine’s parameters. The bond graph based QS was examined on an AC induction motor (ACIM) which had confirmed the high efficiency of QS based approach in achieving diagnostic results for broken rotor bar. The QS approach uses the temporal causal graph (TCG) and qualitative equations of the system. The applied qualitative analysis predicts the effects of parameter changes (increased-decreased) on the whole system behavior. The results indicate that the proposed QS technique is an effective method for extracting diagnostic information, leading to an accurate diagnosis by combining TCG and qualitative equations with qualitative reasoning.
Optimal Detection and Ambiguity Function of Hybrid Distributed MIMO Radar

This paper proposes a notional radar concept, named hybrid distributed multi-input multi-output (MIMO) radar, which brings together the distributed MIMO radar concept and the phased-array (PA) radar one. In particular, the hybrid distributed MIMO radar is composed of K sub-arrays which are randomly distributed with wide separations. Each sub-array consists of Q all-digital colocated antenna elements with T/R modules separated by half-wavelength inter-element spacing. In the transmitting end, each sub-array operating as a PA radar, is driven by an independent waveform that is orthogonal with waveforms transmitted by other sub-arrays. In the receiving end, the reflected signals are received by all elements in the whole array and jointly processed. The hybrid distributed MIMO radar is a promising technique, due to its capability of providing spatial diversity gains and high resolutions. In this paper, as the first stage of studying the target detection and localization problem for hybrid distributed MIMO radar, we present its signal model, and then develop the optimal detection theory and ambiguity function. Finally, sufficient numerical examples are provided to verify the effectiveness of the proposed radar system.
An optimal hybrid learning approach for attack detection in linear networked control systems

A novel learning-based attack detection and estimation scheme is proposed for linear networked control systems ( NCS ) , wherein the attacks on the communication network in the feedback loop are expected to increase network induced delays and packet losses, thus changing the physical system dynamics. First, the network traffic flow is modeled as a linear system with uncertain state matrix and an optimal Q-learning based control scheme over finite-horizon is utilized to stabilize the flow. Next, an adaptive observer is proposed to generate the detection residual, which is subsequently used to determine the onset of an attack when it exceeds a predefined threshold, followed by an estimation scheme for the signal injected by the attacker. A stochastic linear system after incorporating network-induced random delays and packet losses is considered as the uncertain physical system dynamics. The attack detection scheme at the physical system uses the magnitude of the state vector to detect attacks both on the sensor and the actuator. The maximum tolerable delay that the physical system can tolerate due to networked induced delays and packet losses is also derived. Simulations have been performed to demonstrate the effectiveness of the proposed schemes.
The reproductive health of adolescents

Adolescent sexual activity and parenthood are of concern to Ecuador and the Americas because of the problems they pose for the couple and the society at large. The adolescent mother’s health and education and the infant’s health and social development are likely to suffer. Society suffers because the young couple is unable to assume the full role of productive and independent citizens. Adolescent pregnancy with its biological psychological and socioeconomic risks is the entryway to a cycle of failure and poverty. The risks are reflected in elevated rates of maternal and perinatal morbidity and mortality. Early unprotected sexual activity may expose the young woman to the risks of illegal abortion or rejection by her family and put an end to her education. Furthermore the children of adolescent parents are at higher risk of abandonment. Although contraception remains controversial in some settings voluntary parenthood is now accepted as a basic human right. Contraceptive use is increasing among young women aged 15-19 especially among those who are married and more educated and who were older at first intercourse. Unmarried adolescents tend not to use contraception at the first intercourse. Information and access to family planning services for adolescents should be improved especially for those at high risk of early parenthood.
Beam Scanning UHF RFID Reader Antenna with High Gain and Wide Axial Ratio Beamwidth

A novel ultra-high-frequency (UHF) RFID reader antenna with 2 dimensional (2D) beam-scanning capability and a wide 3 dB axial ratio (AR) beamwidth of over 136° is presented. The proposed antenna consists of three concentric metal rings, each having a branch wire to stimulate circular polarization (CP) and a separate feed. The antenna is able to perform a 2D beam-scan with a relatively stable gain (9-11 dBic) from 0° to 360° in azimuth, and -40° to 40° in zenith. The minima of the antenna’s AR beamwidth follows the peak gain direction, giving an equivalent 3 dB AR beamwidth of 136° for every phi cut, wider than current state-of-the-art [1]. Moreover, the proposed antenna array presents a $50 \Omega$ impedance with no impedance matching network, and requires only two phase shifters to steer, significantly reducing the design complexity. The antenna’s 10 dB return loss bandwidth is from 860 MHz to 880 MHz, covering the lower band UHF RFID frequencies in the European Telecommunications Standards Institute (ETSI) region.
Deep Convolutional Neural Network Based Small Space Debris Saliency Detection

The drastic increase of the number of space debris in orbit around the earth poses a serious threat to operational spacecraft. Rapid small space debris detection with space-based surveillance platform is very important for spacecraft emergency avoidance at distance. Nevertheless, small space debris detection is a great challenge because of its fast movement and noise caused by cosmic rays in space-based surveillance platform. Inspired by strong pattern recognition capacity of deep learning, a deep convolutional neural network based small space debris saliency detection method for space-based surveillance system is proposed. First, the spatial contrast map of the space debris image is generated using local contrast method. Then, the spatiotemporal saliency information is captured incorporating with the above contrast map. The experimental results prove the applicability of the proposed method.
A New Control Algorithm for Directly Interconnected Offshore Wind Turbine Generators

A new control algorithm for Directly Interconnected offshore Wind Turbines with permanent magnet synchronous Generators (DIWTG) is presented. In the DΓWTG offshore wind park configuration, Wind Turbines with Permanent Magnet Synchronous Generators (WTPMSG) are directly connected to the offshore AC collection grid without using a power converter. The offshore AC collection grid is then connected, via a transformer, to the offshore AC transmission grid. In order to achieve maximum power point tracking, the (collective) speed of DΓWTGs is controlled by an onshore back to back converter. By measuring the active power and speed of the permanent magnet generators, wind speed at each turbine is estimated and used for calculation of the reference speed of WTPMSGs. Voltage control at the power converter side is performed in a way which allow the DΓWTGs to be operated at a constant V/f where the maximum resultant frequency at nominal wind speed is 16.67 Hz.
Monolithically Integrated RRAM- and CMOS-Based In-Memory Computing Optimizations for Efficient Deep Learning

Resistive RAM (RRAM) has been presented as a promising memory technology toward deep neural network (DNN) hardware design, with nonvolatility, high density, high ON/OFF ratio, and compatibility with logic process. However, prior RRAM works for DNNs have shown limitations on parallelism for in-memory computing, array efficiency with large peripheral circuits, multilevel analog operation, and demonstration of monolithic integration. In this article, we propose circuit-/device-level optimizations to improve the energy and density of RRAM-based in-memory computing architectures. We report experimental results based on prototype chip design of 128 × 64 RRAM arrays and CMOS peripheral circuits, where RRAM devices are monolithically integrated in a commercial 90-nm CMOS technology. We demonstrate the CMOS peripheral circuit optimization using input-splitting scheme and investigate the implication of higher low resistance state on energy efficiency and robustness. Employing the proposed techniques, we demonstrate RRAM-based in-memory computing with up to 116.0 TOPS/W energy efficiency and 84.2% CIFAR-10 accuracy. Furthermore, we investigate four-level programming with single RRAM device, and report the system-level performance and DNN accuracy results using circuit-level benchmark simulator NeuroSim.
Mobile Payment Adoption: A Systematic Review of the UTAUT Model

Recently, Mobile payment (M-payment) is fast becoming an essential research field in the information technology domain. Although numerous sectors have adopted the M-payment in different contexts, the factors that affect its adoption is still not apparent. Different information systems (IS) theories/models were developed in order to study the adoption of a specific new technology. One of the most commonly used and validated models in empirical studies is the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Based on the literature, research neglects to review the M-payment adoption studies. The main objective of this study is to systematically review the M-payment adoption studies that involve the UTAUT as the theoretical model. In that, a total of 25 research articles were synthesized and analyzed. The major findings include that 48% of the analyzed studies were conducted in the industrial sectors. Additionally, 80% of the analyzed studies were mainly relied on questionnaire surveys for data collection. Besides, our results revealed that perceived risk and perceived trust are regarded as the most frequent factors that were used to extend the UTAUT to study the M-payment adoption. These findings provide a comprehensive reference for researchers who intend to conduct further research in M-payment.
Real Time Emotion Recognition from Facial Expressions Using CNN Architecture

Emotion is an important topic in different fields such as biomedical engineering, psychology, neuroscience and health. Emotion recognition could be useful for diagnosis of brain and psychological disorders. In recent years, deep learning has progressed much in the field of image classification. In this study, we proposed a Convolutional Neural Network (CNN) based LeNet architecture for facial expression recognition. First of all, we merged 3 datasets (JAFFE, KDEF and our custom dataset). Then we trained our LeNet architecture for emotion states classification. In this study, we achieved accuracy of 96.43% and validation accuracy of 91.81% for classification of 7 different emotions through facial expressions.
Artificial Intelligence: Alternating the Highest Human Cognizing

Progressing outcomes of Artificial Intelligence in constructing basic, root cognizers able to develop to the artificial ones functionally equal to the highest human cognizers produce a significant consequences. In fact, AI proves that realities not only of cellular nature but also the ones constructed, assembled from ordinary available to humans units of matter can attain the highest cognizing of the universe, at least, comparable with one of humans.And since those artificial, constructed highest cognizers are only the assembles of units of matter unconstrained from the mysteries of the origin of cells it is expected to question the necessity of humans or some ones else in assembling, constructing the highest cognizers.In other words, to question the feasibility of origin of highest cognizers in frame of the acknowledged physics.This paper aims to enlighten the base of the above consequences and to track some steps of their grounding.
ABORTION PREVALENCE AND SOCIO-DEMOGRAPHIC DIFFERENTIALS

This study was performed to estimate the prevalence of abortions and to evaluate its association with the socio-demographic factors in an urban slum. The data was collected based on recall obstetric history from186 females through systematic random sampling from 01.04. 2001 to 30.06. 2001. It was found that the abortion rate was 419.35 / 1000 women of reproductive age group. Abortion ratio was 109.39 / 1000 live births. There was strong association of induced abortion with non-Muslim family, working of female and illiteracy of husband. On the other hand gravidity 5 or above (increasing gravidity) was statistically associated with total abortions. It was concluded that there is an unmet need for controlling unwanted pregnancies and promoting birth spacing among Pakistani women. Some important social and demographic factors determine the abortion behavior in Pakistan.
Toed-in vs Parallel Displays in Video See-Through Head-Mounted Displays for Close-Up View

In non-orthostereoscopic video see-through (VST) head-mounted displays (HMDs), the perception of the three-dimensional space is negatively altered by geometrical aberrations, which may lead to perceptual errors, problems of hand-eye coordination, and discomfort for the user. Parallax-free VST HMDs have been proposed, yet their embodiments are generally difficult to create. The present study investigates the guidelines for the development of non-orthostereoscopic VST HMDs capable of providing perceptually coherent augmentations for close-up views, hence specifically devoted to guide high-precision manual tasks. Our underlying rationale is that, under VST view, a perspective-preserving conversion of the camera frames is sufficient to restore the natural perception of the relative depths around a pre-defined working distance in non-orthostereoscopic VST HMDs. This perspective conversion needs to account for the geometry of the visor and the working distance. A simulation platform was designed to compare the on-image displacements between the direct view of the world and the perspective-corrected VST view, considering three different geometrical arrangements of cameras and displays. A user study with a custom-made VST HMD was then conducted to evaluate quantitatively and qualitatively which of the three configurations was the most effective in mitigating the impact of the geometrical aberrations around the reference distance. The results of the simulations and of the user study both proved that, in non-orthostereoscopic VST HMDs, display convergence can be prevented, as the perspective conversion of the camera frames is sufficient to restore the correct stereoscopic perception by the user in the peripersonal space.
Using Machine Learning Methods for Detecting Alzheimer's Disease through Hippocampal Volume Analysis

Alzheimer's disease (AD) is a neurodegenerative disorder that gradually destroys memory and thinking skills. It is the most common cause of dementia and the incidence of the disease increases with age. As the elderly population increases, the incidence of the disease is expected to increase further in the coming years so developing new treatments and diagnostic methods is getting more important. The work presented in this paper assesses the utility of image processing on the Magnetic Resonance Imaging (MRI) scans to estimate the probability of early diagnosis of dementia in Alzheimer's Disease. We analyzed the data diagnosed by the Alzheimer's Disease Neuroimaging Initiative (ADNI) protocol. The analyzed data were T1-weighted magnetic resonance images of 159 patients with Alzheimer's disease, 217 patients with mild cognitive impairment and 109 cognitively healthy older people. Within the scope of the study, 3-dimensional modeling of the hippocampus, considered to be one of the first and most affected brain regions of dementia, was calculated by means of semi-automatic segmentation software. Then, a data set was formed based on age, gender, diagnosis, right and left hippocampal volume values. The diagnosis via hippocampal volume information was made by using machine learning techniques. By using this approach, we conclude that brain MRIs can be used to separate the patients with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) and Cognitive Normal (CN); while most of the researches were only be able to separate AD with CN. Results revealed that our approach improves the performance of the computer-aided diagnosis of the Alzheimer's disease.
A Hopfield Neural Networks Based Mechanism for Coexistence of LTE-U and WiFi Networks in Unlicensed Spectrum

Long-Term Evolution in the unlicensed spectrum (LTE-U) is considered as an indispensable technique to mitigate the spectrum scarcity in wireless networks. Typical LTE transmissions are contention-free and centrally controlled by the base station (BS); however, the wireless networks that work in unlicensed bands use contention-based protocols for channel access, which raises the need to derive an efficient and fair coexistence mechanism among different radio access networks. In this work, we propose a novel neural networks (NNs) based mechanism for the coexistence of an LTE-U base station (BS) in the unlicensed spectrum alongside with a WiFi access point (WAP). Specifically, we model the coexistence problem as a Hopfield Neural Network (HNN) based optimization problem that aims a fair coexistence considering both the LTE-U data rate and the QoS requirements of the WiFi network. Using the energy function of HNN, precise investigation of its minimization property can directly provide the solution of the optimization problem. Numerical results show that the proposed mechanism allows the LTE-U BS to work efficiently in the unlicensed spectrum while protecting the WiFi network.
Use Of The Lower Extremity Functional Scale (LEFS) In A Patient After A First Metatarsophalangeal Joint Implant: A Case Report

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A Multilayer Perceptron-Based Approach for Stator Fault Detection in Permanent Magnet Wind Generators

The permanent magnet synchronous-based generator (PMSG) is a common type of wind generator that recurrently interrupts its operation due to stator faults occurrences. These faults are difficult to detect and can softly lead to machine damages since they occur between turns (turn-to-turn fault), or between turn and machine housing (turn-to-ground fault). Thus, these generators must be constantly monitored so that these faults can be detected in their initial stage. In fact, early detection reduces the cost of maintenance, while also decreases the inactivity time of the turbines. This work proposes a strategy to detect stator faults at the first moments by means of a classifier module that analyzes the stator electrical current patterns. This classifier is based on a multilayer perceptron (MLP) neural network, which was trained using a dataset of instances created by means of a mathematical model of the PMSG. The results show the MLP classifier is able to identify the problem with more than 94 % of accuracy, considering both turn-to-turn and turn-to-ground faults. Moreover, the detection was achieved in a very initial stage (1 % of faulty turns), contributing to the low-cost maintenance and correct operation of the turbines.
TSNSCHED: Automated Schedule Generation for Time Sensitive Networking

Time Sensitive Networking (TSN) is a set of standards enabling high performance deterministic communication using different scheduling mechanisms. Due to the size of industrial networks, configuring TSN networks is challenging to be done manually. We present TSNsched, a tool for automatic generation of schedules for TSN. TSNsched takes as input the logical topology of a network, expressed as flows, and outputs schedules for TSN switches using an SMT-solver. The generated schedule guarantees the desired network performance (specified in terms of latency and jitter), if such schedules exist. TSNsched can synthesize IEEE 802.1Qbv schedules and supports unicast and multicast flows, such as, in Publish/Subscribe networks. TSNsched can be run as a standalone tool and also allows rapid prototyping with the available JAVA API. We evaluate TSNsched on a number of realistic-size network topologies. TSNsched can generate high performance schedules, with average latency less than $1000\mu \text{s}$, and average jitter less than $20\mu \text{s}$, for TSN networks, with up to 73 subscribers and up to 10 multicast flows.