The new coronavirus (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people’s everyday lives. Fever, cough, sore throat, headache, exhaustion, muscular aches, and difficulty breathing are all typical symptoms of COVID-19. A reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus’s transmission. The most accessible method for COVID-19 identification is RT-PCR; however, due to its time commitment and false-negative results, alternative options must be sought. Indeed, compared to RT-PCR, chest CT scans and chest X-ray images provide superior results. Because of the scarcity and high cost of CT scan equipment, X-ray images are preferable for screening. In this paper, a pre-trained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method (ANOVA) to reduce computations and time complexity while overcoming the curse of dimensionality to improve predictive accuracy. Finally, selected features were classified by XGBoost. The ChestX-ray8 dataset, which was employed to train and evaluate the proposed method. This method reached 98.72% accuracy for two-class classification (COVID-19, healthy) and 92% accuracy for three-class classification (COVID-19, healthy, pneumonia).

Pumped-storage hydropower is seen as a promising solution for efficient, large-scale energy storage. One competitive technical solution is the variable-speed hydropower plant (VSHP) configured with a converter-fed synchronous machine (CFSM). These machines are operated with one extra degree of freedom that is not usually optimized, where the CFSM’s rotor-side DC excitation interacts with the stator-side AC excitation. Depending on machine loading, the CFSM will be utilized in conditions far from its original design. In order to deal with this issue, this paper presents a stator flux control (SFC) method for regulating VSHPs in a more efficient way by adjusting the field current to prevent the machine from operating with over-magnetization independent of loading condition, as well as better utilizing the stator-fed converter current, maximizing the utilization of the CFSM. The derived first-principle analytical equations for the proposed SFC have been validated and analyzed in the Matlab/Simulink environment for a large 45 MVA, 375 rpm CFSM, with the measured saturation curve as input. Finally, dynamic transitions between different levels of pumping power reveal the SFC’s ability to help to maintain a unity stator flux in the machine, enabling optimal operation independent of loading level.

An algorithm for the numerical simulation of the propagation of electromagnetic waves in randomly inhomogeneous magnetic media by the FDTD method has been developed. The formulated algorithm is suitable for analyzing the main timing characteristics, as well as identifying the features of the propagation of various types of signals in both time-independent and time-dependent layered randomly inhomogeneous media. The simulation of the propagation of the sine pulse, sine signal and square wave in time-independent magnetic randomly inhomogeneous media with a various levels of phase contrast of two types - with a “diffuse” distribution of inhomogeneities and their “close packing” is carried out. The influence of the concentration of magnetic granules and the type of their distribution on the characteristics of the transmitted and reflected signals is revealed.

Utility-scale and small scale wind and solar power installations along with electric vehicle charging stations, and other active sources of energy are increasing at the medium and lower voltage levels in the distribution grid. This situation required a better understanding of the impact of high penetration of weather-dependent renewable energy sources on the operating conditions of the distribution network at both medium and low voltage levels. Despite the need, a multi-voltage level distribution network model, based on real network data and weather-dependent renewable generation data, has not been presented for distribution grid studies. This paper presents a comprehensive multi-voltage level active distribution network model based on real network data along with load and generation time-series for about a year. The network topology is modelled based on geographical data for various rural, semi-urban, and urban locations. The distribution network is embodied with a large share of renewable generation sources, with generation time-series simulated from meteorological data. The network is also flexible to incorporate other assets such as electric vehicle charging stations, storage, etc. Thus, the presented active distribution network model can be used to study, optimize, and control the effects of weather dependent generation and other network assets in the distribution grid.

This work examines the convex optimization problem. The objective is to minimize the task duration by optimal allocation of the resources like local and edge computational capabilities, transmission power, and optimal task segmentation. For optimal allocation of resources, an algorithm name Estimation of Optimal Resource Allocator (EORA) is designed to optimize the function by keeping track of statistics of each candidate of the population.

We prove that under stochastic geometric modelling of cellular networks, the coverage probability is not a function of base stations density—contrary to widespread belief. That is, we reveal that the base station density, $\lambda$, that appears in a plethora of published cellular coverage probability expressions is superfluous.

Objective: The recently-introduced hypnodensity graph provides a probability distribution over sleep stages per data window (i.e. an epoch). This work explored whether this representation reveals continuities that can only be attributed to intra-and inter-rater disagreement of expert scorings, or also to co-occurrence of sleep stage-dependent features within one epoch. Approach: We proposed a simplified model for time series like the ones measured during sleep, and a second model to describe the annotation process by an expert. Generating data according to these models, enabled controlled experiments to investigate the interpretation of the hypnodensity graph. Moreover, the influence of both the supervised training strategy, and the used softmax non-linearity were investigated. Polysomnography recordings of 96 health sleepers (of which 11 were used as independent test set), were subsequently used to transfer conclusions to real data. Main results: A hypnodensity graph, predicted by a supervised neural classifier, represents the probability with which the sleep expert(s) assigned a label to an epoch. It thus reflects annotator behavior, and is thereby only indirectly linked to the ratio of sleep stage-dependent features in the epoch. Unsupervised training was shown to result in hypnodensity graph that were slightly less dependent on this annotation process, resulting in, on average, higher-entropy distributions over sleep stages (Hunsupervised = 0.41 vs Hsupervised = 0.29). Moreover, pre-softmax predictions were, for both training strategies, found to better reflect the ratio of sleep stage-dependent characteristics in an epoch, as compared to the postsoftmax counterparts (i.e. the hypnodensity graph). In real data, this was observed from the linear relation between pre-softmax N3 predictions and the amount of delta power. Significance: This study provides insights in, and proposes new, representations of sleep that may enhance our comprehension about sleep and sleep disorders.

This paper proposes a new estimation and compensation approach to mitigate several linear and widely linear effects in coherent optical systems using digital signal processing (DSP) algorithms. Compared to most of the available strategies that employ local estimation and/or compensation algorithms, this approach performs a global impairments estimation and compensation based on Nonlinear Least Squares. The proposed method estimates and compensates for the chromatic dispersion (CD), carrier frequency offset (CFO), in-phase/quadrature (IQ) imbalance, and laser phase noise (PN) in two steps. Firstly, it estimates the quasi-static parameters related to the CD, CFO, and both transmitter and receiver IQ imbalance. Secondly, it estimates both transmitter and receiver lasers’ phases and compensates for all the imperfections by using a Zero-Forcing (ZF) equalizer. Simulations show the effectiveness of the approach in terms of statistical performance and computational time. The estimation performance is assessed by computing the Cramér Rao Lower Bound (CRLB), while the detection performance is compared to a modified Clairvoyant equalizer.

Gait disorders in children with cerebral palsy (CP) affect their mental, physical, economic, and social lives. Gait assessment is one of the essential steps of gait management. It has been widely used for clinical decision making and evaluation of different treatment outcomes. However, most of the present methods of gait assessment are subjective, less sensitive to small pathological changes, time-taking and need a great effort of an expert. This work proposes an automated, comprehensive gait assessment score (A-GAS) for gait disorders in CP. Kinematic data of 356 CP and 41 typically developing subjects is used to validate the performance of A-GAS. For the computation of A-GAS, instance abnormality index (AII) and abnormality index (AI) are calculated. AII quantifies gait abnormality of a gait cycle instance, while AI quantifies gait abnormality of a joint angle profile during walking. AII is calculated for all gait cycle instances by performing probabilistic and statistical analyses. Abnormality index (AI) is a weighted sum of AII, computed for each joint angle profile. A-GAS is a weighted sum of AI, calculated for a lower limb. Moreover, a graphical representation of the gait assessment report, including AII, AI, and A-GAS is generated for providing a better depiction of the assessment score. Furthermore, the work compares A-GAS with a present rating-based gait assessment scores to understand fundamental differences. Finally, A-GAS's performance is verified for a high-cost multi-camera set-up using nine joint angle profiles and a low-cost single camera set-up using three joint angle profiles. Results show no significant differences in performance of A-GAS for both the set-ups. Therefore, A-GAS for both the set-ups can be used interchangeably.

A static metasurface reflector based on a novel coupled resonator configuration is proposed to independently control the reflection phase and magnitude of linearly polarized incident fields, and is demonstrated experimentally in the millimeter-wave Ka-band around 30 GHz. The proposed concept is illustrated using a unit cell design consisting of a rectangular ring coupled with a rectangular slot resonator backed by a grounded dielectric slab. By geometrically tuning various dimensions of the two resonators, a near-perfect amplitude-phase coverage is achieved at a fixed design frequency of 30 GHz. To demonstrate the flexible beam-forming capability of the proposed metasurface reflectors, illustrative examples of fixed beam steering with varying reflection magnitudes, and asymmetric dual-beam patterns with specified reflection magnitude, reflection angles and beam-widths, are successfully shown. Compared to the standard method based on polarization rotation and resistive loadings with discrete values, the proposed technique does not generate undesired cross-polarization field reflection, and provides a continuous magnitude tuning including full absorption, along with wide phase coverage.

In this letter, we study the temporal and spatial variability of ionospheric irregularities by generating high-resolution maps of the observed amplitude scintillation index (S4) using data from a multi-constellation and multi-frequency GNSS receiver. The study is located in the Arabian Peninsula region, which falls under the northern crest of the equatorial ionization anomaly (EIA). Even though the study was conducted during a solar minimum period, considerable pre-sunset scintillation occurrences have been observed between 15-17 local time, particularly during the winter solstices. While most scintillation occurrences have been observed at low elevation (20 to 25 degrees), a considerable number of scintillation causing ionospheric irregularities have been observed towards the north, east, and southeast of the receiver location, for elevation ranging from 40 to 60 degrees. Out of all the GNSS constellations with medium-earth-orbit (MEO) satellites, GPS was the most impacted by amplitude scintillation, while BeiDou and Galileo satellites were the least affected. It is anticipated that the patches of ionospheric irregularities reported in this work will be further enhanced as solar activity increases in the coming years. Therefore, this work can serve as a reference for future studies during periods of increased geomagnetic activity.

I have made some revisions according to the advices of other researchers and submitted a new version of the theory (v4). I also have added a supplementary material for the the new version, including some detailed explanations and derivations. In the proposed theory, the total electromagnetic energy of a radiator is separated into three parts: a Coulomb-velocity energy, a radiative energy, and a macroscopic Schott energy. The Coulomb-velocity energy is considered to be attached to the sources as the same in the charged particle theory. It becomes zero as soon as its sources have disappeared. The radiative energy leaves the radiator and propagates to the surrounding space. The macroscopic Schott energy continues to exist for a short time after the sources have disappeared. It is a kind of oscillating energy and is considered to be responsible for energy exchange between the reactive energy and the radiative energy, performing like the Schott energy in the charged particle theory. As the Poynting vector describes the total power flux density related to the total electromagnetic energy, it should include the contributions of the real radiative power and a pseudo power flow caused by the fluctuation of the reactive energy. The energies involved in the electromagnetic mutual coupling are interpreted in a similar way. In the theory, all energies are defined with explicit expressions in which the vector potential plays an important role. The time domain formulation and the frequency domain formulation of the theory are in consistent with each other. The theory is also verified with Hertzian dipole. Numerical examples demonstrate that the theory may provide insightful interpretation for electromagnetic radiation and mutual coupling problems.

The quality of the technical textiles, in particular nonwovens, depends on the process parameters involved in producing them. It is not practical to investigate the effect of these parameters in real-time. Hence, a digital twin has been developed to analyze nonwoven production processes. The quality of the virtually produced nonwovens from the digital twin is then analyzed and mapped back to the process parameters. Different visualization techniques can be incorporated to guide engineers in finding optimal combinations of these process parameters for obtaining the desired product quality. This would eliminate the traditional trial and error strategies and reduce the domain expertise required in such an analysis. In this paper, we present a visual analytic tool that aids the engineer in analyzing and optimizing the quality of the nonwovens, thereby helping to make beneficial decisions in the industrial production process.

This paper uses active flux concept to review fundamental frequency sensorless algorithms for both induction and permanent magnet motors in one framework. Fundamentally, sensorless torque estimation can be directly solved using voltage model (VM) estimator, or indirectly solved using current model (CM) estimator. The latter turns the torque estimation problem into a speed estimation problem. The stator flux in VM and the d-axis angle in CM are deemed as the two sets of original states for sensorless drive. Through change of states, the direct torque estimation can be realized via observer designs; whereas the speed dependency of the unknown state (e.g., active flux and emf) gives rise to a class of speed estimation methods, known as model reference adaptive system (MRAS). The idea of a general speed observer is proposed to summarize various separate speed estimation methods needed for direct torque estimation. It is suggested to adopt inherently sensorless designs such that two-way coupling between torque estimation and speed estimation is avoided. For induction motors, it turns out the unmodelled voltage in the active flux dynamics reveals current flowing in rotor bars and can be further modelled, for which the solutions to regeneration instability problem are discussed, and change of states is recommended to attain global stability. Finally discussed are the results of slow reversal test, where local weak observability of ac motors can be potentially preserved.

A document by Yuwei Sun . Click on the document to view its contents.

Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual effort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitated deep representation learning where hierarchical representations are automatically learned in a data-driven manner. This paper presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER.

The pervasiveness of online mis/disinformation escalated during the COVID-19 pandemic. To address the proliferation of online mis/disinformation, it is critical to build reliability into the tools older adults use to seek health information. On average, older adult populations demonstrate disproportionate susceptibility to false messages spread under the guise of accuracy and were the most engaged with false information about COVID-19 across online platforms when compared to other age-groups. In a design-thinking challenge posed by AARP to graduate students in a Digital Health course at Tufts University School of Medicine, students leveraged existing solutions to design a web browser extension that is responsive to both passive and active health information-seeking methods utilized by older adults in the United States. This paper details the design-thinking process employed, insights gained from primary research, an overview of the prototyped solution, and insights relating to the design of effective health information-seeking platforms for older adults.

Abstract—Non-orthogonal multiplexing (NOM) is a novel superposition coding inspired scheme that has been recently proposed for improving the power, spectrum efficiency and delay of wireless links with packet error rate (PER) constraints. Despite its efficiency, restricting the number of multiplexed packets to two limits the throughput improvement to 100%. Therefore, this work presents a novel NOM design with unlimited number of multiplexed packets by manipulating the repeated transmissions in automatic repeat request (ARQ) to enhance the power and spectrum efficiency by multiplexing new and repeated packets while taking into account the channel conditions and varying the power per packet in different transmissions. The proposed scheme employs an efficient heuristic algorithm to perform the power assignment and multiplexing decisions. Moreover, the complexity of the proposed NOM can be controlled by enforcing a limit on the maximum number of multiplexed packets per transmission, making it suitable for different types of Internet of Things (IoT) nodes with various computational capabilities. The obtained results demonstrate the effectiveness of proposed scheme, which offers up to 200% spectral efficiency improvement at moderate signal to noise ratios (SNRs), and up to 700% at high SNRs. Furthermore, the new scheme can reduce the transmission power consumption by up to 6 dB in the high SNR region.