Emotion is defined (from Britannica) as a complex combination of experiences of consciousness, bodily sensation and behavior, reflecting personal significance of externally and/or internally triggered changes. As an indispensable part of human communication, emotions could be represented within the digital world as means that could control the human interaction with the digital artifacts. When this is combined with gamification, a more engaging communication for the player could be established. In this article, we explore how multisensed emotions of players could be used as adaptation controllers within the emotion-aware serious neurogames (SNGs). We first briefly describe the emotion sensing and recognition landscape, examining the emotion-source sensing and emotion inferring approaches. We then discuss the SNGs design framework and the way human-to-SNG communication is set. Additionally, we describe the way the adaptation of the SNG characteristics can be driven by the inferred emotions, towards the achievement of the desired SNG goals. In addition, we demonstrate the feasibility and design characteristics of such multisensed emotion-aware SNGs, by presenting a related application case, namely EmoSense, and evaluate its acceptance as a technological solution in the field of neurofeedback. Finally, we discuss challenges and opportunities in the field.

Ultrasound image quality is of utmost importance for a clinician to reach a correct diagnosis. Conventionally, image quality is evaluated using metrics to determine the contrast and resolution. These metrics requires localization of specific regions and targets in the image such as a region of interest (ROI), a background region, and or, a point scatterer. Such objects can all be difficult to identify in in-vivo images, especially for automatic evaluation of image quality in large amounts of data. Using a matrix array probe, we have recorded a Very Large cardiac Channel data Database (VLCD) to evaluate coherence as an in-vivo image quality metric.  The VLCD consists of 33 280 individual image frames from 538 recordings of 106 patients. We also introduce a Global Image Coherence (GIC), an in-vivo image quality metric that does not require any identified ROI since it is defined as an average coherence value calculated from all the data pixels used to form the image, below a pre-selected range. The GIC is shown to be a quantitative metric for in-vivo image quality when applied to the VLCD. We demonstrate, on a subset of the dataset, that the GIC correlates well with the conventional metrics contrast ratio (CR) and the generalized contrast-to-noise ratio (gCNR) with R=0.74 (p<0.005) and R=0.62 (p<0.005) respectively. There exists multiple methods to estimate the coherence of the received signal across the ultrasound array. We further show that all coherence measures investigated in this study are highly correlated (R>0.9, p<0.001) when applied to the VLCD. Thus, even though there are differences in the implementation of coherence measures, all quantify the similarity of the signal across the array and can be averaged into a GIC to evaluate image quality automatically and quantitatively.

Backgrounds: Based on SYK-related models, Majorana particle is a fermion that is its own antiparticle. Recent discovery on simulating traversable wormhole by quantum processor based on Majorana particle is a significant progress in quantum simulation. By adding possible extendable approaches, more possibility could be suggested. Objectives: Discovering symmetry conservation attribute from Majorana particle to expand its usage for wormhole. Methods: By applying to the simplified non-unit model, additional considerations for simulating wormhole would be discovered. Results: Additional considerations of expanding holographic wormhole could be discussed in model.

This work seeks a novel design for a compact leaky-wave array antenna for high-power microwave (HPM) applications. The leaky-wave waveguide antenna is made of rectangular waveguide with groups of non-resonant closely-spaced slots in the waveguide’s broad-wall. By this means, more slots can be set in a unit length with lower radiation power through each slot, then higher power-handling capacity and a compact longitudinal dimension and height can be achieved. An L-band leaky-wave waveguide antenna centered at 1.57 GHz has been designed and experimentally studied. The results show that the radiation efficiency of the antenna is more than 97% and the power-handling capacity of a single waveguide exceeds 1.3 GW. In addition, a 5-waveguide array antenna was also designed. The array antenna has an aperture efficiency of 76.6% and the beam scanning range can reach ±40° in H-plane, which would be suitable for many HPM applications.

On the basis of the constructed multi-channel metasurface lens array, the design scheme of the integrated infrared spectrum detector is given. The integrated infrared spectrum detector consists of surface plasmon filter array, multi-channel metasurface lens array and infrared detector array. Effective detection of infrared absorption peaks and reference wavelengths of three gases. First, a metal hole-type surface plasmon filter array is designed. By adjusting the structural parameters of metal holes, the resonance characteristics of surface plasmons can be effectively adjusted, thereby realizing the adjustment of electromagnetic wave filtering characteristics. By adjusting the relevant structural parameters, four groups of metal hole arrays with different structural parameters are finally designed, which can achieve effective filtering of electromagnetic waves of the desired wavelength, and their transmittance is exceed 70%, and the FWHM of the transmission peak is less than 0.1 μm. Secondly, an infrared detector array composed of a photothermal conversion area and a thermoelectric conversion area is designed, and two high-efficiency electromagnetic wave absorbers based on the FP cavity resonance principle and the surface plasmon resonance principle are proposed. In the end, the difficulties of narrow-band filtering, wavefront manipulation, and efficient absorption of electromagnetic waves in the mid- and long-wave infrared band were successfully solved, laying a technical foundation for the development of integrated multi-component infrared gas sensors.

This paper presents a finite-difference time-domain (FDTD) framework that accelerates the modeling of the interaction of infinite periodic structures with practical sources (such as Gaussian or Bessel beams) by exploiting periodic boundary conditions (PBCs). The technique involves the introduction of arbitrary beam-generating sources in periodic simulations via the array-scanning method (ASM). The proposed method is both efficient and practical, being naturally broadband and highly parallelizable. We present examples of Gaussian beams in free space and interacting with grating geometries, efficiently replicating the results of multi-period brute-force simulations.

This work focuses on exploiting the constructive interference among different users’ data waveforms to introduce new coding and decoding techniques, which are specifically designed for non-orthogonal multiple access (NOMA) systems. In this paper, a structured coding scheme is devised. In essence, the proposed technique focuses on finding a relationship between the sent users’ data waveforms and then uses this relationship in the decoding process at the receiving destination. It is worth pointing out that the proposed coding and decoding techniques exhibit better performance and reduced the complexity compared with the conventional uncoded NOMA. The complexity order evaluation shows that the proposed scheme attains a reduction in the required number of the floating point operations (FLOPs) of 5N and 6N at the second and third users, respectively, compared with that of the uncoded NOMA. Moreover, we have derived a closed-form expression for the bit-error rate (BER), which is verified using the Monte Carlo simulation. To demonstrate the practicality of the proposed system, the obtained results are compared with those of the uncoded and convolutional coding NOMA systems. Finally, the performance of the proposed system outperformed conventional systems by an average of 5 dB in the case of two users and an average of 15 dB in the case of three users in the same work environment.

Modern systems (e.g., assistive technology and self-driving) can place significant demands on the user’s working memory (WM), which can adversely impact performance (i.e., elevated risk of errors) and increase the cognitive load (CL). Robust prediction of CL from EEG remains a challenge due to the small sample problem, noisy recordings, ineffective data representation, and lack of robust models. This paper presents a holistic approach to developing a reliable prediction of CL. We used EEG data recorded following a modified Stenberg WM task in which four levels of CL were defined based on the encoding of 2, 4, 6, and 8 English characters. First, we address the problem of noise and “small sample” by generating large low noise data using eigenspace-based bootstrap sampling and generative adversarial network (GAN). Second, we transform EEG recordings into spatial-spectral images to capture spatial information. Third, we built parameter-optimized CNN models to predict four levels of CL using single-frequency bands (i.e., θ, α, β) and stacked (i.e., all three bands) representations. In our quest to provide interpretable models, we applied Gradientweighted Class Activation Mapping (Grad-CAM) to our models to localize the brain regions responsible for the prediction of CL. Empirical analysis of models trained using θ, α, β, and stacked representation show accuracy of 90%, 89%, 91%, and 94%, respectively. Grad-CAM visualizations showed that the prefrontal, cerebellum, frontal, and parietal areas have the highest contribution to the prediction of CL.

This paper describes a new optical spectroscopy instrument achieving lock-in detection in all its spectral output channels in a parallel and simultaneous fashion.  It is based on two identical compact array spectrometers working in phase quadrature thanks to optical modulators synchronized with an external reference signal. In the application example, the optical lock-in spectrometer was used to perform spectral irradiance measurements with commercial LED lamps exhibiting temporal light modulation at different frequencies. A remote sensing optical device was designed to measure from a distance the temporal luminous waveform of a specific LED lamp and send it to the reference input of the optical lock-in spectrometer. When the lamps were operating together on top of a large continuous luminous background, the optical lock-in spectrometer successfully retrieved the respective spectral irradiance distributions and color parameters of each lamp, allowing their characterization to be performed independently of the other light sources illuminating the sensor during the measurements. © 2023 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

The log-sum minimization has been widely employed in signal and image processing. In this paper, in order to analyze its applicable conditions and achievable boundary for compressed sensing scenario, some theoretical derivations of the log-sum function are studied based on the mutual coherence (MC) of measurement matrix. Firstly, the existence condition of the global minimum is demonstrated for the log-sum minimization without noise perturbation. Secondly, for the iterative threshold log-sum (ITL) algorithm, the theoretical performance guarantee of recovering sparse signal disturbed by noise is proposed. Additionally, comprehensive experimental results verify the theoretical analysis for the log-sum one and display the advantages and disadvantages of four sparse estimation algorithms, i.e., the ITL algorithm, ℓ1/2 regularization, ℓ1 regularization and orthogonal matching pursuit (OMP).

Since hybrid power plants (HPPs) play an intensive role in the energy supply balance of future energy systems, there is today an increased attention on co-located wind-battery HPPs both in industry and academia. However, the profitability of HPPs in sequential electricity markets to overcome initial investment costs has not been yet well examined, especially with respect to  balancing services provision. This article proposes a novel energy management system (EMS) for optimal participation of wind-battery HPPs in two sequential electricity markets, namely in spot market and balancing market. The methodology consists of three optimization models, which allow HPPs to achieve energy arbitrage, to provide balancing services, and to reduce real-time imbalance costs. Furthermore, the profitability of HPPs in future (2030) energy scenario is analyzed based on the new designed and developed EMS taking balancing service into account. The results of the overplanting case show that HPPs operated based on the proposed EMS achieve net present value 3.6 times as high as sole operation in spot market.

Background: Currently, neural network model needs understanding on what derives conclusion by execution. Neural network model imitates human intelligence but it has a limitation of cannot explaining what it is to construct the knowledge which it derives. Object: By inducing universal framework from existing neural network models, common reasoning that regulates the intelligence that derives from the models would be introduced. Method: Starting from minimizing model’s bias, integrative method of understanding how neural network takes place in numerical ordinals and cardinals would be introduced. After that, explanation of intelligence by neural network would be described. Result: Foundation of artificial neural network intelligence is an optimization with a grid of minimized error from ground truth.

Manuscript Version 3 - accepted by the IEEE Journal of Photovoltaics. The final version is available at https://doi.org/10.1109/JPHOTOV.2023.3242117 Abstract: Bifacial photovoltaic modules are able to convert the solar radiation reaching their front and rear-sides, which means that more electricity can be produced using the same array area as monofacial modules with similar ratings. In some locations, the cost per power unit for such a technology has already become cost-competitive with conventional monofacial modules. The so-called effective irradiance and the bifacial gain are useful metrics, respectively, to assess the solar resource and the performance of bifacial arrays. To calculate the effective irradiance, studies previously published employ rear-side irradiance measurements, whereas to compute the bifacial gain, other works make use of monofacial modules with rating similar to those of the bifacial modules under analysis. In this paper, a straightforward method is presented, allowing to calculate the effective irradiance from the maximum power current, and to calculate the bifacial gain using a power scaling relation. The proposed method was experimentally tested using an outdoor platform with a dual-axis tracking system with bifacial modules. The effective irradiance calculated using the novel method presented nRMSE of 2.88 %, relative to the results obtained using the consolidated method. The bifacial gains obtained were 6.24 % and 6.69 %, respectively, using the proposed and traditional calculation methods. The procedure presented in this study might be useful for the quantification of the effective irradiance and the bifacial gain for PV installations which do not have extensive monitoring hardware.

Coal-fired power plants (CFPP) are a significant resource for flexibility regulation of power systems containing a high proportion of renewable energy (RE). This paper presents an extended study of the technology for co-firing coal and ammonia, which can decarbonize CFPP. The excess RE produces green hydrogen, converted into green ammonia for CFPP combustion. Moreover, this system has energy storage features that can cope with short-term fluctuations and store and utilize RE seasonally. In this paper, this system’s complex coupled thermal and electrical rocesses are simplified and characterized as a mixed integer linear programming (MILP) model for the first time. Furthermore, the operation of the green ammonia co-firing system is simulated at two time scales of five days and a full year. Finally, sensitivity and efficiency analyses are performed. The results shows that as the maximum co-firing ratio of ammonia and coal increases, the total operation cost (TOC), carbon emission (CE), and RE curtailment shows a decreasing trend. Therefore, the ratio of ammonia-coal co-firing is recommended to be 25% to compromise the TOC and exhaust gas emission. In this situation, the TOC and CE will decrease by 8.59% and 12.74%, respectively, compared with the scenario without co-firing technology. The overall efficiency of green ammonia synthesis and combustion for generation is about 13.92%.

To increase torque and power conversion of permanent magnet synchronous motors (PMSMs) at high-speed operation, the DC-link voltage of the inverter must be fully utilized during steady-state and transient operations. This paper proposes the incorporation of a harmonic reference generator (HRG) with a pulse clipping (PC) scheme into a model predictive control framework to achieve highest power output. The HRG calculates flux references in the stator-fixed coordinate system for an underlying continuous control set model predictive flux control (CCS-MPFC). These reference trajectories contain the harmonic flux component induced by the voltage hexagon in the overmodulation range and specific switching states (e.g., six-step operation) of the inverter are ensured by the proposed PC scheme. This enables a seamless transition to the overmodulation range including six-step operation and increases the drive’s power conversion to its maximum extent. Since the flux differential equation in the stator-fixed coordinate system, used as motor model for the HRG and CCS-MPFC, is able to represent PMSMs with linear and nonlinear magnetization, the proposed approach is well suited for the control of PMSMs with nonlinear magnetization. Extensive transient and steady-state experimental investigations on a highly utilized PMSM in the entire modulation and speed range prove the performance of the proposed approach.

Studying genetic variation underlying phenotypes is an important topic in genomics. In plant genomic research, for example, scientists analyze the variation between cultivars and wild types to develop crops with improved resistance to diseases. This analysis is commonly based on comparison to a single reference genome. Because the number of genomes is growing rapidly and to avoid bias towards a single reference genome, the field is shifting towards the use of pangenomes, i.e., abstract representations of multiple genomes in a species or population. While pangenomes allow for a more complete picture of the genetic variation, their large size and complex data structure hinder analysis. To deal with this, genome scientists need visual analytics tools that support interactive and exploratory analysis of pangenomes to identify relevant information for variant analysis. A major challenge is to handle multiple references together with providing the adequate context of heterogeneous (meta)data, such as annotations, evolutionary relationships, and phenotypes. To address this challenge, we developed PanVA, a visual analytics design for variant analysis in pangenomes. PanVA supports a novel strategy for pangenomic variant analysis that was designed with the active participation of genomics researchers. PanVA uniquely allows researchers to get a complete picture of the variation within genes in a large set of genomes, and identify associations with phenotypes. The design combines tailored visual representations with interactions such as sorting, grouping and aggregation, allowing the user to navigate and explore different perspectives. The realization of the PanVA design is possible through PanTools. Through user evaluation in the context of plants and pathogen research, we demonstrate that PanVA helps researchers explore regions of interest and generate hypotheses about genetic variants and their role in phenotypic variation.

This paper deals with predictive maintenance of infrastructure objects, focusing on the inspection of bridges. To achieve this goal, unmanned aerial vehicles place measuring units for data collection at different points of the bridge. This work deals with the question of how these measuring units can be found and removed by them later. For this purpose, a magnetic localization method based on the STAR method is presented. In contrast to the STAR method, which only detects the center of a magnetic target, a modified version called SRIOD is implemented here, where the orientation of the magnetic target can also be detected. This is essential information if the unmanned aerial vehicle is to grab the measuring unit with a robotic arm. In this regard, two identical permanent magnets are integrated into the measuring unit, whose gradient tensor contraction of the magnetic flux density distribution has a directed asymmetric surface when considering the contours of constant values. This creates a magnetic invisible reference plane that can be detected with a magnetic gradiometer. The method is verified both in simulation and experimentally in a laboratory setup.

Over the last decade, video-enabled mobile devices have become almost ubiquitous, while advances in markerless pose estimation allow an individual’s body position to be tracked across the frames of a video. Previous work by this and other groups has shown that pose-extracted kinematic features can be used to reliably measure motor impairment in Parkinson’s disease (PD). This presents the prospect of developing an asynchronous and scalable, video-based assessment of motor dysfunction. Crucial to this endeavour is the ability to automatically recognise the class of an action being performed, without which manual labelling is required. Representing the evolution of body joint locations as a spatio-temporal graph, we implement a deep-learning model for frame-level classification of activities performed according to part 3 of the Movement Disorder Society Unified PD Rating Scale (MDS-UPDRS). We train and validate this system using a dataset of n=7220 video clips, recorded at 5 independent sites. This approach achieves human-level performance in classifying and labelling periods of activity within monocular video clips. Our framework could support clinical workflows and patient care at scale through applications such as quality monitoring of clinical data collection, automated labelling of video streams, or a module within a remote self-assessment system.