A theory for analyzing the radiative and reactive energies for pulse radiators in free space is presented. With the proposed definition of reactive energies and radiative energies, power balance at arbitrarily chosen observation surfaces are established, which intuitively shows that the Poynting vector contains not only the power flux density associated with the radiative energies, but also the influence of the fluctuation of the reactive energies dragging by the sources. A new vector is defined for the radiative power flux density. The radiative energies passing through observation surfaces enclosing the radiator are accurately calculated. Numerical results verifies that the proposed radiative flux density is more proper for expressing the radiative power flux density than the Poynting vector.

This paper proposed a 4-dimensional approach to solar photovoltaic energy, featuring a 3-D porous metal cell with a continuous flow of photovoltaic fluids (e.g. Colloidal Quantum Dots or perovskites) traversing throughout. Rationale and proposed governing equations indicate that significantly more power per unit 2-D panel area could be achieved, offering a method of de facto overcoming power limitations such as Shockley-Queisser and recombination in conventional 2-D passive solar panels. The main goal is to initiate thought and collaboration with experts in electrical engineering, physics, colloidal quantum dots, perovskites, capacitors/batteries, and any other related field that could help this approach become a commercial reality if there is a market fit. Potential applications include those where maximum solar power is desired yet space is limited- such as an on-board solar panel for electric vehicles.

Contact tracing has widely been adopted to control the spread of Coronavirus-2019 (COVID-19). It enables to identify, assess, and manage people who have been exposed to COVID-19, thereby preventing from its further transmission. Today’s most of the contact tracing approaches, tools, and solutions fall short in providing decentralized, transparent, traceable, immutable, auditable, secure, and trustworthy features. In this paper, we propose a decentralized blockchain-based COVID-19 contact tracing solution. Contact tracing can greatly suffice the need for a speedy response to a pandemic. We leverage the immutable and tamper-proof features of blockchain to enforce trust, accountability, and transparency. Trusted and registered oracles are used to bridge the gap between on-chain and off-chain data. With no third parties involved or centralized servers, the users’ medical information is not prone to invasion, hacking, or abuse. Each user is registered using their digital medical passports. To respect the privacy of the users, their locations are updated with a time delay of 20 minutes. Using Ethereum smart contracts, transactions are executed on-chain with emitted events and immutable logs. We present details of the implemented algorithms and their testing analysis. We evaluate the proposed approach using security, cost, and privacy parameters to show its effectiveness. The smart contracts code is publicly made available on GitHub.

This paper presents distributed and asynchronous active fault management (DA-AFM) to manage renewable energy upon faults. Addressed here are two challenges in fault management for photovoltaic (PV) farms and wind farms. The first one is the activation of crowbars in doubly-fed induction generator (DFIG) wind turbine systems during fault ride-though. The activation undesirably makes DFIG-based wind farms lose control and absorb reactive power. The second challenge is implementation of distributed fault management for distinct PV farms with different objectives and constraints. Coordination for large number of PV farms facilitates integration of themselves and other renewable energy. To prevent crowbars from being activated, DA-AFM controls nearby PV farms’ interface converters to smooth voltage drops so that DFIGs experience voltages with a lower dropping speed. To enable distributed computation of DA-AFM’s optimization formulation, a distributed and asynchronous surrogate Lagrangian relaxation (DA-SLR) method is devised to coordinate a cluster of PV farms. Simulation results have demonstrated DA-AFM’s effectiveness in preventing crowbars’ activation in wind farms and in coordinating diverse PV farms.

Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis [1-7]. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 Tera-OPS (TOPS - operations per second), generating convolutions of images of 250,000 pixels with 8-bit resolution for 10 kernels simultaneously — enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition.

Microcombs provide a potential compact and efficient light source for multi-Terabit-per-second optical superchannels. However, as the bandwidth of these multi-wavelength light sources is increased, this can result in low per-line power. Optical amplifiers can be used to overcome power limitations, but the accompanying spontaneous optical noise can degrade performance in optical systems. To overcome this issue, we propose wideband noise reduction for comb lines using a high-Q microring resonator, whose resonances align with comb lines. When applying the proposed distillation to a superchannel system with 18 Gbaud, 64-QAM sub-channels in a > 10 Tb/s optical superchannel, we find that noise-corrupted comb lines can reduce the optical signal-to-noise ratio required for the comb by ~ 9 dB when used as optical carriers at the transmitter side, and by ~ 12 dB when used as a local oscillator at the receiver side.

Large-scale testing involving more than a trillion comparisons between the iris patterns of different persons has confirmed that the entropy of IrisCodes is large enough to deliver collision avoidance (absence of False Matches) in population sizes of national, continental, and even planetary scale. This short paper explains how the entropy of this biometric achieves it.

A novel power system transient simulation scheme based on frequency response optimized integrators considering second order derivative is applied to a single-phase grid-feeding converter system in this paper. The converter system under study is introduced in detail as well as how the different numerical integrators are utilized for each constituting component. Numerical case studies show that the novel transient simulation scheme is able to achieve computational efficiency without sacrificing accuracy. This paper has been accepted by the 2021 IEEE PES Innovative Smart Grid Technologies Conference.

We describe in the paper a ring voltage-controlled oscillator (VCO) indicating an improved phase noise over a wide range of frequency offsets and an extended frequency/voltage tuning range. The phase noise is improved by leveraging a better linearity approach, while reducing the VCO gain and maintaining wide tuning range. The proposed VCO is a block of a time-domain comparator embedded in a monitoring and readout circuit of an industrial sensor interface. An analytical model is extracted resulting in closed-form expressions for both input-referred noise and phase noise of the VCO. Employing the analytical expressions, the contributed noise and phase noise limitations are fully addressed, and all the effective factors are investigated. The prototype of the proposed VCO was implemented and fabricated in a 0.35 µm CMOS process. The integrated VCO consumes 0.903 mW from a 3.3 V supply, when running at its maximum frequency of 9.37 MHz. The measured phase noise of the proposed VCO is -147.57 dBc/Hz at 1 MHz offset from the 9.37 MHz oscillation frequency, and the occupied silicon area of circuit is 0.005 mm2.

Abstract—While deep learning-based methods have gained popularity and have made remarkable progress in remote sensing (RS) image change detection (CD), the limited amount of available data hinders the performance of most supervised methods. The CD networks transferred or derived from other fields can be fronted with a weak generalization capability. Developing a universal benchmark for performance evaluations based on the available datasets is urgent. To address these problems, we proposed a lightweight network, termed 3M-CDNet, which only requires about 3.12 M parameters. The lighter the network, the easier it is to train and alleviate overfitting the limited amount of data, resulting in a better generalization capability. 3M-CDNet has a flexible modular design that achieves performance improvements by incorporating plug-and-play modules. 3M-CDNet gains accuracy improvements in two ways: (1) the application of deformable convolutions (DConv) in the backbone network to gain a good geometric transformation modeling capacity for CD and (2) the application of an effective two-level feature fusion strategy to enhance the feature representation capacity. 3M-CDNet gains a good generalization capacity by incorporating effective “tricks” to alleviate overfitting, in which online data augmentation (Online DA) is applied to increase the diversity of the training samples, and Dropout regularization is applied in the classifier. Extensive ablation studies have proved the effectiveness of the core components. Experiment results suggest that 3M-CDNet outperforms state-of-the-art methods on several optical RS datasets and serves as a new universal benchmark. Specifically, 3M-CDNet achieves the best F1-score, i.e., LEVIR-CD (0.9161), Season-Varying (0.9473), and DSIFN (0.7031).

The study ameliorates the feasibility of IR thermometer, and to introduce a novel design with upgraded applications & functions. The custom compact device (shape illustrates a “Badge”) measures the surface temperature of a body and received data processed through a microcontroller (AtMega328P). The device functions in a way that if there is any thermal change from ambient temperature (D-1 m), the mode is activated and triggers a pre-defined alert. For distance measuring, it measures the intensity of IR radiation emitted by a body from a particular direction. It also reads the temperature when it comes across a body. It will provide an optimum way in the primary healthcare instrumentation and helpful for noncontact, quick, and accurate measurement of moving and high temperature body.

The work physical model was designed through a physical model, and then the polymer adsorption which causing blockage resulting in permeability decrease computed by mathematical model.

This brief presents a novel level-shifter circuit for high-frequency high-voltage (HV) gate-drives. The proposed level shifter (LS) is designed based on a capacitive-coupler/current mirror/ latch structure which helps to extend operation voltage of a floating supply into the negative range, achieves sub-ns and constant delay, and consumes very low power from the floating supply. Additionally, common-mode noise cancellers based on a cross-current mirror and transmission gates are also presented to enhance the dV/dt immunity of the LS against slewing of the floating ground. Implemented in 0.18 µm HV BCD-on-SOI (bipolar-CMOS-DMOS on silicon-on-isolator) process, the post-layout simulation of the proposed design shows a delay of 680 ps, 200 V/ns of dVSSF/dt slew rate immunity, It dissipates no static power and only 8.1 pJ/transition from the floating supply, improving FoM1 and FoM2 of the proposed LS by 3 times and 11.7 times compared to respective state-of-the-art works.

As a novel layered noble metal dichalcogenide material, palladium diselenide (PdSe2) has attracted wide interest due to its excellent optical and electronic properties. In this work, a strong third-order nonlinear optical response of 2D PdSe2 films is reported. We conduct both open-aperture (OA) and closed-aperture (CA) Z-scan measurements with a femtosecond pulsed laser at 800 nm to investigate the nonlinear absorption and nonlinear refraction, respectively. In the OA experiment, we observe optical limiting behaviour originating from large two photo absorption (TPA) in the PdSe2 film of β = 3.26 ×10-8 m/W. In the CA experiment, we measure a peak-valley response corresponding to a large and negative Kerr nonlinearity of n2 = -1.33×10-15 m2/W – two orders of magnitude larger than bulk silicon. In addition, the variation of n2 as a function of laser intensity is also characterized, with n2 decreasing in magnitude when increasing incident laser intensity, becoming saturated at n2 = -9.96×10-16 m2/W at high intensities. Our results show that the extraordinary third-order nonlinear optical properties of PdSe2 have strong potential for high-performance nonlinear photonic devices.

Projects can be seen as the crucial building blocks whereby organizations execute and implement their short, and long-term strategic vision. Projects are thought to solve problems, drive change, satisfy unique needs, add value, or exploit opportunities, just to name a few. In order to successful deliver projects, project management tools and techniques are applied throughout a project´s lifecycle, essentially to efficiently and in a timely manner, identify and manage project risks. However, according to latest reviewed literature, projects keep failing at an impressive rate. Although research in the project management field argues that such failure rate is due to a huge variety of reasons, it highlights particular importance to a still underexplored and not quite well understood (regarding how it emerges and evolves) risk type, that may lead projects to failure. This risk type, called as corporate behavioral risks, usually emerge, and evolve as organizations work together across a finite period of time (for example, across a project lifecycle) to deliver projects, and is characterized by the mix of countless formal and informal dynamic interactions between the different elements that constitute the different organizations. Understanding the extent to which such corporate behavior influences project´s outcomes, is a breakthrough of high importance that positively impacts two dimensions; first, enables organizations that deliver projects (but not only), to increase the chances of project success, which in turn is a driver of sustainable business, because it allows the development and implementation of effective, and timely corrective measures to project´s tasks and activities, and second, it contributes to the scientific community (on the organizations field), to generate valuable and actionable new knowledge regarding the emergence and evolution of such cooperative risks, which can lead to the development of new theories and approaches on how to manage them. In this work, we propose a heuristic model to efficiently identify and analyze how corporate behavioral risks may influence project´s outcomes. The proposed model in this work, lays its foundations on four fundamental fields ((1) project management, (2) risk management, (3) corporate behavior, and (4) social network analysis), and will quantitatively measure four critical project social networks ((1) communication, (2) problem-solving, (3) advice, and (4) trust) that usually emerge as projects are being delivered, by applying the theory of social network analysis (SNA), more concretely, SNA centrality metrics. The proposed model in this work is supported with a case study to illustrate its implementation across a project lifecycle, and how organizations can benefit from its application.

Studies on prospective memory (PM) predominantly assess either event- or time-based PM by implementing non-ecological laboratory-based tasks. The results deriving from these paradigms have provided findings that are discrepant with ecologically valid research paradigms that converge on the complexity and cognitive demands of everyday tasks. The Virtual Reality Everyday Assessment Lab (VR-EAL), an immersive virtual reality (VR) neuropsychological battery with enhanced ecological validity, was implemented to assess everyday event- and time-based PM, as well as the influence of other cognitive functions on everyday PM functioning. The results demonstrated the importance of delayed recognition, planning, and visuospatial attention on everyday PM. Delayed recognition and planning ability were found to be central in event- and time-based PM respectively. In order of importance, delayed recognition, visuospatial attention speed, and planning ability were found to be involved in event-based PM functioning. Comparably, planning, visuospatial attention accuracy, delayed recognition, and multitasking/task-shifting ability were found to be involved in time-based PM functioning. These findings further suggest the importance of ecological validity in the study of PM, which may be achieved using immersive VR paradigms.

Recent research has focused on assessing either event- or time-based prospective memory (PM) using laboratory tasks. Yet, the findings pertaining to PM performance on laboratory tasks are often inconsistent with the findings on corresponding naturalistic experiments. Ecologically valid neuropsychological tasks resemble the complexity and cognitive demands of everyday tasks, offer an adequate level of experimental control, and allow a generalisation of the findings to everyday performance. The Virtual Reality Everyday Assessment Lab (VR-EAL), an immersive virtual reality neuropsychological battery with enhanced ecological validity, was implemented to comprehensively assess everyday PM (i.e., focal and non-focal event-based, and time-based). The effects of the length of delay between encoding and initiating the PM intention and the type of PM task on everyday PM performance were examined. The results revealed that everyday PM performance was affected by the length of delay rather than the type of PM task. The effect of the length of delay differentially affected performance on the focal, non-focal, and time-based tasks and was proportional to the PM cue focality (i.e., semantic relationship with the intended action). This study also highlighted methodological considerations such as the differentiation between functioning and ability, distinction of cue attributes, and the necessity of ecological validity.

We present theoretical designs of high performance optical filters in integrated silicon photonic nanowire resonators. We use mode interference in formed by zig-zag waveguide coupled Sagnac loop reflectors (ZWC-SLRs), tailored to achieve diverse filtering functions with good performance. These include compact bandpass filters with improved roll-off, optical analogues of Fano resonances with ultrahigh spectral extinction ratios (ERs) and slope rates, and resonance mode splitting with high ERs and low free spectral ranges. The analysis verifies the feasibility of multi-functional integrated photonic filters based on ZWC-SLR resonators for flexible spectral engineering in diverse applications.