Young’s double slit experiments, which represent the mystery of quantum mechanics (Feynman), have been described by optics, electromagnetic wave theory, quantum wave theory and Bohm’s trajectory theory. The double slit has the varieties of the applications. To test the description theories, we perform the comprehensive double slit experiments, which show that (1) before passing through the double slit/cross-double slit and before landing on the screen, the laser light behaves as particles, not as waves; (2) the experiments challenge the computer simulation of the trajectory theory at near field, however support the trajectory theory at far field. Those novel phenomena provide the comprehensive information for further theoretical development and applications.

Two algorithms for establishing PACSIC (parametric cubic spline interpolation curve) for any given dataset as an open or closed PLF (piecewise linear function) were established. Both algorithms do not require any prescribed tangent directions or any boundary conditions, though these directions can also be pre-defined if necessary. The direction at each vertex was simply determined by the two neighboring vertices. The LS (least square) G1 PACSIC, in particular, can be applied to approximate any 2d or 3d dataset. Simple examples were used to illustrate the flexibility of the algorithms, and various illustrative examples were also shown to demonstrate the efficiency of both algorithms. For completeness, the G2 PACSIC was briefly investigated, where the G2 conditions were given in terms of elastic parameters. Certainly, similar algorithms for generic PACSIC and LS PACSIC can also be developed if the tangent directions at vertices are evaluated by using three or more neighboring vertices; or even by raising the degree from cubic to quartic.

Before Automated Vehicles (AVs) can enter the market, academic and industrial partners must tackle system homologation to ensure the functional safety of AVs. For that task, a scenario-driven approach has gained acceptance in recent years since it allows facing the overall system’s complexity. Real-world drivings are a valuable source of scenarios. However, their extraction and analysis are still the subjects of research. This work addresses this issue and proposes a novel methodology to extract routing scenarios on urban intersections. Therefore, trajectories are annotated with routing maneuvers using a semantically enriched road network and map matching for route backtracking. The accuracy of the approach and consistency of the methodology is evaluated with Brunswick’s inner-city ring and by extracting u-turn scenarios on the Application Platform for Intelligent Mobility (AIM) Research Intersection of the German Aerospace Center (DLR).

The concept of embodiment has gained widespread popularity within prosthetic research. Embodiment has been claimed to be an indicator for the efficacy of sensory feedback and control strategies. Moreover, it has even been claimed to be necessary for prosthetic acceptance, albeit unfoundedly. Despite the popularity of the term, an actual consensus on how prosthetic embodiment should be used in an experimental framework has yet to be reached. The lack of consensus is in part due to terminological ambiguity and the lack of an exact definition of prosthetic embodiment itself. In a review published parallel to this article, we summarized the definitions of embodiment used in the prosthetics literature and concluded that treating prosthetic embodiment as a combination of ownership and agency allows for embodiment to be quantified, and thus useful in translational research. Here, we reviewed the potential mechanisms that give rise to ownership and agency considering temporal, spatial, and anatomical constraints. We then use this to propose a multi-dimensional framework where prosthetic embodiment arises within a spectrum dependent on the integration of volition and multi-sensory information as demanded by the degree of interaction with the environment. This framework allows for different experiments on sensory feedback and control to be put into perspective. Considering embodiment in a spectrum tied to the interaction with the environment led us to conclude on the importance of evaluating prosthetic technologies operating in environments as close to daily life as possible.

Machine Learning (ML) workloads have rapidly grown in importance, but raised concerns about their carbon footprint. Four best practices can reduce ML training energy by up to 100x and CO2 emissions up to 1000x. By following best practices, overall ML energy use (across research, development, and production) held steady at <15% of Google’s total energy use for the past three years. If the whole ML field were to adopt best practices, total carbon emissions from training would reduce. Hence, we recommend that ML papers include emissions explicitly to foster competition on more than just model quality. As estimates of emissions in papers that omitted them have been off 100x–100,000x, publishing emissions has the added benefit of ensuring accurate accounting. Given the importance of climate change, we must get the numbers right to make certain that we work on its biggest challenges.

We investigate the problem of infection diagnosis in hydrocephalic images. While traditional deep learning methods can be used for this problem - they require generous training data and produce class activations that are not meaningful. A novel 2D brain attention regularized network (BAR-Net) is proposed in our work, which encourages the network to focus inside the brain region for classification decisions. The newly proposed regularizer yields more interpretable class activations and experimental results confirm that the issue of over-fitting is mitigated. Extending the 2D BAR-Net, a hybrid 2-D/3-D CNN is proposed, that facilitates interactions between the 2D and 3D branches. Furthermore, a mutual attention regularization term is introduced in order to enable branches to exchange knowledge learned from the other by transferring attention maps for regularization.

The pandemic has precipitated a shift of value creation toward economic activity that is ignored in measures of economic growth such as GDP. As a consequence, economic policy is increasingly fumbling in the dark. Not least innovation policies and growth-oriented R & D now lack a relevant yardstick of success. This article provides an overview of the challenge and suggests better ways to measure the impact of innovations. Ultimately this concerns what kind of development nations aim for and how they assess success.

Semantic communications target to reliably convey the semantic meaning of messages. It is different from existing communication systems focusing on reliable bit transmission. To achieve the goal of semantic communications, we propose a signal shaping method by minimizing the semantic loss, which is measured by the pretrained bidirectional encoder representation from transformers (BERT) model. The signal set optimization problem is transformed to a vector optimization subject to a power constraint. We propose an efficient projected gradient descent method to solve the problem and prove its convergence. Simulation results show that the proposed method outperforms existing signal shaping methods in minimizing the semantic loss.

Internet-of-drones (IoD) systems require enhanced data transmission security and efficient interference management to accommodate the rapidly growing drone-based and rate-intensive applications. This paper develops a novel resource allocation scheme to jointly manage interference and enhance the physical layer security  of  cellular-connected IoD networks in the presence of a multi-band eavesdropping drone. Our envisioned cellular-connected IoD network has multiple full-duplex cellular base stations (CBSs), where each CBS reserves an orthogonal cellular  radio resource block (RRB) for the aerial communication links. To efficiently utilize the cellular RRBs, each CBS is connected to a cluster of data transmitting drones using uplink  non-orthogonal multiple access (NOMA) scheme. In addition, all the CBSs simultaneously transmit  artificial noise signals to weaken the eavesdropper links.  A joint optimization problem, considering the transmit power allocation and clustering of the legitimate drones, and the jamming power allocation of the CBSs, is formulated to maximize the worst-case average sum-secrecy-rate of the  network. The joint optimization problem  is decomposed into  drone-clustering and power allocation sub-problems to obtain an efficient solution. A multi-agent reinforcement-learning framework is devised to solve the drone-clustering sub-problem.  Meanwhile, the transmit and jamming power allocation sub-problem is solved by employing fractional programming, successive convex approximation, and alternating optimization techniques. By iteratively solving these two sub-problems, a convergent resource allocation algorithm, namely, security and interference management with reinforcement-learning and NOMA (SIREN), is proposed.  The superiority of SIREN over several benchmark schemes is verified via extensive simulations.

Shortages in therapists, along with the COVID-19 pandemic, have created a gap in care for people with cerebral palsy, stroke, and many other conditions requiring rehabilitation. Socially assistive robots and telehealth are two tools which could help fill this gap and may be able to work together, in socially assistive robot-augmented telerehabilitation, to create meaningful telerehab encounters. We present results from a survey completed between Jun 31, 2020, and Dec 31, 2020, asking 379 therapists in the United States for their opinions on features which may be useful for a telerehabilitation platform and for their general feelings towards robots. We explore their answers and reduce the response space by identifying six factors underlying the answers. We show that therapists want high quality video, control over robots, and more regulations. We also find that they are positive about the features which could be used in socially assistive robot-augmented telerehabilitation.

Early detection of an inter-turn short circuit fault (ISCF) can reduce repair costs and downtime of an electrical machine. In an induction machine (IM) driven by an inverter with a model predictive control (MPC) algorithm, the controller outputs are influenced by a fault due to the fault-controller interaction. Based on this observation, this study developed neural network models using inverter switching statistics to detect the ISCF of an IM. The method was non-invasive, and it did not require any additional sensors. In the fault detection task, an area under receiver operating characteristics curve value of 0.9950 (95% Confidence IntervaI: 0.9949 - 0.9951) was obtained. At the rated operating conditions, the neural network model detected and located an ISCF of 2-turns (out of 104 turns per phase) under 0.1 seconds, a speedup of more than two times compared to the thresholding-based method. Moreover, we published the switching vector data collected at various load torque and shaft speed values for healthy and faulty states of the IM, becoming the first publicly available ISCF detection dataset. Together with the dataset, we provided performance baselines for three main neural network architectures, namely, multi-layer perceptron, convolutional neural network, and recurrent neural network.

This review explores mild cognitive impairment support, focusing on virtual reality (VR) applications coupled with electrodermal activity (EDA). A systematic review and meta-analysis using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement were performed from an original inquiry of 698 articles from 11 digital libraries. An independent review of selected studies assessed the risk of bias and obtained data. The meta-analysis included 44 studies, with a total number of 9099 participants in all the papers reviewed were split into (5200) cognitive or (2304) non-cognitive impairments. The proportion of cognitive versus non-cognitive had a first risk difference (RD = 0.32; p-value = 0.0001). The comparison of female to male participants showed (OR = 6.10; p-value = 0.00001). Anxiety, depression, and stress conditions had the effect of (SMD = -0.39; p-value = 0.09). In the analysis, the general lack of research restricts virtual reality and electrodermal activity to sustain mild cognitive impairments. Forty-five excluded papers applied data from pre-2017. Two studies with 46 participants had a result of (SMD = 0.60; p-value = 0.31). Identifying VR and EDA-based studies investigating training and rehabilitation to support MCI symptoms in individuals.

This communication presents a corrected formulation, differing significantly from a traditional and widely employed one, for the precision of topographic measurements by means of space-based or airborne radar interferometry. The reasons for the differences are explained. The results are applicable to both downlooking topography and conventional geometries for interferometric synthetic aperture radar. Comparisons between the opposing formulations are exhibited for interferometer baseline tilt angles relative to the horizontal of five and sixty degrees. The discrepancies are especially wide at near-nadir viewing. The corrected formulation for measurement precision is presented in both its exact form and a convenient approximate expression, the two differing by less than one percent for the spaceborne interferometer illustrated.

This work presents a formal review of smart contracts, including definitions, technical requirements, and potential power and energy-related use cases. This includes in-depth discussions covering cybersecurity, legality and interoperability goals that must be taken into consideration by potential end-users. The paper presents a first attempt towards the standardization of smart contracts (SCs) within the field of power and energy as a work in progress activity under the IEEE Standards Association (IEEE SA) P2418.5 Working Group. This work also proposes a holistic, language-agnostic reference model that is intended to accelerate the adoption of Distributed Ledger Technology (DLT) by industry stakeholders by providing standardized processes. Finally, the paper discusses key takeaways that must continue to be developed to increase SC usage within the energy industry.

Development of ultra-compact, low-to-medium precision analog-to-digital converters (ADCs) with unprecedented energy-efficiency is essential to meet the ever-increasing demand for data converters in advanced computing systems including neuromorphic accelerators based on emerging non-volatile memories. To this end, in this work, for the first time, we propose a feedforward neural network ADC based on a network of highly scalable, CMOS-compatible, and energy-efficient ferroelectric-FinFET (Fe-FinFET) synaptic elements. Our lower triangular neural network (LTNN) ADC design, implemented using 7-nm technology from ARM along with an experimentally calibrated compact model for Fe-FinFETs, consumes 5.44 μW of power, 1.03 μm2 of area while operating at a speed of 1.23 megasamples per second for 4-bit precision. The proposed neural network ADC may pave the way for realization of highly efficient neuromorphic processing engines and neuro-optimizers based on cross-point array of emerging non-volatile memories.

Despite the use of High-k gate stacks, poor channel electrostatics at short channel lengths has limited the applicability of conventional Bulk MOSFETs. With the use of Ferroelectric materials in the gate stack, enhanced transverse (gate) electric field has the potential to enable greater scalability, which needs to be further explored in the context of planar Bulk MOSFETs. In this paper, we firstly present the design of an optimized N-channel Partially Junction-less Bulk MOSFET with a view to suppressing various leakage mechanisms, while also taking channel quantization effects into account. Through detailed process simulations, the process steps required to achieve this device design are determined, thus providing a more practical appraisal of the feasibility of the device. We then identify the thickness of the Ferroelectric layer over the existing gate stack with a view to achieve steep sub-threshold slope (SS) while simultaneously ensuring hysteresis-free characteristics, including enhanced Analog as well as Digital performance of the device.

Abstract—Aiming to address the growth and the integration of renewable sources and electronic loads, different types of dc-dc topologies with specific features have been proposed, going from medium frequency isolated Modular Multilevel Converter to Switched Capacitor and Hybrid Switched Capacitor topologies. In this paper, a bidirectional Modular Multilevel Converter based Hybrid Switched Capacitor dc-dc converter is presented, featuring transformerless structure, automatic total arm voltages clamping/balancing, voltage static gain independent of the submodules quantity and efficiency with reduced duty cycle influence. The topology operates with a Quasi-Two-Level modulation, which provides passive components volume/weight reduction, a degree of freedom regarding the dv/dt applied on the magnetic windings and basis to perform the submodule capacitor voltages equalization within each arm. The topology operating principles are approached by an average value static analysis while an instant value static analysis is used to estimate the operating current spike worst case. A 1.17 kW, 350/200 V laboratory scale prototype using IGBTs is implemented to experimentally confirm the theoretical approaches.

Integrated photonic devices operating via optical nonlinearities offer a powerful solution for all-optical information processing, yielding processing speeds that are well beyond that of electronic processing as well as providing the added benefits of compact footprint, high stability, high scalability, and small power consumption. The increasing demand for high-performance nonlinear integrated photonic devices has facilitated the hybrid integration of novel materials to address the limitations of existing integrated photonic platforms, such as strong nonlinear optical absorption or an inadequate optical nonlinearity. Recently, graphene oxide (GO), with its large optical nonlinearity, high flexibility in altering its properties, and facile fabrication processes, has attracted significant attention, enabling many hybrid nonlinear integrated photonic devices with improved performance and novel capabilities. This paper reviews the applications of GO to nonlinear integrated photonics. First, an overview of GO’s optical properties and the fabrication technologies needed for its on-chip integration is provided. Next, the state-of-the-art GO nonlinear integrated photonic devices are reviewed, together with comparisons of the nonlinear optical performance of different integrated platforms incorporating GO. Finally, the challenges and perspectives of this field are discussed.