In this paper, a high sensitive area-variant type MEMS capacitive sensor was proposed for nano-indentation measurement. The proposed bionic swallow structure processed a high mechanical sensitivity without influences from the load coupling effect. Six comb arrays were optimized and integrated into the proposed sensor with a novel comb sensing configuration for eliminating electrostatic interferences resulting from multiple combs and maintaining compact chip size. Based on the proposed micro-machined process, comb arrays have been fabricated with great consistency, which exhibited a static differential output of 0.0554 pF. The fabricated sensor has been characterized with rigid glass cube and AFM cantilevers, and the measured force sensitivity can reach 98.54 aF/nN for a larger measurement range of 142 µN with a linearity of 0.9996. The fabricated sensor also exhibited a high resolution of 0.9819 nN, indicating its great feasibility in nano-indentation measurement at the sub-nano-Newton level.

In this paper, we bring forward the use of the recently developed Signature Transform as a way to measure the similarity between image distributions and provide detailed acquaintance and extensive evaluations. We are the first to pioneer RMSE and MAE Signature, along with log-signature as an alternative to measure GAN convergence, a problem that has been extensively studied. We are also forerunners to introduce analytical measures based on statistics to study the goodness of fit of the GAN sample distribution that are both efficient and effective. Current GAN measures involve lots of computation normally done at the GPU and are very time consuming. In contrast, we diminish the computation time to the order of seconds and computation is done at the CPU achieving the same level of goodness. Lastly, a PCA adaptive t-SNE approach, which is novel in this context, is also proposed for data visualization.

In this work, we describe step by step a complete workflow to apply machine learning (ML) classification for chipless RFID tag identification, covering: i) the tag implementation criteria for circular ring resonator (CRR) arrays for ML interoperability; ii) the data collection procedure to get a sufficiently representative dataset of real measurements; iii) the ML techniques to visualize the data and reduce its dimensionality; iv) the evaluation of the ML classifier to ensure high accuracy predictions on new measurements; and v) a thresholding scheme to increase the certainty of the predictions. The differences of the tags’ frequency responses are maximized by optimizing the Hamming distance between the tag identifiers and by controlling the radar cross section (RCS) level of each CRR array. We show on two scenarios, fixed and flexible range (up to 160 cm), that the proposed workflow can achieve perfect accuracy in most cases for the identification of four tags.

Public facilities are important transmission places for respiratory infectious diseases (e.g., COVID-19), due to the frequent crowd interactions inside. Usually, changes of obstacle factors can affect the movements of human crowds and result in different epidemic transmission among individuals. Most related studies only focus on the specific scenarios, but the common rules are usually ignored for the impacts of obstacles’ spatial elements on the epidemic transmission. To tackle these problems, this study aims to evaluate the impacts of three spatial factors of obstacles (i.e., size, quantity, and placement) on infection spreading trends in two-dimension, which can provide scientific and concise spatial design guidelines for indoor public places. Firstly, we used the obstacle area proportion as the indicator of the size factor, gave the mathematical expression of the quantity factor, and proposed the walkable-space distribution indicator to represent the placement factor. Secondly, two epidemic spreading indicators (i.e., daily new cases and people’s average exposure risk) were estimated based on the fundamental model named exposure risk with the virion-laden droplets, which forecasted the disease spreading between individuals accurately. Thirdly, 120 indoor scenarios were built and simulated, based on which the value of independent and dependent variables can be measured. Besides, the Pearson correlation analysis and linear regression analysis were employed to examine the relations between obstacle factors and epidemic transmissions. Finally, several design guidelines were provided for policymakers to mitigate the disease spreading: minimizing the size of obstacles; increasing the obstacle quantity and adopting the uniform obstacle placement by lifting the smallest size of the walkable convex space.

By their very nature, Spin Waves (SWs) excited at the same frequency but different amplitudes, propagate through waveguides and interfere with each other at the expense of ultra-low energy consumption. In addition, all (part) of the SW energy can be moved from one waveguide to another by means of coupling effects. In this paper we make use of these SW features and introduce a novel non Boolean algebra based paradigm, which enables domain conversion free ultra-low energy consumption SW based computing. Subsequently, we leverage this computing paradigm by designing a non-binary spin wave adder, which we validate by means of micro-magnetic simulation. To get more inside on the proposed adder potential we assume a 2-bit adder implementation as discussion vehicle, evaluate its area, delay, and energy consumption, and compare it with conventional SW and 7nm CMOS counterparts. The results indicate that our proposal diminishes the energy consumption by a factor of 3.14x and 6x, when compared with the conventional SW and 7nm CMOS functionally equivalent designs, respectively. Furthermore, the proposed non-binary adder implementation requires the least number of devices, which indicates its potential for small chip real-estate realizations.

With the increasing population of the silver economy, many potential economic opportunities appear, in particular, in developed countries. Recently, the European Union and the Baltic Sea Region have been very active with their research on active aging and to tackle the challenges arising from the exponentially increasing ageing population. The OSIRIS project is one such EU-funded initiative to develop a digital platform for the elderly named the Digital Silver Hub, which aims to serve as an ecosystem for the quadruple helix actors (private sector, public sector, academic institutions, senior citizens) to participate in knowledge exchange, collaboration and co-creation of innovative technological solutions to facilitate the elderly population. In this paper, 30 interviews were conducted from the partner heads of the OSIRIS project as well as quadruple helix actors from each region in the Baltic Sea Region (Estonia, Latvia, Finland, Lithuania, and Denmark) in order to deeply understand the functionalities that are needed to be offered by the Digital Silver Hub. A deductive thematic analysis is conducted to analyse these functionalities in terms of Collective Intelligence (CI) components (namely staffing, processes, goals and motivation) based on a most recent generic CI model. The functionalities were further evaluated by experts working in the field of science and technology in the silver economy. As results, the needed functionalities are identified and explained in depth. As an outlook, we discuss how these functionalities can be translated into architectural components of a system design.

We investigate the problem of electromagnetic radiation in massive electromagnetism with focus on the directive radiation characteristics of Proca antennas (sources embedded into Proca metamaterial domains.) It is theoretically demonstrated that the recently proposed nonlocal Proca metamaterial implies the existence of point nonlocal sources possessing a perfectly isotropic radiation pattern. Based on this rigorous analysis, we derive exact expressions for the directive radiation patterns of arbitrary discrete distributions of Proca sources, i.e., radiating arrays emitting massive photons. Examples of linear discrete Proca arrays are given and comparison with massless photon radiation sources is provided.

In this paper, a massively parallel approach of the multilevel fast multipole algorithm (PMLFMA) on graphics processing unit (GPU) heterogeneous platform, noted as GPU-PMLFMA, is presented for solving extremely large electromagnetic scattering problems involving tens of billions of unknowns, In this approach, the flexible and efficient ternary partitioning scheme is employed at first to partition the MLFMA octree among message passing interface (MPI) processes. Then the computationally intensive parts of the PMLFMA on each MPI process, matrix filling, aggregation and disaggregation, etc., are accelerated by using the GPU. Different parallelization strategies in coincidence with the ternary parallel MLFMA approach are designed for GPU to ensures a high computational throughput. Special memory usage strategy is designed to improve the computational efficiency and benefit data re-using. The CPU/GPU asynchronous computing pattern is designed with the OpenMP and CUDA respectively for accelerating the CPU and GPU execution parts and computation time overlapped. GPU architecture-based optimization strategies are implemented to further improve the computational efficiency. Numerical results demonstrate that the proposed GPU-PMLFMA can achieve over 3 times speed-up, compared with the 8-threaded conventional PMLFMA. Solutions of scattering by electrically large and complicated objects with about 24000 wavelengths and over 41.8 billion unknowns, are presented.

Arguments about the appropriate valuation of life in public policy have generally moved from accounting resource costs to revealed preference valuations. In medical and public health, the valuation of the ‘quality” life experience over time (a very different concept) has focused on equivalents to an unconstrained life experience. The user perspective on these valuations is largely missing and has substantial implications for both individuals and organizations. As these summary measures are fundamental to current triage and assessment processes, they have considerable importance for individuals. This highlights a number of different channels that can bias the assessment of individuals in specific circumstances when AI support is used, as a wide range of inferences from diverse data is then brought to bear and amplifies health data security scope and importance. These intermediate aggregate channel security vulnerabilities have not previously been highlighted, in spite of the importance attached to these summary measures in public health. The collision of totally individualized assumed impacts and end user consequences are amplified rapidly by Ai methods in support of decisions, and the security and privacy-if any left-of individual medical AND life data are thus jointly at cyber risk.

This paper aims to develop a traffic control approach that provides an optimal real-time solution for large-scale highway networks. The traffic flow model’s nonlinearity is the main reason for the complex optimization problems that consequently require high computational effort. Feedback linearization is a well-known approach in nonlinear systems control. It can provide an exact linear representation of the original nonlinear system. Thus, it facilitates further steps in the controller design. This research combines the feedback linearization method and linear MPC to simultaneously guarantee optimal performance and real-time feasibility. While developing feedback linearization for METANET model, we discovered a pattern that describes the expansion of the control signals (ramp metering and variable speed limits) and disturbances effects through the highway networks. Utilizing this pattern, we design a generic feedback linearization control law for METANET model. The control law provides the key connection between the linearized and original model. Finally, we complete the methodology by employing a linear MPC to regulate the linearized model. The performance of the designed method is evaluated by conducting comprehensive simulation studies, including a large-scale network. The simulation results are promising. Eventually, comparing the developed methodology with an equivalent nonlinear MPC verifies a substantial improvement in computational costs.

In this paper, MATLAB based computational approaches for the design and analysis of time-varying capacitors using the finite-difference time-domain (FDTD) technique and the Simulink design environment are presented. The FDTD formulation for multiple lumped capacitors loaded in series on a transmission line is discussed and extended to include time variation of capacitance. The design methodology for the same is also discussed using MATLAB’s Simulink using the RF Blockset Library. The developed FDTD formulation and the Simulink method are then used to design a mixer with time-varying capacitors loaded transmission line.

Situational awareness based on the data collected by the vehicle perception sensors (i.e. LiDAR, RADAR, camera and ultrasonic sensors) is key for achieving assisted and automated driving functions in a safe and reliable way. However, the data rates generated by the sensor suite are difficult to support over traditional wired data communication networks on the vehicle, hence there is an interest in techniques that reduce the amount of sensor data to be transmitted without losing key information or introducing unacceptable delays. These techniques must be analysed in combination with the consumer of the data, which will most likely be a machine learning algorithm based on deep neural networks (DNNs). In this paper we demonstrate that by compression tuning the DNNs (i.e. transfer learning by re-training with compressed data) the DNN average precision and recall can significantly improve when uncompressed and compressed data are transmitted. This improvement is achieved independently from the compression standard used for compression-training (we used AVC and HEVC), and also when training and transmitted data use the same compression standard or different compression standards. Furthermore, the performance of the DNNs is stable when transmitting data with increasing lossy compression rate, up to a compression ratio of approximately 1200:1; above this value the performance starts to degrade. This work paves the way for the use of compressed sensor data in assisted and automated driving in combination with the optimisation of compression- tuned DNNs.

We present a unified system model and framework for the analytical performance study of two heterogeneous and physically-distinct, but coexisting, networks that work harmoniously at the same time, space, and frequency domains. The two-tier network model considered in this paper is an overlaying of femtocells on a macrocell. Overlaying femtocells improves  the performance by offloading traffic from macrocells and providing spatial diversity. The mmWave channel model employed considers the number of clusters and rays within each cluster to vary due to the end-user mobility. This is a new and different model compared to the widely used channel models for mmWave two-tier networks. Optimal power control is formulated as a sum-rate maximization problem for downlink and uplink transmissions at two-tier networks and a power allocation scheme is proposed by following Shannon-Hartley theorem. A comprehensive and interesting performance investigation is provided, where it is shown that the upper bound on the number of admitted secondary users has a linear relationship with the outage probability threshold, logarithmic relationship with SINR and exponential relationship with channel gain factors. Simulation results show that the proposed scheme with sub-channel iterative Lagrange multipliers search algorithm is very effective at managing the cross-tier interference and can outperform a competitive scheme from literature that is based on cognitive radio technology. The computational complexity analysis of proposed algorithms are also given, since the complexity of second algorithm can be a performance-complexity trade-off issue for systems with limited computation power and time requirements.

If each node in a wireless network has information about only its $1$-hop neighborhood, then what are the limits to performance? This problem is considered for wireless networks where each communication link has a minimum bandwidth quality-of-service (QoS) requirement. Links in the same vicinity contend for the shared wireless medium. The conflict graph captures which pairs of links interfere with each other and depends on the MAC protocol. In IEEE 802.11 MAC protocol-based networks, when communication between nodes $i$ and $j$ takes place, the neighbors of both $i$ and $j$ remain silent. This model of interference is called the $2$-hop interference model because the distance in the network graph between any two links that can be simultaneously active is at least $2$. In the admission control problem studied in the present paper, the objective is to determine, using only localized information, whether a given set of flow rates is feasible. While distance-$d$ distributed algorithms have been analyzed for the $1$-hop interference model, an open problem in the literature is to extend these results to the $K$-hop interference model, and the present work initiates the generalization to the $K$-hop interference model. We show that the centralized version of the problem is NP-hard and then investigate distributed, low-complexity solutions for this problem. We propose a distributed algorithm for this problem where each node has information about only its $1$-hop neighborhood. The worst-case performance of the distributed algorithm, i.e. the largest factor by which the performance of this distributed algorithm is away from that of an optimal, centralized algorithm, is analyzed. Lower and upper bounds on the suboptimality of the distributed algorithm are obtained, and both bounds are shown to be tight. The exact worst-case performance is obtained for some ring topologies. The performance of the distance-$1$ distributed algorithm is compared with that of the row constraints, and these two distributed algorithms are shown to be incomparable.

Coupled lines with a double shielding in weakly and strongly inhomogeneous dielectric media are studied. They have specific properties that allow creation of impedance-transforming directional couplers with given directivity type. The proposed models and synthesis techniques takes into account both electric asymmetry and dielectric inhomogeneity. Numerical simulations and measurements prove the correctness of the general theory, and the possibility of creating co-, contra- and trans-directional couplers with impedance-transforming and phase-shifting properties. The results will be useful for finding new design solutions of advanced couplers and microwave circuits.

All signals are generated and received using the M9800A Vector Network Analyzer (VNA). To obtain the data, 16-antenna array is designed and placed around the SSB. The array used is the EMvision 3B headset, attached to an EMvi? sion trolley, with calibration PCBs installed(03 series (+20)).

This paper focuses on a phase transition from the asymptotic safety approach of renormalizing the quantum gravity (QG) to a more granular approach of the loop quantum gravity (LQG) and then merging it with the Regge calculus for deriving the spin-(2) graviton. From loop-(2) onwards, the higher derivative curvatures make the momentum go to infinity which assaults a problem in renormalizing the QG. If the Einstein-Hilbert (E-H) action, is computed, and a localized path integral (or partition functions) is defined over a curved space, then that action is shown to be associated with the higher order dimension in a more compactified way, resulting in an infinite winding numbers being accompanied through the exponentiality coefficients of the partition integrals in the loop expansions of the second order term onwards. Based on that localization principle, the entire path integral got collapsed to discrete points that if corresponds the aforesaid actions, results in negating the divergences’ with an implied bijections’ and reverse bijections’ of a diffeormorphism of a continuous differentiable functional domains. If those domains are being attributed to the spatial constraints, Hamiltonian constraints and Master constraints then, through Ashtekar’s variables, it can be modestly shown that the behavior of quantum origin of asymptotic safety is similar to the LQG granules of spinfoam spacetime. Then, we will proceed with the triangulation of the entangled-points that results in the inclusion of Regge poles via the quantum number (+2,-2,0) as the produced variables of the spin-(2) graviton and spin-(0) dilaton.

This paper presents an overview of the M-ary Aggregate Spread Pulse Modulation (M-ASPM), and provides an assessment of its suitability and advantages for use in low-power wide-area networks (LPWANs). M-ASPM combines high energy-per-bit efficiency, robustness, resistance to interference, and a number of other favorable technical characteristics, with the spread-spectrum ability to maintain the network capacity while extending its range. Throughout the paper, LoRa is used for benchmark comparison and quantification of various M-ASPM features. In particular, we demonstrate that, while sharing many essential properties with LoRa, M-ASPM provides far more effective network range extension. When used in the same manner as LoRa, M-ASPM can serve as an appealing LoRa alternative. In addition, while being different LPWAN solutions, M-ASPM and LoRa can be designed to concurrently operate in the same spectral band and geographical area, cooperatively complementing each other’s coverage.