Over-the-air computing (AirComp) has recently attracted considerable attention as an efficient method of data fusion by integrating uncoded communication transmissions with computation thanks to the signal superposition offered by the multiple access channels. However, appropriate processing is required to neutralize the wireless channel effect. As, internet-of-things (IoT) applications through low-cost devices is the main target of AirComp, perfect availability of channel state information (CSI) is not always practical, there is the need to investigate the effect of imperfect CSI on AirComp. Specifically, we present novel closed-form expressions for tight approximations that can be used to design and evaluate AirComp systems. Furthermore, we design a general optimization framework that takes into account both magnitude and phase errors in the CSI. Finally, a pilot retransmission policy is designed, that offers trade-off between resources cost and the gain in the accuracy of the computations. In order to validate its application, a utility function of the cost of retransmission is introduced, namely, Retransmission Policy Cost (RPC), which can incorporate the power or throughput cost opposing to the expected gain of the selected policy. Simulations show the deterioration caused by the imperfect CSI and highlight the added value of the proposed policy under various system conditions.Â

A comprehensive comparison between two multi-line thru-reflect-line calibration techniques is provided. One of the methods is based on the classical combination of N measurements following the Gauss-Markov theorem. The other one has been developed along this paper in a complete and detailed way using the recently published idea of considering each of the N measurements as part of a tensor. The measurement of a calibration kit that covers from 500 MHz to 20.5 GHz has been carried out for both methods and the results discussed. From the analysis of the results, it seems that the tensor approach could show some advantages in the (not uncommon) cases where a strictly white and Gaussian noise is not the only perturbation of the measurements. On the other hand, the decomposition of the tensor requires a higher computational effort, which would better be reduced.

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. The main disadvantage of calculations on quantum computers is their non-determinism. For optimization problems, of course, it is possible to get surprisingly good results but without a guarantee of the actual optimality of the result, i.e, an assurance that there exists no better solution which could potentially be found by the quantum machine. In this paper, we propose an novel approach that provides such a guarantee of optimality. We generate a solution that is optimal in the strict mathematical sense, without probabilistic considerations. To accomplish this, we use the D-Wave quantum machine working as a sampler implementing quantum annealing – an approach considered a hardware metaheuristic – to obtain upper and lower bounds on the value of the objective function of the problem under consideration. Then, we use the Branch and Bound scheme controlled by quantum annealing. This allows us to obtain very quickly – in constant time – the boundaries of the considered subproblems. Our approach is a combination of calculations realized on a quantum machine controlled by procedures implemented on a classical computer, allowing us to generate optimal solutions to the NP-hard problems of task scheduling on a single machine with a total weighted tardiness criterion.  The proposed quantum algorithm shows a significant advantage over the classical CPU approach both in terms of calculation time and the number of generated solution tree nodes.

The concept of mode division multiplexing also known as space division multiplexing was introduced as an alternative to combat the approaching capacity crunch in single mode fibers. Just like single mode fibers, space division multiplexed fibers will experience non-linearity at a different level and studies have shown that some linear effects can be beneficial in combating the nonlinear interference. This study aims to identify the benefits accrued when these linear effects are implemented by exploring the already existing models defined in the literature.

The paper addresses the effect of the ionosphere on the ELF and lower frequency waves excited in the Earth-ionosphere waveguide by a controlled source. The experiment carried out on the Kola Peninsula is described and the results of measurements in the frequency range 0.4--95 Hz are presented. Non-monotonic behavior of the magnetic field with time is revealed. It is shown that variations in the magnetic field are related to the state of the ionosphere and depend on the geomagnetic activity. The importance of the effect of the topside ionosphere on the structure of the studied field is discussed.

Automated individual tree crown (ITC) delineation plays an important role in forest remote sensing. Accurate ITC delineation benefits biomass estimation, allometry estimation, and species classification among other forest related tasks, all of which are used to monitor forest health and make important decisions in forest management.  In this paper, we introduce Neuro-Symbolic DeepForest,  a convolutional neural network (CNN) based ITC delineation algorithm that uses a neuro-symbolic framework to inject domain knowledge (represented as rules written in probabilistic soft logic) into a CNN.  We create rules that encode concepts for competition, allometry, constrained growth, mean ITC area, and crown color.  Our results show that the delineation model learns from the annotated training data as well as the rules and that under some conditions, the injection of rules improves model performance and affects model bias.  We then analyze the effects of each rule on its related aspects of model performance.

The exponential growth of cloud computing within the last decade has led to a severe consideration about the power requirement of the cloud data center. However, variations in the computing resource demands and fixed size of virtual machines (VMs) lead to a poor resource utilization, load imbalance, and excessive energy consumption within the data center. Dynamic VM consolidation effectively handles these problems, where VMs are placed on as few physical machines (PMs) as possible. Concurrently, VM allocation must be done strategically by considering various factors of the computing resources for minimal exploitation of them. The high frequency of VM consolidation may lead to the degradation of the system’s reliability by assigning VMs to unreliable PMs. A novel Quantum Machine learning driven Reliable Resource Allocation (QM-RRA) model is proposed to address these problems and enhance the data center’s reliability. We handle the problem by applying an Evolutionary Quantum C-Not Neural Network (QCNN) unit to forecast future resource usage at the cloud data center. In addition, Quantum Evolutionary Adaptive Algorithm (QEAA) is utilized to optimize qubit network weights. It offers energy saving by minimizing the number of active PMs, maximizing resource utilization, and enhancing the reliability of the overall data center. The QM-RRA model is evaluated by performing experiments on the Google Cluster Data set (GCD). The observed results are compared with the state-of-art methods and shows its supremacy in terms of different performance parameters like resource utilization, power consumption, and the number of active PMs. There is a significant reduction in power consumption and the number of active PMs up to 31.38%  and 44.93%, respectively. The enhancement in resource utilization is up to 25.61%, and overall system reliability is 63.35%.

Industrial Metaverse is a novel ecological system for modern industry. One of the most inspiring parts of Industrial Metaverse lies in the potential to restructure the industrial chain and to provide added value to all the stakeholders therein. It is being enabled by the deep intertwining of SOTA ICT (information & communication technology) and the real industrial economy. In this paper, we envision how the industrial metaverses form a higher-dimensional world on top of the physical world we live in and unfold discussion along the dimensions of “the enabling technologies” and “potential industrial application scenarios”. We will report some notable observations of pioneering projects from today’s industry/market, and then discuss the industrial metaverses from the perspectives of developers and users, respectively showing the technical requirements and the main benefits. A summary is given regarding the growing interest of industrial metaverses.

The paper deals with the processing and analysis of the results of electro-optical studies of the pre-breakdown stage of an electric discharge in water using the Kerr effect. For this, the first chronogram was selected, obtained in the process of electro-optical chronographic research of water breakdown. In this work, it was possible to explain not only the features of the chronogram but also to obtain an estimate of the intensity of the appearance of the anode streamer in water E ~ 40-50 MV/cm. Using the method of computer simulation in the point-plane system near the anode point, the distributions of the electric field strength and the phase difference were constructed. The calculations were carried out both at a constant relative permittivity and its nonlinear dependence on the electric field strength. It turned out that dipole saturation is observed near the tip. As a result, the Kerr bands become denser and, at a spatial resolution of the recording equipment of 30 μm, in the pre-measuring zone, they are not recorded on the chronogram.

In the foreseeable Intelligent Transportation System (ITS), Intelligent Connected Vehicles (ICVs) will play an important role in improving travel efficiency and safety. However, it is challenging for ICVs to support the resource-hungry autonomous driving applications due to the limitation of hardware computing power. Fortunately, the emergence of Multi-access Edge Computing (MEC) helps overcome this limitation effectively. In this paper, we investigate the vehicle and edge server collaborative computation offloading problem by jointly optimizing the partial offloading ratio and resource allocation ratio.

This letter proposes a time-efficient algorithm applicable for time-varying wireless networks, which sorts a predefined number of elements in a large data set that are larger or smaller than the other or located between two parts. Based on the mean and standard deviation, a theoretical analysis for Gaussian, uniform, negative exponential, Rayleigh, and unknown distributions is presented, which finds exact and approximate thresholds. Then, the theoretical and numerical analyses show the superiority of the proposed algorithm to the well-known Merge, Quick, and K-S mean-based sorting algorithms in terms of the time complexity, the running time, and the similarity measure.

This paper proposes a fault location identification method for power distribution systems with inverter-interfaced distributed generations (IIDGs). The proposed method is not restricted to any specific types of data and is able to fully utilize all available types of data in power distribution systems for enhancing the accuracy of results. The measurement set may include synchronized voltage and current measurements collected by Micro-phasor measurement units (Micro-PMUs) and/or unsynchronized measurements collected by legacy measuring devices, active and reactive power measurements, pseudo measurements of load values, and virtual measurements based on Kirchhoff’s laws. The proposed method explicitly considers uncertainties in the measurement set and is applicable for different load types. Simulations results on IEEE 34-bus feeder demonstrate the accuracy of the proposed method.

This brief proposes a low power mixed-signal foreground calibration algorithm of a pipeline ADC, using a digitally controlled re-configurable switched capacitor multiplying DAC (MDAC) gain controller. The proposed calibration technique forces the front-end stage MDAC gain towards its ideal value to achieve the ideal ADC output linearity. In this paper, a feedback mechanism has been employed to nullify the effect of change in MDAC gain from its ideal value by sensing a digital backend unit response. The proposed technique has been verified in 0.18 um CMOS process using the cadence virtuoso tool by simulating an 11- bit pipeline ADC which contains a 1.5-bit nonideal pipeline stage followed by a 10-bit linear back-end ADC (BE-ADC). Simulation results demonstrate the recovery of the lost linearity of a 11 bit pipeline ADC post calibration.

A method to assess the performance of control loops, based on closed-loop system identification is proposed. This method allows to take into account the trade-off between process variable and manipulated variable energy, thus over-coming one of the most important criticisms to Harris’ index. To illustrate the proposed approach, a numerical example is given, for which the proposed index is 8% greater than Harris’ index.

The intersection of Artificial Intelligence (AI) and art heralds a transformative phase in the evolution of creative expression, offering unprecedented possibilities and raising profound ethical questions. As AI-driven tools begin to occupy a prominent position in the art world, from generative visual artworks to music composition, critical concerns surrounding authorship, authenticity, cultural sensitivity, economic impacts, and audience manipulation emerge. This article delves into the multifaceted ethical landscape of AI’s application to art, exploring the tension between technological advancement and traditional notions of artistic value and integrity. Through various case studies, such as the ethical conundrums posed by DeepFakes in visual arts and AI-driven restorations of historic artworks, we scrutinize the broader implications for artists, audiences, and the art market. Conclusively, we reflect on the balance between innovation and ethical artistic practice, emphasizing the need for interdisciplinary discourse to navigate this nascent domain.

Flight delays represent a significant issue to airline profits and passenger satisfaction. Many factors can lead to a flight being delayed and/or canceled. The study evaluates flight delays, cancellations, and incident data with the goal of visualizing which airports, airlines, and cities, to visualize or states are experiencing the highest number of flight disruptions relative to others. Databases are commonly used for data analysis to allow pattern recognition and large data distribution and organization. The study will mainly serve the purpose of flight data retrieval, the compilation of data and database design, and finally output data visualizations.

In the past few years, 3D imaging technology has received a lot of attention. Time-of-Flight (ToF) cameras based on the indirect ToF principle calculate depth information by measuring the phase shift between the emitted periodic signal and the reflected signal. In this work, we aim to study the effect of dirt on 3D imaging systems quantitatively. To this end, ten sheets with different transmittances, between 50% and 100% are used. To the best of our knowledge, for the first time we provide standardized methods for evaluating the effect of dirt on protective covers in the depth estimation of “flash lidar”. The experiments have shown that the depth measurements obtained using the multi-frequency estimation method are more accurate in the presence of dust than the single-frequency estimation method.

Compared to simple mirrors, meta-mirrors have the ability to conserve the polarization of incident light in reflection. In previous decade, a lot of meta-mirrors designed for various applications. In this paper, a meta-mirror is demonstrated to simultaneously conserve the polarization of incident light and the circular dichroism.  A conventional chiral structure is used to design the meta-mirror in microwave regime. The chiral structure is C2-symmetric (breaking the rotational and mirror symmetries) for chirality inclusion into the meta-mirror. The design methodology uses the principle of febry-perot cavity which includes a sandwiched geometry. The substrate used to design the structure is Roger 5880 (a low-loss material at higher frequency bands) with copper material for chiral structure and back reflector.  Moreover, the meta-mirror shows the insensitivity towards incident angle of light. This meta-mirror can have a lot of applications in optical setup, communication, chiral absorption, spin-dependent reflections etc.