This letter addresses the challenge of determining the maximum sum rate achievable in high mobility scenarios with time and frequency selectivity in 5G V2X communications, particularly in the context of IoT/sensor networks. We derive a closed-form expression for the maximum sum rate of a multicarrier non-orthogonal multiple access (NOMA) system with 𝑛 users, and validate our theoretical results through simulations by comparing them with traditional orthogonal schemes. Our work provides valuable insights into the potential of NOMA for high-speed V2X communications, and offers a novel contribution to the existing literature by presenting a closed-form expression allowing the development of efficient and reliable 5G V2X systems.

The term ”Virtual Reality’‘ (VR) has been in use since 1938, but the recent barrage of jargon, confusing new terminology, and the hype around VR and a plethora of related realities (AR, MR, etc.) have made it necessary to bring some order to the ”Realities’‘; in fact, it has been largely recognized that there are a lot of ”grey’‘ areas between the Realities, making it desirable to embrace an overarching vision. Throughout the world, many have chosen to use the framework of eXtended Reality (XR) as this unifying overarching concept to interpolate between the ”Realities’‘ and to eXtrapolate beyond them. Together with XR, there is also XI (eXtended Intelligence) for which the IEEE has convened the Council on eXtended Inteligence, or CXI. A related concept is the metaverse, i.e. shared VR, introduced in 1974 as ”Metavision’‘ and ”Metaveillance’‘ and recently popularized by Facebook. In this paper we propose XV as an overarching term, concept, and taxonomy for shared (social) XR across all of the ”Verses’‘, including the universe (physical reality, i.e. ”atoms’‘), the metaverse (virtuality, i.e. ”bits”), the omniverse and multiverse, etc.  We also briefly outline the state-of-the-art in the various realties and verses covered by XV.

The development of the networked battlespace as a concept, and the wealth of wired and wireless communications to support it have opened the door to truly multidimensional radar systems. Whether as a static network of radars coordinating to provide national coverage, or a local network for UAV's providing a swarm aperture for rapidly reconfigurable airborne radar, there is an urgent need for a flexible radar channel model to empower the engineer and scientist to develop new systems, sensing algorithms and concepts of operation. LyceanEM, an open source electromagnetics model is presented as a solution to this issue, allowing for multi source, heterogeneous antenna arrays to be simulated together with a radar target.

We report high-power modified uni-traveling carrier photodiodes with 150-GHz bandwidth. To improve the RF output power, the photodiode chips are flip-chip bonded on diamond submounts with high thermal conductivity to enhance heat dissipation. Measured 3-dB bandwidths of 4-μm, 6-μm, 8-μm, and 10-μm diameter photodiodes reach 150 GHz, 129 GHz, 117 GHz, and 105 GHz, respectively. RF output power with 12.7 dBm at 90 GHz, 11.3 dBm at 100 GHz, 8.5 dBm at 110 GHz, -3 dBm at 150GHz, and -5.7 dBm at 165GHz are achieved.

Correct use of home appliances is intended to avoid property damage and life unsafety. Also, serious fire accidents must affect the safety of one’s neighborhood. Therefore, this study uses the Internet of Things (IoT) platform to build an intelligent system that aims to measure the temperature rise of home appliances, which is employed to monitor the usage states of home appliances in real time. Hereby, the microcontroller, Node Micro Control Unit (NodeMCU-32S), is used to develop an IoT platform combined with sensors for measuring AC voltage, current, and ambient temperature so that the electrical power and temperature rise of load can be detected. Meanwhile, the smart measurement system is integrated with mobile devices to upload the detected datasets to the Google cloud database system. Thus, it can be used as samples for future calibration of system parameters with machine learning. Moreover, the verification of feasibility and soundness of a system model is performed by using Petri net tool, WoPeD, for the purpose of eliminating the improper states to optimize the system performance. Finally, the experimental results show that the proposed IoT-Enabled system has promising precision, 94.17％, and recall, 92.26％, which obviously outperforms other existing systems.

Picture segmentation is a crucial task in  computer vision that involves dividing an image into multiple  regions or segments based on various criteria, such as color,  texture, or edge characteristics. Changing artifacts  identification involves identifying and removing artifacts that  are present in the image and affecting the accuracy of segmentation. Artifacts can be caused by various factors, such  as noise, lighting conditions, or image compression.  Identifying and removing these artifacts can improve the  accuracy of segmentation algorithms. Bias modification  involves adjusting the biases present in segmentation  algorithms to improve their accuracy. These biases can lead  to errors in segmentation, such as over-segmentation or  under-segmentation. Bias modification can involve adjusting  the algorithm’s parameters or using different training data to  improve its performance. The use of changing artifacts  identification and bias modification can significantly improve  the accuracy of picture segmentation. By identifying and  removing artifacts and adjusting biases, segmentation  algorithms can more accurately identify and classify objects  in an image, leading to better object detection and  classification. This model is expected to be useful for  damaged images and applications where artifacts and bias are  actual features of interest, such as lesion detection(problem  within medical imaging analysis) and bias field correction in  medical imaging, e.g., Magnetic resonance imaging (MRI).

Reconfigurable aerial platforms such as multicopter unmanned aerial vehicles (UAVs) allow the design of fail-safe systems because of inherent redundancy in actuators and sensors to maintain stability with a reduction in flight performance. The methods based on univariate and multivariate time series analysis of just the attitude signals can pave the way for model-free systems that can be generalized across a class of UAVs like multicopters. In this paper, we present a critical analysis of real-flight attitude time-series signals and investigate them for data-driven motor fault and failure detection and isolation (FDI), specifically for multicopters configurations like quadcopters and hexacopters. We analyze flight data for different scenarios of outdoor flights, healthy and faulty, hovering and cruising, loss of efficiency, and single-rotor failure of every motor. We tested it for small to medium-sized multicopters. The failure detection and classification are performed without relying on analytical system modeling or the knowledge of the controller. Thus, we perform three major assessments: vector auto-regression (VAR) using residual variance, time-frequency analysis, and dimensionality analysis of the recorded variables to support the classification framework. To the author’s best knowledge, this is the first attempt at providing an algorithmic foundation that works on existing hardware and flight controllers using streaming flight attitude data rather than simulations. This allows us to implement these online methods in real-time on multicopters, which drastically increases levels of safety and scalability of unmanned flights in drone applications.

Learned Pair Weighting and Contextual Memory for Deep Metric Learning.

This paper presents a differential delay cell-based ring voltage-controlled oscillator (RVCO). The proposed RVCO (PRVCO) introduces auxiliary charging and discharging paths in conventional ring VCO (CRVCO) that enhance the maximum operating frequency and frequency range. Adding auxiliary paths does not increase the maximum current in PRVCO within different time instants in one period compared to other available RVCOs. Thus, it maintains the phase noise performance. The post-layout simulations of the four-stage PRVCO were performed in 65nm CMOS technology with 1V supply voltage. The maximum operating frequency of the four-stage CRVCO, and PRVCO, are 585MHz and 3.2 GHz, respectively. The PRVCO improves the maximum operating frequency significantly compared to CRVCO. The PRVCO has a linear tuning range from 1.8 GHz to 3.2 GHz, which is 78% better than the CRVCO. The phase noise of the PRVCO is -103.8 dBc/Hz at an offset frequency of 1 MHz. The figure of merit (FoM) is improved by 11 dB compared to CRVCO in the same technology node of 65nm CMOS.

This paper presents the collection of 154 new images and results, obtained after performing many known image processing algorithms and digital image transformations techniques like adding noises namely Gaussian with variances, Poisson and speckle with variances; double precision, methods of Sobel, Prewitt and Robert; de- hazing algorithm, pixel intensities, vector quantization, k-means clustering, fuzzy logic and morphological segmentation. Images used in this paper are two X-Rays having the lateral and Posterior to Anterior (PA) views of the patient’s lungs who was diagnosed from Chlamydia Pneumonia which is a lung infection caused by bacteria of family Chlamydiaceae and order Chlamydiales, which causes respiratory tract infections and illness by damaging the lining of the respiratory tract including the throat, windpipe, and lungs. Chlamydia pneumoniae is a small gram-negative bacterium (0.2 - 1 μm). All results are included in the paper as well as separate images have also been uploaded on the open-access page. The data has been analyzed with histogram and mathematical polynomial fitting equations and matrices have been also submitted which can used in the digital testing of machines or computer diagnostic solutions. As these results reveal certain patterns, these further processed X-Ray results can help researchers or doctors in understanding ailment or diagnosing and to pharmacologists in making medicine or machine/therapy.

This is the second part of a two-part paper series. In the Part 1 paper, the authors summarize the utility workshop outcomes and low-voltage network modeling approaches. In this Part 2 paper, we developed and validated an accurate network protector relay model and interfaced a commonly used network protector relay hardware with our real-time simulation system. Hardware-in-the-loop protection studies are performed to assess the impact of distributed energy resources and benchmark a mitigation strategy. Simulation results suggest that the network protector reverse trip and auto-reclose functions are negatively impacted by the high distributed energy resource penetration. To accommodate DER backfeed while remaining secure and reliable for faults on primary feeders, we recommend options for a rate-of-change-based blocking scheme and a protection setting change. Finally, future mitigation ideas and standard revisions are discussed.

The impending sixth-generation wireless communication networks are anticipated to guarantee mass connectivity, high integration, and lower power consumption for generating the required beamforming. To achieve these goals, an artificial intelligence (AI) framework is proposed by utilizing holographic MIMO-assisted integrated sensing, localization, and communication. The proposed AI framework ensures lower power consumption to activate the minimum number of grids from the holographic grid array for the generation of holographic beamforming. An optimization problem is formulated to maximize the signal-to-interference-plus-noise ratio received by the users, which in turn maximizes the utility function for sensing considering the user distances, beampattern gains, sensingcommunication loss, and dense locations controlling parameter. A novel AI-based framework is proposed to solve the formulated NP-hard optimization problem by decomposing it into two subproblems: the sensing problem and the communication resource allocation problem. First, a variational autoencoder (VAE) based mechanism is devised to solve the sensing problem mitigating the disputes to obtain the users’ exact location. Second, a sequential neural network-based scheme is utilized to allocate the communication resources to the heterogeneous users for generating the desired beamforming based on the findings of the VAE-based mechanism. Moreover, an extreme case power allocation strategy is presented once a large number of users enter the system. The extreme case power allocation strategy applies when the total power prediction exceeds the total system power for allocating the communication resources to the users. Finally, simulation results validate that the proposed AI-based framework outperforms the long short-term memory method with a cumulative power savings of 34.02% taking the ground truth power into account. Therefore, the proposed AI framework generates effective beamforming to serve the communication users.

In this human activity recognition dataset, there are a total of 53 participants, consisting of 33 adults and 20 youths. In a laboratory experiment, individuals were given a set of synchronized sensors that were attached to their wrist and ankle using customized Velcro bands. These sensors, which were called Triaxial Wocket accelerometers, were lightweight, small, and thin. While wearing these sensors, the participants performed everyday tasks, and the raw acceleration data was collected at a rate of 90 Hz and transmitted wirelessly to a smartphone via Bluetooth.

This paper proposes a Bellman Deviation algorithm for the detection of man-in-the-middle (MITM) attacks occurring when an agent controls a Markov Decision Process (MDP) system using  model-free reinforcement learning. We show an intuitive necessary and sufficient “informational advantage” condition  for  the proposed algorithm to guarantee the detection of attacks  with high probability, while  also avoiding false alarms.

This paper proposes a novel methodology to overcome the outstanding challenges in effectively assessing technology maturity to final real-world applications. The Engineering Severity Level (ESL) is a conceptual tool that provides a simple, standardised and quantitative method of assessing technology suitability to real-world environments. It is a standalone tool offering advantages in generating an evidence base for accurate technology maturity assessment and early identification of developmental roadblocks. Moreover, the tool can be applied as a universal basis to enhance TRL classification and provide further context and quantitative analysis. The benefits are exemplified in the assessment of quantum technologies for Position, Navigation and Timing (PNT) requirements in Defence applications.

An unsupervised machine learning algorithm utilizing nonlinear dimensionality reduction has been developed to interface with spectroscopic measurements from an atomic rubidium optical magnetometer. The magnetometer uses Electromagnetically Induced Transparency (EIT) optical spectroscopy with atomic vapor cells in order to observe magneto-sensitive spin transitions of the 87Rb atom. The Rb EIT measurements result in the observation of seven transmission peaks associated with the available two-photon resonances between the Rb D1 line Zeeman sublevels. The developed algorithm correlates the waveform of the EIT spectra and the directionality of the local magnetic field using kernel principal component analysis (KPCA). The analysis, implemented with a radial basis function (RBF) kernel trick, was found to neatly cluster the EIT spectra according to the direction of the local magnetic field in the reduced dimensional feature space. The KPCA projections demonstrated a smooth curve in a three-dimensional feature space, and the resulting curve was modeled with a support vector regression (SVR) machine. We found that if the direction of the magnetic field is undefined initially, the measurement accuracy is better than 3 degrees in both azimuthal and longitudinal angles with respect to the real direction of the magnetic field. If the magnetometer is configured so that the azimuthal angle of the field is defined, the KPCA- SVR algorithm is capable of predicting the longitudinal angle of the local magnetic field within 1 degree of accuracy. These results suggest that the dimensionality reduction methods may be a powerful tool for approaching regression problems in optical spectroscopy.

When humans and robots work together, ensuring safe cooperation must be a priority. This research aims to develop a novel real-time planning algorithm that can handle unpredictable human movements by both slowing down task execution and modifying the robot's path based on the proximity of the human operator. To achieve this, an efficient method for updating the robot's motion is developed using a two-fold control approach that combines B-Splines and Hidden Markov Models. This allows the algorithm to adapt to a changing environment and avoid collisions. The proposed framework is thus validated using the Franka Emika Panda robot in a simple start-goal task. Our algorithm successfully avoids collision with the moving hand of an operator monitored by a fixed camera.

The growing distributed energy resources (DER) penetration in the low-voltage network (600V and below) challenges the existing protection philosophy and practice. To assess the impact of high DER penetration, the authors built a representative low-voltage network model in real-time electromagnetic transient software and performed hardware-in-the-loop (HIL) protection studies. In the first stage of the effort, the authors invited four major U.S. utilities with low-voltage networks to a technical workshop to survey the modeling and study needs. Guided by the workshop discussions, the authors developed various real-time simulation models, including a low-voltage network model, a model of a commonly used network protector relay, and DER models. Finally, the authors conducted hardware-in-the-loop protection studies to investigate and mitigate the high DER penetration impacts. Part 1 of the paper summarizes the technical workshop outcomes and low-voltage network modeling approaches. Part 2 of the paper reports the HIL simulation setup, high DER penetration impact assessment, and benchmark results of a promising mitigation solution.