This work deals with the spatial characteristics of the wind fields evaluated from synthetic aperture radar (SAR) images and simulated by the weather research and  forecasting (WRF) atmospheric model in the Gulf of  Oristano, a small coastal area about 10 km × 18 km wide in western coast of Sardinia (Western Mediterranean Sea). The SAR-derived wind fields have been obtained analyzing images of the COSMO-SkyMed, Radarsat-2, and Sentinel-1A satellites through a fully two-dimensional continuous wavelet transform (2-D-CWT) method. The analysis of the wind directions has shown that the model variability is limited if compared to that inferred by 2-D-CWT method, which mostly respects the variability evidenced by in situ data. As the use of model directions to compute the SAR wind fields is a standard in many studies, the impact on the SAR wind speed retrieval of using the model instead of the SAR-derived directions has been assessed: differences of wind speed greater than ±10% occur for about the 20% of data. The spatial variability of the SAR and model wind speed fields results quite different at both local and domain scales. The knowledge of the spatial variations of the surface wind fields can be very important for the oceanographic applications and constitutes the added value brought by SAR in the description of the coastal wind. For this reason, the SAR-derived wind fields should be taken as reference in many kind of applications.

Synthetic aperture radar (SAR) images of the sea surface often show roll-vortex structures and other features which, in general, are spread out over several length scales and may present spatial periodicity as well as intermittence.  Standard techniques, such as two-dimensional (2-D)  Fourier analysis, are unsuitable both when it is of interest to detect intermittent phenomena and to analyze the spatial disposition of the backscatter structures inside the SAR images. For the above reasons, the 2-D continuous wavelet transform analysis has been applied to two European Remote Sensing mission SAR images over the Mediterranean Sea, showing wind rolls and atmospheric gravity waves. Through the evaluation of the wavelet variance map, which ideally corresponds to the 2-D Fourier spectrum, it has been possible to assess the presence of two main energy areas at large (from 7–28 km) and small (from 0.5–2 km) spatial scales. While the large-scale fluctuations may be ascribed to atmospheric gravity waves and other features induced by the surroundings, the small-scale fluctuations reveal the inner structure of the atmospheric wind rolls. The SAR-like maps, obtained by adding the wavelet coefficient maps pertaining to the small scales, have permitted to highlight the high- and low-intensity backscatter cells associated with the wind rolls. These cells have been statistically characterized by means of the frequency distributions of the size of the cells maximum and minimum axes, of the orientation of the maximum axis, and of their area. The results indicate that high- and low-intensity backscatter cells have similar characteristics in both cases studied: they appear of elliptic shape, with the major axis along the wind roll direction; the average axes ratio is 2.5:1. The frequency distributions of the cells area indicate a continuous distribution of sizes, without significant gaps.

preprint version of the paper submitted to Transactions of Automation Science and Engineering

We present the follow-up of our previously published work, where we described a wavelet-based method to characterize the sea surface backscatter structures in Synthetic Aperture Radar (SAR) images. The method relies on the ability of the 2-D continuous wavelet technique to detect the spatial structure of the Marine Atmospheric Boundary Layer (MABL) and to isolate wind-related cells and features. The analysis of the cells’ geometry, molded by the radiometric characteristics of the sea surface, permits the identification of the wind direction inside the cells, due to the along-wind asymmetry of backscatter structures, and thus the computation of the wind speed through standard algorithms. Twenty-one SAR images (ERS-2 and Envisat ASAR Wide Swath) over the Mediterranean Sea have been analyzed, and the results are compared with satellite wind fields. The images cover a range of meteorological conditions from low to moderate winds. Comparison of the SAR-derived wind fields with those provided by satellite scatterometers indicates a good score of success (roughly 70%–80%). The developed methodology, once tested over an adequate number of images to derive statistically reliable results, could be routinely used to enrich SAR images with the wind field as well as to characterize the MABL in terms of size, distribution, and shape of the backscatter cells.

Vehicle-mounted multichannel ground penetrating radar (MC-GPR) facilitates acquisition of volume images of subsurface structures, but inconsistencies arise due to different transmitted wavelets from each antenna. This paper introduces a methodology, Reflectivity-Consistent Sparse Blind Deconvolution (RC-SBD), that estimates transmitted wavelets and stationary clutter for each antenna, thereby calibrating GPR volume images. Previous Sparse Blind Deconvolution was achieved by alternating minimization, producing a single transmitted wavelet and sparse ground reflectivity. RC-SBD utilizes an assumption of subsurface reflectivity smoothness in the horizontal direction, expressed via a total variation regularization term. This allows a wavelet to be derived for each channel. Stationary clutter variables, such as reflection from the vehicle itself and direct waves, are integrated into the model for simultaneous estimation. The objective function incorporates several ℓ2 and ℓ1 regularization terms and is solved using the Split Bregman algorithm augmented by a gradient method. Hyperparameters are determined through Bayesian optimization, aiming to maximize kurtosis of the frequency domain calibrated volume image. The proposed methodology was validated with synthetic data, demonstrating accurate wavelets estimation and significant denoising of volume image. Real-world data application revealed substantial enhancements in the channel depth cross section, visualizing responses of structures such as rebar and steel plates. Furthermore, calibrated image remained stable across diverse datasets, including earthwork and bridge sections, indicating the estimates’ versatility.

This work aims to assess the impact of computational resources on the Quality of Experience (QoE) of video streaming services delivered over vRAN-based networks. The obtained results prove that inadequate resource assignment to vRAN instances leads to degradation of the QoE, even if the allocation of radio resources is adequate for the service

Surface electromyography (sEMG) is a non-invasive technique that records the electrical signals generated by muscles through electrodes placed on the skin. sEMG is the state-of-the-art method used to control active upper limb prostheses because of the association between its amplitude and the neural drive sent from the spinal cord to muscles. However, accurately estimating the kinematics of a freely moving human hand using sEMG from extrinsic hand muscles remains a challenge. Deep learning has been recently successfully applied to this problem by mapping raw sEMG signals into kinematics. Nonetheless, the optimal number of EMG signals and the type of pre-processing that would maximize performance have not been investigated yet. Here, we analyze the impact of these factors on the accuracy in kinematics estimates. For this purpose, we processed monopolar sEMG signals that were originally recorded from 320 electrodes over the forearm muscles of 13 subjects. We used a previously published deep learning method that can map the kinematics of the human hand with real-time resolution. While state-of-the-art myocontrol algorithms essentially use the temporal envelope of the EMG signal as the only EMG feature, we show that our approach requires the full bandwidth of the signal in the temporal domain for accurate estimates. Spatial filtering had a smaller impact and low-order spatial filters may be suitable. Moreover, reducing the number of channels by ablation resulted in large performance losses. The highest accuracy was reached with the highest number of available sensors (320). Importantly and unexpected, our results also showed that increasing the number of channels above those used in this study may further enhance the accuracy in predicting the kinematics of the human hand. We conclude that full bandwidth high-density EMG systems of hundreds of electrodes are needed for accurate kinematic estimates of the human hand.

A document by Kyoya Uemura . Click on the document to view its contents.

Providing high throughput and quality of service in modern stadiums necessitates the placement of hundreds of access points (APs). Optimizing the locations of APs in such venues via measurements requires significant resources. Even simulation methods, such as ray-tracing, can be computationally costly. We provide a solution to this problem by building a propagation model based on machine learning (ML) that rapidly predicts received signal strengths in stadiums. We train the model with a small set of simulated data generated by a ray-tracer. We use input features, such as the electrical distance between the transmitter and the receiver and the antenna gain along the direct path between the two, to generalize to new transmitter locations, antenna patterns and stadium geometries. Geometry and pattern generalization have not been included in existing propagation models for stadiums. Finally, we present a novel sampling approach for the input features in a given stadium, ensuring the computational efficiency and accuracy of the ML model. The results demonstrate the accuracy of our propagation model for new transmitter locations, patterns and stadiums. The trained model is also considerably faster than a ray-tracer, making it an efficient tool for resource planning tasks, such as optimal placement of APs.

Ensuring dependable quality of service (QoS) and quality of experience (QoE) for computation-intensive and delay-sensitive applications in vehicles can be a challenging task that impacts performance. While multi-access edge computing (MEC) based vehicular edge computing network (VECN) and vehicular cloudlets (VC) enable task offloading, but their prompt and optimal accessibility is another challenge. The conventional wireless technologies may not suffice to meet the stringent ultra-low latency and cost constraints of such applications. Nonetheless, the combination of different wireless technologies can enhance network performance and satisfy these requirements. Focusing on the computational efficacy of VECN, this paper proposes a  mobility, contact, and computational load-aware (MCLA) task offloading scheme for heterogeneous VECN. The MCLA scheme dynamically considers the mobility, contact, and computational load of vehicles for making task offloading decisions. To optimize the performance, the MCLA scheme integrates the Mode-1 and Mode-2 of the 5G-NR-V2X standard, along with mmWave communications. The MCLA scheme provides an opportunistic switching mechanism between these modes and heterogeneous radio access technologies (RATs) to reduce communication delays and costs. Moreover, the MCLA scheme leverages public vehicles (i.e., public buses), in proximity by using their computational power to manage computational latency and cost. Furthermore, it also considers the shareable computations from passengers’ mobile equipment within the public vehicle to improve the computation capacity of the public vehicles. Extensive evaluations and numerical results show that the proposed MCLA scheme significantly improves the task turnover ratio by 4%-15% with 4.7%-29.8% lower transmission and computation costs.

Microwave structure behavior prediction enables the estimation of circuit response over a frequency range, playing a crucial role in the design of radio frequency (RF) structures. Deep neural network (DNN) approaches have demonstrated their capability to simulate microwave structure behaviors. Nonetheless, the quality and utility of the model are constrained by the availability of data and computational capabilities. These inherent disadvantages hinder the extensive application of DNN in microwave structure behavior prediction. Transfer learning has recently been produced as a method offering improved accuracy and speed for predicting microwave circuit behavior. This paper proposes a novel transfer learning-based model to expedite the prediction process for a sequence of frequency samples. Through experimental validation, it is illustrated that the proposed methodology outperforms the conventional DNN techniques for microwave structure behavior prediction by effectively reducing the required data and shortening the training time. The proposed model also facilitates the fine-tuning of hyperparameters and reduces the simulator computing load.

A four-dimensional (4D) general super-resolution imaging method (called PSF modulation method) was developed via modulation of an arbitrary point-spread-function (PSF) of an imaging system such as an ultrasound and optical system. The highest image resolution achievable will only be limited by the noises in the images. Theory, computer simulations, and experiments were presented. A pulse-echo image of a super-resolution (0.65 mm versus a two-way diffraction-limited resolution of 2.65 mm, or 4.08-fold improvement) and 31-dB signal-to-noise ratio (SNR) was obtained with an ultrasound experiment using a 0.5-mm diameter modulator and 1.483-mm wavelength. Another ultrasound experiment shows that a wave source was imaged at a super-resolution (0.5 mm versus a one-way diffraction limited resolution of 3.63 mm, or 7.26-fold improvement) at about 30-dB SNR, using the same modulator size and wavelength. In addition, a PSF-weighted super-resolution imaging method based on the PSF modulation method was developed. This method is easier to implement but may have some limitations. With a proper choice of a modulator (e.g., a quantum dot) and imaging system, nanoscale (a few nanometers) imaging is possible using the methods developed.Â

A document by Leandro Carísio Fernandes . Click on the document to view its contents.

This article describes the design and analysis of a 2.5-MW, 5000-rpm electric motor with a slotted armature employing REBCO high-temperature superconductors (HTS). The alternating current and field in the armature induces AC losses in the superconductors, requiring cryogenic cooling. Therefore, the aim is to design a machine with sufficiently low losses to make this cooling realistic, which simultaneously outperforms the state-of-the-art. The reasoning behind the key design choices is presented before the model used for two-dimensional (2-D) finite element analysis (FEA) is described. Then, HTS AC losses are studied with the T-A-formulation, examining the impact of various operating conditions. Aligning the HTS tapes with the field was found to successfully reduce AC losses, while filamentization was only successful for more than 10 filaments. The final design had an estimated efficiency of 99.8%, an active torque density of 50.9 Nm/kg, and a cryogenic cooling power requirement of 0.05% of the output power.

This study considers a dual-polarized intelligent reflecting surface (DP-IRS)-assisted multiple-input multiple-output (MIMO) single-user wireless communication system. The transmitter and receiver are equipped with DP antennas, and each antenna features a separate phase shifter for each polarization. We attempt to maximize the system’s spectral efficiency (SE) by optimizing the operations of the reflecting elements at the DP-IRS, precoder/combiner at the transmitter/receiver, and vertical/horizontal phase shifters at the DP antennas. To address this problem, we propose a three-step alternating optimization (AO) algorithm based on the semi-definite relaxation method. Next, we consider asymptotically low/high signal-to-noise ratio (SNR) regimes and propose low-complexity algorithms. In particular, for the lowSNR regime, we derive computationally low-cost closed-form solutions. According to the obtained numerical results, the proposed algorithm outperforms the various benchmark schemes. Specifically, our main algorithm exhibits a 65.6 % increase in the SE performance compared to random operations. In addition, we compare the SE performance of DP-IRS with that of simple IRS (S-IRS). For N = 50, DP-IRS achieves 24.8 %, 28.2 %, and 30.3 % improvements in SE for 4×4, 8×8, and 16×16 MIMO, respectively, compared to S-IRS.

Mechanics are one of the pillars of gameplay, enabled by the underlying implementation of the game. In particular, self-amending mechanics are mechanics that change themselves dynamically and are a common source of duplicate and coupled code because they occur in multiple situations using specific interactions. The Rulebook is an architectural pattern that generalizes how developers deal with these issues, based on a careful research process including a systematic literature review, semi-structured interviews with professional developers, and quasi-experiments. The pattern codifies changes to the game state as “effect” objects, which it matches against a dynamic pool of rules. Each rule may amend, resolve, or chain effects. This design prevents the rest of the game from becoming coupled to the specific interactions of these mechanics while also promoting an extensible and flexible structure for self-amendment. This paper details the Rulebook pattern and presents a case study demonstrating its design process in three different implementations of open-source jam games. Together with the typification of self-amending mechanics, this article formalizes a novel, state-of-the-art toolset for architecting games.

Vertical cavity surface emitting lasers (VCSEL) are of great interest for photonic and telecom applications, however challenges in fabrication of efficient VSCEL GaN devices are yet to be resolved. In this work we present a study of micro-photoluminescence (PL) emission from a novel InGaN quantum well (QW) emitting structure with integrated micro-cavity, which can be used for VCSEL applications. The micro-cavity exhibiting Tamm plasmon optical states is formed by porous GaN distributed Bragg reflector (DBR) bottom mirror and top plasmonic silver metal mirror. Results of PL, Fourier imaging spectroscopy and finite-difference time-domain simulations are presented and discussed. An estimated 8.5 times enhancement of QW PL intensity at around 480-500 nm and a red-shift of QW peak emission is attributed to the Tamm plasmon resonance in the cavity at around 514 nm.

Most existing road infrastructures were constructed before the emergence of automated vehicles (AV) without considering their operational needs. Whether and how AV could safely adapt to as-built highway geometry remain inconclusive, and a plausible concern is a challenge from vertical alignments. To fill this gap, this study uses virtual simulation to investigate the available sight distance (ASD) for AV on vertical alignments subject to the current highway geometric design specification, and its implications for speed limits. According to the scenario generation framework, several scenarios featuring vertical geometric elements and the light detection and ranging (LiDAR) sensor were created and tested. Moreover, the maximum speed for adequate ASD is calculated to determine the AV speed limit, considering safe sight distance and speed consistency requirements. The results indicate that crest curves are not disadvantaged in ASD compared with either the sag curves or tangent grades. Only equipped with multi-channel LiDAR and advanced perception algorithms enabling a lower detection threshold, would Level 4 AV be compatible with the as-built vertical alignment with a design speed (Vd) of 100 km/h. However, Level 3 AV can only adapt to the vertical profile with Vd = 60 km/h. The findings of this study should be of interest to the road-oriented operational design domain and support road administrators in regulating AV safe speeds.