Typical video compression systems consist of two main modules: motion coding and residual coding. This general architecture is adopted by classical coding schemes (such as international standards H.265 and H.266) and ...
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Induction motors can be operated as induction generators when additional capacitors are added to the stator terminals. Capacitors connected to induction generators can generate voltage and can provide reactive power. ...
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Advanced battery management systems (ABMSs) rely on mathematical models to ensure high battery safety and performance. One of the key tasks of a BMS is state estimation. In the following, we consider a single lithium-...
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Advanced battery management systems (ABMSs) rely on mathematical models to ensure high battery safety and performance. One of the key tasks of a BMS is state estimation. In the following, we consider a single lithium-ion cell described with a dual polarization equivalent circuit model. To consider a realistic scenario, where the parameters have been identified from experimentally collected data, both parametric and measurement uncertainties are taken into account in the model. In particular, unknown but bounded uncertainties are assumed. In this setup, we address state estimation through a set-based approach using Constrained Zonotopes (CZ). Due to the model nonlinearities, a method able to propagate CZ through nonlinear mappings is demanded. Within this context, mean value and first-order Taylor CZ-based extensions were proposed which, however, might lead to conservative overestimation due to the sensitivity to the wrapping and dependency effects inherited from interval arithmetic. In the following, we suggest the use of DC programming as an alternative. The effectiveness of the proposed scheme is demonstrated in simulation for the considered Li-ion model.
Selective thermal emitters can boost the efficiency of heat-to-electricity conversion in thermophotovoltaic systems only if their spectral selectivity is high. We demonstrate a non-Hermitian metasurface-based selectiv...
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The use of Unmanned Aerial Vehicles (in short, UAVs, aka drones) for cultural and entertainment purposes, such as drone light shows, has grown exponentially. One such innovative and creative application is the visual ...
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ISBN:
(数字)9798350357882
ISBN:
(纸本)9798350357899
The use of Unmanned Aerial Vehicles (in short, UAVs, aka drones) for cultural and entertainment purposes, such as drone light shows, has grown exponentially. One such innovative and creative application is the visual arts using drones to explore long-exposure photography. Light painting are generally performed indoors and outdoors in a dedicated space with a human using a moving light source to create spectacular images and save the movement perception in a picture. In this work, we propose a robotic perception system designed to choreograph UAV movements based on time-parametric curves or image edges, serving as reference motions. Our framework begins by processing a digital image, extracting its contours through boundary tracing, and subsequently generating a safe, navigable, and precise path for UAV motion planning. This process involves optimizing waypoints within the UAV workspace to determine a feasible trajectory that encompasses all designated points or computes safe trajectories utilizing established mathematical equations. The validation of the motion planning is performed through light painting, where the UAV can either fly through the motion reference to mimic the original image. The generated trajectories on light painting mode by physical robots are compared against the ground-truth demonstrating the accuracy of the applied control scheme.
This study proposes a low-cost microwave sensor for the monitoring of water quality contamination in irrigation systems. The sensor was employed for monitoring the concentration of specific compounds in mixtures of gl...
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Automatic target recognition (ATR) for 3D synthetic aperture sonar (SAS) imagery is an intrinsic challenge in highly cluttered ocean environments, especially for objects partially or completely buried in the sediment....
Automatic target recognition (ATR) for 3D synthetic aperture sonar (SAS) imagery is an intrinsic challenge in highly cluttered ocean environments, especially for objects partially or completely buried in the sediment. Conventional dynamic range compression (DRC) techniques such as log-compression, which is a type of tone mapping intended to appeal to the human visual system, can further obscure the sonar signatures of these already physically occluded objects and lead to suboptimal downstream ATR performance, particularly for convolutional neural networks (CNNs). In this paper, we present a novel machine learning-based approach for tone mapping sub-bottom SAS imagery as a pre-processing stage in the 3D SAS ATR pipeline. This learned tone mapping function can be jointly optimized with a CNN-based ATR algorithm. We train and validate our method on measured volumetric SAS data captured by the Sediment Volume Search Sonar (SVSS) system.
Hypertension is a noncommunicable disease (NCD) that causes global concern, high costs and a high number of deaths. Internet of Things, Ubiquitous Computing, and Cloud Computing enable the development of systems for r...
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Searching for high-index dielectrics, we identify materials that break the index upper bound set by Moss’ rule. We highlight the promise of such super-Mossian materials by demonstrating nanophotonic devices made of F...
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ISBN:
(纸本)9781957171258
Searching for high-index dielectrics, we identify materials that break the index upper bound set by Moss’ rule. We highlight the promise of such super-Mossian materials by demonstrating nanophotonic devices made of FeS 2 and MoS 2 .
The convolutional neural network's ability to learn images has reigned in computer vision tasks of object detection, classification, and segmentation. In segmentation, the CNN architectures of U-Net and SegNet hav...
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