A deep learning framework based on generative adversarial networks (GAN) for ovarian ultrasound (US) images synthesis is investigated. This method offers an effective solution for addressing the issue of insufficient ...
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Considering the need for high precision control in the flight process of quadrotor unmanned aerial vehicle (UAV), a kind of fault-Tolerant control strategy based on fixed-Time prescribed performance control (FXTPPC) a...
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The contribution at hand presents a novel method for optimizing the illumination of matrix headlights in simulation to improve the environment perception of camera-based computer vision and the object detection qualit...
The contribution at hand presents a novel method for optimizing the illumination of matrix headlights in simulation to improve the environment perception of camera-based computer vision and the object detection quality for automated driving. With high-definition (HD) matrix headlights in combination with the developed novel algorithm, different surfaces and materials in the environment can be illuminated with different intensities, resulting in material-based and -optimized illumination that is individually adapted to each material. This environment-optimal illumination improves the detection quality of computer vision, which is better than conventional homogenous headlamp illumination. Additionally, it is possible to achieve a similar detection quality using the novel proposed optimization approach with potential energy savings of up to 88%.
With the development of affective computing and computer science, electroencephalogram (EEG) based emotion recognition has attracted much more attention. In this paper, we collected EEG signals from fifteen healthy pe...
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The contribution at hand presents a novel control system for projecting symbols onto the road by high-definition matrix headlights in automobiles. The control system dynamically adjusts the projection of symbols on un...
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ISBN:
(数字)9798350372694
ISBN:
(纸本)9798350372700
The contribution at hand presents a novel control system for projecting symbols onto the road by high-definition matrix headlights in automobiles. The control system dynamically adjusts the projection of symbols on uneven road surfaces, improving the legibility and distortion-free appearance of these symbols. This contribution addresses the challenge of projecting symbols onto irregular surfaces that state-of-the-art flat plane models cannot accurately capture. The proposed system requires neither a detailed 3D model of the projection surface nor computationally intensive inverse ray casting. It calculates control errors directly from the symbol's ego-vehicle camera image, resulting in a closed-loop system that adapts to real-world conditions. With the new feedback control system, the robustness of maintaining the correct position and shape of the symbols is significantly improved, ensuring the safety of communication between vehicles and humans outside the vehicle.
Accurate 3D object detection is vital for automated driving. While lidar sensors are well suited for this task, they are expensive and have limitations in adverse weather conditions. 3+1D imaging radar sensors offer a...
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As a distributed energy source, the photovoltaic system will affect the stability of the power grid in the grid connected operation mode. To meliorate the control effect of the inverter and the power quality of the gr...
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Sign language plays an important role in information transmission and emotional communication between the hearing-impaired and the outside world. It is expressed through a rapid and complex combination of gestures, bo...
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This contribution presents the concept and implementation of a catalog (i.e., a structured collection) of dynamical system models, relevant to controltheory. The key features of the catalog are: (a) a combination of ...
In order to identify the tilt direction of the self-mixing interference (SMI) signals under weak feedback regime interfered by noise， a deep learning method is proposed. The one-dimensional U-Net (1D U-Net) neural ne...
In order to identify the tilt direction of the self-mixing interference (SMI) signals under weak feedback regime interfered by noise， a deep learning method is proposed. The one-dimensional U-Net (1D U-Net) neural network can discriminate the direction of self-mixing fringes accurately and quickly. For experimental SMI signals， the 1D U-Net can be used for discriminant direction after a one-step normalization. Simulation and experimental results show that the proposed method is suitable for SMI signals with noise within the whole weak feedback regime， and can maintain a high discrimination accuracy for signals interfered by 5dB noise. Combined with fringe counting method， accurate and rapid SMI signal displacement reconstruction can be realized.
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