This paper presents a characterization of the modulation bandwidth in high-frequency voltage-controlled oscillators (VCOs) fabricated using 130 nm SiGe BiCMOS technology. The study focuses on analyzing the modulation ...
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ISBN:
(纸本)9798350377217;9798350377200
This paper presents a characterization of the modulation bandwidth in high-frequency voltage-controlled oscillators (VCOs) fabricated using 130 nm SiGe BiCMOS technology. The study focuses on analyzing the modulation bandwidth's dependency on key parameters such as tuning sensitivity, oscillation frequency, and amplitude of the modulating signal for LC cross-coupled oscillators with MOS-varactors for frequency adjustment. Experimental investigations demonstrate the conditions under which the modulation bandwidth can be approximated by the frequency deviation (Delta f). The results highlight the complex interplay between VCO's tuning sensitivity (K-VCO), modulation frequency, and linearity, shedding light on the factors influencing the modulation bandwidth's behavior. The findings provide valuable insights for the design and optimization of VCOs in high-frequency applications, particularly in radar systems, communication circuits, and frequency synthesizers employed in frequency-modulated continuous-wave (FMCW) radar setups.
Low-light image enhancement technology is mainly used to process the images which are taken under the conditions of short exposure time of the device or dark ambient light. Aiming at the effective enhancement of the b...
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ISBN:
(纸本)9798350309461
Low-light image enhancement technology is mainly used to process the images which are taken under the conditions of short exposure time of the device or dark ambient light. Aiming at the effective enhancement of the brightness of images and the preservation of the details as well, a light enhancement algorithm based on Retinex is proposed. First of all, a decomposition network is built based on Convolutional Neural Networks (CNN). The input is the low-illumination image with its maximum channel. Attention modules in space and channel dimensions are used to improve the capability of feature extraction. Then, a block adaptive illumination enhancement function is proposed to enhance the light of the divided image blocks, which can significantly improve the light of extremely low-light images. The loss function consists of three parts: reconstruction loss, reflection loss and illumination loss. The proposed loss function can effectively retain image details. The experiment demonstrates that the algorithm has an ideal result in low illumination image enhancement. The details of the enhanced image are clear, the noise suppression effect is better and the color distortion is smaller. Our approach is better in terms of subjective effects and objective evaluation indicators.
The State Council's latest Guidance on Actively Promoting 'internet +' emphasizes that 'the development of a new generation of mobile communication networks and the next generation of the internet shou...
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Sign language is the primary means of communication for the deaf and mute community. The regional and national differences in sign language have led to barriers between different sign language systems, making communic...
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The quality of imagesignals directly affects the performance of intelligent communication systems. This paper proposes a set of image enhancement and denoising algorithms to address image quality degradation in intel...
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Implicit neural representations (INRs) have demonstrated their effectiveness in continuous modeling for imagesignals. However, INRs typically operate in a continuous space, which makes it difficult to integrate the d...
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ISBN:
(纸本)9798350344868;9798350344851
Implicit neural representations (INRs) have demonstrated their effectiveness in continuous modeling for imagesignals. However, INRs typically operate in a continuous space, which makes it difficult to integrate the discrete symbols and structures inherent in human language. Despite this, text features carry rich semantic information that is helpful for visual representations, alleviating the demand of INR-based generative models for improvement in diverse datasets. To this end, we propose EIDGAN, an Efficient scale-Invariant Dual-modulated generative adversarial network, extending INRs for text-to-image generation while balancing network's representation power and computation costs. The spectral modulation utilizes Fourier transform to introduce global sentence information into the channel-wise frequency domain of image features. The cross attention modulation, as second-order polynomials incorporating the style codes, introduces local word information while recursively increasing the expressivity of a synthesis network. Benefiting from the column-row entangled bi-line design, EIDGAN enables text-guided generation of any-scale images and semantic extrapolation beyond image boundaries. We conduct experiments on text-to-image tasks based on MS-COCO and CUB datasets, demonstrating competitive performance on INR-based methods.
Multi-source remote sensing image matching is crucial for remote sensing technology applications. However, the variations in factors such as grayscale, perspective, and sensors between multi-source images present cert...
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ISBN:
(数字)9798350350920
ISBN:
(纸本)9798350350920
Multi-source remote sensing image matching is crucial for remote sensing technology applications. However, the variations in factors such as grayscale, perspective, and sensors between multi-source images present certain challenges for image matching. In response to the challenges in matching multi-source remote sensing images, a matching method based on texture-enhanced region features is proposed. Initially, Gabor filters and the gray-level co-occurrence matrix (GLCM) are used to obtain the texture energy maps, followed by the extraction of maximally stable extremal regions (MSER) on the texture energy maps to acquire region features. Subsequently, the contour descriptors of the features are computed using Fourier descriptors. Finally, feature matching and refinement of the matching results are conducted in conjunction with the fast sample consensus (FSC). We conducted experimental region feature matching on multiple pairs of multi-source remote sensing images, and the results validate the effectiveness of our method.
The internet, big data, and intelligent robots have transformed online education. This study introduces an Enhanced Convolutional Neural Network (CNN+) model optimized with refined weight initialization and strategic ...
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Global Navigation Satellite System based bistatic Synthetic Aperture Radar Interferometry (GNSS-based InBSAR) is an effective means for deformation monitoring. But researches focused on dangerous rock scenario are rar...
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Explainable Artificial Intelligence (xAI) has the potential to enhance the transparency and trust of AI-basedsystems. Although accurate predictions can be made using Deep Neural Networks (DNNs), the process used to a...
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ISBN:
(纸本)9798350349405;9798350349399
Explainable Artificial Intelligence (xAI) has the potential to enhance the transparency and trust of AI-basedsystems. Although accurate predictions can be made using Deep Neural Networks (DNNs), the process used to arrive at such predictions is usually hard to explain. In terms of perceptibly human-friendly representations, such as word phrases in text or super-pixels in images, prototype-based explanations can justify a model's decision. In this work, we introduce a DNN architecture for image classification, the Enhanced Prototypical Part Network (EPPNet), which achieves strong performance while discovering relevant prototypes that can be used to explain the classification results. This is achieved by introducing a novel cluster loss that helps to discover more relevant human-understandable prototypes. We also introduce a faithfulness score to evaluate the explainability of the results based on the discovered prototypes. Our score not only accounts for the relevance of the learned prototypes but also the performance of a model. Our evaluations on the CUB-200-2011 dataset show that the EPPNet outperforms state-of-theart xAI-based methods, in terms of both classification accuracy and explainability.
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