This paper presents a novel method to implement low-complexity and reconfigurable adaptive filters using processing in memory (PIM) technique. The proposed scheme includes an array of memristive devices, which are pro...
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ISBN:
(数字)9798350351859
ISBN:
(纸本)9798350351866;9798350351859
This paper presents a novel method to implement low-complexity and reconfigurable adaptive filters using processing in memory (PIM) technique. The proposed scheme includes an array of memristive devices, which are programmed by the filter coefficients to realize the desired frequency response of the filter. Additionally, the other fundamental operations of the adaptive filter, such as error calculation and error minimization, are implemented using the proposed memristive-based circuits. As a result, the proposed scheme provides flexibility and parallelism while reducing the computational complexity. Moreover, the idea proposed in this paper can be utilized to implement adaptive filters with arbitrary impulse responses and filter orders.
image dehazing is an important task to obtain clear images from blurry vision in low vision individuals. Although traditional methods and deep learning have made progress in this field, there are still challenges, esp...
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This paper presents a novel approach to remove non homogeneous haze from real images. The proposed method consists mainly of image feature extraction, haze removal, and image reconstruction. To accomplish this challen...
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ISBN:
(纸本)9781665464956
This paper presents a novel approach to remove non homogeneous haze from real images. The proposed method consists mainly of image feature extraction, haze removal, and image reconstruction. To accomplish this challenging task, we propose an architecture based on transformers, which have been recently introduced and have shown great potential in different computer vision tasks. Our model is based on the SwinIR an image restoration architecture based on a transformer, but by modifying the deep feature extraction module, the depth level of the model, and by applying a combined loss function that improves styling and adapts the model for the non-homogeneous haze removal present in images. The obtained results prove to be superior to those obtained by state-of-the-art models.
With the rapid development of today's internet of Things technology, the integration of communication and sensing is becoming more and more important. Sensing position by the received signal strength (RSS) is a wi...
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Firstly, the interference characteristics of photodetector for optical communication are analyzed. The interference characteristics of photodetector are analyzed and simulated by means of bit interference and clock ex...
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Photon-limited deblurring is a complex and demanding problem encountered in various applications where low-light conditions prevail. The scarcity of photons in such situations leads to the introduction of shot noise, ...
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ISBN:
(纸本)9798350350463;9798350350456
Photon-limited deblurring is a complex and demanding problem encountered in various applications where low-light conditions prevail. The scarcity of photons in such situations leads to the introduction of shot noise, resulting in a degradation of image quality. Solving this problem with Neural networks often involves constructing models empirically, making the behavior of the underlying architecture challenging to comprehend. A recent technique known as algorithm unrolling has enabled the connection of iterative algorithms with neural networks, where the Convolutional Neural Network (CNN) acts as a denoiser. This paper introduces a reduced parameter denoiser to enhance image quality and preserve finer details or avoid over-smoothing of the image during reconstruction. As a result, the unrolled model surpasses existing deblurring methods for improving image quality in low-light conditions. The proposed denoiser reduces the number of parameters by a factor of 3.84 and preserves the finer details while reconstructing. Our model improves computational efficiency and storage requirements compared to the state-of-the-art.
This paper analyzes the advantages of using edge computing technology to realize video monitoring function and gives the video monitoring system architecture based on edge computing, as well as the composition, functi...
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Gaze estimation is a fundamental aspect of many visual tasks. However, the high cost of acquiring gaze datasets with 3D annotations hinders the optimization and application of gaze estimation models. In this work, we ...
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ISBN:
(纸本)9798350344868;9798350344851
Gaze estimation is a fundamental aspect of many visual tasks. However, the high cost of acquiring gaze datasets with 3D annotations hinders the optimization and application of gaze estimation models. In this work, we propose a novel Head-Eye redirection parametric model based on Neural Radiance Field. This model allows for dense gaze data generation with view consistency and accurate gaze direction. Furthermore, our head-eye redirection parametric model can decouple the face and eyes for separate neural rendering, which enables us to separately control the attributes of the face, identity, illumination, and eye gaze direction. As a result, diverse 3D-aware gaze datasets can be obtained by manipulating the latent code belonging to different face attributes in an unsupervised manner. Our method has achieved state-of-the-art performance in image quality and accuracy gaze annotations compared with existing gaze data synthesis methods. Extensive experiments on several benchmarks demonstrate that our method can effectively improve domain generalization and domain adaptation in the gaze estimation task.
We now live in a technologically advanced society where the use of mobile phones, multimedia, and the internet has increased significantly due to the rapid advancement of these technologies. In order to secure these t...
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Global navigation satellite systems (GNSS) provide all-weather timing, positioning and navigation services, playing a crucial role in both military and civilian applications. Despite their widespread use, GNSS is vuln...
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