Compressive sensing (CS) has seen extensive use in signal processing, particularly in tasks related to image reconstruction. CS simplifies the sampling and compression procedures, but leaves the difficulty to the noli...
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ISBN:
(纸本)9798350377859;9798350377842
Compressive sensing (CS) has seen extensive use in signal processing, particularly in tasks related to image reconstruction. CS simplifies the sampling and compression procedures, but leaves the difficulty to the nolinear reconstruction. Traditional CS reconstruction algorithms are usually iterative, having a complete theoretical foundation. However, these iterative algorithms are constrained by significant computational complexity. While modern deep network-based methods can achieve high-precision reconstruction in compressed sensing (CS) with satisfactory speed, they often lack theoretical analysis and interpretability. To leverage the strengths of both types of CS methods, the deep unfolding networks (DUNs) have been developed. In this paper, a novel DUN named supervised transmission-augmented network (SuperTA-Net) is proposed. Based on the framework of our previous work PIPO-Net, the multi-channel transmission strategy is put forward to reduce the influence of critical information loss between modules and improve the reliability of data. Besides, in order to avoid the issues such as high information redundancy and high computational burden when too many channels are set, the attention based supervision scheme is presented to dynamically adjust the weight of each channel and remove the redundant information. Through experiments focused on reconstructing CS images, the proposed neural network architectures are shown to be highly effective.
With the help of social networks, Deepfakes enable false information to be presented to netizens in a highly credible manner, which will have a huge impact on individuals, organizations, and countries. It will also po...
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Device studying (ML) is an effective device that has been used in many packages, consisting of virtual sign processing, communications structures, and modulation reputation. In particular, ML has been extensively stud...
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ISBN:
(纸本)9798350395334;9798350395327
Device studying (ML) is an effective device that has been used in many packages, consisting of virtual sign processing, communications structures, and modulation reputation. In particular, ML has been extensively studied for modulation recognition, which is a hard hassle because of the always-changing and unpredictable nature of the sign. With the aid of leveraging ML strategies, which can be used to quantify and check specific houses of a signal, reliable modulation popularity and signal processing strategies can be evolved. For instance, ML can be used to automatically discover modulation class when the image shape of the signal is unknown. It is achieved via reading the sign with device learning techniques consisting of Fourier transform;waveletbased totally function extraction, and convolutional neural networks. Different applications encompass channel estimation, signal detection, and optimization of verbal exchange networks. Further to recognizing modulation codecs, ML-based algorithms may be used to enhance signal processing and communique systems. It could be done with the aid of developing smarter systems that are capable of studying global data. ML can be used to robotically apprehend anomalous alerts and allow networks to quickly adapt to converting situations. ML techniques can also be used to optimize community parameters, which include power management, coding schemes, and modulation codecs, so as to maximize their performance.
The Traffic Sign Recognition (TSR) system is an essential component of Advanced Driver Assistance systems (ADAS), which assists drivers in detecting and understanding traffic signs. However, recognizing traffic signs ...
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Process control of advanced semiconductor nodes is not only pushing the limits of metrology equipment requirements in terms of resolution and throughput but also in terms of the richness of data to be extracted to ena...
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ISBN:
(纸本)9781510672178;9781510672161
Process control of advanced semiconductor nodes is not only pushing the limits of metrology equipment requirements in terms of resolution and throughput but also in terms of the richness of data to be extracted to enable engineers to fine-tune the process steps for increased yield. The move towards 3D structures requires extraction of critical dimension parameters from structures which can vary largely from layer to layer. For in-line process control, the necessary automation forces the development of layer and equipment-specific dedicated imageprocessingalgorithms. Similarly, with the increase in stochastic defects in the EUV era, detection of defects at the nm scale requires the identification of features captured in low resolution to meet the throughput requirements of HVM fabs, which can again lead to custom algorithm development. With the emergence of ML-based imageprocessing methods, this process of algorithm development for both cases can be accelerated. In this work, we provide the general framework under which the images obtained from high-speed scanning probe microscopy-based systems can be used to train a network for either feature detection for parameter extraction or defect identification.
In multipath-Assisted indoor positioning systems, the parameters of multipath channels, including delays and angles of radio signals, are harnessed to enhance the accuracy and resilience of indoor positioning. This sy...
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The current research background of digital robot visual defect detection focuses on the application of virtual artificial intelligence algorithms. Convolutional neural networks (CNNS) perform well in the field of imag...
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Rice is a fundamental staple crop worldwide, and monitoring the health and growth of rice plants is crucial for ensuring optimal yield and quality. Automated rice leaf segmentation from images plays a vital role in pl...
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Diabetic Retinopathy (DR) is a retinal condition resulting in damage to blood vessels within the eye, serving as a leading cause of vision impairment or blindness when not addressed. Manual identification of diabetic ...
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Calibration is a well-studied property of predictors which guarantees meaningful uncertainty estimates. Multicalibration is a related notion - originating in algorithmic fairness - which requires predictors to be simu...
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