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Superresolution imaging: a survey of current techniques

Superresolution imaging: a survey of current techniques

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Conference on advanced signal processing algorithms, architectures, and Implementantions XvIII

作者： Cristobal, G. Gil, E. Sroubek, F. Flusser, J. Miravet, C. Rodriguez, F. B. CSIC Inst Opt Serrano 121 E-28006 Madrid Spain ASCR Inst Informat Theory & Automat Prague Czech Republic Univ Autonoma Madrid Escuela Politecn Super Madrid Spain

ISBN: (纸本)9780819472946

Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of sensors) and instability of the observed scene (e.g., object motion, media turbulence), acquired images can be indistinct, noisy, and may, exhibit insufficient spatial and temporal resolution. In particular, several external effects blur images. Techniques for recovering the original image include blind deconvolution (to remove blur) and superresolution (SR). The stability of these methods depends on having more than one image of the same frame. Differences between images are necessary to provide new information, but they can be almost unperceivable. State-of-the-art SR techniques achieve remarkable results in resolution enhancement by estimating the subpixel shifts between images, but they lack any apparatus for calculating the blurs. In this paper, after introducing a review of current SR, techniques we describe two recently developed SR methods by the authors. First, we introduce a variational method that minimizes a regularized energy function with respect to the high resolution image and blurs. In this way we establish a unifying way to simultaneously estimate the blurs and the high resolution image. By estimating blurs we automatically estimate shifts with subpixel accuracy, which is inherent for good SR performance. Second, an innovative learning-based algorithm using a neural architecture for SR is described. Comparative experiments on real data illustrate the robustness and utilization of both methods.

关键词： superresolution deconvolution energy minimization probabilistic neural network sequence processing

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FPGA design and implementation of a wavelet-domain video denoising system

FPGA design and implementation of a wavelet-domain video den...

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7th International Conference on advanced Concepts for Intelligent vision Systems

作者： Katona, M Pizurica, A Teslic, N Kovacevic, v Philips, W Univ Novi Sad Chair Comp Engn Novi Sad 21000 Serbia Monteneg Univ Ghent Dept Telecommun & Informat Proc B-9000 Ghent Belgium

ISBN: (纸本)354029032X

Multiresolution video denoising is becoming an increasingly popular research topic over recent years. Although several wavelet based algorithms reportedly outperform classical single-resolution approaches, their concepts are often considered as prohibitive for real-time processing. Little research has been done so far towards hardware customization of wavelet domain video denoising. A number of recent works have addressed the implementation of critically sampled orthogonal wavelet transforms and the related image compression schemes in Field Programmable Gate Arrays (FPGA). However, the existing literature on FPGA implementations of overcomplete (non-decimated) wavelet transforms and on manipulations of the wavelet coefficients that are more complex than thresholding is very limited. In this paper we develop FPGA implementation of an advanced wavelet domain noise filtering algorithm, which uses a non-decimated wavelet transform and spatially adaptive Bayesian wavelet shrinkage. The standard composite television video stream is digitalized and used as source for real-time video sequences. The results demonstrate the effectiveness of the developed scheme for real time video processing.

关键词： video signal processing

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Generalized parametric estimator for multiple wide-band signals

Generalized parametric estimator for multiple wide-band sign...

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advanced signal-processing algorithms, architectures, and implementations

作者： Wu, Q. Reilly, J.P. Wong, K.M. McMaster Univ Hamilton Canada

This paper presents a generalized parametric estimator for the directions of arrival (DOA) of wide-band signals. This estimator is derived by extenting the geometrical explanation of the ML estimator of narrow-band signals to the focussed correlation matrix. The consistency of the estimator for estimating DOA has been proved. This estimator can be considered as a coherent signal processing method by which the computation complexity can be reduced approximately by the number of the frequency bins. We have also shown that under certain condition the proposed estimator is equivalent to the ML estimator derived by applying the likelihood principle on the Fourier coefficients of each frequency bin. Such an equivalence implies that the MLE has some inherent advantages from the perspective of improving performance and that the focussing techniques are not necessary for the ML estimator.

关键词： signal processing

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Truncated sampling for the Fourier-Mellin transform with applications to wide-band WvD computation

Truncated sampling for the Fourier-Mellin transform with app...

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advanced signal-processing algorithms, architectures, and implementations

作者： Allen, Jeffrey C. Naval Ocean Systems Cent San Diego United States

The Fourier-Mellin transform (FMT) of an input function is defined as and is the magnitude squared of the Mellin transform of the magnitude squared of the Fourier transform of the input function. As such, the FMT is unchanged by translations and dilations of the input function. While the FMT has found applications in optical pattern recognition, ship classification by sonar and radar, and image processing, only cursory attention has been paid to the truncation error incurred by using a finite number of samples of the input function. This paper establishes truncation bounds for computing the FMT for band-limited functions from a finite number of samples of the input function. These bounds naturally suggest an implementation of the FMT by the method of direct expansions. This approach readily generalizes to a direct expansion for the Wigner-ville distribution and the Q distribution.

关键词： signal processing

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Deep Learning Techniques For signal processing On Spherical Manifolds In Near-Field Applications 13

Deep Learning Techniques For Signal Processing On Spherical ...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Mohseni, Milad Kapula, Prabhakara Rao Dutt, Amit Pravallika, B. Rajalakshmi, B. Geetha, B.T. Islamic Azad University Department of Computer and Engineering Tabriz Iran B v Raju Institute of Technology Department of Ece Telangana State Narsapur India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Computer Science Bangalore India Saveetha School of Engineering Simats Department of Ece Tamil Nadu India

ISBN: (纸本)9798350305463

Improvements in deep learning have benefited signal processing on spherical surfaces in particular. This work addresses deep learning for close-range spherical surface signal processing. Traditional signal processing algorithms may struggle to handle complex data on spherical surfaces. Deep learning has the potential to close this gap. Our deep learning in signal processing examination begins with its principles, difficulties, and accomplishments. The article goes on to illustrate how spherical geometry influences data encoding and signal processing, underscoring the need for understanding signal processing on spherical manifolds. The research emphasises the need for cutting-edge solutions to tackle non-Euclidean data representation challenges quickly. We are experimenting with several deep learning architectures for spherical manifold data. Among these architectures are attention mechanisms, GNNs, CNNs, and so on. Integrating these models with spherical data layers and methodologies should allow for a thorough evaluation of their ability to detect complex close-range *** learning algorithms increase near-field signal processing on spherical manifolds in terms of accuracy, efficiency, and resilience, according to the results. Deep learning architectures for curved spherical data processing are advanced in this work. This objective necessitates research on its theoretical underpinnings, existing approaches, and practical applications. These unique and adaptable data management solutions provide remarkable data management on spherical manifolds for near-field applications. Their data management systems are more precise and trustworthy than earlier ones. © 2024 IEEE.

关键词： Network architecture

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Probabilistic model for the evaluation of fault-tolerant multiprocessor systems using concurrent error detection

Probabilistic model for the evaluation of fault-tolerant mul...

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advanced signal-processing algorithms, architectures, and implementations

作者： Nair, v.S.S. Abraham, J.A. Univ of Illinois at Urbana-Champaign Urbana United States

The analysis of fault-tolerant multiprocessor systems that use concurrent error detection (CED) schemes is much more difficult than the analysis of conventional fault-tolerant architectures. various analytical techniques have been proposed to evaluate CED schemes deterministically. However, these approaches are based on the worst-case assumptions related to the failure of system components. Often, the evaluation results are not good measures for comparing the fault tolerance capabilities of a system. In this paper, we develop a probabilistic approach to evaluate and compare fault-tolerant multiprocessor systems. various probabilities associated with CED schemes are identified and used in the framework of the matrix-based model which we had proposed previously. Based on these probabilistic matrices, we analytically derive estimates for the fault tolerance capabilities of various systems.

关键词： Computer Systems, Digital

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Review on Energy Harvesting Techniques for Future Wireless Generation Networks 8

Review on Energy Harvesting Techniques for Future Wireless G...

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8th International Conference on advanced Computing and Communication Systems, ICACCS 2022

作者： Ashok, K. Santhosh Krishna, B.v. Nagras, Aishwarya Halli, Aishwarya M Gowthami, B. Chandana, C. New Horizon College of Engineering Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9781665408165

World is experiencing an explosive growth in wireless communication and wireless networks which has led to a huge increase in the energy consumption. In low-power scenarios like wireless sensors and networks, it is highly impractical or expensive to replace batteries of low-cost devices. To overcome these problems, relying on energy harvesting has proved to be the best solution and this is due to the potential for mobile devices to scan power from their surrounding that is solar, wind, vibration, thermo-electric effects, ambient radio power and so on. The use of Energy harvesting nodes in wireless communication is a promising approach for maximizing the energy efficiency. However, it requires signal processing algorithms and allied architectures to harvest the energy along with the information transfer. This paper deals with a comprehensive presentation of new research contributions on Wireless Energy Harvesting techniques, algorithms, architectures, performance metrics, and applications which are suitable for future wireless networks. Different architectures are examined in this paper that imposes optimization targets on various parameters that are very much essential to design energy efficient high speed wireless systems. © 2022 IEEE.

关键词： Energy harvesting

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Dynamically Reconfigurable architectures for Real-time Baseband processing

Dynamically Reconfigurable Architectures for Real-time Baseb...

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作者： Chenxin Zhang University of Lund

学位级别：博士

Motivated by challenges from today's fast-evolving wireless communication standards and soaring silicon design cost, it is important to design a flexible hardware platform that can be dynamically reconfigured to adapt to current operating scenarios, provide seamless handover between different communi- cation networks, and extend the longevity of advanced systems. Moreover, increasingly sophisticated baseband processing algorithms pose stringent re- quirements of real-time processing for hardware implementations, especially for power-budget limited mobile terminals. With existing hardware platforms such as Application-Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), and Digital signal Processors (DSPs), the contradictory design requirements of flexibility, computational performance, and hardware ef- ficiency cannot be attained at the same time. To achieve a balance between the aforementioned design requirements, a coarse-grained dynamically reconfigurable cell array architecture is proposed. The architecture is constructed from an array of heterogeneous function units interconnected through a hierarchical on-chip network. The adopted in-cell configuration scheme enables fast context switching between standards and be- tween computational tasks during run-time. Although cell array is a generic hardware platform, this thesis focuses on the architectural development of the cell array tailored specifically for digital baseband processing of contemporary wireless communication systems. various degrees of flexibilities among operat- ing scenarios, algorithms, tasks, and supporting standards are exploited. Be- sides, high hardware efficiency is attained by conducting algorithm-architecture, hardware-software, and processing-memory co-design. In this thesis, flexibility, performance and efficiency of the proposed archi- tecture are demonstrated through two case studies. First, the cell array is de- ployed in a digital front-end receiver, aiming t

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New matrix decomposition based on transforming the basis sets of the singular value decomposition yields principal features for time-frequency distributions

New matrix decomposition based on transforming the basis set...

引用

Advance signal processing algorithms, Atchitectures, and implementations X

作者： Groutage, Dale Bennink, David Naval Surface Warfare Cent Bremerton United States

We present a matrix decomposition that can be used to derive features from processes that are described by discrete-time, time-frequency representations. These include, among others, electrocardiograms, brain wave signals, seismic signals, vibration and shock signals, speech signals for voice recognition, and acoustic transient signals. The new decomposition is based on a transformation of the basis vectors of the singular value decomposition (SvD) which we call transformed singular value decomposition or TSvD. The transformed basis vectors are obtained by forming linear combinations of the original SvD basis vectors in a way such that the means of the transformed vectors are extrema of each other. The TSvD basis vectors are used to identify concentrations of energy density in the discrete-time, time-frequency representation by time and frequency descriptors. That is, descriptors such as the location in time, the spread in time, the location in frequency and the spread in frequency for each principal concentration of energy density can be obtained from the TSvD terms in the matrix decomposition series. Several examples are presented which illustrate the application of the new matrix decomposition for deriving principal time and frequency features from the discrete-time, time-frequency representations of nonstationary processes. Two of the examples illustrate how the derived time and frequency features can be used to classify individual short duration transient signals into respective classes, that is,: (1) automatically classify sonar signals as belonging to one of ten classes, and (2) automatically classify heartbeat signals as belonging to one of two people.

关键词： signal processing

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System level fixed-point design based on an interpolative approach 97

System level fixed-point design based on an interpolative ap...

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34th Design Automation Conference

作者： Willems, M Bursgens, v Keding, H Grotker, T Meyr, H Institute for Integrated Systems in Signal Processing Aachen University of Technology Templergraben 55 52056-Aachen Germany

ISBN: (纸本)0897919203

The design process for fixed-point implementations either in software or in hardware requires a bit-true specification of the algorithm in order to analyze quantization effects on an algorithmical level, abstracting from implementational details. On the other hand, system design starts from a floating-point description, so that a transformation of a floating-point description into a fixed-point description becomes necessary. Within this paper we present a tool that allows an automated, interactive transformation from floating-point ANSI-C into a bit-true specification based on a new data type fixed that is introduced as an extension to ANSI-C. The concept is rooted in a sophisticated data dependency analysis that allows to handle control structures as well as pointers. It is part of the fixed-point design environment FRIDGE (1) which includes an advanced simulator that covers the extended ANSI-C syntax as well as target specific compilers which allow to generate efficient fixed-point implementations either for HW or for SW, starting from the bit-true algorithm specification.

关键词： algorithms

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