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5th IEEE International conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT)

作者： Sushmitha, M. Chetan, S. Sarkar, Sayantam Dr Ambedkar Inst Technol Dept Elect & Commun Engn VLSI Design & Embedded Syst Bengaluru India Dr Ambedkar Inst Technol Dept Elect & Commun Engn Bengaluru India Vijaya Vittala Inst Technol Dept Elect & Commun Engn Bengaluru India

ISBN: (纸本)9781728197722

There are various Discrete wavelet Transform architectures that are designed to fulfil certain requirements and criteria's. The convolution method which is an old traditional method which requires more multipliers, hardware resources and huge memory storage which is not apt to yield high speed and efficient image processing, signal processing application designs when compared to lifting method. In this paper, we have proposed an architecture for lifting scheme based CDF-5/3 2D-DWT, which includes less mathematical computations and high speed is achieved compared to existing works. The proposed architecture design is multiplier-less and utilizes lesser number of components hence it is area efficient and also achieves high throughput rate. The RTL of this design is described in VHDL language and is synthesized in Xilinx 14.5 and target device is Virtex-5 series XC5VLX110T for testing the resulting parameters like LUTs, flip- flops and slices. Higher PSNR values and lesser MSE values are achieved when tested for different kind of images. The maximum operating frequency achieved for the designed 2D-DWT architecture is 258.358MHz. Hence the proposed 2D-DWT architecture has increased speed, less computational complexity and good hardware utilization when compared to other existing works.

关键词： Lifting scheme CDF-5/3 wavelet Filter DWT 1D-DWT 2D-DWT

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Orthogonality and Zero DC Tradeoffs in Biorthogonal Graph Filterbanks

Orthogonality and Zero DC Tradeoffs in Biorthogonal Graph Fi...

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IEEE International conference on Acoustics, Speech and signal processing

作者： Dion E. O. Tzamarias Eduardo Pavez Benjamin Girault Antonio Ortega Ian Blanes Joan Serra-Sagrista Cerdanyola del Vallès Universitat Autònoma de Barcelona Barcelona SPAIN University of Southern California Los Angeles California USA Université de Rennes ENSAI CNRS CREST-UMR 9194 Rennes FRANCE

Biorthogonal graph wavelet filterbanks, also known as GraphBior, are one of the most popular graph transforms used in image compression, but up to now, they could be designed based on two known admissible fundamental matrices: i) the random walk Laplacian, which heavily penalizes low degree pixels, and ii) the normalized Laplacian, which lacks a zero-DC response. By exploiting a new extension of the admissibility condition in GraphBior we propose a new fundamental matrix with the goal of distributing the errors of GraphBior more uniformly across pixels with different node degrees. Furthermore the proposed matrix preserves high energy compaction linked to the zero-DC GraphBior variation.

关键词： graph signal processing biorthogonal filter-banks image compression graph Fourier transform irregularity-aware graph Fourier transforms

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U-net based MRA framework for segmentation of remotely sensed images

U-net based MRA framework for segmentation of remotely sense...

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International conference on Artificial Intelligence and signal processing (AISP)

作者： Ranjan, Pranjal Patil, Sarvesh Ansari, Rizwan Ahmed Veermata Jijabai Technol Inst Dept Elect Engn Mumbai Maharashtra India

ISBN: (数字)9781728144580

ISBN: (纸本)9781728144580;9781728144566

Segmentation of remotely sensed images lies in the intersection of the domains of remote sensing and computer vision. It is used to systematically extract information from data collected by various airborne and spaceborne sensors, resulting in a simpler representation. This method is used in various applications which include change detection, land cover and land use classification, resource exploration, the study of natural hazards, and mapping. This paper proposes a composite architecture for segmentation of remotely sensed images derived from the U-Net aided by processing the information obtained using multi-resolution analysis (MRA). This increases the contextual overview of the network as the same data on multiple scales is available for feature extraction and learning. This work further analyzes the effects of different wavelets for decomposition on segmentation performance. On comparison with the plain U-Net model performance, it is observed that the MRA aided network can segment areas with higher accuracy and mean intersection over union, the best performance being with Daub basis of MRA.

关键词： image Segmentation Convolutional Neural Network U-Net Multiresolution Analysis wavelet Transform Remotely Sensed images

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Natural image Deblurring using Recursive Deep Convolutional Neural Network (R-DbCNN) and Second-Generation wavelets

Natural Image Deblurring using Recursive Deep Convolutional ...

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IEEE International conference on signal and image processing applications (IEEE ICSIPA)

作者： Khmag, Asem Kamarudin, Noraziahtulhidayu Univ Zawia Dept Comp Syst Engn Fac Engn Az Zawia Libya Univ Coll Technol Sarawak Sch Comp & Creat Sarawak Malaysia

ISBN: (纸本)9781728133775

Deconvolution in blind digital images is a common issue in image enhancement techniques, which basically was a notion of many researches. In this study, spatial varying blind deconvloution is stated and implemented. In addition, image noise removal approach which utilizes the normalized platform of second-generation wavelet transform is applied as pre-processing step. The low and high frequencies are decomposed in this step in order to be extracted. Practically, the main merit of wavelet transform is its efficiency in reduction of data redundancy in digital images. This feature helps a lot in terms of data classification where it is easy to distinguish the signal from its noisy counterpart. The second step, a recursive deep convolutional neural network (R-DbCNN) is implemented to suppress any image blur affected by second-generation wavelet transform to further remove the blur of noisy image. The experimental results depict that the suggested method outperforms recent blur removal techniques for different bluer image types in terms of image quality and time consumption.

关键词： image deblurring second-generation wavelet transforms image enhancement convolution neural network image denoising

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"Refining image Quality: Deep Neural Network applications in Plane Wave Medical Imaging"

"Refining Image Quality: Deep Neural Network Applications in...

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International conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： D. Ferlin Deva Shahila L. Padma Suresh A. Merry Ida D. Lilly Evangelin P Aruna Jeyanthy Valantina Stephen Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Baselios Mathews II College of Engineering Kollam Chennai India Department of CSE Loyola Institute of Technology and Science Loyola Nagar Thovalai Kanya Kumari India Department of CSE Loyola Institute of Technology and Science Loyola Nagar Thovalai Chennai India Department of EEE Loyola Institute of Technology and Science Loyola Nagar Thovalai Kanya Kumari India Department of EEE Kalasalingam Academy of Research and Education Srivilliputhur Tamil Nadu India Department of EEE Saveetha School of Engineering Thandalam Chennai India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

With the goal to attain a high frame rate, plane wave imaging (PWI), a common swift ultrasonography imaging in medicine method, uses a solitary plane wave emission with no focal point. However, as compared to the widely utilized targeted line scan mode, the imaging quality is significantly reduced. Imaging performance can be enhanced with conventional adaptive beamformers, albeit at the expense of more processing. In the present study, we suggest employing a deep neural network (DNN) to maintain a high frame rate despite improving PWI performance. Specifically, the key contribution of this technology is The network input is the PWI response from a single point target, and the narrower scan response from the same point appears as the desired output. An amalgam of phantom investigations, in vivo research, and computations are used to assess the effectiveness of the suggested approach. For comparison coherence factor (CF), a prior anticipated deep learning-based technique, the delay-and-sum (DAS), and the DAS with a customized scan are employed. The performance is measured using numerical metrics, such as the speckle the contrast ratio (CR), signal-to-noise ratio, and contrast-to-noise ratio (CNR) (SSNR). The outcomes show that the suggested strategy is capable of achieving better contrast and resolution. In particular, the suggested approach outperforms the DAS across the board. While the CF offers a greater CR, the suggested The SSNR and CNR of this strategy are significantly greater. In addition, the total performance is comparable with concentrated scan performance and superior to the conventional deep learning technique. Furthermore, the suggested technique ensures good temporal resolution with less additional computation needed comparison to the DAS. The aforementioned results confirm that the suggested strategy is capable of producing high-quality images while preserving the high frame rate required for PWI.

关键词： In vivo image resolution Refining Phantoms Artificial neural networks Ultrasonography Speckle

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Multi-level Stress Assessment Using Multi-domain Fusion of ECG signal 42

Multi-level Stress Assessment Using Multi-domain Fusion of E...

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42nd Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Ahmad, Zeeshan Khan, Naimul Mefraz Ryerson Univ Dept Elect & Comp Engn 350 Victoria St Toronto ON M5B 2K3 Canada

ISBN: (纸本)9781728119908

Stress analysis and assessment of affective states of mind using ECG as a physiological signal is a burning research topic in biomedical signal processing. However, existing literature provides only binary assessment of stress, while multiple levels of assessment may be more beneficial for healthcare applications. Furthermore, in present research, ECG signal for stress analysis is examined independently in spatial domain or in transform domains but the advantage of fusing these domains has not been fully utilized. To get the maximum advantage of fusing diferent domains, we introduce a dataset with multiple stress levels and then classify these levels using a novel deep learning approach by converting ECG signal into signal images based on R-R peaks without any feature extraction. Moreover, We made signal images multimodal and multidomain by converting them into time-frequency and frequency domain using Gabor wavelet transform (GWT) and Discrete Fourier Transform (DFT) respectively. Convolutional Neural networks (CNNs) are used to extract features from different modalities and then decision level fusion is performed for improving the classification accuracy. The experimental results on an in-house dataset collected with 15 users show that with proposed fusion framework and using ECG signal to image conversion, we reach an average accuracy of 85.45%.

关键词： Frequency domain analysis

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image Super-Resolution Based on Multi-scale Fusion Network

Image Super-Resolution Based on Multi-scale Fusion Network

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7th International conference on Communications, signal processing, and Systems (CSPS)

作者： Lin, Leping Huang, Huiling Ouyang, Ning Guilin Univ Elect Technol Key Lab Cognit Radio & Informat Proc Minist Educ Guilin 541004 Guangxi Peoples R China Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Guangxi Peoples R China

ISBN: (纸本)9789811365041;9789811365034

It is important and necessary to obtain high-frequency information and texture details in the image reconstruction applications, such as image super-resolution. Hence, it is proposed the multi-scale fusion network (MCFN) in this paper. In the network, three pathways are designed for different receptive fields and scales, which are expected to obtain more texture details. Meanwhile, the local and global residual learning strategies are employed to prevent overfitting and to improve reconstruction quality. Compared with the classic convolutional neural network-based algorithms, the proposed method achieves better numerical and visual effects.

关键词： Multi-pathways Residual learning Multi-scale Receptive field Texture details

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Pavement images Denoising with Cracks Detection and Classification Using 2D Discrete wavelet Transform and Savitzky-Golay Filters

Pavement Images Denoising with Cracks Detection and Classifi...

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IEEE International conference on signal and image processing applications (IEEE ICSIPA)

作者： Hamici, Zoubir Obaidat, Turki I. Al-Suleiman Al Zaytonnah Univ Jordan Elect Engn Dept Amman Jordan Al Zaytonnah Univ Jordan Civil Engn Dept Amman Jordan

ISBN: (纸本)9781728133775

image processing has gained an increased usage and impact in modern pavement networks automatic distress severity classification (DSC). DSC defines priorities and maintenance resources optimum allocation in order to achieve a cost-effective rehabilitation process. This paper presents a novel computer vision algorithm having the ability to process, isolate and evaluate the distress severity level of a pavement. A pavement color image is converted to grayscale and then processed for image denoising of the granularity and complex texture that represent and artifact in cracks edge detection. The processing is achieved by a 2D dual-tree double density wavelet transform filter banks that significantly reduces the granularity noise while preserving the pavement cracks for edge detection. The 2D wavelet FIR filters perform analysis, soft thresholding then a synthesis of the image. The second step is then an edge detection process followed by morphological filtering and labeled components size-histogram filter to isolate false edges as residuals of denoising. A final step is performed by two Savitzky-Golay filters for the detection of longitudinal and transverse alligator cracks projections. A weighted score function with multiple parameters is used for DSC.

关键词： Computer vision wavelets filter-banks Denoising Savitzky-Golay Filters Pavement Cracks

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A Novel 3-D Radar Imaging Method Based on Sparse Optimization 2

A Novel 3-D Radar Imaging Method Based on Sparse Optimizatio...

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conference on Big Data ii - Learning, Analytics, and applications

作者： Fu, Jixiang Xing, Mengdao Amin, Moeness G. Xidian Univ Natl Lab Radar Signal Proc Xian Shaanxi Peoples R China Villanova Univ Ctr Adv Commun Villanova PA 19085 USA

ISBN: (纸本)9781510635685

Three-dimensional (3-D) radar imaging is an important task underpinning many applications that seek target detection, localization, and classification. However, traditional 3-D radar imaging is computationally cumbersome and needs a large memory space to store the cubic data matrix. These challenges have hindered 3-D radar imaging development and limited its applications. Owing to the sparsity of the radar image of typical targets, the data size could be significantly reduced by exploiting compressive sensing and sparse reconstruction techniques. These techniques prove important to mitigate the sidelobe levels that arise from successive Fourier-based processing. Towards this end, we first perform polar reformatting to the 2-D data matrix in azimuth and range. Then, a series of images over different values of the vertical variable z are generated by using different focusing filters. Sparse optimization is afterwards applied to the 3-D data cube to produce high-resolution, significantly reduced sidelobe 3-D image. Compared with the conventional 3-D radar imaging methods, the proposed method, in addition to high fidelity images, requires fewer data measurements and offers computationally efficient processing. Numerical simulations are provided to evaluate the effectiveness of the proposed method.

关键词： Three-dimensional Radar Imaging Polar Reformatting Sparse Optimization High-resolution

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EDITORIAL SPECIAL ISSUE: PART IV-iiI-ii-I SERIES - FRACTALS-FRACTIONAL AI-BASED ANALYSES AND applications TO COMPLEX SYSTEMS finestre of creativity, complexity and universality…

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2023年第10期31卷 2302005-2302005页

作者： Karaca, Yeliz Baleanu, Dumitru Moonis, Majaz Zhang, Yu-dong Gervasi, Osvaldo Univ Massachusetts Chan Med Sch 55 Lake Ave North Worcester MA 01655 USA Lebanese Amer Univ Beirut 11022 Lebanon Inst Space Sci Bucharest 077125 Romania Univ Leicester Univ Rd Leicester LE1 7RH England Univ Perugia I-06123 Perugia Italy

Complex systems, as interwoven miscellaneous interacting entities that emerge and evolve through self-organization in a myriad of spiraling contexts, exhibit subtleties on global scale besides steering the way to understand complexity which has been under evolutionary processes with unfolding cumulative nature wherein order is viewed as the unifying framework. Indicating the striking feature of non-separability in components, a complex system cannot be understood in terms of the individual isolated constituents' properties per se, it can rather be comprehended as a way to multilevel approach systems behavior with systems whose emergent behavior and pattern transcend the characteristics of ubiquitous units composing the system itself. This observation specifies a change of scientific paradigm, presenting that a reductionist perspective does not by any means imply a constructionist view;and in that vein, complex systems science, associated with multiscale problems, is regarded as ascendancy of emergence over reductionism and level of mechanistic insight evolving into complex system. While evolvability being related to the species and humans owing their existence to their ancestors' capability with regards to adapting, emerging and evolving besides the relation between complexity of models, designs, visualization and optimality, a horizon that can take into account the subtleties making their own means of solutions applicable is to be entailed by complexity. Such views attach their germane importance to the future science of complexity which may probably be best regarded as a minimal history congruent with observable variations, namely the most parallelizable or symmetric process which can turn random inputs into regular outputs. Interestingly enough, chaos and nonlinear systems come into this picture as cousins of complexity which with tons of its components are involved in a hectic interaction with one another in a nonlinear fashion amongst the other related systems

关键词： Applied Mathematics Computational Mathematics Pure Mathematics Mathematical Sciences Discrete Mathematics Mathematical Physics Theoretical Sciences Exact Science Computational Methods Computational Intelligence Applied Sciences Computer Science Computational Science Data Science Engineering Electrical and Electronics Engineering Exact Sciences Natural Sciences Social Sciences Nonlinear Dynamics Nonlinear Systems Linear Systems Fractional Calculus AI Control Control Systems Optimization Complexity Chaos Uncertainty Causality Symmetry Oscillations Bifurcation Stability Sensitivity Quantum Medicine Biology Physics Chemistry Mathematical Bioengineering Mathematical Neuroscience Mathematical Biology Artificial Intelligence Neural Networks Artificial Neural Networks Data-Driven Models Encryption Soft Computing Coding Theory Machine Learning Deep Learning Intelligent Systems Probabilities and Stochastic Processes Game Theory Solitons, Fractals, Multifractals Fractional Calculus Fractional Dynamics Fractional Mathematics Fractional Systems Fractional Models Fractals Multifractals Waves wavelet Entropy Algebraic Methods Computational (-Algorithmic) Complexity Fractional Order Differential Equations Fractional Differential Equations Fuzzy Fractional Differential Equations Special Functions Functional Analysis Error Estimations Fuzzy Calculus Differential/Integral Equations, Stochastic Differential Equations, Fractal-Fractional Differential Equations Fractional-Order Controllers Fractional-Order Systems Intelligent Fractional Order Systems Variational Principles image and/or signal processing Mathematical Models Fractal AI Fractional AI Fractals-Fractional AI Quantum AI Dynamical systems Chaotic Systems Complex Systems

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