Due to the irrational coefficients, the orthogonal wavelet filter banks (FBs) need a lot of resources when implemented on hardware. As a result, there is a decrease in operating speed, a significant memory requirement...
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Due to the irrational coefficients, the orthogonal wavelet filter banks (FBs) need a lot of resources when implemented on hardware. As a result, there is a decrease in operating speed, a significant memory requirement, and an increase in power consumption. This paper proposes an orthogonal low complex symmetric Daubechies-2 (4-tap) wavelet filter bank (FB) to address these problems. This is accomplished by obtaining dyadic filter coefficients and making the suggested FB symmetric by marginally modifying the perfect reconstruction (PR) criterion. By using fewer adders and shifters, the suggested wavelet FB achieves a significant reduction in dynamic power consumption without the need for multipliers. This is confirmed by implementing the suggested wavelet FB on the Zedboard ZYNQ-7000 AP-SoC (Zynq FPGA from xilinx) field programmable gate array (FPGA). The suitability of the suggested FB is tested in medical image retrieval and image compression applications. Results from simulations demonstrate that, when compared to state-of-the-art techniques, the suggested wavelet FB performs better in terms of retrieval accuracy (ARP, ARR) for medical image retrieval and PSNR for image compression on the benchmark image datasets.
Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signalprocessing and machine learning techniques, including c...
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Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signalprocessing and machine learning techniques, including convolutional neural networks. Increasingly, modern data also reside on networks and other irregular domains whose structure is better captured by a graph. To process and learn from such data, graph filters account for the structure of the underlying data domain. In this article, we provide a comprehensive overview of graph filters, including the different filtering categories, design strategies for each type, and trade-offs between different types of graph filters. We discuss how to extend graph filters into filter banks and graph neural networks to enhance the representational power;that is, to model a broader variety of signal classes, data patterns, and relationships. We also showcase the fundamental role of graph filters in signalprocessing and machine learning applications. Our aim is that this article provides a unifying framework for both beginner and experienced researchers, as well as a common understanding that promotes collaborations at the intersections of signalprocessing, machine learning, and application domains.
This study focuses on evaluating how well a wireless voice over internet protocol (VOIP) system works for 4G and 5G communications by using wavelets. It suggests creating a transmission system for wireless VOIP based ...
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This study focuses on evaluating how well a wireless voice over internet protocol (VOIP) system works for 4G and 5G communications by using wavelets. It suggests creating a transmission system for wireless VOIP based on data from the wavelet transform (both image and audio data). Each wavelet's performance will be assessed using several metrics, including signal-to-noise ratio, peak signal-to-noise ratio, root mean squared error, percentage of retained signal energy, and compression ratio. Additionally, other factors such as packet loss, Delay, and Throughput will be analyzed. These parameters will be evaluated with both raw data (without wavelets) and data processed using different wavelets. The simulation results show that the proposed model performs better with various wavelets compared to Markov's model. Notably, the Daubechies wavelet family yields superior results compared to other types. This model could be beneficial for high-speed IP-based cellular systems where minimizing delay (latency) is crucial. For 5G systems, parallel computing techniques may be used to implement this model effectively.
We introduce graph wedgelets - a tool for data compression on graphs based on the representation of signals by piecewise constant functions on adaptively generated binary graph partitionings. The adaptivity of the par...
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We introduce graph wedgelets - a tool for data compression on graphs based on the representation of signals by piecewise constant functions on adaptively generated binary graph partitionings. The adaptivity of the partitionings, a key ingredient to obtain sparse representations of a graph signal, is realized in terms of recursive wedge splits adapted to the signal. For this, we transfer adaptive partitioning and compression techniques known for 2D images to general graph structures and develop discrete variants of continuous wedgelets and binary space partitionings. We prove that continuous results on best $m$-term approximation with geometric wavelets can be transferred to the discrete graph setting and show that our wedgelet representation of graph signals can be encoded and implemented in a simple way. Finally, we illustrate that this graph-based method can be applied for the compression of images as well.
Recently, as an emerging signalprocessing technology, the semi-tensor product compressed sensing (STP-CS) has attracted widespread attention in the fields of imageprocessing, communications, and bioinformatics. This...
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Recently, as an emerging signalprocessing technology, the semi-tensor product compressed sensing (STP-CS) has attracted widespread attention in the fields of imageprocessing, communications, and bioinformatics. This article reviews the theoretical foundations, algorithmic designs, and practical applications of STP-CS. It begins by revisiting the basic concepts of compressed sensing (CS) and the definition of the semi-tensor product (STP), followed by a detailed discussion on the theoretical model of STP-CS, optimization of the measurement matrix, and reconstruction algorithms. Furthermore, the article explores the practical applications of STP-CS in areas such as sensor nodes, visual security, image encryption, and spectrum sensing, analyzing its performance advantages and potential challenges in these fields. A comprehensive analysis indicates that STP-CS offers significant benefits in saving storage space, reducing computational complexity, and enhancing data security, making it a promising technology in the field of signalprocessing.
In digital image analysis and processing field of study, noise reduction and suppression have been stated as a common query. However, it is mostly essential issue to demesne the fine edges and ridges and tiny texture ...
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In digital image analysis and processing field of study, noise reduction and suppression have been stated as a common query. However, it is mostly essential issue to demesne the fine edges and ridges and tiny texture while suppressing the noise in processing of the digital images. In order to avoid causing "Over-strangling" phenomenon, semi-soft thresholding model is exploited to classify the sharp edges of the contaminated images. In this study, a self-adjusting generative adversarial network GAN is utilized. This procedure is used to extract the fine edge of the noised digital images in order to improve the actual signal in the high frequency components where the main parts of the clean pixels may consider as noise pixels, and as a result delete the unwanted noise from the tested image that might cause over smoothing to the resulted images. In order to further denoise the contaminated digital image, adaptive learning GAN model throughout scoring machine is exploited. Therefore, it preserves the information of input image and feature maps, learns the correlation between global and local features, improves image restoration performance, and suppresses phenomena such as over-smoothing that tend to occur in wavelets-based denoising. The proposed method is an end-to-end network structure with CNN-based preprocessing methods. Experimental results demonstrate that, in comparison with state-of-the-art noise removal techniques, the proposed method has better visual quality, and the proposed method improves PSNR by 2.27 dB and 0.85 dB on average compared with state-of-the-art- denoising methods. In addition, the proposed method could shorten the processing time noticeably.
This work presents, for the first time, the use of Continuous Wavelet Transform (CWT) for ultrasonic ranging with air-coupled pMUTs. wavelets is a powerful mathematical tool which to date has been used towards audio a...
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ISBN:
(纸本)9798350371918;9798350371901
This work presents, for the first time, the use of Continuous Wavelet Transform (CWT) for ultrasonic ranging with air-coupled pMUTs. wavelets is a powerful mathematical tool which to date has been used towards audio analysis and imageprocessing. Given that wavelets are characterized by two properties, namely scale and time, they could be suitable for pMUT-based time-of-flight (ToF) ranging. The proposed signalprocessing methodology is experimentally validated with the method achieving a impressive mean accuracy of 95.5%. These experimental results highlight the potential usage of the proposed signalprocessing methodology for pMUT-based airborne ranging applications.
The recent interest of face image manipulation detection has been directed towards providing the ability of detecting various types of manipulations. In the best scenario, the available methods can detect the manipula...
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The recent interest of face image manipulation detection has been directed towards providing the ability of detecting various types of manipulations. In the best scenario, the available methods can detect the manipulations and localize the manipulated face region. The ability of recovering the face region after manipulation localization will be very useful in practical applications, however, this has not been highlighted in the previous researches. In this paper, a new face image authentication (FIA) scheme is presented based on image watermarking and Cohen-Daubechies-Feauveau (CDF) wavelets. In the proposed scheme, the CDF is used to generate the recovery bits from the face region in order to be used for recovering the face region when manipulations exist. Several experiments have been conducted to evaluate the performance of the proposed scheme which proved its efficiency in generating high quality watermarked images, detecting various types of manipulations, localizing the manipulated blocks in the face region, and recovering the face region with good visual quality. The comparison with the state-of-the-art detection schemes proved the superiority of the proposed scheme.
Inexact computing brings benefits to error-tolerant applications, including multimedia and signalprocessing. Although inexact computing reduces precision, it provides meaningful and faster results yet, with lower ene...
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Inexact computing brings benefits to error-tolerant applications, including multimedia and signalprocessing. Although inexact computing reduces precision, it provides meaningful and faster results yet, with lower energy consumption and lower system architecture complexity. We introduce two 4:2 inexact compressors into the unsigned 8 x 8 multiplier circuit by truncating and combining the presented compressors, which in terms of dynamic power shows 25 and 57.14%, respectively, improvement in comparison with the exact multiplier and the best value of similar models. The proposed multiplier with the truncating and combining method and the second proposed compressor shows 7.78 and 67.97% improvement in the MED and PDUEP, respectively, improvement in comparison with the best value of similar models. In design of the transistor level, the proposed models improve the power up to 0.007 mu W. In the imageprocessing application, the proposed multipliers with the truncating and combining method, improve the PSNR up to13.23 and 16.17%, respectively, and improve the SSIM up to 0.90 and 0.94%.The routing and mapping of the proposed circuits are conducted using the hybrid method to improve the evaluation criteria for imageprocessingapplications. The validity of the performance of the proposed multipliers are examined with simulations of FPGA circuits (Virtex-6 family) and ASIC circuits (RF_CMOS 0.18 mu m), and simulation of applications in imageprocessing is carried out in MATLAB software.
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