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Design of a Hardware-Efficient Floating-Point Multiplier with Dynamic Segmentation

Design of a Hardware-Efficient Floating-Point Multiplier wit...

引用

Ph.D. Research in Microelectronics and Electronics (PRIME)

作者： Luca Tegazzini Gennaro Di Meo Davide De Caro Antonio G. M. Strollo DIETI- Univ. di Napoli Federico II Napoli Italy

ISBN: (数字)9798350386301

ISBN: (纸本)9798350386318

In this paper, we present a novel low-power floating-point multiplier design that leverages dynamic segmentation to enhance efficiency. Our approach approximates the mantissa product through a simple addition of dynamically extracted input segments, supplemented by an additional correction term implemented through a small, hardwired lookup table. Compared to previous proposals, our FPM shows optimal results in terms of the power-accuracy trade-off, with power savings exceeding 85% compared to an exact implementation. The proposed multiplier offers remarkable performance in image processing applications, with Structural Similarity Index (SSIM) values approaching 1 and Peak signal-to-Noise Ratio (PSNR) reaching up to 66dB.

关键词： Measurement image segmentation PSNR Accuracy Table lookup Microelectronics Proposals

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Airbus Ship Detection from Satellite imagery using Frequency Domain Learning 27

Airbus Ship Detection from Satellite Imagery using Frequency...

引用

conference on image and signal processing for Remote Sensing XXVii

作者： Al-Saad, Mina Aburaed, Nour Panthakkan, Alavikunhu Al Mansoori, Saeed Al Ahmad, Hussain Marshall, Stephen Univ Dubai Dubai U Arab Emirates Mohammed Bin Rashid Space Ctr Dubai U Arab Emirates Univ Strathclyde Glasgow Lanark Scotland

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

Ship detection from remote sensing images has been a topic of interest that gradually gained attention over the years due to the wide variety of its applications in the field of maritime surveillance, such as oil discharge control, sea pollution monitoring, and harbour management. Even though there is an extensive amount of methods developed for ship detection, there are still several challenges that remain unsolved, especially in complex environments. These challenges include occlusions due to shadows, clouds, and fog. Nowadays, deep learning algorithms, especially Deep Convolutional Neural Networks (DCNNs), are considered as a powerful approach for automatically detecting ships in satellite imagery. In this paper, enhanced Faster R-CNN (FRCNN) model will be used to overcome the aforementioned unsolved challenges. The enhanced FRCNN, which combines high level features with low level features, will be trained and tested in the frequency domain using the publicly available satellite imagery dataset, Airbus Ship Detection, provided by Kaggle. The performance will be compared to the original FRCNN based on their Overall Accuracy (OA) and Mean Average Precision (mAP) metrics.

关键词： Deep Learning FRCNN Discrete wavelet Transform Remote Sensing Ship Detection

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DWT-QR based blind image watermarking method using firefly algorithm 28

DWT-QR based blind image watermarking method using firefly a...

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28th signal processing and Communications applications conference (SIU)

作者： Altay, Seyma Yucel Ulutas, Guzin Ataturk Univ Bilgisayar Muhendisligi Bolumu Erzurum Turkey Karadeniz Tech Univ Bilgisayar Muhendisligi Bolumu Trabzon Turkey

ISBN: (纸本)9781728172064

Digital watermarking is a method used to protect intellectual property rights and copyrights. This study represents a blind method based on Discrete wavelet Transform (DWT) and QR decomposition. In this method, the cover image is firstly divided into non-overlapping blocks, and then the blocks with low standard deviation are selected to embed the watermark. After each of the selected blocks is decomposed by DWT, low frequency sub-bands are subjected to QR decomposition. The first row of the R matrix resulting from QR decomposition is used to embed the watermark bits. Firefly Algorithm (FA) is utilized to optimize the gain factor of embedding, which is effective in determining the robustness and imperceptibility of the watermarking scheme. To appraise the performance of the study, Peak signal to Noise Ratio (PSNR) is used to measure the perceptual quality and Normalized Correlation (NC) is used to evaluate robustness. According to experimental results, the perceptual quality of the proposed method is found to be higher than that of similar studies in the literature. In addition, it is established that the robustness of the method against attacks such as noise, cropping, compression, and filtering is better.

关键词： Firefly algorithm discrete wavelet transform robustness QR decomposition digital watermarking

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A High-Speed Computational Pipeline Single MAC-Based VLSI Architecture for Real-Time signal and image processing

A High-Speed Computational Pipeline Single MAC-Based VLSI Ar...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International conference on

作者： Dhandapani Vaithiyanathan Britto Pari James Karuthapandian Mariammal National Institute of Technology Delhi Delhi India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Madras Institute of Technology Anna University Chennai India

VLSI architectures play a vital role in most real-time applications because of their optimal performance. In this work, a single MAC (Multiply-Accumulate) based pipelined architecture is proposed for faster computation of the one-dimension (1-D) discrete wavelet transform (DWT). The main objective of this work is to reduce computational complexity by utilizing resource sharing and to enhance the speed of operation by employing a pipelining scheme. The designed single MAC-based architecture for computing DWT is suitable for infrasound data processing which employs a high-pass filter (HPF) and low-pass filter (LPF). The architecture efficiently implements a resource-sharing scheme by adopting only these two filters. In addition, in this realization, pipelining is utilized to achieve high-speed computation of DWT. Further, this design makes use of common elements several times which provide a convenient and suitable implementation. The proposed structure is coded using Verilog and implemented in FPGA (Field Programmable Gate Array) hardware with the Xilinx platform. Intending easily transported and real-time utilization, this architecture is realized using Xilinx Virtex-5 series FPGA. The area of the proposed architecture is reduced by about 44.8% when examining conventional parallel MAC architecture. Likewise, this pipelined design offers power conservation of about 31.97% over the parallel design. Further, the proposed architecture offers an operating speed of 276MHz with a power conservation of 200mW at 276MHz clock frequency.

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Fault Diagnosis of Rolling Element Bearing Using a Mesh of Continuous wavelet Transform and Visual Geometry Group 19 (VGG-19)

Fault Diagnosis of Rolling Element Bearing Using a Mesh of C...

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2021 IEEE International conference on Artificial Intelligence and Computer applications, ICAICA 2021

作者： Ali Sher, Muhammad Muhammad, Umar Yu, Xiaojie Hu, Qiao Xi'an Jiaotong University School of Mechanical Engineering Xi'an China Xi'an Jiaotong University School of Mechanical Engineering Xi'an China

ISBN: (纸本)9781665418676

Health management of machines is a very crucial part for any industry. The reason being, a slight ignorance in this field can lead to huge financial losses, and in some cases can even cause causalities. Talking about industrial machines like lathe, milling, laser cutting, and CNCs;every structure has a common mechanical part i.e. bearing. So, fault diagnosis of bearing needs the most attention. This article describes an approach for fault diagnosis of bearing using a non-conventional three step procedure (data acquisition, signal processing, and data classification). For data acquisition, ceramic shear piezoelectric accelerometer having a sensitivity of 10.2 mV/(m/s2), has been used. To convert time domain vibration data into time-frequency domain image, signal processing is performed using continuous wavelet transform (CWT). Unlike conventional approaches, in this method, feature extraction and data classification are performed using a single technique i.e. visual geometry group 19 (vgg-19). For the testing and validation of proposed method, four types of bearing samples (different health conditions) i.e. normal bearing, inner race faulty bearing (fault 1), outer race faulty bearing (fault 2), and rolling element faulty bearing (fault 3) have been used. The results using the proposed approach are very encouraging, and a classification accuracy of 100% was achieved using 5 epochs. In future, this strategy can be used for investigation and condition monitoring of different mechanical parts including gear, pulley, rotor, and reducer. © 2021 IEEE.

关键词： Failure analysis

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DEEP MULTI-SCALE GABOR wavelet NETWORK FOR image RESTORATION

DEEP MULTI-SCALE GABOR WAVELET NETWORK FOR IMAGE RESTORATION

引用

IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Dong, Hang Zhang, Xinyi Guo, Yu Wang, Fei Xi An Jiao Tong Univ Coll Artificial Intelligence Xian Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China

ISBN: (纸本)9781509066315

Due to the limitations of the imaging processors and complex weather conditions, image degradation is often inevitable. Existing deep learning-based image restoration methods often rely on the powerful feature representation capacity of deep networks and pay less attention to the inherent properties of the degradation signal, e.g. variations in spatial scale and orientations across the image, which makes them ineffective for the image restoration tasks. In this paper, we propose a Multi-scale Gaborwavelet Network (MsGWN) for image restoration. We apply the multi-scale architecture to extract the contaminated feature from input at different spatial scales, and thus the contaminated feature can be effectively restored in a corse-to-fine manner. However, using multi-scale architecture alone cannot remove the degradations with different orientations. To overcome this problem, we introduce a Gabor wavelet Module (GWM) to further extract the contaminated features from four orientations. By decomposing the features into four multi-orientation components, the restoration process can be facilitated by avoiding learning the mixed degradations all-in-one. We evaluate the proposed method on image demoireing, image deraining, and image dehazing. Experiments on these applications demonstrate that the proposed method can achieve favorable results against the state-of-the-art approaches.

关键词： image restoration Multi-scale Multi-orientation Gabor wavelet Deep learning

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Feature Level Medical image Fusion with Deep Learning

Feature Level Medical Image Fusion with Deep Learning

引用

Evolutionary Algorithms and Soft Computing Techniques (EASCT), International conference on

作者： R. Prathipa R. Ramadevi Sathyabama Institute of Science and Technology Chennai India Department of Biomedical Engineering Saveetha School of Engineering Chennai India

In recent years, medical image fusion has emerged as a comprehensive analytical method that frequently employs various modification techniques. The objective of multi-modal medical image fusion is to enhance image quality while preserving specific characteristics by combining images from different imag- ing modalities. This field encompasses cutting-edge domains such as image processing, computer vision, pattern recognition, ma- chine learning, and artificial intelligence. Furthermore, medical image fusion has found extensive clinical applications, enabling healthcare professionals to gain a deeper understanding of lesions by integrating medical images from multiple modalities. In this study, we propose the utilization of a Grey Wolf algorithm, which comprises various learning models, to minimize squared error and optimize the process through weighted entropy during image fusion. The successful results are attributed to a hybridization of the Discrete wavelet Transform (DWT) and the Discrete Curvelet Transform (DCT). By employing Principal Component Analysis, Grey Wolf Optimization, and a Recurrent Neural Network, we have developed a novel fusion model for unsupervised categorization known as GWO-RNN. This model is designed to work with multi-modality images that incorporate structural and sparse constraints. The flexibility and precision of image fusion are further enhanced through correlation and covariance analysis using eigenvalues and eigenvectors. Matlab simulations of the proposed workbench are conducted, comparing it against state-of-the-art methods to assess performance metrics, including peak signal-to-noise ratio, mean square error, correlation, and accuracy. The proposed workbench not only meets but surpasses all performance benchmarks while also reducing computational overhead.

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Exploring the use of Optical Domain Information for Automated Target Detection and Recognition 2

Exploring the use of Optical Domain Information for Automate...

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conference on Virtual, Augmented, and Mixed Reality (XR) Technology for Multi-Domain Operations ii

作者： Hickman, Duncan L. Tektonex Ltd Argyll Rd Kilcreggan Argyll & Bute Scotland

ISBN: (数字)9781510643567

ISBN: (纸本)9781510643567

Automated Target Detection and Recognition (ATD/R) of targets in a cluttered background presents major challenges for the designers of military sensor systems. To achieve acceptable levels of detection and recognition performance, extensive use is often made of complex image processing techniques such as neural network architectures which attempt to replicate the capabilities of the human vision system. Although such methods generate good levels of performance, they are often not suitable for those applications where smaller and lower cost sensor systems are used across a more diverse range of scene content and imaging conditions. A new approach is proposed here where system performance is achieved through a more effective balance between optical domain and post-detection processing. Specifically, the signal to Clutter Ratio (SCR) is maximised by using broadband spectral and polarisation information to offset performance deficiencies associated with simple image and data processing functions. It is shown that this approach offers a basis for introducing ATD/R functionality in low cost imaging systems such as those flown on drones. A simple imaging system is used to demonstrate the concept, which compromises two broadband cameras operating in the visible and near infrared bands, and with one of the sensors additionally providing polarimetric information. The concept of a joint spectral polarisation weight map is proposed and the potential performance gain is illustrated using targets in moderate to high clutter situations. The results obtained indicate potential benefits for future ATD/R systems and it is hoped that this will encourage future design engineers to consider the wider use of optical domain information.

关键词： Target detection and recognition ATD/R spectral processing polarimetry colour weight map polarisation weight map joint weight map signal to clutter optical domain processing

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signal Restoration with Fractional Transform Sparse Representation and Autoencoder

Signal Restoration with Fractional Transform Sparse Represen...

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International conference on Computer and Communication Engineering, ICCCE

作者： Shivani Saxena Surbhi Mistry Nilesh Patel Mohendra Roy Ahsan Z. Rizvi Computer Sciences and Engineering Institute of Advanced Research Gandhinagar India Biotechnology and Bioengineering Institute of Advanced Research Gandhinagar India Information and Communication Technology Pandit Deendayal Energy University Gandhinagar India

Signa1 denoising is a crucial task in image processing, audio analysis, and communications. Traditional methods often struggle to strike a balance between reducing noise and preserving important signal details. Existing methods exploit the potential of fractional transforms combined with sparse representation techniques. However, these approaches face challenges in achieving the desired balance due to limited sensitivity to signal variations, high computational complexity, restricted adaptability to diverse noise types, and the need for careful selection of transform parameters. To overcome these limitations, we propose a novel method called Fractional Transform Sparse Representation Denoising (FTSRD), which integrates fractional wavelet transform (FWT), fractional DCT (FDCT), sparse representation, and Deep Denoising Autoencoder (DDAE) techniques. The integration of DDAE in FTSRD provides several advantages. DDAE, being a deep learning-based model, has the capability to learn complex noise patterns and extract meaningful signal features. Experimental evaluations were conducted on synthetic signals and ECG signals. The results demonstrate the superior performance of FTSRD in reducing noise while preserving essential structural details. It also surpasses traditional methods in subjective and objective quality measures. In conclusion, FTSRD addresses the challenges associated with noise reduction and signal detail preservation, making it a valuable tool in various denoising applications.

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A wavelet Based Edge Detection Algorithm

A Wavelet Based Edge Detection Algorithm

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

作者： Sun, Qingfeng Anhui Tech Coll Mech & Elect Engn Dept Elect Engn Wuhu 241000 Anhui Peoples R China

ISBN: (纸本)9789811365041;9789811365034

Edge is in the place where image gray scale changes severely, it contains abundant image information. image edge detection is a hot and difficult research field. Compare and analyze several classic edge detection method, aim at the advantages and disadvantages, respectively, propose a multi-scale edge detection algorithm based on the wavelet. The simulation shows the algorithm obtains an ideal effect in edge location and noise suppression.

关键词： Edge Gray scale Edge detection Multi-scale wavelet transform

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