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2nd International Conference on signal image processing and Communication, ICSIPC 2022

作者： Yang, Chenglong Lai, Yushu Li, Qiuyue School of Electronic and information Engineering Chongqing Three Gorges University Chongqing404130 China

ISBN: (纸本)9781510657694

In order to investigate the feasibility of acoustic emission phenomenon aroused by electromagnetic excitation in metal specimens, electromagnetic acoustic emission model based on COMSOL software was established. On this basis, electromagnetic acoustic emission experiments were carried out and real signals with a lot of noise were obtained. To remove the noise of the electromagnetic acoustic emission signal, wavelet packet transform and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) was introduced. Firstly, the noise signal of low-frequency was removed by the wavelet packet transform, and the retained node signal was reconstructed. Then the reconstructed signal was resolved by CEEMDAN, and the effective component signal was obtained by the variance contribution rate. Finally, to obtain denoised electromagnetic acoustic emission signal, the effective component signal was reconstructed. The result of simulation and experiment shows that the electromagnetic acoustic emission signal could be effectively obtained by loading electromagnetic excitation, which has greater practical value in engineering applications. © 2022 SPIE.

关键词： Empirical mode decomposition

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Efficient VLSI architecture of 3D discrete transformation

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INTEGRATION-THE VLSI JOURNAL 2022年 82卷 136-146页

作者： Basiri, M. Mohamed Asan IIITDM Dept ECE Kurnool 518007 India

Discrete transforms are used in many signal processing applications such as audio compression, image compression, video compression, high efficiency video coding (HEVC), and so on. This paper proposes a generic VLSI architecture for performing (N x N x N)-point discrete transformation. This proposed generic architecture can be used to perform the 3D discrete transformations such as Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), Discrete Hartley Transform, Integer Discrete Cosine Transform (Integer DCT), Discrete Hadamard Transform, and Discrete Walsh Transform. Also, this (N x N x N)-point discrete transformation's proposed architecture is to perform [N/2(i)] numbers of (2 x 2(i) x 2(i))-point discrete transforms in parallel, where i is varied from 2, 3, 4, ...log(2) N for Discrete Hartley Transform, i = 3 for DCT/DST, and i = 2, 3, 4,5, 6 for Integer DCT/Discrete Walsh/Discrete Hadamard Transforms. Also, our proposed architecture eliminates the requirement of larger storage buffer in between the row, column, and temporal processes. The trade-off in our proposed design is the number of cycles to complete the operation. All the existing and proposed techniques are implemented with 45 nm CMOS technology using Cadence. The synthesis results show that our proposed architecture achieves 73% of reduction in critical path delay as compared with the parallel Butterfly architecture based 3D-DCT.

关键词： DCT Discrete wavelet transform Digital signal processing image compression

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Secure biometric systems based on bio-signals and DNA encryption of optical spectrograms

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OPTICS EXPRESS 2023年第3期31卷 3927-3944页

作者： Salama, Gerges M. Omar, Basma El-shafai, Walid El-banby, Ghada M. Hamed, Hesham F. A. El-Gazar, Safaa Soliman, Naglaa F. El-Samie, Fathi E. Abd Minia Univ Fac Engn Dept Elect Engn Al Minya 61111 Egypt Prince Sultan Univ Dept Comp Sci Secur Engn Lab Riyadh 11586 Saudi Arabia Menoufia Univ Fac Elect Engn Dept Elect & Elect Commun Engn Menoufia 32952 Egypt Menoufia Univ Fac Elect Engn Dept Ind Elect & Control Engn Menoufia 32952 Egypt Egyptian Russian Univ Fac Engn Dept Elect Engn Cairo Egypt Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia

Recently, biometrics has become widely used in applications to verify an individual's identity. To address security issues, biometrics presents an intriguing window of opportunity to enhance the usability and security of the Internet of Things (IoT) and other systems. It can be used to secure a variety of newly emerging IoT devices. However, biometric scenarios need more protection against different hacking attempts. Various solutions are introduced to secure biometrics. Cryptosystems, cancelable biometrics, and hybrid systems are efficient solutions for template protection. The new trend in biometric authentication systems is to use bio-signals. In this paper, two proposed authentication systems are introduced based on bio-signals. One of them is unimodal, while the other is multimodal. Protected templates are obtained depending on encryption. The deoxyribonucleic acid (DNA) encryption is implemented on the obtained optical spectrograms of bio-signals. The authentication process relies on the DNA sensitivity to variations in the initial values. In the multimodal system, the singular value decomposition (SVD) algorithm is implemented to merge bio-signals. Different evaluation metrics are used to assess the performance of the proposed systems. Simulation results prove the high accuracy and efficiency of the proposed systems as the equal error rate (EER) value is close to 0 and the area under the receiver operator characteristic curve (AROC) is close to 1. The false accept rate (FAR), false reject rate (FRR), and decidability (D) are also estimated with acceptable results of 1.6 x 10-8, 9.05 x 10-6, and 29.34, respectively. Simulation results indicate the performance stability of the proposed systems in the presence of different levels of noise. (c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： image encryption image processing Neural networks Power spectral density signal processing wavelet transforms

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Development of Integrated Sensor System for Intelligent Transportation System 18

Development of Integrated Sensor System for Intelligent Tran...

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18th IEEE International Colloquium on signal processing and applications (CSPA)

作者： Azares, John Emmanuel G. Centino, Mark Joshua A. Dela Cruz, Jennifer Nopia, John Paul A. Amado, Timothy M. Mapua Univ Sch Elect Elect & Comp Engn Manila Philippines

ISBN: (纸本)9781665485296

Traffic congestion has always been a major problem in the Philippines. This project study developed an integrated sensor system for intelligent transportation, which addressed the lack of automatic traffic monitoring systems to achieve traffic efficiency and safety. The researchers utilized different sensors in gathering data such as water level, temperature, and humidity within a certain area as well as a camera in capturing images and compared the three image compression algorithms, mainly the DCT (Discrete Cosine Transform), DWT (Discrete wavelet Transform) and SVD (singular value decomposition) in terms of different parameters used in describing and identifying the best type of image compression to be applied prior to transmission to other different nodes. MSE (mean squared error) value, PSNR (Peak signal-to-noise ratio), Compress Ratio, and Process Time were the values of comparison in determining the best compression technique or algorithm for the gathered images. As a result, it was found that DCT characterized the best parameters for image compression. DCT produced the highest PSNR (52.41979dB), the lowest value for MSE (0.37247), and the lowest process time (0.16181s), while SVD was able to produce the most compressed image. This project also utilized solar renewable energy for the power management system, which enabled the system to run independently without any other external power source. This will be beneficial for the community to identify which roads can be used to optimize the mobility of the vehicles and maximize the use of renewable energy;hence will help reduce the traffic congestion issues in the country.

关键词： sensor system transportation DCT DWT solar energy image compression algorithm

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Listless Block Cube Tree Coding for Low Resource Hyperspectral image Compression Sensors 5

Listless Block Cube Tree Coding for Low Resource Hyperspectr...

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5th International Conference on Multimedia, signal processing and Communication Technologies (IMPACT)

作者： Chandra, Harshit Bajpai, Shrish Integral Univ Fac Engn & Informat Technol ECE Dept Lucknow Uttar Pradesh India

ISBN: (纸本)9781665476478

Hyperspectral (HS) image has rich spectral information content, which facilitates multiple applications including remote sensing. Due to the big data size of the HS image, compression is a required process for the efficiency of image storage and transmission. However, the complexity of the compression algorithms turns real-time compression into a very challenging task. A novel listless set partitioned hyperspectral image compression algorithm is proposed. The proposed compression algorithm uses zero block cube tree structure to exploit the inter and intra sub-band correlation to achieve the compression. From the result, it has been clear that the proposed compression algorithm has low coding complexity with at-par coding efficiency. Thus, it can be a suitable contender for low-resource hyperspectral image sensors.

关键词： Coding Complexity Compression Hyperspectral image Set Partitioned Hyperspectral image Compression wavelet Transform

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Efficient FPGA Implementations of Lifting based DWT using Partial Reconfiguration 36

Efficient FPGA Implementations of Lifting based DWT using Pa...

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36th International Conference on VLSI Design (VLSID) / 22nd International Conference on Embedded Systems (ES)

作者： Basiri, Mohamed Asan M. Bharadwaja, P. V. S. R. IIITDM Dept ECE Kurnool India

ISBN: (纸本)9798350346787

Discrete wavelet Transform (DWT) is an ubiquitous mathematical technique used in a wide range of applications. The fundamental advantages of DWT are its high compression ratio, lack of blocking artifacts, and strong time & frequency domain localization. This proposed work is an efficient implementation of (9,7) lifting based 1D, 2D, and 3D DWTs, where the hardware software codesign with partial reconfiguration (PR) has made a significant reduction in resource utilization. Additionally, the suggested approach yields a very low mean square error (MSE), resulting in a substantially higher peak signal to noise ratio (PSNR). The constant multiply-add unit based proposed work with single core implementation achieves 80% decrease in the number of look up tables (LUT) and 32% decrease in number of slices utilized when compared with the variable multiply-add unit based implementation. To prove the architecture's originality, it is tested for six distinct applications such as mono audio, stereo audio, grayscale image, colour image, gray scale video, and colour video. The implementation of the work is done on Zynq 7000 (XC7Z020CLG484-1) field programmable gate array (FPGA) with Xilinx Vivado.

关键词： Convolution based DWT Digital signal processing Lifting based DWT and Partial Reconfiguration

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WDIG: a wavelet domain image generation framework based on frequency domain optimization

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EURASIP JOURNAL ON ADVANCES IN signal processing 2023年第1期2023卷 66页

作者： Zhu, Qing Li, Xiumei Sun, Junmei Bai, Huang Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou Peoples R China Hangzhou Normal Univ Key Lab Cryptog Zhejiang Prov Hangzhou Peoples R China

In the end-to-end image generation task, the spatial domain of pixel space cannot explicitly separate the low-frequency general information such as texture and color from the high-frequency detail information such as structure and identity. The loss function calculated in the spatial domain fails to effectively constrain the maintenance of detail information, and the generated image quality is insufficient. In this paper, a wavelet domain image generation (WDIG) framework is proposed to preserve the frequency information of images, in which the loss functions are constructed in the pixel space and wavelet space. In the pixel space, the low-frequency and high -frequency characteristic information of the signal are obtained by setting the appropriate Gaussian kernel and adopting the Gaussian fuzzy method. The loss function of l1 norm spatial domain is constructed for the low-frequency and high-frequency characteristic information. In the wavelet space, the corresponding channel sub-band coefficients are obtained by wavelet transform, and the image is explicitly separated into high frequency information and low-frequency information. The l1 norm frequency domain loss function is constructed respectively for the sub-band coefficients. The WDIG can constrain model training more accurately and optimize model more precisely, so as to better maintain the details and quality of generated image. The WDIG framework is evaluated in the image generation applications including style transfer, image translation and Generative Adversarial Nets (GAN) Inversion. Experimental results show that the WDIG framework can effectively retain the details of images and generate more realistic images, and improve the image quality of the above applications in image generation.

关键词： image generation Pixel space wavelet space Frequency domain Detail information

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Comparative Study on Classification of images in Compressed and Uncompressed Domains

Comparative Study on Classification of Images in Compressed ...

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International Conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Ashwini K Perla Sree Neha S Jaiadityan Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita school of computing Chennai India

ISBN: (数字)9798350350845

ISBN: (纸本)9798350350852

image Classification is the basis of Computer vision. Classification with image data finds a variety of applications in various fields. A comparative study of Classifying the images in the compressed and uncompressed domain is exploited in this research. A garbage classification dataset with 6 different categories is employed for this purpose. Naitve Convolutional neural network is utilized as the classifier. Compressed classification is achieved by compressing the images using discrete wavelet transform of three levels and by feeding the compressed data to the model for classification. The accuracy achieved by classification in the uncompressed domain is about 82.1% with a loss of 0.892. The accuracy achieved in the compressed domain by the three levels of the discrete wavelet transform is 81.7%,81.2%, and 74.6% with losses of 0.793, 0.891, and 1.071 respectively. There is minimal difference in accuracy achieved between uncompressed and compressed domains. image compression by discrete wavelet transform overperforms the uncompressed domain by several factors like size, elapsed time and memory space.

关键词： Wireless communication image coding wavelet domain Memory management Transforms signal processing Data models

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A Multiplier-Less Level-3 Haar wavelet Transform Approximation Requiring Five Additions Only 15

A Multiplier-Less Level-3 Haar Wavelet Transform Approximati...

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15th IEEE Dallas Circuit and System Conference (DCAS)

作者： da Rosa, Morgana M. A. Paim, Guilherme Seidel, Henrique B. Almeida, Sergio da Costa, Eduardo A. C. Bampi, Sergio Univ Catolica Pelotas UCPel Grad Program Elect Engn & Comp Pelotas RS Brazil Fed Univ Rio Grande do Sul UFRGS Grad Program Microelect PGMICRO Porto Alegre RS Brazil

ISBN: (纸本)9781665498852

Approximate transform kernels have emerged as an alternative design to reduce the hardware complexity of digital signal processors by exploiting the intrinsic error resilience of many embedded consumer electronics applications such as signal, image and video processing, and computer vision. The approximate level-4 Haar wavelet transform (AxHWT-4) is the state-of-the-art approximate transform for ECG (electrocardiogram) signal processing and reduces more than nine times the energy consumption compared to the exact transform. In this work, we propose an approximate level-3 Haar wavelet transform (AxHWT-3) for increasing the hardware design efficiency with related to the AxHWT-4 while obtaining higher R-peak detection accuracy in the electrocardiogram signal. Our AxHWT-3 approximation is performed in the Haar matrix kernel to eliminate its multiplications which in the hardware design results in savings of energy consumption and VLSI hardware area. Our AxHWT-3 proposal achieves an R-peak detection accuracy of more than 99% and reduces the energy consumption by 15.98 times and the VLSI area by 12.64 times compared to the exact solution (i.e., the original level-3 Haar). In addition, the proposed AxHWT-3 outperforms the state-of-the-art AxHWT-4 with a savings of about 32% in power dissipation and 19.5% in VLSI area.

关键词： Approximate Computing Approximate Transforms Haar wavelet ECG Digital signal processing Hardware Design

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Diffusion-Based Bit-Depth Expansion

Diffusion-Based Bit-Depth Expansion

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IEEE Workshop on Multimedia signal processing

作者： Riyu Lu Lingyu Zhu Baoliang Chen Xiaopeng Fan Shiqi Wang City University of Hong Kong Harbin Institute of Technology Hong Kong SAR City University of Hong Kong China Harbin Institute of Technology Harbin China

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

Diffusion-based generative models have achieved remarkable success across a variety of applications. However, the potential application for bit-depth expansion has not been extensively studied. This paper introduces a wavelet-based diffusion model for the bit-depth expansion task. In this method, the image is first decomposed into low and high-frequency components via wavelet transformation. This decomposition allows for targeted processing by specialized modules and reduces computational complexity by lowering the image resolution. The low-frequency component is processed in both the forward diffusion and reverse denoising stages. Meanwhile, the high-frequency components are filtered by the High Frequency Denoising Filter (HFDF) to eliminate noise and artifacts. Finally, the low and high-frequency components are recombined into a predicted high-bit-depth image through inverse wavelet transformation. Experimental results demonstrate the superiority of the proposed method in producing perceptually compelling outputs that outperform previous methods.

关键词： wavelet transforms Learning systems image resolution Noise reduction Noise signal processing algorithms Imaging Diffusion models Prediction algorithms High frequency

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