Meng Wang^*, Yixuan Shao, Haipeng Liu

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5229-5254, 2023, DOI:10.32604/cmc.2023.036631

Abstract In recent years, deep generative models have been successfully applied to perform artistic painting style transfer (APST). The difficulties might lie in the loss of reconstructing spatial details and the inefficiency of model convergence caused by the irreversible en-decoder methodology of the existing models. Aiming to this, this paper proposes a Flow-based architecture with both the en-decoder sharing a reversible network configuration. The proposed APST-Flow can efficiently reduce model uncertainty via a compact analysis-synthesis methodology, thereby the generalization performance and the convergence stability are improved. For the generator, a Flow-based network using Wavelet additive coupling (WAC) layers is implemented to… More >

Abdul Quawi^1,*, Y. Mohamed Shuaib¹, M. Manikandan²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3469-3486, 2023, DOI:10.32604/iasc.2023.035001

Abstract In this work, an Artificial Neural Network (ANN) based technique is suggested for classifying the faults which occur in hybrid power distribution systems. Power, which is generated by the solar and wind energy-based hybrid system, is given to the grid at the Point of Common Coupling (PCC). A boost converter along with perturb and observe (P&O) algorithm is utilized in this system to obtain a constant link voltage. In contrast, the link voltage of the wind energy conversion system (WECS) is retained with the assistance of a Proportional Integral (PI) controller. The grid synchronization is tainted with the assistance of… More >

Peng Geng^*, Ji Lu, Hongtao Ma, Guiyi Yang

Structural Durability & Health Monitoring, Vol.17, No.1, pp. 1-22, 2023, DOI:10.32604/sdhm.2023.018632

Abstract Accurate and reliable crack segmentation is a challenge and meaningful task. In this article, aiming at the characteristics of cracks on the concrete images, the intensity frequency information of source images which is obtained by Discrete Wavelet Transform (DWT) is fed into deep learning-based networks to enhance the ability of network on crack segmentation. To well integrate frequency information into network an effective and novel DWTA module based on the DWT and scSE attention mechanism is proposed. The semantic information of cracks is enhanced and the irrelevant information is suppressed by DWTA module. And the gap between frequency information and… More >

Jin Wang¹, Zhen Liu^1,*, Ying Wang¹, Caifeng Wen^2,3, Jianwen Wang^2,3

Energy Engineering, Vol.120, No.5, pp. 1149-1162, 2023, DOI:10.32604/ee.2023.025209

Abstract Analyzing the strain signal of wind turbine blade is the key to studying the load of wind turbine blade, so as to ensure the safe and stable operation of wind turbine in natural environment. The strain signal of the wind turbine blade under continuous crosswind state has typical non-stationary and unsteady characteristics. The strain signal contains a lot of noise, which makes the analysis error. Therefore, it is very important to denoise and extract features of measured signals before signal analysis. In this paper, the joint algorithm of ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is used for… More >

Sachin Sharma^1,*, Meena Malik², Chander Prabha³, Amal Al-Rasheed⁴, Mona Alduailij⁴, Sultan Almakdi⁵

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 393-407, 2023, DOI:10.32604/cmc.2023.033536

Abstract Watermarking of digital images is required in diversified applications ranging from medical imaging to commercial images used over the web. Usually, the copyright information is embossed over the image in the form of a logo at the corner or diagonal text in the background. However, this form of visible watermarking is not suitable for a large class of applications. In all such cases, a hidden watermark is embedded inside the original image as proof of ownership. A large number of techniques and algorithms are proposed by researchers for invisible watermarking. In this paper, we focus on issues that are critical… More >

Amel Ali Alhussan^1,*, Hanaa A. Abdallah², Sara Alsodairi², Abdelhamied A. Ateya³

Computer Systems Science and Engineering, Vol.46, No.1, pp. 403-416, 2023, DOI:10.32604/csse.2023.035048

Abstract Securing medical data while transmission on the network is required because it is sensitive and life-dependent data. Many methods are used for protection, such as Steganography, Digital Signature, Cryptography, and Watermarking. This paper introduces a novel robust algorithm that combines discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) digital image-watermarking algorithms. The host image is decomposed using a two-dimensional DWT (2D-DWT) to approximate low-frequency sub-bands in the embedding process. Then the sub-band low-high (LH) is decomposed using 2D-DWT to four new sub-bands. The resulting sub-band low-high (LH₁) is decomposed using 2D-DWT to four new sub-bands.… More >

Dong-Wook Kim¹, Gun-Yoon Shin¹, Myung-Mook Han^2,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 153-164, 2023, DOI:10.32604/csse.2023.034589

Abstract Unlike external attacks, insider threats arise from legitimate users who belong to the organization. These individuals may be a potential threat for hostile behavior depending on their motives. For insider detection, many intrusion detection systems learn and prevent known scenarios, but because malicious behavior has similar patterns to normal behavior, in reality, these systems can be evaded. Furthermore, because insider threats share a feature space similar to normal behavior, identifying them by detecting anomalies has limitations. This study proposes an improved anomaly detection methodology for insider threats that occur in cybersecurity in which a discrete wavelet transformation technique is applied… More >

Shixin Tu, Yuanyuan Jia, Jinglong Du^*, Baoru Han^*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 293-321, 2023, DOI:10.32604/cmes.2023.022308

Abstract The field of healthcare is considered to be the most promising application of intelligent sensor networks. However, the security and privacy protection of medical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention. Fortunately, digital watermarking provides an effective method to solve this problem. In order to improve the robustness of the medical image watermarking scheme, in this paper, we propose a novel zero-watermarking algorithm with the integer wavelet transform (IWT), Schur decomposition and image block energy. Specifically, we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,… More >

Mohanapriya Marimuthu¹, Santhosh Rajendran², Reshma Radhakrishnan², Kalpana Rengarajan³, Shahzada Khurram⁴, Shafiq Ahmad⁵, Abdelaty Edrees Sayed⁵, Muhammad Shafiq^6,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4729-4745, 2023, DOI:10.32604/cmc.2023.033020

Abstract Continuous improvements in very-large-scale integration (VLSI) technology and design software have significantly broadened the scope of digital signal processing (DSP) applications. The use of application-specific integrated circuits (ASICs) and programmable digital signal processors for many DSP applications have changed, even though new system implementations based on reconfigurable computing are becoming more complex. Adaptable platforms that combine hardware and software programmability efficiency are rapidly maturing with discrete wavelet transformation (DWT) and sophisticated computerized design techniques, which are much needed in today’s modern world. New research and commercial efforts to sustain power optimization, cost savings, and improved runtime effectiveness have been initiated… More >

V. Harshini¹, T. Dhanwin¹, A. Shahina^1,*, N. Safiyyah², A. Nayeemulla Khan²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1851-1867, 2023, DOI:10.32604/csse.2023.028136

Abstract Biometrics, which has become integrated with our daily lives, could fall prey to falsification attacks, leading to security concerns. In our paper, we use Transient Evoked Otoacoustic Emissions (TEOAE) that are generated by the human cochlea in response to an external sound stimulus, as a biometric modality. TEOAE are robust to falsification attacks, as the uniqueness of an individual’s inner ear cannot be impersonated. In this study, we use both the raw 1D TEOAE signals, as well as the 2D time-frequency representation of the signal using Continuous Wavelet Transform (CWT). We use 1D and 2D Convolutional Neural Networks (CNN) for… More >