With the rapid growth of technology and the proliferation of data in this digital age, current image and audio applications require greater resolution, higher data transmission rates and better data compression techni...
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ISBN:
(纸本)9781665434294
With the rapid growth of technology and the proliferation of data in this digital age, current image and audio applications require greater resolution, higher data transmission rates and better data compression techniques to meet the ever increasing demands placed on them. The research presented here investigates the impact of data compression in the automatic recognition of handwritten digit images and spoken digit audio. A Haar wavelet transform (HWT) is used to compress the original image and audio data, which is input to an artificial neural network (ANN) where the automatic digit recognition is performed. The HWT generates a signature, or fingerprint, for the data by removing redundant data using a cut-off function, a number of which are investigated. This reduced data signature enables the ANN-based recogniser to be simplified and computationally more efficient. Experimental results show that for handwritten digit images, the recognition accuracy is 94.3% with compression ratios of 80%;for spoken audio digits, the recognition accuracy is 98.8% with compression ratios of 82%.
The Quality of vision is a practical objective in the image *** of the applications of imageprocessing procedures is image Fusion. To get the subjective vision of a image by gathering the best data from source images...
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Using DWT-SVD and SHA3 Hash function, this research aims to develop an ownership protection and image authentication technique that embeds the watermark information and hash authentication key in a hybrid domain. The ...
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ISBN:
(纸本)9781665462204
Using DWT-SVD and SHA3 Hash function, this research aims to develop an ownership protection and image authentication technique that embeds the watermark information and hash authentication key in a hybrid domain. The experiment was conducted with multispectral images from the KhalifaSat. The Performance of the proposed method is evaluated using wavelet domain signal to noise ratio (WSNR), structural similarity index measurement (SSIM) and peak signal to noise ratio (PSNR). To analyse the efficacy of the recovered watermark, two metrics are used: Normalized Correlation (NC) and image Quality Index (IQI). The method presented is robust against many intended and unintended attacks. Without sacrificing transparency, our proposed watermarking approach meets the objectives of imperceptibility and robustness. It accurately detects the manipulated locations on the satellite image and is sensitive to even small changes.
Intelligent fault diagnosis techniques such as deep learning methods have gained immense attention recently for identifying bearing faults with higher accuracy and adaptability based on vibration analysis. This work p...
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Intelligent fault diagnosis techniques such as deep learning methods have gained immense attention recently for identifying bearing faults with higher accuracy and adaptability based on vibration analysis. This work proposes a fault diagnosis method for rolling bearings under extreme noise conditions based on the time-frequency representation (TFR) of signals with deep learning techniques. The bearing signals are masked with -5dB and -10dB of white Gaussian noise to create a noisy environment. Short-time Fourier transforms (STFT), Continuous wavelet transforms, and Gabor transforms (GT) are utilized to obtain spectrogram, magnitude scalogram, and constant-Q transform images to visualize the time-frequency relationship of the signal from one-dimensional vibration signals. These TFR images are directly given as input to VGG16-CNN deep learning architecture to classify the bearing faults. The effectiveness of each TFR is measured and compared based on classification accuracy. This work also studies the effect of TFR with and without overlapping the segmented signals. The case western reserve university (CWRU) standard-bearing dataset is used in this work and has achieved a satisfactory result. The result suggests that the magnitude scalogram of the vibration signal is an effective TFR that works efficiently with deep learning for bearing fault classification under different conditions, such as original signal with no added noise, -5dB and -10 dB of added noise, with an accuracy of 99.42%, 90.2 % and 85.03%, respectively when trained with 80% of the sample with 75% of the overlapping index.
The internal defects of industrial components such as magnetic tiles seriously affect their performance. With the development of intelligent manufacturing technology, industrial manufacturing enterprises need an autom...
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ISBN:
(纸本)9781728192536
The internal defects of industrial components such as magnetic tiles seriously affect their performance. With the development of intelligent manufacturing technology, industrial manufacturing enterprises need an automatic method to efficiently and accurately detect the internal defects of magnetic tiles. In this paper, a signal pre-processing algorithm based on Empirical Mode Decomposition (EMD) and wavelets denoising is proposed for echo signals for defect detection. Then the variance curve and the adaptive processing method are used to locate the defects accurately. The experimental results show that the algorithm proposed in this paper can been successfully used in defect specimen with different transducer frequency, different defect size and different defect depth. Compared with the original B-scan image, and the internal defects of the specimen could be detected more prominently in enhanced B-scan image, and the accuracy of the defect depth could reach 98.76%, which is better than existing state of the art. Thus, the proposed method has been proved to be effective for optimizing ultrasonic B-mode scanning and accurately locating defects inside magnetic tiles.
The excessive use of synthetic pesticides in the fields is the cause of numerous problems to human health and biodiversity. Knowing the location and severity of the emergence of pests enables targeted treatment, helpi...
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ISBN:
(数字)9798350367560
ISBN:
(纸本)9798350367577
The excessive use of synthetic pesticides in the fields is the cause of numerous problems to human health and biodiversity. Knowing the location and severity of the emergence of pests enables targeted treatment, helping to reduce the use of pesticides. Given that traditional field inspection requires considerable effort with limited effectiveness, remote sensing combined with machine learning is an essential complement for large-scale, efficient monitoring. This study therefore aims to develop a machine learning-based solution for detecting cotton jassids from free satellite imagery data in Alibori, northern Benin. Multispectral and thermal infrared images respectively from the Sentinel-2 and Landsat 9 satellites were fused in the most correlated bands using signalprocessing algorithms Pseudo Wigner Distribution (PWD), Nonsubsampled Contourlet Transform (NSCT), and Discrete wavelet transform (DWT). Two configurations, in terms of data augmentation, were used. Areas infested by jassids were identified in merged and unmerged satellite images (Sentinel-2, Landsat 9, and PlanetScope, a commercial satellite) using Support Vector Machine (SVM), Random Forest (RF), and CatBoost supervised machine learning algorithms. The green and Thermal Infrared Sensor 1 (TIRS1) bands are the most correlated in configuration 1 and the blue and TIRSI bands in configuration 2 for respectively Sentinel-2 and Landsat 9. The DWT and NSCT signalprocessing algorithms produced the least Root Mean Square Deviation (RMSE) for image fusion (0.46 for DWT and 0.33 for NSCT). The best result in terms of detecting performance was obtained with the composite treatment of Landsat 9 images and the RF algorithm, with an overall accuracy of 88.23%, an Fl-score of 82%, and a Kappa index of 79.92% in configuration 1 and in configuration 2 for composite treatment of Sentinel-2 an overall accuracy of 92.36%, an Fl-score of 90.75 % and a Kappa index of 86.36% with CatBoost algorithm.
Engagement analysis finds various applications in healthcare, education, advertisement, services. Deep Neural Networks, used for analysis, possess complex architecture and need large amounts of input data, computation...
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ISBN:
(数字)9798350365474
ISBN:
(纸本)9798350365481
Engagement analysis finds various applications in healthcare, education, advertisement, services. Deep Neural Networks, used for analysis, possess complex architecture and need large amounts of input data, computational power, inference time. These constraints challenge embedding systems into devices for real-time use. To address these limitations, we present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain and boost processing speed, we introduce a "TC" stream that uses Continuous wavelet Transform (CWT) to represent information in a 2D tensor form. Evaluated on the EngageNet dataset, the proposed method outperforms existing baselines, utilizing only two behavioral features (head pose rotations) compared to the 98 used in baseline models. Furthermore, comparative analysis shows TCCT-Net’s architecture offers an order-of-magnitude improvement in inference speed compared to state-of-the-art image-based Recurrent Neural Network (RNN) methods. The code will be released at https://***/vedernikovphoto/TCCT_Net.
Several discrete wavelet transform architectures have already been introduced due to their wide range of applications in the field of image, video and speech signalprocessing. In this paper, an area efficient VLSI ar...
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Biometric Wrist Authentication (BWA) is one of the best-known authentication schemes in many access control systems. The use of fingerprint biometrics as humans attempt to communicate with robots/machines, and their p...
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ISBN:
(纸本)9783030870072;9783030870065
Biometric Wrist Authentication (BWA) is one of the best-known authentication schemes in many access control systems. The use of fingerprint biometrics as humans attempt to communicate with robots/machines, and their physical environments have inherent setbacks. However, various efforts have been proposed to fix the limitations. Most biometric efforts suffer from lack of computational derivatives and do not support optimal image compression. Motivated by these concerns, the goal of this paper is fivefold. First, we proposed BWA using Discrete Cosine Transform (DCT) to compress palm print images and develop Wrist-Print Biometric Identification System (WPBIS). Second, we developed a process model for DCT and characterized it for wrist templates considering both original and decoded images. Third, Bits per pixel (Bpp) and Compression ratio (Cr) for a wrist template/bioscript are used as metrics for evaluation. Fourth, after adopting various timestamps, we observed that the image template Bpp yielded 1.256 Bpp and compression of 63.26% based on DCT. Fifth, we showed a typical experimental scenario with a digital signal processor feeding images with DCT. Identification and verification of various wrist-prints (test-point samples) are equally carried out. From the results, WPBIS DCT offered higher image intensity compared with wavelet transform.
Different characteristics of satellite images are reflected in different channels, so the monitoring and early warning of meteorological disasters based on satellite image data of a single channel may not achieve sati...
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