Deep unfolding compressive sensing (CS) has experienced remarkable advancements. However, there still exist two challenges: (1) Many algorithms either use uniform block-based sampling, which ignore the fact that the c...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Deep unfolding compressive sensing (CS) has experienced remarkable advancements. However, there still exist two challenges: (1) Many algorithms either use uniform block-based sampling, which ignore the fact that the content of different blocks is different, or allocate the sampling rate referring to complete signal before CS sampling, which is not always feasible in real-world scenarios. (2) Traditional CNN is difficult to capture broader contextual priors during iterative recovery. In this paper, we propose a novel network ASMFNet to solve the above two issues. Specifically, to address the first issue, we introduce a dual-branch network featuring a basic sampling branch to acquire reference image and an adaptive sampling branch by median filtering for allocating remaining sampling rate adaptively. For the second problem, we use Swin Transformer and feature fusion block to increase the feature interactions. Experimental results demonstrate that our proposed method outperforms existing methods.
The systematic process of detecting, gathering, and disposing of all waste in a specific manner so as not to jeopardize human or environmental life is known as waste management. If not correctly managed, medical waste...
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The systematic process of detecting, gathering, and disposing of all waste in a specific manner so as not to jeopardize human or environmental life is known as waste management. If not correctly managed, medical waste, which includes items like old needles, sharps, and infectious materials, poses a serious risk to the environment and the general public's health, especially in developing and underdeveloped countries. In this study, we used IoT and machine learning technologies to automate waste identification, information tracking, and monitoring, improve the accuracy and efficiency of waste bins, and enable real-time monitoring and analysis of waste information. By utilizing smart waste bins fitted with sensors and machine learning algorithms to automatically detect and classify various forms of waste, IoT and machine learning are being applied to the management of medical waste. These intelligent bins can then notify waste management staff when they need to be emptied and can offer useful information on waste streams for analysis and practice improvement. Only persons who have been authenticated will be permitted to collect medical waste, and the system will keep a record of all data. Using IoT and machine learning technologies, we attempted to reduce the danger of unintentional exposure to hazardous materials, which enhanced overall public health and safety in addition to increasing the effectiveness of medical waste management. Overall, compared to current manual medical waste management systems, the convergence of IoT and machine learning has the potential to significantly increase the sustainability and efficiency of medical waste management, reducing the environmental impact of healthcare operations and enhancing public health.
With the rapid development of intelligent systems and the advent of the era of big data, the continuous development of computers is being promoted. Exporting and tracking moving targets in video images is one of the m...
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ISBN:
(数字)9781665490092
ISBN:
(纸本)9781665490092
With the rapid development of intelligent systems and the advent of the era of big data, the continuous development of computers is being promoted. Exporting and tracking moving targets in video images is one of the most important research contents of computer vision. It combines many advanced technologies in the field of computing, such as imageprocessing, pattern recognition, automatic control and artificial intelligence, and is widely used in intelligent surveillance. In various fields such as traffic control, machine intelligence and medical diagnosis, visual effects are obtained through image or imageprocessing. Record videos from the computer and perform specific mechanical tasks. In terms of intelligent tracking, as the demand for applications in various complex environments continues to grow, how to improve the robustness and accuracy of moving target tracking and tracking algorithms has become the focus of ongoing target tracking research. This paper studies the image target detection algorithm based on computer vision technology. Firstly, the literature research method is used to summarize the existing problems of image target detection based on computer vision technology and the existing algorithms. The experiment is used to analyze the image target based on computer vision technology. The detection algorithm is verified, and the error rate of image target detection of the algorithm proposed in this paper is compared. According to the experimental results, it can be seen from Figure 1 that in experiment 1, the target detection of the GMM-STMRF algorithm is more accurate than other methods based on the calculation of the false detection rate. The maximum false detection rate is only 2.3%, and the other algorithms have 5.4%-11.1% false detection rate The GMM-STMRF algorithm increases the multi-frame calculation in the time dimension, so the calculation time has increased. algorithms such as GMM and MeanShift need to estimate the multi-frame parameters, an
The Intermediate Frequency Direct Sampling (IFDS) mode in Inverse Synthetic Aperture Radar (ISAR) imaging preserves the original information of the echo signal and radar system characteristics. However, the massive da...
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Aiming at the problem of large noise and artifacts generated by the first 3D image virtual reconstruction system, a 3D image virtual reconstruction system based on visual communication technology is proposed. In this ...
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The recently proposed adaptive kernel Kalman filter (AKKF) is an efficient method for highly nonlinear and high-dimensional tracking or estimation problems. Compared to other nonlinear Kalman filters (KFs), the AKKF h...
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ISBN:
(纸本)9798350337327
The recently proposed adaptive kernel Kalman filter (AKKF) is an efficient method for highly nonlinear and high-dimensional tracking or estimation problems. Compared to other nonlinear Kalman filters (KFs), the AKKF has significantly improved performance, reducing computational complexity and avoiding resampling. It has been applied in various tracking scenarios, such as multi-sensor fusion and multi-target tracking. By using existing Stone Soup components, along with newly established kernel-based prediction and update modules, we demonstrate that the AKKF can work in the Stone Soup platform by being applied to a bearing-only tracking (BOT) problem. We hope that the AKKF will enable more applications for tracking and estimation problems, and the development of a whole class of derived algorithms in sensor fusion systems.
This paper proposes an innovative algorithm for optimizing intelligent image data systems based on deep learning. The algorithm combines image feature extraction, data preprocessing and efficient optimization strategi...
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ISBN:
(数字)9798350377033
ISBN:
(纸本)9798350377040
This paper proposes an innovative algorithm for optimizing intelligent image data systems based on deep learning. The algorithm combines image feature extraction, data preprocessing and efficient optimization strategies to improve the performance and accuracy of image data processingsystems. First, by designing a deep CNN architecture, important features in the image are extracted to achieve efficient completion of image recognition and classification tasks. Subsequently, a new multi-level data processing method is proposed, which can optimize image data at different levels, thereby improving processing speed and reducing noise interference. Through a series of simulation experiments, the results show that the image classification accuracy of the algorithm is improved by about 12 % , from 85.6% of the traditional method to 97.3%. In addition, the processing efficiency is improved by about 20%, the data processing time is reduced from 2.5 seconds of the traditional method to 2 seconds, and the stability of the system is significantly enhanced by introducing optimization strategies, and the stability is improved by about 18%. The optimized algorithm shows significant advantages in both accuracy and efficiency, meeting the needs of efficient intelligent imageprocessingsystems.
Cyber-Physical System (CPS) is used in industries and automated plants as they have modern administration abilities and are capable of performing real-time processing in a distributed architecture. In mechanical plant...
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Semantic communication is considered the key promoter and basic paradigm of future 6G networks and applications. In this paper, we investigate a multi-unmanned aerial vehicle (UAV) semantic communication framework, wh...
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In recent years, the application of artificial intelligence (AI) techniques for fire detection has gained significant attention due to its potential for enhancing early fire detection systems. This study aims to compa...
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ISBN:
(数字)9798350309249
ISBN:
(纸本)9798350309256
In recent years, the application of artificial intelligence (AI) techniques for fire detection has gained significant attention due to its potential for enhancing early fire detection systems. This study aims to compare the performance of deep learning convolutional neural networks (CNN) and support vector machine (SVM) machine learning algorithms in the context of fire detection. We present a comprehensive analysis and evaluation of the two approaches, highlighting their strengths and weaknesses, and discussing their potential for real-world fire detection applications.
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