It is necessary to regularly enhance the traffic light system's control using the newest technology. The PLC-basedsystems that are now in use make it difficult to adopt modern technologies like artificial intelli...
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The internet, big data, and intelligent robots have transformed online education. This study introduces an Enhanced Convolutional Neural Network (CNN+) model optimized with refined weight initialization and strategic ...
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Pests impact agricultural production, prompting the need for efficient disease detection in plants. Traditional methods, reliant on manual observation, are time- consuming and imprecise. It uses the Convolutional Neur...
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ISBN:
(纸本)9798331540661;9798331540678
Pests impact agricultural production, prompting the need for efficient disease detection in plants. Traditional methods, reliant on manual observation, are time- consuming and imprecise. It uses the Convolutional Neural Network (CNN) for prediction of diseases. The CNNs enhance precision, offering farmers a more effective and streamlined method for disease detection, potentially transforming agricultural practicesUsing transfer learning and pre-trained models, the system successfully extracts information from photographs submitted via a user-friendly website. The CNN based algorithm analyzes these photos using powerful image pre-processing techniques, giving farmers and individuals precise insights into recognized diseases or pest infestations. Plant photos obtained with cellphones are processed using CNN, a specialized image recognition system. A CNN is an effective method in the AI, which scans the image and produces the maximum correct output. It is primarily used in image recognition tasks, where it processes input in the form of pixels. The website design not only allows for easy image submission but also provides results and probable therapy recommendations. This comprehensive solution, which combines cutting-edge CNN technology, transfer learning, and an easy-to-use web interface, enables users to proactively protect their crops, resulting in a more resilient and sustainable agricultural ecosystem.
This paper proposes a radio frequency signal identification method based on deep neural network. First, this article abstracts the radio frequency signal into a plane diagram and converts the radio frequency signal id...
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The deep learning enhanced channel estimation is presented for the OFDM communication in the internet of Vehicles (10V). Using the image enhancement and denoising, the channel responses at unknown positions are predic...
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An internet of things is a system of connecting various smart devices which are able to gather and transfer data over the network. The expansion of internet of Things can be used in smart agriculture to boost the qual...
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Single image Super Resolution (SISR) is one of the essential tasks in the image processing field, which aims to enhance the resolution of images. With the aid of Deep Learning (DL), SISR recently has made significant ...
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ISBN:
(纸本)9781665464956
Single image Super Resolution (SISR) is one of the essential tasks in the image processing field, which aims to enhance the resolution of images. With the aid of Deep Learning (DL), SISR recently has made significant improvements and achieved promising results. This paper introduces a novel training technique, called Per-Channel training. This novel training technique proved to obtain the best results in terms of the common Super Resolution (SR) metrics across all testing datasets used, reaching a maximum average Peak signal-to-Noise Ratio (PSNR) of 39.34 dB and Structural Similarity Index Measure (SSIM) of 0.9714 in one of the test datasets.
New navigation signal broadcasts from low-orbit communications satellites. In comparison with the GNSS signal, these new navigation signals are designed based on Doppler measurements and share the transmission channel...
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In response to the insufficient sample issue in the electromagnetic signal data of high-speed railway pantograph arcs, this study proposes an improved Deep Convolutional Generative Adversarial Network (DCGAN) to augme...
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ISBN:
(纸本)9798350379860;9798350379877
In response to the insufficient sample issue in the electromagnetic signal data of high-speed railway pantograph arcs, this study proposes an improved Deep Convolutional Generative Adversarial Network (DCGAN) to augment image datasets. By optimizing the network structure, the proposed method significantly improves the quality of generated images and introduces a lightweight design that reduces the computational demands of the model, making it more suitable for resource-constrained environments. After 5,000 iterations, the proposed method achieves a Frechet Inception Distance (FID) score of 36.33, compared to 45.54 for the original DCGAN and 50.21 for the WGAN. Moreover, the Inception Score (IS) of the proposed method is 1.91 +/- 0.11, outperforming the original DCGAN (1.81 +/- 0.23) and WGAN (1.77 +/- 0.04), further demonstrating its effectiveness. This method provides a new technical reference for research on electromagnetic signals in high-speed railways.
This paper describes a work on an acoustic user authentication system using smartphones. The system implements two-factor authentication for Windows workstations, where the authentication procedure, including locking ...
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ISBN:
(纸本)9798350348194;9798350348187
This paper describes a work on an acoustic user authentication system using smartphones. The system implements two-factor authentication for Windows workstations, where the authentication procedure, including locking and unlocking the workstation is transparent to the user. Since workstations and smartphones have built-in microphones and speakers, the system does not require additional hardware. The uniqueness of the solution is being based on acoustic signals. These signals are transmitted by the user's smartphone and received by the workstation microphone. The system is "pure play acoustic" since no wiring or radio transmission is used. The system configuration supports multiple users in the same area. Eavesdropping prevention is provided by sequentially generated random one-time keys. Acoustic communication can be applied either in the audible range or beyond the human hearing range depending on the sampling rate of the smartphone and the workstation.
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