As the 16th most common cancer globally, oral cancer yearly accounts for some 355,000 new cases. this study underlines that an early diagnosis can improve the prognosis and cut down on mortality. It discloses a multif...
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As the 16th most common cancer globally, oral cancer yearly accounts for some 355,000 new cases. this study underlines that an early diagnosis can improve the prognosis and cut down on mortality. It discloses a multifaceted approach to the detection of oral cancer, including clinical examination, biopsies, imaging techniques, and the incorporation of artificial intelligence and deep learning methods. this study is distinctive in that it provides a thorough analysis of the most recent AI-based methods for detecting oral cancer, including deep learning models and machine learning algorithmsthat use convolutional neural networks. By improving the precision and effectiveness of cancer cell detection, these models eventually make early diagnosis and therapy possible. this study also discusses the importance of techniques in image pre-processing and segmentation in improving image quality and feature extraction, an essential component of accurate diagnosis. these techniques have shown promising results, with classification accuracies reaching up to 97.66% in some models. Integrating the conventional methods withthe cutting-edge AI technologies, this study seeks to advance early diagnosis of oral cancer, thus enhancing patient outcomes and cutting down on the burden this disease is imposing on healthcare systems.
As machine learning (ML) models for image perception continue to advance, ensuring their robustness and reliability under various real-world scenarios remains a significant challenge. image quality factors, such as bl...
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ISBN:
(纸本)9798350393613
As machine learning (ML) models for image perception continue to advance, ensuring their robustness and reliability under various real-world scenarios remains a significant challenge. image quality factors, such as blur, brightness, and other environmental conditions, can significantly affect the performance of these algorithms, leading to inaccurate detection and potential failures in critical applications. In this paper, we propose a comprehensive diagnosis framework that leverages image quality metrics to assess and enhance the performance of these algorithms. To accomplish this goal, we deliberately introduce disturbances in parameters such as brightness, saturation, and other relevant factors. Subsequently, we compute a set of full-reference image quality metrics to evaluate the image quality after the perturbations. Once we have obtained the metrics, we apply a nonlinear transformation to these values. Based on the transformed metrics, we create a regression model that predicts the detection Intersection over Union (IOU). To validate our framework, we conducted experiments using three state-of-the-art machine learning models for object detection and instance segmentation. the models were subjected to various scenarios with different levels of image quality perturbations. Our experimental results clearly demonstrate the possibility of establishing a strong correlation between image quality metrics and the performance of the algorithms.
Hyperspectral image unmixing estimates a collection of constituent materials (called endmembers) and their corresponding proportions (called abundances), which is a critical preprocessing step in many remote sensing a...
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In the process of generating Synthetic Aperture Radar (SAR) images, the noise based on speckle is produced due to physical reasons, and its presence seriously affects the interpretation and post-processing of the imag...
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Assuring the reliability of the OS boot process is essential to realize reliable computer systems. Secure Boot enables it by introducing the verification of the boot image with digital signature and hash values. this ...
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ISBN:
(纸本)9798350393613
Assuring the reliability of the OS boot process is essential to realize reliable computer systems. Secure Boot enables it by introducing the verification of the boot image with digital signature and hash values. this can be the basis for various security mechanisms. However, Secure Boot requires a long boot time due to their expensive computation costs, resulting in extended downtime. In this paper, we first implement Secure Boot in an ordinary RISC-V boot process on U-Boot, a representative open-source bootloader, and clarify the overhead introduced by the verification process. Based on the insight obtained above, we propose a parallelization of the verification process on a multi-core in Secure Boot. It accelerates the boot process while securely authenticating the boot image. We implement the proposed parallel verification process on U-Boot. the evaluation on a HiFive Unmatched RISC-V board shows that the parallelized hash computation on four cores achieves 3.96 times better performance than the original.
Exploring fluid flow phenomena has always been a hot topic in fluid mechanics research. Particle image Velocimetry (PIV) is a non-contact technique applied in fluid mechanics laboratories for measuring the velocity fi...
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the MCU is the core component of the motor drive because the optimized control algorithm is implemented using the MCU. Monitoring motor control variables becomes important because it provides better insights and infor...
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ISBN:
(纸本)9798350393613
the MCU is the core component of the motor drive because the optimized control algorithm is implemented using the MCU. Monitoring motor control variables becomes important because it provides better insights and information to analyze and optimize the performance of the control algorithms. MicroSD card, with its advantages of compact size, interface connection, and reasonable price on acceptable performance, is used as an external storage device. However, the microSD card has a serious disadvantage of its extended busy time in the writing procedure, which affects motor control performance. In this study, we propose a microSD card writing scheme by exploiting the dual-core structure of MCUs to overcome the microSD card busy time and implement a data logger based on the proposed algorithm. the experimental results show that the proposed scheme provides remarkable performance and the capability to log data over a long period without any missing or corrupted data.
Convolutional Neural Networks (CNNs) often require a huge amount of multiplication. the current approach of multiplication reduction requires data preprocessing, which is power-hungry and time-consuming. the paper pro...
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ISBN:
(纸本)9798350393613
Convolutional Neural Networks (CNNs) often require a huge amount of multiplication. the current approach of multiplication reduction requires data preprocessing, which is power-hungry and time-consuming. the paper proposed an image-wised Selective processing Engine (SPE-I) for accelerating CNN processing by eliminating unessential operations through algorithm-hardware co-designs. the SPE-I compares the similarity of two input images and identifies any redundant calculations that can be skipped. A modified LeNet-5 network, LeNet3x3 was designed to validate the performance improvement of SPE-I using the MNIST dataset. LeNet3x3 with and without SPE-I were implemented in TSMC 90-nm CMOS technology at 87.5 MHz operating frequency. Compared to the network without SPE-I, the network with SPE-I only has 0.12% - 1.79% accuracy drop, achieving 43.1% power saving due to 73% - 81% multiplication reduction. Regarding timing, SPE-I takes 20% of total clock cycles to provide convolutional data compared to the convolutional layer using preprocessing.
Industry 4.0, the digitalization of manufacturing promises to lead to lowered cost, efficient processes and even discovery of new business models. However, many of the enterprises have huge investments in legacy machi...
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ISBN:
(纸本)9783031702587;9783031702594
Industry 4.0, the digitalization of manufacturing promises to lead to lowered cost, efficient processes and even discovery of new business models. However, many of the enterprises have huge investments in legacy machines which are not 'smart'. In this study, we thus designed a cost-efficient solution to retrofit a legacy conveyor belt-based cutlery washing machine with a commodity web camera. We then applied computer vision (using both traditional imageprocessing and deep learning techniques) to infer the speed and utilization of the machine. We detailed the algorithmsthat we designed for computing both speed and utilization. Withthe existing operational constraints of our client, frequent re-training of the deep learning model for object detection is not feasible. thus, we compared the generalizability of the two techniques across 'unseen' cutleries and found traditional imageprocessing to be generalizable across 'unseen' images. Our proposed final solution uses traditional imageprocessing for computation of utilization but a hybrid of traditional imageprocessing and deep learning model for speed computation as it is more reliable. Our client has implemented our proposed solution for one conveyor belt-based cutlery washing machine and will be planning to scale this to multiple conveyor belt-based cutlery washing machines.
To address the problem of weak robustness of some current image encryption algorithms, this paper proposes a strong robust encryption algorithm. the Logistic chaotic system is improved to produce more random sequence ...
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