Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of tra...Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.展开更多
X-ray image might be corrupted by noise or blurring because of signal transmission or the bad X- ray lens. This paper presents a two-stage shock filter based on Partial Differential Equations (PDE) to restore noisy bl...X-ray image might be corrupted by noise or blurring because of signal transmission or the bad X- ray lens. This paper presents a two-stage shock filter based on Partial Differential Equations (PDE) to restore noisy blurred X-ray image. Shock filters are popular morphological methods. They are used for noise removal, edge enhancement and image segmentation. Our experimental results show that the performances of shock filter are excellent in X-ray image. The peak signal-to-noise ratio (PSNR) values are 38 dB at least in restoring the noisy X-ray image. The sharpness of image’s edges increase in enhancing the blurred X-ray image. Furthermore, this paper proposes a VLSI architecture for accelerating the high-definition (HD) X-ray image (944 p) process. This paper implements the architecture in FPGA. The hardware cost is low because the computation of shock filter is low complex. To achieve the real-time processing specification, this paper uses a 5-series shock filter architecture to implement computation of HD X-ray image. This paper demonstrates a 944 p, 43.1-fps solution on 100 MHz with 133 k gate counts in Design Compiler, and with 2904 logic elements in FPGA.展开更多
This paper introduces bit-interleaved polar coded modulation with iterative detection/decoding(BIPCM-ID).In order to enable the soft output of successive cancellation list(SCL)decoding,two types of re-encoders are pro...This paper introduces bit-interleaved polar coded modulation with iterative detection/decoding(BIPCM-ID).In order to enable the soft output of successive cancellation list(SCL)decoding,two types of re-encoders are proposed,namely the max-re-encoder and min-re-encoder,respectively.Regarding the iterative decoding,we analytically verify that the average mutual information(AMI)between the transmitted and decoded symbols can approach the Shannon bound with the proposed schemes.Moreover,bit error rate(BER)and block error rate(BLER)in single-user and multi-user scenarios are studied.Finally,simulation results show that the performance of BIPCMID outperforms other bit-interleaved coded modulation(BICM)systems with LDPC and Turbo codes,while also reducing the computational complexity.展开更多
This paper describes the hardware implementation of the RANdom Sample Consensus (RANSAC) algorithm for featured-based image registration applications. The Multiple-Input Signature Register (MISR) and the index registe...This paper describes the hardware implementation of the RANdom Sample Consensus (RANSAC) algorithm for featured-based image registration applications. The Multiple-Input Signature Register (MISR) and the index register are used to achieve the random sampling effect. The systolic array architecture is adopted to implement the forward elimination step in the Gaussian elimination. The computational complexity in the forward elimination is reduced by sharing the coefficient matrix. As a result, the area of the hardware cost is reduced by more than 50%. The proposed architecture is realized using Verilog and achieves real-time calculation on 30 fps 1024 * 1024 video stream on 100 MHz clock.展开更多
Electrical impedance tomography (EIT) is a fast and cost-effective technique that provides a tomographic conductivity image of a subject from boundary current-voltage data. This paper proposes a time and memory effici...Electrical impedance tomography (EIT) is a fast and cost-effective technique that provides a tomographic conductivity image of a subject from boundary current-voltage data. This paper proposes a time and memory efficient method for solving a large scale 3D EIT image reconstruction problem and the ill-posed linear inverse problem. First, we use block-based sampling for a large number of measured data from many electrodes. This method will reduce the size of Jacobian matrix and can improve accuracy of reconstruction by using more electrodes. And then, a sparse matrix reduction technique is proposed using thresholding to set very small values of the Jacobian matrix to zero. By adjusting the Jacobian matrix into a sparse format, the element with zeros would be eliminated, which results in a saving of memory requirement. Finally, we built up the relationship between compressed sensing and EIT definitely and induce the CS: two-step Iterative Shrinkage/Thresholding and block-based method into EIT image reconstruction algorithm. The results show that block-based compressed sensing enables the large scale 3D EIT problem to be efficient. For a 72-electrodes EIT system, our proposed method could save at least 61% of memory and reduce time by 72% than compressed sensing method only. The improvements will be obvious by using more electrodes. And this method is not only better at anti-noise, but also faster and better resolution.展开更多
Due to the content bundling and the dramatic increase of content size, the download performance in peer-to-peer networks has become a research focus again recently. In this paper, we propose a novel approach to improv...Due to the content bundling and the dramatic increase of content size, the download performance in peer-to-peer networks has become a research focus again recently. In this paper, we propose a novel approach to improve the download performance based on the classical space-time trade-off. With the approach, a peer can speed up local downloads in peer-to-peer networks by contributing a portion of local hard disks for the content distribution in peer-to-peer networks. The contribution can bring performance improvement to each peer following the approach and in the meantime improve the overall content distribution performance in a peer-to-peer network. Based on the approach, we propose BISTRO, a BitTorrent based on space-time trade-off. The BISTRO is compatible with the vanilla BitTorrent. Our extensive experiments show that BISTRO can significantly reduce the download time.展开更多
文摘Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.
文摘X-ray image might be corrupted by noise or blurring because of signal transmission or the bad X- ray lens. This paper presents a two-stage shock filter based on Partial Differential Equations (PDE) to restore noisy blurred X-ray image. Shock filters are popular morphological methods. They are used for noise removal, edge enhancement and image segmentation. Our experimental results show that the performances of shock filter are excellent in X-ray image. The peak signal-to-noise ratio (PSNR) values are 38 dB at least in restoring the noisy X-ray image. The sharpness of image’s edges increase in enhancing the blurred X-ray image. Furthermore, this paper proposes a VLSI architecture for accelerating the high-definition (HD) X-ray image (944 p) process. This paper implements the architecture in FPGA. The hardware cost is low because the computation of shock filter is low complex. To achieve the real-time processing specification, this paper uses a 5-series shock filter architecture to implement computation of HD X-ray image. This paper demonstrates a 944 p, 43.1-fps solution on 100 MHz with 133 k gate counts in Design Compiler, and with 2904 logic elements in FPGA.
文摘This paper introduces bit-interleaved polar coded modulation with iterative detection/decoding(BIPCM-ID).In order to enable the soft output of successive cancellation list(SCL)decoding,two types of re-encoders are proposed,namely the max-re-encoder and min-re-encoder,respectively.Regarding the iterative decoding,we analytically verify that the average mutual information(AMI)between the transmitted and decoded symbols can approach the Shannon bound with the proposed schemes.Moreover,bit error rate(BER)and block error rate(BLER)in single-user and multi-user scenarios are studied.Finally,simulation results show that the performance of BIPCMID outperforms other bit-interleaved coded modulation(BICM)systems with LDPC and Turbo codes,while also reducing the computational complexity.
文摘This paper describes the hardware implementation of the RANdom Sample Consensus (RANSAC) algorithm for featured-based image registration applications. The Multiple-Input Signature Register (MISR) and the index register are used to achieve the random sampling effect. The systolic array architecture is adopted to implement the forward elimination step in the Gaussian elimination. The computational complexity in the forward elimination is reduced by sharing the coefficient matrix. As a result, the area of the hardware cost is reduced by more than 50%. The proposed architecture is realized using Verilog and achieves real-time calculation on 30 fps 1024 * 1024 video stream on 100 MHz clock.
文摘Electrical impedance tomography (EIT) is a fast and cost-effective technique that provides a tomographic conductivity image of a subject from boundary current-voltage data. This paper proposes a time and memory efficient method for solving a large scale 3D EIT image reconstruction problem and the ill-posed linear inverse problem. First, we use block-based sampling for a large number of measured data from many electrodes. This method will reduce the size of Jacobian matrix and can improve accuracy of reconstruction by using more electrodes. And then, a sparse matrix reduction technique is proposed using thresholding to set very small values of the Jacobian matrix to zero. By adjusting the Jacobian matrix into a sparse format, the element with zeros would be eliminated, which results in a saving of memory requirement. Finally, we built up the relationship between compressed sensing and EIT definitely and induce the CS: two-step Iterative Shrinkage/Thresholding and block-based method into EIT image reconstruction algorithm. The results show that block-based compressed sensing enables the large scale 3D EIT problem to be efficient. For a 72-electrodes EIT system, our proposed method could save at least 61% of memory and reduce time by 72% than compressed sensing method only. The improvements will be obvious by using more electrodes. And this method is not only better at anti-noise, but also faster and better resolution.
文摘Due to the content bundling and the dramatic increase of content size, the download performance in peer-to-peer networks has become a research focus again recently. In this paper, we propose a novel approach to improve the download performance based on the classical space-time trade-off. With the approach, a peer can speed up local downloads in peer-to-peer networks by contributing a portion of local hard disks for the content distribution in peer-to-peer networks. The contribution can bring performance improvement to each peer following the approach and in the meantime improve the overall content distribution performance in a peer-to-peer network. Based on the approach, we propose BISTRO, a BitTorrent based on space-time trade-off. The BISTRO is compatible with the vanilla BitTorrent. Our extensive experiments show that BISTRO can significantly reduce the download time.
