A widely used computationally intensive scientific kernel, the matrix multiplication algorithm is at the heart of many scientific routines. Resurging fields, such as artificial intelligence (AI), strongly benefit from...
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the study proposes a method to assist physicians who are not respiratory specialists to diagnose specific diseases from lung sounds without advanced medical equipment. the method uses frequency feature images to estim...
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ISBN:
(数字)9798350353235
ISBN:
(纸本)9798350353242
the study proposes a method to assist physicians who are not respiratory specialists to diagnose specific diseases from lung sounds without advanced medical equipment. the method uses frequency feature images to estimate the presence and progression of disease by performing in parallelthe process of comprehensively detecting fine crackles suggestive of disease and the process of identifying the respiratory phases in which the abnormal sounds occur. the validation results of this study showed a detection of fine crackles with an accuracy of 0.90 and an identification accuracy of 0.85 for the respiratory phases. these results are integrated to estimate the progression of the disease. the method provides a visual indication of the location of abnormal sounds, which helps physicians and patients understand and explain their diagnosis.
In recent years, autonomous driving technology has developed rapidly in the fast lane, but its application and popularization still face many challenges. How to test and verify the autonomous driving system is a techn...
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In medical imaging, efficient segmentation of colon polyps plays a pivotal role in minimally invasive solutions for colorectal cancer. this study introduces a novel approach employing two parallel encoder branches wit...
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ISBN:
(数字)9798350350920
ISBN:
(纸本)9798350350937
In medical imaging, efficient segmentation of colon polyps plays a pivotal role in minimally invasive solutions for colorectal cancer. this study introduces a novel approach employing two parallel encoder branches within a network for polyp segmentation. One branch of the encoder incorporates the dual convolution blocks that have the capability to maintain feature information over increased depths, and the other block embraces the single convolution block withthe addition of the previous layer's feature, offering diversity in feature extraction within the encoder, combining them before transpose layers with a depth-wise concatenation operation. Our model demonstrated superior performance, surpassing several established deep-learning architectures on the Kvasir and CVC-ClinicDB datasets, achieved a Dice score of 0.919, a mIoU of 0.866 for the Kvasir dataset, and a Dice score of 0.931 and a mIo U of 0.891 for the CVC-ClinicDB. the visual and quantitative results highlight the efficacy of our model, potentially setting a new model in medical image segmentation.
Many modern multicore processors integrate asymmetric core clusters. Withthe trend towards Multi-Chip-Modules (MCMs) and interposer-based packaging technologies, platforms will feature heterogeneity at the level of c...
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Monitoring network traffic to identify malicious applications is an active research topic in network security. In the era of big data, withthe increasing number of access devices, networks will become more and more d...
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ISBN:
(数字)9798350376548
ISBN:
(纸本)9798350376555
Monitoring network traffic to identify malicious applications is an active research topic in network security. In the era of big data, withthe increasing number of access devices, networks will become more and more dense and require more rapid response. Traditional anomaly detection methods cannot achieve both detection accuracy and latency. To study a high-performance network traffic anomaly detection method is imperative. In this paper, We proposed a novel anomaly detection framework based on big data analytics for network traffic to enhance the detection of sophisticated cyber threats. the framework is divided into two stages: online and offline. the online stage uses a distributed algorithm utilizing customized network traffic characteristics. In the offline stage, the multi-modal method of accurate classification is adopted, and the identification result is used as an expert system of online algorithm to realize data authentication. By integrating advanced machine learning algorithms and big data parallel anomaly detection techniques, the proposed framework aims to strike a balance between accuracy and efficiency in detecting emerging cyber threats. the framework has been tested on both open datasets and real-world datasets. A large number of experiments have been conducted to validate the feasibility and practicality of the framework.
Blockchain technology inherently necessitates redundant computation to achieve consensus among untrusted parties because of its fundamental threat model. this requirement, however, compromises system performance and i...
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ISBN:
(数字)9798331515966
ISBN:
(纸本)9798331515973
Blockchain technology inherently necessitates redundant computation to achieve consensus among untrusted parties because of its fundamental threat model. this requirement, however, compromises system performance and impedes the widespread adoption of blockchain. To leverage existing physical resources, current research on high-performance consortium blockchain algorithms and architectures frequently employs cluster-node architectures to expand the parallelprocessing capability of traditional single physical nodes. Our investigation reveals a significant trend as the parallel capability of individual nodes improves. the idle time caused by synchronization of all transactions within each block, previously considered negligible, has become increasingly significant. To address this, we present BachLedger, which implements Seamless Scheduling to fully utilize inter-block thread idle time, thereby augmenting system resource utilization and achieving overall performance improvements. Our experimental results demonstrate that our algorithm surpasses current state-of-the-art (SOTA) performance levels in high-performance consortium blockchains and effectively resolves the aforementioned synchronization issue. Furthermore, this scheduling algorithm offers enhanced scalability for BachLedger, positioning it as a promising solution for future blockchain implementations.
Spatial keyword queries have been popular in the research community for over a decade due to the explosive growth in user-generated data and its prime applications in different domains. kNN queries make a major catego...
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ISBN:
(纸本)9781450395298
Spatial keyword queries have been popular in the research community for over a decade due to the explosive growth in user-generated data and its prime applications in different domains. kNN queries make a major category of spatial keyword queries that is heavily studied. However, the expressiveness of existing kNN queries is limited in supporting negative keyword predicates, e.g., find tweets with keywords "Chipotle" but NOT "Chipotle sauce", which have prime applications. In addition, existing architectures suffer from a lack of generality for different types of kNN queries. this paper proposes U-ASK;a Unified Architecture for Spatial-Keyword query supporting negative keyword predicates. U-ASK includes an indexing framework named TEQ (Textual-Enhanced Quadtree) and a query processor POWER (parallel bOttom-up search With incrEmental pRuning) that handle various forms of kNN spatial keyword queries with negative keyword predicates. the experimental evaluation on real tweet datasets demonstrates up to 30x faster runtime compared to the state-of-the-art algorithms.
the digital Finite Impulse Response (FIR) filter emerges as a highly promising solution for enhancing Moving Target Indication (MTI) capabilities, particularly within the short-range Frequency-Modulated Continuous-Wav...
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To address the limitations of energy-efficient but computationally limited ARM64-based AI edge devices and general-purpose edge servers, this paper proposes a Decentralized Collaborative Heterogeneous Edge Computing (...
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