Magnetic resonance imaging(MRI) plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and exten...
Magnetic resonance imaging(MRI) plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI) algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw data(ISMRMRD) ***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.
Hyperspectral image super resolution aims to improve the spatial resolution of given hyperspectral images, which has become a highly attractive topic in the field of image processing. Existing techniques typically foc...
Hyperspectral image super resolution aims to improve the spatial resolution of given hyperspectral images, which has become a highly attractive topic in the field of image processing. Existing techniques typically focus on super resolution with sufficient training data. However, restricted by data acquisition conditions, certain hyperspectral images or band images are very different to obtain, resulted in insufficient training data. In order to solve this problem, a new hyperspectral image super resolution method is proposed in this paper in an effort to conduct the super resolution task over insufficient (sparse) training data, by applying the recently introduced ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation method. Particularly, the training dataset is divided into several subsets. For the subsets with sufficient training data, the relevant ANFIS models are trained using standard ANFIS learning algorithm, while for the subsets with sparse training data, the corresponding ANFIS models are interpolated through the use of ANFIS interpolation. Experimental results indicate that compared with the methods using sufficient training data, the proposed method can achieve very similar result, showing its effectiveness for situations where only sparse training data is available.
An excessive proliferation of aberrant cells in the brain, which may result in a brain tumor. Moreover, it is very difficult for doctors to detect brain tumors at an early stage. However, with the help of MRI images, ...
An excessive proliferation of aberrant cells in the brain, which may result in a brain tumor. Moreover, it is very difficult for doctors to detect brain tumors at an early stage. However, with the help of MRI images, they can now monitor the condition. Due to the complexity of the disease, it is very difficult for doctors to determine its exact causes. With the help of brain tumor images, we can now monitor the condition and identify the possible cause of the disease. In this paper, we have proposed a deep learning technique for brain tumor detection. We employed a pre-trained VGG-16 architecture for detection of brain tumour. Our proposed approach outperformed the state-of-the-art algorithms with an accuracy of 86.6%. We compared the results by computing different statistical measures such as AUC, precision, recall, and F1 score.
The rapid improvement of remote systems is being driven by the growing use of fake intelligence. The shift from "interdependent items" to "associated insights" is expected to be fundamentally alter...
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ISBN:
(数字)9798331541583
ISBN:
(纸本)9798331541590
The rapid improvement of remote systems is being driven by the growing use of fake intelligence. The shift from "interdependent items" to "associated insights" is expected to be fundamentally altered by sixth-generation (6G) distant innovation. However, because of their vast analytics foundations and deep neural networks, state-of-the-art AI frameworks entirely rely on computer and interactions. As a result, excessive inactivity, excessive energy use, organizational obstruction, and protection leakage occur throughout the planning and deduction phases. Edge AI might be a game-changer for 6G, improving the practicality, efficacy, security, and validity of 6G systems in general, with its steady integration of sensing, communication, processing, and intelligence. It does this by coordinating modelling with duction and teaching skills at the organise edge. In this research, we will outline our methodology for robust and flexible uncontrolled neural network models and coordinated Bluetooth connections in edge AI contexts. A comprehensive end-to-end frame plan for supporting edge AI, service-driven use efficiency tactics, and contemporary concepts in remote organize building will all be safeguarded. In addition, uniformity, phases for software and hardware components, state-of-the-art AI applications, and real-world examples are presented to support industrialization and subsequently deployment.
Unsupervised text simplification has attracted much attention due to the scarcity of high-quality parallel text simplification corpora. Recent an unsupervised statistical text simplification based on phrase-based mach...
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Unsupervised text simplification has attracted much attention due to the scarcity of high-quality parallel text simplification corpora. Recent an unsupervised statistical text simplification based on phrase-based machine translation system (UnsupPBMT) achieved good performance, which initializes the phrase tables using the similar words obtained by word embedding modeling. Since word embedding modeling only considers the relevance between words, the phrase table in UnsupPBMT contains a lot of dissimilar words. In this paper, we propose an unsupervised statistical text simplification using pre-trained language modeling BERT for initialization. Specifically, we use BERT as a general linguistic knowledge base for predicting similar words. Experimental results show that our method outperforms the state-of-the-art unsupervised text simplification methods on three benchmarks, even outperforms some supervised baselines.
Alzheimer’s disease is a progressive form of memory loss that worsens over time. Detecting it early, when memory issues are mild, is crucial for effective interventions. Recent advancements in computer technology, sp...
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This paper presents the development of an object detection system based on the deep learning approach of computer vision to support the laparoscopic surgical robotic position control system. The system comprises two m...
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Erasure Codes are widely implemented in distributed storage systems to achieve fault tolerance with high storage efficiency. Reed-Solomon code is commonly deployed in data centers due to its optimal storage efficiency...
Erasure Codes are widely implemented in distributed storage systems to achieve fault tolerance with high storage efficiency. Reed-Solomon code is commonly deployed in data centers due to its optimal storage efficiency, but it requires massive repair bandwidth for node repair. Minimum Storage Regenerating (MSR) code and Locally Repairable (LR) code are proposed to reduce repair bandwidth. However, MSR code usually carries a heavy disk I/O burden and LR code is not optimal in storage efficiency. In this paper, we take disk I/O, storage efficiency, repair bandwidth, sub-packetization level, and repair degree (number of helper nodes) into consideration together, and propose explicit construction of MDS array code (named LDIO code) with low disk I/O and reduced repair bandwidth, where the sub-packetization level is 2. Besides the low disk I/O consumption, LDIO code also achieves a small repair bandwidth of ${\bar \gamma _{sys}} = \frac{1}{2} + \frac{1}{{4(r - 1)}}$ for systematic nodes with repair degree d = k+1. Normally, LDIO code can provide bandwidth savings of 25% to 50% for the repair of systematic nodes compared with RS code, and almost savings of 6% compared with piggybacking code. For an LDIO code of length n and redundancy r, the size of the finite field needed is Θ(n r+1 ).
Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often...
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The research herein proposes a position control system, designed for a single-port surgical robot, based on the lockup table method. The programmable logic controller (PLC) was implemented to move the single- port sur...
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