The mortar pumpability is essential in the construction industry,which requires much labor to estimate manually and always causes material *** paper proposes an effective method by combining a 3-dimensional convolutio...
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The mortar pumpability is essential in the construction industry,which requires much labor to estimate manually and always causes material *** paper proposes an effective method by combining a 3-dimensional convolutional neural network(3D CNN)with a 2-dimensional convolutional long short-term memory network(ConvLSTM2D)to automatically classify the mortar *** results show that the proposed model has an accuracy rate of 100%with a fast convergence speed,based on the dataset organized by collecting the corresponding mortar image *** work demonstrates the feasibility of using computer vision and deep learning for mortar pumpability classification.
Suspicious activity recognition (SAR) is an active research field in computer vision and image processing due to the rapid demand for intelligent video surveillance systems. However, current automated systems focus to...
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Automated analysis of breast cancer (BC) histopathology images is a challenging task due to the high resolution, multiple magnifications, color variations, the presence of image artifacts, and morphological variabilit...
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Histopathological images serve as pivotal assets within the domain of breast cancer diagnosis, demanding profound comprehension for precise interpretation. This paper introduces a histopathological image classificatio...
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This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactiv...
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This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.
Today for an organisation, data security is the most crucial topic. An organisation needs to protect its information against cyberattacks. Cryptography, DLT, and blockchain technology provide higher security for data ...
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The amount of exploration done for the available medical literature is quite less and at the same time, there is less awareness of information mining in this specific field. The accessibility of immense quantity of bi...
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To improve the effectiveness of online learning, the learning materials recommendation is required to be personalised to the learner material recommendations must be personalized to learners. The existing approaches a...
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Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause signi...
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Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause significant performance degradation for containerized applications and enhanced resource ***,current studies have almost not discussed the isolation problems of page cache which is a key resource for *** leverage memory cgroup to control page cache ***,existing policy introduces two major problems in a container-based ***,containers can utilize more memory than limited by their cgroup,effectively breaking memory ***,the Os kernel has to evict page cache to make space for newly-arrived memory requests,slowing down containerized *** paper performs an empirical study of these problems and demonstrates the performance impacts on containerized *** we propose pCache(precise control of page cache)to address the problems by dividing page cache into private and shared and controlling both kinds of page cache separately and *** do so,pCache leverages two new technologies:fair account(f-account)and evict on demand(EoD).F-account splits the shared page cache charging based on per-container share to prevent containers from using memory for free,enhancing memory *** EoD reduces unnecessary page cache evictions to avoid the performance *** evaluation results demonstrate that our system can effectively enhance memory isolation for containers and achieve substantial performance improvement over the original page cache management policy.
Diabetic Retinopathy is a disease,which happens due to abnormal growth of blood vessels that causes spots on the vision and vision *** techniques are applied to identify the disease in the early stage with different m...
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Diabetic Retinopathy is a disease,which happens due to abnormal growth of blood vessels that causes spots on the vision and vision *** techniques are applied to identify the disease in the early stage with different methods and *** Learning(ML)techniques are used for analyz-ing the images andfinding out the location of the *** restriction of the ML is a dataset size,which is used for model *** problem has been overcome by using an augmentation method by generating larger datasets with multidimensional *** models are using only one augmentation tech-nique,which produces limited features of dataset and also lacks in the association of those data during DR detection,so multilevel augmentation is proposed for *** proposed method performs in two phases namely integrated aug-mentation model and dataset correlation(***).It eliminates overfit-ting problem by considering relevant *** method is used for solving the Diabetic Retinopathy problem with a thin vessel identification using the UNET *** based image segmentation achieves 98.3%accuracy when com-pared to RV-GAN and different UNET models with high detection rate.
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