A deep learning-based model for High-Speed Users’ Mobility Prediction in Small Cell and Femtocell Networks

Femtocell networks have been proposed for indoor communications as the extension of cellular networks for enhancing coverage performance. Because femtocells have small coverage radius, typically from 15 to 30 meters, a femtocell user (FU) walking at low speed can still make several femtocell-to-femtocell handovers during its connection. When performing a femtocell-to-femtocell handover, femtocell selection used to select the target handover femtocell has to be able not only to reduce unnecessary handovers and but also to support FU’s quality of service (QoS). In the paper, we propose a femtocell selection scheme for femtocell-tofemtocell handover, named Mobility Prediction and Capacity Estimation based scheme (MPCE-based scheme), which has the advantages of the mobility prediction and femtocell’s available capacity estimation methods. Performance results obtained by computer simulation show that the proposed MPCE-based scheme can reduce unnecessary femtocell-tofemtocell handovers, maintain low data delay and improve the throughput of femtocell users. DOI: 10.32913/rd-ict.vol3.no14.536

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Faculty Opinions recommendation of Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.734021700.793563646 ◽

2019 ◽

Author(s):

Saikat Chakrabarti

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Small Cell Lung ◽

Histopathology Images ◽

Mutation Prediction

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Power information network intrusion detection based on deep learning and cloud computing

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219104 ◽

2021 ◽

pp. 1-9

Author(s):

Xiangbing Zhao ◽

Jianhui Zhou

Keyword(s):

Cloud Computing ◽

Deep Learning ◽

Intrusion Detection ◽

High Speed ◽

Information Networks ◽

Network Intrusion Detection ◽

Information Network ◽

Advantages And Disadvantages ◽

Network Intrusion ◽

Capture Mechanism

With the advent of the computer network era, people like to think in deeper ways and methods. In addition, the power information network is facing the problem of information leakage. The research of power information network intrusion detection is helpful to prevent the intrusion and attack of bad factors, ensure the safety of information, and protect state secrets and personal privacy. In this paper, through the NRIDS model and network data analysis method, based on deep learning and cloud computing, the demand analysis of the real-time intrusion detection system for the power information network is carried out. The advantages and disadvantages of this kind of message capture mechanism are compared, and then a high-speed article capture mechanism is designed based on the DPDK research. Since cloud computing and power information networks are the most commonly used tools and ways for us to obtain information in our daily lives, our lives will be difficult to carry out without cloud computing and power information networks, so we must do a good job to ensure the security of network information network intrusion detection and defense measures.

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Fully Automated Cultivation of Adipose-Derived Stem Cells in the StemCellDiscovery—A Robotic Laboratory for Small-Scale, High-Throughput Cell Production Including Deep Learning-Based Confluence Estimation

Processes ◽

10.3390/pr9040575 ◽

2021 ◽

Vol 9 (4) ◽

pp. 575

Author(s):

Jelena Ochs ◽

Ferdinand Biermann ◽

Tobias Piotrowski ◽

Frederik Erkens ◽

Bastian Nießing ◽

...

Keyword(s):

Stem Cells ◽

Deep Learning ◽

High Throughput ◽

High Speed ◽

New Technologies ◽

Human Mesenchymal Stem Cells ◽

Simulation Software ◽

System Capacity ◽

Small Scale ◽

Production Environment

Laboratory automation is a key driver in biotechnology and an enabler for powerful new technologies and applications. In particular, in the field of personalized therapies, automation in research and production is a prerequisite for achieving cost efficiency and broad availability of tailored treatments. For this reason, we present the StemCellDiscovery, a fully automated robotic laboratory for the cultivation of human mesenchymal stem cells (hMSCs) in small scale and in parallel. While the system can handle different kinds of adherent cells, here, we focus on the cultivation of adipose-derived hMSCs. The StemCellDiscovery provides an in-line visual quality control for automated confluence estimation, which is realized by combining high-speed microscopy with deep learning-based image processing. We demonstrate the feasibility of the algorithm to detect hMSCs in culture at different densities and calculate confluences based on the resulting image. Furthermore, we show that the StemCellDiscovery is capable of expanding adipose-derived hMSCs in a fully automated manner using the confluence estimation algorithm. In order to estimate the system capacity under high-throughput conditions, we modeled the production environment in a simulation software. The simulations of the production process indicate that the robotic laboratory is capable of handling more than 95 cell culture plates per day.

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A deep learning forecasting method for frost heave deformation of high-speed railway subgrade

Cold Regions Science and Technology ◽

10.1016/j.coldregions.2021.103265 ◽

2021 ◽

pp. 103265

Author(s):

Jing Chen ◽

Anyuan Li ◽

Chunyan Bao ◽

Yanhua Dai ◽

Minghao Liu ◽

...

Keyword(s):

Deep Learning ◽

High Speed ◽

Frost Heave ◽

High Speed Railway ◽

Forecasting Method

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LiDAR Point Cloud Recognition and Visualization with Deep Learning for Overhead Contact Inspection

Sensors ◽

10.3390/s20216387 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6387 ◽

Cited By ~ 1

Author(s):

Xiaohan Tu ◽

Cheng Xu ◽

Siping Liu ◽

Shuai Lin ◽

Lipei Chen ◽

...

Keyword(s):

Deep Learning ◽

Computational Complexity ◽

Point Cloud ◽

High Speed ◽

Point Cloud Data ◽

Embedded Devices ◽

Cloud Data ◽

Manual Inspection ◽

Order Of Magnitude ◽

Supply Systems

As overhead contact (OC) is an essential part of power supply systems in high-speed railways, it is necessary to regularly inspect and repair abnormal OC components. Relative to manual inspection, applying LiDAR (light detection and ranging) to OC inspection can improve efficiency, accuracy, and safety, but it faces challenges to efficiently and effectively segment LiDAR point cloud data and identify catenary components. Recent deep learning-based recognition methods are rarely employed to recognize OC components, because they have high computational complexity, while their accuracy needs to be improved. To track these problems, we first propose a lightweight model, RobotNet, with depthwise and pointwise convolutions and an attention module to recognize the point cloud. Second, we optimize RobotNet to accelerate its recognition speed on embedded devices using an existing compilation tool. Third, we design software to facilitate the visualization of point cloud data. Our software can not only display a large amount of point cloud data, but also visualize the details of OC components. Extensive experiments demonstrate that RobotNet recognizes OC components more accurately and efficiently than others. The inference speed of the optimized RobotNet increases by an order of magnitude. RobotNet has lower computational complexity than other studies. The visualization results also show that our recognition method is effective.

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Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽

10.3390/s20102778 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2778 ◽

Cited By ~ 12

Author(s):

Mohsen Azimi ◽

Armin Eslamlou ◽

Gokhan Pekcan

Keyword(s):

Deep Learning ◽

Structural Health Monitoring ◽

Health Monitoring ◽

High Speed ◽

Deep Neural Networks ◽

State Of The Art ◽

Data Driven ◽

Structural Health ◽

Promising Tool ◽

Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

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