scholarly journals Network Traffic Prediction via Deep Graph-Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kai Zhang ◽  
Xiaohu Zhao ◽  
Xiao Li ◽  
XingYi You ◽  
Yonghong Zhu
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Traffic Flow ◽  
Network Traffic ◽  
Mobile Network ◽  
Temporal Correlation ◽  
Spatiotemporal Modeling ◽  
Convolutional Network ◽  
Spatiotemporal Features ◽  
Gated Recurrent Unit

Accurate and real-time network traffic flow forecast holds an important role for network management. Especially at present, virtual reality (VR), artificial intelligence (AI), vehicle-to-everything (V2X), and other technologies are closely combined through the mobile network, which greatly increases the human-computer interaction activities. At the same time, it requires high-throughput, low delay, and high reliable service guarantee. In order to achieve ondemand real-time high-quality network service, we must accurately grasp the dynamic changes of network traffic. However, due to the increase of client mobility and application behavior diversity, the complexity and dynamics of network traffic in the temporal domain and the spatial domain increase sharply. To accurate capture the spatiotemporal features, we propose the spatial-temporal graph convolution gated recurrent unit (GC-GRU) model, which integrates the graph convolutional network (GCN) and the gated recurrent unit (GRU) together. In this model, the GCN structure could handle the spatial features of traffic flow with network topology, and the GRU is used to further process spatiotemporal features. Experiments show that the GC-GRU model has better prediction performance than other baseline models and can obtain spatial-temporal correlation in traffic lows better.

scholarly journals Automatic Spotting of Sceptical Activity with Visualization Using Elastic Cluster for Network Traffic in Educational Campus

2020 ◽  
Vol 2 (2) ◽  
pp. 88-97
Author(s):  
Dr. Suma V
Keyword(s):  
Open Source ◽  
Real Time ◽  
Traffic Flow ◽  
Network Traffic ◽  
Network Attacks ◽  
Accurate Evaluation ◽  
Recognition Model ◽  
Malicious Activity ◽  
Campus Networks ◽  
Prevention System

An automatic sceptical recognition model to identify the suspicious or the malicious activity in the network of the educational institutional campus is laid out in the paper. The carried out work in the paper kindles the network traffic flow in the educational campus and identifies the unwanted activities and stops them. The detected activities are visualized in the real time using a personalized reportage dash board. The design integrates the open source tools to provide an accurate evaluation utilizing the engine for the identifying and preventing the suspicious activities. The suspicious events identified are computed in the elastic cluster to visualize the intimidations. The laid out model computes the events identified and raises alarms. The elastic cluster founded on the No-SQL reports the happenings occurring in real time. The system is initially allowed to learn the various type of network attacks, once trained it the designed model automatically stops the malicious activities in the network traffic. This enhances the security for the campus networks by utilizing the open source libraries as well as minimizes cost imposed by the commercial identification and the prevention system.

Prediction of Congestion and Bursting Phenomena in Network Traffic Based on Multifractal Spectrums

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Yan Liu ◽  
Li-Guang Liu ◽  
Hang Wang
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Flow Rate ◽  
Network Traffic ◽  
Local Area Network ◽  
Early Time ◽  
Time Sequence ◽  
Area Network ◽  
Bursting Phenomena ◽  
Time Sequences

From the viewpoint of nonlinear dynamics, a numerical method for predicting the traffic of the local area network (LAN) is presented based on the multifractal spectrums; in particular, for predicting the typical congestion and bursting phenomena, by analyzing real time sequences. First, the multifractal spectrums available to the LAN traffic are derived in some detail and their physical meanings are consequently explained. Then an exponent factor is introduced to the measurement or description of the singularity of the time sequence and the correlations between multifractal spectrums and traffic flow rate are studied in depth. Finally, as an example, the multifractal spectrums presented are used to predict the network traffic of an Ethernet by analyzing its real time sequence. The results show that there exists a distinct relationship between the multifractal spectrums and the traffic flow rate of networks and the multifractal spectrum could be used to efficiently and feasibly predict the traffic flow rate, especially for predicting the singularities of the real time sequences, which are closely related to the congestion and bursting phenomena. Thus, this method can be applied to the prediction and management of the congestion and bursting in the network traffic at an early time. Furthermore, the prediction will become much more accurate and powerful over a long period, since the fluctuations of the traffic flow rate are remarkable.

scholarly journals Telemanipulation of an Articulated Robotic Arm using a Commercial Virtual Reality Controller

2020 ◽  
Vol 6 (3) ◽  
pp. 127-130
Author(s):  
Max B. Schäfer ◽  
Kent W. Stewart ◽  
Nico Lösch ◽  
Peter P. Pott
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Robotic Arm ◽  
Input Device ◽  
Robot Assisted ◽  
Promising Alternative ◽  
Input Devices ◽  
Current Input ◽  
Current Systems ◽  
Robot Assisted Surgery

AbstractAccess to systems for robot-assisted surgery is limited due to high costs. To enable widespread use, numerous issues have to be addressed to improve and/or simplify their components. Current systems commonly use universal linkage-based input devices, and only a few applicationoriented and specialized designs are used. A versatile virtual reality controller is proposed as an alternative input device for the control of a seven degree of freedom articulated robotic arm. The real-time capabilities of the setup, replicating a system for robot-assisted teleoperated surgery, are investigated to assess suitability. Image-based assessment showed a considerable system latency of 81.7 ± 27.7 ms. However, due to its versatility, the virtual reality controller is a promising alternative to current input devices for research around medical telemanipulation systems.

scholarly journals Virtual Reality Technology and Remote Digital Application for Tele-Simulation and Global Medical Education: An Innovative Hybrid System for Clinical Training

2021 ◽  
pp. 104687812110082
Author(s):  
Omamah Almousa ◽  
Ruby Zhang ◽  
Meghan Dimma ◽  
Jieming Yao ◽  
Arden Allen ◽  
...  
Keyword(s):  
Medical Education ◽  
Virtual Reality ◽  
Real Time ◽  
Global Education ◽  
Clinical Training ◽  
Simulation Training ◽  
Virtual Space ◽  
Control Panel ◽  
Clinical Simulation ◽  
Clinical Scenarios

Objective. Although simulation-based medical education is fundamental for acquisition and maintenance of knowledge and skills; simulators are often located in urban centers and they are not easily accessible due to cost, time, and geographic constraints. Our objective is to develop a proof-of-concept innovative prototype using virtual reality (VR) technology for clinical tele simulation training to facilitate access and global academic collaborations. Methodology. Our project is a VR-based system using Oculus Quest as a standalone, portable, and wireless head-mounted device, along with a digital platform to deliver immersive clinical simulation sessions. Instructor’s control panel (ICP) application is designed to create VR-clinical scenarios remotely, live-stream sessions, communicate with learners and control VR-clinical training in real-time. Results. The Virtual Clinical Simulation (VCS) system offers realistic clinical training in virtual space that mimics hospital environments. Those VR clinical scenarios are customizable to suit the need, with high-fidelity lifelike characters designed to deliver interactive and immersive learning experience. The real-time connection and live-stream between ICP and VR-training system enables interactive academic learning and facilitates access to tele simulation training. Conclusions. VCS system provides innovative solutions to major challenges associated with conventional simulation training such as access, cost, personnel, and curriculum. VCS facilitates the delivery of academic and interactive clinical training that is similar to real-life settings. Tele-clinical simulation systems like VCS facilitate necessary academic-community partnerships, as well as global education network between resource-rich and low-income countries.

scholarly journals SURG-03. IMMERSIVE VIRTUAL REALITY APPLICATIONS IN NEUROSURGICAL ONCOLOGY

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii461-iii461
Author(s):  
Andrea Carai ◽  
Angela Mastronuzzi ◽  
Giovanna Stefania Colafati ◽  
Paul Voicu ◽  
Nicola Onorini ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
New Technologies ◽  
Young Patients ◽  
Spatial Relationship ◽  
3D Models ◽  
Immersive Virtual Reality ◽  
Global Improvement ◽  
3D Rendering ◽  
Before And After

Abstract Tridimensional (3D) rendering of volumetric neuroimaging is increasingly been used to assist surgical management of brain tumors. New technologies allowing immersive virtual reality (VR) visualization of obtained models offer the opportunity to appreciate neuroanatomical details and spatial relationship between the tumor and normal neuroanatomical structures to a level never seen before. We present our preliminary experience with the Surgical Theatre, a commercially available 3D VR system, in 60 consecutive neurosurgical oncology cases. 3D models were developed from volumetric CT scans and MR standard and advanced sequences. The system allows the loading of 6 different layers at the same time, with the possibility to modulate opacity and threshold in real time. Use of the 3D VR was used during preoperative planning allowing a better definition of surgical strategy. A tailored craniotomy and brain dissection can be simulated in advanced and precisely performed in the OR, connecting the system to intraoperative neuronavigation. Smaller blood vessels are generally not included in the 3D rendering, however, real-time intraoperative threshold modulation of the 3D model assisted in their identification improving surgical confidence and safety during the procedure. VR was also used offline, both before and after surgery, in the setting of case discussion within the neurosurgical team and during MDT discussion. Finally, 3D VR was used during informed consent, improving communication with families and young patients. 3D VR allows to tailor surgical strategies to the single patient, contributing to procedural safety and efficacy and to the global improvement of neurosurgical oncology care.

scholarly journals Real-Time Detection of Dictionary DGA Network Traffic Using Deep Learning

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Kate Highnam ◽  
Domenic Puzio ◽  
Song Luo ◽  
Nicholas R. Jennings
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Traffic ◽  
Short Term Memory ◽  
Domain Names ◽  
Control Networks ◽  
Detection Techniques ◽  
Lstm Network ◽  
And Control

AbstractBotnets and malware continue to avoid detection by static rule engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses. Common DGA detection techniques fail to reliably detect DGA variants that combine random dictionary words to create domain names that closely mirror legitimate domains. To combat this, we created a novel hybrid neural network, Bilbo the “bagging” model, that analyses domains and scores the likelihood they are generated by such algorithms and therefore are potentially malicious. Bilbo is the first parallel usage of a convolutional neural network (CNN) and a long short-term memory (LSTM) network for DGA detection. Our unique architecture is found to be the most consistent in performance in terms of AUC, $$F_1$$ F 1 score, and accuracy when generalising across different dictionary DGA classification tasks compared to current state-of-the-art deep learning architectures. We validate using reverse-engineered dictionary DGA domains and detail our real-time implementation strategy for scoring real-world network logs within a large enterprise. In 4 h of actual network traffic, the model discovered at least five potential command-and-control networks that commercial vendor tools did not flag.

Real-Time Deformation Simulation of Kidney Surgery Based on Virtual Reality

2021 ◽  
Vol 26 (3) ◽  
pp. 290-297
Author(s):  
Mengjie Jing ◽  
Zhixin Cui ◽  
Hang Fu ◽  
Xiaojun Chen
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Time Deformation ◽  
Kidney Surgery ◽  
Deformation Simulation
Sign in / Sign up

Export Citation Format

Share Document