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This paper studies the two-hop transmission relying unmanned aerial vehicle (UAV) relays which is suitable to implement in the internet of things (IoT) systems. To enhance system performance in order to overcome the large scale fading between the base station (BS) and destination as well as achieve the higher spectrum efficiency, where non-orthogonal multiple access (NOMA) strategies were typically applied for UAV relays to implement massive connections transmission. In particular, outage probability is evaluated via signal to noise ratio (SNR) criterion so that the terminal node can obtain reasonable performance. The derivations and analysis results showed that the considered fixed power allocation scheme provides performance gap among two signals at destination.The numerical simulation confirmed the exactness of derived expressions in the UAV assisted system.
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Accessing the Alfresco repository from SharePoint is a big opportunity for some organizations who are looking to avoid to store everything in SharePoint and to centralize their documents in Alfresco. Taking the opportunity with the new version 1.1 of CMIS, we decided to build UI components on top of SharePoint using only common web technologies i.e. JS, HTML or CSS. The goal of these components is to be highly configurable and easy to customize depending of use cases.
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What’s the characteristics, strengths and weaknesses of traditional fabric defect detection method
Why textile industry can benefit from edge computing infrastructure
How to design and implement an edge-enabled application for fabric defect detection in real-time
Insights, synergy and future research directions
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What’s edge computing and why it’s important to intelligence manufacturing
What’s the characteristics, strengths and weaknesses of traditional fabric defect detection method
Why textile industry can benefit from edge computing infrastructure
How to design and implement an edge-enabled application for fabric defect detection in real-time
Insights, synergy and future research directions
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See https://medium.com/@jamessirota for a series of blog entries that goes with this deck...
Defense in Depth for Big Data
Network Anomaly Detection Overview
Volume Anomaly Detection
Feature Anomaly Detection
Model Architecture
Deployment on OpenSOC Platform
Questions
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All Things Cloud Developer Meetup.
Filtering From the Firehose: Real Time Social Media Streaming with Jim Moffitt from Gnip. Gnip is the world's largest and most trusted provider of social data.
Learn about collecting and filtering social media data with streaming APIs. Jim will cover best practices, use case examples and live demos of filtering data from Twitter.
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The performance, flexibility, and expressivity of a native graph platform are truly transformative for these challenging disciplines. Register and learn how you can leverage graph technology for your next generation service assurance solution.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/09/covid-19-safe-distancing-measures-in-public-spaces-with-edge-ai-a-presentation-from-the-government-technology-agency-of-singapore/
Ebi Jose, Senior Systems Engineer at GovTech, the Government Technology Agency of Singapore, presents the “COVID-19 Safe Distancing Measures in Public Spaces with Edge AI” tutorial at the May 2022 Embedded Vision Summit.
Whether in indoor environments, such as supermarkets, museums and offices, or outdoor environments, such as parks, maintaining safe social distancing has been a priority during the COVID-19 pandemic. In this talk, Jose presents GovTech’s work developing cloud-connected edge AI solutions that count the number of people present in outdoor and indoor spaces, providing facility operators with real-time information that allows them to manage spaces to enable social distancing.
Jose provides an overview of the system architecture, hardware, algorithms, software and backend monitoring elements of GovTech's solution, highlighting differences in how it addresses indoor vs. outdoor spaces. He also presents results from deployments of GovTech’s solution.
2. • Simple to Use
• DynusT is Windows-based software. DynusT is
aimed at integrating with travel demand
models and microscopic simulation models,
supporting application areas in which realistic
traffic dynamic representation is needed for a
large-scale regional or corridor network.
3. NETWORK DATA – DYNUST
Input information of nodes comes from a
shapefile from the planning model
Nodes
Node I.D. Latitude
(required) (required)
TAZ association with
Longitude
nodes
(required)
(suggested/optional)
4. LINKS – DYNUST
Input information of links comes from a shapefile from the
planning model
From node (required)
To node
(required)
Link Length
(required)
Links
Link Direction ID (required)
Functional Class ID
(required)
Number of lanes per link
(required)
5. Incident Description
• An incident is described as a time-dependent
event, such as a car accident or a temporary
special event that impedes the traffic way thus
causing a reduction in capacity. The "severity" of
the incident will be input by the user as the
severity is the fraction of link capacity lost due to
the incident. For an active incident, DynusT will
reduce the physical capacity (lane-miles) and
maximum flow rate of the incident link.
•
7. • Data that can be inputed into the program include Scenario, Demand,
Capacity and Traffic Flow Model Data. Also, traffic control consists of 4
main types – Stop Sign, Yield Signs, Actuated Signals, and Pretimed
Signals.
8. Scenario Development
Traffic Flow
rerouting plan
Additional Tolling
Connectivity Scenarios
Ramp Scenario Phased
Metering Development evacuation
Strategies strategies
Signal Progress
& optimization Capacity ITS Strategies
increase on
links
9. DynusT Major Control Devices
4 Control devices (from left to right)
Stop Sign
Yield Sign
Pre-timed Signal
Actuated Signal
17. A Sample Output File for this Project
• ==========================================================
• ==========================================================
• H DynusT H
• H H
• H Dynamic Urban Systems for Transportation H
• H H
• H Version (2.0.1 Beta) H
• H H
• H H
• H Released by: Federal Highway Administration H
• H Copyright: Yi-Chang Chiu H
• H H
• H Scheduled Release Date: October, 2009 H
• H H
• ==========================================================
• ==========================================================
•
• ****************************************
• * Basic Information *
• ****************************************
•
• NETWORK DATA
• ------------
• Number of Nodes : 1139
• Number of Links : 2640
• Number of Zones : 247
• ***************************************
•
• INTERSECTION CONTROL DATA
• -------------------------
• Number of No Control : 1133
• Number of Yield Signs : 0
• Number of 4-Way STOP Signs : 2
• Number of 2-Way STOP Signs : 4
• Number of Pretimed Control : 0
• Number of Actuated Control : 0
• ***************************************
•
20. My Simulation
• Added an incident on a freeway segment
• Changed the parameters of the incident
– Duration: 15 min., 30 min., 45 min., 1 hour, etc.
– Changed the severity percentage (0, .33, .67, .99)
– From there, ran the output
24. Results of my Simulation
• ==========================================================
The link is defined
• ========================================================== (842-844)
• H DynusT H
• H H The duration is
• H Dynamic Urban Systems for Transportation H
• H H defined (30 min.)
•
•
H
H
Version (2.0.1 Beta)
H
H
33% = 1 lane closed
• H H
• H Released by: Federal Highway Administration H
• H Copyright: Yi-Chang Chiu H
• H H
• H Scheduled Release Date: October, 2009 H
• H H
• ==========================================================
• ==========================================================
•
• ****************************************
• * Basic Information *
• ****************************************
•
•
•
• CAPACITY REDUCTION
• ------------------
• -- Incident --
• Location 842 -- 844 From min 0.0 To min 30.0, 33.0 % Capacity Reduction
•
•
25. Sample Data Graph - Speed
Link 402,642
Scenario No. VHT VMT SPEED = VMT/VHT)
0-30 MIN
Base Cond. - No Inc. 5194 329243 63.4
0.17 (SHOULDER) 5192 328895.656 63.3
0.33 (1 LANE CLOSED) 5163 326620 63.3
0.67 (2 LANES CLOSED) 5254 328809 62.6
0.99 (3 LANES CLOSED) 5146 326989 63.5
329500
329000
328500
328000
Series1
327500
Poly. (Series1)
327000
326500
326000
5140 5160 5180 5200 5220 5240 5260
26. Overall Results and Observations
The longer the incident, the more vehicle miles traveled (VMT).
The higher the severity, the higher the vehicle hours traveled time
(VHT). (I.E., .33 VS. .67)
• The results for my testing were representative of the entire network
and since it was a large network huge impacts were not always
observed but many incidents in network would have bigger effect.
The incident would hinder capacity more on areas of close
proximity vs. the entire network. If you multiply results on entire
network, which was composed of 31000+ vehicles, 1139
nodes, 2640 links and 247 zones, then you will observe that in the
big picture, there is indeed a big impact on all traffic involved and
ITS technology can greatly assist in this dilemma.