This document proposes an intelligent traffic management system using sensors and wireless communication. The system aims to create smooth traffic flow, allow for unobstructed movement of emergency vehicles, reduce travel time, and increase road capacity without changing road layout. It would work by polling sensor data, processing it to modify traffic light timers, and transmitting messages to notify special vehicles and dynamic message boards. Pseudocode and design models are provided. The conclusion states the system would ensure smooth traffic flow via green waves, prioritize special vehicles, make intelligent decisions based on traffic volume, and be simple and efficient.
about this presentation:
1) this presentation was a quickie for non-tech employees, who wanted a basic understanding of html/css, as it related to a white-label SAAS product;
2) the back-end/front-end definitions relate to the specific application (it's inaccurate if node.js is in the picture)
In software engineering, an entity–relationship model (ER model) is a data model for describing the data or information aspects of a business domain or its process requirements
about this presentation:
1) this presentation was a quickie for non-tech employees, who wanted a basic understanding of html/css, as it related to a white-label SAAS product;
2) the back-end/front-end definitions relate to the specific application (it's inaccurate if node.js is in the picture)
In software engineering, an entity–relationship model (ER model) is a data model for describing the data or information aspects of a business domain or its process requirements
A Platform for Data Intensive Services Enabled by Next Generation Dynamic Opt...Tal Lavian Ph.D.
The new architecture is proposed for data intensive enabled by next generation dynamic optical networks
Offers a Lambda scheduling service over Lambda Grids
Supports both on-demand and scheduled data retrieval
Supports bulk data-transfer facilities using lambda-switched networks
Provides a generalized framework for high performance applications over next generation networks, not necessary optical end-to-end
Supports out-of-band tools for adaptive placement of data replicas
LightHouse 4.0 Distribution Monitoring platform, with support for GSM cellular networks. Additional platform enhancements include Auto-Phase ID, GPS-based time synchronization of sensors and Substation Monitoring. These features allow utilities to have more accurate and actionable information from their distribution networks and enable better fault detection, power restoration and Predictive Grid® Analytics.
ROLE OF DIGITAL SIMULATION IN CONFIGURING NETWORK PARAMETERSDeepak Shankar
Selecting the right Ethernet standard and configuring all the network devices in the embedded systems accurately is an extremely hard and rigorous job. The configuration depends on the topology, workloads of the connected devices, processing overhead at the switches, and the external interfaces. Network calculus, mathematical models and analytical techniques provide worst case execution time (WCET), but their probability of activity is extremely wide. This leads to overdesign which leads to higher costs, power consumption, weight, and size. Simulating the network is the best way to measure the throughput of the entire system. Digital system simulation provides better latency and throughput accuracy, but the accuracy is still limited because it does not consider the latency associated with the network OS, cybersecurity processing and scheduling. In many cases, these factors can reduce the throughput by 20-40%.
In this paper, we will present our research on modeling the entire Ethernet network, including the workloads, network flow control, scheduling, switch hardware, and software. To substantially increase the coverage and compare topologies, we have developed a set of benchmarks that provides coverage for different combination of deterministic, rate-constrained, and best effort traffic. During the presentation, we will cover the benchmarks, the list of attributes required to accurately model the traffic, nodes, switches, and the scheduler settings. We will also look at the statistics and reports required to make the configuration decision. In addition, we will discuss how the model must be constructed to study the impact of future requirements, failures, network intrusions, and security detection schemes.
Key Takeaways:
1. Learn how to efficiently use network simulation to design Ethernet systems
2. Develop a reusable benchmark and associated statistics to test different configurations
3. The role and impact of the CDT slots, guard band, send slope, idle slope, shuffle scheduling, flow control and virtual channels
Introduction to National Instrument Data Logging Machine Monitoring and Pow...slemoslideshare
Presentation by Stephen Plumb about National Instruments' Data Logging Machine Monitoring and Power Monitoring during a seminar at the Ecole National Superieure Polytechnique de Yaounde.
Intelligent Traffic Controller is designed and developed for the purpose of efficient traffic management, minimize pollution, increase current safety standards , smart toll collection system , theft detection and also to provide services to emergency vehicles.
Telecordia NIST/WSTS Workshop: Mobile Backhaul SynchronizationADVA
Check out the Mobile Backhaul Synchronization slides that Gil Biran will be presenting at this week's Telecordia NIST/WSTS workshop in San Jose, California
Network-based UE mobility estimation in mobile networksPopescu Dalia
The co-existence of small cells and macro cells is a key feature of 4G and future networks. This heterogeneity with the increased mobility of user devices can generate a high handover frequency that could lead to unreasonably high call drop probability or poor user experience. By performing smart mobility management, the network can pro-actively adapt to the user and guarantee seamless and smooth cell transitions. In this work, we demonstrate how sounding reference signal (SRS) measurements available at the base station (a.k.a. eNodeB in 4G systems) can be used with a low computational requirement to estimate the mobility level of the user and with no modification at the user device/equipment (UE) side. The performance of the algorithm is showcased using realistic data and mobility traces. Results show that the classification of UE’s speed to the three mobility classes can be achieved with accuracy of 87% for low mobility, 93% for medium mobility and 94% for high mobility, respectively.
1. N SRI KRISHNA YADAV(2011H140031H)
PEEYUSH PASHINE(2011H140033H)
PRAVESH TAMRAKAR(2011H140036H)
B ANUSHA(2011H140039H)
2. CURRENT SCENARIO
Independent traffic light controllers with pre-set
timings
Traffic congestion leading to increase in travel time
Possibility of emergency vehicles being caught up in
traffic
Transportation infrastructure cannot be altered
3. PURPOSE
Smooth traffic flow
Unobstructed movement of emergency & VIP vehicles
causing minimum disruption of normal traffic
Travel time reduction
Increased road capacity without changing the road
layout
4. INTRODUCTION
Traffic management using intelligent decisions based
on current traffic situation
Sustainable & balanced transportation system
Application of ICT in traffic management solutions
Data gathering and provision of feedback
Improved safety,energy efficiency,environmental
quality,travel time etc
5. APPLICATION
Users’ perspective
Green wave effect creation on roads with additional
feature of special vehicle pre-emption
Target elements
Traffic light controllers
Message boards
Special vehicles
6. ASSUMPTIONS
SECONDARY
ROAD
PRIMARY
ROAD
•Two-way primary and secondary roads
•Traffic volume in primary road is greater than in
secondary ones
•Special vehicles equipped with mechanism to notify
themselves to the traffic controller
7. USE CASE APPROACH
COMPUTE
& MODIFY
TIMER
INTERVAL
TRAFFIC
SENSORS
LIGHT
DISPLAY
COMPUTE
& BLOCK
SPECIAL TRAFFIC
VEHICLES MESSAGE DISPLAY
BOARD
11. DESIGN MODEL
Assertion
Data 1
Sources
Switching
timer
0
Assertion
Data
Sources
12. DESIGN MODEL
Assertion 1
Data
Sources Switching
timer
0
Assertion
Data
Sources
13. CONCLUSION
Smooth traffic flow ensured by green wave effect.
Priority given to special vehicles.
Intelligent decisions based on traffic volume.
Simple and computationally efficient.