This document summarizes a journal article about using wireless sensor networks for disaster management. It discusses several potential applications:
1) Flood detection networks that monitor water levels and send alerts.
2) Forest fire detection networks that use sensors to detect temperature, smoke and send alerts.
3) Earthquake detection networks that use sensor data to detect earthquakes and estimate their magnitude.
4) Tsunami detection networks that use underwater pressure sensors to detect tsunamis and activate barriers.
5) Drought forecasting networks that use sensors to monitor factors like rainfall and temperature to predict drought and send alerts.
A wireless sensor network consists of spatially distributed autonomous sensor nodes that monitor physical or environmental conditions. Sensor nodes gather data and relay it back to a main location. Wireless sensor networks enable monitoring of conditions such as temperature, sound, pollution levels, pressure, or motion over large areas. They have applications in industries such as agriculture, environmental protection, health care, home and building automation, and transportation.
Development in the technology of sensor such as Micro Electro Mechanical Systems (MEMS), wireless communications, embedded systems, distributed processing and wireless sensor applications have contributed a large transformation in Wireless
Sensor Network (WSN) recently. It assists and improves work performance both in the field of industry and our daily life. Wireless Sensor Network has been widely used in many areas especially for surveillance and monitoring in agriculture and habitat monitoring. Environment monitoring has become an important field of control and protection, providing real-time system and control communication
with the physical world. An intelligent and smart Wireless Sensor Network system can gather and process a large amount of data from the beginning of the monitoring and manage air quality, the conditions of traffic, to weather situations.
Wireless sensor networks consist of hundreds or thousands of sensor nodes that are distributed to monitor various environmental conditions through sensing, processing, and communicating with each other and a base station. These sensor nodes have limitations in terms of power, memory, and processing capabilities compared to other networks. Wireless sensor networks have a wide range of applications including military surveillance, environmental monitoring, smart homes/buildings, and healthcare.
This document provides an overview of wireless sensor networks, including their applications in various fields such as military, environment, health, home, and automotive. It discusses the key factors influencing sensor network design such as fault tolerance, scalability, and power consumption. It also describes the typical components of sensor nodes, communication architectures, operating systems like TinyOS, and simulators used for wireless sensor networks.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Applications of wireless sensor networks include environmental monitoring, habitat monitoring, structural health monitoring, precision agriculture, and disaster relief management. Researchers have deployed sensor networks to monitor storm petrels on Great Duck Island, measure soil moisture levels over hectares of land, and detect endangered plant species in Hawaii Volcanoes National Park. Wireless sensor networks can also be applied to monitor landfill gas emissions, track water quality parameters, and assess the health of buildings and bridges.
This document provides an overview of wireless sensor networks. It discusses key definitions, advantages, applications and challenges. Sensor networks can provide energy and detection advantages over traditional systems. They enable applications in various domains including military, environmental monitoring, healthcare and home automation. The document also outlines enabling technologies and discusses important considerations like network architectures, hardware components, energy consumption and optimization goals.
A wireless sensor network consists of spatially distributed autonomous sensor nodes that monitor physical or environmental conditions. Sensor nodes gather data and relay it back to a main location. Wireless sensor networks enable monitoring of conditions such as temperature, sound, pollution levels, pressure, or motion over large areas. They have applications in industries such as agriculture, environmental protection, health care, home and building automation, and transportation.
Development in the technology of sensor such as Micro Electro Mechanical Systems (MEMS), wireless communications, embedded systems, distributed processing and wireless sensor applications have contributed a large transformation in Wireless
Sensor Network (WSN) recently. It assists and improves work performance both in the field of industry and our daily life. Wireless Sensor Network has been widely used in many areas especially for surveillance and monitoring in agriculture and habitat monitoring. Environment monitoring has become an important field of control and protection, providing real-time system and control communication
with the physical world. An intelligent and smart Wireless Sensor Network system can gather and process a large amount of data from the beginning of the monitoring and manage air quality, the conditions of traffic, to weather situations.
Wireless sensor networks consist of hundreds or thousands of sensor nodes that are distributed to monitor various environmental conditions through sensing, processing, and communicating with each other and a base station. These sensor nodes have limitations in terms of power, memory, and processing capabilities compared to other networks. Wireless sensor networks have a wide range of applications including military surveillance, environmental monitoring, smart homes/buildings, and healthcare.
This document provides an overview of wireless sensor networks, including their applications in various fields such as military, environment, health, home, and automotive. It discusses the key factors influencing sensor network design such as fault tolerance, scalability, and power consumption. It also describes the typical components of sensor nodes, communication architectures, operating systems like TinyOS, and simulators used for wireless sensor networks.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Applications of wireless sensor networks include environmental monitoring, habitat monitoring, structural health monitoring, precision agriculture, and disaster relief management. Researchers have deployed sensor networks to monitor storm petrels on Great Duck Island, measure soil moisture levels over hectares of land, and detect endangered plant species in Hawaii Volcanoes National Park. Wireless sensor networks can also be applied to monitor landfill gas emissions, track water quality parameters, and assess the health of buildings and bridges.
This document provides an overview of wireless sensor networks. It discusses key definitions, advantages, applications and challenges. Sensor networks can provide energy and detection advantages over traditional systems. They enable applications in various domains including military, environmental monitoring, healthcare and home automation. The document also outlines enabling technologies and discusses important considerations like network architectures, hardware components, energy consumption and optimization goals.
This document provides an introduction to wireless sensor networks. It discusses the differences between wireless sensor networks and ad hoc networks, and describes some key applications including monitoring of areas, objects, and interactions. It outlines the characteristics of wireless sensor networks including constraints of sensors like limited power and computational ability. The document also discusses design challenges, enabling technologies, and the future of wireless sensor networks. It provides examples of sensor network hardware including motes, sensor boards, and programming boards.
An overview of a wireless sensor network communication pptphbhagwat
This document provides an overview of wireless sensor network communication architectures and their design challenges. It describes that wireless sensor networks consist of spatially distributed sensors that cooperatively monitor physical conditions. The key components of sensor nodes are described as well as common communication architectures and protocols used. Some examples of wireless sensor network applications are also mentioned such as environmental monitoring, precision agriculture, and health monitoring. Design challenges for wireless sensor networks include energy efficiency, distributed processing, and operating in harsh environments.
1. Wireless sensor networks consist of distributed sensor nodes that communicate wirelessly to monitor physical or environmental conditions, such as temperature, sound, or pollution levels.
2. The sensor nodes gather and route data back to a central sink/gateway node where the information can be analyzed.
3. Communication protocols and algorithms are required for efficient multi-hop routing of data between sensor nodes and the sink node.
Wireless sensor networks are composed of densely deployed sensor nodes that can cooperatively monitor phenomena. The document outlines applications of sensor networks like environmental monitoring and health monitoring. It discusses factors influencing sensor network design such as fault tolerance, scalability, hardware constraints, and power consumption. It also describes the communication architecture of sensor networks including the physical, data link, network, transport, and application layers and open research issues at each layer.
This document summarizes wireless sensor networks and motes. It discusses that motes are low-cost, low-power computers that monitor sensors and communicate wirelessly. Wireless sensor networks are formed from many motes that pass data along to each other. Example applications of wireless sensor networks include habitat monitoring, fire detection, and preventative maintenance. The document also discusses TinyOS, an open-source operating system for sensor networks, and some contributions to wireless sensor networks from the Dialog Lab, including developing a mote clone and researching data mules to extend network connectivity.
Wireless sensor networks consist of distributed autonomous sensors that monitor physical or environmental conditions. Sensor nodes gather data and transmit it to a central location. Wireless sensor networks have applications in fields like military surveillance, environmental monitoring, healthcare, home automation, and traffic control. The design of wireless sensor networks is influenced by factors like fault tolerance, scalability, hardware constraints, topology, and power consumption.
A sensor node, also known as a mote (chiefly in North America), is a node in a sensor network that is capable of performing some processing, gathering sensory information and communicating with other connected nodes in the network. A mote is a node but a node is not always a mote.
The document describes a wireless sensor network (WSN) modeling framework that consists of three modeling languages: the software architecture modeling language, the node configuration modeling language, and the physical environment modeling language. The software architecture modeling language allows modeling the structure and behavior of WSN applications using components, ports, connections, events, conditions, and actions. The node configuration modeling language specifies attributes of node types like the operating system, MAC protocol, installed sensors/actuators. The physical environment modeling language models attributes of the deployment environment.
This document discusses solutions to extend the life of wireless sensor networks. It begins by introducing wireless sensor networks and describing their typical node architecture and energy consumption. Most energy is used by the transceiver unit for radio communication. The lifetime of a network is defined as the time until a certain percentage of nodes fail. Several solutions are proposed to enhance network lifetime, including efficient node deployment, data aggregation, routing, and using mobile sinks to balance energy consumption across nodes. Reducing radio communication and balancing energy usage across the network can effectively increase the lifetime of wireless sensor networks.
Wireless sensor network and its applicationRoma Vyas
The document discusses wireless sensor networks (WSN) and their applications. It defines a WSN as a collection of sensor nodes that communicate wirelessly and self-organize after deployment. Sensor nodes collect data at regular intervals, convert it to electrical signals, and send it to a base station. The document outlines the components of sensor nodes and describes how WSNs are used for applications like forest fire detection, air/water pollution monitoring, landslide detection, and military surveillance. It also discusses the TinyOS operating system commonly used for WSNs and its features for efficiently utilizing energy in sensor nodes.
Wireless sensor networks (WSNs) consist of distributed sensor nodes that communicate wirelessly. Routing protocols for WSNs include flooding, gossiping, SPIN, and GEAR. Flooding broadcasts data to all neighbors while gossiping randomly selects neighbors, avoiding duplicated data. SPIN and GEAR use data negotiation and geographical information to route packets efficiently. Common networking technologies in WSNs are Bluetooth, ZigBee, UWB, and Wi-Fi, with each having advantages for different applications depending on data rates and power requirements. TinyOS and Contiki are lightweight operating systems used in WSNs. WSNs have a variety of applications including environmental monitoring, pollution monitoring, and detection of fires, landslides
The main tasks of a Wireless Sensor Network
(WSN) are data collection from its nodes and communication
of this data to the base station (BS). The protocols used for
communication among the WSN nodes and between the WSN
and the BS, must consider the resource constraints of nodes,
battery energy, computational capabilities and memory. The
WSN applications involve unattended operation of the network
over an extended period of time. In order to extend the lifetime
of a WSN, efficient routing protocols need to be adopted. The
proposed low power routing protocol based on tree-based
network structure reliably forwards the measured data towards
the BS using TDMA. An energy consumption analysis of the
WSN making use of this protocol is also carried out. It is
found that the network is energy efficient with an average
duty cycle of 0:7% for the WSN nodes. The OmNET++
simulation platform along with MiXiM framework is made
use of.
The document discusses a wireless sensor network project that involves collecting sensor data from nodes in the network. It describes the architecture of the sensor nodes and how they communicate with a base station. The project involves nodes sensing data, storing it locally, and aggregating it before the base station fetches and displays the results. The nodes use Zigbee networking and MSP430 microcontrollers to sense temperature and other environmental data. Future work includes improving data aggregation and displaying results on smartphones.
A wireless sensor network consists of various wireless nodes that communicate with base stations via radio links to form an ad-hoc network. Each node contains sensors to measure conditions, a microcontroller to manage data collection and power, and a radio to communicate with other nodes. Nodes can use star, mesh, or hybrid networks to relay data to base stations. Standards like Zigbee are designed for wireless sensor networks and allow for low power communication and networking. Wireless sensor networks have applications in environmental monitoring, industrial monitoring, smart homes, and other areas where distributed sensing is needed.
Wireless sensor networks make use of sensor nodes distributed in a sensor node field. There are many factors that influence the sensor network design. Sensor networks have their own protocol stack aligned with the OSI model.
This document provides an introduction to wireless sensor networks. It discusses how sensor networks are composed of spatially distributed sensor nodes that monitor physical conditions and work cooperatively to gather and transmit sensor data via wireless communication. Each sensor node contains basic computing and communication capabilities. The document outlines common network topologies used in sensor networks and compares the capabilities of modern sensor nodes to early personal computers. Finally, it lists several example application domains for wireless sensor networks, including environmental/infrastructure monitoring, smart homes/offices, traffic control, medical care, industrial processes, and military surveillance.
The document discusses wireless sensor networks (WSNs). It describes WSNs as consisting of distributed sensors that monitor conditions like temperature, sound, and pressure and transmit data to a central location. Modern networks are bidirectional, enabling sensor control. WSNs were initially developed for military surveillance but are now used in industrial and consumer applications. They pose challenges in deployment, location tracking, coverage, and integration of different sensor types on a single platform. Advances in energy harvesting and self-organizing networks could enable millions of low-cost sensor nodes. WSNs have applications in intrusion detection, health monitoring, and location detection.
Wireless sensor networks use multiple sensor nodes that communicate wirelessly to monitor environments. Each node contains sensors to capture phenomena, processing capabilities, and wireless communication. Common sensor types include temperature, pressure, optical, acoustic, and chemical sensors. Challenges for wireless sensor networks include limited energy, decentralized operation, and security. Example applications are structural health monitoring, traffic control, and precision agriculture.
Wireless sensor networks are distributed sensor nodes that communicate wirelessly to monitor environmental conditions like temperature, sound, and pressure. They work cooperatively to pass data through the network and can be used for applications like forest fire detection, healthcare monitoring, and pollution monitoring. Each sensor node has components like a radio transceiver, a microcontroller, an electronic circuit for interfacing with sensors and an energy source. They provide advantages over wired networks like easy installation and accommodation of new devices but also have disadvantages like lower speed, complex configuration and sensitivity to surroundings.
The document discusses wireless sensor networks and describes their key characteristics. It notes that wireless sensor networks consist of low-power smart sensor nodes distributed over a large field to enable wireless sensing and data networking. The sensor nodes contain sensors, processors, memory, and radios. Wireless sensor networks can be either unstructured with dense node distribution or structured with few scattered nodes.
This document discusses wireless sensor networks. It outlines their applications such as environmental monitoring, health care, and military uses. It also examines factors that influence sensor network design like fault tolerance, scalability, production costs, and power consumption. The communication architecture of sensor networks is presented, including the application, transport, network, data link, and physical layers. Sensor networks have the potential to be widely used in many applications due to their flexibility and fault tolerance.
This document provides an introduction to wireless sensor networks. It discusses the differences between wireless sensor networks and ad hoc networks, and describes some key applications including monitoring of areas, objects, and interactions. It outlines the characteristics of wireless sensor networks including constraints of sensors like limited power and computational ability. The document also discusses design challenges, enabling technologies, and the future of wireless sensor networks. It provides examples of sensor network hardware including motes, sensor boards, and programming boards.
An overview of a wireless sensor network communication pptphbhagwat
This document provides an overview of wireless sensor network communication architectures and their design challenges. It describes that wireless sensor networks consist of spatially distributed sensors that cooperatively monitor physical conditions. The key components of sensor nodes are described as well as common communication architectures and protocols used. Some examples of wireless sensor network applications are also mentioned such as environmental monitoring, precision agriculture, and health monitoring. Design challenges for wireless sensor networks include energy efficiency, distributed processing, and operating in harsh environments.
1. Wireless sensor networks consist of distributed sensor nodes that communicate wirelessly to monitor physical or environmental conditions, such as temperature, sound, or pollution levels.
2. The sensor nodes gather and route data back to a central sink/gateway node where the information can be analyzed.
3. Communication protocols and algorithms are required for efficient multi-hop routing of data between sensor nodes and the sink node.
Wireless sensor networks are composed of densely deployed sensor nodes that can cooperatively monitor phenomena. The document outlines applications of sensor networks like environmental monitoring and health monitoring. It discusses factors influencing sensor network design such as fault tolerance, scalability, hardware constraints, and power consumption. It also describes the communication architecture of sensor networks including the physical, data link, network, transport, and application layers and open research issues at each layer.
This document summarizes wireless sensor networks and motes. It discusses that motes are low-cost, low-power computers that monitor sensors and communicate wirelessly. Wireless sensor networks are formed from many motes that pass data along to each other. Example applications of wireless sensor networks include habitat monitoring, fire detection, and preventative maintenance. The document also discusses TinyOS, an open-source operating system for sensor networks, and some contributions to wireless sensor networks from the Dialog Lab, including developing a mote clone and researching data mules to extend network connectivity.
Wireless sensor networks consist of distributed autonomous sensors that monitor physical or environmental conditions. Sensor nodes gather data and transmit it to a central location. Wireless sensor networks have applications in fields like military surveillance, environmental monitoring, healthcare, home automation, and traffic control. The design of wireless sensor networks is influenced by factors like fault tolerance, scalability, hardware constraints, topology, and power consumption.
A sensor node, also known as a mote (chiefly in North America), is a node in a sensor network that is capable of performing some processing, gathering sensory information and communicating with other connected nodes in the network. A mote is a node but a node is not always a mote.
The document describes a wireless sensor network (WSN) modeling framework that consists of three modeling languages: the software architecture modeling language, the node configuration modeling language, and the physical environment modeling language. The software architecture modeling language allows modeling the structure and behavior of WSN applications using components, ports, connections, events, conditions, and actions. The node configuration modeling language specifies attributes of node types like the operating system, MAC protocol, installed sensors/actuators. The physical environment modeling language models attributes of the deployment environment.
This document discusses solutions to extend the life of wireless sensor networks. It begins by introducing wireless sensor networks and describing their typical node architecture and energy consumption. Most energy is used by the transceiver unit for radio communication. The lifetime of a network is defined as the time until a certain percentage of nodes fail. Several solutions are proposed to enhance network lifetime, including efficient node deployment, data aggregation, routing, and using mobile sinks to balance energy consumption across nodes. Reducing radio communication and balancing energy usage across the network can effectively increase the lifetime of wireless sensor networks.
Wireless sensor network and its applicationRoma Vyas
The document discusses wireless sensor networks (WSN) and their applications. It defines a WSN as a collection of sensor nodes that communicate wirelessly and self-organize after deployment. Sensor nodes collect data at regular intervals, convert it to electrical signals, and send it to a base station. The document outlines the components of sensor nodes and describes how WSNs are used for applications like forest fire detection, air/water pollution monitoring, landslide detection, and military surveillance. It also discusses the TinyOS operating system commonly used for WSNs and its features for efficiently utilizing energy in sensor nodes.
Wireless sensor networks (WSNs) consist of distributed sensor nodes that communicate wirelessly. Routing protocols for WSNs include flooding, gossiping, SPIN, and GEAR. Flooding broadcasts data to all neighbors while gossiping randomly selects neighbors, avoiding duplicated data. SPIN and GEAR use data negotiation and geographical information to route packets efficiently. Common networking technologies in WSNs are Bluetooth, ZigBee, UWB, and Wi-Fi, with each having advantages for different applications depending on data rates and power requirements. TinyOS and Contiki are lightweight operating systems used in WSNs. WSNs have a variety of applications including environmental monitoring, pollution monitoring, and detection of fires, landslides
The main tasks of a Wireless Sensor Network
(WSN) are data collection from its nodes and communication
of this data to the base station (BS). The protocols used for
communication among the WSN nodes and between the WSN
and the BS, must consider the resource constraints of nodes,
battery energy, computational capabilities and memory. The
WSN applications involve unattended operation of the network
over an extended period of time. In order to extend the lifetime
of a WSN, efficient routing protocols need to be adopted. The
proposed low power routing protocol based on tree-based
network structure reliably forwards the measured data towards
the BS using TDMA. An energy consumption analysis of the
WSN making use of this protocol is also carried out. It is
found that the network is energy efficient with an average
duty cycle of 0:7% for the WSN nodes. The OmNET++
simulation platform along with MiXiM framework is made
use of.
The document discusses a wireless sensor network project that involves collecting sensor data from nodes in the network. It describes the architecture of the sensor nodes and how they communicate with a base station. The project involves nodes sensing data, storing it locally, and aggregating it before the base station fetches and displays the results. The nodes use Zigbee networking and MSP430 microcontrollers to sense temperature and other environmental data. Future work includes improving data aggregation and displaying results on smartphones.
A wireless sensor network consists of various wireless nodes that communicate with base stations via radio links to form an ad-hoc network. Each node contains sensors to measure conditions, a microcontroller to manage data collection and power, and a radio to communicate with other nodes. Nodes can use star, mesh, or hybrid networks to relay data to base stations. Standards like Zigbee are designed for wireless sensor networks and allow for low power communication and networking. Wireless sensor networks have applications in environmental monitoring, industrial monitoring, smart homes, and other areas where distributed sensing is needed.
Wireless sensor networks make use of sensor nodes distributed in a sensor node field. There are many factors that influence the sensor network design. Sensor networks have their own protocol stack aligned with the OSI model.
This document provides an introduction to wireless sensor networks. It discusses how sensor networks are composed of spatially distributed sensor nodes that monitor physical conditions and work cooperatively to gather and transmit sensor data via wireless communication. Each sensor node contains basic computing and communication capabilities. The document outlines common network topologies used in sensor networks and compares the capabilities of modern sensor nodes to early personal computers. Finally, it lists several example application domains for wireless sensor networks, including environmental/infrastructure monitoring, smart homes/offices, traffic control, medical care, industrial processes, and military surveillance.
The document discusses wireless sensor networks (WSNs). It describes WSNs as consisting of distributed sensors that monitor conditions like temperature, sound, and pressure and transmit data to a central location. Modern networks are bidirectional, enabling sensor control. WSNs were initially developed for military surveillance but are now used in industrial and consumer applications. They pose challenges in deployment, location tracking, coverage, and integration of different sensor types on a single platform. Advances in energy harvesting and self-organizing networks could enable millions of low-cost sensor nodes. WSNs have applications in intrusion detection, health monitoring, and location detection.
Wireless sensor networks use multiple sensor nodes that communicate wirelessly to monitor environments. Each node contains sensors to capture phenomena, processing capabilities, and wireless communication. Common sensor types include temperature, pressure, optical, acoustic, and chemical sensors. Challenges for wireless sensor networks include limited energy, decentralized operation, and security. Example applications are structural health monitoring, traffic control, and precision agriculture.
Wireless sensor networks are distributed sensor nodes that communicate wirelessly to monitor environmental conditions like temperature, sound, and pressure. They work cooperatively to pass data through the network and can be used for applications like forest fire detection, healthcare monitoring, and pollution monitoring. Each sensor node has components like a radio transceiver, a microcontroller, an electronic circuit for interfacing with sensors and an energy source. They provide advantages over wired networks like easy installation and accommodation of new devices but also have disadvantages like lower speed, complex configuration and sensitivity to surroundings.
The document discusses wireless sensor networks and describes their key characteristics. It notes that wireless sensor networks consist of low-power smart sensor nodes distributed over a large field to enable wireless sensing and data networking. The sensor nodes contain sensors, processors, memory, and radios. Wireless sensor networks can be either unstructured with dense node distribution or structured with few scattered nodes.
This document discusses wireless sensor networks. It outlines their applications such as environmental monitoring, health care, and military uses. It also examines factors that influence sensor network design like fault tolerance, scalability, production costs, and power consumption. The communication architecture of sensor networks is presented, including the application, transport, network, data link, and physical layers. Sensor networks have the potential to be widely used in many applications due to their flexibility and fault tolerance.
This document describes a landslide warning system that uses sensors, a microcontroller, GPS, and Zigbee wireless communication. Three sensors (an angle sensor, liquid level sensor, and temperature sensor) are connected to an ARM microcontroller to collect data on slope angle, water depth, and temperature. The microcontroller sends this sensor data along with location information from a GPS module to a Zigbee transmitter. The Zigbee transmits the data to a receiver Zigbee connected to an LCD and GSM module. The LCD displays the sensor readings and location at the receiver station, and the GSM sends an SMS alert about the landslide risk to warn people. The system was tested and able to accurately detect landslide
This document discusses tsunamis and tsunami warning systems. It defines a tsunami as a series of ocean waves generated by earthquakes or other disturbances under the sea. It then provides examples of historic tsunamis in locations like Lisbon, Japan, and India. The document goes on to explain that tsunami warning systems were first attempted in Hawaii in the 1920s and have since been improved. Major warning centers include the Pacific Tsunami Warning Center and the National Tsunami Warning Center. After the devastating 2004 Indian Ocean tsunami, several regional warning systems were also established.
IRIS Recognition Based Authentication System In ATMIJTET Journal
Security and Authentication of individuals is necessary for our daily lives especially in ATMs. It has been improved by using biometric verification techniques like face recognition, fingerprints, voice and other traits, comparing these existing traits, there is still need for considerable computer vision. Iris recognition is a particular type of biometric system that can be used to reliably identify a person uniquely by analyzing the patterns found in the iris. Initially Iris images are collected as datasets and maintained in agent memory. Then the Iris and pupil are detected from the image, removing noises. The features of the iris were encoded by convolving the normalized iris region with 2DGabor filter. The Hamming distance was chosen as a matching metric, which gave the measure of how many bits disagreed between the templates of the iris.
This report will give idea of key steps in developing an algorithm for \’Iris based Recognition system\’.Experimental observations as well are also shown.
This document discusses tsunamis and tsunami warning systems. It provides background on the history of notable tsunamis such as those in Lisbon in 1755 and Japan in 1779. It then describes the devastating 2004 Indian Ocean tsunami, noting details like its height of 51 meters. The document outlines the development of tsunami warning systems starting in Hawaii in the 1920s to better detect tsunamis and issue warnings. It provides details on the Indian Ocean Tsunami Warning System established in 2006 to warn nations bordering the Indian Ocean of approaching tsunamis. Both the advantages of reducing loss of life and damage and the shortcomings around costs and maintenance requirements of tsunami warning systems are mentioned.
The document describes a tsunami warning system, explaining that tsunamis are large ocean waves usually caused by undersea earthquakes, volcanic eruptions, or landslides. The system uses seismometers to detect earthquakes, tide gauges to measure changes in sea level, and NOAA and DART stations that can detect tsunamis, allowing warnings to be issued to protect coastal areas from potential damage from an approaching tsunami.
A wireless sensor network consists of spatially distributed sensor nodes that monitor physical conditions and communicate wirelessly. Nodes sense data, process it, and transmit it to other nodes or a central gateway. The gateway provides a connection to the wired world to collect, process, analyze and present measurement data. Routers can extend the communication range between nodes and the gateway. Sensor nodes are small, require little power, are programmable and cost-effective to purchase and maintain.
The document discusses tsunami detection methods including:
1. Current methods use deep water pressure sensors anchored to the seafloor which can detect tsunami-induced pressure changes but have high costs and maintenance needs.
2. A proposed coastal alert system uses anchored buoys that detect the receding water of an approaching tsunami wave and trigger alarms to warn local communities.
3. Another proposal involves deployable underwater sensors that are dropped from buoys after seismic events to take pressure readings at multiple depths in a lower-cost and more redundant method.
This document outlines a project to develop an automatic street light controller using an AT89c51 microcontroller. It will include a photo sensor that switches the street lights on and off based on light levels, reducing power consumption. The project description provides a block diagram and overview of the main components, including the microcontroller, photo sensor, relays to control the lights, and an LCD display. It also describes the interfaces that will be used, such as the sensor, LCD, relays, switches and LED, and the software needed like a compiler and downloader.
This document discusses tsunamis and tsunami warning systems. It explains that tsunamis are caused by large displacements of water, often due to earthquakes, landslides, or volcanic eruptions. It then describes how tsunami warning systems work, including how the Meteorological Agency issues warnings within three minutes of an earthquake with estimates of tsunami arrival times and heights. However, it notes shortcomings in that actual tsunami heights and times may differ from forecasts. It suggests developing more accurate forecasts based on observed data while still issuing warnings that assume maximum tsunami scales.
The document discusses tsunamis, including their causes, characteristics, and historical examples. It provides details on underwater earthquakes triggering tsunamis and describes tsunamis as consisting of multiple waves rather than a single wave. Examples of destructive tsunamis throughout history are given for various regions.
This document provides contact information for Prateek Technologies and lists 190 embedded systems, robotics, and other technology projects they have completed or can complete. It includes their address, phone numbers, email and website. The projects listed cover a wide range of applications including industrial automation, medical devices, transportation, security, and more. This document is marked as for private circulation only.
Tsunamis are powerful waves that can reach over 100 feet tall and travel at speeds over 60 mph. They have the force to lift vehicles and demolish buildings, maintaining their energy as they cross entire cities. Hawaii faces the greatest risk from tsunamis in the United States, experiencing around one per year on average, and the waves can sound like a freight train as they approach land.
The document describes an automatic street light control system that uses a light dependent resistor and transistor circuit to switch street lights on and off automatically based on light levels. It removes the need for manual operation by turning lights on when darkness reaches a certain level and off when another light source is detected. This saves energy by precisely controlling light times. The system uses a transistor as a switch that is activated by a light dependent resistor sensor similar to the human eye.
Tsunamis are caused by large displacements of water, usually in oceans, that can be triggered by earthquakes, volcanic eruptions, landslides or meteorite impacts. While tsunamis have extremely long wavelengths and periods in deep ocean waters, they can travel very fast at over 600 mph. When they reach shallow coastal waters, their energy causes the sea level to rise dramatically and flood inland areas. Proper planning, awareness of warning signs and evacuation routes can help minimize damage and save lives during a tsunami.
Wireless sensor networks can play a vital role in disaster management by enabling early detection of disasters like floods, forest fires, earthquakes, and tsunamis. The document discusses several wireless sensor network architectures for disaster management, including networks for flood detection, forest fire detection, earthquake sensing, and tsunami detection. It also discusses a drought forecast and alert system that uses sensor networks and neural networks to predict drought conditions 7 days in advance. Finally, it provides an overview of Sahana, an open source disaster management software system that aims to facilitate information sharing between organizations during disasters.
1) Wireless sensor networks can play a vital role in disaster management by detecting disasters and alerting emergency response teams. They allow for near real-time detection of events like fires and floods.
2) The document describes different wireless sensor network architectures that could be used for flood monitoring and forest fire detection. It involves deploying sensor nodes in affected areas that communicate with local base stations and emergency response centers.
3) The goal is to detect disasters early to minimize loss of life and property by alerting rescue teams so they can respond quickly.
Wireless sensor networks carry out cooperative activities due to limited resources and nowadays, the applications of these networks are copious, varied and the applications in agriculture are still budding. One interesting purpose is in environmental monitoring and greenhouse control, where the crop conditions such as weather and soil do not depend on natural agents. To control and observe the environmental factors, sensors and actuators are necessary. Under these conditions, these devices must be used to make a distributed measure, scattering sensors all over the greenhouse using distributed clustering mechanism. This paper reveals an initiative of environmental monitoring and greenhouse control using a sensor network.
Low cost wireless sensor networks and smartphone applications for disaster ma...eSAT Publishing House
This document describes a low-cost wireless sensor network and smartphone application system for disaster management. The system uses an Arduino-based wireless sensor network comprising nodes with various sensors to monitor the environment. The sensor data is transmitted to a central gateway and then to the cloud for analysis. A smartphone app connected to the cloud can detect disasters from the sensor data and send real-time alerts to users to help with early evacuation. The system aims to provide low-cost localized disaster detection and warnings to improve safety.
This document discusses how wireless sensor networks (WSNs) can help manage disasters. WSNs consist of low-cost, low-power sensor nodes that cooperate to sense the environment and communicate wirelessly. They are proposed as an alternative to satellite monitoring for disaster management. WSNs can provide early warnings of disasters and help search and rescue operations by locating victims. The document outlines key design considerations for using WSNs for disaster detection, monitoring and response, including how to deploy the sensors and which sensing modalities to use based on the type of disaster.
Wireless Sensor Network and Monitoring of Crop FieldIOSRJECE
1) The document discusses the use of wireless sensor networks (WSNs) to monitor environmental conditions in crop fields for precision agriculture. WSNs can collect data on soil moisture, temperature, and other parameters to help farmers optimize crop production.
2) It reviews the literature on WSN evolution and applications in agriculture such as environmental monitoring and irrigation management. WSNs offer benefits like low-cost, flexible deployment, and real-time data collection compared to traditional monitoring methods.
3) The document outlines the basic components and characteristics of WSNs, including sensor node structure, multi-hop data transmission, and connectivity options like Bluetooth, WiFi, and GPS. This information helps farmers implement effective WSNs for
IJRET-V1I1P1 - Forest Fire Detection Based on Wireless Image Processing ISAR Publications
This document proposes an intelligent forest fire detection system using image processing and artificial intelligence techniques. It discusses how traditional fire detection methods are inadequate and outlines the proposed system. The system would use temperature sensors, a GPS module, and small satellites to continuously monitor forests for fires, detect fires rapidly, and transmit location alerts to aid fire station response before fires spread severely. If a temperature threshold is exceeded, the system would transmit an alert along with the fire's GPS location to orbiting small satellites and ultimately to fire stations. The goal is early and accurate fire detection and localization to minimize ecological and economic damage from forest fires.
Forest-Fires Surveillance System Based On Wireless Sensor NetworkIJERA Editor
We present the design and evaluation of a wireless sensor network for early detection of forest fires. Wild fires
cause to damage on forest and a mountain which have valuable natural resources during the dry winter season
Where it becomes very paramount to cover the area caused by fire by the forest fighters. Current surveillance
systems utilize a camera, an infrared sensor system and a satellite system. These systems cannot support
authentic time surveillance, monitoring and automatic alarm. Even though it gives information about fire caused
area,but asthe forest looks same in all areas as it is covered with dense trees it is very hard to recognize the exact
area andimage transmition through the transmitter to the officers computer takes too much time .It takes too
much time to load the image. Which in turns waste the time and fire caused area goes on increasing. Taking in
toconsideration all this faults of the prior system in the forest we have designed our modified project.In our
project, we develop a forest fires surveillance system.
This research looks about a counsel structure that uses degree-supervised snitch to consider allocated sensor networks. Level managed snitch is a proposed process that combines evening out and invading together. This
strategy reduces the number of possible messages by delivering them via the base station mechanism, hence increasing the sensor neighborhood’s
presence time. The sensor district, which contains numerous sensor centers,
is dynamically assigned into phases of extended clear by the use of various energy ranges at the base station. The game design divides the entire sensor
neighborhood into distinct concentric zones based on distance from the base station, with the group being routed from high-capacity center to center locations within the lower-capacity zone. The transmission of information
proximity of the forest fire to the base station will increase the opportunity. The primary benefit of the display is that it sends a basic event with a higher probability while also conserving the presence time of the neighborhood destiny noticing.
Modelling of wireless sensor networks for detection land and forest fire hotspotTELKOMNIKA JOURNAL
Indonesia located in South East Asia countries with tropical region, forest fires in Indonesia is
one of big issue and disaster because it happens in almost of every year, this is because of some of region
consist of peat land that high risk for fire especially in dry season. Riau Province is one of region that
regularly incident of forest fire with affected the length and breadth of Indonesia. Propose development of
Wireless Sensor Networks (WSNs) for detection of land and forest fire hotspot in Indonesia as well as one
of the main consents in this research, case location in Riau province is at one of the regions that high risk
forest fire in dry season. WSNs technology used for ground sensor system to collect environmental data.
Data training for fire hotspot detection is done in data center to determine and conclude of fire hotspot then
potential to become big fire. The deployment of sensors located at several locations that has potential for
fire incident, especially as data shown in previous case and forecast location with potential fire happen.
Mathematical analysis is used in this case for modelling number of sensors required to deploy and the size
of forest area. The design and development of WSNs give high impact and feasibility to overcome current
issues of forest fire and fire hotspot detection in Indonesia. The development of this system used WSNs
highly applicable for early warning and alert system for fire hotspot detection.
Abstract: In this paper, Landslide detection is used to describe the Down slope faction of soil, rock& unrefined Materials under the force of gravity .It can be triggered by steady processes such as Weathering or by external mechanism. We need to prevent the loss of life & spoil to communication routes, Human settlement, Agricultural fields & Forest land etc. We will discuss the techniques which includes circuits for Railway Track cutting, Fogg recognition, Heavy Rainfall & to provide alert system. This system uses a wireless sensor network which is Digital sensor network. This sensor nodes are one of the progressive technologies, it provides high sensitivity, large coverage area, accurate processing & Transmission of critical data in Run time with high resolution. The sensor module detects the vibration from field & sends data to monitoring station through RFID module. We have implement this work by using competent microcontroller AT89s52.
This document summarizes an article that proposes an IoT-based intelligent modeling system for fire prevention and safety in smart homes. The system uses a wireless sensor network with multiple sensors to detect fires early and reduce false alarms by using Global System for Mobile Communications. It detects fires efficiently by requiring confirmation from two sources - the user responding to a GSM alert or two or more sensors detecting fire. The system was evaluated through simulation and showed it could detect fires early even if a sensor failed while keeping energy consumption low.
IRJET - Forest Fire Detection, Prevention and Response System using IoTIRJET Journal
The document proposes an IoT-based system to detect, monitor, and respond to forest fires using sensors connected to a microcontroller that communicates with the cloud, which notifies emergency services if a fire is detected and triggers a water pump to control the spread of the fire. The system is intended to help prevent loss of wildlife, property, and ecosystems from large destructive forest fires. Key components of the system include temperature, light, and flame sensors, a Node MCU microcontroller, cloud connectivity, a water pump, and notifications to emergency responders.
IRJET- IoT based Wireless Sensor Network for Prevention of Crop Yields fr...IRJET Journal
This document proposes an IOT-based wireless sensor network system to prevent crop damage from animals and enable smart farming. The system uses various sensors like IR, motion, camera, soil moisture, rain, gas, temperature and humidity sensors connected over a wireless network to detect animal intrusions. When an animal is detected, the camera captures an image, a minor shock is administered to scare away the animal, and alerts are sent to the farmer's phone. The sensor data is stored in the cloud for analytics. This system aims to help farmers protect their crops and yields from losses due to animals.
The document proposes a method to enhance security in wireless sensor networks for agriculture by addressing the problem of network congestion. Sensors would be installed in agricultural fields to monitor soil conditions and other variables. One node would act as a sink node to receive data from other sensor nodes. Congestion could occur if sensor nodes send too much data, overwhelming the sink node. The proposed method would apply an AODV routing protocol to detect congestion and notify the user, allowing corrective action. It would also simulate data flow using NS2 to track packets over time and show how congestion impacts packet loss. Addressing congestion could improve network performance and benefit farmers' ability to increase crop yields.
A Comparative Performance Analysis of Centralized and Distributed Hierarchica...IRJET Journal
This document discusses and compares centralized and distributed hierarchical routing protocols in wireless sensor networks (WSNs). It analyzes the performance of several hierarchical routing protocols (LEACH, LEACH-C, MOD LEACH, HEED, EAMMH, EAMRP) with respect to parameters like throughput, alive nodes, energy consumption, dead nodes, and scalability. Hierarchical clustering protocols are among the most energy efficient routing techniques for WSNs. The document also discusses the importance of environmental monitoring using WSNs given the increasing impacts of climate-related disasters.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document summarizes a research article that proposes an intelligent environment design using three wireless communication networks to handle urban flood alert and rescue scenarios. A Wireless Sensor Network would measure river levels and alert people in flood-prone areas. A Mobile Ad Hoc Network would route messages from victims to care facilities. A Wireless Mesh Network in temporary shelters would use RFID technology to provide information and services to sheltered people. The design aims to provide communication when existing infrastructure is disrupted by disasters like floods.
An overview of how Wireless Sensor Networks are being extended to a system which has tremendous capabilities. The future is all about Smart Dust. Trillions of sensors may be planted across the world to improve the ecosystem as well as the lives of human beings. Although the aim of reducing the volume to orders of micrometer has not yet been fulfilled, considerable developments have been made to build motes that combine sensing, computing, wireless communication capabilities and autonomous power supply within volume of only few millimeters and that too at low cost.
Anti-poaching System to Detect Poachers and Conserve Forest EcosystemIRJET Journal
This document proposes an anti-poaching system that uses sensors to detect poaching activity in forests and notify authorities. The system uses a cell phone detector sensor to detect signals from cell phones between 0.8-2.5GHz and trigger an alert with the phone's location. It also uses a PIR motion sensor with a range of 7-12 meters to detect intruders, then triggers a camera to take an image and send it along with location data via GPRS to a central server for authorities to monitor. The system is designed to be low-power, ruggedized and mounted in forests to automatically detect and report poaching activities to help conservation efforts.
Similar to Wireless sensor networks for disaster managment (20)
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
This document provides a comparative study of two-way finite automata and Turing machines. Some key points:
- Two-way finite automata are similar to read-only Turing machines in that they have a finite tape that can be read in both directions, but cannot write to the tape.
- Turing machines have an infinite tape that can be read from and written to, allowing them to recognize recursively enumerable languages.
- Both models are examined in their ability to accept the regular language L={anbm|m,n>0}.
- The time complexity of a two-way finite automaton for this language is O(n2) due to making two passes over the
This document analyzes and compares the performance of the AODV and DSDV routing protocols in a vehicular ad hoc network (VANET) simulation. Simulations were conducted using NS-2, SUMO, and MOVE simulators for a grid map scenario with varying numbers of nodes. The results show that AODV performed better than DSDV in terms of throughput and packet delivery fraction, while DSDV had lower end-to-end delays. However, neither protocol was found to be fully suitable for the highly dynamic VANET environment. The document concludes that further work is needed to develop improved routing protocols optimized for VANETs.
This document discusses the digital circuit layout problem and approaches to solving it using graph partitioning techniques. It begins by introducing the digital circuit layout problem and how it has become more complex with increasing circuit sizes. It then discusses how the problem can be decomposed into subproblems using graph partitioning to assign geometric coordinates to circuit components. The document reviews several traditional approaches to solve the problem, such as the Kernighan-Lin algorithm, and discusses their limitations for larger circuit sizes. It also discusses more recent approaches using evolutionary algorithms and concludes by analyzing the contributions of various approaches.
This document summarizes various data mining techniques that have been used for intrusion detection systems. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using detection models, a data warehouse for storage, and a model generator. It then discusses supervised and unsupervised learning approaches that have been applied, including neural networks, support vector machines, K-means clustering, and self-organizing maps. Finally, it reviews several related works applying these techniques and compares their results, finding that combinations of approaches can improve detection rates while reducing false alarms.
This document provides an overview of speech recognition systems and recent progress in the field. It discusses different types of speech recognition including isolated word, connected word, continuous speech, and spontaneous speech. Various techniques used in speech recognition are also summarized, such as simulated evolutionary computation, artificial neural networks, fuzzy logic, Kalman filters, and Hidden Markov Models. The document reviews several papers published between 2004-2012 that studied speech recognition methods including using dynamic spectral subband centroids, Kalman filters, biomimetic computing techniques, noise estimation, and modulation filtering. It concludes that Hidden Markov Models combined with MFCC features provide good recognition results for large vocabulary, speaker-independent, continuous speech recognition.
This document discusses integrating two assembly lines, Line A and Line B, based on lean line design concepts to reduce space and operators. It analyzes the current state of the lines using tools like takt time analysis and MTM/UAS studies. Improvements are identified to eliminate waste, including methods improvements, workplace rearrangement, ergonomic changes, and outsourcing. Paper kaizen is conducted and work elements are retimed. The goal is to integrate the lines to better utilize space and manpower while meeting manufacturing standards.
This document summarizes research on the exposure of microwaves from cellular networks. It describes how microwaves interact with biological systems and discusses measurement techniques and safety standards regarding microwave exposure. While some studies have alleged health hazards from microwaves, independent reviews by health organizations have found no evidence that exposure to microwaves below international safety limits causes harm. The document concludes that with precautions like limiting exposure time and using phones with lower SAR ratings, microwaves from cell phones pose minimal health risks.
This document summarizes a research paper that examines the effect of feature reduction in sentiment analysis of online reviews. It uses principle component analysis to reduce the number of features (product attributes) from a dataset of 500 camera reviews labeled as positive or negative. Two models are developed - one using the original set of 95 product attributes, and one using the reduced set. Support vector machines and naive Bayes classifiers are applied to both models and their performance is evaluated to determine if classification accuracy can be maintained while using fewer features. The results show it is possible to achieve similar accuracy levels with less features, improving computational efficiency.
This document provides a review of multispectral palm image fusion techniques. It begins with an introduction to biometrics and palm print identification. Different palm print images capture different spectral information about the palm. The document then reviews several pixel-level fusion methods for combining multispectral palm images, finding that Curvelet transform performs best at preserving discriminative patterns. It also discusses hardware for capturing multispectral palm images and the process of region of interest extraction and localization. Common fusion methods like wavelet transform and Curvelet transform are also summarized.
This document describes a vehicle theft detection system that uses radio frequency identification (RFID) technology. The system involves embedding an RFID chip in each vehicle that continuously transmits a unique identification signal. When a vehicle is stolen, the owner reports it to the police, who upload the vehicle's information to a central database. Police vehicles are equipped with RFID receivers. If a stolen vehicle passes within range of a receiver, the receiver detects the vehicle's ID signal and displays its details on a tablet. This allows police to quickly identify and recover stolen vehicles. The system aims to make it difficult for thieves to hide a vehicle's identity and allows vehicles to be tracked globally wherever the detection system is implemented.
This document discusses and compares two techniques for image denoising using wavelet transforms: Dual-Tree Complex DWT and Double-Density Dual-Tree Complex DWT. Both techniques decompose an image corrupted by noise using filter banks, apply thresholding to the wavelet coefficients, and reconstruct the image. The Double-Density Dual-Tree Complex DWT yields better denoising results than the Dual-Tree Complex DWT as it produces more directional wavelets and is less sensitive to shifts and noise variance. Experimental results on test images demonstrate that the Double-Density method achieves higher peak signal-to-noise ratios, especially at higher noise levels.
This document compares the k-means and grid density clustering algorithms. It summarizes that grid density clustering determines dense grids based on the densities of neighboring grids, and is able to handle different shaped clusters in multi-density environments. The grid density algorithm does not require distance computation and is not dependent on the number of clusters being known in advance like k-means. The document concludes that grid density clustering is better than k-means clustering as it can handle noise and outliers, find arbitrary shaped clusters, and has lower time complexity.
This document proposes a method for detecting, localizing, and extracting text from videos with complex backgrounds. It involves three main steps:
1. Text detection uses corner metric and Laplacian filtering techniques independently to detect text regions. Corner metric identifies regions with high curvature, while Laplacian filtering highlights intensity discontinuities. The results are combined through multiplication to reduce noise.
2. Text localization then determines the accurate boundaries of detected text strings.
3. Text binarization filters background pixels to extract text pixels for recognition. Thresholding techniques are used to convert localized text regions to binary images.
The method exploits different text properties to detect text using corner metric and Laplacian filtering. Combining the results improves
This document describes the design and implementation of a low power 16-bit arithmetic logic unit (ALU) using clock gating techniques. A variable block length carry skip adder is used in the arithmetic unit to reduce power consumption and improve performance. The ALU uses a clock gating circuit to selectively clock only the active arithmetic or logic unit, reducing dynamic power dissipation from unnecessary clock charging/discharging. The ALU was simulated in VHDL and synthesized for a Xilinx Spartan 3E FPGA, achieving a maximum frequency of 65.19MHz at 1.98mW power dissipation, demonstrating improved performance over a conventional ALU design.
This document describes using particle swarm optimization (PSO) and genetic algorithms (GA) to tune the parameters of a proportional-integral-derivative (PID) controller for an automatic voltage regulator (AVR) system. PSO and GA are used to minimize the objective function by adjusting the PID parameters to achieve optimal step response with minimal overshoot, settling time, and rise time. The results show that PSO provides high-quality solutions within a shorter calculation time than other stochastic methods.
This document discusses implementing trust negotiations in multisession transactions. It proposes a framework that supports voluntary and unexpected interruptions, allowing negotiating parties to complete negotiations despite temporary unavailability of resources. The Trust-x protocol addresses issues related to validity, temporary loss of data, and extended unavailability of one negotiator. It allows a peer to suspend an ongoing negotiation and resume it with another authenticated peer. Negotiation portions and intermediate states can be safely and privately passed among peers to guarantee stability for continued suspended negotiations. An ontology is also proposed to provide formal specification of concepts and relationships, which is essential in complex web service environments for sharing credential information needed to establish trust.
This document discusses and compares various nature-inspired optimization algorithms for resolving the mixed pixel problem in remote sensing imagery, including Biogeography-Based Optimization (BBO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). It provides an overview of each algorithm, explaining key concepts like migration and mutation in BBO. The document aims to prove that BBO is the best algorithm for resolving the mixed pixel problem by comparing it to other evolutionary algorithms. It also includes figures illustrating concepts like the species model and habitat in BBO.
This document discusses principal component analysis (PCA) for face recognition. It begins with an introduction to face recognition and PCA. PCA works by calculating eigenvectors from a set of face images, which represent the principal components that account for the most variance in the image data. These eigenvectors are called "eigenfaces" and can be used to reconstruct the face images. The document then discusses how the system is implemented, including preparing a face database, normalizing the training images, calculating the eigenfaces/principal components, projecting the face images into this reduced space, and recognizing faces by calculating distances between projected test images and training images.
This document summarizes research on using wireless sensor networks to detect mobile targets. It discusses two optimization problems: 1) maximizing the exposure of the least exposed path within a sensor budget, and 2) minimizing sensor installation costs while ensuring all paths have exposure above a threshold. It proposes using tabu search heuristics to provide near-optimal solutions. The research also addresses extending the models to consider wireless connectivity, heterogeneous sensors, and intrusion detection using a game theory approach. Experimental results show the proposed mobile replica detection scheme can rapidly detect replicas with no false positives or negatives.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Things to Consider When Choosing a Website Developer for your Website | FODUUFODUU
Choosing the right website developer is crucial for your business. This article covers essential factors to consider, including experience, portfolio, technical skills, communication, pricing, reputation & reviews, cost and budget considerations and post-launch support. Make an informed decision to ensure your website meets your business goals.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
CAKE: Sharing Slices of Confidential Data on BlockchainClaudio Di Ciccio
Presented at the CAiSE 2024 Forum, Intelligent Information Systems, June 6th, Limassol, Cyprus.
Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16