AI
Artificial intelligence refers to computer systems that are capable of performing tasks traditionally associated with human intelligence — such as making predictions, identifying objects, interpreting speech and generating natural language. AI systems learn how to do so by processing massive amounts of data and looking for patterns to model in their own decision-making. In many cases, humans will supervise an AI’s learning process, reinforcing good decisions and discouraging bad ones, but some AI systems are designed to learn without supervision.
Over time, AI systems improve on their performance of specific tasks, allowing them to adapt to new inputs and make decisions without being explicitly programmed to do so. In essence, artificial intelligence is about teaching machines to think and learn like humans, with the goal of automating work and solving problems more efficiently.
Artificial intelligence aims to provide machines with similar processing and analysis capabilities as humans, making AI a useful counterpart to people in everyday life. AI is able to interpret and sort data at scale, solve complicated problems and automate various tasks simultaneously, which can save time and fill in operational gaps missed by humans.
AI serves as the foundation for computer learning and is used in almost every industry — from healthcare and finance to manufacturing and education — helping to make data-driven decisions and carry out repetitive or computationally intensive tasks.
Many existing technologies use artificial intelligence to enhance capabilities. We see it in smartphones with AI assistants, e-commerce platforms with recommendation systems and vehicles with autonomous driving abilities. AI also helps protect people by piloting fraud detection systems online and robots for dangerous jobs, as well as leading research in healthcare and climate initiatives.
Over time, AI systems improve on their performance of specific tasks, allowing them to adapt to new inputs and make decisions without being explicitly programmed to do so. In essence, artificial intelligence is about teaching machines to think and learn like humans, with the goal of automating work and solving problems more efficiently.
Artificial intelligence aims to provide machines with similar processing and analysis capabilities as humans, making AI a useful counterpart to people in everyday life. AI is able to interpret and sort data at scale, solve complicated problems and automate various tasks simultaneously, which can save time and fill in operational gaps missed by humans.
AI serves as the foundation for computer learning and is used in almost every industry — from healthcare and finance to manufacturing and education — helping to make data-driven decisions and carry out repetitive or computationally intensive tasks.
Many existing technologies use artificial intelligence to enhance capabilities. We see it in smartphones with AI assistants, e-commerce platforms with recommendation systems and vehicles with autonomous driving abilities. AI also helps protect people by piloting fraud detection systems online and robots for dangerous jobs, as well as leading research in healthcare and climate initiatives.
Autonomous vehicles 3
Autonomous vehicles (AVs) use technology to partially or entirely replace the human driver in navigating a vehicle from an origin to a destination while avoiding road hazards and responding to traffic conditions.1 The Society of Automotive Engineers (SAE) has developed a widely-adopted classification system with six levels based on the level of human intervention. The U.S. National Highway Traffic Safety Administration (NHTSA) uses this classification system.
AV research started in the 1980s when universities began working on two types of AVs: one that required roadway infrastructure and one that did not.1 The U.S. Defense Advanced Research Projects Agency (DARPA) has held “grand challenges” testing the performance of AVs on a 150-mile off-road course.1 No vehicles successfully finished the 2004 Grand Challenge, but five completed the course in 2005.1 In 2007, six teams finished the third DARPA challenge, which consisted of a 60-mile course navigating an urban environment obeying normal traffic laws.1 In 2015, the University of Michigan built Mcity, the first testing facility built for autonomous vehicles. Research is conducted there into the safety, efficiency, accessibility, and commercial viability of AVs.4 Unmanned aircraft systems (UAS), or drones, are being deployed for commercial ventures such as last-mile package delivery, medical supply transportation, and inspection of critical infrastructure.
There are several limitations and barriers that could impede adoption of AVs, including: the need for sufficient consumer demand, assurance of data security, protection against cyberattacks, regulations compatible with driverless operation, resolved liability laws, societal attitude and behavior change regarding distrust and subsequent resistance to AV use, and the development of economically viable AV technologies.
Weather can adversely affect sensor performance on AVs, potentially impeding adoption. Ford recognized this barrier and started conducting AV testing in the snow in 2016 at the University of Michigan’s Mcity testing facility, utilizing technologies suited for poor weather conditions.
Although AVs alone are unlikely to have significant direct impacts on energy consumption and GHG emissions, when AVs are effectively paired with other technologies and new transportation models, significant indirect and synergistic effects on economics, the environment, and society are possible.20,21 One study found that when eco-driving, platooning, intersection connectivity and faster highway speeds are considered as direct effects of connected and automated vehicles, energy use and GHG emissions can be reduced by 9%.
AV research started in the 1980s when universities began working on two types of AVs: one that required roadway infrastructure and one that did not.1 The U.S. Defense Advanced Research Projects Agency (DARPA) has held “grand challenges” testing the performance of AVs on a 150-mile off-road course.1 No vehicles successfully finished the 2004 Grand Challenge, but five completed the course in 2005.1 In 2007, six teams finished the third DARPA challenge, which consisted of a 60-mile course navigating an urban environment obeying normal traffic laws.1 In 2015, the University of Michigan built Mcity, the first testing facility built for autonomous vehicles. Research is conducted there into the safety, efficiency, accessibility, and commercial viability of AVs.4 Unmanned aircraft systems (UAS), or drones, are being deployed for commercial ventures such as last-mile package delivery, medical supply transportation, and inspection of critical infrastructure.
There are several limitations and barriers that could impede adoption of AVs, including: the need for sufficient consumer demand, assurance of data security, protection against cyberattacks, regulations compatible with driverless operation, resolved liability laws, societal attitude and behavior change regarding distrust and subsequent resistance to AV use, and the development of economically viable AV technologies.
Weather can adversely affect sensor performance on AVs, potentially impeding adoption. Ford recognized this barrier and started conducting AV testing in the snow in 2016 at the University of Michigan’s Mcity testing facility, utilizing technologies suited for poor weather conditions.
Although AVs alone are unlikely to have significant direct impacts on energy consumption and GHG emissions, when AVs are effectively paired with other technologies and new transportation models, significant indirect and synergistic effects on economics, the environment, and society are possible.20,21 One study found that when eco-driving, platooning, intersection connectivity and faster highway speeds are considered as direct effects of connected and automated vehicles, energy use and GHG emissions can be reduced by 9%.
fog computing 2
Fog Computing is the term introduced by Cisco that refers to extending cloud computing to an edge of the enterprise’s network. Thus, it is also known as Edge Computing or Fogging. It facilitates the operation of computing, storage, and networking services between end devices and computing data centers.
The term fog computing was coined by Cisco in January 2014. This was because fog is referred to as clouds that are close to the ground in the same way fog computing was related to the nodes which are present near the nodes somewhere in between the host and the cloud. It was intended to bring the computational capabilities of the system close to the host machine. After this gained a little popularity, IBM, in 2015, coined a similar term called “Edge Computing”.
Finally, fog computing delivers cloud capabilities to the edge of networks, increasing efficiency, lowering latency, and improving data processing capabilities. It is perfect for real-time data analysis, low-latency applications such as IoT, and situations where data privacy and security are critical. While it provides scalability and lower bandwidth usage, it also has issues in managing data congestion and increasing power consumption. Fog computing is making progress in applications such as healthcare monitoring, industrial IoT, and real-time analytics across a variety of industries.
The term fog computing was coined by Cisco in January 2014. This was because fog is referred to as clouds that are close to the ground in the same way fog computing was related to the nodes which are present near the nodes somewhere in between the host and the cloud. It was intended to bring the computational capabilities of the system close to the host machine. After this gained a little popularity, IBM, in 2015, coined a similar term called “Edge Computing”.
Finally, fog computing delivers cloud capabilities to the edge of networks, increasing efficiency, lowering latency, and improving data processing capabilities. It is perfect for real-time data analysis, low-latency applications such as IoT, and situations where data privacy and security are critical. While it provides scalability and lower bandwidth usage, it also has issues in managing data congestion and increasing power consumption. Fog computing is making progress in applications such as healthcare monitoring, industrial IoT, and real-time analytics across a variety of industries.
2024
, . (2024). KAZE-SAR: SAR image registration using KAZE detector and modified SURF descriptor for tackling speckle noise. IEEE Transactions on Geoscience and Remote Sensing, 60(1), 1-12.
, , , , . (2024). PCTO-SIM: Multiple-point geostatistical modeling using parallel conditional texture optimization. Computers & Geosciences, 102(1), 116-138.