Skip to main content

Ubiquitous Computing Vision in IoT

Ubiquitous Computing vision begins with the technology that captures and stores images and then transforms images into information that can be processed further. It comprises of numerous technologies working together. Computing vision engineering is an interdisciplinary subject requiring cross-functional and systems expertise in a number of these technologies.

A version of the Deep Neural Network (DNN) also called the Convolutional Neural Network (CNN), which demonstrates a huge leap in accuracy. That development drove renewed interest and excitement into the field of computer vision engineering. In applications requiring image classification and facial recognition, deep learning algorithms even exceeded their human counterparts. Deep learning has created an era of entering cognitive technology where deep learning and computer vision come together to address high level and complex problems of the human brain.
Many technology developments and advancements are also happening rapidly at many levels beyond conventional camera sensors. 
Some recent advancements are:
·   Lasers and infrared sensors  both combined to sense the depth and distance, which are one of the critical enablers of 3D mapping applications and self-driving cars
·    Nonintrusive sensors that track vital signs of medical patients without physical contact
·   Ultra low cost and low power vision sensors that can be deployed anywhere for a longer period of time
·   High frequency cameras that can capture subtle movements not perceivable by human   eyes to help athletes analyze their gaits

Some other interesting applications:
·       Agricultural drones that monitors the health of crops
·       Next-generation home security cameras
·       Transportation infrastructure management UAV drone inspections

For furthermore updates on the availing research proficiency, do visit: https://neuralnetworks.conferenceseries.com/abstract-submission.php

For details about the webpage, go through the link provided; PS: https://neuralnetworks.conferenceseries.com/  


Comments

Post a Comment

Popular posts from this blog

Neural Networks and Deep Learning

Neural Networks and Deep Learning have grown widely over the last few years. By using neural network architecture, softwares of AI can go through and check millions of images to find the right tone to fit any image. This method could be used to colorize still frames of white and black movies, surveillance footage or any number of images. Because neural networks can derive data from any number of resources with access to millions of sounds and videos, it can make predictive judgments. Neural network architecture can now synthesize audio to fill in the blank spots of a silent video. Neural network architecture can perform translations of text without preprocessing the sequence so that the algorithm can learn word relationships. The network then processes these relationships through its image mapping technology to create a contextual solution to a translation issue. By getting access to a wide variety of images and learning the context of each one, neural network architecture can...
Post a Comment
Read more

Market Analysis: Cognitive Computing, recent industry developments

In the ever dynamical world of data technology, business organizations are left with a massive amount of data with them. This data includes very crucial info for business use, however business organizations are solely ready to utilize 200th of whole data accessible with them with the use of traditional data analytics technology. To method and interpret the reaming 80th of the data that's within the form of videos, images, and human voice (also referred to as dark data), there's a requirement of cognitive computing systems. Cognitive computing  systems are a typical combination of hardware and software that constitute natural language processing (NLP) and machine language, and have the capability to collect, process, and interpret the dark data available with business organizations. Cognitive computing systems process and interpret the data in a probabilistic manner, unlike conventional big data analytic tools. However, to cope with the continuously evolving technolog...
2 comments
Read more

Artificial Neural Networks can Detect Human Ambiguity

Artificial Neural Networks (ANNs) computational model based on the structure and functions of biological neural networks, it became a strong tool for researching artificial intelligence and information analysis and are utilised in robotics, social sciences and neuroscience for classification, prediction and pattern recognition. A global scientific team which incorporates scientists from Russia has created an artificial neural network that detects human ambiguity. They assist to classify neural signals, observe pathological activity of the brain (for example, with epilepsy), and neurodegenerative diseases. ANNs have three layers that are interconnected. The primary layer consists of input neurons. Those neurons send information on to the second layer that successively sends the output neurons to the third layer. Training an artificial neural network involves selecting from allowed models for which there are several associated algorithms. In this analysis, the scientist...
Post a Comment
Read more