Session details: Signal & image processing for ambient intelligence, pervasive computing

2015 ◽  
Author(s):  
Eric Larson
Keyword(s):  
Image Processing ◽  
Pervasive Computing ◽  
Ambient Intelligence ◽  
Signal Image

Pervasive Computing

2010 ◽  
pp. 234-252
Author(s):  
Varuna Godara
Keyword(s):  
Grid Computing ◽  
Science Fiction ◽  
Pervasive Computing ◽  
Ambient Intelligence ◽  
New Products ◽  
Value Systems ◽  
Smart Environments ◽  
Fiction Writers ◽  
Computing Grid ◽  
Networking Technologies

Pervasive computing is trying to make the dreams of the science fiction writers come true—where you think of some type of convenience and you have it. It appears that pervasive computing is allowing tiny computers, sensors, networking technologies, and human imagination to blend and mould into new products and services. This chapter introduces pervasive computing, grid computing, and ambient intelligence with explanation of how these technologies are merging to create sensor embedded smart environments. Along with description and scope of e-business and m-business, different views of p-business are illustrated. Finally, different smart environments including smart consumer-to-consumer, smart value systems, smart p-education, p-governance, and so forth, are explained.

Concoction of Ambient Intelligence and Big Data for Better Patient Ministration Services

2020 ◽  
pp. 171-183
Author(s):  
Arushi Jain ◽  
Vishal Bhatnagar
Keyword(s):  
Big Data ◽  
Ubiquitous Computing ◽  
Pervasive Computing ◽  
Ambient Intelligence ◽  
Hospital Staff ◽  
Automatic Analysis ◽  
Political Environment ◽  
Medical Centers ◽  
Health Parameters

The term Ambient Intelligence (AmI) encompasses other technologies such as ubiquitous communication, pervasive computing and ubiquitous computing. Hospitals can improve their working by monitoring the health of the patients and performing automatic analysis of various and health parameters inside the room. Security mechanisms can also be enhanced by only allowing authorized hospital staff and attendants in the ward. With the advent of Ambient Intelligence and the congenial political environment, the focus is now shifting to providing better healthcare at homes than at traditional medical centers. In this paper, we implemented an algorithm in which we consider a specific room of a hospital as the environment, with a patient monitored for health and security reasons. If anything is not allowed for the particular patient or there are some unwanted variations in the health parameters of the patient, the alarm was rang and the patient's assistants were notified.

Concoction of Ambient Intelligence and Big Data for Better Patient Ministration Services

2017 ◽  
Vol 8 (4) ◽  
pp. 19-30 ◽  
Author(s):  
Arushi Jain ◽  
Vishal Bhatnagar
Keyword(s):  
Big Data ◽  
Ubiquitous Computing ◽  
Pervasive Computing ◽  
Ambient Intelligence ◽  
Hospital Staff ◽  
Automatic Analysis ◽  
Political Environment ◽  
Medical Centers ◽  
Health Parameters

The term Ambient Intelligence (AmI) encompasses other technologies such as ubiquitous communication, pervasive computing and ubiquitous computing. Hospitals can improve their working by monitoring the health of the patients and performing automatic analysis of various and health parameters inside the room. Security mechanisms can also be enhanced by only allowing authorized hospital staff and attendants in the ward. With the advent of Ambient Intelligence and the congenial political environment, the focus is now shifting to providing better healthcare at homes than at traditional medical centers. In this paper, we implemented an algorithm in which we consider a specific room of a hospital as the environment, with a patient monitored for health and security reasons. If anything is not allowed for the particular patient or there are some unwanted variations in the health parameters of the patient, the alarm was rang and the patient's assistants were notified.

Explicit Haar–Schauder multiwavelet filters and algorithms. Part II: Relative entropy-based estimation for optimal modeling of biomedical signals

2019 ◽  
Vol 17 (05) ◽  
pp. 1950038 ◽  
Author(s):  
Makerem Zemni ◽  
Malika Jallouli ◽  
Anouar Ben Mabrouk ◽  
Mohamed Ali Mahjoub
Keyword(s):  
Image Processing ◽  
Relative Entropy ◽  
Simulation Study ◽  
Wavelet Theory ◽  
Biomedical Signal ◽  
New Approach ◽  
Biomedical Signals ◽  
Image Processing And Analysis ◽  
Signal Image ◽  
Optimal Modeling

Biomedical signal/image processing and analysis are always fascinating tasks in scientific researches, both theoretical and practical. One of the powerful tools in such topics is wavelet theory which has been proved to be challenging since its discovery. One of the best measures of the optimality of reconstruction of signals/images is the well-known Shannon’s entropy. In wavelet theory, this is very well known and researchers are familiar with it. In the present work, a step forward is proposed based on more general wavelet tools. New approach is proposed for the reconstruction of signals/images provided with multiwavelets Shannon-type entropy to evaluate the order/disorder of the reconstructed signals/images. Efficiency and accuracy of the approach is confirmed by a simulation study on several models such as ECG, EEG and DNA/Proteins’ signals.

A flexible mixed-signal image processing pipeline using 3D chip stacks

2016 ◽  
Vol 14 (3) ◽  
pp. 517-534 ◽  
Author(s):  
Lan Shi ◽  
Christopher Soell ◽  
Benjamin Pfundt ◽  
Andreas Baenisch ◽  
Marc Reichenbach ◽  
...  
Keyword(s):  
Image Processing ◽  
Mixed Signal ◽  
Processing Pipeline ◽  
Signal Image

Multichannel transforms for signal/image processing

1996 ◽  
Vol 5 (10) ◽  
pp. 1402-1413 ◽  
Author(s):  
I. Pitas ◽  
A. Karasaridis
Keyword(s):  
Image Processing ◽  
Signal Image
Sign in / Sign up

Export Citation Format

Share Document