Abstract
Background: Kalman filter and its variants had achieved great success in many applications in the field of technology. However, the kalman filter is under heavy computations burden. Under big data, it becomes pretty slow. On the other hand, the computer industry has now entered the multicore era with hardware computational capacity increased by adding more processors (cores) on one chip, the sequential processors will not be available in near future, so we should have to move to parallel computations.
Objective: This paper focuses on how to make Kalman Filter faster on multicore machines and implementing the parallel form of Kalman Filter equations to denoise sound wave as a case study.
Method: Splitting the all signal points into large segments of data and applying equations on each segment simultaneously. After that, we merge the filtered points again in one large signal.
Results: Our Parallel form of Kalman Filter can achieve nearly linear speed-up.
Conclusion: Through implementing the parallel form of Kalman Filter equations on the noisy sound wave as a case study and using various numbers of cores, it is found that a kalman filter algorithm can be efficiently implemented in parallel by splitting the all signal points into large segments of data and applying equations on each segment simultaneously.
Keywords: Optimal state estimation, kalman filter, sound model and kalman filter, parallel computations, sound wave.
Graphical Abstract
Call for Papers in Thematic Issues
?The New Era of Computational Intelligence: Big Data Applications in Health Care?
Analyzing healthcare data has remained a tedious task for data analysts in the current age of research due the nonlinear nature of data. With data sources multiplying and their complexity rising, the most common challenge for medical analysts today is obtaining relevant data for those that need it. The challenge ...read more
Advanced Applications of Artificial Intelligence in Manufacturing Technologies
As one of the most advanced fields of study and technology in existence today, artificial intelligence (AI) is finding more and more applications in production and daily life, especially in the industrial sector. This showcases the many applications of AI in mechanical production, including but not limited to: improving worker ...read more
Advancements in AI and Machine Learning for Enhanced Computer Vision Applications
This special thematic issue is meticulously crafted to provide an extensive exploration of the intricate interplay between Artificial Intelligence (AI) and Machine Learning (ML) within the expansive domain of Computer Vision. The scope of this issue is ambitiously designed to cover a diverse range of topics, incorporating cutting-edge research, advanced ...read more
Advancing Computer Vision and Multimedia Communication for Seamless Human-Machine Interaction
The rapid advancements in computer vision and multimedia communication technologies are revolutionizing the way humans interact with machines. These technologies have the potential to enable seamless and natural human-machine interaction, creating new possibilities for communication, collaboration, and entertainment. The findings will have a significant impact on the development of new ...read more
Related Books
IoT-enabled Sensor Networks: Architecture, Methodologies, Security, and Futuristic Applications
AIoT and Big Data Analytics for Smart Healthcare Applications
Research Trends in Artificial Intelligence: Internet of Things
Artificial Intelligence and Multimedia Data Engineering
Basics of Python Programming: A Quick Guide for Beginners
Blockchain Technology in Healthcare - Concepts, Methodologies, and Applications
Artificial Intelligence and Knowledge Processing: Methods and Applications
Handbook of Artificial Intelligence
Amazon Web Services: the Definitive Guide for Beginners and Advanced Users
The Role of AI in Enhancing IoT-Cloud Applications
10