Special Issue "Information Fusion and Machine Learning for Sensors"
Deadline for manuscript submissions: 30 September 2020.
Interests: data fusion; machine learning; Internet of Things (IoT); ambient intelligent; AAL; privacy
Special Issues and Collections in MDPI journals
Interests: machine learning; visual processing; deep learning; IoT
In today’s digital world, information is the key factor in making decisions. Ubiquitous electronic sources, such as sensors and video, provide a steady stream of data, while text-based data from databases, the Internet, email, chat, VOIP (Voice over Internet Protocol), and social media are growing exponentially. The ability to make sense of data by fusing them into new knowledge would provide clear advantages in making decisions.
Fusion systems aim to integrate sensor data and information in databases, knowledge bases, contextual information, etc., in order to describe situations. In a sense, the goal of information fusion is to attain a global view of a scenario in order to make the best decision.
One of the main goals of future research in data fusion (DF) is the application of machine learning (ML) techniques on this fused information to extract knowledge. How to apply ML in these large data sets and what techniques could be applied depending on the data stored are the main points of this Special Issue.
The key aspect in modern DF applications is the appropriate integration of all types of information or knowledge—observational data, knowledge models (a priori or inductively learned), and contextual information. Each of these categories has a distinctive nature and potential support for the result of the fusion process:
Observational Data: Observational data are the fundamental data about a dynamic scenario, as collected from some observational capability (sensors of any type). These data are about the observable entities in the world that are of interest;
Contextual Information: Contextual information has become fundamental to developing models in complex scenarios. Context and the elements of what could be called contextual information could be defined as “the set of circumstances surrounding a task that are potentially of relevance to its completion”. Due to its task relevance, fusion or estimating/inferring the task implies the development of a best-possible estimate taking into account this lateral knowledge.
Learned Knowledge: DF systems combine multisource data to provide inferences, exploiting models of the expected behaviors of entities (physical models, such as cinematics, or logical models, such as expected behaviors, depending on the context). In those cases where a priori knowledge for DF process development cannot be formed, one possibility is to try and excise knowledge through online machine learning processes, operating on observational and other data. These are procedural and algorithmic methods for discovering the relationships among, and the behaviors of, the entities of interest.
This Special Issue invites contributions on the following topics (but is not limited to them):
Data fusion of distributed sensors
Context definition and management
Machine learning techniques
Reduction complexity in data sets
Integration of IA techniques
Reasoning systems in data fusion environments
Integration of data fusion
Data fusion on autonomous systems
Virtual and augmented reality
Human computer interaction
Visual pattern recognition
Environment modeling and reconstruction from images
Data fusion and ML in UAVs
Big data analytics platforms and tools for data fusion and analytics
Cloud computing technologies and their use for big data, data fusion, and data analytics
Prof. Dr. Jose Manuel Molina López
Dr. Miguel Angel Patricio
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- machine learning
- data fusion
- data analytics