Special Issue “Multimodal Sensing Technologies for IoT and AI-Enabled Systems”

In today’s world, multimodal data and sensing technologies have emerged as crucial components within the Internet of Things (IoT) and artificial intelligence (AI) paradigms, influencing multiple fields, from healthcare to industry, media, education, robotics, transportation, and environmental monitoring, shaping broader multidisciplinary research and application projects. Due to time, location and contextual awareness, integrating IoT with AI has led to enhanced smart systems capable of performing complex tasks autonomously, thereby contributing to the development of intelligent societies. This Special Issue aims to bring together cutting-edge research and the latest advancements in multimodal sensing technologies, IoT, and AI-enabled systems, combining imaging applications, audiovisual reaction monitoring, and broader sensing technologies (e.g., temperature, humidity, air pollution, interaction recording, etc.), thus forming multimodal fusion decision systems. The proposed Special Issue is an excellent match to the objectives of Sensors, in addition to aligning itself perfectly with the journal’s multidisciplinary nature.

We encourage the submission of high-quality papers demonstrating these technologies’ potential to shape our future, drive innovation, and offer solutions to real-world problems. Authors are invited to submit original research works, viewpoint articles, case studies, reviews, theoretical, and critical perspectives.

Find the topics of interest and other information at the following link: https://www.mdpi.com/journal/sensors/special_issues/T1G1Y2K445 

Dr. Emmanouil Tsardoulias

Prof. Dr. Charalampos Dimoulas

Prof. Dr. Andreas L. Symeonidis

A new publication in the Software journal – A Framework for Rapid Robotic Application Development for Citizen Developers

It is common knowledge among computer scientists and software engineers that ”building robotics systems is hard”: it includes applied and specialized knowledge from various scientific fields, such as mechanical, electrical and computer engineering, computer science and physics, among others. To expedite the development of robots, a significant number of robotics-oriented middleware solutions and frameworks exist that provide high-level functionality for the implementation of the in-robot software stack, such as ready-to-use algorithms and sensor/actuator drivers. While the aforementioned focus is on the implementation of the core functionalities and control layer of robots, these specialized tools still require extensive training, while not providing the envisaged freedom in design choices. In this paper, we discuss most of the robotics software development methodologies and frameworks, analyze the way robotics applications are built and propose a new resource-oriented architecture towards the rapid development of robot-agnostic applications. The contribution of our work is a methodology and a model-based middleware that can be used to provide remote robot-agnostic interfaces. Such interfaces may support robotics application development from citizen developers by reducing hand-coding and technical knowledge requirements. This way, non-robotics experts will be able to integrate and use robotics in a wide range of application domains, such as healthcare, home assistance, home automation and cyber–physical systems in general.

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You can access the full article in the following link: https://www.mdpi.com/2674-113X/1/1/4/htm

Practical Machine Learning in R – a new book by ISSEL

A new book is published by the ISSEL group. The book is about quickly entering the world of creating machine learning models in R. The theory is kept to minimum and there are examples for each of the major algorithms for classification, clustering, features engineering and association rules. The book is a compilation of the leaflets the authors give to their students during the practice labs, in the courses of Pattern Recognition and Data Mining, in the Electrical and Computer Engineering Department of the Aristotle University of Thessaloniki.

You can find it at: https://leanpub.com/practical-machine-learning-r