Learning Hyperparameters in Efficient Spatial Model by Robotic Sensors

2019 ◽  
Author(s):  
Jinho Jeong ◽  
Soo Jeon ◽  
Jongeun Choi
Keyword(s):  
Mobile Robots ◽  
Learning Strategies ◽  
Spatial Model ◽  
Markov Random Fields ◽  
Spatial Models ◽  
Future Research ◽  
Gaussian Markov Random Fields ◽  
New Class ◽  
Future Research Directions ◽  
Proximity Graph

Abstract Recently, a new class of spatial models over a continuum domain that builds on hidden Gaussian Markov Random Fields (GMRFs) was proposed for resource-constrained networked mobile robots dealing with non-stationary physical processes. The hidden GMRF was realized with respect to a proximity graph over a surveillance region. In this paper, we investigate learning strategies based on the maximum likelihood (ML) and the maximum a posteriori (MAP) estimators to find the locational generating points for the spatial model so that mobile robots can efficiently make the prediction. Some promising simulation results and future research directions are discussed.

Integrating Odor Sensing in Robotics

2018 ◽  
pp. 224-243
Author(s):  
Alauddin Yousif Al-Omary
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Chemical Sensors ◽  
Mobile Robotics ◽  
Future Research ◽  
Research Directions ◽  
The Future ◽  
The Status ◽  
Future Research Directions ◽  
Technical Challenges

In this chapter, the benefit of equipping the robot with odor sensors is investigated. The chapter addresses the types of tasks the mobile robots can accomplish with the help of olfactory sensing capabilities, the technical challenges in mobile robot olfaction, the status of mobile robot olfaction. The chapter also addresses simple and complex electronic olfaction sensors used in mobile robotics, the challenge of using chemical sensors, the use of many types of algorithms for robot olfaction, and the future research directions in the field of mobile robot olfaction.

Metacognition in Higher Education

2017 ◽  
pp. 1-21
Author(s):  
Elena Railean
Keyword(s):  
Higher Education ◽  
Learning Strategies ◽  
Theory And Practice ◽  
Future Research ◽  
Academic Learning ◽  
Third Way ◽  
Future Research Directions ◽  
Holistic Integration ◽  
Research Questions ◽  
Open Content

Globalization forces Higher Education to adopt metacognition towards successful learning strategies for teacher training, students' learning and content(s) development. Researchers and practitioners use metacognition to study principles of educational system(s), learning environment(s), open content(s), and all possible processes (e.g. metacognitive, psycho-motoric, didactic, assessment etc.). Existing efforts can be divided into three categories: 1) separate strategy and tactics; 2) a holistic integration of strategy in existing successful practices, and 3) frontier research in university pedagogy. This chapter explores the third way. Within the context of the interest in metacognition and successful learning strategies in higher education, the chapter critically explores the 21st century theory and practice of the academic learning and synthesis responses to the following research questions: What is the correlation between theory and practice in Higher Education? What models are required? The conclusion is provided and future research directions are emphasized.

scholarly journals “Texas-Sized” Molecular Boxes: From Chemistry to Applications

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2426
Author(s):  
Xiaodong Chi ◽  
Jinya Tian ◽  
Dan Luo ◽  
Han-Yuan Gong ◽  
Feihe Huang ◽  
...  
Keyword(s):  
Self Assembly ◽  
First Generation ◽  
Controlled Drug Release ◽  
Information Storage ◽  
Future Research ◽  
Research Directions ◽  
Design And Synthesis ◽  
New Class ◽  
Future Research Directions ◽  
Molecular Recognition Features

The design and synthesis of novel macrocyclic host molecules continues to attract attention because such species play important roles in supramolecular chemistry. However, the discovery of new classes of macrocycles presents a considerable challenge due to the need to embody by design effective molecular recognition features, as well as ideally the development of synthetic routes that permit further functionalization. In 2010, we reported a new class of macrocyclic hosts: a set of tetracationic imidazolium macrocycles, which we termed “Texas-sized” molecular boxes (TxSBs) in homage to Stoddart’s classic “blue box” (CBPQT4+). Compared with the rigid blue box, the first generation TxSB displayed considerably greater conformational flexibility and a relatively large central cavity, making it a good host for a variety of electron-rich guests. In this review, we provide a comprehensive summary of TxSB chemistry, detailing our recent progress in the area of anion-responsive supramolecular self-assembly and applications of the underlying chemistry to water purification, information storage, and controlled drug release. Our objective is to provide not only a review of the fundamental findings, but also to outline future research directions where TxSBs and their constructs may have a role to play.

Polarization-based Robot Orientation and Navigation

2015 ◽  
Vol 3 (1) ◽  
pp. 73-89
Author(s):  
Abd El Rahman Shabayek ◽  
Olivier Morel ◽  
David Fofi
Keyword(s):  
Machine Vision ◽  
Future Research ◽  
Animal Kingdom ◽  
Orientation Estimation ◽  
Research Directions ◽  
Specific Target ◽  
New Class ◽  
Future Research Directions ◽  
Smart Machines ◽  
Robotic Devices

From insects in your garden to creatures in the sea, inspiration can be drawn from nature to design a whole new class of smart robotic devices. These smart machines may move like living creatures. They can be launched toward a specific target for a pre-defined task. Bio-inspiration is developing to meet the needs of many challenges particularly in machine vision. Some species in the animal kingdom like cephalopods, crustaceans and insects are distinguished with their visual capabilities which are strongly improved by means of polarization. This work surveys the most recent research in the area of bio-inspired polarization based robot orientation and navigation. Firstly, the authors will briefly discuss the polarization based orientation and navigation behavior in the animal kingdom. Secondly, a comprehensive cover of its mapping into robotics navigation and orientation estimation will be given. Finally, the future research directions will be discussed.

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