scholarly journals Fusion of the SLAM with Wi-Fi-Based Positioning Methods for Mobile Robot-Based Learning Data Collection, Localization, and Tracking in Indoor Spaces

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5182
Author(s):  
Gunwoo Lee ◽  
Byeong-Cheol Moon ◽  
Sangjae Lee ◽  
Dongsoo Han
Keyword(s):  
Viterbi Algorithm ◽  
Real Life ◽  
Dead Reckoning ◽  
Location Tracking ◽  
Localization And Mapping ◽  
Signal Fluctuation ◽  
Quality Learning ◽  
Indoor Spaces ◽  
The Internet Of Things ◽  
Learning Data

The ability to estimate the current locations of mobile robots that move in a limited workspace and perform tasks is fundamental in robotic services. However, even if the robot is given a map of the workspace, it is not easy to quickly and accurately determine its own location by relying only on dead reckoning. In this paper, a new signal fluctuation matrix and a tracking algorithm that combines the extended Viterbi algorithm and odometer information are proposed to improve the accuracy of robot location tracking. In addition, to collect high-quality learning data, we introduce a fusion method called simultaneous localization and mapping and Wi-Fi fingerprinting techniques. The results of the experiments conducted in an office environment confirm that the proposed methods provide accurate and efficient tracking results. We hope that the proposed methods will also be applied to different fields, such as the Internet of Things, to support real-life activities.

scholarly journals LumNet

2021 ◽  
Vol 5 (2) ◽  
pp. 1-20
Author(s):  
Prince U.C. Songwa ◽  
Aaqib Saeed ◽  
Sachin Bhardwaj ◽  
Thijs W. Kruisselbrink ◽  
Tanir Ozcelebi
Keyword(s):  
Control Systems ◽  
Real Life ◽  
Visual Performance ◽  
Lighting Control ◽  
Domain Specific ◽  
Real Life Setting ◽  
Added Benefit ◽  
Measurement Devices ◽  
Indoor Spaces ◽  
Learned Features

High-quality lighting positively influences visual performance in humans. The experienced visual performance can be measured using desktop luminance and hence several lighting control systems have been developed for its quantification. However, the measurement devices that are used to monitor the desktop luminance in existing lighting control systems are obtrusive to the users. As an alternative, ceiling-based luminance projection sensors are being used recently as these are unobtrusive and can capture the direct task area of a user. The positioning of these devices on the ceiling requires to estimate the desktop luminance in the user's vertical visual field, solely using ceiling-based measurements, to better predict the experienced visual performance of the user. For this purpose, we present LUMNET, an approach for estimating desktop luminance with deep models through utilizing supervised and self-supervised learning. Our model learns visual representations from ceiling-based images, which are collected in indoor spaces within the physical vicinity of the user to predict average desktop luminance as experienced in a real-life setting. We also propose a self-supervised contrastive method for pre-training LUMNET with unlabeled data and we demonstrate that the learned features are transferable onto a small labeled dataset which minimizes the requirement of costly data annotations. Likewise, we perform experiments on domain-specific datasets and show that our approach significantly improves over the baseline results from prior methods in estimating luminance, particularly in the low-data regime. LUMNET is an important step towards learning-based technique for luminance estimation and can be used for adaptive lighting control directly on-device thanks to its minimal computational footprint with an added benefit of preserving user's privacy.

scholarly journals CNN-BASED PLACE RECOGNITION TECHNIQUE FOR LIDAR SLAM

2020 ◽  
Vol XLIV-M-2-2020 ◽  
pp. 117-122
Author(s):  
Y. Yang ◽  
S. Song ◽  
C. Toth
Keyword(s):  
Urban Areas ◽  
Recognition Task ◽  
Dead Reckoning ◽  
Point Clouds ◽  
Navigation Systems ◽  
Place Recognition ◽  
3D Point Clouds ◽  
Localization And Mapping ◽  
Sensing Platform ◽  
3D Descriptors

Abstract. Place recognition or loop closure is a technique to recognize landmarks and/or scenes visited by a mobile sensing platform previously in an area. The technique is a key function for robustly practicing Simultaneous Localization and Mapping (SLAM) in any environment, including the global positioning system (GPS) denied environment by enabling to perform the global optimization to compensate the drift of dead-reckoning navigation systems. Place recognition in 3D point clouds is a challenging task which is traditionally handled with the aid of other sensors, such as camera and GPS. Unfortunately, visual place recognition techniques may be impacted by changes in illumination and texture, and GPS may perform poorly in urban areas. To mitigate this problem, state-of-art Convolutional Neural Networks (CNNs)-based 3D descriptors may be directly applied to 3D point clouds. In this work, we investigated the performance of different classification strategies utilizing a cutting-edge CNN-based 3D global descriptor (PointNetVLAD) for place recognition task on the Oxford RobotCar dataset.

Public Administration Curriculum-Based Big Data Policy-Analytic Epistemology

2019 ◽  
pp. 467-488
Author(s):  
Emmanuel N. A. Tetteh
Keyword(s):  
Big Data ◽  
Action Learning ◽  
Real Life ◽  
Big Data Analytics ◽  
Social Challenges ◽  
Political Policy ◽  
New Information ◽  
Data Policy ◽  
The Internet Of Things

The equilibration that underscores the internet of things (IoT) and big data analytics (BDA) cannot be underestimated at the behest of real-life social challenges and significant policy data generated to redress the concerns of epistemic communities, such as political policy actors, stakeholders, and the citizenry. The cognitive balancing of new information gathered by BDA and assimilated across the IoT is at the crossroads of ascertaining how the growing increases of such BDA can be better managed to transition from the big data state of disequilibration to reach a more stable equilibrium of policy data usefulness. In the quest for explicating the equilibration of policy data usefulness, an account of the curriculum-based MPA policy analysis and analytics concentration program at Norwich University is described as a case example of big data policy-analytic epistemology. The case study offers a symbolic ideology of an IoT action-learning solution model as a recommendation for fostering the stable equilibration of policy data usefulness.

Assurance of Learning and Accreditations Through Assessment in Business Schools

2019 ◽  
pp. 53-67
Author(s):  
Mounir Kehal
Keyword(s):  
Faculty Development ◽  
Intellectual Capital ◽  
Business Schools ◽  
Real Life ◽  
Colleges And Universities ◽  
Academic Institutions ◽  
Web Based ◽  
Reporting Requirements ◽  
Assurance Of Learning ◽  
Learning Data

The use of web-based technologies in academic institutions for their diverse practices has been widespread in colleges and universities for several decades. These applications include surveying stakeholders, assessing classes, reporting on faculty development, and assurance of learning data to mention a few. Further advances have led to the integration of applications that not only enable the sharing of knowledge, but which also support the reporting requirements necessary to obtain and retain accreditation; likewise satisfy the supply of intellectual capital to the employment marketplace. In this chapter, the authors aim to portray relationship between assurance of learning and assessment at large with real life examples and approaches.

scholarly journals Concept, Possibilities and Pilot-Testing of a New Smartphone Application for the Social and Life Sciences to Study Human Behavior Including Validation Data from Personality Psychology

J ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 102-115 ◽  
Author(s):  
Christian Montag ◽  
Harald Baumeister ◽  
Christopher Kannen ◽  
Rayna Sariyska ◽  
Eva-Maria Meßner ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Human Behavior ◽  
Real Life ◽  
Smartphone Application ◽  
Self Report ◽  
The Internet ◽  
Validation Data ◽  
Personality Psychology ◽  
The Social ◽  
The Internet Of Things

With the advent of the World Wide Web, the smartphone and the Internet of Things, not only society but also the sciences are rapidly changing. In particular, the social sciences can profit from these digital developments, because now scientists have the power to study real-life human behavior via smartphones and other devices connected to the Internet of Things on a large-scale level. Although this sounds easy, scientists often face the problem that no practicable solution exists to participate in such a new scientific movement, due to a lack of an interdisciplinary network. If so, the development time of a new product, such as a smartphone application to get insights into human behavior takes an enormous amount of time and resources. Given this problem, the present work presents an easy way to use a smartphone application, which can be applied by social scientists to study a large range of scientific questions. The application provides measurements of variables via tracking smartphone–use patterns, such as call behavior, application use (e.g., social media), GPS and many others. In addition, the presented Android-based smartphone application, called Insights, can also be used to administer self-report questionnaires for conducting experience sampling and to search for co-variations between smartphone usage/smartphone data and self-report data. Of importance, the present work gives a detailed overview on how to conduct a study using an application such as Insights, starting from designing the study, installing the application to analyzing the data. In the present work, server requirements and privacy issues are also discussed. Furthermore, first validation data from personality psychology are presented. Such validation data are important in establishing trust in the applied technology to track behavior. In sum, the aim of the present work is (i) to provide interested scientists a short overview on how to conduct a study with smartphone app tracking technology, (ii) to present the features of the designed smartphone application and (iii) to demonstrate its validity with a proof of concept study, hence correlating smartphone usage with personality measures.

Guest Editorial of the Special Issue on Computational Intelligence and Its Applications for Robotics

2017 ◽  
Vol 14 (02) ◽  
pp. 1702001 ◽  
Author(s):  
Young-Jae Ryoo ◽  
Takahiro Yamanoi
Keyword(s):  
Computational Intelligence ◽  
Fuzzy Controller ◽  
Mathematical Reasoning ◽  
Real Life ◽  
Humanoid Robots ◽  
Robotic System ◽  
Human Beings ◽  
Special Issue ◽  
Life Problems ◽  
Localization And Mapping

The special issue topics focus on the computational intelligence and its application for robotics. Its areas reach out comprehensive ranges; context-awareness software, omnidirectional walking and fuzzy controller of dynamic walking for humanoid robots, pet robots for treatment of ASD children, fuzzy logic control, enhanced simultaneous localization and mapping, fuzzy line tracking for mobile robots, and so on. Computational intelligence (CI) is a method of performing like humans. Generally computational intelligence means the ability of a computer to learn a specific task from data or experimental results. Meanwhile robotic system has many limits to behave like human beings. The robotic system might be too complex for mathematical reasoning, it might contain some uncertainties during the process, or the process might simply be stochastic in real life. Real-life problems cannot be translated into binary code for computers to process it. Computational intelligence might solve such problems.

The Key Technology and Application of the Internet of Things

2014 ◽  
Vol 644-650 ◽  
pp. 2812-2815 ◽  
Author(s):  
Cui Mei Li ◽  
Rou Wang ◽  
Le Huang
Keyword(s):  
Internet Of Things ◽  
Real Life ◽  
The Internet ◽  
Third Wave ◽  
Information Industry ◽  
Key Technology ◽  
The Third ◽  
World Information ◽  
Strategic Advice ◽  
The Internet Of Things

The Internet of Things, which is another revolution in the information industry following the computer and the Internet, is referred to as the third wave of the world information industry. In this paper, the concepts, the architecture system and the principle, and the key technology in the Internet of Things and its application in real life are presented. Finally, a strategic advice on the development of the Internet of Things in China is put forward.

scholarly journals A Low-Cost Consistent Vehicle Localization Based on Interval Constraint Propagation

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Zhan Wang ◽  
Alain Lambert
Keyword(s):  
Constraint Satisfaction Problem ◽  
Low Cost ◽  
Dead Reckoning ◽  
Constraint Propagation ◽  
Vehicle Localization ◽  
Practical Applications ◽  
Localization And Mapping ◽  
Position And Orientation ◽  
Map Data ◽  
Interval Constraint

Probabilistic techniques (such as Extended Kalman Filter and Particle Filter) have long been used to solve robotic localization and mapping problem. Despite their good performance in practical applications, they could suffer inconsistency problems. This paper proposes an interval analysis based method to estimate the vehicle pose (position and orientation) in a consistent way, by fusing low-cost sensors and map data. We cast the localization problem into an Interval Constraint Satisfaction Problem (ICSP), solved via Interval Constraint Propagation (ICP) techniques. An interval map is built when a vehicle embedding expensive sensors navigates around the environment. Then vehicles with low-cost sensors (dead reckoning and monocular camera) can use this map for ego-localization. Experimental results show the soundness of the proposed method in achieving consistent localization.

PDR-Aided Algorithm with WiFi Fingerprint Matching for Indoor Localization

2014 ◽  
Vol 701-702 ◽  
pp. 989-993
Author(s):  
Wen Bin Yu ◽  
Peng Li ◽  
Zhi Chen ◽  
Chang Li
Keyword(s):  
Social Network ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Location Based Services ◽  
Fingerprint Matching ◽  
Network Applications ◽  
Wireless Signal ◽  
Signal Fluctuation ◽  
Pedestrian Dead Reckoning ◽  
Wifi Fingerprint

Recently, indoor localization is essential to enable location-based services for many mobile and social network applications. Due to fluctuation of the wireless signal, the accuracy of a simple WiFi fingerprint-based localization is not high. In this paper, we first exploit Pedestrian Dead Reckoning (PDR) technology to overcome the problem of the wireless signal fluctuation, then propose a PDR-aided algorithm with WiFi fingerprint matching for indoor localization, which using the PDR technology aided indoor localization. Experiments show that our algorithm has better accuracy than other indoor localization methods.

scholarly journals Guidelines for using empirical studies in software engineering education

2017 ◽  
Vol 3 ◽  
pp. e131 ◽  
Author(s):  
Fabian Fagerholm ◽  
Marco Kuhrmann ◽  
Jürgen Münch
Keyword(s):  
Software Engineering ◽  
Software Development ◽  
Engineering Education ◽  
Empirical Studies ◽  
Real Life ◽  
Distributed Software ◽  
Software Engineering Education ◽  
Technological Advances ◽  
Quality Learning ◽  
Industry Training

Software engineering education is under constant pressure to provide students with industry-relevant knowledge and skills. Educators must address issues beyond exercises and theories that can be directly rehearsed in small settings. Industry training has similar requirements of relevance as companies seek to keep their workforce up to date with technological advances. Real-life software development often deals with large, software-intensive systems and is influenced by the complex effects of teamwork and distributed software development, which are hard to demonstrate in an educational environment. A way to experience such effects and to increase the relevance of software engineering education is to apply empirical studies in teaching. In this paper, we show how different types of empirical studies can be used for educational purposes in software engineering. We give examples illustrating how to utilize empirical studies, discuss challenges, and derive an initial guideline that supports teachers to include empirical studies in software engineering courses. Furthermore, we give examples that show how empirical studies contribute to high-quality learning outcomes, to student motivation, and to the awareness of the advantages of applying software engineering principles. Having awareness, experience, and understanding of the actions required, students are more likely to apply such principles under real-life constraints in their working life.

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