Dynamic bolus phantoms for the evaluation of the spatio-temporal resolution of MPI scanners. - In: Biomedical engineering, ISSN 1862-278X, Bd. 67 (2022), S. 47
https://doi.org/10.1515/bmt-2022-2001
Multi-class extension of common spatial pattern for motor imagery brain computer interfaces. - In: Biomedical engineering, ISSN 1862-278X, Bd. 67 (2022), S. 32
https://doi.org/10.1515/bmt-2022-2001
A deep learning approach for direction of arrival estimation using automotive-grade ultrasonic sensors. - In: Journal of physics, ISSN 1742-6596, Bd. 2234 (2022), 012009, insges. 12 S.
In this paper, a deep learning approach is presented for direction of arrival estimation using automotive-grade ultrasonic sensors which are used for driving assistance systems such as automatic parking. A study and implementation of the state of the art deterministic direction of arrival estimation algorithms is used as a benchmark for the performance of the proposed approach. Analysis of the performance of the proposed algorithms against the existing algorithms is carried out over simulation data as well as data from a measurement campaign done using automotive-grade ultrasonic sensors. Both sets of results clearly show the superiority of the proposed approach under realistic conditions such as noise from the environment as well as eventual errors in measurements. It is demonstrated as well how the proposed approach can overcome some of the known limitations of the existing algorithms such as precision dilution of triangulation and aliasing.
https://doi.org/10.1088/1742-6596/2234/1/012009
SON function coordination in campus networks using machine learning. - In: 2022 IEEE Wireless Communications and Networking Conference (WCNC), (2022), S. 2130-2135
With the advent of 5G, network lifecycle operations such as service initial deployment, configuration changes, upgrades, optimization, and self-healing to name a few, should be fully automated processes to reduce capital expenditure (CAPEX) and operational expenditure (OPEX), and also to allow new players such as industry owners, to come into the scene as nontraditional network operators. To this end, self-organized networks functions (SF) have been proposed as a first attempt to provide self-adaptation capabilities to mobile networks on different fronts and to reduce the error-prone human intervention. Nevertheless, deploying multiple optimization functions in a network brings demanding challenges in terms of conflicting objectives in coordination. Automatically coordinating all those functions is paramount for industry owners in campus networks (CN) since they often do not have a deep expertise to carry out network optimization in an agile manner. Typically, each SF aim at individual goals modifying coupled network parameters, generally in dissonant directions with respect to other SF, jeopardizing the global stability of the system. This work presents an explicit formulation of the joint optimization problem when load balancing optimization (LBO) and coverage and capacity optimization (CCO) are instantiated in a CN.
https://doi.org/10.1109/WCNC51071.2022.9771586
Hybrid Take-Home Labs for the STEM education of the future. - In: Smart Education and e-Learning - Smart Pedagogy, (2022), S. 17-26
The acceptance of digitally supported teaching has increased strongly in recent years - and not only due to Corona. In the STEM subjects, online labs are increasingly being used to ensure that the requirements for availability, usability and granularity of the offerings are met. This ensures the connection of theoretically taught fundamentals and their application and deepening in the form of practical courses in the basic subjects. However, practical experimentation and the associated haptic learning is somewhat lost as a result. The Hybrid Take-Home Labs project aims to develop and test the basis for practical support of learning processes in STEM subjects, which allows students to conduct even complex virtual and remote-controlled laboratory experiments from home using their own resources, combined as needed for student-centered teaching to meet the requirements of future-oriented competence-based learning. It is one of nine projects supported by the Thuringian Ministry of Economics, Science and Digital Society and the German Stifterverband.
https://doi.org/10.1007/978-981-19-3112-3_2
Virtual environment smart house for hybrid laboratory GOLDi. - In: Mobility for Smart Cities and Regional Development - Challenges for Higher Education, (2022), S. 250-257
The necessity to integrate virtual laboratories into the study process is becoming more significant, especially in pandemic time. Virtual based e-learning is seen as a reliable and effective support of teaching and learning process in different fields of study. The hybrid laboratory GOLDi uses the possibilities of remote and virtual experiments actively. At the same time, the implementation of the new experiment for teaching students in the area of Smart House systems will expand the functionality of the laboratory. The implementation of virtual experiments in the field of home automation systems provides an interactive learning environment that allows to engage students in an active educational process and increase their motivation to study modern information technologies and processes. The paper presents the results of the development of educational virtual environment for learning basics of Smart House systems development and control.
https://doi.org/10.1007/978-3-030-93904-5_25
New ways for distributed remote web experiments. - In: Learning with Technologies and Technologies in Learning, (2022), S. 257-284
Remote Laboratories are widely used in the education of stem subjects. While the first generation of remote labs was based on individually developed local experiments with an integrated web interface, the next generation combined multiple experiments in a remote laboratory management system, making it possible to share whole experiments between institutions. At the same time, with cheap hardware available more and more experiments are conducted by the students at home. The sharing of experiments is already a step towards a more prosperous learning environment. The next step is to collaboratively develop and operate experiments by offering parts of experiments that are coupled over the internet to execute the whole experiment. This form of remotely coupled experiments allows for better collaboration between different institutions and also has benefits within a single institution. By remixing the components, the curriculum can be adapted to changing teaching scenarios, especially when considering that components from other institutions might be used. Also, extensive or expensive apparatuses can be hosted by the institution while more mobile parts are given to the students creating a hybrid take-home lab which is an improvement compared to an all virtual or all remote laboratory in terms of immersion.
https://doi.org/10.1007/978-3-031-04286-7_13
Utilizing the natural dynamics of elastic legged robots for periodic jumping motions. - In: 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids), (2022), S. 261-268
This work focuses on the energy efficient control of a planar bipedal robot by using elastic elements in the joints for short-term energy storage. The considered biped exhibits three degrees of freedom per leg and each joint is equipped with a series-elastic actuator (SEA). A controller is developed to enable point foot hopping based on the spring loaded inverted pendulum template. The control design was split into the high-level rigid-body and the elastic dynamics and is validated for hopping motions by numerical simulations. A high-level reference design is proposed, which enables that the elastic system can outperform the corresponding rigid counterpart with regard to energy efficiency in stance phase by more than 50 percent.
https://doi.org/10.1109/Humanoids53995.2022.10000146
Application of fractional-order partial pole-zero-cancelation to an inverted pendulum. - In: The 2022 10th International Conference on Systems and Control (ICSC'22), (2022), S. 54-59
Within this work we apply the concept of partial fractional-order pole-zero cancelation to the experimental setup of an inverted pendulum on a cart. We design a state feedback and extend it with a fractional-order compensator. The introduced algebraic convergence of the fractional-order terms is reduced by increased controller gains. Finally the adopted controller shows an improved input disturbance sensitivity and reduces the peaks in the control signal maintaining the designed closed-loop bandwidth. The controller is evaluated in simulation and experiment, showing its effectiveness compared to the simple state-feedback.
https://doi.org/10.1109/ICSC57768.2022.9993917
EVOLVE&hahog;INFOMAX: an unsupervised learning principle of invariances for nonlinear dynamic systems. - In: Industrial & engineering chemistry research, ISSN 1520-5045, Bd. 61 (2022), 49, S. 18004-18016
Invariant features characterize the essential nature of things behind the apparently rapid and noisy changes. Thus, learning invariances become one of the key problems of machine learning. Slow-feature analysis (SFA) is one such method. However, slowness in the original SFA, which is used as the learning criterion, is defined based on the linear temporal dependency assumption. To overcome this limitation, a new learning principle is introduced in this paper to define slowness that is suitable for nonlinear dynamic systems from an information perspective. This new principle is called EVOLVE&hahog;INFOMAX as it seeks to maximize the information preservation of system states during dynamic evolution, while aligning each feature to having the same uncertainty and constraining the features to be quasi-independent. The theoretical properties of this new principle are rigorously justified, which shows the characteristics of the model behavior, the optimality of the induced estimator, and the relationship with maximum likelihood estimation. The equivalence to the original definition of SFA is also analyzed, and the existence of a solution is shown. Two case studies show the potential capabilities and flexibility of the proposed method in both slow-feature extraction and downstream tasks, such as process monitoring.
https://doi.org/10.1021/acs.iecr.2c03330