A phantom for noninvasive fetal pulsoximetry with a high-precision positioning system for the optical sensor. - In: Biomedical engineering, ISSN 1862-278X, Bd. 67 (2022), S. 229
https://doi.org/10.1515/bmt-2022-2001
Spectral separation of short latency tibial nerve evoked potentials from cortical background activity - implications for signal-to-noise management. - In: Biomedical engineering, ISSN 1862-278X, Bd. 67 (2022), S. 139
https://doi.org/10.1515/bmt-2022-2001
Real-time system for physiologically controlled closed-loop neurostimulation. - In: Biomedical engineering, ISSN 1862-278X, Bd. 67 (2022), S. 78
https://doi.org/10.1515/bmt-2022-2001
Large single domain iron oxide nanoparticles as thermal markers for lateral flow assays. - In: Biomedical engineering, ISSN 1862-278X, Bd. 67 (2022), S. 63
https://doi.org/10.1515/bmt-2022-2001
Passage of magnetic nanoparticles through a differentiating blood-placenta barrier. - In: Biomedical engineering, ISSN 1862-278X, Bd. 67 (2022), S. 61
https://doi.org/10.1515/bmt-2022-2001
Adapting magnetic microspheres to several applications: hyperthermia, drug delivery and immunomagnetic separation. - In: Biomedical engineering, ISSN 1862-278X, Bd. 67 (2022), S. 60
https://doi.org/10.1515/bmt-2022-2001
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