Semiconductor utilization for the capture of ionizing radiation

Tato bakalářská práce popisuje typy a vlastnosti nukleárních článků a analyzuje vzájemné působení ionizujícího záření a polovodičů. Polovodičové nukleární články jsou stroje, které přeměňují energii ionizujícího záření na elektřinu, detektory využívají polovodičové materiály k měření veličin. Součástí tohoto projektu je popis ionizujícího záření, polovodičů, nukleárních článků a detektorů. Následně je popsán betavoltaický nukleární článek a jeho součásti. Je realizováno měření polovodičového nukleárního článku a je proveden rozbor potvrzující pravdivost teoretických poznatků.This Bachelor’s thesis describes the types and characteristics of nuclear cells and analyses the interaction between ionising radiation and semiconductors. Semiconductor nuclear cells convert the radiation energy into electricity, while detectors utilise the semiconductors for measurement. The thesis introduces the reader to ionizing radiation, semiconductors, describes the nuclear cells and detectors. Then the betavoltaic nuclear cell and its components are described. The measurement and its evaluation is realised to prove the concept of semiconductor nuclear cell.

Big Data Analytics In The Context Of Mobile Network Performance Optimization

Crowdsourcing is a modern and growing technique of acquiring large amount of information. This project utilizes the data on mobile connectivity gathered by RTR NetzTest application to evaluate the performance indicators of the network. The software tool capable of assessing the network based on location, operator and other parameters will be created and utilized for benchmarking of the Austrian network operators. The software tool will be able to monitor the individual telecommunication nodes to estimate their performance in time

EWOk: towards efficient multidimensional compression of indoor positioning datasets

Indoor positioning performed directly at the end-user device ensures reliability in case the network connection fails but is limited by the size of the RSS radio map necessary to match the measured array to the device’s location. Reducing the size of the RSS database enables faster processing, and saves storage space and radio resources necessary for the database transfer, thus cutting implementation and operation costs, and increasing the quality of service. In this work, we propose EWOk, an Element-Wise cOmpression using k-means, which reduces the size of the individual radio measurements within the fingerprinting radio map while sustaining or boosting the dataset’s positioning capabilities. We show that the 7-bit representation of measurements is sufficient in positioning scenarios, and reducing the data size further using EWOk results in higher compression and faster data transfer and processing. To eliminate the inherent uncertainty of k-means we propose a data-dependent, non-random initiation scheme to ensure stability and limit variance. We further combine EWOk with principal component analysis to show its applicability in combination with other methods, and to demonstrate the efficiency of the resulting multidimensional compression. We evaluate EWOk on 25 RSS fingerprinting datasets and show that it positively impacts compression efficiency, and positioning performance.This work was supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreements No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints, http://www.a-wear.eu/) and No. 101023072 (ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories, http://orientate.dsi.uminho.pt) and Academy of Finland (grants #319994, #323244)

Forum gesundheitsziele.de - Gesundheitsziele auf Bundesebene

Der Beschluss der 72. Gesundheitsministerkonferenz (GMK) der Länder war ein wichtiges Startsignal für gesundheitsziele.de, das Forum zur Entwicklung und Umsetzung von Gesundheitszielen in Deutschland. In ihrem Beschluss von 1999 hat die GMK die Verantwortlichen in Bund, Ländern und Gemeinden aufgefordert, ihre Gesundheitspolitik künftig zielorientierter als bisher auszurichten und tragfähige Gesundheitsziele zu entwickeln. Im Dezember 2000 nahm das Forum gesundheitsziele.de als Modellprojekt des Bundesministeriums für Gesundheit (BMG) und der GVG seine Arbeit auf. Das Modellprojekt verfolgte zwei Ziele: (1) Die Erarbeitung exemplarischer Gesundheitsziele für Deutschland und (2) die Etablierung von Gesundheitszielen in Deutschland, komplementär zu bestehenden Instrumenten der Gesundheitspolitik. Mehr als 70 Organisationen und Institutionen, zentrale Akteure im Gesundheitswesen arbeiten dabei zusammen. Der vorliegende Beitrag stellt vier Zielbereiche vor, die entwickelt wurden, nämlich (1) Gesundheitsziele mit Krankheitsbezug, (2) Gesundheitsziele zu Gesundheitsförderung und Prävention, (3) Gesundheitsziele für Bevölkerungs- und Altersgruppen und (4) Gesundheitsziele mit Bürger- und Patientenorientierung. Ein Grund für die Auswahl der verschiedenen Zielbereiche war, dass gesundheitsziele.de zunächst als exemplarischer Zieleprozess startete, der auch zeigen sollte, ob und wie sich Gesundheitsziele für Deutschland entwickeln und umsetzen lassen und ob sich bestimmte Zielbereiche besonders eignen. In der 6-jährigen Modellphase ist es den beteiligten Akteuren gelungen, den nationalen Zieleprozess im föderal und sektoral gegliederten Gesundheitssystem aufzubauen. Das Forum gesundheitsziele. de konnte zeigen, dass exemplarische nationale Gesundheitsziele und Vorschläge zur Umsetzung im Konsens entwickelt werden können - auch in einem gegliederten Gesundheitssystem. Ferner hat sich eine funktionsfähige und stabile Gremien- und Arbeitsstruktur etabliert als Grundlage langfristiger Prozesse, an deren Weiterentwicklung die Akteure kontinuierlich arbeiten. (ICD2

Nuclear Cells

Nuclear cells are the devices converting the energy of ionizing radiation into electricity. This project describes the types and characteristics of nuclear cells. It introduces the reader to nuclear cells, their types and applications. Then the betavoltaic nuclear cell and its components are described. As the next step the available materials will be analysed and the measuring will be performed to check the functionality of the principle

Big Data Analytics In The Context Of Mobile Network Performance Optimization

Crowdsourcing is a modern and growing technique of acquiring large amount of information. This project utilizes the data on mobile connectivity gathered by RTR NetzTest application to evaluate the performance indicators of the network. The software tool capable of assessing the network based on location, operator and other parameters will be created and utilized for benchmarking of the Austrian network operators. The software tool will be able to monitor the individual telecommunication nodes to estimate their performance in time

Deep learning-based cell-level and beam-level mobility management system

The deployment with beamforming-capable base stations in 5G New Radio (NR) requires an efficient mobility management system to reliably operate with minimum effort and interruption. In this work, we propose two artificial neural network models to optimize the cell-level and beam-level mobility management. Both models consist of convolutional, as well as dense, layer blocks. Based on current and past received power measurements, as well as positioning information, they choose the optimum serving cell and serving beam, respectively. The obtained results show that the proposed cell-level mobility model is able to sustain a strong serving cell and reduce the number of handovers by up to 94.4% compared to the benchmark solution when the uncertainty (representing shadowing, interference, etc.) is introduced to the received signal strength measurements. The proposed beam-level mobility management model is able to proactively choose and sustain the strongest serving beam, even when high uncertainty is introduced to the measurements.publishedVersionPeer reviewe

Transfer Learning for Convolutional Indoor Positioning Systems

Fingerprinting is a widely used technique in indoor positioning, mainly due to its simplicity. Usually, this technique is used with the deterministic k- Nearest Neighbors (k-NN )algorithm. Utilizing a neural network model for fingerprinting positioning purposes can greatly improve the prediction speed compared to the k-NN approach, but requires a voluminous training dataset to achieve comparable performance. In many indoor positioning datasets, the number of samples is only at a level of hundreds, which results in poor performance of the neural network solution. In this work, we develop a novel algorithm based on a transfer learning approach, which combines samples from 15 different Wi-Fi RSS indoor positioning datasets, to train a single convolutional neural network model, which learns the common patterns in the combined data. The proposed model is then fine-tuned to optimally fit the individual databases. We show that the proposed solution reduces the positioning error by up to 25% compared to the benchmark model while reducing the number of outlier predictions.acceptedVersionPeer reviewe

Machine Learning Based NLOS Radio Positioning in Beamforming Networks

In this paper, we address the challenging problem of radio positioning in non-line-of-sight (NLoS) conditions. To this end, we utilize measurements in the form of time-of-flight and gNodeB angular information in the context of 5G New Radio (NR) networks. Such measurements are processed by artificial neural networks with different snapshot and sequence-processing architectures to track the positions of the terminals. For model training, we consider a crowdsensing data acquisition scheme to effortlessly gather the desired measurements with the synchronized location tags. Realistic ray-tracing based evaluations on the so-called Madrid map at 28 GHz millimeter-wave band are provided, to assess the achievable performance while also varying the amount of uncertainties within the data. The obtained results show that radio positioning is feasible with accuracy in the order of 1 meter, or even below, also in challenging NLOS scenarios if the data and measurement uncertainties are small. The results also show that the sequence processing approach offers superior performance under practical measurement uncertainties.acceptedVersionPeer reviewe