Elektrotechnik und Informatik (ETI)
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Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in diamond nano or microcrystals is a promising technology for sensitive, integrated magnetic-field sensors. Currently, this technology is still cost-intensive and mainly found in research. Here we propose one of the smallest fully integrated quantum sensors to date based on nitrogen vacancy (NV) centers in diamond microcrystals. It is an extremely cost-effective device that integrates a pump light source, photodiode, microwave antenna, filtering and fluorescence detection. Thus, the sensor offers an all-electric interface without the need to adjust or connect optical components. A sensitivity of 28.32nT/Hz−−−√ and a theoretical shot noise limited sensitivity of 2.87 nT/Hz−−−√ is reached. Since only generally available parts were used, the sensor can be easily produced in a small series. The form factor of (6.9 × 3.9 × 15.9) mm3 combined with the integration level is the smallest fully integrated NV-based sensor proposed so far. With a power consumption of around 0.1W, this sensor becomes interesting for a wide range of stationary and handheld systems. This development paves the way for the wide usage of quantum magnetometers in non-laboratory environments and technical applications.
The demand for energy storage is increasing massively due to the electrification of transport and the expansion of renewable energies. Current battery technologies cannot satisfy this growing demand as they are difficult to recycle, as the necessary raw materials are mined under precarious conditions, and as the energy density is insufficient. Metal–air batteries offer a high energy density as there is only one active mass inside the cell and the cathodic reaction uses the ambient air. Various metals can be used, but zinc is very promising due to its disposability and non-toxic behavior, and as operation as a secondary cell is possible. Typical characteristics of zinc–air batteries are flat charge and discharge curves. On the one hand, this is an advantage for the subsequent power electronics, which can be optimized for smaller and constant voltage ranges. On the other hand, the state determination of the system becomes more complex, as the voltage level is not sufficient to determine the state of the battery. In this context, electrochemical impedance spectroscopy is a promising candidate as the resulting impedance spectra depend on the state of charge, working point, state of aging, and temperature. Previous approaches require a fixed operating state of the cell while impedance measurements are being performed. In this publication, electrochemical impedance spectroscopy is therefore combined with various machine learning techniques to also determine successfully the state of charge during charging of the cell at non-fixed charging currents.
Keywords:
electrochemical impedance spectroscopy; artificial neural networks; support vector regression; zinc-air battery; state estimation; state of charge
Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in nano- or micro-diamonds is a promising technology for precise magnetic-field sensors. Here, we propose a new, low-cost and stand-alone sensor setup that employs machine learning on an embedded device, so-called edge machine learning. We train an artificial neural network with data acquired from a continuous-wave ODMR setup and subsequently use this pre-trained network on the sensor device to deduce the magnitude of the magnetic field from recorded ODMR spectra. In our proposed sensor setup, a low-cost and low-power ESP32 microcontroller development board is employed to control data recording and perform inference of the network. In a proof-of-concept study, we show that the setup is capable of measuring magnetic fields with high precision and has the potential to enable robust and accessible sensor applications with a wide measuring range.
Technical and organizational steps are necessary to mitigate cyber threats and reduce risks. Human behavior is the last line of defense for many hospitals and is considered as equally important as technical security. Medical staff must be properly trained to perform such procedures. This paper presents the first qualitative, interdisciplinary research on how members of an intermediate care unit react to a cyberattack against their patient monitoring equipment. We conducted a simulation in a hospital training environment with 20 intensive care nurses. By the end of the experiment, 12 of the 20 participants realized the monitors’ incorrect behavior. We present a qualitative behavior analysis of high performing participants (HPP) and low performing participants (LPP). The HPP showed fewer signs of stress, were easier on their colleagues, and used analog systems more often than the LPP. With 40% of our participants not recognizing the attack, we see room for improvements through the use of proper tools and provision of adequate training to prepare staff for potential attacks in the future.
ALPACA: Application Layer Protocol Confusion - Analyzing and Mitigating Cracks in TLS Authentication
(2021)
Reviewing Cyber Security Research of Implantable Medical Rhythm Devices regarding Patients’ Risk
(2020)
Introduction: The recent publication of several critical cyber security issues in cardiac implantable devices and the resulting press coverage upsets affected users and their trust in medical device producers. Reviewing the published security vulnerabilities regarding networked medical devices, it raises the question, if the reporting media, the responsible security researchers, and the producers handle security vulnerabilities appropriately. Are the media reports of security vulnerabilities in medical devices meaningful in a way that patients can assess their respective risk for an attack via the security vulnerability? The collaboration between IT-security experts and clinicians aims at reviewing published security vulnerabilities of rhythm devices, and evaluate overall patients risks.
Methodology: We performed a literature review on security vulnerabilities in implantable medical devices with a focus on cardiac devices. We analyzed (Fig. 1) the (1) requirements for an attacker and the (2) technical feasibility and clustered them in three different scenarios: The first scenario requires that the attacker physically approaches a victim with a programming device. The second scenario requires proximity to the victim, e.g., within a few meters. The third and strongest attacker scenario is a remote attack that doesn’t require any physical proximity to the victim. We then compare the attacker scenarios and (3) the overall patients’ risks with the press coverage (overhyped, adequate, underhyped). (4) The resulting overall patients’ risk was rated by clinicians (security vulnerability of patients’ data, dangerous programming possible).
Results: Out of the three analyzed incidents, we found one to be underhyped, one to be overhyped, and one was appropriate compared to the medial coverage (Fig. 2). The most occurring technical issues were based on the absence of basic security primitives. The patient damage for all of the analyzed incidents was fatal in the worst-case scenario. Further, the patient damage and the overall patient risks are disjunct due to the missing capability of performing large scale attacks.
Conclusion: The resulting overall patients’ risks may not adequately reflect the patient damage in the considered cases. Often, the overall patient risk is not as severe as the necessary attacker capabilities are high and it would require strongly motivated attackers to perform the attack. Therefore, most of the reviewed cases are considered with a smaller overall patient risk than implied by press reports. Reviewing the ongoing IT-Security trends regarding implantable medical devices shows an increasing focus on researching in the field of medical device security. Therefore, further findings in the near future are to be expected. To deal with this fact in a responsible way, proper proactive knowledge management is mandatory. We recommend medical staff to critically reflect reports in mass media due to possible sensationalism. Therefore, we propose a joint approach in combining the technical expertise of cyber security experts with clinical aspects of medical experts, to ensure a solid understanding of a newly published vulnerability. The combination of both communities promises to result in better predictions for patients’ risks from security vulnerabilities in implanted cardiac devices.
Die wachsenden Anteile fluktuierender regenerativer Energien in der Energieversorgung (bis 2020 sollen 30 % und 2050 sogar So % des Stroms aus regenerativen Energiequellen stammen) sowie die Steigerung der Elektromobilität machen deutlich: Das Thema der Zwischenspeicherung elektrischer Energie ist von höchster gesellschaftlicher Relevanz und verlangt zwingend nach einer Lösung. Neue Technologien, die umweltfreundlich, sicher, leistungsfähig und bezahlbar zugleich sind, müssen deshalb entwickelt werden.
Magnetic field sensors based on quantum mechanic effects are often
susceptible to misalignments of the magnetic field or need advanced
procedures to compensate for these. Also, the record breaking sensitivities reported for superconducting quantum interference devices and alkali vapor magnetometers come along with large and complex experimental setups. The nitrogen vacancy center in diamond can be used to design a simple, small, and robust sensor without employing microwave radiation. By using compressed nanodiamond particles, it is possible to eliminate the need of an alignment of the magnetic field and still obtain the absolute magnetic flux density in a single measurement. In order to demonstrate the capabilities of this approach, a centimeter-sized modified automotive demo board is employed as a complete sensor with a sensitivity of 78 µT/Wurzel Hz.
Accurate self-localisation is a fundamental ability of any mobile robot. In Monte Carlo localisation, a probability distribution over a space of possible hypotheses accommodates the inherent uncertainty in the position estimate, whereas bounded-error localisation provides a region that is guaranteed to contain the robot. However, this guarantee is accompanied by a constant probability over the confined region and therefore the information yield may not be sufficient for certain practical applications. Four hybrid localisation algorithms are proposed, combining probabilistic filtering with non-linear bounded-error state estimation based on interval analysis. A forward-backward contractor and the Set Inverter via Interval Analysis are hybridised with a bootstrap filter and an unscented particle filter, respectively. The four algorithms are applied to global localisation of an underwater robot, using simulated distance measurements to distinguishable landmarks. As opposed to previous hybrid methods found in the literature, the bounded-error state estimate is not maintained throughout the whole estimation process. Instead, it is only computed once in the beginning, when solving the wake-up robot problem, and after kidnapping of the robot, which drastically reduces the computational cost when compared to the existing algorithms. It is shown that the novel algorithms can solve the wake-up robot problem as well as the kidnapped robot problem more accurately than the two conventional probabilistic filters.