FG Mikro- und Nanosysteme
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- Open Access (13)
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- Intracranial hypertension detection (2)
- Inverse problem (2)
- Multisensor fusion (2)
- Non-Gaussian distributions (2)
- Two-sample test (2)
- 5G networks (1)
- 5G, Sensor networks, Edge computing, Energy efficiency, KPIs, Design space, AI (1)
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- Acoustic electrostatic actuators (1)
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The scaling limitations of electrical interconnects are driving the demand for efficient optical chip-to-chip links. We report the first monolithic integration of air-clad optical through-silicon waveguides in silicon, fabricated via Bosch and cryogenic deep reactive-ion etching. Rib, single-bridge, and double-bridge designs with 50 μm cores and up to 150 μm propagation lengths have been evaluated. Cryogenic-etched rib waveguides achieve the highest median transmission (66%, −1.80 dB), compared to Bosch-etched ribs (62%, −2.08 dB). Across all geometries, 3 dB alignment windows range from 9.3 μm to 49.2 μm, with Bosch-etched double-bridge waveguides providing the broadest tolerance. We show that geometric fidelity outweighs sidewall roughness for transmission and alignment in these large-core, multimode optical through-silicon waveguides. This technology provides a scalable, complementary metal-oxide semiconductor-compatible pathway toward 3D photonic interconnects.
The research tackles the challenge of monitoring and managing a complex system by distinguishing consecutive states while observing multiple variables, especially in non-Gaussian environment. We introduce a novel methodology – MIDAST – aimed at fusion-based multivariate data segmentation and grounded in multivariate statistical tests, including the Kolmogorov–Smirnov test, a Maximum Mean Discrepancy-based test, and a kernel-based test. The performance of the method is evaluated through a comparative analysis against selected baseline techniques, specifically e-Divisive and Kernel Change Point Analysis methods, with a focus on segmentation accuracy. Additionally, the computational complexity of the proposed methodology is assessed.
Computer simulation experiments, conducted across two distinct data models: (a) multivariate sub-Gaussian and (b) multivariate Student’s t distributions, has been performed to evaluate the efficiency of designed methodology. Various scenarios have been examined, with different change factors considered, such as strength of the correlation between components, number of degrees of freedom (for the Student’s t distribution), and the stability index (for the sub-Gaussian distribution). Finally, to depict a practical meaning of the proposed approach we successfully demonstrated invasiveness minimization of intracranial hypertension events detection by identifying the temporal distribution structure in multivariate data. MIDAST supplemented with a windowing mechanism enables screening temporal changes in one or many statistical parameters describing multivariate distribution of measured time series, uncovering single or multiple data change points marking the boundaries within which the homogeneous laws governing the evolution of the physical system and/or process apply.
Despite significant advances in telecommunications technologies, as well as the use of artificial intelligence in 5G and the anticipated reliance of 6G on trustworthy artificial intelligence, there are still insufficiently automated, inefficient, and unreliable areas of wireless network operation and maintenance that rely on human expert decisions. In this article, we point out the gap in automated performance monitoring and prediction as a factor preventing the design of autonomous mobile network management systems. In addition, we systematize the basics of modeling KPIs measured in a mobile network and conditioned by their configuration and environmental states. KPI modeling recommendations summarized for different network levels were demonstrated for two applications using datasets collected from operator networks, that is, quantifying the performance change in a population of 350 5G cells with unique configuration settings updates, and predicting the performance profile for an unknown configuration in a model trained with historical data associated with different configurations applied to a population of network cells.
A novel concept of an acoustic flowmeter, based on single-mode waveguides, is proposed, implemented, and analysed in this work. Instead of transmitting a pulse diagonally across the duct's cross-section, this device operates with two ducts that operate simultaneously as pipes and as waveguides. Below the frequency threshold for single-mode propagation, acoustic waves are forced to traverse the waveguides with a plane front, precluding the possibility of beam drifting, inner reflections, and spreading losses. This enables the designer to flexibly increase the sound path and perform a highly sensitive measurement of the flow velocity and speed of sound, even if the excitation frequency is required to be kept below a relatively low value. A device based on this principle was constructed and tested for flow measurements in air. It consists of two waveguides of a circular cross-section (5 mmdiameter) coupled to electroacoustic transducers for the transmission of a wideband chirp (9.8–18.2 kHz). Usage of a wideband signal was possible due to the combined frequency response of a special kind of micromachined ultrasound transducer (MUT) and a commercial micro-electromechanical system (MEMS) microphone. The constructed flowmeter was capable of measuring flow velocities up until the transition to turbulent flow at 16 Lmin-1with a resolution of 0.3 Lmin-1, and it also detected changes of less than 0.2 ms-1in the speed of sound. This topology for flow measurement could prove advantageous for applications where gases of variable composition are conducted in ducts of diameters in the millimetre range.