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End date: 2021 × Start date: 2021 × DDC: 621.3 × Version: publishedVersion × WGL Institute: IHP ×

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    Dual-Band Transmitter and Receiver With Bowtie-Antenna in 0.13 μm SiGe BiCMOS for Gas Spectroscopy at 222 - 270 GHz
    (New York, NY : IEEE, 2021) Schmalz, Klaus; Rothbart, Nick; Gluck, Alexandra; Eissa, Mohamed Hussein; Mausolf, Thomas; Turkmen, Esref; Yilmaz, Selahattin Berk; Hubers, Heinz-Wilhelm
    This paper presents a transmitter (TX) and a receiver (RX) with bowtie-antenna and silicon lens for gas spectroscopy at 222-270 GHz, which are fabricated in IHP's 0.13 μm SiGe BiCMOS technology. The TX and RX use two integrated local oscillators for 222 - 256 GHz and 250 - 270 GHz, which are switched for dual-band operation. Due to its directivity of about 27 dBi, the single integrated bowtie-antenna with silicon lens enables an EIRP of about 25 dBm for the TX, and therefore a considerably higher EIRP for the 2-band TX compared to previously reported systems. The double sideband noise temperature of the RX is 20,000 K (18.5 dB noise figure) as measured by the Y-factor method. Absorption spectroscopy of gaseous methanol is used as a measure for the performance of the gas spectroscopy system with TX- and RX-modules.
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    A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions
    (New York, NY : IEEE, 2021) Assafo, Maryam; Langendorfer, Peter
    Anomaly detection modeled as a one-class classification is an essential task for tool condition monitoring (TCM) when only the normal data are available. To confront with the real-world settings, it is crucial to take the different operating conditions, e.g., rotation speed, into account when approaching TCM solutions. This work mainly addresses issues related to multi-operating-condition TCM models, namely the varying discriminability of sensory features with different operating conditions; the overlap between normal and anomalous data; and the complex structure of input data. A feature selection scheme is proposed in which the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is presented as a tool to aid the multi-objective selection of sensory features. In addition, four anomaly detection approaches based on Self-Organizing Map (SOM) are studied. To examine the stability of the four approaches, they are applied on different single-operating-condition models. Further, to examine their robustness when dealing with complex data structures, they are applied on multi-operating-condition models. The experimental results using the NASA Milling Data Set showed that all the studied anomaly detection approaches achieved a higher assessment accuracy with our feature selection scheme as compared to the Principal Component Analysis (PCA), Laplacian Score (LS), and extended LS in which we added a final step to the original LS method in order to eliminate redundant features.