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- ItemPredictive Modeling of Antibiotic Susceptibility in E. Coli Strains Using the U-Net Network and One-Class Classification(New York, NY : IEEE, 2020) Ali, Nairveen; Kirchhoff, Johanna; Onoja, Patrick Igoche; Tannert, Astrid; Neugebauer, Ute; Popp, Jürgen; Bocklitz, ThomasThe antibiotic resistance of bacterial pathogens has become one of the most serious global health issues due to misusing and overusing of antibiotics. Recently, different technologies were developed to determine bacteria susceptibility towards antibiotics; however, each of these technologies has its advantages and limitations in clinical applications. In this contribution, we aim to assess and automate the detection of bacterial susceptibilities towards three antibiotics; i.e. ciprofloxacin, cefotaxime and piperacillin using a combination of image processing and machine learning algorithms. Therein, microscopic images were collected from different E. coli strains, then the convolutional neural network U-Net was implemented to segment the areas showing bacteria. Subsequently, the encoder part of the trained U-Net was utilized as a feature extractor, and the U-Net bottleneck features were utilized to predict the antibiotic susceptibility of E. coli strains using a one-class support vector machine (OCSVM). This one-class model was always trained on images of untreated controls of each bacterial strain while the image labels of treated bacteria were predicted as control or non-control images. If an image of treated bacteria is predicted as control, we assume that these bacteria resist this antibiotic. In contrast, the sensitive bacteria show different morphology of the control bacteria; therefore, images collected from these treated bacteria are expected to be classified as non-control. Our results showed 83% area under the receiver operating characteristic (ROC) curve when OCSVM models were built using the U-Net bottleneck features of control bacteria images only. Additionally, the mean sensitivities of these one-class models are 91.67% and 86.61% for cefotaxime and piperacillin; respectively. The mean sensitivity for the prediction of ciprofloxacin is only 59.72% as the bacteria morphology was not fully detected by the proposed method.
- Item168-195 GHz Power Amplifier with Output Power Larger Than 18 dBm in BiCMOS Technology(New York, NY : IEEE, 2020) Ali, Abdul; Yun, Jongwon; Giannini, Franco; Ng, Herman Jalli; Kissinger, Dietmar; Colantonio, PaoloThis paper presents a 4-way combined G-band power amplifier (PA) fabricated with a 130-nm SiGe BiCMOS process. First, a single-ended PA based on the cascode topology (CT) is designed at 185 GHz, which consists of three stages to get an overall gain and an output power higher than 27 dB and 13 dBm, respectively. Then, a 4-way combiner/splitter was designed using low-loss transmission lines at 130-210 GHz. Finally, the combiner was loaded with four single-ended PAs to complete the design of a 4-way combined PA. The chip of the fabricated PA occupies an area of 1.35mm2. The realized PA shows a saturated output power of 18.1 dBm with a peak gain of 25.9 dB and power-added efficiency (PAE) of 3.5% at 185 GHz. A maximum output power of 18.7 dBm with PAE of 4.4% is achieved at 170 GHz. The 3-dB and 6-dB bandwidth of the PA are 27 and 42 GHz, respectively. In addition, the PA delivers a saturated output power higher than 18 dBm in the frequency range 140-186 GHz. To the best of our knowledge, the power reported in this paper is the highest for G-band SiGe BiCMOS PAs. © 2013 IEEE.
- ItemRidge Gap Waveguide Based Liquid Crystal Phase Shifter(New York, NY : IEEE, 2020) Nickel, Matthias; Jiménez-Sáez, Alejandro; Agrawal, Prannoy; Gadallah, Ahmed; Malignaggi, Andrea; Schuster, Christian; Reese, Roland; Tesmer, Henning; Polat, Ersin; Schumacher, Peter; Jakoby, Rolf; Kissinger, Dietmar; Maune, HolgerIn this paper, the gap waveguide technology is examined for packaging liquid crystal (LC) in tunable microwave devices. For this purpose, a line based passive phase shifter is designed and implemented in a ridge gap waveguide (RGW) topology and filled with LC serving as functional material. The inherent direct current (DC) decoupling property of gap waveguides is used to utilize the waveguide surroundings as biasing electrodes for tuning the LC. The bed of nails structure of the RGW exhibits an E-field suppression of 76 dB in simulation, forming a completely shielded device. The phase shifter shows a maximum figure of merit (FoM) of 70 °/dB from 20 GHz to 30 GHz with a differential phase shift of 387° at 25 GHz. The insertion loss ranges from 3.5 dB to 5.5 dB depending on the applied biasing voltage of 0 V to 60 V. © 2013 IEEE.
- ItemIn-Vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools(New York, NY : IEEE, 2020) Zarrin, Pouya Soltani; Roeckendorf, Niels; Wenger, ChristianChronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease and a major cause of morbidity and mortality worldwide. Although a curative therapy has yet to be found, permanent monitoring of biomarkers that refiect the disease progression plays a pivotal role for the effective management of COPD. The accurate examination of respiratory tract fiuids like saliva is a promising approach for staging disease and predicting its upcoming exacerbations in a Point-of-Care (PoC) environment. However, the concurrent consideration of patients' demographic and medical parameters is necessary for achieving accurate outcomes. Therefore, Machine Learning (ML) tools can play an important role for analyzing patient data and providing comprehensive results for the recognition of COPD in a PoC setting. As a result, the objective of this research work was to implement ML tools on data acquired from characterizing saliva samples of COPD patients and healthy controls as well as their demographic information for PoC recognition of the disease. For this purpose, a permittivity biosensor was used to characterize dielectric properties of saliva samples and, subsequently, ML tools were applied on the acquired data for classification. The XGBoost gradient boosting algorithm provided a high classification accuracy and sensitivity of 91.25% and 100%, respectively, making it a promising model for COPD evaluation. Integration of this model on a neuromorphic chip, in the future, will enable the real-time assessment of COPD in PoC, with low cost, low energy consumption, and high patient privacy. In addition, constant monitoring of COPD in a near-patient setup will enable the better management of the disease exacerbations.