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- ItemFast-Slow-Scale Interaction Induced Parallel Resonance and its Suppression in Voltage Source Converters(New York, NY : IEEE, 2021) Ma, Rui; Qiu, Qi; Kurths, Jürgen; Zhan, MengMulti-timescale interaction of power electronics devices, including voltage source converter (VSC), has made the stability and analysis of high penetrating renewable power systems very complicated. In this paper, the impedance model is used to analyze the multi-timescale characteristics and interaction of the VSC. Firstly, the multi-timescale impedance characteristics of VSC are investigated based on the Bode plots. It is found that the slow-timescale (within the DC-link voltage control scale) and fast-timescale (within the AC current control scale) models are separately consistent with the full-order model perfectly within their low- and high-frequency ranges. In addition, there exists a high impedance peak within the intermediate frequency range (roughly from 10 Hz to 100 Hz). Then, the impedance peak is theoretically estimated and explained by the slow-fast-scale impedance parallel resonance through transfer-function diagram analysis. Moreover, it is found that the impedance peak is more related to some outer controllers, such as the alternative voltage control and active power control. Specifically, larger proportional coefficients can greatly suppress the resonance peak. Finally, simulations and experiments are conducted to verify the generality of the multi-timescale characteristics and interaction of the VSC. Hence these findings are not only significant to provide a physical insight into the inner key structure of the impedance of VSC, but also expected to be helpful for controller and parameter design of the VSC.
- 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.
- ItemDual-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-WilhelmThis 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.
- ItemResorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media Posts(New York, NY : IEEE, 2022) Sakor, Ahmad; Singh, Kuldeep; Vidal, Maria-EstherSocial media networks have become a prime source for sharing news, opinions, and research accomplishments in various domains, and hundreds of millions of posts are announced daily. Given this wealth of information in social media, finding related announcements has become a relevant task, particularly in trending news (e.g., COVID-19 or lung cancer). To facilitate the search of connected posts, social networks enable users to annotate their posts, e.g., with hashtags in tweets. Albeit effective, an annotation-based search is limited because results will only include the posts that share the same annotations. This paper focuses on retrieving context-related posts based on a specific topic, and presents PINYON, a knowledge-driven framework, that retrieves associated posts effectively. PINYON implements a two-fold pipeline. First, it encodes, in a graph, a CORPUS of posts and an input post; posts are annotated with entities for existing knowledge graphs and connected based on the similarity of their entities. In a decoding phase, the encoded graph is used to discover communities of related posts. We cast this problem into the Vertex Coloring Problem, where communities of similar posts include the posts annotated with entities colored with the same colors. Built on results reported in the graph theory, PINYON implements the decoding phase guided by a heuristic-based method that determines relatedness among posts based on contextual knowledge, and efficiently groups the most similar posts in the same communities. PINYON is empirically evaluated on various datasets and compared with state-of-the-art implementations of the decoding phase. The quality of the generated communities is also analyzed based on multiple metrics. The observed outcomes indicate that PINYON accurately identifies semantically related posts in different contexts. Moreover, the reported results put in perspective the impact of known properties about the optimality of existing heuristics for vertex graph coloring and their implications on PINYON scalability.
- ItemA 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, PeterAnomaly 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.
- ItemIEEE Access Special Section Editorial: Recent Advances on Hybrid Complex Networks: Analysis and Control(New York, NY : IEEE, 2021) Lu, Jianquan; Ho, Daniel W. C.; Huang, Tingwen; Kurths, Jurgen; Trajkovic, LjiljanaComplex networks typically involve multiple disciplines due to network dynamics and their statistical nature. When modeling practical networks, both impulsive effects and logical dynamics have recently attracted increasing attention. Hence, it is of interest and importance to consider hybrid complex networks with impulsive effects and logical dynamics. Relevant research is prevalent in cells, ecology, social systems, and communication engineering. In hybrid complex networks, numerous nodes are coupled through networks and their properties usually lead to complex dynamic behaviors, including discrete and continuous dynamics with finite values of time and state space. Generally, continuous and discrete sections of the systems are described by differential and difference equations, respectively. Logical networks are used to model the systems where time and state space take finite values. Although interesting results have been reported regarding hybrid complex networks, the analysis methods and relevant results could be further improved with respect to conservative impulsive delay inequalities and reproducibility of corresponding stability or synchronization criteria. Therefore, it is necessary to devise effective approaches to improve the analysis method and results dealing with hybrid complex networks.
- 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.
- 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.