De-Embedding for Coupled Three-Port Devices

In many applications, the device under test (DUT) is embedded into a test setup. Various de-embedding techniques have been proposed to expose the real electrical behaviors of a DUT, e.g., the traditional thru-reflect-line and short-open-load-thru algorithms, where the T-matrix and its inverse form are adopted in the mathematical process. In the fields of radiofrequency and electromagnetic compatibility, a DUT may have three coupled ports, and the symmetry in the associated S-matrix breaks down, because the numbers of entry and exist ports are not equal, which results in a non-square T-matrix based upon the definitions. Given that the inverse expression of a non-square matrix does not exist, the conventional de-embedding methods are not applicable for a coupled three-port network. In this paper, a de-embedding algorithm which is feasible for coupled three-port devices is proposed and verified through the measurement-based studies. The de-embedding technique may also be applied on devices with more than three ports

Effects of Climate Warming on Net Primary Productivity in China During 1961–2010

The response of ecosystems to different magnitudes of climate warming and corresponding precipitation changes during the last few decades may provide an important reference for predicting the magnitude and trajectory of net primary productivity (NPP) in the future. In this study, a process-based ecosystem model, Carbon Exchange between Vegetation, Soil and Atmosphere (CEVSA), was used to investigate the response of NPP to warming at both national and subregional scales during 1961–2010. The results suggest that a 1.3°C increase in temperature stimulated the positive changing trend in NPP at national scale during the past 50 years. Regardless of the magnitude of temperature increase, warming enhanced the increase in NPP; however, the positive trend of NPP decreased when warming exceeded 2°C. The largest increase in NPP was found in regions where temperature increased by 1–2°C, and this rate of increase also contributed the most to the total increase in NPP in China\u27s terrestrial ecosystems. Decreasing precipitation depressed the positive trend in NPP that was stimulated by warming. In northern China, warming depressed the increasing trend of NPP and warming that was accompanied by decreasing precipitation led to negative changing trends in NPP in large parts of northern China, especially when warming exceeded 2°C. However, warming stimulated the increase in NPP until warming was greater than 2°C, and decreased precipitation helped to increase the NPP in southern China

Insertion Loss Reduction using Rounded Corners to Mitigate Surface Roughness Effect in Pcb Transmission Lines

Signal integrity (SI) can be interpreted as a measure of the distortion of the incident pulse, which is attributed to various contributors, e.g., inter-symbol interference (ISI), crosstalk, jitter, etc. The channel insertion loss is generally the most critical concern in SI designs, since it determines the working bandwidth of a high-speed channel, and the bandlimited channels are known as the root cause of ISI. At the tens of Gigabit rates in use today, PCB transmission lines may have appreciable losses, which can be divided into frequency-dependent dielectric loss and conductor loss, and noticeable amount of losses can be generated at high-frequencies due to the skin effect and copper rough surfaces. In order to reduce the additional conductor loss due to the surface roughness, the employment of low-profile copper foils is a common practice in high-speed digital design. However, this existing method is not cost-effective. In this paper, insertion loss reduction using rounded corners are proposed and verified using both 2D and 3D full-wave simulations for the first time. Rounded corners can mitigate the increased insertion loss due to copper surface roughness in PCB transmission lines and can be applied in high-speed interconnect designs to increase eye margins. The impact of applying rounded corners on far-end crosstalk is also discussed

Analysis and Modeling Framework of Common Mode Noise in a Three-Phase Motor System

Electromagnetic Interference (EMI) Issue is One of the Major Constraints of Power Electronic Converters, Especially for Variable-Speed Systems. in This Work, the Common Mode Noise in a Three-Phase Motor System is Analyzed and Modeled. the Three-Phase Pulse-Width Modulation (PWM) Inverter Creates High Magnitudes of Dv/dt and Di/dt, Resulting in Common Mode Noise and Disturbance Power. in This Paper, the Common Mode Noise and Disturbance Power Modeling Method Are Proposed for Three-Phase Motor Systems. the Proposed Equivalent Circuit Model Comprises Detailed Models for Insulated Gate Bipolar Transistors (IGBTs), EMI Filters, a Three-Phase Motor and a Printed Circuit Board (PCB). the Proposed Model of a Three-Phase System Was Verified by Measurement with and Without Additional Y-Capacitors. the Measured and Modeled Common-Mode Noise Shows a Correlation in Broadband Common-Mode Noise Reduction

Undesired-Resonance Analysis and Modeling of Differential Signals Due to Narrow Ground Lines Without Stitching Vias

Undesired Resonances on High-Speed Differential Signals Are Studied in This Paper, which is Caused by the Adjacent Narrow Ground Line Without Stitching Vias. Due to Space Limitations in the High-Speed Channel Layouts of Certain Package Applications, the Ground (GND) Line is Often Narrow and Has Insufficient Stitching Vias, Potentially Causing Undesired Resonance in High-Speed Differential Signals. in This Study, These Undesired Resonances Were Investigated using 3D Simulations, revealing that They Can Be Modeled as Parallel-Coupled Half-Wavelength Resonance. the Resonance Frequency of the Parallel-Coupled Half-Wavelength Resonance Structure Can Be Predicted Well using the Formula based on the GND Line Length. Moreover, Three Potential Solutions to Undesired Resonance Are Proposed, Providing a Practical Guide for GND Line Routing in Specific Applications

Machine Learning Empowered Thin Film Acoustic Wave Sensing

Thin film based surface acoustic wave (SAW) technology has been extensively explored for physical, chemical and biological sensors. However, these sensors often show inferior performance for a specific sensing in complex environments, as they are affected by multiple influencing parameters and their coupling interferences. To solve these critical issues, we propose a methodology to extract critical information from the scattering parameter and combine machine learning method to achieve multi-parameter decoupling. We used AlScN film-based SAW device as an example, in which highly c-axis orientated and low stress AlScN film was deposited on silicon substrate. The AlScN/Si SAW device showed a Bode quality factor value of 228 and an electro-mechanical coupling coefficient of ~2.3. Two sensing parameters (i.e., ultraviolet or UV and temperature) were chosen for demonstration and the proposed machine-learning method was used to distinguish their influences. Highly precision UV sensing and temperature sensing were independently achieved without their mutual interferences. This work provides an effective solution for decoupling of multi-parameter influences and achieving anti-interference effects in thin film based SAW sensing

Genome-wide association and genomic prediction for resistance to southern corn rust in DH and testcross populations

Southern corn rust (SCR), caused by Puccinia polysora Underw, is a destructive disease that can severely reduce grain yield in maize (Zea mays L.). Owing to P. polysora being multi-racial, it is very important to explore more resistance genes and develop more efficient selection approaches in maize breeding programs. Here, four Doubled Haploid (DH) populations with 384 accessions originated from selected parents and their 903 testcross hybrids were used to perform genome-wide association (GWAS). Three GWAS processes included the additive model in the DH panel, additive and dominant models in the hybrid panel. As a result, five loci were detected on chromosomes 1, 7, 8, 8, and 10, with P-values ranging from 4.83×10-7 to 2.46×10-41. In all association analyses, a highly significant locus on chromosome 10 was detected, which was tight chained with the known SCR resistance gene RPPC and RPPK. Genomic prediction (GP), has been proven to be effective in plant breeding. In our study, several models were performed to explore predictive ability in hybrid populations for SCR resistance, including extended GBLUP with different genetic matrices, maker based prediction models, and mixed models with QTL as fixed factors. For GBLUP models, the prediction accuracies ranged from 0.56-0.60. Compared with traditional prediction only with additive effect, prediction ability was significantly improved by adding additive-by-additive effect (P-value< 0.05). For maker based models, the accuracy of BayesA and BayesB was 0.65, 8% higher than other models (i.e., RRBLUP, BRR, BL, BayesC). Finally, by adding QTL into the mixed linear prediction model, the accuracy can be further improved to 0.67, especially for the G_A model, the prediction performance can be increased by 11.67%. The prediction accuracy of the BayesB model can be further improved significantly by adding QTL information (P-value< 0.05). This study will provide important valuable information for understanding the genetic architecture and the application of GP for SCR in maize breeding

Machine learning-based anomaly detection of groundwater microdynamics: case study of Chengdu, China

Abstract Detection of subsurface hydrodynamic anomalies plays a significant role in groundwater resource management and environmental monitoring. In this paper, based on data from the groundwater level, atmospheric pressure, and precipitation in the Chengdu area of China, a method for detecting outliers considering the factors affecting groundwater levels is proposed. By analyzing the factors affecting groundwater levels in the monitoring site and eliminating them, simplified groundwater data is obtained. Applying sl-Pauta (self-learning-based Pauta), iForest (Isolated Forest), OCSVM (One-Class SVM), and KNN to synthetic data with known outliers, testing and evaluating the effectiveness of 4 technologies. Finally, the four methods are applied to the detection of outliers in simplified groundwater levels. The results show that in the detection of outliers in synthesized data, the OCSVM method has the best detection performance, with a precision rate of 88.89%, a recall rate of 91.43%, an F1 score of 90.14%, and an AUC value of 95.66%. In the detection of outliers in simplified groundwater levels, a qualitative analysis of the displacement data within the field of view indicates that the outlier detection performance of iForest and OCSVM is better than that of KNN. The proposed method for considering the factors affecting groundwater levels can improve the efficiency and accuracy of detecting outliers in groundwater level data

Investigation of Segmentation Method for Enhancing High Frequency Simulation Accuracy of Q3D Extractor

ANSYS Q3D Extractor is a fast quasi-static 3D solver for extracting lumped RLGC parameters and SPICE models. Although Q3D Extractor is faster than full wave simulation, it results in inaccuracies at high frequency simulation, especially for PCB sructures. This paper proposes a method of improving the accuracy of high frequency simulations by using segmentation. For the convenience of study, a transmission line on PCB is selected as the device under test without sacrificing generality, and HFSS simulation results and measurement data are used as reference. Extensive simulations are performed to investigate the effectiveness of segmentation method. It is found that by dividing the transmission line into a set of electrically small segments, the high frequency simulation accuracy can be effectively enhanced. Some other relevant tips for using Q3D are also presented