Accurate modeling techniques for power delivery

“Power delivery is essential in electronic systems to provide reliable power from voltage sources to load devices. Driven by the ambitious user demands and technology evolutions, the power delivery design is posed serious challenges. In this work, we focus on modeling two types of power delivery paths: the power distribution network (PDN) and the wireless power transfer (WPT) system. For the modeling of PDN, a novel pattern-based analytical method is proposed for PCB-level PDN impedance calculations, which constructs an equivalent circuit with one-to-one correspondences to the PCB’s physical structure. A practical modeling methodology is also introduced to optimize the PDN design. In addition, a topology-based behavior model is developed for the current-mode voltage regulator module (VRM). This model includes all the critical components in the power stage, the voltage control loop, and the current control loop of a VRM device. A novel method is also proposed to unify the modeling of the continuous and discontinuous conduction modes for transient load responses. Cascading the proposed VRM model with the PCB-level PDN model enables a combined PDN analysis, which is much needed for modern PDN designs. For the modeling of WPT system, a system-level model is developed for both efficiency and power loss of all the blocks in WPT systems. A rectifier characterization method is also proposed to obtain the accurate load impedance. This model is capable of deriving the power capabilities for both the fundamental and higher order harmonics. Based on the system model, a practical design methodology is introduced to simultaneously optimize multiple system parameters, which greatly accelerates the design process”--Abstract, page iv

Automated channel emulator based on MEMS switch and improvement of signal transition

Channel Emulator, which is widely used in communication system development, is an instrument that emulates the real-world signal propagation environment between transmitter and. [sic] To overcome the disadvantages of traditional channel emulator, we propose a novel structure of the automated channel emulator in Section 1, which can be controlled by software and integrated into auto-testing system. MEMS switch, with good RF performance, is used to connect and isolate multiple channels. In Section 2, we divide the whole channel emulator system into Channel, Support, and Controller Board, and provide detailed design procedures with critical parameters of each board. The well-designed high frequency channel traces are validated by both 2D/3D simulation models and analytical calculations. The automated control logic and driven mechanism are also illustrated by sequence and block diagram. In Section 3, we perform post-simulation after the completion of PCB layout to check the RF performance of the real PCB board. Then manufacture and assemble the whole system of the automated channel emulator. In Section 4, we study the discontinuities in channel path in a systematically approach, including: channel trace turns, connector transient tapering, wire-bonding and solder parasitic effects. Analysis, simulations and measurements are performed to provide improvement solutions of signal transition. Section 5 concludes this thesis work and discuss about the future plan to expand our channel emulator design to differential solution --Abstract, page iii

Human Pose Estimation using Global and Local Normalization

In this paper, we address the problem of estimating the positions of human joints, i.e., articulated pose estimation. Recent state-of-the-art solutions model two key issues, joint detection and spatial configuration refinement, together using convolutional neural networks. Our work mainly focuses on spatial configuration refinement by reducing variations of human poses statistically, which is motivated by the observation that the scattered distribution of the relative locations of joints e.g., the left wrist is distributed nearly uniformly in a circular area around the left shoulder) makes the learning of convolutional spatial models hard. We present a two-stage normalization scheme, human body normalization and limb normalization, to make the distribution of the relative joint locations compact, resulting in easier learning of convolutional spatial models and more accurate pose estimation. In addition, our empirical results show that incorporating multi-scale supervision and multi-scale fusion into the joint detection network is beneficial. Experiment results demonstrate that our method consistently outperforms state-of-the-art methods on the benchmarks.Comment: ICCV201

Interaction Mechanism of Benzene and Phenanthrene in Condensed Organic Matter: Importance of Adsorption (Nanopore-Filling)

Although microporosity and surface area of natural organic matter (NOM) are crucial to mechanistic evaluation of the sorption process for nonpolar organic contaminants (NOCs), they have wrongly been estimated by the N2 adsorption technique. Nuclear magnetic resonance spectroscopy (13C NMR), and benzene, carbon dioxide, and nitrogen adsorption techniques were used to characterize structural and surface properties for different condensed NOM samples, which were related to the sorption behavior of phenanthrene (Phen). It was found that the revised Freundlich model by taking the chemical activity into account can well describe the isotherms for benzene and Phen. The benzene and Phen adsorption volumes for the coal samples are similar to or lower than the CO2-nanopore volumes. Adsorption volumes of both benzene and Phen are significantly related to the aliphatic carbon structure, and their correlation lines are nearly overlapped, suggesting that the nanopore filling for Phen and benzene on the investigated samples is the dominating mechanism, and also is not affected by water molecules. The entrapment of benzene and/or the pore deformation in the NOM nanopore are likely responsible for the observed hysteresis of benzene. The above results demonstrate that Phen and benzene adsorption on the condensed NOM is closely associated with the aliphatic carbon structure of the investigated samples

Exploring Effective Factors for Improving Visual In-Context Learning

The In-Context Learning (ICL) is to understand a new task via a few demonstrations (aka. prompt) and predict new inputs without tuning the models. While it has been widely studied in NLP, it is still a relatively new area of research in computer vision. To reveal the factors influencing the performance of visual in-context learning, this paper shows that prompt selection and prompt fusion are two major factors that have a direct impact on the inference performance of visual context learning. Prompt selection is the process of identifying the most appropriate prompt or example to help the model understand new tasks. This is important because providing the model with relevant prompts can help it learn more effectively and efficiently. Prompt fusion involves combining knowledge from different positions within the large-scale visual model. By doing this, the model can leverage the diverse knowledge stored in different parts of the model to improve its performance on new tasks. Based these findings, we propose a simple framework prompt-SelF for visual in-context learning. Specifically, we first use the pixel-level retrieval method to select a suitable prompt, and then use different prompt fusion methods to activate all the knowledge stored in the large-scale model, and finally ensemble the prediction results obtained from different prompt fusion methods to obtain the final prediction results. And we conduct extensive experiments on single-object segmentation and detection tasks to demonstrate the effectiveness of prompt-SelF. Remarkably, the prompt-SelF has outperformed OSLSM based meta-learning in 1-shot segmentation for the first time. This indicated the great potential of visual in-context learning. The source code and models will be available at \url{https://github.com/syp2ysy/prompt-SelF}

Interaction Mechanism of Benzene and Phenanthrene in Condensed Organic Matter: Importance of Adsorption (Nanopore-Filling)

Although microporosity and surface area of natural organic matter (NOM) are crucial to mechanistic evaluation of the sorption process for nonpolar organic contaminants (NOCs), they have wrongly been estimated by the N2 adsorption technique. Nuclear magnetic resonance spectroscopy (13C NMR), and benzene, carbon dioxide, and nitrogen adsorption techniques were used to characterize structural and surface properties for different condensed NOM samples, which were related to the sorption behavior of phenanthrene (Phen). It was found that the revised Freundlich model by taking the chemical activity into account can well describe the isotherms for benzene and Phen. The benzene and Phen adsorption volumes for the coal samples are similar to or lower than the CO2-nanopore volumes. Adsorption volumes of both benzene and Phen are significantly related to the aliphatic carbon structure, and their correlation lines are nearly overlapped, suggesting that the nanopore filling for Phen and benzene on the investigated samples is the dominating mechanism, and also is not affected by water molecules. The entrapment of benzene and/or the pore deformation in the NOM nanopore are likely responsible for the observed hysteresis of benzene. The above results demonstrate that Phen and benzene adsorption on the condensed NOM is closely associated with the aliphatic carbon structure of the investigated samples