ESTIMATING AND MITIGATING RADIATED EMISSIONS FROM PCB HEATSINKS

The advance of VLSI technology requires heatsinks to take away the heat generated by ICs and keep the temperature below an acceptable limit. These metal heatsinks radiate and cause EMI problems when electromagnetic noises are coupled to them. Thus it is important to study the possible radiation of a VLSI heatsink and the effective methods to reduce the radiated emissions. This dissertation includes three chapters on estimating and mitigating VLSI heatsink radiation. In the first chapter, a closed-form expression is derived for determining the maximum possible radiated emissions from a heatsink over a printed circuit board or chassis plane as a function of the maximum voltage between the heatsink and plane. The relevant parameters are the dimensions of the heatsink. The closed-form expression is validated by comparing its results to full-wave simulation results. This analysis was done for rectangular heatsinks, but the results can be applied to other heatsink shapes. The second chapter discusses a method to damp the unintended radiated emissions from PCB-chassis (or heatsink-PCB) resonances with lossy posts mounted near the four corners of the rectangular cavity formed. A simple closed-form expression was derived for determining an optimal series resistance for damping these cavity resonances over a wide range of frequencies. A similar analysis could be done to determine the optimal resistance values for other cavity shapes and mounting post locations. For the 4-post configuration, shorting one or more of the posts does not affect the optimum resistance value for the remaining posts. The third chapter discusses the reduction of a tall heatsink radiation by using shorting posts that bypass some of the noise current to the PCB ground. At high frequencies, the size of a tall heatsink may be comparable to a quarter-wavelength and the heatsink/board geometry can be an efficient antenna. The effectiveness of shorting posts was examined for reducing heatsink radiation. The use of lossy components for damping LC resonances is also discussed

An Intelligent Dissolved Oxygen Microsensor System with Electrochemically Actuated Fluidics

A new dissolved oxygen monitoring microsystem is proposed to achieve in situ intelligent self-calibration by using an electrochemically actuated fluidic system. The electrochemical actuation, based on water electrolysis, plays two critical roles in the proposed microsystem. First, the electrochemically generated gases serve as the calibrants for the in situ 2-point calibration/diagnosis procedure of the microsensor in a chip. Secondly, the electrochemical generation and collapse of gas bubbles provide the driving force of the bidirectional fluidic manipulation for sampling and dispensing of the sample solution. A microsystem including a dissolved oxygen microprobe, electrochemical actuators, and a fluidic structure are prepared by microfabrication technology and its performance is characterized

Human-robot co-carrying using visual and force sensing

In this paper, we propose a hybrid framework using visual and force sensing for human-robot co-carrying tasks. Visual sensing is utilized to obtain human motion and an observer is designed for estimating control input of human, which generates robot's desired motion towards human's intended motion. An adaptive impedance-based control strategy is proposed for trajectory tracking with neural networks (NNs) used to compensate for uncertainties in robot's dynamics. Motion synchronization is achieved and this approach yields a stable and efficient interaction behavior between human and robot, decreases human control effort and avoids interference to human during the interaction. The proposed framework is validated by a co-carrying task in simulations and experiments

Neural control for constrained human-robot interaction with human motion intention estimation and impedance learning

In this paper, an impedance control strategy is proposed for a rigid robot collaborating with human by considering impedance learning and human motion intention estimation. The least square method is used in human impedance identification, and the robot can adjust its impedance parameters according to human impedance model for guaranteeing compliant collaboration. Neural networks (NNs) are employed in human motion intention estimation, so that the robot follows the human actively and human partner costs less control effort. On the other hand, the full-state constraints are considered for operational safety in human-robot interactive processes. Neural control is presented in the control strategy to deal with the dynamic uncertainties and improve the system robustness. Simulation results are carried out to show the effectiveness of the proposed control design

Admittance-based controller design for physical human-robot interaction in the constrained task space

In this article, an admittance-based controller for physical human-robot interaction (pHRI) is presented to perform the coordinated operation in the constrained task space. An admittance model and a soft saturation function are employed to generate a differentiable reference trajectory to ensure that the end-effector motion of the manipulator complies with the human operation and avoids collision with surroundings. Then, an adaptive neural network (NN) controller involving integral barrier Lyapunov function (IBLF) is designed to deal with tracking issues. Meanwhile, the controller can guarantee the end-effector of the manipulator limited in the constrained task space. A learning method based on the radial basis function NN (RBFNN) is involved in controller design to compensate for the dynamic uncertainties and improve tracking performance. The IBLF method is provided to prevent violations of the constrained task space. We prove that all states of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB) by utilizing the Lyapunov stability principles. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experiment platform. Note to Practitioners-This work is motivated by the neglect of safety in existing controller design in physical human-robot interaction (pHRI), which exists in industry and services, such as assembly and medical care. It is considerably required in the controller design for rigorously handling constraints. Therefore, in this article, we propose a novel admittance-based human-robot interaction controller. The developed controller has the following functionalities: 1) ensuring reference trajectory remaining in the constrained task space: A differentiable reference trajectory is shaped by the desired admittance model and a soft saturation function; 2) solving uncertainties of robotic dynamics: A learning approach based on radial basis function neural network (RBFNN) is involved in controller design; and 3) ensuring the end-effector of the manipulator remaining in the constrained task space: different from other barrier Lyapunov function (BLF), integral BLF (IBLF) is proposed to constrain system output directly rather than tracking error, which may be more convenient for controller designers. The controller can be potentially applied in many areas. First, it can be used in the rehabilitation robot to avoid injuring the patient by limiting the motion. Second, it can ensure the end-effector of the industrial manipulator in a prescribed task region. In some industrial tasks, dangerous or damageable tools are mounted on the end-effector, and it will hurt humans and bring damage to the robot when the end-effector is out of the prescribed task region. Third, it may bring a new idea to the designed controller for avoiding collisions in pHRI when collisions occur in the prescribed trajectory of end-effector

Diff-Privacy: Diffusion-based Face Privacy Protection

Privacy protection has become a top priority as the proliferation of AI techniques has led to widespread collection and misuse of personal data. Anonymization and visual identity information hiding are two important facial privacy protection tasks that aim to remove identification characteristics from facial images at the human perception level. However, they have a significant difference in that the former aims to prevent the machine from recognizing correctly, while the latter needs to ensure the accuracy of machine recognition. Therefore, it is difficult to train a model to complete these two tasks simultaneously. In this paper, we unify the task of anonymization and visual identity information hiding and propose a novel face privacy protection method based on diffusion models, dubbed Diff-Privacy. Specifically, we train our proposed multi-scale image inversion module (MSI) to obtain a set of SDM format conditional embeddings of the original image. Based on the conditional embeddings, we design corresponding embedding scheduling strategies and construct different energy functions during the denoising process to achieve anonymization and visual identity information hiding. Extensive experiments have been conducted to validate the effectiveness of our proposed framework in protecting facial privacy.Comment: 17page