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Several research studies have investigated the degradation of BaTiO3-based dielectric capacitor materials, focusing on the impact of composition, defect chemistry, and microstructural design to limit the electromigration of oxygen vacancies under electric fields at finite temperatures. Electromigration can be a dominant mechanism that controls failure rates in the individual multilayer ceramic capacitor (MLCC) components in testing the reliability of failures with highly accelerated lifetime testing (HALT) to determine the mean time to failure of MLCCs surface mounted onto printed circuit boards (PCBs). Conventional assumptions often consider these failures as independent, with no interaction between components on the PCB. However, this study employs a Physics of Failure (PoF) approach to closely examine transient degradation and its impact on MLCC reliability, emphasizing thermal crosstalk and its influence on dependent and independent failure rates. Finite element analysis thermal modeling and infrared thermography were used to assess the impact of circuit layout and component spacing on heat dissipation and thermal crosstalk under various electrical stress conditions. The study distinguishes between dependent and independent failures under a HALT, quantified through a β′ factor reflecting common cause failures due to thermal crosstalk. Through a series of experimental and statistical analyses, the β′ factor is evaluated with respect to temperature, voltage, and component spacing. These insights highlight the importance of understanding the nature of the data in reliability testing of MLCCs and optimizing the layout design of high-density circuits to mitigate dependent failures, improving overall reliability and informing better design and packaging strategies. 

We present a magnetic camera system developed to detect ferrous or ferromagnetic objects. The main motivation is detection and tracking of underwater pipelines. Many industries, such as oil and gas, must perform inspection and maintenance of pipelines and automation is desirable. An electromagnet generates a static magnetic field which is read by an array of Hall-effect sensors. The presence of ferromagnetic materials distorts this field, which can be detected by the sensors and creates a magnetic image. The grid configuration of the camera allows for quick computation of the center of mass and general orientation of detected pipes, facilitating tracking. This camera is carried by an ROV and tested in a pool environment. 

We present progress on the problem of reconfiguring a 2D arrangement of building material by a cooperative group of robots. These robots must avoid collisions, deadlocks, and are subjected to the constraint of maintaining connectivity of the structure. We develop two reconfiguration methods, one based on spatio-temporal planning, and one based on target swapping, to increase building efficiency. The first method can significantly reduce planning times compared to other multi-robot planners. The second method helps to reduce the amount of time robots spend waiting for paths to be cleared, and the overall distance traveled by the robots. 

Millimeter-scale magnetic rotating swimmers have multiple potential medical applications. They could, for example, navigate inside the bloodstream of a patient toward an occlusion and remove it. Magnetic rotating swimmers have internal magnets and propeller fins with a helical shape. A rotating magnetic field applies torque on the swimmer and makes it rotate. The shape of the swimmer, combined with the rotational movement, generates a propulsive force. Visual feedback is suitable for in-vitro closed-loop control. However, in-vivo procedures will require different feedback modalities due to the opacity of the human body. In this paper, we provide new methods and tools that enable the 3D control of a magnetic swimmer using a 2D ultrasonography device attached to a robotic arm to sense the swimmer’s position. We also provide an algorithm that computes the placement of the robotic arm and a controller that keeps the swimmer within the ultrasound imaging slice. The position measurement and closed-loop control were tested experimentally. 

Abstract To fulfill the demands of more bandwidth in 5G and 6G communication technology, new dielectric substrates that can be co‐fired into packages and devices that have low dielectric loss and improved thermal conductivity are desired. The motivation for this study is to design composites with low dielectric loss (tan δ) and high thermal conductivity (κ), while still limiting the electrical conductivity, for microwave applications involving high power and high frequency. This work describes the fabrication of high‐density electroceramic composites with a model dielectric material for cold sintering, namely sodium molybdate (Na₂Mo₂O₇), and fillers with higher thermal conductivity such as hexagonal boron nitride. The physical properties of the composites were characterized as a function of filler vol.%, temperature, and frequency. Understanding the variation in measured properties is achieved through analyzing the respective transport mechanisms. 

Magnetic induction localization is an inverse problem that determines the relative position and orientation (pose) between transmitting and receiving coils by analyzing the received signals. Related work has established methods to resolve the localization into two candidate poses. However, these methods require having signed signals, or periodic signals whose starting point is unambiguously determined with respect to an absolute reference (the transmitted signal). For distributed systems, the signal signs are difficult to resolve. This paper presents a method to extract partial information about the signs from unsigned signals. The method is tested in a hardware experiment. 

Background: Multivariate pattern analysis (MVPA or pattern decoding) has attracted considerable attention as a sensitive analytic tool for investigations using functional magnetic resonance imaging (fMRI) data. With the introduction of MVPA, however, has come a proliferation of methodological choices confronting the researcher, with few studies to date offering guidance from the vantage point of controlled datasets detached from specific experimental hypotheses. New method: We investigated the impact of four data processing steps on support vector machine (SVM) classification performance aimed at maximizing information capture in the presence of common noise sources. The four techniques included: trial averaging (classifying on separate trial estimates versus condition-based averages), within-run mean centering (centering the data or not), method of cost selection (using a fixed or tuned cost value), and motion-related denoising approach (comparing no denoising versus a variety of nuisance regressions capturing motion-related reference signals). The impact of these approaches was evaluated on real fMRI data from two control ROIs, as well as on simulated pattern data constructed with carefully controlled voxel- and trial-level noise components. Results: We find significant improvements in classification performance across both real and simulated datasets with run-wise trial averaging and mean centering. When averaging trials within conditions of each run, we note a simultaneous increase in the between-subject variability of SVM classification accuracies which we attribute to the reduced size of the test set used to assess the classifier's prediction error. Therefore, we propose a hybrid technique whereby randomly sampled subsets of trials are averaged per run and demonstrate that it helps mitigate the tradeoff between improving signal-to-noise ratio by averaging and losing exemplars in the test set. Comparison with existing methods: Though a handful of empirical studies have employed run-based trial averaging, mean centering, or their combination, such studies have done so without theoretical justification or rigorous testing using control ROIs. Conclusions: Therefore, we intend this study to serve as a practical guide for researchers wishing to optimize pattern decoding without risk of introducing spurious results. 

This paper investigates using a sampling-based approach, the RRT*, to reconfigure a 2D set of connected tiles in complex environments, where multiple obstacles might be present. Since the target application is automated building of discrete, cellular structures using mobile robots, there are constraints that determine what tiles can be picked up and where they can be dropped off during reconfiguration. We compare our approach to two algorithms as global and local planners, and show that we are able to find more efficient build sequences using a reasonable amount of samples, in environments with varying degrees of obstacle space.