Obtaining high-density tactile field information is a critical aspect of research in the field of robotic haptics, as it plays a decisive role in determining the precision of robot manipulations. Vision-based tactile sensors have unique high-resolution features, which make them promising for related research. However, previous studies have mainly focused on reconstructing the shape of rigid objects or predicting the three-dimensional force of rigid objects, neglecting the analysis of flexible objects. Moreover, due to the resolution limitations of existing commercial sensors, the performance evaluation of previous force prediction models relied solely on the total force. To overcome these limitations and in order to explore the tactile field information of objects with more attributes, this paper presents a detailed high-density tactile field data acquisition method based on a mechanical simulation environment. Additionally, we constructed a network to learn the mapping relationship between tactile images and six-dimensional tactile field information. Our results demonstrate that the proposed method can predict the three-dimensional force and displacement information of the object. Notably, the prediction error is within the tolerance range for fine manipulation by robots.

A compact 300 kW air-cooled SiC power electronics building block (PEBB) that integrates discrete SiC power switches, advanced gate drivers, low inductance loop design, DC link capacitors, and forced air cooling heatsink is developed. This paper discusses the design consideration of the PEBB focusing on device selection and efficiency. A high-speed gate driver is designed to drive paralleled SiC devices. Direct Bonded Copper (DBC) thermal management is implemented and the junction to ambient thermal resistance for each SiC switch is experimentally extracted to be less than 1oC/W with forced air. PEBB power density of 43.9kW/L is achieved. The PEBB is tested in inverter mode and demonstrated an efficiency of 99.4% at 40 kW.

Flush based cache attacks have become a practical threat to data privacy and information security due to their advantages such as high accuracy and resolution. However, their accuracy and resolution still has room for improvement. In addition, although most of the attacks have been demonstrated on x86 processors, few of them have been executed on ARM devices. We propose a high accuracy, high resolution flush based cache attack, Flush+Time. This technique solves two important challenges for cache attacks on ARM: how to flush cache lines and how to achieve precise timing. Experiments show that Flush+Time increases accuracy from 95.1% of Flush+Reload, the most powerful general cache attack so far, to 99.3%. Flush+Time has a 30.5% higher resolution than Flush+Reload, but its execution time is only 0.59 times that of Spectre.