Large Margin Object Tracking with Circulant Feature Maps

Structured output support vector machine (SVM) based tracking algorithms have shown favorable performance recently. Nonetheless, the time-consuming candidate sampling and complex optimization limit their real-time applications. In this paper, we propose a novel large margin object tracking method which absorbs the strong discriminative ability from structured output SVM and speeds up by the correlation filter algorithm significantly. Secondly, a multimodal target detection technique is proposed to improve the target localization precision and prevent model drift introduced by similar objects or background noise. Thirdly, we exploit the feedback from high-confidence tracking results to avoid the model corruption problem. We implement two versions of the proposed tracker with the representations from both conventional hand-crafted and deep convolution neural networks (CNNs) based features to validate the strong compatibility of the algorithm. The experimental results demonstrate that the proposed tracker performs superiorly against several state-of-the-art algorithms on the challenging benchmark sequences while runs at speed in excess of 80 frames per second. The source code and experimental results will be made publicly available

Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques

Real-time safety assessment (RTSA) of dynamic systems is a critical task that has significant implications for various fields such as industrial and transportation applications, especially in non-stationary environments. However, the absence of a comprehensive review of real-time safety assessment methods in non-stationary environments impedes the progress and refinement of related methods. In this paper, a review of methods and techniques for RTSA tasks in non-stationary environments is provided. Specifically, the background and significance of RTSA approaches in non-stationary environments are firstly highlighted. We then present a problem description that covers the definition, classification, and main challenges. We review recent developments in related technologies such as online active learning, online semi-supervised learning, online transfer learning, and online anomaly detection. Finally, we discuss future outlooks and potential directions for further research. Our review aims to provide a comprehensive and up-to-date overview of real-time safety assessment methods in non-stationary environments, which can serve as a valuable resource for researchers and practitioners in this field.Comment: Accepted by the 2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS 2023

Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger

Robotic fingers made of soft material and compliant structures usually lead to superior adaptation when interacting with the unstructured physical environment. In this paper, we present an embedded sensing solution using optical fibers for an omni-adaptive soft robotic finger with exceptional adaptation in all directions. In particular, we managed to insert a pair of optical fibers inside the finger's structural cavity without interfering with its adaptive performance. The resultant integration is scalable as a versatile, low-cost, and moisture-proof solution for physically safe human-robot interaction. In addition, we experimented with our finger design for an object sorting task and identified sectional diameters of 94\% objects within the $\pm$6mm error and measured 80\% of the structural strains within $\pm$0.1mm/mm error. The proposed sensor design opens many doors in future applications of soft robotics for scalable and adaptive physical interactions in the unstructured environment.Comment: 8 pages, 6 figures, full-length version of a submission to IEEE RoboSoft 202

Modeling of a Microstrip Line Referenced to a Meshed Return Plane

Transmission Lines Referenced to Meshed Return Planes Are Widely Used Because of the Physical Flexibility Imparted by the Meshed Plane. Poor Accounting for the Meshed Ground, However, Can Lead to Severe Signal Integrity and Radio Frequency Interference Issues. Full-Wave Simulation Can Characterize the Electrical Performance at an Early Design Stage, But It is Both Time and Computational Resource Consuming. to Make the Simulation More Efficient, a Method is Proposed in This Study to Model Transmission Lines with a Meshed Reference Ground using 2D Analysis. the 2D Analysis is Performed at Several Locations Along the Length of the Trace above the Meshed Return to Determine Per-Unit-Length RLGC Parameters and Partial Self - and Mutual-Inductances of the Trace and Meshed Return. the Partial Self-Inductance of the Return is Then Corrected to Account for the Current Direction Along the Mesh. Cascading the Corrected S-Parameters for Each Segment is Then Used to Estimate the overall Characteristics of the Transmission Line. Results Found using This Approach Closely Match Those Found with 3D Full-Wave Simulation

Extraction of Stripline Surface Roughness using Cross-Section Information and S-Parameter Measurements

To characterize additional conductor loss introduced by conductor surface roughness, various models have been proposed to describe the relationship between foil roughness levels and surface roughness correction factor. However, all these empirical or physical models require a PCB sample to be manufactured and analyzed in advance. The procedure requires dissecting the PCB and is time- and labor-consuming. To avoid such a process, a new surface roughness extraction process is proposed here. Only the measured S-parameter and nominal cross-sectional information of the board are needed to extract the roughness level of conductor foils. Besides, this method can also deal with boards having non-equal roughness on different conductor surfaces, which is common in the manufactured printed circuit boards (PCB). The roughness level on each surface can be extracted separately to accurately model their contribution to the total conductor loss. The presented method is validated by both simulation and measurement. A good correlation is achieved between extracted roughness level and the measured value from the microscope