Search for: All records

From a three-phase experiential learning experience at Michigan Technological University (Michigan Tech) to develop the Mechatronics workforce, the authors aim to describe here the impacts and lessons learned. Mechatronics is the science of developing, interfacing, and operating automation in industrial environments. Though a mechatronics-educated workforce is highly sought by companies, due to advancements in biotechnology, manufacturing, and artificial intelligence (AI), the required experiential learning opportunities to create such a workforce are limited. In this study, the authors conducted an accessible experiential learning program aimed at educating and promoting the mechatronics workforce at Michigan Tech. Specifically, the program consisted of three phases: (1) an online Mechatronics Education Portal (MEP), (2) in-person Mechatronics Practice (MP) labs, and (3) a Mechatronics Industry Pathways Rotation (MIPR). The proposed experience was conducted with a cohort of nine participants in its first year and resulted in significant improvement in technical test scores of 2.56 out of 10 and with at least 75% of the participants rating the MEP, MP, and MIPR as good or better. 

Michigan Tech, West Shore Community College (WSCC), and Gogebic Community College (GCC) collaborate on the NSF ExLENT project aims to provide experiential learning opportunities in Mechatronics for a diverse STEM workforce. The program and its impacts are aligned with the regional economic needs of the Upper Peninsula and Northern Michigan areas. The emerging technology field of Mechatronics focuses on developing and implementing advanced automation for industrial applications. Thus, Mechatronics encompasses advanced fields, including robotics, Artificial Intelligence (AI), and cybersecurity. Though the demand for mechatronics expertise is growing, experiential workforce development opportunities in mechatronics are limited. This project will provide ExLENT participants with experiential opportunities through an online Mechatronics Education Portal (MEP), experiential Mechatronics Practice initiatives at Michigan Tech, and a Mechatronics Industry Pathways Rotation organized at WSCC and GCC. The MEP and MP modules will be focused on the five Mechatronics pillars of Robotics, Mechanics, Electronics/Controls, Cybersecurity, and Artificial Intelligence. This project will leverage partnerships among three universities, three nonprofit organizations, and nine regional industry collaborators. Comprehensive program evaluation will ensure that the project meets its objectives in improving interdisciplinary Mechatronics training through experiential learning opportunities, developing a flexible and comprehensive program to promote a diverse and inclusive STEM workforce, and facilitating sustainable collaboration amongst project partners centered around Mechatronic workforce preparation and placement. 

Portrait cartoonization aims at translating a portrait image to its cartoon version, which guarantees two conditions, namely, reducing textural details and synthesizing cartoon facial features (e.g., big eyes or line-drawing nose). To address this problem, we propose a two-stage training scheme based on GAN, which is powerful for stylization problems. The abstraction stage with a novel abstractive loss is used to reduce textural details. Meanwhile, the perception stage is adopted to synthesize cartoon facial features. To comprehensively evaluate the proposed method and other state-of-the-art methods for portrait cartoonization, we contribute a new challenging large-scale dataset named CartoonFace10K. In addition, we find that the popular metric FID focuses on the target style yet ignores the preservation of the input image content. We thus introduce a novel metric FISI, which compromises FID and SSIM to focus on both target features and retaining input content. Quantitative and qualitative results demonstrate that our proposed method outperforms other state-of-the-art methods. 

Syntheses of Rh complexes of the phosphine-amido-silane SiNP ligand are reported. The reaction of the parent (SiNP)H ligand (4) with 0.5 equiv. [(COE)RhCl] 2 (COE = cis -cyclooctene) in the presence of NaN(SiME 3 ) 2 resulted in the formation of (SiNP)Rh(COE) (5). Compound 5 was converted to a series of (SiNP)Rh(P(OR) 3 ) complexes 6–10 (R = Ph, i Pr, n Bu, Et, or Me) by treatment with the corresponding phosphite. NMR and XRD structural data, as well as the DFT computational analysis indicate that compounds 5–10 are divided into two structural Types ( A and B ), differing in the nature of the interaction of the Si–H bond of the SiNP ligand with Rh. 

Counting multi-vehicle motions via traffic cameras in urban areas is crucial for smart cities. Even though several frameworks have been proposed in this task, there is no prior work focusing on the highly common, dense and size-variant vehicles such as motorcycles. In this paper, we propose a novel framework for vehicle motion counting with adaptive label-independent tracking and counting modules that processes 12 frames per second. Our framework adapts hyperparameters for multi-vehicle tracking and properly works in complex traffic conditions, especially invariant to camera perspectives. We achieved the competitive results in terms of root-mean-square error and runtime performance.