The most recent issue of the “Manufacturing Matters” newsletter, published by the Chamber of Commerce Grand Haven, Spring Lake, Ferrysburg, includes a feature article about Michigan Tech’s Mechatronics degree programs and learning lab, and the work that alumnus Mark Gauthier is doing to support and promote Mechatronics careers in southwestern Michigan.
Donald Engineering Supports Mechatronics Playground at Michigan Tech
From the February 2021 issue of Manufacturing Matters, a newsletter published by the Chamber of Commerce Grand Haven, Spring Lake, Ferrysburg.
With increases in smart technology, including IoT and Industry 4.0, things are changing in manufacturing. To help its graduates remain competitive, Michigan Technological University has combined the technologies of mechanical and electrical engineering into one degree: Mechatronics.
The multidisciplinary program combines these disciplines, along with fluid power, robotics, software, and computational hardware for a comprehensive education in the most current Mechatronics standards and products.
Mechatronics courses and degrees can be pursued on-campus, with some courses available online. Co-op options and graduate certificates are available for those interested in expanding their on-the-job knowledge. FANUC industrial certification in robotics is available for those already working in industry.
A publication by Associate Professor Yakov Nekrich, Computer Science, has been accepted to the 53rd Annual ACM Symposium on Theory of Computing (STOC).
The paper, “Optimal-Time Dynamic Planar Point Location in Connected Subdivisions,” describes an optimal-time solution for the dynamic point location problem and answers an open problem in computational geometry.
The data structure described in the paper supports queries and updates in logarithmic time. This result is optimal in some models of computation. Nekrich is the sole author of the publication.
The annual ACM Symposium on Theory of Computing (STOC), is the flagship conference of SIGACT, the Special Interest Group on Algorithms and Computation Theory, a special interest group of the Association for Computing Machinery (ACM).
A scholarly paper co-authored by Assistant Professor Sidike Paheding, Applied Computing, is one of two papers to receive the 2020 Best Paper Award from the open-access journal Electronics, published by MDPI.
The paper presents a brief survey on the advances that have occurred in the area of Deep Learning.
Paheding is a member of the Institute of Computing and Cybersystems’ (ICC) Center for Data Sciences (DataS).
Co-authors of the article, “A State-of-the-Art Survey on Deep Learning Theory and Architectures,” are Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan Westberg, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, and Vijayan K. Asari. The paper was published March 5, 2019, appearing in volume 8, issue 3, page 292, of the journal.
Papers were evaluated for originality and significance, citations, and downloads. The authors receive a monetary award , a certificate, and an opportunity to publish one paper free of charge before December 31, 2021, after the normal peer review procedure.
MDPI, a scholarly open access publishing venue founded in 1996, publishes 310 diverse, peer-reviewed, open access journals.
In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others.
This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began.
Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.
In this position, Dr. Havens will serve as the editor-in-chief for all publications of IEEE CIS conferences, including the flagship conferences IEEE International Joint Conference on Neural Networks (IJCNN), IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE Congress Evolutionary Computation (IEEE CEC), IEEE World Congress Computational Intelligence (WCCI), and IEEE Symposium Series on Computational Intelligence (SSCI).
A conference paper published in IEEE Xplore entitled, “Interfacing Computing Platforms for Dynamic Control and Identification of an Industrial KUKA Robot Arm” has been published by Assistant Professor Nathir Rawashdeh, Applied Computing.
In this work, a KUKA robotic arm controller was interfaced with a PC using open source Java tools to record the robot axis movements and implement a 2D printing/drawing feature.
The paper was presented at the 2020 21st International Conference on Research and Education in Mechatronics (REM). Details available at the IEEE Xplore database.
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