Archives for 2020

A Pattern Recognition Framework for Embedded Systems

Frank Vahid

Tony Givargis

Roman Lysecky

Abstract— Embedded systems often implement behavior for common application domains, such as the control systems domain or the signal processing domain. An increasingly common domain is pattern recognition, such as determining which kind of fruit is passing on a conveyor belt. Embedded system students and designers typically are not experts in such domains and could benefit from simpler platforms to help them gain insight into the problem of pattern recognition and help them develop such algorithms rapidly. Generic frameworks, such as PID (proportional- integral-derivative) for control, or FIR (finite impulse response) for signal filtering, empower non-expert embedded system designers to quickly build robust systems in those domains. We introduce a generic pattern recognition framework, useful for education as well as for various real systems. The framework divides the task into three phases: feature extraction, classification, and actuation (FCA). We provide template code (in C) that a student or designer can modify for their own specific application. Read the full article here “A Pattern Recognition Framework for Embedded Systems”

Project-Based Courses for B.Tech. Program of Robotics in Mechanical Engineering Technology

Zhou Zhang

Department of Mechanical Engineering Technology
New York City College of Technology, CUNY
Brooklyn, New York, USA zhzhang@citytech.cuny.edu

Andy S. Zhang

Department of Mechanical Engineering Technology
New York City College of Technology, CUNY
Brooklyn, New York, USA azhang@citytech.cuny.edu

Mingshao Zhang

Department of Mechanical and Industrial Engineering
Southern Illinois University Edwardsville
Edwardsville, Illinois, USA mzhang@siue.edu

Sven Esche

Department of Mechanical Engineering
Stevens Institute of Technology Hoboken, New Jersey, USA sesche@stevens.edu

Abstract— Robotics program at many Colleges has continued to become more and more popular. However, the students of the Bachelor of Technology (B.Tech.) program of robotics in the Mechanical Engineering Technology (MET) are facing three difficulties: (1) Weak fundamental knowledge related electrical engineering (EE), computer science (CS) and information technology (IT); (2) Difficulty in understanding the advanced concepts and theories of robotics; (3) Limited robotics class hours. Therefore, devising a series of appropriate robotics classes for the MET program is desirable. Read the full article here “Project-Based Courses for B.Tech. Program of Robotics in Mechanical Engineering Technology”

Improving Student Success by Being Automatically Personal

Mark A. Palmer

Formerly IME Department
Kettering University
Currently Flushing, MI
MarkAPalmer@att.net

Abstract – This paper describes the development and use of “automatically-personal e-mail” routines allowing one to send interpretive e-mails to one’s class based on clicking a command in an Excel grade book. The macros are included in a template file which are available under the Attribution-NonCommercial-ShareAlike 4.0 International Creative Commons License. Nudges, in the form of light-touch directed-feedback have been shown to be effective in engaging students, but they are often time consuming for faculty. The author has found that he can send detailed performance updates to students automatically through macros in a well defined Excel Gradebook. This increases student engagement as they see it as a way of demonstrating caring. Using an Engineering Materials Course as an example, the author demonstrates the steps necessary to send 6 such nudges throughout an 11 week term. Sample commented coding and examples of the messages sent to students are provided as examples. Read the full article here “Improving Student Success by Being Automatically Personal”

The Effect of An Automatic Feedback System on Students’ Comments to Improve their Performance

Shaymaa E. Sorour

Dept. of Educational Technology
Faculty of Specific Education Kafrelsheikh University
Egypt
shaymaasorour@gmail.com

Hanan E. Abdelkader

Dept. of Computer Teacher Preparation
Faculty of Specific Education
Mansoura University
Egypt
h_elrefaey@yahoo.com

Abstract—This research focuses on understanding student performance by giving automatic feedback after writing freestyle comment data in each lesson. Writing comments express students’ learning activities, tendencies, attitudes, and situations involved with the lesson. Random Forest and Support Vector Machine were applied to analyze the students’ prediction results. Also, a Majority Vote (MV) method is employed in consecutive lessons during the semester to the predicted results. The proposed system tracks student’s learning activities and attitudes with different courses and provides valuable feedback to improve educational performance.

Keywords—Automatic feedback, Comment data, SVM, RF, MV

I. INTRODUCTION

Improving the performance of students, determining their actual progress, and enhancing their learning process can be extremely valuable in the educational environment. To achieve a high level of student performance, we have to find ways to measure their current progress and predict the results of the learning process at the earliest stages. Read the full article here “The Effect of An Automatic Feedback System on Students’ Comments to Improve their Performance”