Prior research shows that ethical misconduct occurs in all sectors of science and engineering, including laboratory-based research, engineering design, and data science and modeling. Problems have arisen at individual and across organizations. Many of the recent unethical incidents in scientific research involve data fabrication and falsification, data tampering, plagiarism, intellectual theft, and misinformation. A few examples of recently reported unethical behaviors in engineering in industry include the delayed response in the GM ignition switch failure case, the diesel emission software manipulation case at Volkswagen, and the lack of attention to user privacy by companies such as Facebook. These problems arise from numerous sources. One is insufficient ethical policies at the leadership level, as prior research has reported a positive correlation between companies' success and strong ethical policies at that level. Yet the idea that these problems can be solved solely at the leadership level seems flawed, as it is difficult to transfer ethical practices from the leadership to their team members if the latter have a weak or flawed understanding of what ethical responsibility entails, if without a proper ethical research efficacy assessment method and improvement plan. This paper presents preliminary findings from a National Science Foundation (NSF) funded project on ethical and responsible research (ER2) in science and engineering. More specifically, the NSF project aims to enhance ethical self-efficacy and competence in undergraduate engineering students through curricular interventions. While the overall goal of the NSF project also includes development of a validated scale to assess the ethical self-efficacy and competency of engineering students, the objective of this paper is limited to conduct a survey of first-year engineering students at Texas A&M University to establish baseline results for future work. Furthermore, based on the survey results, the paper also investigates differences in the ethical self-efficacy level of students based on their demographic attributes and high school education on ethics.

The proposed framework supports a new and efficient non-rigid body object tracking among objects with similar patterns. Human tracking is used as an example of a non-rigid body tracking. The main objective of this framework is to track the targeted person among people with similar patterns in a series of images frames. The targeted person is identified in the initial frame by the user. This framework consists of three stages: generation of panoramic images for a wider range, detection stage, and tracking stage. In detection stage, all multiple regions of interest (ROIs) are classified and the target is detected using multiple ROIs and fuzzy neural system. In tracking stage, the targeted person is tracked using an adaptive motion frame method that checks the target's movement. This suggested framework explains how multiple ROIs are used for detecting non-rigid body object and how the target can be tracked using previous trajectory and velocity. This algorithm contributes towards the tracking of a desired target that exists among many similar non-rigid body objects.

The paper discusses a panoramic vision system for autonomous-navigation purposes. It describes an economic PC-based method for integrating data from multiple camera sources in real time. The views from adjacent cameras are visualized together as a panorama of the scene using a modified correlation-based stitching algorithm. A separate operator is presented with a particular slice of the panorama matching the user's viewing direction. Additionally, a simulated environment is created where the operator can choose to augment the video by simultaneously viewing an artificial three-dimensional (3-D) view of the scene. Potential applications of this system include enhancing quality and range of visual cues, and navigation under hostile circumstances where direct view of the environment is not possible or desirable.

This paper presents an integrated method to identify an object pattern from an image, and track its movement over a sequence of images. The sequence of images comes from a single perspective video source, which is capturing data from a precalibrated scene. This information is used to reconstruct the scene in three-dimension (3-D) within a virtual environment where a user can interact and manipulate the system. The steps that are performed include the following: i) Identify an object pattern from a two-dimensional perspective video source. The user outlines the region of interest (ROI) in the initial frame; the procedure builds a refined mask of the dominant object within the ROI using the morphological watershed algorithm. ii) The object pattern is tracked between frames using object matching within the mask provided by the previous and next frame, computing the motion parameters. iii) The identified object pattern is matched with a library of shapes to identify a corresponding 3-D object. iv) A virtual environment is created to reconstruct the scene in 3-D using the 3-D object and the motion parameters. This method can be applied to real-life application problems, such as traffic management and material flow congestion analysis.

A review of existing systems for performing manufacturing tasks in a collaborative virtual environment highlights the limitations for modeling object behavioral characteristics. It is difficult for users to describe the tasks to be performed in the environment. A behavioral layer architecture is presented to address these limitations. The layer consists of a virtual manufacturing lattice (VML) structure, an object library, and a virtual manufacturing script (VMS). The VML is a hierarchical structure to encapsulate object behavioral characteristics, which augments the scenegraph structure. The encapsulation is achieved by using a four-tuple structure (C,R,T,E), where C is the composition structure of the node which contains geometry, material, texture, and location data, R is the precedence relationship element, T is the trajectory relationship element, and E is the event control list used to describe the state information about the object. The VMS is implemented as a script and a parser that serves as an interface between the user and collaborative virtual reality software. The script is a grammar-like structure to describe manufacturing tasks. In the behavioral layer architecture, the script is parsed, and calls are issued to the underlying functions to initiate the tasks and to manipulate the lattice structure to perform the specified tasks. Availability of network bandwidth is a key ingredient for using our system for telecollaboration. As part of an example to demonstrate the implementation of VML-VMS, a study of network latency and saturation is performed for different network medium, packet sizes, sampling rate, and network protocols to explore ways of reducing network latency and display frame jitter.

Presents an experiment that investigated the potential benefits of virtual reality (VR) environments in supporting assembly planning. In the experiment, 15 subjects performed an assembly planning task in three different conditions: a traditional engineering (TE) environment, a nonimmersive desktop VR (DVR) environment, and an immersive CAVE (Cave Automatic Virtual Environment) VR (CVR) environment. The effects of the three conditions on the subjects' performance were analyzed. The subjects' performance time in the TE condition was significantly longer than that in the DVR condition and that in the CVR condition, whereas the difference in performance time between the DVR condition and the CVR condition was not significant. The total number of problematic assembly steps in the TE condition was significantly greater than that in the CVR condition. Specifically, the subjects' assembly sequences in the TE condition involved more reorientations than in the DVR condition. The number of difficult assembly steps in the TE condition was significantly greater than that in the DVR condition, which was significantly greater than that in the CVR condition. The number of dissimilar assembly steps in the TE condition was significantly greater than that in the CVR condition, which was significantly greater than that in the DVR condition. Hence, the results revealed advantages of the two VR environments over the TE environment in improving the subjects' overall assembly planning performance and in minimizing the handling difficulty, excessive reorientation and dissimilarity of assembly operations.