Web-Enabled Vision Guided Robotic Tracking Within the Framework of E-Manufacturing

The current trends in industry include an integration of information and knowledge base network with a manufacturing system, which coined a new term, E-Manufacturing. From the perspective of E-Manufacturing, any production equipment and its control functions do not exist alone, but become a part of the holistic operation system with distant monitoring, remote quality control and fault diagnostic capabilities. The key to this new paradigm is the accessibility to a remotely located system and having the means of responding to a changing environment, which is better suited for today’s rapidly changing environment. In this context, this paper presents an innovative method in part tracking using the Ethernet SmartImage Sensor and the web-controllable SCARA robot. Remote controlling of an automation process using Internet can suffer from time lag, if the network is congested with heavy data traffic, which maybe the greatest hurdle for using Internet for real time control. The approach discussed in this paper overcomes the time lag for part tracking and mathematically calculates the product locations on the conveyor at various instances and efficiently guide the robot to the product. The accuracy of the proposed scheme has been verified, which vindicates the industrial applicability of the setup. The web-enabled robotic operations present many benefits, such as ubiquitous access, remote control, programming, monitoring capabilities, and integration of production equipment into information networks for improved efficiency and quality

Videoconference teaching for applied engineering technology students

Presented at the 2006 Annual ASEE Conference, June 21, 2006 - Chicago, Illinois.The development of a fully-interactive videoconference teaching facility for Applied Engineering Technology (AET) students is described in this work. This facility will provide greater program delivery flexibility by offering a non-traditional educational approach that expands student’s horizons. The new facility will allow all AET students at Drexel, as well as students at remote locations, to participate in the same educational and training process. By expanding training opportunities to students who might not otherwise take advantage of them, due to distance and time, this facility helps reduce the shortage of trained specialists in applied electrical, mechanical, and manufacturing technology. The videoconference teaching courses will be designed for undergraduate AET students and may also be taken by other undergraduate/graduate students at Drexel or by the students of other universities and community colleges who have fulfilled the necessary prerequisites and desire to pursue a BS degree in AET. The inter-institutional class sessions will be carried out utilizing Internet II-based access to high-end video and test equipment of Drexel’s AET electrical, electronics, and manufacturing laboratories. Through remote operation, expensive equipment of the AET laboratories, such as the electronics laboratory, nondestructive evaluation of materials laboratory, and web-enabled robotic assembly station, will be accessible to institutions that cannot afford the equipment directly and which do not have faculty with the expertise and training in these specific AET areas

ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentation

We address a weakly-supervised low-shot instance segmentation, an annotation-efficient training method to deal with novel classes effectively. Since it is an under-explored problem, we first investigate the difficulty of the problem and identify the performance bottleneck by conducting systematic analyses of model components and individual sub-tasks with a simple baseline model. Based on the analyses, we propose ENInst with sub-task enhancement methods: instance-wise mask refinement for enhancing pixel localization quality and novel classifier composition for improving classification accuracy. Our proposed method lifts the overall performance by enhancing the performance of each sub-task. We demonstrate that our ENInst is 7.5 times more efficient in achieving comparable performance to the existing fully-supervised few-shot models and even outperforms them at times.Comment: Accepted at Pattern Recognition (PR

Localization Uncertainty Estimation for Anchor-Free Object Detection

Since many safety-critical systems, such as surgical robots and autonomous driving cars, are in unstable environments with sensor noise and incomplete data, it is desirable for object detectors to take into account the confidence of localization prediction. There are three limitations of the prior uncertainty estimation methods for anchor-based object detection. 1) They model the uncertainty based on object properties having different characteristics, such as location (center point) and scale (width, height). 2) they model a box offset and ground-truth as Gaussian distribution and Dirac delta distribution, which leads to the model misspecification problem. Because the Dirac delta distribution is not exactly represented as Gaussian, i.e., for any $\mu$ and $\Sigma$. 3) Since anchor-based methods are sensitive to hyper-parameters of anchor, the localization uncertainty modeling is also sensitive to these parameters. Therefore, we propose a new localization uncertainty estimation method called Gaussian-FCOS for anchor-free object detection. Our method captures the uncertainty based on four directions of box offsets~(left, right, top, bottom) that have similar properties, which enables to capture which direction is uncertain and provide a quantitative value in range~[0, 1]. To this end, we design a new uncertainty loss, negative power log-likelihood loss, to measure uncertainty by weighting IoU to the likelihood loss, which alleviates the model misspecification problem. Experiments on COCO datasets demonstrate that our Gaussian-FCOS reduces false positives and finds more missing-objects by mitigating over-confidence scores with the estimated uncertainty. We hope Gaussian-FCOS serves as a crucial component for the reliability-required task

Posterior Epidural Migration of a Lumbar Intervertebral Disc Fragment Resembling a Spinal Tumor: A Case Report

Posterior epidural migration of a lumbar intervertebral disc fragment (PEMLIF) is uncommon because of anatomical barriers. It is difficult to diagnose PEMLIF definitively because of its relatively rare incidence and the ambiguity of radiological findings resembling spinal tumors. This case report describes a 76-year-old man with sudden-onset weakness and pain in both legs. Electromyography revealed bilateral lumbosacral polyradiculopathy with a mass-like lesion in L2-3 dorsal epidural space on lumbosacral magnetic resonance imaging (MRI). The lesion showed peripheral rim enhancement on T1-weighted MRI with gadolinium administration. The patient underwent decompressive L2-3 central laminectomy, to remove the mass-like lesion. The excised lesion was confirmed as an intervertebral disc. The possibility of PEMLIF should be considered when rim enhancement is observed in the epidural space on MRI scans and electrodiagnostic features of polyradiculopathy with sudden symptoms of cauda equina syndrome