Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices

Internet of Things(IoT) devices, mobile phones, and robotic systems are often denied the power of deep learning algorithms due to their limited computing power. However, to provide time-critical services such as emergency response, home assistance, surveillance, etc, these devices often need real-time analysis of their camera data. This paper strives to offer a viable approach to integrate high-performance deep learning-based computer vision algorithms with low-resource and low-power devices by leveraging the computing power of the cloud. By offloading the computation work to the cloud, no dedicated hardware is needed to enable deep neural networks on existing low computing power devices. A Raspberry Pi based robot, Cloud Chaser, is built to demonstrate the power of using cloud computing to perform real-time vision tasks. Furthermore, to reduce latency and improve real-time performance, compression algorithms are proposed and evaluated for streaming real-time video frames to the cloud.Comment: Accepted to The 11th International Conference on Machine Vision (ICMV 2018). Project site: https://zhengyiluo.github.io/projects/cloudchaser

Impacting device for testing insulation

An electro-mechanical impacting device for testing the bonding of foam insulation to metal is descirbed. The device lightly impacts foam insulation attached to metal to determine whether the insulation is properly bonded to the metal and to determine the quality of the bond. A force measuring device, preferably a load cell mounted on the impacting device, measures the force of the impact and the duration of the time the hammer head is actually in contact with the insulation. The impactor is designed in the form of a handgun having a driving spring which can propel a plunger forward to cause a hammer head to impact the insulation. The device utilizes a trigger mechanism which provides precise adjustements, allowing fireproof operation

Real-Time Grasp Detection Using Convolutional Neural Networks

We present an accurate, real-time approach to robotic grasp detection based on convolutional neural networks. Our network performs single-stage regression to graspable bounding boxes without using standard sliding window or region proposal techniques. The model outperforms state-of-the-art approaches by 14 percentage points and runs at 13 frames per second on a GPU. Our network can simultaneously perform classification so that in a single step it recognizes the object and finds a good grasp rectangle. A modification to this model predicts multiple grasps per object by using a locally constrained prediction mechanism. The locally constrained model performs significantly better, especially on objects that can be grasped in a variety of ways.Comment: Accepted to ICRA 201

Astronaut tool development: An orbital replaceable unit-portable handhold

A tool to be used during astronaut Extra-Vehicular Activity (EVA) replacement of spent or defective electrical/electronic component boxes is described. The generation of requirements and design philosophies are detailed, as well as specifics relating to mechanical development, interface verifications, testing, and astronaut feedback. Findings are presented in the form of: (1) a design which is universally applicable to spacecraft component replacement, and (2) guidelines that the designer of orbital replacement units might incorporate to enhance spacecraft on-orbit maintainability and EVA mission safety

A Lost Dream: Worker Control at Rath Packing

[Excerpted from Introduction by Gene Daniels] The story of Rath Packing Company of Waterloo, Iowa, is alternately a model of the American Dream and the story of a dream turned nightmare. Started in Iowa in 1891 with a work force of 22, Rath employed 8,000 people at its peak. In 1944, workers at Rath slaughtered 12,000 hogs, cattle and sheep a day. It was the largest and most modern packing house in the world. In the 1950s and early 1960s, however, Rath\u27s management failed to make several strategic moves. They failed to market Rath\u27s products to supermarkets, thinking Mom & Pop stores would remain the backbone of community grocery shopping. Little attention was paid to the growing conglomeration within the meatpacking industry itself And, management failed to re-invest in new machinery and processes and failed to build a new facility like the single-story buildings being constructed by competitors. All these factors combined to provide Rath with short-term prof its and long-term headaches. By the 1970s the company was in deep trouble

The Family History of Meghan Redmon

Meghan Redmon authored this family history as part of the course requirements for HIST 550/700 Your Family in History offered online in Spring 2020 and was submitted to the Pittsburg State University Digital Commons. Please contact the author directly with any questions or comments: [email protected]

Cervical Cancer

Cervical Cancer is a major health concern worldwide for women. Human papillomavirus (HPV) is one of the major health risk factors known to causing cervical cancer. Early prevention and detection are key to preventing the cancer. There are vaccinations to prevent HPV and ways to detect abnormal cells by having a Papanicolaou cytology (Pap) test. The underlying signs and symptoms, pathophysiology, and implications for nursing care are discussed within the poster