University of Bridgeport

About: University of Bridgeport is a education organization based out in Bridgeport, Connecticut, United States. It is known for research contribution in the topics: Wireless sensor network & Key distribution in wireless sensor networks. The organization has 1008 authors who have published 1639 publications receiving 22740 citations.

A dual-mode built-in self-test technique for capacitive MEMS devices

Abstract: A dual-mode built-in self-test (BIST) scheme which partitions the fixed (instead of movable) capacitance plates of a capacitive microelectromechanical system (MEMS) device is proposed. The BIST technique divides the fixed capacitance plate(s) at each side of the movable microstructure into three portions: one for electrostatic activation and the other two equal portions for capacitance sensing. Due to such a partitioning method, the BIST technique can be applied to surface- and bulk-micromachined MEMS devices and other technologies. Further, the sensitivity and symmetry dual BIST modes based on this partitioning can also be developed. The combination of both BIST modes covers a larger defect set, so a more robust testing result for the device can be expected. The BIST technique is verified by three typical capacitive MEMS devices. Simulation results show that the proposed technique is an effective BIST solution for various capacitive MEMS devices.

Detection of obstructive sleep apnea through ECG signal features

Abstract: Obstructive sleep apnea (OSA) is a common disorder in which individuals stop breathing during their sleep. Most of sleep apnea cases are currently undiagnosed because of expenses and practicality limitations of overnight polysomnography (PSG) at sleep labs, where an expert human observer is needed to work over night. New techniques for sleep apnea classification are being developed by bioengineers for most comfortable and timely detection. In this paper, an automated classification algorithm is presented which processes short duration epochs of the electrocardiogram (ECG) data. The automated classification algorithm is based on support vector machines (SVM) and has been trained and tested on sleep apnea recordings from subjects with and without OSA. The results show that our automated classification system can recognize epochs of sleep disorders with a high degree of accuracy, approximately 96.5%. Moreover, the system we developed can be used as a basis for future development of a tool for OSA screening.

Intervertebral disc detection in X-ray images using faster R-CNN

Abstract: Automatic identification of specific osseous landmarks on the spinal radiograph can be used to automate calculations for correcting ligament instability and injury, which affect 75% of patients injured in motor vehicle accidents. In this work, we propose to use deep learning based object detection method as the first step towards identifying landmark points in lateral lumbar X-ray images. The significant breakthrough of deep learning technology has made it a prevailing choice for perception based applications, however, the lack of large annotated training dataset has brought challenges to utilizing the technology in medical image processing field. In this work, we propose to fine tune a deep network, Faster-RCNN, a state-of-the-art deep detection network in natural image domain, using small annotated clinical datasets. In the experiment we show that, by using only 81 lateral lumbar X-Ray training images, one can achieve much better performance compared to traditional sliding window detection method on hand crafted features. Furthermore, we fine-tuned the network using 974 training images and tested on 108 images, which achieved average precision of 0.905 with average computation time of 3 second per image, which greatly outperformed traditional methods in terms of accuracy and efficiency.

The Worldview Matrix as a Strategy When Designing Mixed Methods Research

Abstract: Researchers in the social sciences have seen a strong shift known as mixed methods. Despite the widespread utilization of mixed methods across disciplines, the field is evolving both as a methodology, and in terms of the methods used. Mixed methods advocates including Creswell, Collins, Greene, Johnson, Onwuegbuzie, Mertens, Morse, Tashakkori, Teddlie, and interpretive research advocates including Denzin, Flick, and Morgan have all cited pragmatism as the dominant paradigm in mixed methods research. Christ, Maxwell, and Lipscomb recently indicated that alternative paradigms also are viable and available. Considering how paradigms are used in the field of mixed methods research helps the field continue to grow. This paper addresses current discussions about alternatives to pragmatism including critical realism, and, then, presents a tool called the worldview matrix that is designed to assist researchers, methodology teachers, and students to clarify and to organize their mixed methods research designs.

Performance evaluation of routing protocols in wireless sensor networks

Abstract: The efficiency of sensor networks strongly depends on the routing protocol used In this paper, we analyze three different types of routing protocols: LEACH, PEGASIS, and VGA Sensor networks are simulated using Sensoria simulator Several simulations are conducted to analyze the performance of these protocols including the power consumption and overall network performance The simulation results, using same limited sensing range value, show that PEGASIS outperforms all other protocols while LEACH has better performance than VGA Furthermore, the paper investigates the power consumption for all protocols On the average, VGA has the worst power consumption when the sensing range is limited, while VGA is the best when the sensing range is increased