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Full-Text Articles in Engineering
Predicting Vasovagal Responses: A Model-Based And Machine Learning Approach, Theodore Raphan, Sergei B. Yakushi
Predicting Vasovagal Responses: A Model-Based And Machine Learning Approach, Theodore Raphan, Sergei B. Yakushi
Publications and Research
Vasovagal syncope (VVS) or neurogenically induced fainting has resulted in falls, fractures, and death. Methods to deal with VVS are to use implanted pacemakers or beta blockers. These are often ineffective because the underlying changes in the cardiovascular system that lead to the syncope are incompletely understood and diagnosis of frequent occurrences of VVS is still based on history and a tilt test, in which subjects are passively tilted from a supine position to 20◦ from the spatial vertical (to a 70◦ position) on the tilt table and maintained in that orientation for 10–15 min. Recently, is has been shown …
Self-Driving Toy Car Using Deep Learning, Fahim Ahmed, Suleyman Turac, Mubtasem Ali
Self-Driving Toy Car Using Deep Learning, Fahim Ahmed, Suleyman Turac, Mubtasem Ali
Publications and Research
Our research focuses on building a student affordable platform for scale model self-driving cars. The goal of this project is to explore current developments of Open Source hardware and software to build a low-cost platform consisting of the car chassis/framework, sensors, and software for the autopilot. Our research will allow other students with low budget to enter into the world of Deep Learning, self-driving cars, and autonomous cars racing competitions.
Pathological Brain Detection By A Novel Image Feature—Fractional Fourier Entropy, Shuihua Wang, Yudong Zhang, Xiaojun Yang, Ping Sun, Zhengchao Dong, Aijun Lu, Ti-Fei Yuan
Pathological Brain Detection By A Novel Image Feature—Fractional Fourier Entropy, Shuihua Wang, Yudong Zhang, Xiaojun Yang, Ping Sun, Zhengchao Dong, Aijun Lu, Ti-Fei Yuan
Publications and Research
Aim: To detect pathological brain conditions early is a core procedure for patients so as to have enough time for treatment. Traditional manual detection is either cumbersome, or expensive, or time-consuming. We aim to offer a system that can automatically identify pathological brain images in this paper.Method: We propose a novel image feature, viz., Fractional Fourier Entropy (FRFE), which is based on the combination of Fractional Fourier Transform(FRFT) and Shannon entropy. Afterwards, the Welch’s t-test (WTT) and Mahalanobis distance (MD) were harnessed to select distinguishing features. Finally, we introduced an advanced classifier: twin support vector machine (TSVM). Results: A 10 …