Schleiermacher's Doctrine of Biblical Authority: An Alternative to Content-Based/Supernaturalist and Function-Based Rationalist Models

Author: Kerry W. Holton Title: SCHLEIERMACHER’S DOCTRINE OF BIBLICAL AUTHORITY: AN ALTERNATIVE TO CONTENT-BASED/SUPERNATURALIST AND FUNCTION- BASED/RATIONALIST MODELS Advisor: Theodore M. Vial, Jr. Degree Date: August 2015 This dissertation examines Friedrich Schleiermacher’s understanding of biblical authority and argues that, as an alternative to strictly supernaturalistic and rationalistic models, his understanding allows the New Testament to speak authoritatively in Christian religion in an age of critical, historical awareness. After classifying Schleiermacher’s position in a typology of the doctrine of biblical authority, this dissertation explores his conception of divine revelation and inspiration vis-à-vis scripture. It demonstrates that although he did not believe there is warrant for the claim of a direct connection between divine revelation and scripture, or that scripture is the foundation of faith, he nonetheless asserted that the New Testament is authoritative. He asserted the normative authority of the New Testament on the basis that it is the first presentation of Christian faith. This dissertation examines Schleiermacher’s “canon within the canon,” as well as his denial that the Old Testament shares the same normative worth and inspiration of the New. Although this dissertation finds difficulty with some of Schleiermacher’s views regarding the Old Testament, it names two significant strengths of what is identified as his evangelical, content-based, and rationalist approach to biblical authority. First, it recognizes and values the co-presence and co-activity of the supernatural and the natural !ii in the production of the New Testament canon. This allows both scripture and the church to share religious authority. Second, it allows Christian faith and the historical-method to coexist, as it does not require people to contradict what they know to be the case about science, history, and philosophy. Thus, this dissertation asserts that Schleiermacher’s understanding of biblical authority is a robust one, since, for him, the authority of scripture does not lie in some property of the texts themselves that historians or unbelievers can take away.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.