Exploring the Role of Female Successor “Amrah Bint Abd Al-Raḥmān” in Narration of Prophetic Traditions

Although several studies have investigated the contribution of Muslim women about their lives and works in large corpus of the ḥadīth sciences until now, but much are needed to be explored. Doubtlessly 'Amrah bint 'Abd al-Raḥmān had contributed to the large corpus of the ḥadīth sciences and also the core of Islamic knowledge parallel to her male counterparts. By highlighting her efforts for learning and her struggle for transmission of fundamental text, reveals how a female successor acquired exemplary reputations in her era. Particularly, her rich insight, deep knowledge in ḥadīth sciences and exemplary piety attracted students and placed parallel to her male counterparts in Islamic knowledge of her time. In sum, this study not merely sheds light upon the prolific contribution of an eminent female successor 'Amrah bint 'Abd al-Raḥmān in ḥadīth narrations, but argues that these scholarly females should be given more credit for being interacting educators and not just transmitters. Doubtlewssly, the study would be helpful to revise our understandings of Islamic knowledge during early period of Islam regarding gendered dynamics and their great contribution to dissemination and transmission of classical text and Islamic knowledge.

Multivariate Mutual Information for Audio Video Fusion

Information fusion is an essential part of distributed wireless sensor networks as well as perceptual user interfaces. Irrelevant and redundant data severely affect the performance of the information fusion framework. The work presented in this thesis addresses the problem of acceptability of data from multiple sources. A uni-modal information-theoretic framework for face recognition is first developed. Performance of the proposed method is compared with existing principal component analysis based face recognition algorithms. The uni-modal framework is extended to develop a multi-modal algorithm. The algorithm uses multivariate mutual information to validate the acceptability of data from two sources. Unlike the preceding algorithms, the framework does not require any preprocessing, such as automatic face recognition, etc. Moreover, it also does not condition any statistical modeling or feature extraction and learning algorithms to extract the maximum information regions. The various cases containing a single speaker as well as a group of speakers are analyzed. The applicability of the algorithm is tested by incorporating it with Haar like features of Voila and Jones for lip activity detection.