قانون ٹارٹ كا فقہ اسلامى كى روشنى میں جائزہ

Tort law is an umbrella term for  laws  which cover issues of civil wrongs like defamation, trespassing and the other actions involving  violation of law. In case a person has undergone a physical, legal or any economic harm then he can file a suit under the tort law. Torts are civil wrongs recognized by law as grounds for a lawsuit. It is also generally known that tort in Islamic fiqh as “Jinayet”. This paper attempts to analys  by Islamic law in thel light of the  relevant verses for the Qur’an followed by the rules stated in traditions from the Prophet (Peace by on him). Jinayat the part of Shari’a that applies to homicide or physical  injury is called jinayat and is based on the pre-Islamic rules of Arab blood feud, as modified by Prophet (Peace be on him). The punishment is either retaliation or blood money (diyat). Retaliation occurs only upon  the request of the victim, if alive, or his nearest kin if the victim is dead, and is to be inflicted by victim or kin. In the case of homicide retaliation means death, in the case of injury it means imposing an identical injury. Where retaliation is one of the options, the victim or his closest kinsman may demand blood money instead, or negotiate an out of court settlement. Jinayat, like modern tort law, is based on private action; there is no official responsible for initiating the case.

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.