Hadith refers to the speeches, actions, tacit approval and characteristics of Prophet Muhammad PBUH. It contains many aspects of Islam and life such as creed, ibadah, ethics and others. Ibadah (Act of Worship) is pivotal for Muslims as it depicts the ritual of devotion to Allah. The word sami'a which defines hearing is well-known in the world of hadith as it has been used as one of the methods of narration. Apart from that, the word sami'a has also been mentioned numerous times in the matan section of hadith. However, the study pertaining to the extraction of the word sami'a in matan is limited. Moreover, the number of research pertaining to the relationship between the word sami'a with ibadah in hadith is also restricted. Hence, this study intends to analyze the word sami'a, its synonym and morphology from the aspect of ibadah (Act of Worship) in Hadith Qawli from Sahih al-Bukhari. In addition, the context of the hadith is also extracted and described, in order to expand the understanding of each hadith. Several phases involving data collection and data analysis processes were conducted in order to achieve the purposes of this study. First, the word sami'a, its synonym and morphology were listed and referred to kitab al-Takhrij and hadith software. Then, the Hadith Qawli containing word sami'a were extracted prior to data analysis phase. Next, Hadith Qawli related to ibadah were chosen and referred to the commentaries book. Lastly, the descriptions of hadith on aspect of ibadah were described according to their own context. Results of the study showed that, there were a few hadith containing the word sami'a in the context of ibadah (Act of Worship). It can be concluded that the hearing aspect has its own function and it plays a pivotal role in ibadah perspective.

Vehicle detection is a fundamental step in urban traffic surveillance systems, since it provides necessary information for further processing. Conventional techniques utilize either background subtraction or foreground appearance-based detection, which involves either poor adaptation or high computation. The complexity of urban traffic scenarios lies in pose and orientation variations, slow or temporarily stopped vehicles and sudden illumination variations. In this work, a foreground-background bimodal is proposed to adapt for scene variation and complexity. Cumulative frame differencing and sigma-delta estimation are used to model foreground and background respectively. A correction feedback updates each model iteratively and recursively based on the detection mask of the other model. Variance update for sigma-delta estimation was limited to update background temporal activities, while cumulative frame differencing account for moving foreground by discarding limited background variations. Comparative experimental results for typical urban traffic sequences show that the proposed technique achieves robust and accurate detection, which improves adaptation, reduce false detection and satisfy real-time requirements.