Running average is a simple and effective background modeling method that generates adaptive background image for moving object detection. Fuzzy Running Average (FRA) improves the selectivity of Standard Running Average (SRA). However, its background restoration rate is slow. This leads to false object detection when a static object becomes dynamic. To overcome this problem, an Extended Fuzzy Running Average (EFRA) is proposed. The results show that the EFRA not only retains the selectivity benefit of FRA, but also improves the restoration rate significantly. Keywords: fuzzy background modeling, vehicle detection, thermograph Classification: Science and engineering for electronics

of the most high-risk areas for patient violence.5 Indeed, the prevalence of patient violence in EDs has risen to an alarming figure worldwide. Around 25% of the nurse respondent's experienced physical assaults more than 20 times in the 3 years preceding Gacki-Smith and his colleagues' study, 6 and almost one-fifth of them experienced frequent verbal abuse. Impact of workplace in health care settingThe short-term and longer-term impacts of patient violence are both substantial at both individual and organizational level. At the individual level, violence leads to both physical and psychological trauma, including frustration and anger.7 Staff may become afraid of patients and their job performance may be affected after an incident of patient violence.8 At the organizational level, patient violence is associated with lower quality of care provided for their patients. 9It also produces significant longer-term effects on recruitment and retention of nurses. 10 Contributing factors of violenceThere are many factors involved in the presentation of patient violence in EDs. From the theoretical perspective, it can be the result of patients' genetic abnormalities, unresolved personal conflicts, and a response to frustration at perceived unmet needs. 11,12 However, none of these theories can fully explain the complexity of patient violence in the ED. It also assumes patients are largely responsible for their violent behaviors and fails to consider other contextual factors. Indeed, there is a growing recognition on understanding the causes of patient violence from multiple angles are broadly classified into patient-related; environment-related and interaction-related factors, which include staff morale, patient/staff ratio and ward policy and staff variables such as their educational level and years of clinical experience. 13 Nurses' attitude towards violenceWith more understanding of these risk factors, the frontline nurses may feel much safer and more confident in management of patient aggression, as well as in establishing therapeutic nursepatient communication and relationships.14 However, as suggested by many nurse researchers that nurses' attitudes towards causes and management of patient violence have a far-reaching impact, which can largely vary across patient populations and clinical settings. Therefore, the nurses' perception towards patient aggression and its correlated factors are worthwhile to study because nurse is a decision maker of how to improve their patient care and prevent any harms or injury on both staff and patients. 15,16 Besides, nurses' attitudes can be mutually transferred to colleagues through communication and forming a culture in wards. The future managing style of nursing students after graduation also may be affected through professional socialization, 17 which is the process by which individuals acquire the values, attitudes, morals, knowledge and skills owned by the group. 18Thus, it is important to obtain present nurses' attitude regarding patient violence and take measures to avoid cult...

Neuroinverse problem are often associated with complex neuronal activity. It involves locating problematic neuron which usually a highly challenging task. While epileptic foci localization is possible with the aid of EEG signals, it relies greatly on the ability to extract hidden information or pattern within EEG signals. Flat EEG being an enhancement of EEG is a way of viewing electroencephalograph on the real plane. In the perspective of dynamical systems, the equivalency of epileptic seizure and Flat EEG is important since the underlying structure of Flat EEG is dynamic. In this paper, a basis for the topological space of Flat EEG is presented. It is expected to facilitate in the constructions of dynamic transformation from epileptic seizure to Flat EEG. It provides a better understanding on Flat EEG itself.