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• Surveillance anomaly detection is critical in our daily life that replaces inefficient human monitoring with an automated system and provides various pattern recognition applications. • In this paper, novel architecture ITAE learns normal appearance and motion patterns by implicitly capturing static and dynamic features. • By utilizing normalizing flow generative model, we are the first to estimate the distribution of appearance and motion surveillance video features. • The proposed approach achieves superior performance on six surveillance anomaly detection benchmarks and demonstrates its effectiveness of generalization ability which is crucial issue in real-world scenarios. In contemporary society, surveillance anomaly detection, i.e., spotting anomalous events such as crimes or accidents in surveillance videos, is a critical task. As anomalies occur rarely, most training data consists of unlabeled videos without anomalous events, which makes the task challenging. Most existing methods use an autoencoder (AE) to learn to reconstruct normal videos; they then detect anomalies based on their failure to reconstruct the appearance of abnormal scenes. However, because anomalies are distinguished by appearance as well as motion, many previous approaches have explicitly separated appearance and motion informationfor example, using a pre-trained optical flow model. This explicit separation restricts reciprocal representation capabilities between two types of information. In contrast, we propose an implicit two-path AE (ITAE), a structure in which two encoders implicitly model appearance and motion features, along with a single decoder that combines them to learn normal video patterns. For the complex distribution of normal scenes, we suggest normal density estimation of ITAE features through normalizing flow (NF)-based generative models to learn the tractable likelihoods and identify anomalies using out-of-distribution detection. NF models intensify ITAE performance by learning normality through implicitly learned features. Finally, we demonstrate the effectiveness of ITAE and its feature distribution modeling on six benchmarks, including databases that contain various anomalies in real-world scenarios. [ABSTRACT FROM AUTHOR]

In this present work, the physical characteristics of the graphene/MWCNTs composite have been studied. Surface modification has been done by grinding process and acid treatment where planetary ball milling (PBM) machine for grinding in aqueous circumstance and a mixture of HNO 3 and H 2 SO 4 for acid treatment have been executed. Though, there are many researches of GN and MWCNTs nanoparticles based on individually as well as combined with other nanoparticles have been accomplished, reports on the combination of these two nanoparticles are negligible. In this paper, IR thermography, morphological and structural characteristics of modified GN–MWCNTs by field emission scanning electron microscopy, Raman spectroscopy and dispersibility analysis by sedimentation, UV–Vis spectroscopy, electric conductivity and thermal conductivity measurements have been presented in order to report the characteristics of GN/MWCNTs composite. Acid treatment as well as wet grinding of GN/MWCNTs composite at 500 rpm for 2 h gives the highest thermal conductivity enhancement which is 4.29% at 40 °C. [ABSTRACT FROM AUTHOR]

This paper reports the effect of microwave on the physical properties of multi-walled carbon nanotubes (MWCNTs) where different power levels of microwave were applied on MWCNTs in order to apprehend the effect of microwave on MWCNTs distinctly. A low energy ball milling in aqueous circumstance was also applied on both MWCNTs and microwave treated MWCNTs. Temperature profile, morphological analysis by field emission scanning electron microscopy (FESEM), defect analysis by Raman spectroscopy, thermal conductivity, thermal diffusivity as well as heat transfer coefficient enhancement ratio were studied which expose some strong witnesses of the effect of microwave on the both purification and dispersion properties of MWCNTs in base fluid distilled water. The highest thermal conductivity enhancement (6.06% at 40 °C) of MWCNTs based nanofluid is achieved by five minutes microwave treatment as well as wet grinding at 500 rpm for two hours. [ABSTRACT FROM AUTHOR]