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Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.

Abstract. Digital watermarking has drawn extensive attention for copyright protection of multimedia data. This paper introduces a blind audio watermarking scheme in discrete cosine transform (DCT) domain based on singular value decomposition (SVD), exponential operation (EO), and logarithm operation (LO). In our proposed scheme, initially the original audio is segmented into non-overlapping frames and DCT is applied to each frame. Low frequency DCT coefficients are divided into sub-bands and power of each sub band is calculated. EO is performed on

Proof of ownership on multimedia data exposes users to significant threats due to a myriad of transmission channel attacks over distributed computing infrastructures. In order to address this problem, in this paper, an efficient blind symmetric image watermarking method using singular value decomposition (SVD) and the fast Walsh-Hadamard transform (FWHT) is proposed for ownership protection. Initially, Gaussian mapping is used to scramble the watermark image and secure the system against unauthorized detection. Then, FWHT with coefficient ordering is applied to the cover image. To make the embedding process robust and secure against severe attacks, two unique keys are generated from the singular values of the FWHT blocks of the cover image, which are kept by the owner only. Finally, the generated keys are used to extract the watermark and verify the ownership. The simulation result demonstrates that our proposed scheme is highly robust against numerous attacks. Furthermore, comparative analysis corroborates its superiority among other state-of-the-art methods. The NC of the proposed method is numerically one, and the PSNR resides from 49.78 to 52.64. In contrast, the NC of the state-of-the-art methods varies from 0.7991 to 0.9999, while the PSNR exists in the range between 39.4428 and 54.2599.

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Human fall identification can play a significant role in generating sensor based alarm systems, assisting physical therapists not only to reduce after fall effects but also to save human lives. Usually, elderly people suffer from various kinds of diseases and fall action is a very frequently occurring circumstance at this time for them. In this regard, this paper represents an architecture to classify fall events from others indoor natural activities of human beings. Video frame generator is applied to extract frame from video clips. Initially, a two dimensional convolutional neural network (2DCNN) model is proposed to extract features from video frames. Afterward, gated recurrent unit (GRU) network finds the temporal dependency of human movement. Binary cross-entropy loss function is calculated to update the attributes of the network like weights, learning rate to minimize the losses. Finally, sigmoid classifier is used for binary classification to detect human fall events. Experimental result shows that the proposed model obtains an accuracy of 99%, which outperforms other state-of-the-art models.

This study presents the characteristics of aerosol black carbon (BC) from a rural continental site, Agartala, located in the North-Eastern part of India using two year measurements from September 2010 to September 2012. Diurnal and seasonal variations are examined in relation to the unique geographical location, changeable meteorological conditions and distinct source characteristics. Winter season is characterized by extremely high BC concentration (17.8 ± 9.2 µg/m 3 ) comparable to those seen in urban environments of India, dropping off to much lower values during the monsoon (2.8 ± 1.7 µg/m 3 ). Even this lowest seasonal mean is rather high, given the rural nature of Tripura. Examination of the spectral dependence of aerosol absorption coefficients indicates that the main source of aerosol to total BC burden at Agartala is the fossil fuel combustions. Concentration weighted trajectory (CWT) analysis indicate that the characteristic high BC during winter is mostly associated with the advection from the Indo-Gangetic Plains (IGP), while the air mass pattern is constricted to the oceanic region during monsoon making BC aloft due to local pollution only.