In this paper, an artificial intelligence technique called Back Propagation Network (BPN) is proposed to cancel the electrocardiogram (ECG) interference in electromyogram (EMG) signal. Conventional filtering techniques are not suitable due to an overlap in spectral content of the EMG and the ECG. The performance evaluation of the proposed technique is done in terms of signal to noise ratio, mean square error, and convergence time. It shows that BPN successfully cancel the interference in EMG signal.

With the continuous enhancement in the speed and architecture of mobile processors it has become possible to play even high resolution videos on them. However the compilers have lagged behind in taking full utilization of the processing capabilities of the underlying hardware. H.264, known for its high compression ratio and computational complexity, needs performance optimizations for a computationally constraint environment. Some of these techniques are described in this paper.Index Terms-H.264 video decoder, Cortex Optimizations, cache optimizations.

Down syndrome is a genetically born disorder among infants that occurs during the development of the foetus. Trisomy 21, a chromosome imbalance disorder is a leading cause of the Down syndrome. Numerous Machine Learning (ML) models have been used to identify Down syndrome in ultrasound images of foetuses, but the development of Deep Learning (DL), offers an enormous advantage over ML models in accuracy. However, the existing models have focused on Down syndrome as a Nasal bone length or Nuchal translucency. In this paper, an Automatic dense convolution neural network (DConN) is proposed to isolate and measure the Down syndrome marker particularly Nasal bone length and Nuchal translucency. It is necessary to extract texture features precisely from ultrasound images to classify them accurately. Initially, the test image is processed using an Anisotropic Diffusion Filter (ADF) to remove the noise. Then the ROI region is segmented and classified using a dense convolution neural network. The parameters namely sensitivity, accuracy, specificity, F1 score, and precision are considered for validating the effectiveness of the proposed model. The proposed method improves the overall accuracy of 3.9%, 1.6% and 0.41% better than cascaded ML, SIFT+GRNN and Modified AdaBoost respectively.

No abstract