Nowadays, Motion Estimation (ME) plays a vital role in traffic signals, movie making, and security purpose to identify the motion of an object. In this paper, Smart and Low power (SLP) based ME is introduced with Level Convertor and Carry SeLect Adder (LC-CSLA-ME). Normal SLP architecture requires more power for performing ME, but in the LC-CSLA-ME architecture, 0.9v supply voltage is used to carry-out the ME. The LC is used to supply the required voltage to the respective sub blocks, which helps to reduce the power consumption of the overall architecture. Instead of using normal adder, CSLA adder is used in prediction and classification unit for improving the hardware utilization. The performance of Application Specified Integrated Chips (ASIC) and Field Programmable Gate Array (FPGA) analysed for both existing and proposed methods. In 180nm technology, LC-CSLA-ME architecture occupied 0.065mm 2 area, 2.10mW power, and 104ms delay. The different dataset such as Johnny, Vidyo 1, and Vidyo 4 is analysed for ME. The Coding Time Saving (CTS) evaluated for both existing and LC-CSLA-ME architectures. In the LC-CSLA-ME, average CTS of Operating Power Points (OPP1, OPP2, OPP3, OPP4, and OPP5) are 34.021, 54.316, 67.021, 75.027, and 80.071 that is high compared to the existing methods.

Abstract-Recently, there has been a significant research in automatic text summarization using featurebased techniques in which most of them utilized any one of the soft computing techniques. But, making use of syntactic structure of the sentences for text summarization has not widely applied due to its difficulty of handling it in summarization process. On the other hand, feature-based technique available in the literature showed efficient results in most of the techniques. So, combining syntactic structure into the feature-based techniques is surely smooth the summarization process in a way that the efficiency can be achieved. With the intention of combining two different techniques, we have presented an approach of text summarization that combines feature and syntactic structure of the sentences. Here, two neural networks are trained based on the feature score and the syntactic structure of sentences. Finally, the two neural networks are combined with weighted average to find the sentence score of the sentences. The experimentation is carried out using DUC 2002 dataset for various compression ratios. The results showed that the proposed approach achieved F-measure of 80% for the compression ratio 50 % that proved the better results compared with the existing techniques.

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