Vignan University

About: Vignan University is a education organization based out in Guntur, Andhra Pradesh, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 1138 authors who have published 1381 publications receiving 7798 citations.

Energy Efficient and Accurate Hybrid CSA-CIA Adders

Abstract: An adder can be treated as a fundamental component to perform arithmetic operations. A large number of operations can be performed by using adders such as additions, subtractions, multiplications, and divisions. In this paper two structures for hybrid CSA-CIA adder were proposed. This paper gives a comparative study of existing hybrid CSA-CIA adder and proposed hybrid CSA-CIA adders. The existing CSA-CIA hybrid adder does not work for all the combinations of inputs. The proposed designs work good for all input combinations. Also the proposed hybrid CSA-CIA adder2 has less energy and delay values compared to existing CSA-CIA hybrid adder. The code is written in Verilog hardware description language (HDL) and the simulations done by using Cadence Nclaunch tool. The layout reports are generated using Cadence Encounter tool.

A Novel Decision Tree Approach for the Prediction of Precipitation Using Entropy in SLIQ

Abstract: The rising tendency of population of every nation is one of the severe stumbling blocks to arrest its economic growth, particularly in the third world countries like India, not able to address even the basic needs of its people. It is high time to have introspection for the deficiencies and find a remedy. The major basic need is food, a product of agriculture. Agriculture mainly depends on rainfall. Prediction of precipitation is a complex phenomenon. Till now many of the researchers have tried their best for predicting the precipitation but in vain, since the prediction is quite complex with neural networks, back propagation, fuzzy logic etc. Hence, we found that data mining is an emerging, efficient, easily implementable tool, which predicts the useful patterns for the prediction of rainfall in a very short time. Supervised Learning In Quest, an efficient data mining decision tree algorithm is applied in the prediction of precipitation. The present research illustrates the Supervised Learning In Quest decision tree algorithm using entropy, which estimates the prediction of precipitation with an average accuracy of 74.92% and the knowledge extraction is purely based on historical data.

New zinc complexes derived from "self-adaptable" acyclic diiminodipyrromethanes as potent catalysts for the reduction of curing temperature of bisphenol-A/F benzoxazines

Abstract: The simple modification of the Schiff-base ligands often brings significant changes in the coordination properties of the metal-complexes, providing newer prospects for their unexplored applications. In this context, the present work utilized the “self-adaptable” acyclic diiminodipyrromethane Schiff's bases (2a and 2b) for the synthesis of their Zn-based complexes and explored their potential in the ring-opening polymerization of benzoxazines. The two zinc complexes of composition [Zn{(Ph)(CH3)C(2,6-iPr2C6H3–NCH–C4H2N)(2,6-iPr2C6H3–NCH–C4H2NH)}2] (3) and [ZnCl2{(Ph)(CH3)C(Ph3C–NHCH–C4H2N)2}] (4) were synthesized in good yields, and the structures were confirmed by single crystal X-ray diffraction (XRD). Later, zinc complexes (3 & 4) were used as catalysts to reduce the curing (ring-opening polymerization) temperature of benzoxazine monomers such as Bisphenol-A (BA-a) and Bisphenol-F (BF-a) benzoxazines. Dynamic scanning calorimetry (DSC) studies revealed that the on-set curing (Tp) temperatures were reasonably decreased upto 20% for the benzoxazines. Furthermore, the thermal stabilities of the polybenzoxazines (PBzs) derived in the presence of zinc catalysts (3 and 4) were compared with PBz obtained in the absence of catalyst under similar conditions. The thermal studies reveled that there is no significant changes in the initial degradation of polymers. However, the thermal stability in terms of char yields at 800 °C improved upto 10–21% for the bisphenol-A/F benzoxazines.

SAE-PD-Seq: sequence autoencoder-based pre-training of decoder for sequence learning tasks

Abstract: Sequence learning approaches require careful tuning of parameters for their success. Pre-trained sequence models exhibit a superior performance compared to the sequence models that are randomly initialized. This work presents a sequence autoencoder based pre-trained decoder approach for sequence learning. This approach is applicable for the tasks in which the output is a sequence. In the proposed method, a SAE (Sequence Auto Encoder) is trained with an objective of reconstructing the input sequence. The weights of the pre-trained SAE are then used to initialize the decoder in the sequence model developed based on the encoder–decoder paradigm. The proposed pre-trained decoder-based approach achieves superior performance as compared to the pre-trained encoder-based approach and the pre-trained encoder- and decoder-based approach. The behavior of the suggested approach is examined using unsupervised pre-training. The proposed method is evaluated for neural machine translation and image caption generation tasks. Outcomes of the experimental studies on benchmark datasets indicate the effectiveness of the proposed approach.