Institution
Vignan University
Education•Guntur, Andhra Pradesh, India•
About: Vignan University is a education organization based out in Guntur, Andhra Pradesh, India. It is known for research contribution in the topics: Control theory & CMOS. The organization has 1138 authors who have published 1381 publications receiving 7798 citations.
Topics: Control theory, CMOS, Cement, Machining, Wireless sensor network
Papers
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TL;DR: Using stircasting, the hybrid aluminium metal matrix composites are prepared with the reinforcement of SiC and graphite particulates by varying equally 2, 4, 6, and 8% by weight as discussed by the authors.
Abstract: Using stir-casting, the hybrid aluminium metal matrix composites are prepared with the reinforcement of SiC and graphite particulates by varying equally 2%, 4%, 6%, and 8% by weight. The wear and frictional force for the prepared specimens were investigated through pin on disc wear testing machine. Exercising ANOVA technique, the wear rate and coefficient of friction was accomplished with the impact of applied load, sliding speed and sliding distance. Using Taguchi technique, experiments have been performed depending on the design of experiments. For analysis of data L9 Orthogonal array was preferred. Wear resistance and frictional force were influenced majorly with the reinforcement of graphite. The morphology of the depleted surfaces and the wear fragments were analysed to recognize the wear property. Distinguished to other percentages of reinforcements, 6% wt. of SiC and 6% wt. of graphite has demonstrated high wear resistance.
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02 Jul 2020TL;DR: A dynamic channel state interval (CSI) based feedback design for improving the features of relay selection to improve the performance of the D2D communication networks.
Abstract: Relay selection in Device-to-Device (D2D) communication network is a prominent task due to the imperfect channel conditions and sensing intervals. This paper presents a dynamic channel state interval (CSI) based feedback design for improving the features of relay selection to improve the performance of the D2D communication networks. Based on the dynamic detection of imperfect channel conditions and sensing intervals, the precoding process is employed for the signal derivative in the relay selection process. The relay with a high order of signal output determines its selection. The performance of the proposed method is verified for the sum rate metric for the varying CSI bits, transmit power and inter-user distance.
1 citations
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TL;DR: In this paper, a new framework DeepLight Weight is proposed to resolve the high server latency and high usage of memory issues in online advertising by pruning redundant parameters and the dense embedding vectors.
Abstract: Online advertising has expanded to a hundred-dollar billion industry in recent years, with sales growing at faster rate in every year. Prediction of the click-through rate (CTR) is an important role in recommended systems and online ads. Click through rating (CTR) is the newest evolution in the advertising and marketing digital world. It is essential for any online advertising company in real time to display the appropriate ads to the right users in the correct context. A huge amount of research work proposed considers each ad separately and does not takes in the relationship with other ads that may have an impact on Click Through Rate. A Factorization machine, a more generalized predictor like support vector machines (SVM) is not able to estimate reliable parameters under sparsity. The main drawback is that the primary features and existing algorithms considers the large weighted parameters. KGCN (Knowledge graph-based convolution network) overcomes the drawback and works on alternating graphs which creates additional clustering and node comparison with high latency and performance. A new framework DeepLight Weight is proposed to resolve the high server latency and high usage of memory issues in online advertising. This work presents a framework to improve the CTR predictions with an objective to accelerate the model inference, prune redundant parameters and the dense embedding vectors. Field Weighed Factorization machine helps to organize the data features with high structure to improve the accuracy. For clearing latency issues, structural pruning makes the algorithm work with dense matrices by combining and executing the individual matrix values or neural nodes.
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TL;DR: The present work contributes and implies the biological significance of Ti-complex and the correlation between the structure and the biological activities of such Ti- complexes supported by Schiff base systems opens up opportunities for further exploitation of similar biologically active titanium systems.
Abstract: Titanium(IV)-complex of chemical composition [{(NNO)2Ti}3O3] (2) bearing bidentate heteroditopic Schiff base [(C5H4OH)-N=CH-C4H3-NH] (L1) ligand in titanium coordination sphere was reported and its biological significance was evaluated. The in vitro cytotoxicity of L1 and 2 were evaluated by using MTT assay on cancer cell lines (MCF-7 & A549) and observed significant cytotoxicity. Further, the LDH and NO assay studies on both L1 and 2 on cancer cell lines revealed that the enhanced cytotoxicity compared to standard anticancer drug i.e. cisplatin. The DNA binding studies of tested compounds with Ct-DNA molecule by using UV-visible and fluorescence spectra and molecular docking studies revealed that moderate to good binding interactions with test molecules. Thus, the present work contributes and implies the biological significance of Ti-complex (2) and the correlation between the structure and the biological activities of such Ti-complexes supported by Schiff base systems opens up opportunities for further exploitation of similar biologically active titanium systems.
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Authors
Showing all 1166 results
Name | H-index | Papers | Citations |
---|---|---|---|
Muthukaruppan Alagar | 40 | 316 | 5914 |
Ebenezer Daniel | 40 | 180 | 5597 |
P. B. Kavi Kishor | 30 | 123 | 3486 |
V. Purnachandra Rao | 26 | 59 | 1723 |
Muddu Sekhar | 24 | 135 | 1929 |
Anandarup Goswami | 23 | 44 | 5427 |
Reddymasu Sreenivasulu | 20 | 58 | 925 |
Murthy Chavali | 20 | 105 | 1699 |
Krishna P. Kota | 20 | 42 | 1172 |
Naveen Mulakayala | 17 | 39 | 937 |
Tondepu Subbaiah | 16 | 65 | 773 |
Bharat Kumar Tripuramallu | 15 | 34 | 574 |
Avireni Srinivasulu | 13 | 97 | 626 |
Abhinav Parashar | 13 | 29 | 375 |
Umesh Chandra | 13 | 39 | 550 |