Radial basis function neural networks are used in a variety of applications such as pattern recognition, nonlinear identification, control and time series prediction. In this paper, the learning algorithm of radial basis function neural networks is analyzed in a feedback structure. The robustness of the learning algorithm is discussed in the presence of uncertainties that might be due to noisy perturbations at the input or to modeling mismatch. An intelligent adaptation rule is developed for the learning rate of RBFNN which gives faster convergence via an estimate of error energy while giving guarantee to the l 2 stability governed by the upper bounding via small gain theorem. Simulation results are presented to support our theoretical development.
Devices in a visual sensor network (VSN) are mostly powered by batteries, and in such a network, energy consumption and bandwidth utilization are the most critical issues that need to be taken into consideration. The most suitable solution to such issues is to compress the captured visual data before transmission takes place. Compressive sensing (CS) has emerged as an efficient sampling mechanism for VSN. CS reduces the total amount of data to be processed such that it recreates the signal by using only fewer sampling values than that of the Nyquist rate. However, there are few open issues related to the reconstruction quality and practical implementation of CS. The current studies of CS are more concentrated on hypothetical characteristics with simulated results, rather than on the understanding the potential issues in the practical implementation of CS and its computational validation. In this paper, a low power, low cost, visual sensor platform is developed using an Arduino Due microcontroller board, XBee transmitter, and uCAM-II camera. Block compressive sensing (BCS) is implemented on the developed platform to validate the characteristics of compressive sensing in a real-world scenario. The reconstruction is performed by using the joint multi-phase decoding (JMD) framework. To the best of our knowledge, no such practical implementation using off the shelf components has yet been conducted for CS.
Lung Cancer (LC) is among the most death-causing cancers, has caused the most destruction and is a gender-neutral cancer, and WHO has kept this cancer on its priority list to find the cure. We have used high-throughput virtual screening, standard precision docking, and extra precise docking for extensive screening of Drug Bank compounds, and the uniqueness of this study is that it considers multiple protein targets of prognosis and metastasis of LC. The docking and MM\GBSA calculation scores for the Tiaprofenic acid (DB01600) against all ten proteins range from -8.422 to -5.727 kcal/mol and - 47.43 to -25.72 kcal/mol, respectively. Also, molecular fingerprinting helped us to understand the interaction pattern of Tiaprofenic acid among all the proteins. Further, we extended our analysis to the molecular dynamic simulation in a neutralised SPC water medium for 100 ns. We analysed the root mean square deviation, fluctuations, and simulative interactions among the protein, ligand, water molecules, and protein-ligand complexes. Most complexes have shown a deviation of <2 Å as cumulative understanding. Also, the fluctuations were lesser, and only a few residues showed the fluctuation with a huge web of interaction between the protein and ligand, providing an edge that supports that the protein and ligand complexes were stable. In the MTT-based Cell Viability Assay, Tiaprofenic Acid exhibited concentration-dependent anti-cancer efficacy against A549 lung cancer cells, significantly reducing viability at 100 μg/mL. These findings highlight its potential as a therapeutic candidate, urging further exploration into the underlying molecular mechanisms for lung cancer treatment.