Bio: Vinal Patel is an academic researcher from Indian Institute of Information Technology and Management, Gwalior. The author has contributed to research in topics: Active noise control & Adaptive filter. The author has an hindex of 8, co-authored 22 publications receiving 228 citations. Previous affiliations of Vinal Patel include Indian Institute of Technology Gandhinagar & University of Southampton.
TL;DR: A novel nonlinear filter, which incorporates the concept of exponential sinusoidal models into nonlinear filters based on functional link networks (FLNs) has been developed in this paper and an adaptive exponential filtered-s least mean square algorithm has been derived.
Abstract: A novel nonlinear filter, which incorporates the concept of exponential sinusoidal models into nonlinear filters based on functional link networks (FLNs) has been developed in this paper. The proposed filter is designed to provide improved convergence characteristics over traditional FLN filters. The conventional trigonometric FLN may be considered as a special case of the proposed adaptive exponential FLN (AEFLN). An adaptive exponential least mean square (AELMS) algorithm has been derived and the same has been successfully applied for identification of a couple of nonlinear plants. The AEFLN-based nonlinear active noise control (ANC) system has also been designed and an adaptive exponential filtered-s least mean square (AEFsLMS) algorithm has been developed to update the weights as well as the exponential factor. Simulation study has revealed the improved noise mitigation offered by the AEFLN-based nonlinear ANC system.
TL;DR: A kernel extreme learning machine (KELM) based CPP prediction model called KELM-CPPpred is developed that outperformed existing prediction approaches based on SVM, RF, and ANN and tested the prediction accuracy with the existing artificial neural network (ANN), random forest, and support vector machine (SVM) approaches on respective benchmark data sets used in the previous studies.
Abstract: Cell-penetrating peptides (CPPs) facilitate the transport of pharmacologically active molecules, such as plasmid DNA, short interfering RNA, nanoparticles, and small peptides. The accurate identification of new and unique CPPs is the initial step to gain insight into CPP activity. Experiments can provide detailed insight into the cell-penetration property of CPPs. However, the synthesis and identification of CPPs through wet-lab experiments is both resource- and time-expensive. Therefore, the development of an efficient prediction tool is essential for the identification of unique CPP prior to experiments. To this end, we developed a kernel extreme learning machine (KELM) based CPP prediction model called KELM-CPPpred. The main data set used in this study consists of 408 CPPs and an equal number of non-CPPs. The input features, used to train the proposed prediction model, include amino acid composition, dipeptide amino acid composition, pseudo amino acid composition, and the motif-based hybrid features. We further used an independent data set to validate the proposed model. In addition, we have also tested the prediction accuracy of KELM-CPPpred models with the existing artificial neural network (ANN), random forest (RF), and support vector machine (SVM) approaches on respective benchmark data sets used in the previous studies. Empirical tests showed that KELM-CPPpred outperformed existing prediction approaches based on SVM, RF, and ANN. We developed a web interface named KELM-CPPpred, which is freely available at http://sairam.people.iitgn.ac.in/KELM-CPPpred.html.
TL;DR: An update rule has been derived for the proposed ANC system, which not only updates the weights of the linear network, but also updates the nature of the activation function.
Abstract: A spline adaptive filter (SAF) based nonlinear active noise control (ANC) system is proposed in this paper. The SAF consists of a linear network of adaptive weights in cascade with an adaptive nonlinear network. The nonlinear network, in-turn consists of an adaptive look-up table followed by a spline interpolation network and forms an adaptive activation function. An update rule has been derived for the proposed ANC system, which not only updates the weights of the linear network, but also updates the nature of the activation function. An extensive simulation study has been conducted to evaluate the noise mitigation performance of the proposed scheme and the new method has been shown to provide improved noise cancellation efficiency with a lesser computational load in comparison with other popular ANC systems.
TL;DR: An adaptive feedback canceller, which is trained using a set of sparse adaptive algorithms is designed in this paper to take advantage of the sparseness of the acoustic feedback path in a hearing aid.
Abstract: Cancelling the effect of acoustic feedback is a challenging task in the design of a behind the ear digital hearing aid. In traditional behind the ear digital hearing aids, feedback cancellation is usually achieved using an adaptive finite impulse response filter, the weights of which are updated using a suitable learning rule. However, the impulse response of the acoustic feedback path in a hearing aid is sparse in nature and traditional feedback cancellation systems are not designed to utilize this sparseness. An adaptive feedback canceller, which is trained using a set of sparse adaptive algorithms is designed in this paper to take advantage of the sparseness. Further, an attempt has been made to enhance the convergence of the feedback cancellation mechanism by introducing an adaptive de-correlation filter as well as using the concept of probe noise injection. The proposed feedback cancellation schemes are shown to provide improved and accurate feedback cancellation over traditional feedback cancellation mechanisms.
TL;DR: An adaptive infinite impulse response (IIR) spline filter for non-linear ANC based on a feedback spline adaptive filter that can compensate for acoustic feedback and nonlinearities.
Abstract: Implementation of a feed-forward active noise control (ANC) system in a short duct may cause acoustic feedback between the active loudspeaker and the reference microphone. The conventional filtered-x least mean square (FxLMS) algorithm based ANC systems are not designed to handle this situation. Similarly, an FxLMS algorithm based ANC system fails to effectively mitigate noise when non-linearities are present in the system. In an endeavor to overcome these two limitations of traditional ANC systems, in this paper, we propose an adaptive infinite impulse response (IIR) spline filter for non-linear ANC. The adaptive nature of the spline activation function in the proposed scheme enables the filter to effectively switch from a linear to non-linear nature and vice versa, depending upon the scenario in which the ANC system is implemented. HighlightsANC system based on a feedback spline adaptive filter.Can compensate for acoustic feedback and nonlinearities.The nonlinearity of the filter is adaptive.Improved noise cancellation over other schemes.
01 Jan 2017
TL;DR: An updated review on key developments of computational modeling of peptide–protein interactions (PepPIs) with an aim to assist experimental biologists exploit suitable docking methods to advance peptide interfering strategies against PPIs.
Abstract: Protein–protein interactions (PPIs) execute many fundamental cellular functions and have served as prime drug targets over the last two decades. Interfering intracellular PPIs with small molecules has been extremely difficult for larger or flat binding sites, as antibodies cannot cross the cell membrane to reach such target sites. In recent years, peptides smaller size and balance of conformational rigidity and flexibility have made them promising candidates for targeting challenging binding interfaces with satisfactory binding affinity and specificity. Deciphering and characterizing peptide–protein recognition mechanisms is thus central for the invention of peptide-based strategies to interfere with endogenous protein interactions, or improvement of the binding affinity and specificity of existing approaches. Importantly, a variety of computation-aided rational designs for peptide therapeutics have been developed, which aim to deliver comprehensive docking for peptide–protein interaction interfaces. Over 60 peptides have been approved and administrated globally in clinics. Despite this, advances in various docking models are only on the merge of making their contribution to peptide drug development. In this review, we provide (i) a holistic overview of peptide drug development and the fundamental technologies utilized to date, and (ii) an updated review on key developments of computational modeling of peptide–protein interactions (PepPIs) with an aim to assist experimental biologists exploit suitable docking methods to advance peptide interfering strategies against PPIs.
TL;DR: Overall, it is shown that using ML models in peptide research can streamline the development of targeted peptide therapies and avoid the common pitfalls and challenges of using ML approaches for peptide therapeutics.
Abstract: Discovery and development of biopeptides are time-consuming, laborious, and dependent on various factors. Data-driven computational methods, especially machine learning (ML) approach, can rapidly and efficiently predict the utility of therapeutic peptides. ML methods offer an array of tools that can accelerate and enhance decision making and discovery for well-defined queries with ample and sophisticated data quality. Various ML approaches, such as support vector machines, random forest, extremely randomized tree, and more recently deep learning methods, are useful in peptide-based drug discovery. These approaches leverage the peptide data sets, created via high-throughput sequencing and computational methods, and enable the prediction of functional peptides with increased levels of accuracy. The use of ML approaches in the development of peptide-based therapeutics is relatively recent; however, these techniques are already revolutionizing protein research by unraveling their novel therapeutic peptide functions. In this review, we discuss several ML-based state-of-the-art peptide-prediction tools and compare these methods in terms of their algorithms, feature encodings, prediction scores, evaluation methodologies, and software utilities. We also assessed the prediction performance of these methods using well-constructed independent data sets. In addition, we discuss the common pitfalls and challenges of using ML approaches for peptide therapeutics. Overall, we show that using ML models in peptide research can streamline the development of targeted peptide therapies.
TL;DR: This work provides a comprehensive review on the biological importance of CPPs, CPP database and existing ML-based methods for CPP prediction, and finds that existing prediction tools tend to more accurately predict C PPs and non-CPPs with the length of 20-25 residues long than peptides in other length ranges.
Abstract: Cell-penetrating peptides (CPPs) facilitate the delivery of therapeutically relevant molecules, including DNA, proteins and oligonucleotides, into cells both in vitro and in vivo. This unique ability explores the possibility of CPPs as therapeutic delivery and its potential applications in clinical therapy. Over the last few decades, a number of machine learning (ML)-based prediction tools have been developed, and some of them are freely available as web portals. However, the predictions produced by various tools are difficult to quantify and compare. In particular, there is no systematic comparison of the web-based prediction tools in performance, especially in practical applications. In this work, we provide a comprehensive review on the biological importance of CPPs, CPP database and existing ML-based methods for CPP prediction. To evaluate current prediction tools, we conducted a comparative study and analyzed a total of 12 models from 6 publicly available CPP prediction tools on 2 benchmark validation sets of CPPs and non-CPPs. Our benchmarking results demonstrated that a model from the KELM-CPPpred, namely KELM-hybrid-AAC, showed a significant improvement in overall performance, when compared to the other 11 prediction models. Moreover, through a length-dependency analysis, we find that existing prediction tools tend to more accurately predict CPPs and non-CPPs with the length of 20-25 residues long than peptides in other length ranges.
TL;DR: In this article, a data-driven approach for condition monitoring of generator bearing using temporal temperature data is presented, where four algorithms, the support vector regression machine, neural network, extreme learning machine, and the deep belief network are applied to model the bearing behavior.
Abstract: Wind turbines are widely installed as the new source of cleaner energy production. Dynamic and random stress imposed on the generator bearing of a wind turbine may lead to overheating and failure. In this paper, a data-driven approach for condition monitoring of generator bearings using temporal temperature data is presented. Four algorithms, the support vector regression machine, neural network, extreme learning machine, and the deep belief network are applied to model the bearing behavior. Comparative analysis of the models has demonstrated that the deep belief network is most accurate. It has been observed that the bearing failure is preceded by a change in the prediction error of bearing temperature. An exponentially-weighted moving average (EWMA) control chart is deployed to trend the error. Then a binary vector containing the abnormal errors and the normal residuals are generated for classifying failures. LS-SVM based classification models are developed to classify the fault bearings and the normal ones. The proposed approach has been validated with the data collected from 11 wind turbines.