A Novel Method for Acoustic Noise Cancellation

Over the last several years Acoustic Noise Cancellation (ANC) has been an active area of research and various adaptive techniques have been implemented to achieve a better online acoustic noise cancellation scheme. Here we introduce the various adaptive techniques applied to ANC viz. the LMS algorithm, the Filtered-X LMS algorithm, the Filtered-S LMS algorithm and the Volterra Filtered-X LMS algorithm and try to understand their performance through various simulations. We then take up the problem of cancellation of external acoustic feedback in hearing aid. We provide three different models to achieve the feedback cancellation. These are - the adaptive FIR Filtered-X LMS, the adaptive IIR LMS and the adaptive IIR PSO models for external feedback cancellation. Finally we come up with a comparative study of the performance of these models based on the normalized mean square error minimization provided by each of these feedback cancellation schemes

GSM Based Display ToolKit

Wireless communication has announced its arrival on big stage and the world is going mobile. We want to control everything and without moving an inch. This remote control of appliances is possible through Embedded Systems. The use of “Embedded System in Communication” has given rise to many interesting applications that ensures comfort and safety to human life. The main aim of the project will be to design a SMS driven automatic display toolkit which can replace the currently used programmable electronic display. It is proposed to design receive cum display toolkit which can be programmed from an authorized mobile phone. The message to be displayed is sent through a SMS from an authorized transmitter. The toolkit receives the SMS, validates the sending Mobile Identification Number (MIN) and displays the desired information after necessary code conversion. The system is made efficient by using ‘clone’ SIMs of same MIN in a geographical area so that the same SMS can be received by number of display boards in a locality using techniques of time division multiple access. Started of as an instantaneous News display unit, we have improved upon it and tried to take advantage of the computing capabilities of microcontroller. We envision a toolkit that will not only display message but also can be used to do some mechanical work

Methodological Issues in Management Research

In order to produce information that will be dependably useful to managers, research must be carefully planned, carried out, and interpreted, Good research does not just happen. It is the result of deliberate application of well-tested methods. The aim of this paper is to outline some of those methods and why they are important

Techniques to Minimize Energy Consumption in Cloud System

Cloud computing is being widely applied to a variety of large size computational problems. These computational environments consist of many heterogeneous computing modules; these modules incorporate with each other to implement the solution of various problems. A typical cloud deployment consumes a significant amount of energy, and higher energy consumption has an adverse impact on the environment. Reducing energy consumption in the cloud environment is both a research and an operational challenge for the current research community and industry. The objective of this research is to minimize the energy consumed by the cloud system, in particular, considering the execution of tasks (service requests) with the help of virtual machines. A survey of the state-of-the-art in an energy-efficient cloud computing system is presented. In this thesis, we have used four different approaches: (i) task allocation, (ii) virtual machine consolidation, (iii) virtual machine selection using Dynamic Voltage and Frequency Scaling (DVFS), and (iv) resource allocation in mobile cloud system to optimize energy consumption of the cloud system. All the proposedalgorithms are simulated with the help of CloudSim simulator. The task allocation problem is a well known NP-complete problem. We have presented two different approaches of task allocation to optimize the energy consumption and makespan of the cloud system. The first approach deals with three task allocation algorithms based on metaheuristics techniques namely, Particle Swarm Optimization (PSO), Binary PSO (BPSO), and BAT. In the second approach, a deterministic Adaptive Task Allocation Algorithm (ATAA) is proposed to allocate tasks to the cloud system. The task allocated to the cloud system is represented with the help of an ETC (Expected Time to Compute) matrix. The ETC matrix holds the time required to compute a specific task on different Virtual Machines (VMs). The simulation is carried out to compare the performance of three proposed metaheuristic based task allocation algorithms by varying the number of VMs and tasks. And, also the performance of the proposed Adaptive Task Allocation Algorithm (ATAA) is analyzed by comparing with the random, and Round-Robin (inbuilt algorithm in CloudSim) algorithms. Simulation results indicate in favor of the proposed scheme (ATAA). In a cloud system, VM consolidation deals with the allocation of VMs to hosts. In this thesis, a task-based VM-consolidation algorithm is proposed to minimize the energy consumption, makespan, and task rejection rate of the cloud system. The proposed algorithm efficiently allocate tasks to VMs and then VMs to hosts. The performance of the proposed algorithm, i.e., Energy-aware Task-based Virtual Machine Consolidation (ETVMC) and the existing algorithms: First Come First Serve (FCFS), Round-Robin, and EERACC proposed in [31] are compared by varying the number task and number of VM with the help of CloudSim simulator. The simulation results indicate minimum energy consumed by the proposed algorithm in comparison to other existing algorithms Dynamic Voltage and Frequency Scaling (DVFS) technique is a technique through which energy consumption can be minimized for computing resources. We have proposed a heuristic algorithm, i.e., Energy-Efficient DVFS-based Task Scheduling Algorithm (EEDTSA) for the selection of VM for each task to optimize the energy utilization by applying the DVFS technique. The DVFS Mechanism is applied to the virtual machines level to reduce the energy of the cloud system. Moreover, the performance of the diverse algorithms (Random allocation, and FCFS) are compared with the proposed DVFS-based VM selection algorithm. It can be observed from the simulation results that the proposed algorithm (EEDTSA) offers greater energy saving as compared to other existing techniques. We have proposed a mobile cloud system with edge data center interfacing mobile user to the cloud system. There are three computing entities (VMs that runs on the top of the host in the data center, VMs that runs on the top of the edge computing devices, and mobile computing devices) used by the mobile user. An energy efficient task allocation in mobile cloud system scheme, i.e., Energy-Efficient Task Allocation in Mobile Cloud System (EETAMCS) is proposed where the selection of appropriate VM for a task with a deadline is explained. Instead of offloading of tasks directly to the cloud data center, in the proposed scheme, the tasks can be offloaded to the edge data center to minimize the energy consumption and execution delay. The result analysis of the proposed algorithm obtained indicates the utilization of edge data centers reduces energy consumption and execution dela

Protein sequence-structure-dynamics-function relationships: The close association of dynamics with protein function

The intrinsic dynamics of globular proteins is the key to the understanding of their function, being a consequence of protein structure and geometry. The view of protein structures has recently changed from native structures being considered to be a single rigid, static object into one where conformational ensembles coexist. Besides, allostery, the transmission of signals from a distant site to the active site, is a direct outcome of the detailed dynamics of a given protein. Investigating how dynamics controls protein function is one of the overall aims of our studies. It is essential to probe protein function by combining information from all three types of data: sequence, structure and dynamics, which combine to define their functions. The abundance of protein sequence data in repositories like UniProt and Pfam is huge and is strongly complementary to the rich data of protein structures in PDB. Exploiting this wealth of information and coupling it with molecular simulations that provide information on protein dynamics, facilitates the understanding and predicting of protein function, which is the underlying motivation and overall objective of the present work. The dynamic behavior of proteins is often altered upon the binding of ligands, partner proteins or other biological macromolecules such as DNA and RNA. This work describes the influence of binding on the intrinsic dynamics of proteins through studies on homooligomeric protein assemblies which are comprised of multiple subunits of the same protein. Specifically, this work compares the dynamics of functionally important residues of a single subunit in isolation with those in its assembled form. Next, is presented a systematic investigation of the extent of similarity between the protein dynamic communities obtained from molecular dynamics with those from a simpler molecular simulation method, the elastic network models. The focus is on the separate dynamic communities, which are those groups of residues, highly cohesive in terms of their motions and which move like a rigid unit. Elastic network models are models for protein cohesion and are particularly appropriate for application to this task. We also show how they can effectively capture the differences in community distributions for mutant and wild type forms of T4 lysozyme. Finally, a machine learning classification method is developed wherein protein dynamics information is coupled with structure, evolutionary and physicochemical properties to predict regulatory and functional binding sites. This work emphasizes the collective interplay between sequence, structure and dynamics as the key to the understanding of protein function. It also highlights the use of simplified molecular representations for simulations, i.e., the elastic network model, which can often be suitable as a substitute for atomic molecular dynamics. The machine learning models developed as a part of this work strongly point up the importance of including protein dynamics to improve predictions. The methods developed have potential practical applications, for instance as predictive models for identification of hot spot residues for site-directed mutagenesis or even for the prediction of sites where potential therapeutics could bind to restore dynamics and other disturbed functions, or even to suggest ways to generate new functions.</p

Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network

Worldwide, oral cancer is the sixth most common type of cancer. India is in 2nd position, with the highest number of oral cancer patients. To the population of oral cancer patients, India contributes to almost one-third of the total count. Among several types of oral cancer, the most common and dominant one is oral squamous cell carcinoma (OSCC). The major reason for oral cancer is tobacco consumption, excessive alcohol consumption, unhygienic mouth condition, betel quid eating, viral infection (namely human papillomavirus), etc. The early detection of oral cancer type OSCC, in its preliminary stage, gives more chances for better treatment and proper therapy. In this paper, author proposes a convolutional neural network model, for the automatic and early detection of OSCC, and for experimental purposes, histopathological oral cancer images are considered. The proposed model is compared and analyzed with state-of-the-art deep learning models like VGG16, VGG19, Alexnet, ResNet50, ResNet101, Mobile Net and Inception Net. The proposed model achieved a cross-validation accuracy of 97.82%, which indicates the suitability of the proposed approach for the automatic classification of oral cancer data

Protein dynamic communities from elastic network models align closely to the communities defined by molecular dynamics

Dynamic communities in proteins comprise the cohesive structural units that individually exhibit rigid body motions. These can correspond to structural domains, but are usually smaller parts that move with respect to one another in a protein’s internal motions, key to its functional dynamics. Previous studies emphasized their importance to understand the nature of ligand-induced allosteric regulation. These studies reported that mutations to key community residues can hinder transmission of allosteric signals among the communities. Usually molecular dynamic (MD) simulations (~ 100 ns or longer) have been used to identify the communities—a demanding task for larger proteins. In the present study, we propose that dynamic communities obtained from MD simulations can also be obtained alternatively with simpler models–the elastic network models (ENMs). To verify this premise, we compare the specific communities obtained from MD and ENMs for 44 proteins. We evaluate the correspondence in communities from the two methods and compute the extent of agreement in the dynamic cross-correlation data used for community detection. Our study reveals a strong correspondence between the communities from MD and ENM and also good agreement for the residue cross-correlations. Importantly, we observe that the dynamic communities from MD can be closely reproduced with ENMs. With ENMs, we also compare the community structures of stable and unstable mutant forms of T4 Lysozyme with its wild-type. We find that communities for unstable mutants show substantially poorer agreement with the wild-type communities than do stable mutants, suggesting such ENM-based community structures can serve as a means to rapidly identify deleterious mutants.This article is published as Mishra SK, Jernigan RL (2018) Protein dynamic communities from elastic network models align closely to the communities defined by molecular dynamics. PLoS ONE 13(6): e0199225. doi: 10.1371/journal.pone.0199225.</p

Performance and Cost Evaluation of Query Plans from the Student Database Using Specific G.A. Technique

Abstract Many educational institutions in India have already established online teaching and learning methodologies with different capabilities and approaches. After inspired from foreign universities, they have successfully adopted the learning online network with computer assisted personalized approaches. Usually, two kinds of large data sets are involved with the system, e.g. educational resources such as web pages, demonstrations, simulations, and individualized problems designed for use on homework assignments, and information about users who create, modify, assess, or use these resources. Genetic Algorithms (GAs) may be implemented as an effective tool to use in pattern recognition. The important aspect of GAs in a learning context is their use in pattern recognition. There are two different approaches for application of GA in pattern recognition. First of all apply a GA directly as a classifier. In this case G.A. may be applied to find the decision boundary in N dimensional feature space. Then use a GA as an optimization tool for resetting the parameters in other classifiers. Most applications of GAs in pattern recognition optimize some parameters in the classification process. In many applications of GAs, feature selection has been used. GAs has also been applied to find an optimal set of feature weights which improve classification accuracy. In this paper, it is intended to use a GA to optimize a combination of classifiers. The objective is to predict the students&apos; semester grades of a reputed Engineering college of India based on some acquired features. It is also intended to evaluate the size of each chromosome, e.g. student level at each query level as well as cost of query plans which may be associated with the student level. The objective is also aimed to evaluate the performance of the queries as well as query plans associated with the student database