Prasant Kumar Pattnaik

Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization

Development of a learning method for optimization of network topology of ANN.Use of PSO trained ANN in channel equalization.PSO trained ANN equalizers performs better than PSO based equalizers as well as ANN based equalizers. In this paper, we apply Artificial Neural Network (ANN) trained with Particle Swarm Optimization (PSO) for the problem of channel equalization. Existing applications of PSO to Artificial Neural Networks (ANN) training have only been used to find optimal weights of the network. Novelty in this paper is that it also takes care of appropriate network topology and transfer functions of the neuron. The PSO algorithm optimizes all the variables, and hence network weights and network parameters. Hence, this paper makes use of PSO to optimize the number of layers, input and hidden neurons, the type of transfer functions etc. This paper focuses on optimizing the weights, transfer function, and topology of an ANN constructed for channel equalization. Extensive simulations presented in this paper shows that, as compared to other ANN based equalizers as well as Neuro-fuzzy equalizers, the proposed equalizer performs better in all noise conditions.

Performance study of some dynamic load balancing algorithms in cloud computing environment

In Cloud computing environment the utilization of computing resources may be available through the service that redernal by cloud consumers to service providers over the Internet. Due to popularity of Cloud computing environment, the cloud computing users are increasing day by day and that has become one of the important challenges for the cloud providers in terms of load balancing. The top most challenge of cloud service providers is load balancing which is mostly used for effective operation in cloud computing environment. However, load balancing distributes the traffic evenly over multiple paths by minimizing the Response time, optimizing the resources, maximizing the throughput and by avoiding single resource overloading. In this present work, we have focused on dynamic load balancing algorithms namely modified throttled, FCFS and Particle Swam Optimization based on their performance using Cloud Analyst Simulator. Simulation outcomes are recorded in terms of the Response time and datacenter processing time of the three algorithms along with its performance and arrival cost details.

A Novel Scheduling Algorithm for Cloud Computing Environment

Cloud computing is the most recent computing paradigm, in the Information Technology where the resources and information are provided on-demand and accessed over the Internet. An essential factor in the cloud computing system is Task Scheduling that relates to the efficiency of the entire cloud computing environment. Mostly in a cloud environment, the issue of scheduling is to apportion the tasks of the requesting users to the available resources. This paper aims to offer a genetic based scheduling algorithm that reduces the waiting time of the overall system. However the tasks enter the cloud environment and the users have to wait until the resources are available that leads to more queue length and increased waiting time. This paper introduces a Task Scheduling algorithm based on genetic algorithm using a queuing model to minimize the waiting time and queue length of the system.

Application of cellular automata in symmetric key cryptography

This paper presents a symmetric key cryptography technique that uses cellular automata(CA). Proposed cryptosystem has been implemented in C. State transitions of programmable cellular automata (PCA) are the basis to define certain fundamental transformations to encrypt and decrypt in the cryptographic system. Different rule configurations are used to form group cellular automata that would be used in encryption and decryption.

An expeditious cum efficient algorithm for salt-and-pepper noise removal and edge-detail preservation using cardinal spline interpolation

This work proposes a faster and an efficient way to remove salt-and-pepper impulse noise and edge-preserving regularization of the henceforth obtained image. In this paper, we propose a two phase mechanism where the noisy pixels are identified and removed in the first phase. The detected noisy pixels in the first phase are involved in cardinal spline edge regularization process in the second phase. Promising results were found even for Noise levels as high as 95% with the proposed algorithm. The results were found to be much better than the previously proposed nonlinear filters or regularization methods both in terms of noise removal as well as edge regularization.

Some Popular Usability Evaluation Techniques for Websites

Usability is widely considered to be an essential attribute for fulfilling the quality requirements of any software product. In context to web based products especially web sites, usability is the process for making the navigational prospects more user friendly and task effectiveness to meet user’s satisfaction. If any website fails to navigate a user to his respective goals, then the user loses his interest and considers it as a less usable website. For simplicity, if any software fails to meet user’s need and satisfaction then it is considered as less usable and poor productive. This paper aims to survey on some usability evaluation techniques and factors that are helpful for usability measurement of web and intelligence computing.

The impact of delayed ACK on TCP variants protocols in wireless network

TCP is a connection oriented and reliable protocol of transport layer. TCP receiver generates one acknowledgment denoted as ACK for each data packet that received to acknowledges the sender. If the data packet and ACK uses the same path, then there may be collision between them due to channel contention. Finally TCP throughput is reduced due to huge ACK packet generation. TCP performance is very poor due to wireless channel characteristics. Since the packet loss is always related to path length, choosing appropriate delayed window size is an important parameter in wireless network. This paper, analyzes the impact of delayed acknowledgment on TCP variants say Newreno and Vegas in multi hop wireless networks. However the issues of congestion and loss recovery techniques for these two mechanisms are different. In this paper we study how well the TCP variants react with delayed acknowledgement strategy in multi hop network with different topologies and various flow patterns with respect to throughput, average delay and number of retransmission. The simulation result shows that TCP-Vegas achieve higher throughput, fewer retransmissions and less delay than Newreno. The performance of TCP-Vegas with delayed ack gets best performance among TCP variants.

Classification and Live Migration of Data-Intensive Cloud Computing Environment

Cloud computing is an emerging technology providing software and hardware resources to the users’ as pay-per-use basis, on the other hand in surge demand of current needs of anywhere and anytime the concept of mobile computing came into view. Both aims to provide service to the users as per their requirements and cloud computing provide better flexibility in terms of PaaS, SaaS and IaaS. Database handling is the important consideration of above type of computing environments. Many researchers are proposed issues of database using SQL and NoSQL space suitably for cloud computing scenario. Again available classification for the cloud database sited as a structured and unstructured schema-based along with small and big databases concepts. In this paper we have conducted a study of cloud databases as well as its classification in terms of ACID and NoACID. Again our work focuses on the architectural issues on Database as a Service (DaaS) based on the live migration from ACID based Database to NoACID based Database and vice-versa.

Maintaining Consistency in Data-Intensive Cloud Computing Environment

Cloud is a service that offers its users to access the shared pool of computing resources based on pay per use basis. Presently, cloud computing is adopted by most of the start-up companies and research areas. Data-intensive cloud computing is adopted in order to handle enormous amount of transactional data. This paper proposes a model in order to achieve the consistency under data-intensive cloud computing environment and compares the proposed model with an existing improved consistency model.

Analysis of TCP Variant Protocol Using Active Queue Management Techniques in Wired-Cum-Wireless Networks

In this work, we analyzed the performance of TCP variant protocols in wired-cum-wireless networks considering active queue management (AQM) techniques such as random exponential marking (REM) and adaptive virtual queue (AVQ) along with Droptail. For analysis, we consider Reno, Newreno, Sack1, and Vegas as TCP variants and proposed a network model for wired-cum-wireless scenario. Then, the performance of TCP variants is analyzed using delayed acknowledgement (DelACK) technique. The simulation results show that Newreno performs better than others when DelACK is not used. However, when DelACK is used, the performance of Vegas is better than others irrespective of AQM techniques.