Raj Kumari

Sensitivity of transverse flow toward isospin-dependent cross sections and symmetry energy

We study the transverse flow for systems having various N/Z ratios. We find the transverse flow is sensitive to N/Z ratio and, in fact, increases with N/Z of the system. The relative contribution of symmetry energy and isospin dependence of nucleon-nucleon cross section is also investigated. We find the greater sensitivity of symmetry energy in the lighter systems compared to heavier ones.

An efficient data offloading to cloud mechanism for smart healthcare sensors

The amalgamation of Mobile Cloud Computing (MCC) and Wireless Body Area Networks (WBANs) is the latest trend in the field of pervasive mobile health care monitoring systems. WBANs are low power sensor networks whose performance can be enhanced by integrating WBANs with cloud computing. Cloud Computing provides all the required resources (storage, memory, processing power) with minimum cost. An efficient health record management service is proposed in this article. It is based on real time data offloading to cloud from WBANs. The major goal of the proposed model is to effectively aggregate the data of patients in the various network segments in order to remove the communication overhead of headers and acknowledgements. A novel algorithm is proposed for data aggregation and offloading to the cloud. The proposed approach is energy efficient to deliver the data effectively to the cloud based health record management services. The performance of the proposed system is assessed on the basis of aggregation efficiency, total transmission time and end-to-end delay using matlab simulations. The experimental results show that the proposed model has added a minimum possible delay on the communication link and thus the data delivery has been efficient and quicker.

User preference-driven offloading scheme for mobile cloud computing

The concept of cloud computing is becoming prominent now a days. The phenomenon of cloud computing comprises the rationing of data and resources over an accessible network. Cloud comprises a large network of nodes, which provides services to the mobile nodes and the application which falls back on to the cloud are known as the cloud applications. In the previous researches, migration of the task to cloud is a static process. This research paper focuses on the optimization of execution time along with the cost needed to execute the task on to the cloud. The tasks are migrated to the cloud server node dynamically after comparing the deadline time for execution with the execution time of task on mobile device and the execution time of task on the cloud server node. If the execution time of task on mobile device is less than the deadline then the task is executed on the mobile device else the task is to be migrated to the cloud server. Along with the execution time optimization, cost for migrating the tasks is being calculated separately for every task. In this way, this paper concentrates on finding tradeoff between the execution time and the cost.

An efficient resource utilization based integrated task scheduling algorithm

Cloud computing is the network of distributed remote servers to access the information anytime, anywhere. Thus, it also referred to as ubiquitous computing. Cloud computing offers the high performance environment that has great sharing of resources across the distributed servers. Though there are large numbers of tasks to be allocated to these resources, it becomes very necessary to efficiently assign the tasks to the resources so as to gain the maximum resource utilization. Due to enormous data available, scheduling task appears to be NP hard problem. As it becomes critical to find the exact solutions, various meta-heuristic techniques are used to attain approximate optimal solutions. In this research paper, an optimal task scheduling method is implemented. Tasks are scheduled by using Particle Swarm Optimization (PSO) algorithm by means of max-min algorithm. The main attention is to reduce the makespan and maximize the CPU utilization. From result simulations, it has been observed that makespan value shows makespan improvement by an average of 5.01% over the existing scheduling technique which is integration of Bee colony optimization algorithm and Particle swarm optimization. Similarly, for resource utilization parameter, this technique shows an average improvement by 3.63% over the previous existing method.

A Review on Comparison of Workflow Scheduling Algorithms with Scientific Workflows

Cloud computing is a technology that uses web and the clients can access the information by means of web programs. Rather than storing data on the desktop, it is stored on the cloud. Cloud as the name alludes is an abstraction of some complex infrastructure. Scheduling of tasks with minimum usage of resources and achieving maximum profit is an important concern in cloud computing. Load balancing is an important mechanism taken into account to handle the load on various dependent nodes in distributed environment. Due to large number of tasks in distributed environment, workflows are used for scheduling the tasks. In this paper, Scientific workflows are used to carry out the simulation on task scheduling algorithms with cloud resources. This review paper compares various scheduling algorithms on the basis of parameters like execution time and total cost that includes communication cost for input and output the data and computation cost. From simulation results, it is concluded that all algorithms shows different results depending upon the specific workflow due to varying size of their tasks.

Power and delay analysis of CMOS multipliers using Vedic algorithm

This paper presents an effective Vedic algorithm called as Urdhva-Tiryyagbhyam Sutra implementation and design for multipliers using 45nm technology. Multiplier is one of the most important parts of almost all digital system hardware, so a high speed, reduced delay, reduced area and low power consumption multiplier design will results in effective digital system designs. Thus this paper present an effective design and implementation of a multiplier with high speed, reduced delays, less area and low power consumption using our ancient methodology of Vedic mathematics that is Urdhva-Tiryagbhyam Sutra. This implementation is done in 45nm technology using chip designing tool Cadence Virtuoso at backend. Implemented Multiplier consumes very low power because of its carry skip addition methodology, reduced hardware and reduced delays. A 2 Bit multiplier consumes a very low power 5.5489×10 −11 watt and delay of 1.924×10 −12 sec and 4 Bit multiplier consumes power of 0.0002854 watt and delay of 2.0873×10−7 sec.

Effective adaptive noise canceller design using normalized LMS

This paper presents the application of Adaptive filters in noise cancellation during various communication processes, where non-stationary signals are transmitted. Adaptive filter estimates the noise signal and by applying the appropriate weights the estimated noise signal is eliminated from the information. For noise cancellation applications most efficient Adaptive filter Algorithms LMS and its normalized form NLMS are used and their comparative analysis is done in form of their output power, error power and SNR. In both of the algorithms concept of negative feedback is utilized in the cancellation of noise from the signal and thus both of these are also called negative feedback algorithms. Implementation and analysis is done by applying different step sizes on different order of filter. Order of filter is taken as 4, 8, 12 and then by changing the values of coefficients the graphical and computational analysis is done. Finally an efficient design using NLMS algorithm is implemented where order is taken as 8 and step size 0.2. This results as a low error power (11.7221 db) and a high value of SNR (1.1445) than that of LMS algorithms.