EMEEDP: Enhanced Multi-hop Energy Efficient Distributed Protocol for Heterogeneous Wireless Sensor Network

In WSN (Wireless Sensor Network) every sensor node sensed the data and transmit it to the CH (Cluster head) or BS (Base Station). Sensors are randomly deployed in unreachable areas, where battery replacement or battery charge is not possible. For this reason, Energy conservation is the important design goal while developing a routing and distributed protocol to increase the lifetime of WSN. In this paper, an enhanced energy efficient distributed protocol for heterogeneous WSN have been reported. EMEEDP is proposed for heterogeneous WSN to increase the lifetime of the network. An efficient algorithm is proposed in the form of flowchart and based on various clustering equation proved that the proposed work accomplishes longer lifetime with improved QOS parameters parallel to MEEP. A WSN implemented and tested using Raspberry Pi devices as a base station, temperature sensors as a node and xively.com as a cloud. Users use data for decision purpose or business purposes from xively.com using internet.Comment: 6 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:1409.1412 by other author

Jordan-Schwinger realizations of three-dimensional polynomial algebras

A three-dimensional polynomial algebra of order $m$ is defined by the commutation relations $[P_0, P_\pm]$ $=$ $\pm P_\pm$, $[P_+, P_-]$ $=$ $\phi^{(m)}(P_0)$ where $\phi^{(m)}(P_0)$ is an $m$-th order polynomial in $P_0$ with the coefficients being constants or central elements of the algebra. It is shown that two given mutually commuting polynomial algebras of orders $l$ and $m$ can be combined to give two distinct $(l+m+1)$-th order polynomial algebras. This procedure follows from a generalization of the well known Jordan-Schwinger method of construction of $su(2)$ and $su(1,1)$ algebras from two mutually commuting boson algebras.Comment: 10 pages, LaTeX2

Performance of Improved Forage Species under Dry Temperate Conditions of North Western Himalayas

The dry temperate region of Himalayas is characterized by low precipitation, low temperature and high snowfall. In this region generally, all the areas excluding the intensively cultivated one are used as pasture and grasslands. The area is characterized by sloppy desert mountains with crop growing season of 5-6 months (April to September). In the region due to continuous heavy grazing and lack of management indigenous grass species presently represent the third or fourth stage of degradation. In north western Himalayas livestock plays a significant role in sustaining the livelihood of people, but in the region all forage resources are hardly enough to meet the forage requirement of even 40-50 per cent of the existing livestock population. Under this situation the planting of ecologically adaptable improved grasses and forage legumes appears to be a viable preposition to increase the forage production and availability in the region. Keeping in view this, the present study was undertaken to study the comparative performance of improved grasses and legume species under dry temperate climatic conditions

PERFORMANCE ANALYSIS USING CODE CONVERTER APPROACH AND THE APPLICATION OF APPROXIMATE ENTROPY AS POST CLASSIFIER FOR THE CLASSIFICATION OF EPILEPSY RISK LEVELS FROM EEG SIGNALS

ABSTRACTObjective: The electroencephalogram (EEG) is actually a measure of the cumulative firing of neurons in various parts of the brain. The EEG containsthe information with regard to the changes in the electrical potential of the brain which is obtained from a set of recording electrodes. The aim of thispaper is to give a performance analysis by considering the advantage of Code Converter technique and Approximated Entropy (ApEn) is used as a postclassifier for the classification of the epilepsy risk levels obtained from EEG signals.Methods: The Data Acquisition of EEG signals are done initially from the hospital. Then the code converter approach is presented, as working ondefinite alphabets is much easier when compared to that of working on numericals. Finally, ApEn is used as a Post Classifier for the classification ofepilepsy risk levels from EEG signals.Results: The Performance Index and Quality Values are the two important parameters that are used to assess the performance of the Code Convertersand the Classifier. The Perfect Classification rate of 83.94% is achieved along with an Accuracy of 91.97% and a Quality Value of 18.5.Conclusion: The computation of this procedure seems to be very simple and versatile. Future works may use different Dimensionality Reductiontechniques to analyze its performance with Approximated Entropy as Post Classifier.Keywords: Electroencephalogram signals, Code converter, Performance index, Quality values