A tensile wearable SHF antenna with efficient communication in defense beacon technology

The study projects a flexible and compact wearable pear-shaped Super High Frequency(SHF)antenna that can provide detailed location recognition and tracking applicable to defense beacon technology.This mini aperture with electrical dimensions of 0.12λ_(0)×0.22λ_(0)×0.01λ_(0)attains a vast bandwidth over 3.1-34.5 GHz Super High Frequency(SHF)frequency band at S_(11)≤-10 dB,peak gain of 7.14 dBi and proportionately homogeneous radiation pattern.The fractional bandwidth(%BW)acquired is 168%that envelopes diversified frequency spectrum inclusive of X band specifically targeted to all kinds of defense and military operations.The proposed antenna can be worn on a soldier's uniform and hence the Specific Absorption Rate simulation is accomplished.The Peak SAR Value over 1 g of tissue is 1.48 W/kg and for 10 g of tissue is 0.27 W/kg well under the safety standards.The flexibility is proven by analyzing the full electromagnetic simulations for various bending conditions.Time response analysis is attained with its Fidelity Factor and Group Delay.Communication excellence is determined using Link Budget Analysis and it is seen that margin at 100 Mbps is 62 m and at 200 Mbps is 59 m.Prototype is fabricated along with experimental validation.All the results show harmony in shaping the antenna to provide critical situational awareness and data sharing capabilities required in defense beacon technology for location identification.

Stem cell technologies in human health: Boon or bane?

The stem cells of an organism only possess extraordinary capacity to change into different cell types during the early life and growth of an organism. When these stem cells divide into different new cells, these either remain as stem cells or develop to become other cells with specialized function. For this reason, stem cells offer direct relevance to human health, as theoretically, using stem cell technology, different organs are expected to be regenerated. To this, the Human Embryonic Stem Cells (HESCs) are natural pluripotent cell, but ethical issues covering many countries have put research work on a bit back-foot. However, the Induced Pluripotent Stem Cells (iPSCs) technology has completely revitalized the world to use this technology universally and it therefore seems that more research on this technology will surely be of enormous help in public health. In addition, application of the stem cell technology in personalized-medicine has been started recently. In this concern, the stem cell banking facilities have provided new avenues for preserving the cord blood of the new-borne child and treat them in future by using her/his own preserved stem cells. However, like all new technologies, the output from stem cell research requires to be evaluated more closely. Furthermore, with proper guidelines on ethical issues and extended research following these strategies, the stem cell technology is expected to not only be of huge benefit to human health, but also the benefit can be extended to the survival of endangered animals as well.

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Data is always a crucial issue of concern especially during its prediction and computation in digital revolution.This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication.It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data.The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means.The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data.The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables.This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach.It also demonstrates the generative function forKalman-filer based prediction model and its observations.This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration(CPE)for Python.

Adaptive Three-phase Estimation of Sequence Components and Frequency Using H∞ Filter Based on Sparse Model

The estimation of sequence or symmetrical components and frequency in three-phase unbalanced power system is of great importance for protection and relay.This paper proposes a new H∞filter based on sparse model to track the sequence components and the frequency of three-phase unbalanced power systems.The inclusion of sparsity improves the error convergence behavior of estimation model and hence short-duration non-stationary PQ events can easily be tracked in the time domain.The proposed model is developed using l1 norm penalty in the cost function of H∞filter,which is quite suitable for estimation across all the three phases of an unbalanced system.This model uses real state space modeling across three phases to estimate amplitude and phase parameters of sequence components.However,frequency estimation uses complex state space modeling and Clarke transformation generates a complex measurement signal from the unbalanced three-phase voltages.The state vector used for frequency estimation consists of two state variables.The proposed sparse model is tested using distorted three-phase signals from IEEE-1159-PQE database and the data generated from experimental laboratory setup.The analysis of absolute and mean square error is presented to validate the performance of the proposed model.