Search

Change in environment and climate in parallel with rapid increase in population becomes a greatest health challenge in this 21st century especially in India. Since India is the second largest population in the world, the comprehensive study of communicable diseases along with the impact of non-communicable disease becomes mandatory. In this paper, we have surveyed the various occurrence of communicable disease in India from 19th century till 21st century. It is observed that year by year the prevalence of communicable disease increases spatially. Especially in the 21st century, every year one or the other communicable diseases are prevailed in India either as outbreak or epidemic or pandemic. Not only the communicable diseases but also the prevalence of non-communicable diseases (NCD) increased spatially in India. The significant annotation is that, apart from COVID'19 the NCDs fatality rate is much higher than the communicable disease throughout the world. In our study, we also found that the environment and the climate change becomes a greatest threat for the diseases. We also studied one prominent reason for the dramatic change in environment and climate is the advancement of technology. So with the same advanced technology, it is high-time to enlighten our research towards exploring the technology to prevent the cause of increase in both communicable and non-communicable diseases spatially in India. [ABSTRACT FROM AUTHOR]

Variational Autoencoders (VAEs) is a type of generative model that can be used for image compression. The steps involved in using VAEs for lossless image compression are pre-processing, encoding, sampling, decoding, loss function, training and compression. The input image is first pre-processed to convert it into a format that can be used as input to the VAE. Typically, this process entails normalizing the pixel values to fall within the range of 0 to 1, followed by transforming the image into a flattened vector. The encoder network receives the pre-processed image and condenses it into a reduced-dimensional representation. The encoder network consists of multiple layers of neural networks that reduce the dimensionality of the input image while capturing the important features of the image. The output of the encoder network is a mean vector and a variance vector, which are used to sample a compressed representation of the image. The compressed representation of the image is sampled from a probability distribution that is defined by the mean vector and variance vector output by the encoder network. This sampling process makes VAEs a generative model. The decoder network takes in the compressed representation of the image as input and reconstructs the original image. The loss function used in VAEs is a combination of a reconstruction loss and a regularization loss. The VAE undergoes training through back-propagation to minimize its loss function. Once the VAE is trained, it can be used to compress new images. The encoder network condenses the input image into a reduced-dimensional representation, which can then be stored or transmitted. In order to achieve secure image transmission we can perform an image encryption using Elliptic Curve Cryptography (ECC). Variational Autoencoders for lossless image compression is a promising research area that will provide good results in achieving high-quality compression while preserving the entirety of the data within the original image. [ABSTRACT FROM AUTHOR]

The aim of the study is to design and analyze a novel coplanar waveguide (CPW) antenna for 2.9 GHz compared with microstrip antenna. Materials and Methods: The antenna was made using a ring structure that was built on a commercially available FR4 Epoxy (4.4) material flexible substrate with a thickness of 1.6 mm. The sample size is estimated to be 40 using the G Power 80% Results: Simulated and experimental results show that novel coplanar waveguide antenna bandwidth is increased to 6.2 dB at the frequency 2.9 GHz. This antenna has been designed and analysed with the aid of Ansoft HFSS version 13.0 software. The significant difference between the two groups was obtained as p<0.05. Conclusion: The obtained results shows that the novel CPW antenna antenna has significantly improved bandwidth weather radar application compared with microstrip antenna. [ABSTRACT FROM AUTHOR]

In a normal way, a man from the Electricity Board comes to each house in-person to the allocated zone, takes each house's reading, and reports it back to the client/user through EB bill. In addition to that, if there is any delay in bill payment, the board worker has to come and remove the fuse carrier. And in the case of the client/user side, they cannot know their unit consumption and bill whenever they want it. To overcome all these disadvantages, we are using a method of a framework that helps the client/user to get their bill and unit consumed whenever they want, and it will also reduce the work of a board member to go in person to each house. In addition to all of these, the major problem we all are experiencing was POWER SHUTDOWN for more hours. In this method, we can get some alternate ways to deal with this power shutdown problem. [ABSTRACT FROM AUTHOR]

In our world, rainfall forecasting is extremely important. Agriculture is seen as the primary source of revenue for the economy. A proper estimate of rainfall is needed to make proper agricultural investments. Rainfall forecasting is needed for people living in coastal areas, in addition to agriculture. People living by the coast are at a higher risk of heavy rain and flooding, so they should be aware of the weather forecast well in advance so that they can schedule their stay accordingly. We use a machine-learning algorithm to predict rainfall for this reason.. Machine Learning algorithm used is Linear Regression. In linear regression, to predict the dependent variable (rainfall) using an independent variable (soil moisture). A comparison of two machine learning algorithms reveals which is more effective. [ABSTRACT FROM AUTHOR]

In the current era, the need of wireless technologies are raising enormously day by day where small in size, low in weight, easy installations are most important. Patch antenna offers effective low profile designs for a broad range of wireless applications. They are very efficient for high frequency applications with light in weight and in expensive to fabricate. In this paper, various shapes of patch antenna like S-slot, H-shape, pyramidal and square shape were designed and the parameters were analyzed using Advanced Design System (ADS) environment. Based on the return loss and power radiated, the spanner shaped antenna is chosen as the optimum design. Further the designed antenna was optimized by changing the dimensions, thickness and material of substrate, dielectric constant and the feed position. The optimized antenna was tested in ADS which achieved a return loss of -25dB at 9.4 GHz and radiated power of about 0.0123 watts. Then the designed antenna is fabricated using FR-4 substrate which has an advantage of low cost and the fabricated antenna was tested for the same parameters using Network Analyzer. The return loss was found to be -22dB at 9.4 GHz and the radiated power was 0.031 watts which is better than the simulated results. The designed antenna can further extended for the use in Wi-Max applications. [ABSTRACT FROM AUTHOR]

The present paper focuses on the optimisation of three different topologies of thermoelectric generators based on silicon nanowire-arrays. The classic heat transfer equations with Fourier's Law and boundary conditions are solved in terms of thermopower efficiency optimization. The numerical technique is based on general, constrained optimisation techniques. The results show that the use of nanowire arrays lead to better thermoelectric conversion efficiency. [ABSTRACT FROM AUTHOR]