
Organized and dependable candidate successful at managing multiple priorities with a positive attitude. Willingness to take on added responsibilities to meet team goals. Hardworking and passionate job seeker with strong organizational skills eager to secure entry-level position. Ready to help team achieve company goals.
Internet of Things based company where we worked with how we can control the operations of electronics with the use of any application on our mobile phones
1. Audio Steganographer
My idea is to encode a message while sending it to the receiver so that the receiver gets to know what the message is only when the encoded message is decoded. The message sent can be a string or a set of bits. We can encode our message with any of the audio format(here we have used .wav format). When the audio format reaches the receiver there will be no change from that of the original audio though a hidden message is hidden within it. Only when we decode that particular format we get the actual message hidden in it which was encoded within the audio format before sending it to the receiver.
2. Texture Synthesis Over Arbitrary Manifold Surfaces Using Masking Neural Style Transfer (NST)
NST is the process of using Convolutional Neural Network to render a content image in different styles. CNN is capable of extracting content information from an arbitrary photograph and style information from a well-known artwork. CNN feature activations were exploited to recombine the content of a given photo and the style of famous artworks. The key idea behind the algorithm is to iteratively optimise an image with the objective of matching desired CNN feature distributions, which involves both the photo's content information and artwork's style information Most of the applications of style transfer thus far have focused on style transfer onto the entire image or on rectangular domains. In this algorithm, we have introduced the concept of masking, which allows to select specific regions in the original image to be altered, while the rest of the original image maintains its appearance and also solving the issue on transferring texture to curved surfaces. Existing algorithms use low-level image features of the target image to inform the texture transfer and is time-consuming. This algorithm minimises the content loss and style loss well during training, which makes a difference in the speed of learning the style objective.