Checkpoint (CP) routing requires a procedure for heavy load and dynamic routing packets over a cluster network, by monitoring network load. High traffic CP network does intense transactions by multiple applications and shares the information among the nodes in the network and delivers the same to the server. The Proposed work assigns time slots and each slot checks the path to send or migrate the data to manage the load and data accumulation between servers and if there arises any data loss it recovers from the previous node buffers. Data rate and delay is checked by each link and adjust the data flow to various servers. Based on time, network state, incoming packet rates, server load, link-based migration is applied. It increases network packets receiving rates, and minimizes the delay in checkpoints.
The increasing amount of research on watermarking over the past decade has been largely driven by its important applications in digital copyrights management and protection. Digital watermarking is the act of hiding a message related to a digital signal (i.e. an image, song, video) within the signal itself. It is a concept closely related to steganography, in that they both hide a message inside a digital signal. However, what separates them is their goal. Watermarking tries to hide a message related to the actual content of the digital signal, while in steganography the digital signal has no relation to the message, and it is merely used as a cover to hide its existence. With the emerging of low cost 3D display devices, different kinds of 3D applications and the amount of 3D content are booming up, recently. Effective digital watermarking systems for 3D content protection will play an important role for developing and promoting the 3D entertainment industry. The proposed 3D watermarking technique employs the method of slice extraction from a 3D volume. An obj file is used and the wave front object data is obtained.DWT technique is used to embed the secret binary data into one of the extracted slices. The inverse operation is performed at the receiver using the same secret key used at the transmitter. Finally, the watermark is extracted and the quality measures of the reconstructed image is calculated.
For a variety of reasons, handwritten text can be digitalized. It is used in a variety of government entities, including banks, post offices, and archaeological departments. Handwriting recognition, on the other hand, is a difficult task as everyone has a different writing style. There are essentially two methods for handwritten recognition: a holistic and an analytic approach. The previous methods of handwriting recognition are time- consuming. However, as deep neural networks have progressed, the approach has become more straightforward than previous methods. Furthermore, the bulk of existing solutions are limited to a single language. To recognise multilanguage handwritten manuscripts offline, this work employs an analytic approach. It describes how to convert Malayalam and Kannada handwritten manuscripts into editable text. Lines are separated from the input document first. After that, word segmentation is performed. Finally, each word is broken down into individual characters. An artificial neural network is utilised for feature extraction and classification. After that, the result is converted to a word document.
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