This paper describes an Optical Character Recognition (OCR) system for printed text documents in Kannada, a South Indian language. The proposed OCR system for the recognition of printed Kannada text, which can handle all types of Kannada characters. The system first extracts image of Kannada scripts, then from the image to line segmentation then segments the words into sub-character level pieces. For character recognition we have used database approach. The level of accuracy reached to 100%.
This paper describes an Optical Character Recognition (OCR) system for printed text documents in Kannada, a South Indian language. The proposed OCR system for the recognition of printed Kannada text, which can handle all types of Kannada characters. The system first extracts image of Kannada scripts, then from the image to line segmentation then segments the words into sub-character level pieces. For character recognition we have used database approach. The level of accuracy reached to 100%.
This chapter contains sections titled: Introduction Communication Complexity Model Computing Functions over Wireless Networks: Spatial Reuse and Block Computation Wireless Networks with Noisy Communications: Reliable Computation in a Collocated Broadcast Network Towards an Information Theoretic Formulation Conclusion Bibliography
Clock synchronization is of critical importance for several applications in wireless and mobile sensor networks; for example to determine the order and time of events. Although several protocols to achieve clock synchronization have been developed, they may not be secure. For instance, a link controlled by an attacker could delay packets it forwards in ways which would cause the nodes sharing that link to obtain faulty time estimates. In this paper, we propose a secure network-wide clock synchronization protocol. It also allows nodes to securely discover the network topology by detecting and isolating links that behave inconsistently. This network-wide clock synchronization protocol is built on ideas in [1] where inconsistent attacks are detected using timing information alone under certain conditions. The proposed protocol has been implemented and the results of experimentation on a twenty-five node network are presented.
We consider the problem of adaptively controlling an unknown Markov chain. No prior information regarding the values of the transition probabilities is provided us (except for a list of forbidden, zero-probability transitions, which is usually obtained as a byproduct of the modeling process itself). The goal is to design an adaptive controller to adequately control the unknown system when its performance is measured by the average cost incurred over a long operating time period. Our main result is the exhibition of a family of adaptive controllers which, when applied to the unknown system, will result in a performance precisely equal to the optimal performance attainable if the system, i.e., the transition probabilities, were known. Hence, the adaptive controllers proposed here are truly optimal, even when operating on an unknown system. The results presented here extend similar results in [1] where we assume to be initially provided with a finite set of possible models, one of which is guaranteed to be the true one. This paper directly addresses those practical situations where a finite set of possible models with such a guarantee is hard to come by.
Current technology trend is to implement multiple solutions in a single device. The devices like Stateful Inspection firewalls and Load balancers use session tables to maintain the session information. They also run traffic analyzers like NetFlow which are separate modules running on these devices. In this paper, we propose a design where session table and other existing processes can be effectively used to achieve the functionality of NetFlow.
Closed-circuit television (CCTV) cameras are being extensively installed in the cities, apartments, homes, restaurants, educational institutions, shops and so on producing lots of data every second. Storing, processing and analyzing these video footages effectively is a challenging task. In the course of analysis of these video sequences, measurement of size of the objects in the scene is an interesting and a very challenging task. Though there are systems that can determine the height of a person, they are not computationally cost effective and very few of these works for CCTV footages.There are so many applications to find out the size of an object or a person with higher cost of maintenance and there is no application which finds the height of a person in video captured by CCTV. This project mainly focuses on the detection and calculation of height of a person in the CCTV footage using deep learning algorithm.The main objective is to build the system which determines the measurements of an object or a person in a pre-recorded video or CCTV footage or in image. The methodology adapted in the project is to find the bounding boxes around all the objects found in an input and is to determine the object class it belongs to. The python programming and machine learning techniques like YOLO algorithm, R-CNN is used for the object recognition.The posture of a person changes accordingly as his/her actions.The posture of a person effects the calculation of height as likely in a bending position of knees, the height may look less and determined accordingly. The input dataset is having various numbers of predefined classes in it, so that the detection of the various objects in the frames will be easier. The model detects the objects in images and objects in CCTV video footage with 81% accuracy. The custom dataset has been used to train and test the model.
In the current digital era, it is essential to provide secure and confidential communication over the internet. In order to accomplish this, Transport Layer Security (TLS), is essential. To create a secure connection, TLS requires the exchange of sensitive data, such as the Server Name Indication (SNI), which could put user privacy at risk. Our study delves into the privacy implications of SNI by creating modules that analyze SNI in the ClientHello Message and TLS certificates. The SNI Parser navigates TLS handshake payload in ClientHello packet, and extracts Server Name Indication. By examining SNI, we gain insights into potential privacy risks associated with unencrypted domain name, which can help in devising effective privacy protection mechanisms. The paper then elucidates how to extract important data from X.509 certificates by providing the server name to the certificate parser that was developed. This research contributes to the comprehension of SNI-related privacy concerns, with the goal of improving user privacy and ensuring a secure online environment.
Data-driven decision making has become critical to every organization. Thus, there is a growing emphasis on adopting robust data governance frameworks for data management. This encompasses data publishing to empower stakeholders with the ability to access and analyze the published data, playing a pivotal role in decision-making. However, data publishing poses a threat to entity-specific information. Privacy Preserving Data Publishing (PPDP) refers to publishing data while protecting the privacy of entity-specific information. K-anonymity is a well-recognized method that achieves PPDP and serves as the foundation for our proposed data transformation algorithm, "Score, Arrange, and Cluster (SAC)". SAC is a clustering-based algorithm that outperforms many existing methods that follow the k-anonymity paradigm. In organizations, it is crucial to address the varying data requirements and role-based access levels of the involved stakeholders for effective data management. SAC was designed to offer only a generic data transformation with minimal data quality degradation. Hence, this work presents an enhancement to SAC that takes into account stakeholder roles and requirements, as illustrated through different scenarios. The scoring mechanism in SAC is augmented to accommodate customization or use the concepts of Genetic Algorithms to enforce role-based access control.
A framework is designed for an ad hoc network, which is a matrix of cells of equal size and are connected in taurus structure. Each cell is of equal size and there are 100 of them and contains number of nodes. The cells are connected in taurus to make the simulation space infinite i.e. if the nodes wanders off the boundary of one side then it appears at the opposite side, this guarantee that all the nodes will participate in the entire simulation. For this a parallel simulator is designed considering routing protocols.
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