Mepco Schlenk Engineering College

About: Mepco Schlenk Engineering College is a based out in . It is known for research contribution in the topics: Wavelet & Wavelet transform. The organization has 1307 authors who have published 1665 publications receiving 18690 citations.

FPGA Implementation of Optimal 3D-Integer DCT Structure for Video Compression

Abstract: A novel optimal structure for implementing 3D-integer discrete cosine transform (DCT) is presented by analyzing various integer approximation methods. The integer set with reduced mean squared error (MSE) and high coding efficiency are considered for implementation in FPGA. The proposed method proves that the least resources are utilized for the integer set that has shorter bit values. Optimal 3D-integer DCT structure is determined by analyzing the MSE, power dissipation, coding efficiency, and hardware complexity of different integer sets. The experimental results reveal that direct method of computing the 3D-integer DCT using the integer set [10, 9, 6, 2, 3, 1, 1] performs better when compared to other integer sets in terms of resource utilization and power dissipation.

Incremental Learning Implementations and Vision for Cyber Risk Detection in IoT

Abstract: The Internet of Things (IoT) has emerged as an Internet-based extension and considerably changed our world. A tremendous amount of IoT applications has highly eased people's daily lives, and improved allocations and usage of a broad range of resources (power bank sharing, bike sharing, etc.). However, the ingrained openness of underlying wireless systems renders these IoT entities vulnerable to a broad range of cyber risks, such as vulnerabilities of spectrum that can be a source of adversarial inference. Furthermore, as our reliance on wireless/connected devices and radios has also been dramatically increasing, public safety, business operations, socializing, and navigation as well as critical national communication infrastructures have become more vulnerable to cyber threats. In addition, the stream of data from IoT devices are also prone to cyber risks, which is of major concern for end users, and could also mislead users and ML models with wrong information during the training and learning phases. Hence, policymakers and industries have recently begun to perceive that the expansion of connected devices and their cyber susceptibility results in a large malicious and inferential risk. As a result, there is a need to identify and comprehend these risks in order to elaborate efficient security solutions. Moreover, the economic impact of IoT devices and associated security vulnerabilities are further growing in accordance with artificial intelligence (AI) integration into human-computer interaction (HCI), including banking, insurance, and so on. Consequently, cyber risks are growing in terms of both frequency and acuteness. Therefore, this article discusses the evolution of common real-time machine learning (RTML) approaches, which are considered as enablers for ensemble learning-based use cases. We discuss a cyber risk detection framework that can be built effectively using Boosting- and Bagging-based ensemble learning approaches.

Structural and magnetic properties of lead doped nickel ferrite nanoparticles using co-precipitation method

Abstract: Lead doped nickel ferrite Ni1-xPbxFe2O4 (x = 0.0 to 1.0) nanoparticles have been successfully prepared via co-precipitation method. The structural and magnetic properties investigated by XRD, FTIR,...

Operations Research in Healthcare Supply Chain Management Under Fuzzy-Stochastic Environment: Operations Research in Healthcare

Abstract: Operations research is for mankind in almost all aspects of our life. Applying the scientific method to the management of organizations, industry, government and other enterprises play a vital role in OR. It is used to increase productivity, to improve customer service, to improve quality and to reduce costs. Healthcare has attracted a great attention of governments in order to provide sufficient health services to the people. The provision of healthcare is very complicated and very responsible, that the right drug to the right people at the right time and in good condition to fight the disease. Today, the importance and significance of planning in healthcare can hardly be over emphasized when providing proper and adequate service continues to be a key concern of most countries. Operations research provides a wide range of methodologies that can help health care systems to significantly improve their operations. It helps to solve approximately all the problems involved in healthcare with its useful modeling techniques. Operations Research in Healthcare Supply Chain Management Under FuzzyStochastic Environment: Operations Research in Healthcare

Throughput Maximization in Wireless Systems Using an Adaptive Cross Layer Scheduling

Abstract: Due to the rapid growth of the Internet and other IP services, there has been a change in the design of recent generation wireless systems to support a variety of data applications from non-real time background traffic to real time streaming video. This has placed an enormous strain on the already tight capacity of wireless systems. Many improvements have been made at the link layer to facilitate diverse QoS support. Further it is necessary to improve packet data admission, scheduling and policing techniques to maximize capacity and user satisfaction. Of the three, efficient scheduling has the greatest impact on increased system capacity and user satisfaction. A number of algorithms have been proposed for scheduling data services. The majority of them focus on either minimizing packet delay or maximizing user throughput. In this paper, a new adaptive cross-layer scheduler design is proposed, which can outperform with respect to both packet delay and user throughput by minimizing a prescribed cost function which includes the current channel and delay states of the packet in the queue.