Evaluating Hybrid Model Performance in Predicting the Impact of Lockdown on Controlling Covid-19 Cases Progression in Middle Income Settings
A Method for the Identification of Denial of Service (DoS) Attack in Network Traffic using Machine Learning Techniques
Agriculture Management System
Unveiling Sentiment Analysis: A Comparative Study of LSTM and Logistic Regression Models with XAI Insights
Resume Screener System
Efficient Agent Based Priority Scheduling and LoadBalancing Using Fuzzy Logic in Grid Computing
A Survey of Various Task Scheduling Algorithms In Cloud Computing
Integrated Atlas Based Localisation Features in Lungs Images
A Computational Intelligence Technique for Effective Medical Diagnosis Using Decision Tree Algorithm
A Viable Solution to Prevent SQL Injection Attack Using SQL Injection
Compression is one of the most important operations in computer applications. There are many compression techniques which are used to compress the image formats for their storage and transmission. Compression is done to reduce the amount of space required to store an image. There is need for a perfect image compression technique which will reduce the size of data for both sharing and storing. In this paper the authors discuss the survey of different compression algorithms for both lossless and lossy compression techniques.
Humans interact with computers in many ways such as Desktop applications, Internet browsers, Handheld computers, Graphical User Interface, Universal User Interface etc. Persons with Disabilities face many difficulties and face many challenges in the society, particularly students with visual impairments face unique challenges in the educational environment. Visually impaired struggle a lot to access information, as well as hearing impaired have difficulty in perceiving information. So, Human computer interface with OMAP 3530 processor from Texas and Spartan 6 FPGA from Xilinx has been proposed. This can be implemented in Unified Technology Learning Platform (UTLP) kit which has two major platforms to go beyond the experiments and to develop products and to solve real life problems.
Network-on-Chip (NoC) is a scalable and flexible communication medium for the design of multi-core based Systemon- Chip (SoC). Communication performance of NoC depends heavily on efficient routing algorithms. Dynamic routing is desirable because of its substantial improvement in communication bandwidth and intelligent adaptation to faulty links and congested traffic. In this paper, the authors propose a fault tolerant Dynamic Adaptive Routing in Networks-on- Chip (NoCs). This algorithm can be implemented on routing to detect transient and permanent faults in the network. That means the packet is able to move around the faults to their destination with a non-minimum path. In addition, the multilevel congestion control mechanism gives the ability to distribute the load and to avoid the faults. Because this procedure is based on adaptive routing, the hardware overhead and computational times are minimal. Experimental results based on an actual Verilog implementation demonstrate that the proposed dynamic adaptive routing algorithm improves the network throughput significantly compared to traditional algorithms.
Reed–Solomon (RS) codes are widely used in digital communication and storage systems. In this paper the authors present a high-speed low-complexity Reed–Solomon (RS) decoder architecture using low power carry skip adder. The carry skip adder's delay and power dissipation are reduced by dividing the adder into variable-sized blocks that balance the delay of inputs to the carry chain. This reduces the active power. Each block also uses highly optimized complementing carry look-ahead logic to reduce delay. A 32-bit carry skip adder is used in the proposed method. Our approach has been implemented in 130 nm CMOS (Complementary Metal Oxide Semiconductor) technology. Compared to the previous designs, the adder architecture decreases the computational complexity with similar or higher coding gain. The 40-bit adder's average power dissipation normalized to 600 MHz operation as 0.928 mW in 130 nm technology and 0.335 mW in 90 nm technology.
Artificial Neural Network (ANN) is being used as a reliable data-oriented modeling technique for past two decades. They learn from examples, generalize and capture delicate functional relationships among the data even if the underlying relationships are unknown or hard to describe. It has the advantages over numerical and hydrodynamic model that it does not require any boundary conditions and excessive data for simulation. Thus ANNs are well suited for non linear real world problems whose solutions require knowledge that is difficult to specify but for which there are enough data or observations. ANNs are general and flexible functional forms than the traditional statistical methods. Extensive research has been carried out using ANN in and around coastal and ocean engineering field. This paper gives an overview of application of artificial neural network for the estimation of wave parameters.
