The enhancement of security measures in advanced encryption standard using double precision floating point multiplication model

In Very Large Scale Integrated (VLSI) and Digital Signal Processing (DSP) applications, a Conventional Combinational Multiplier uses a lot of hardware and propagation delay, and that is one of the main issues. The delay is determined by the ripple carries between the adders. Most techniques used in the combinational multiplier depend on the paper-and-pencil shift-and-add (PPSA) algorithm, which uses ripple carry adder architecture. This paper proposes an architecture called Cell-based Carry-Save Combinational Multiplier (CCCM) that focuses on the propagation delay and area. Each row represents a carry-save adder. The carry outputs are passed to the next row. For validating the proposed architecture, 16x16 multiplication is performed and the overall improvement in the delay is 73.32 % while there is a 3.31 % reduction in the area.

Design of 32-bit cell-based carry-save combinational multiplier with reduced area and propagation delay

D-Flipflops are widely used in designing various analogue, digital and mixed signals. Different shift registers, counters and other circuits are designed using D-Flipflop. To increase the battery life time and reduce the power consumption, the voltage given to the circuit have to be decreased in the Standby mode of operation. In this paper different D Flipflops are designed using techniques like 5 transistor D-Flip Flop, Self-voltage level D-Flipflop and modified SVL (Self Voltage Level) D-Flipflop and Novel sleep transistor technique. Comparisons are made between all the designs in terms of power, delay and leakage power. All the designs are designed and simulated with the help of Cadence Virtuoso in 90nm technology. The leakage power observed in Novel Sleep Transistor technique is 93.5% less than that observed in Modified SVL Technique.

Designing a D-Flipflop Using Novel Sleep Transistor Technique

Design a high performance and low power radix-4 booth multiplier using power reduction techniques

This paper aims to develop a system for controlling the computer cursor using the movement of the user’s eyeballs, providing a hands-free alternative to the traditional mouse. We developed a program using image processing techniques and machine learning algorithms in Python to obtain the eyeball movements and blink and translate them respectively into cursor movements and click actions. Our system was able to achieve a high level of accuracy in tracking the user’s eye movements. Users were easily able to adapt to the new input method. This system has great potential to improve the accessibility and usability of computers for individuals with motor impairments or disabilities. This hands-free control method has great potential in the area of applications in gaming and virtual reality environment etc. 

System Cursor Control Using Human Eyeball Movement

/pdf/double-precession-floating-point-multiplier-using-schonhage-3sgi3jp50e.pdf

Double Precession Floating Point Multiplier using Schonhage – Strassen Algorithm used for FPGA Accelerator

- There are different reasons for which people need an artificial of locomotion such as a virtual keyboard. The number of people, who need to move around with the help of some article means, because of an illness. Moreover, implementing a controlling system in it enables them to move without the help of another person is very helpful. The idea of eye controls of great use to not only the future of natural input but more importantly the handicapped and disabled. Camera is capturing the image of eye movement. First detect pupil center position of eye. Then the different variation on pupil position get different command set for virtual keyboard. The signals pass the motor driver to interface with the virtual keyboard itself. The motor driver will control both speed and direction to enable the virtual keyboard to move forward, left, right and stop.

Eyeball Based Cursor Movement Using Opencv

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Double Precession Floating Point Multiplier using Schonhage – Strassen Algorithm used for FPGA Accelerator

Eyeball Based Cursor Movement Using Opencv