— Quality of Service calls for consistent network data delivery and give some level of assurance for better network performance. In this work, details about the quality of service at the physical layer of wireless local area network... more
— Quality of Service calls for consistent network data delivery and give some level of assurance for better network performance. In this work, details about the quality of service at the physical layer of wireless local area network (WLAN) IEEE 802.11, which uses OFDM technique, is studied. This study is based on simulation of IEEE 802.11 physical layer for QOS performance parameters. OFDM technique has been widely used in systems which use high data rates as IEEE 802.11 which is up to 54Mbps. OFDM increases the robustness against frequency selective fading or narrowband interference. The Quality of Service has been widely studied for MAC layer of IEEE 802.11, but study of OFDM based IEEE 802.11 physical layer for QOS parameters has been done in this work. Different graphical data is generated to study QOS performance parameters. A separate simulation for OFDM and plotting of the graphs is also done to study its aspects.
In essence, all computing devices can perform only computational operation with limited communicating capabilities. Proliferation of sensor take " things " to another level and an era of Internet of Things (IoT) get start. IoT is a... more
In essence, all computing devices can perform only computational operation with limited communicating capabilities. Proliferation of sensor take " things " to another level and an era of Internet of Things (IoT) get start. IoT is a multilayered structure which provides ease of optimization, prediction and strategic evolution. Applications of IoT improve the quality of a common man life as it can be deployed from smart farming to smart home. IoT has the potential to provide a simplified solution to a complex real life problem [1-2]. IoT gives us abundant facilities but can cost us in terms of data security.
Multiplier is one of the hardware block which generally occupies a significant chip area and is required to be minimized which will be fruitful to number of applications in which multiplier blocks constitute an important unit such as... more
Multiplier is one of the hardware block which generally occupies a significant chip area and is required to be minimized which will be fruitful to number of applications in which multiplier blocks constitute an important unit such as digital signal processing (DSP) systems or computational techniques. Battery operated systems require low power devices to be implemented which can be minimized if the hardware required for the device is reduced logically. This paper focuses the DSP applications in which multiplier is significantly used and proposes a technique that helps in reducing the hardware as well as delay leading to the rise in performance of the system thus helping in increasing the operation frequency by a significant value. A 16-bit multiplier has been designed using a radix-8 and radix-16 Booth's multiplication that reduces number of partial products.
Medical Images have always been vulnerable to high level components of noises. Magnetic Resonance Imaging (MRI), X-ray, Computed Tomography and Ultrasound are among most popular techniques for producing medical images, during image... more
Medical Images have always been vulnerable to high level components of noises. Magnetic Resonance Imaging (MRI), X-ray, Computed Tomography and Ultrasound are among most popular techniques for producing medical images, during image capture and transmission noise is added in the images that decreases the image quality and leads to poor image analysis. Various denoising techniques are used to remove the noise or distortion from images while preserving the original quality of the image among which wavelet transform has been proved an efficient one in reducing the noise level. The aim of this paper to characterize the Gaussian noise in wavelet transforms subsequently a threshold based denoising algorithm has been developed using hard and soft thresholding techniques that works on Haar, Daubechies and Symlet Transforms. Firstly the image is decomposed using Haar and Daubechies and symlet transforms, and then the level of soft and hard threshold is selected for reducing the noise in the image and finally the comparison between them has been done on the basis of calculated PSNR& MSE of an image for every wavelet.