Quantization

1.13 Convergence – In the telecommunications industry, convergence refers to the integration of voice, internet, broadcasting, and other telephony servers into one mega-industry from their traditionally separate industries. Companies such as Telstra are an excellent example of this, as Telstra now offers an abundance of products such as, Fixed Phone, Mobile Phone, Dialup Internet, Broadband Internet, Wireless Internet, TV, Music, Tickets, and more. Incentives are given to “bundle” services with the

SOLAR TRACKING FOR OPTIMUM POWER GENERATION NAME: - PRITAM PHILIP KISKU ID: - 201201005 INTRODUCTION “An embedded system is a system that has a software embedded into computer-hardware, which makes a system dedicated for an application(s) or specific part of an application or product or part of a larger system.” – Embedded Systems by McGraw-Hill Education. The topic of the case study is “Solar Tracking for Optimum Power Generation”. The demand for energy is increasing day-by-day and

famous algorithm for analysis of clusters.In this algorithm, n number of data points are divided into k clusters based on some similarity measurement criterion. K-Means Algorithm has fast speed and thus is used commonly clustering algorithm. Vector quantization,cluster analysis,feature learning are some of the application of K-Means.However results generated using this algorithm are mainly dependant on choosing initial cluster centroids.The main shortcome of this algorithm is to provide appropriate number

photocatalysis, microelectronics, energy conversion industries and nonlinear optics [1-3]. These materials have been a new generation of advanced materials due to their novel structural, morphological, optical and electrical properties originating from size quantization [4]. PbS is an important IV-VI semiconductor material because of its direct bandgap of 0.41 eV and its exciton Bohr radius of 18nm at room temperature. Therefore when crystal sizes are smaller than the Bohr radius, such a small bandgap and a large

CONTENT BASED IMAGE COMPRESSION USING DCT AND DWT TECHNIQUE Abstract: Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are the most known methods used in digital image compression. Wavelet transform has better efficiency compared to Fourier transform because it describe any type of signals both in time and frequency domain simultaneously. In this paper, we will discuss the use of Discrete Cosine Transform (DCT) and Discrete wavelet transformation (DWT) based Image compression

1. Introduction Conventional cryptographic algorithms, which are generally aimed at encrypting like text data (known as Naïve approach) not well suited for video encryption. This is due to the fact that conventional cryptographic algorithms cannot process the large volume of video data in real-time. Selective encryption of the H.264/AVC bitstream puts decoder to fully decode the encrypted video with degradation in video quality. This perceptual encryption has low encryption and decryption time. The

III. NUMBER PLATE EXTRACTION The captured input image has number plate covered by vehicle body, so by this step only number plate area is detected and extracted from whole body of vehicle. The number plate extraction phase influence the accuracy of ANPR system because all further step depend on the accurate extraction of number plate area. The input to this stage is vehicle image and output is a portion of image containing the exact number plate. Number plate can be distinguished by its features

Classification is a principle technique in hyperspectral images (HSI) analysis, where a label is assigned to each pixel based on its characteristics. Applying machine learning techniques to these datasets need special consideration, since the hyperspectral images are typically represented by features vectors of extremely high dimensions. A robust HSI classification requires a prudent combination of deep feature extractor and powerful classifier. In the last one decade, extensive classification methods

Abstract—Steganalysis and steganography are two term used for security purpose for embedding and extracting information in media files in imperceptible way. Steganography is the art of secret communication and steganalysis is the art of detecting the hidden messages embedded in digital media using steganography. Many powerful and robust methods of steganography and steganalysis have been introduced in the literature. In this paper, we will discuss various steganalysis techniques used recently for

Abstract: With the increasing usage of digital multimedia, the protection of intellectual property rights problem has become a very important issue. Digital watermarking is now drawing attention as a new method of protecting multimedia content from unauthorized copying. Over the past two decades, many digital watermarking algorithms have been developed, each with its own advantages and disadvantages. The main aim of this paper is to present a survey report of recently done work in audio watermarking