Design Of An Optimized Compressive Sensing

A typical problem in OFDM based systems is a high Peak-to-Average Power Ratio (PAPR). The available RF power amplifiers usually have a limited linear dynamic range. This is not a problem for constant amplitude signals, however, in OFDM, due to IFFT operation, some of the sub-carriers add constructively to large amplitudes while others add destructively. The amplitude of the signals thus varies randomly and for large amplitude signals, non-linear amplifications cause errors. Therefore, error correction codes are employed. In HiperLan2, the data is coded with a convolutional encoder of rate 1/2. The convolutional encoder uses the industry-standard generator polynomials, g0 = 1338 and g1 = 1718, and is shown in Figure 4.3. The bit denoted as “A” is the first output from the encoder while the bit denoted “B” is the second one. Higher code rates are derived via puncturing. In puncturing, some of the encoded bits in the transmitter are removed

Table I displays six solid binary images along with their corresponding compression ratios. On considering Run-Length coding, the alternative encoding scheme produces better performance than the other encoding scheme. On an average, the proposed algorithm performs well on comparison with the standard Huffman coding algorithm by approximately 1.58%.

Today, we can send information all over the world instantly; however movement and activities can be tracked through computers and cell phones. With new and improved technology, it can improve lives, increase public safety, build wealth and promote efficiencies in how we use scarce resources. Researchers,

Lossy compression happens when the compression turns out in pixels and resolution is lost after compression taken places. This is why the file can be used efficiently and at a lower file size. This compression usually happens during video conferencing and during freeze frames or when an image is pixilated. This is accepted as high resolution is not needed as long as the user understands the file. Disadvantage of it is that it could give poor quality which could cause miscommunication.

Frigaard, C., Gade, J.T., Hemmingsen., Sand, T.:Image compression based on a fractal theory. In: Institute for Electronic Systems, Aalborg University (1994)

File compression is when you compress a file to reduce its size. There are 2 kinds of compression lossy and lossless. Lossy compression is when parts of the data that are deemed ‘unnecessary’ are removed to reduce the size. Lossless compression identifies and eliminates statistical redundancy, and also with lossless you do not lose any of your data (Lossless definition found here

Since my high school, with my independent work, I have made achievements from wireless radio and 51 single-chip machine to buzzer, amplifiers and lights, which have triggered to challenge myself continuously. In order to find my clear social and academic position in the information era, I chose the No.1 Electronic university in China, Beijing University of Posts and Telecommunications. Although I did not step into the field of Electronic Engineering immediately, great passion about it has never fade away. Therefore, I have devoted more spare time to studying professional literature in the libraries and have established a habit of discussing with the most learned and inspiring teachers make me to have a better understanding about Communication Engineering. During my undergraduate study, I have conducted various researches about Communications, such as Design and Simplify Electronic Time Clock, Design Digital Display Circuit and Networked wireless meter reading system based on ZigBee technology. “Approaches to minimize network coding resource” is my graduation thesis topic. Based on the analysis of the existing approaches to minimize network

This paper discusses about various Transcoding enable technique regarding display, size, quality on mobiles using wireless connection. Here the author has introduced his idea through Transcoding enabled caching (Tec) for efficient delivery of quality media files to the customers and proxies are used for storing and transcoding files as well as delivering the media file to end user which helps to decrease the browsing time and cost-efficient.

In the paper titled “The dynamical behaviors of complementary correlations under decoherence channels”, Du et.al have investigated the relationship between complementary correlations (CC) and the entanglement over noisy channels. In particular, for bipartite qubit systems, it has been shown that there exists an optimal threshold to which CC has to be compared in order to determine the dynamical behavior of entanglement. Furthermore, authors claim to derive a novel threshold on environment parameter as well which is based on the Pearson correlation between measured outcomes of complementary observables. This topic has already been investigated to some extent in Ref. [1], the main novelty of this work is the inclusion of the decoherence.

Compression is representation of data in a compact form. Compression of data, bit rate reduction or source coding involves encoding of data using fewer number of bits. Compression are of two types lossy and lossless. Lossless compression removes such data bits whose removal does not affects the information of the data on the other hand no information losses takes place in lossless compression. Lossy compression reduces bits by finding unnecessary information and then removing them . The process of reduction of file data size is referred as data compression.

The original video file is about 2GB in compare with the encoded video file about 1.4MB which show the compression rate is over 99.9%.

Abstract – In this paper, we proposed an effective system for lossless image compression using different wavelet transform such as the stationary wavelet transform, the non decimated wavelet transform, and discrete wavelet transform with delta encoding and compare the results without delta encoding. With the development in the field of networking in the process of sending and receiving files needed to effective techniques for image compression as the raw images required large amounts of disk space to defect during transportation and storage operations. We used lossless compression to maintain the original features of the image in this process; there is a major problem which often is low compression ratios. We also use three types of wavelet transform which are discrete wavelet transform, non decimated wavelet transform, and stationary wavelet transform to demonstrate the effectiveness of the proposed system for comparison and to show the differences between each type to overcome previous challenges. This paper proposed a system for lossless image compression based on the use of the three types of wavelet transforms using Arithmetic coding and Huffman coding with delta coding, which helps in the high compression ratio to clarify the extent of the difference and distinction between each type. The results show that the stationary wavelet transform outperforms the non decimated wavelet transform and the discrete wavelet transform and with delta encoding outperforms without delta

performances of two incompatible features PAPR and Bit Error rate can be achieved. Selection of Non Linear Companding transform parameters plays an essential role as overall performance, robustness depends on it. The proposed theoretical work results can be view through MATLAB simulations.

Abstract- Video compression utilizes high computational power. For real-time requirements of multimedia applications faster computations need to be performed. To fulfill such requirements of video compression a parallel computational algorithm can be implemented. In this thesis, the parallelism of multi-core architecture was exploited using block matching algorithm. The test-implementation of the algorithm was done on MATLAB. The parallel model was designed using OpenMP and its implementation was tested on multi-core architectures.

Literature survey is the most important step in software development process. Before developing the tool it is necessary to determine the time factor, economy and company strength. Once these things are satisfied, then next steps are to determine which operating system and language can be used for developing the tool. Once the programmers start building the tool the programmers need lot of external support. This support can be obtained from senior programmers, from book or from websites. Before building the system the above consideration are taken into account for developing the proposed system.