Arithmetic Coding

Correlated data gathering in body area networks calls for systems that perform efficient compression and reliable transmission of the measurements, while imposing a small computational burden at the sensors. Highly-efficient compression mechanisms, e.g., adaptive arithmetic entropy encoding, do not address the problem adequately, as they have high computational demands. In this paper, we propose a new distributed joint source-channel coding (DJSCC) solution for this problem. Following the principles of distributed source coding, our design allows for efficient compression and error-resilient transmission while exploiting the correlation amongst sensors' readings at energy-robust sink nodes. In this way, the computational complexity and in turn, the energy consumption at the sensor node is kept to a minimum. Our DJSCC design is based on a new non-systematic Slepian-Wolf Raptor code construction that achieves good performance at short code lengths, which are appropriate for low-rate data gathering within local or body area sensor networks. Experimental results using a WSN deployment for temperature monitoring reveal that, for lossless compression, the proposed system leads to a 30.08% rate reduction against a baseline system that performs adaptive arithmetic entropy encoding of the temperature readings. Moreover, under AWGN and Rayleigh fading channel losses, the proposed system leads to energy savings between 12.19% to 16.51% with respect to the baseline system.

In this paper, we present image compression techniques to utilizing the visual redundancy and investigated. To effectively define and utilize image compression context for natural image is difficult problem. Inspired by recent research in the advancements of image compression techniques, we propose Daubechies-Wavelet with arithmetic coding towards the improvement over visual quality rather than spatial wise fidelity. Image compression using Daubechies-Wavelet with arithmetic coding is quite simple and good technique of compression to produce better compression results. In this image compression technique we first apply Daubechies-Wavelet transform then 2D Walsh-Wavelet transform on each kxk where (k=2 n ) block of the low frequency sub band. Split all values from each transformed block kxk followed by applying arithmetic coding for image compress.

We propose a fully three-dimensional (3-D) wavelet-based coding system featuring 3-D encoding/two-dimensional (2-D) decoding functionalities. A fully 3-D transform is combined with context adaptive arithmetic coding; 2-D decoding is enabled by encoding every 2-D subband image independently. The system allows a finely graded up to lossless quality scalability on any 2-D image of the dataset. Fast access to 2-D images is obtained by decoding only the corresponding information thus avoiding the reconstruction of the entire volume. The performance has been evaluated on a set of volumetric data and compared to that provided by other 3-D as well as 2-D coding systems. Results show a substantial improvement in coding efficiency (up to 33%) on volumes featuring good correlation properties along the axis. Even though we did not address the complexity issue, we expect a decoding time of the order of one second/image after optimization. In summary, the proposed 3-D/2-D multidimensional layered zero coding system provides the improvement in compression efficiency attainable with 3-D systems without sacrificing the effectiveness in accessing the single images characteristic of 2-D ones.

This paper introduces a new family of reconstruction codes which is motivated by applications in DNA data storage and sequencing. In such applications, DNA strands are sequenced by reading some subset of their substrings. While previous works considered two extreme cases in which all substrings of predefined lengths are read or substrings are read with no overlap for the single string case, this work studies two extensions of this paradigm. The first extension considers the setup in which consecutive substrings are read with some given minimum overlap. First, an upper bound is provided on the attainable rates of codes that guarantee unique reconstruction. Then, efficient constructions of codes that asymptotically meet that upper bound are presented. In the second extension, we study the setup where multiple strings are reconstructed together. Given the number of strings and their length, we first derive a lower bound on the read substrings' length ℓ that is necessary for the existence of multi-strand reconstruction codes with non-vanishing rates. We then present two constructions of such codes and show that their rates approach 1 for values of ℓ that asymptotically behave like the lower bound.

This paper introduces a new family of reconstruction codes which is motivated by applications in DNA data storage and sequencing. In such applications, DNA strands are sequenced by reading some subset of their substrings. While previous works considered two extreme cases in which all substrings of some fixed length are read or substrings are read with no overlap, this work considers the setup in which consecutive substrings are read with some given minimum overlap. First, upper bounds are provided on the attainable rates of codes that guarantee unique reconstruction. Then, we present efficient constructions of asymptotically optimal codes that meet the upper bound.

We present a new image compression technique called \DjVu " that is speci cally geared towards the compression of high-resolution, high-quality images of scanned documents in color. With DjVu , any screen connected to the Internet can access and display images of scanned p ages while faithfully reproducing the font, color, drawing, pictures, and paper texture. A typical magazine page in color at 300dpi can be c ompressed down to between 40 to 60 KB, approximately 5 to 10 times better than JPEG for a similar level of subjective quality. B&W documents are typically 15 to 30 KBytes at 300dpi, or 4 to 8 times better than CCITT-G4. A r eal-time, memory e cient version of the decoder was implemented, and is available as a plug-in for popular web browsers.

Several techniques based on the three-dimensional (3-D) discrete cosine transform (DCT) have been proposed for volumetric data coding. These techniques fail to provide lossless coding coupled with quality and resolution scalability, which is a significant drawback for medical applications. This paper gives an overview of several state-of-the-art 3-D wavelet coders that do meet these requirements and proposes new compression methods exploiting the quadtree and block-based coding concepts, layered zero-coding principles, and context-based arithmetic coding. Additionally, a new 3-D DCT-based coding scheme is designed and used for benchmarking. The proposed wavelet-based coding algorithms produce embedded data streams that can be decoded up to the lossless level and support the desired set of functionality constraints. Moreover, objective and subjective quality evaluation on various medical volumetric datasets shows that the proposed algorithms provide competitive lossy and lossless compression results when compared with the state-of-the-art.

salah satu jenis data adalah data teks. Data teks biasanya dikodekan dalam bentuk kode ASCII yang memiliki panjang binner 8. Kompresi data teks bertujuan untuk mengurangi pemakaian ruang penyimpanan pada memori. Penelitian ini menggunakan Auto-Encoder Neural Network (AENN). Hasil dari penelitian ini menghasilkan rasio sebesar 88,62%, namun masih ada keterbasan jika panjang asli ganjil maka setelah didekompresi menghasilkan panjang yang genap dan keterbasan pembagi hidden yang menyebabkan data tidak kembali ke aslinya.

Perkembangan media penyimpan berkapasitas besar mengakibatkan orang tidak lagi menemui masalah jika mempunyai file dengan ukuran yang besar. Lebih-lebih jika file yang kita punya merupakan file citra. Walaupun demikian, adakalanya ukuran file yang besar tersebut terasa mengganggu jika kita harus memanage media penyimpan yang kita punya untuk bermacam-macam data. Kompresi data menjadi cara yang efisien untuk mengatasi permasalahan di atas. Ada dua tipe kompresi data, yaitu kompresi lossy dan kompresi lossles. Kompresi citra bersifat lossy, citra mengalami penurunan akibat proses yang terjadi didalamnya. Sedangkan pada kompresi citra yang bersifat lossles, citra tidak mengalami penurunan. Ada banyak sekali metode kompresi data yang ada saat ini, namun pada tugas akhir ini akan dibahas tentang salah satu teknik kompresi data Arithmetic Coding pada kompresi citra. Metode Arithmetic Coding ini menggantikan satu deretan simbol input dengan sebuah bilangan floating point. Semakin panjang d...

Image compression is an essential technique for saving time and storage space for the gigantic amount of data generated by images. This paper introduces an adaptive source-mapping scheme that greatly improves bit-level lossless grayscale image compression. In the proposed mapping scheme, the frequency of occurrence of each symbol in the original image is computed. According to their corresponding frequencies, these symbols are sorted in descending order. Based on this order, each symbol is replaced by an 8-bit weighted fixed-length code. This replacement will generate an equivalent binary source with an increased length of successive identical symbols (0s or 1s). Different experiments using Lempel-Ziv lossless image compression algorithms have been conducted on the generated binary source. Results show that the newly proposed mapping scheme achieves some dramatic improvements in regards to compression ratios.

Cryptography is the process of transforming the meaning of information with the aid of an encryption key. DNA cryptography is a new rapidly evolving technology that uses DNA computing principles: Adenine (A), Guanine (G), Cytosine (C), and Thymine (T) to encrypt or hide the meaning of information such that only the intended recipient can understand. This research aims to improve on a twolevel text data encryption system using DNA cryptography. To achieve the research goal, 2-level data security algorithms for encryption, decryption, and key generation were designed using Shift Cipher encryption technique at the first security level and One Time Pad encryption technique at the second security level of the encryption and decryption algorithms. The algorithms were implemented using the PHP programming language. The research findings revealed that the improved 2-level data security approach has better encryption and decryption execution time compared to the 2-level Text Data encryption using DNA cryptography and the encryption algorithms can be used to encrypt and decrypt alphanumeric and special symbol information.

Motion estimation is a fundamental step for high quality, low bandwidth video compression. Recently the MPEG-4 group has proposed some low complexity algorithms. They have almost the same performance in term of PSNR of the Full Search algorithm, but at the same time the complexity is dramatically reduced. However it is difficult to realize these algorithms with conventional hardware structures. For this reason we presented a Hardware Oriented Region Based algorithm (HORB) with similar performances, but that can be implemented with a simple hardware structure. Here we present an architecture tailored to meet the design constraint of the HORB algorithm (and at the same time capable of realizing the Full Search algorithm).

With frequent predictions of upcoming technological and economic difficulties triggered by an impending shortage of information and communications technologies (ICT) professionals, the calls are growing stronger to include coding as a core element of school curriculum. These calls are bolstered by the suggestion that coding supports the development of thinking skills – which echoes a longstanding argument for teaching mathematics. Motivated by the parallel, we attempted to investigate some of the common ground between learning to code and the development of core mathematical concepts. We photographed and video recorded children, aged 9–10, as they learned to build and program Lego Mindstorms TM EV3 robots over four days. Our findings suggest that programming supports children’s understandings of decimal numbers and their transitions from additive to multiplicative thinking.

With louder and more widespread calls to include computer programming as a core element of school curriculum, global efforts to define innovative and distinct coding curricula are underway. We take a different tack in this paper, one oriented by an investigation of the common ground between learning to program and learning mathematics. We observed 9-and 10-year-olds as they learned to build and program Lego Mindstorms EV3 robots over 4 days, attending in particular to the ways that programming robots to move might support the development and integration of powerful instantiations of number, arithmetic and multiplication. Our findings suggest that children's understanding of number, and their transitions from additive to multiplicative thinking, can be powerfully supported by engaging in practical tasks rather than practice exercises. Keywords Robotics . Coding . Elementary mathematics . Instantiations of arithmetic . Metaphor . Video analysis Worldwide, coding is gaining momentum in K-12 education. Estonia and England, for example, have implemented national curricula, making computer programming mandatory for all school-age students across all grades. Other nations 1 are also moving in this direction. In North America, while national-level discussions and calls have yet to gather the same momentum, an increasing number of initiatives are emerging at the local level. For example, the Chicago School District recently launched Computer Science for All, making computer science a core subject in all public high schools. This

We study the Lonely Runner Conjecture (LRC) for speed sets in exact arithmetic progression (AP) with common start. We prove that perfect isolation (minimal circular gap exactly 1/n) occurs precisely at the φ(n) times t with {δt} = r/n and gcd(r, n) = 1, yielding uniform ngrids. An equivalent spectral characterization states that, at these instants, all Fourier modes with frequencies not divisible by n vanish. We formalize a zero-radius structural stability failure: arbitrarily small perturbations of the AP destroy these exact grid instants. Computationally, we give an event-complete "boundary-set" procedure that evaluates isolation only at analytically determined threshold times and empirically reduces work relative to naive sampling. We document floating-point tolerances and provide code/data for reproducibility.

The embedded block coding with optimized truncation (EBCOT) algorithm has been adopted by the JPEG2000 standard. In which, the post compression rate distortion (PCRD)optimization needs a large memory space to store all the code streams of code blocks; however, code blocks of less importance might not be used for the optimal decoded image at a bit rate. To avoid waste of computational power and memory space, a simple context based rate distortion estimation (CBRDE) is proposed to arrange the scanning order of code blocks in an adaptive manner. CBRDE is based on the MQ table, which is available at both encoder and decode. Thus, there is no need to store and transmit the rate distortion information of code blocks. Experimental results show that the rate distortion curves are almost convex; this demonstrates the benefit of the proposed CBRDE.

In this paper it is presented the lossless PPM (Prediction by Partial string Matching) algorithm and it is studied the way the extended alphabet can be used for the PPM encoding so it will allow the use of symbols which are not present in the alphabet at the beginning of the encoding phase. The extended alphabet can contain symbols with the size larger than a byte and at the decoding external words absent at decoding are combined with the adaptive-generated words. The arithmetical algorithm is used to encoding of words with the statistic model generated by the PPM algorithm. Some experimental results on various types of files and important interpretations deducted from these results are presented.

Compression is a process of coding for reducing the total number of bits that are used to represent certain information. The main emphasis of compression is to transmit only minimum information that are mainly required for reconstruction. In this work, Run length coding algorithm is demonstrated by taking different data inputs. ________________________________________________________________________________________________________

This paper aims to present the methods of achieving an optimal encoding in the data communication channels. After a short description of the communication channel and of the data communication channel types, follow briefly a few notions of the data channel enthropy, information, transinformation, with their properties, definitions and mathematical relations connecting them. Chapter 2 presents the concept of optimal code, following a detailed description (using two suggestive examples) of the two main methods used to obtain an optimal code: Shannon-Fano and Huffman.

The nature of the quantities involved in arithmetic problems promotes semantic encodings that affect the strategy chosen to solve them . Such encoding effects might prevent positive transfer to problems sharing the same formal mathematical structure . In this study with 5 th and 6 th graders, we investigated the conditions promoting positive and negative transfer in arithmetic problems that could be solved with two distinct strategies. We showed that basic training cannot overcome the initial impact of semantic encodings, and we provided evidence that a lack of semantic encoding of the training problems leads to transfer errors. This suggests the existence of ontological restrictions on the representation mechanisms involved in problem solving tasks.

We address the problem of stabilizing a continuous-time linear time-invariant process under communication constraints. We assume that the sensor that measures the state is connected to the actuator through a finite capacity communication channel over which an encoder at the sensor sends symbols from a finite alphabet to a decoder at the actuator. We consider a situation where one symbol from the alphabet consumes no communication resources, whereas each of the others consumes one unit of communication resources to transmit. This paper explores how the imposition of limits on an encoder's bit-rate and average resource consumption affect the encoder/decoder/controller's ability to keep the process bounded. The main result is a necessary and sufficient condition for a bounding encoder/decoder/controller which depends on the encoder's bit-rate, its average resource consumption, and the unstable eigenvalues of the process.

The Main Objective of this project is to make available a touch screen panel which will display the schemes on Screen which will be very helpful for rural as well as urban areas. There are number of schemes are available but there is not a simple way to reach or to access the schemes for common people where they are not have more availability of technology in their area. So they are unaware about the facilities and the schemes, they does not have more knowledge about it. We are making it easier by user which have to fill up very basic information about him and according to the basic information it will show a particular schemes which are eligible for him/her along with the documents required to apply it. Then according to the basic information of user after reading or seeing eligible schemes he/she will receive the SMS which we are providing for contact/helpline number for more detail information.

This paper investigates rhe algorithmic complexiq of arithmetic coding in the new H264 video codzng standard and proposes a coprocessor to reduce it by more ihan an order of magnitude. The coprocessor is based on an innovative algOFlfhFi2 named as the MZ-coder and maintains the origieal coding eflciency with a multiplication-free, nonstalling, fui& pipeiined architecture with modest harhvare requirements. The coprocessor delivers a constant throughput for both coding and decoding of I bit per cycle and can be attached to a controlling CPU whose ISA has been extended with arithmetic coding instnrctions.

Efficient image compression technique especially for medical applications is presented. Dyadic wavelet decomposition by use of Antonini and Villasenor bank filters is followed by adaptive space-frequency quantization and zerotree-based entropy coding of wavelet coefficients. Threshold selection and uniform quantization is made on a base of spatial variance estimate built on the lowest frequency subband data set. Threshold value for each coefficient is evaluated as linear function of 9-order binary context. After quantization zerotree construction, pruning and arithmetic coding is applied for efficient lossless data coding. Presented compression method is less complex than the most effective EZW-based techniques but allows to achieve comparable compression efficiency. Specifically our method has similar to SPIHT efficiency in MR image compression, slightly better for CT image and significantly better in US image compression. Thus the compression efficiency of presented method is competitive with the best published algorithms in the literature across diverse classes of medical images.