The Experts below are selected from a list of 53280 Experts worldwide ranked by ideXlab platform
Ilan Ben-zvi - One of the best experts on this subject based on the ideXlab platform.
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Geometric Optimization of the 56MHz SRF Cavity and its Frequency Table Geometric optimization of the 56MHz SRF cavity and its Frequency Table
2020Co-Authors: Xiangyun Chang, Ilan Ben-zviAbstract:Abstract It is essential to know the Frequency of a Superconducting Radio Frequency (SRF) cavity at its "just being fabricated" stage because Frequency is the key parameter in constructing the cavity. In this paper, we report our work on assessing it. We can estimate the Frequency change from stage to stage theoretically andor by simulation. At the operating stage, the Frequency can be calculated accurately, and, from this value, we obtain the frequencies at other stages. They are listed in a Table that serves to check the processes from stage to stage. Equally important is optimizing the geometric shape of the SRF cavity so that the peak electric-field and peak magnetic-field are as low as possible. It is particularly desirable in the 56MHz SRF cavity of RHIC to maximize the Frequency sensitivity of the slow tuner. After undertaking such optimization, our resultant peak electric-field is only 44.1MV/m, and the peak magnetic-field is 1049G at 2.5MV of voltage across the cavity gap. To quench superconductivity in an SRF cavity, it is reported that the limit of the peak magnetic-field is 1800G [l], and that of the peak electric-field is more than lOOMV/m for a SRF cavity [2]. Our simulations employed the codes Superfish and Microwave Studio
Jianjiun Ding - One of the best experts on this subject based on the ideXlab platform.
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improved angle freeman chain code using improved adaptive arithmetic coding
Asia Pacific Conference on Circuits and Systems, 2020Co-Authors: Jianjiun DingAbstract:Binary image is useful in our life. For instance, text, line art, halftone image, tax etc. could use this method, so lossless binary image compression is useful for improve this domain. We found that angle freeman chain code for eight connectivity (AF8) is effective in lossless binary image compression. Therefore, we use improved-adaptive-arithmetic-coding to encode character of AF8, and we also decrease character with global and local Frequency Table thanks to some characteristics of AF8 we found. Then, in experimental result, we show our proposed method is better than AF8 with static arithmetic coding (SAC), and we also show that the context modeling method we choose is better than the compression coding without context modeling. Furthermore, our method is also better than other method like the ZD code and the AAF8 code.
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improved efficiency on adaptive arithmetic coding for data compression using range adjusting scheme increasingly adjusting step and mutual learning scheme
IEEE Transactions on Circuits and Systems for Video Technology, 2018Co-Authors: Jianjiun Ding, Ihsiang Wang, Hungyi ChenAbstract:Context-based adaptive arithmetic coding (CAAC) has high coding efficiency and is adopted by the majority of advanced compression algorithms. In this paper, five new techniques are proposed to further improve the performance of CAAC. They make the Frequency Table (the Table used to estimate the probability distribution of data according to the past input) of CAAC converge to the true probability distribution rapidly and hence improve the coding efficiency. Instead of varying only one entry of the Frequency Table, the proposed range-adjusting scheme adjusts the entries near to the current input value together. With the proposed mutual-learning scheme, the Frequency Tables of the contexts highly correlated to the current context are also adjusted. The proposed increasingly adjusting step scheme applies a greater adjusting step for recent data. The proposed adaptive initialization scheme uses a proper model to initialize the Frequency Table. Moreover, a local Frequency Table is generated according to local information. We perform several simulations on edge-directed prediction-based lossless image compression, coefficient encoding in JPEG, bit plane coding in JPEG 2000, and motion vector residue coding in video compression. All simulations confirm that the proposed techniques can reduce the bit rate and are beneficial for data compression.
Hungyi Chen - One of the best experts on this subject based on the ideXlab platform.
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improved efficiency on adaptive arithmetic coding for data compression using range adjusting scheme increasingly adjusting step and mutual learning scheme
IEEE Transactions on Circuits and Systems for Video Technology, 2018Co-Authors: Jianjiun Ding, Ihsiang Wang, Hungyi ChenAbstract:Context-based adaptive arithmetic coding (CAAC) has high coding efficiency and is adopted by the majority of advanced compression algorithms. In this paper, five new techniques are proposed to further improve the performance of CAAC. They make the Frequency Table (the Table used to estimate the probability distribution of data according to the past input) of CAAC converge to the true probability distribution rapidly and hence improve the coding efficiency. Instead of varying only one entry of the Frequency Table, the proposed range-adjusting scheme adjusts the entries near to the current input value together. With the proposed mutual-learning scheme, the Frequency Tables of the contexts highly correlated to the current context are also adjusted. The proposed increasingly adjusting step scheme applies a greater adjusting step for recent data. The proposed adaptive initialization scheme uses a proper model to initialize the Frequency Table. Moreover, a local Frequency Table is generated according to local information. We perform several simulations on edge-directed prediction-based lossless image compression, coefficient encoding in JPEG, bit plane coding in JPEG 2000, and motion vector residue coding in video compression. All simulations confirm that the proposed techniques can reduce the bit rate and are beneficial for data compression.
Xiangyun Chang - One of the best experts on this subject based on the ideXlab platform.
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Geometric Optimization of the 56MHz SRF Cavity and its Frequency Table Geometric optimization of the 56MHz SRF cavity and its Frequency Table
2020Co-Authors: Xiangyun Chang, Ilan Ben-zviAbstract:Abstract It is essential to know the Frequency of a Superconducting Radio Frequency (SRF) cavity at its "just being fabricated" stage because Frequency is the key parameter in constructing the cavity. In this paper, we report our work on assessing it. We can estimate the Frequency change from stage to stage theoretically andor by simulation. At the operating stage, the Frequency can be calculated accurately, and, from this value, we obtain the frequencies at other stages. They are listed in a Table that serves to check the processes from stage to stage. Equally important is optimizing the geometric shape of the SRF cavity so that the peak electric-field and peak magnetic-field are as low as possible. It is particularly desirable in the 56MHz SRF cavity of RHIC to maximize the Frequency sensitivity of the slow tuner. After undertaking such optimization, our resultant peak electric-field is only 44.1MV/m, and the peak magnetic-field is 1049G at 2.5MV of voltage across the cavity gap. To quench superconductivity in an SRF cavity, it is reported that the limit of the peak magnetic-field is 1800G [l], and that of the peak electric-field is more than lOOMV/m for a SRF cavity [2]. Our simulations employed the codes Superfish and Microwave Studio
Madhu Goel - One of the best experts on this subject based on the ideXlab platform.
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www.IJCSI.org Ternary Tree and Clustering Based Huffman Coding Algorithm
2014Co-Authors: Pushpa R. Suri, Madhu GoelAbstract:In this study, the focus was on the use of ternary tree over binary tree. Here, a new two pass Algorithm for encoding Huffman ternary tree codes was implemented. In this algorithm we tried to find out the codeword length of the symbol. Here I used the concept of Huffman encoding. Huffman encoding was a two pass problem. Here the first pass was to collect the letter frequencies. You need to use that information to create the Huffman tree. Note that char values range from-128 to 127, so you will need to cast them. I stored the data as unsigned chars to solve this problem, and then the range is 0 to 255. Open the output file and write the Frequency Table to it. Open the input file, read characters from it, gets the codes, and writes the encoding into the output file. Once a Huffman code has been generated, data may be encoded simply by replacing each symbol with its code. To reduce the memory size and fasten the process of finding the codeword length for a symbol in a Huffman tree, we proposed a memory efficient data structure to represent the codeword length of Huffman ternary tree. In this algorithm we tried to find out the length of the code of the symbols used in the tree
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ternary tree and clustering based huffman coding algorithm
2010Co-Authors: Pushpa R. Suri, Madhu GoelAbstract:In this study, the focus was on the use of ternary tree over binary tree. Here, a new two pass Algorithm for encoding Huffman ternary tree codes was implemented. In this algorithm we tried to find out the codeword length of the symbol. Here I used the concept of Huffman encoding. Huffman encoding was a two pass problem. Here the first pass was to collect the letter frequencies. You need to use that information to create the Huffman tree. Note that char values range from -128 to 127, so you will need to cast them. I stored the data as unsigned chars to solve this problem, and then the range is 0 to 255. Open the output file and write the Frequency Table to it. Open the input file, read characters from it, gets the codes, and writes the encoding into the output file. Once a Huffman code has been generated, data may be encoded simply by replacing each symbol with its code. To reduce the memory size and fasten the process of finding the codeword length for a symbol in a Huffman tree, we proposed a memory efficient data structure to represent the codeword length of Huffman ternary tree. In this algorithm we tried to find out the length of the code of the symbols used in the tree.
