• The Journal of Korean Institute of Communications and Information Sciences
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• 제32권4C호
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• pp.379-388
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• 2007

In this paper, we propose an adaptive turbo decoding algorithm for high order modulation scheme combined with originally design for a standard rate-1/2 turbo decoder for B/QPSK modulation. A transformation applied to the incoming I-channel and Q-channel symbols allows the use of an off-the-shelf B/QPSK turbo decoder without any modifications. Adaptive turbo decoder process the received symbols recursively to improve the performance. As the number of iterations increase, the execution time and power consumption also increase as well. The source of the latency and power consumption reduction is from the combination of the radix-4, dual-path processing, parallel decoding, and early-stop algorithms. We implemented the proposed scheme on a field-programmable gate array (FPGA) and compared its decoding speed with that of a conventional decoder. From the result of implementation, we confirm that the decoding speed of proposed adaptive decoding is faster than conventional scheme by 6.4 times.

• Journal of electromagnetic engineering and science
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• 제6권3호
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• pp.147-154
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• 2006

In this paper, we propose and present implementation results of a high-speed turbo decoding algorithm. The latency caused by (de) interleaving and iterative decoding in a conventional maximum a posteriori(MAP) turbo decoder can be dramatically reduced with the proposed design. The source of the latency reduction is come from the combination of the radix-4, dual-path processing, parallel decoding, and rearly-stop algorithms. This reduced latency enables the use of the turbo decoder as a forward error correction scheme in real-time wireless communication services. The proposed scheme results in a slight degradation in bit-error rate(BER) performance for large block sizes because the effective interleaver size in a radix-4 implementation is reduced to half, relative to the conventional method. Fixed on the parameters of N=212, iteration=3, 8-states, 3 iterations, and QPSK modulation scheme, we designed the adaptive high-speed turbo decoder using the Xilinx chip (VIRTEX2P (XC2VP30-5FG676)) with the speed of 17.78 Mb/s. From the results, we confirmed that the decoding speed of the proposed decoder is faster than conventional algorithms by 8 times.

• ETRI Journal
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• 제29권3호
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• pp.363-370
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• 2007

In this paper, we propose a flexible turbo decoding algorithm for a high order modulation scheme that uses a standard half-rate turbo decoder designed for binary quadrature phase-shift keying (B/QPSK) modulation. A transformation applied to the incoming I-channel and Q-channel symbols allows the use of an off-the-shelf B/QPSK turbo decoder without any modifications. Iterative codes such as turbo codes process the received symbols recursively to improve performance. As the number of iterations increases, the execution time and power consumption also increase. The proposed algorithm reduces the latency and power consumption by combination of the radix-4, dual-path processing, parallel decoding, and early-stop algorithms. We implement the proposed scheme on a field-programmable gate array and compare its decoding speed with that of a conventional decoder. The results show that the proposed flexible decoding algorithm is 6.4 times faster than the conventional scheme.

• ETRI Journal
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• 제29권6호
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• pp.736-744
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• 2007

We propose a fast macroblock (MB) mode prediction and decision algorithm based on temporal correlation for P-slices in the H.264/AVC video standard. There are eight block types for temporal decorrelation, including SKIP mode based on rate-distortion (RD) optimization. This scheme gives rise to exhaustive computations (search) in the coding procedure. To overcome this problem, a thresholding method for fast inter mode decision using a MB tracking scheme to find the most correlated block and RD cost of the correlated block is suggested for early stop of the inter mode determination. We propose a two-step inter mode candidate selection method using statistical analysis. In the first step, a mode is selected based on the mode information of the co-located MB from the previous frame. Then, an adaptive thresholding scheme is applied using the RD cost of the most correlated MB. Secondly, additional candidate modes are considered to determine the best mode of the initial candidate modes that does not satisfy the designed thresholding rule. Comparative analysis shows that a speed-up factor of up to 70.59% is obtained when compared with the full mode search method with a negligible bit increment and a minimal loss of image quality.

• Proceedings of the Korean Vacuum Society Conference
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• 한국진공학회 2014년도 제46회 동계 정기학술대회 초록집
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• pp.237.1-237.1
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• 2014

Etching is one of the most important steps in semiconductor manufacturing. In etch process control a critical task is to stop the etch process when the layer to be etched has been removed. If the etch process is allowed to continue beyond this time, the material gets over-etched and the lower layer is partially removed. On the other hand if the etch process is stopped too early, part of the layer to be etched still remains, called under-etched. Endpoint detection (EPD) is used to detect the most accurate time to stop the etch process in order to avoid over or under etch. The goal of this research is to develop a hardware and software system for EPD. The hardware consists of an Optical Plasma Monitor (OPM) sensor which is used to continuously monitor the plasma optical emission intensity during the etch process. The OPM software was developed to acquire and analyze the data to perform EPD. Our EPD algorithm is based on the following theory. As the etch process starts the plasma generated in the vacuum is added with the by-products from the etch reactions on the layer being etched. As the endpoint reaches and the layer gets completely removed the plasma constituents change gradually changing the optical intensity of the plasma. Although the change in optical intensity is not apparent, the difference in the plasma constituents when the endpoint has reached leaves a unique signature in the data gathered. Though not detectable in time domain, this signature could be obscured in the frequency spectrum of the data. By filtering and analysis of the changes in the frequency spectrum before and after the endpoint we could extract this signature. In order to do that, first, the EPD algorithm converts the time series signal into frequency domain. Next the noise in the frequency spectrum is removed to look for the useful frequency constituents of the data. Once these useful frequencies have been selected, they are monitored continuously in time and using a sub-algorithm the endpoint is detected when significant changes are observed in those signals. The experiment consisted of three kinds of etch processes; ashing, SiO2 on Si etch and metal on Si etch to develop and evaluate the EPD system.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.112-119
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• 2023

Traditionally used for networking computers and communications, the Internet has been evolving from the beginning. Internet is the backbone for many things on the web including social media. The concept of social networking which started in the early 1990s has also been growing with the internet. Social Networking Sites (SNSs) sprung and stayed back to an important element of internet usage mainly due to the services or provisions they allow on the web. Twitter and Facebook have become the primary means by which most individuals keep in touch with others and carry on substantive conversations. These sites allow the posting of photos, videos and support audio and video storage on the sites which can be shared amongst users. Although an attractive option, these provisions have also culminated in issues for these sites like posting offensive material. Though not always, users of SNSs have their share in promoting hate by their words or speeches which is difficult to be curtailed after being uploaded in the media. Hence, this article outlines a process for extracting user reviews from the Twitter corpus in order to identify instances of hate speech. Through the use of MPCA (Modified Principal Component Analysis) and ECNN, we are able to identify instances of hate speech in the text (Enhanced Convolutional Neural Network). With the use of NLP, a fully autonomous system for assessing syntax and meaning can be established (NLP). There is a strong emphasis on pre-processing, feature extraction, and classification. Cleansing the text by removing extra spaces, punctuation, and stop words is what normalization is all about. In the process of extracting features, these features that have already been processed are used. During the feature extraction process, the MPCA algorithm is used. It takes a set of related features and pulls out the ones that tell us the most about the dataset we give itThe proposed categorization method is then put forth as a means of detecting instances of hate speech or abusive language. It is argued that ECNN is superior to other methods for identifying hateful content online. It can take in massive amounts of data and quickly return accurate results, especially for larger datasets. As a result, the proposed MPCA+ECNN algorithm improves not only the F-measure values, but also the accuracy, precision, and recall.