• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.119-124
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• 2001

In this pattern recognition on the large volumes of remote sensing satellite images, the inference time is much increased. In the case of the remote sensing data [5] having 4 wavebands, the 778 training patterns are learned. Each land cover pattern is classified by using 159, 900 patterns including the trained patterns. For the fuzzy classification, the 778 fuzzy rules are generated. Each fuzzy rule has 4 fuzzy variables in the condition part. Therefore, high performance parallel fuzzy inference system is needed. In this paper, we propose a novel parallel fuzzy inference system on T3E parallel computer. In this, fuzzy rules are distributed and executed simultaneously. The ONE_To_ALL algorithm is used to broadcast the fuzzy input to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL_TO_ONE algorithm. By parallel processing of the fuzzy rules, the parallel fuzzy inference algorithm extracts match parallelism and achieves a good speed factor. This system can be used in a large expert system that ha many inference variables in the condition and the consequent part.

• Journal of the Korea Computer Graphics Society
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• v.2 no.1
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• pp.24-30
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• 1996

The ray tracing method, which is one of many photorealistic rendering techniques, requires heavy computational processing to synthesize images. Parallel processing can be used to reduce the computational processing time. A parallel algorithm for the ray tracing has been implemented and executed for various images on transputer systems. In order to develop a scalable parallel algorithm, a processor farming technique has been exploited. Since each image is divided and distributed to each farming processor, the scalability of the parallel system and load balancing are achieved naturally in the proposed algorithm. Efficiency of the parallel algorithm is obtained up to 95% for nine processors. However, the best size of a distributed task is much higher in simple images due to less computational requirement for every pixel. Efficiency degradation is observed for large granularity tasks because of load unbalancing caused by the large task. Overall, transputer systems behave as good scalable parallel processing system with respect to the cost-performance ratio.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.405-406
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• 2006

Recently, the flat display modules such as plasma or TFT-LCD employ thin crystallized panels which are normally weak to high level transient mechanical energy inputs. As a result, anti-shock performance is one of the most important design specifications for TFT-LCD modules. However, most of large display module designs are generated based on engineers own experiences. Also, a large-scale analysis to evaluate complex material and structural behaviors is one of interesting topic in diverse engineering and scientific fields. The utilization of massively parallel processors has also been a recent trend of high performance computing. The objective of this paper is to introduce a parallel process system which consists of general purpose finite element analysis solver as well as parallelized PC cluster. The parallel processing system is constructed using thirty-two processing elements and the finite element program is developed by adopting hierarchical domain decomposition method. In order to verify the efficiency of the established system, an impact analysis on thin and complex sub-parts of flat display modules is performed. The evaluation results showed a good agreement with the corresponding reference solutions, and thus, the parallel process system seems to be a useful tool fur the complex structural analysis such as IT related products.

• Journal of Internet Computing and Services
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• v.18 no.3
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• pp.1-9
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• 2017

Smart Cities intelligently manage numerous infrastructures, including Smart-City IoT devices, and provide a variety of smart-city applications to citizen. In order to provide various information needed for smart-city applications, Smart Cities require a function to intelligently process large-scale streamed big data that are constantly generated from a large number of IoT devices. To provide smart services in Smart-City, the Smart-City Consortium uses stream reasoning. Our stream reasoning requires real-time processing of big data. However, there are limitations associated with real-time processing of large-scale streamed big data in Smart Cities. In this paper, we introduce one of our researches on cloud computing based real-time distributed-parallel-processing to be used in stream-reasoning of IoT big data in Smart Cities. The Smart-City Consortium introduced its previously developed smart-city middleware. In the research for this paper, we made cloud computing based real-time distributed-parallel-processing available in the cloud computing platform of the smart-city middleware developed in the previous research, so that we can perform real-time distributed-parallel-processing with them. This paper introduces a real-time distributed-parallel-processing method and system for stream reasoning with IoT big data transmitted from various sensors of Smart Cities and evaluate the performance of real-time distributed-parallel-processing of the system where the method is implemented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3543-3557
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• 2017

Compression is a very important technique for remotely sensed hyperspectral images. The lossless compression based on the recursive least square (RLS), which eliminates hyperspectral images' redundancy using both spatial and spectral correlations, is an extremely powerful tool for this purpose, but the relatively high computational complexity limits its application to time-critical scenarios. In order to improve the computational efficiency of the algorithm, we optimize its serial version and develop a new parallel implementation on graphics processing units (GPUs). Namely, an optimized recursive least square based on optimal number of prediction bands is introduced firstly. Then we use this approach as a case study to illustrate the advantages and potential challenges of applying GPU parallel optimization principles to the considered problem. The proposed parallel method properly exploits the low-level architecture of GPUs and has been carried out using the compute unified device architecture (CUDA). The GPU parallel implementation is compared with the serial implementation on CPU. Experimental results indicate remarkable acceleration factors and real-time performance, while retaining exactly the same bit rate with regard to the serial version of the compressor.

• ETRI Journal
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• v.32 no.4
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• pp.493-502
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• 2010

The operation time of an encoder is one of the critical implementation issues for satisfying the timing requirements of Long Term Evolution (LTE) systems because the encoder is based on binary operations. In this paper, we propose a design and implementation of a latency efficient encoder for LTE systems. By virtue of 8-bit parallel processing of the cyclic redundancy checking attachment, code block (CB) segmentation, and a parallel processor, we are able to construct engines for turbo codings and rate matchings of each CB in a parallel fashion. Experimental results illustrate that although the total area and clock period of the proposed scheme are 19% and 6% larger than those of a conventional method based on a serial scheme, respectively, our parallel structure decreases the latency by about 32% to 65% compared with a serial structure. In particular, our approach is more latency efficient when the encoder processes a number of CBs. In addition, we apply the proposed scheme to a real system based on LTE, so that the timing requirement for ACK/NACK transmission is met by employing the encoder based on the parallel structure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1056-1063
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• 1994

In this paper a content addressable memory (CAM) is designed by adopting best-match method. It has a single processing element(PE) architecture with high computational efficiency and throughput. It is composed of three main functional blocks(input MUX, best-match CAM, control part). It support fully parallel processing. Logic simulation is completed by using QUICKSIM, Circuit simulation is performanced by using HSPICE. Its layout is based on the ETRI 3 m n-well process design rules. Its maximum operating frequency is 20 MHz.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.6
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• pp.168-176
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• 1996

A diagnosis system which can monitor tool breakage and chipping in real time was developed using a DSP(Digital Signal Processor) board in face milling operation. AR modelling and band energy method were used to extract the feature of tool states from cutting force signals. Artificial neural network embedded on DSP board discriminates different patterns from features got after signal processing. The features extracted from AR modelling are more accurate for the malfunction of a process than those from band energy method, even though the computing speed of the former is slow. From the processed features, we can construct the real time diagnosis system which monitors malfunction by using a DSP board having a parallel processing capability.

• Journal of Biomedical Engineering Research
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• v.16 no.3
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• pp.317-324
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• 1995

A need exists for the accurate identification of time series models having time varying parameters, as is important in the case of real time identification of nonstationary EMG signal. Thls paper describes real time identification and muscle fatigue monitoring method of nonstationary EMG signal. The method is composed of the efficient identifier which estimates the autoregressive parameters of nonstationary EMG signal model, and its real time implementation by using T805 parallel processing computer. The method is verified through experiment with real EMG signals which are obtained from surface electrode. As a result, the proposed method provides a new approach for real time Implementation of muscle fatigue monitoring and the execution time is 0.894ms/sample for 1024Hz EMG signal.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.481-483
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• 1998

In general, spatial filtering method was proposed to simplify measurement system through parallel Processing hardware. Spatial filtering is a method of detection that we can get a spatial pattern information, as we process a special space pattern, to say, as we process spatial parallel process by using the spatial weighting function. The important processing characteristics will be depended in according to how ire design a spatial weighting function, a spatial sensitive distribution. The form of the weighting function which is realized from the generally used spatial filtering is fixed and the weighting value was already became a binary-value. In this paper, we propose a new method in order to construct adaptive measurement systems. This method is a weighting function design to make multi-valued and variable.