• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.233-240
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• 2021

We propose a novel, highly accurate approximate multiplier using different types of inexact 4-2 compressors. The importance of low hardware costs leads us to develop approximate multiplication for error-resilient applications. Several rules are developed when selecting a topology for designing the proposed multiplier. Our highly accurate multiplier design considers the different error characteristics of adopted compressors, which achieves a good error distribution, including a low relative error of 0.02% in the 8-bit multiplication. Our analysis shows that the proposed multiplier significantly reduces power consumption and area by 45% and 26%, compared with the exact multiplier. Notably, a trade-off relationship between error characteristics and hardware costs can be achieved when considering those of existing highly accurate approximate multipliers. In the image blending, edge detection and image sharpening applications, the proposed 8-bit approximate multiplier shows better performance in terms of image quality metrics compared with other highly accurate approximate multipliers.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.203-216
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• 2004

Recent advances in video coding technology have resulted in rapid growth of application in mobile communication, With this explosive growth reliable transmission and error resilient technique become increasingly necessary to offer high quality multimedia service. In this paper, we present the result of our investigation on the error resilient performance evaluation of the MPEG-4 simple profile under the H.324/M and the H.264/AVC baseline under the IP packet networks. Especially, we have tested error resilient tools of MPEG-4 simple profile such as resynchronization marker insertion, data partitioning, and of H.264/AVC baseline such as the flexible macroblock ordering (FMO) scheme. The objective quality of decoded video is measured in terms of rate and PSNR under various random bit and burst error conditions.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.235-242
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• 2020

This paper analyzes the effect of reduced output width of the truncated logarithmic multiplication and application to inferences using convolutional neural networks (CNNs). For small hardware overhead, output width is reduced in the truncated Mitchell multiplier, so that fractional bits in multiplication output are minimized in error-resilient applications. This analysis shows that when reducing output width in the truncated Mitchell multiplier, even though worst-case relative error increases, average relative error can be kept small. When adopting 8 fractional bits in multiplication output in the evaluations, there is no significant performance degradation in target CNNs compared to existing exact and original Mitchell multipliers.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1616-1623
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• 1999

This paper describes error resilient coding scheme is added in WLL system and its application for robust low-bit rate still image transmission over power controlled W-CDA system Rayleigh fading channels. The baseline JPEG compressing methods are uses in image coding over wireless channel. The channel uses Reed-Solomon(RS) outer codes concatenated with convolutional inner codes, and truncated type I hybrid ARQ protocol based on the selective repeat strategy and the RS error detection capability. Simulation results are proved for the statistics of the frame-error bursts of the proposed system in comparison with conventional WLL system. it gains the 2 dB of the Eb/No in same BER.

• Journal of Communications and Networks
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• v.15 no.2
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• pp.198-206
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• 2013

Recent advances in forward error correction (FEC) coding techniques were focused on addressing the challenges of multicast and broadcast delivery. However, FEC approaches can also be used for unicast content delivery in order to solve transmission control protocol issues found in wireless networks. In this paper, we exploit the error resilient properties of Raptor codes by proposing Raptor-based content delivery protocol (RCDP) - a novel solution for reliable and bidirectional unicast communication in lossy links that can improve content delivery in situations where the wireless network is the bottleneck. RCDP has been designed, validated, optimized, and its performance has been analyzed in terms of throughput and resource efficiency. Experimental results show that RCDP is a highly efficient solution for environments characterized by high delays and packet losses making it very suitable for intelligent transport system oriented applications since it achieves significant performance improvements when compared to traditional transport layer protocols.

• Ecology and Resilient Infrastructure
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• v.3 no.3
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• pp.177-181
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• 2016

In order to prevent the fragmentation of animal habitat due to road construction, the most widely applied solution is building animal passes worldwide. In Korea, animal passes were introduced in the early 2000s, and through trial and error, the national guidelines for them and their design standards were published in 2010. These were criticized by politicians because of their relative inefficiency considering their high construction cost and their lack of animal usage. This study investigated the extent to which animals used the facilities. For this study, two types of animal passes, eco-bridges and ecoducts, were considered and the test sites were chosen from the Baekdu Mountains. The animal usage data was captured using infra-red CCTV cameras. The results showed that various types of animals used eco-bridges and ecoducts. Interestingly various types of birds were captured by cameras and endangered animals were also in them. The season, weather, and their surrounded vegetation also had effects on their usages. The infrared CCTV allowed detailed captures of animals but the electricity shortage was one disadvantage. During the last decades, a number of eco-bridges were constructed throughout the country and now we need to focus on their monitoring and maintenance for their successful efficiency and application.

• Ecology and Resilient Infrastructure
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• v.7 no.2
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• pp.114-125
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• 2020

Despite the various artificial neural networks that have been developed, most of the discharge models in previous studies have been developed using deep neural networks. This study aimed to develop a discharge model using a convolution neural network (CNN), which was used to solve classification problems. Furthermore, the applicability of CNN was evaluated. The photographs (pictures or images) for input data to CNN could not clearly show the characteristics of the study area as well as precipitation. Hence, the model employed in this study had to use numerical images. To solve the problem, the CN of NRCS was used to generate images as input data for the model. The generated images showed a good possibility of applicability as input data. Moreover, a new application of CN, which had been used only for discharge prediction, was proposed in this study. As a result of CNN training, the model was trained and generalized stably. Comparison between the actual and predicted values had an R² of 0.79, which was relatively high. The model showed good performance in terms of the Pearson correlation coefficient (0.84), the Nash-Sutcliffe efficiency (NSE) (0.63), and the root mean square error (24.54 ㎥/s).