• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.1
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• pp.13-20
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• 2004

In this paper, we have analyzed the current MPEG-4 video encoding tools and proposed efcient coding techniques that reduce the complexity of the encoder. Until recently, encoder optimization without shape coding has been a major concern in video for wire/wireless low bit rate coding services. Recently, we found out that the computational complexity of MPEG-4 shape coding plays a very important role in the object-based coding through experiments. We have made an experiment whether we could get optimized object-based coding method through successfully combining latest optimized texture coding techniques with our proposed optimized shape coding techniques. In texture coding, we applied the MVFAST method for motion estimation. We chose not to use IVOPF(Intelligent VOP Formation) but to use TRB(Tightest Rectangular Boundary) for positioning VOP and, finally, to eliminate the spiral search of shape motion estimation to reduce the complexity in shape coding. As a result of experiment, our proposed scheme achieved improved time complexity over the existing reference software by $57.3\%$ and over the optimized method on which only shape coding was applied by $48.7\%$, respectively.

• The KIPS Transactions:PartD
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• v.14D no.4 s.114
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• pp.381-388
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• 2007

Consumers changed to the subject to produce a digital contents as data transmission technique is advanced and a digital machine is diffused variously. Users are interested greatly in a user creative movie (UCM) production among various online contents. The UCM production method which uses the music and picture is the method that users make the UCM more easily. However, the UCM production service has the problem that any association does not exist in the music and picture and that the picture changes according to fixed time interval without the relation at a music rhythm. To solve this problem, we propose the UCM production method which uses a music analysis and picture analysis in the paper. A music analysis finds a picture change time according to the rhythm and a picture analysis finds the association of the picture. A music analysis finds strong parts of the sound which uses Root-Mean-Square (RMS). And a picture analysis classifies the picture as a scenery picture and people picture which uses structure simplicity of the picture(SSP) and face region detection. A picture analysis got correct result of 86.4% in the experiment and we can finds the association at each picture and arranges the sequence which the picture appears. Therefore, if we use a music and picture analysis at the UCM production, users may make natural and efficient movie.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.183-190
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• 2016

This paper proposes algorithm to reduce the computing time in a neural network that reduces transmission of data for tracking mobile objects in surveillance networks in terms of detection and communication load. Object Detection can be defined as follows : Given image sequence, which can forom a digitalized image, the goal of object detection is to determine whether or not there is any object in the image, and if present, returns its location, direction, size, and so on. But object in an given image is considerably difficult because location, size, light conditions, obstacle and so on change the overall appearance of objects, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact object detection which overcomes some restrictions by using neural network. Proposed system can be object detection irrelevant to obstacle, background and pose rapidly. And neural network calculation time is decreased by reducing input vector size of neural network. Principle Component Analysis can reduce the dimension of data. In the video input in real time from a CCTV was experimented and in case of color segment, the result shows different success rate depending on camera settings. Experimental results show proposed method attains 30% higher recognition performance than the conventional method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.9
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• pp.598-605
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• 2014

In this paper, we suggest a modified slotted ALOHA algorithm for fast tag collection in active RFID system and implement the reader and tag operation using CC2530 chip of Texas Instruments Co. to prove the performance of the proposed algorithm. In the present international standard related with active RFID including ISO/IEC 18000-7 the reader sends sleep command to each tag after successful obtaining tag's information. Meanwhile, in this paper, the tags decide to sleep after checking the second command from the reader resulting in enormously decreased tag collection time. We tested the proposed algorithm with 30 tags over the range of 0-3m and the results showed that the tag collection process was completed in 400msec at average. And 30 tags are collected in one second with 99.7% and the collection rate is 100% in 2m distance between reader and tag. The collection rates are 99.94% and 99.7% for distance 2.5m and 3m, respectively. The average collection rate is 99.91% over all range and it is concluded that the proposed algorithm is enough to apply to real fields.

• Journal of Navigation and Port Research
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• v.39 no.4
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• pp.293-297
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• 2015

Underwater acoustic communications has been limited use for military purposes in the past. However, the fields of underwater applications expend to detection, submarine and communication in recent. The excessive multipath encountered in underwater acoustic communication channel is creating inter symbol interference, which is limiting factor to achieve a high data rate and bit error rate performance. To improve the performance of a received signal in underwater communication, many researchers have been studied for channel coding scheme with excellent performance at low SNR. In this paper, we applied BCJR decoder based ( 2,1,7 ) convolution codes and to compensate for the distorted data induced by the multipath, we applying the turbo equalization method. Through the underwater experiment on the Gyeungcheun lake located in Mungyeng city, we confirmed that turbo equalization structure of BCJR has better performance than hard decision and soft decision of Viterbi decoding. We also confirmed that the error rate of decoder input is less than error rate of $10^{-1}$, all the data is decoded. We achieved sucess rate of 83% through the experiment.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.521-530
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• 2018

This paper proposes "The Agriculture Decision-making System(ADS) based on Deep Learning for improving crop productivity" that collects weather information based on location supporting precision agriculture, predicts current crop condition by using the collected information and real time crop data, and notifies a farmer of the result. The system works as follows. The ICM(Information Collection Module) collects weather information based on location supporting precision agriculture. The DRCM(Deep learning based Risk Calculation Module) predicts whether the C, H, N and moisture content of soil are appropriate to grow specific crops according to current weather. The RNM(Risk Notification Module) notifies a farmer of the prediction result based on the DRCM. The proposed system improves the stability because it reduces the accuracy reduction rate as the amount of data increases and is apply the unsupervised learning to the analysis stage compared to the existing system. As a result, the simulation result shows that the ADS improved the success rate of data analysis by about 6%. And the ADS predicts the current crop growth condition accurately, prevents in advance the crop diseases in various environments, and provides the optimized condition for growing crops.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.4
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• pp.155-162
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• 2013

Routing protocol in ad hoc mobile networking has been an active research area in recent years. However, the environments of ad hoc network tend to have vulnerable points from attacks, because ad hoc mobile network is a kind of wireless network without centralized authentication or fixed network infrastructure such as base stations. Also, existing routing protocols that are effective in a wired network become inapplicable in ad hoc mobile networks. To address these issues, several secure routing protocols have been proposed: SAODV and SRPTES. Even though our protocols are intensified security of networks than existing protocols, they can not deal fluidly with frequent changing of wireless environment. Moreover, demerits in energy efficiency are detected because they concentrated only safety routing. In this paper, we propose an energy efficient secure routing protocol for various ad hoc mobile environment. First of all, we provide that the nodes distribute security information to reliable nodes for secure routing. The nodes constitute tree-structured with around nodes for token escrow, this action will protect invasion of malicious node through hiding security information. Next, we propose multi-path routing based security level for protection from dropping attack of malicious node, then networks will prevent data from unexpected packet loss. As a result, this algorithm enhances packet delivery ratio in network environment which has some malicious nodes, and a life time of entire network is extended through consuming energy evenly.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.1-12
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• 2008

In this paper, we present a real-time algorithm for recognizing the vehicle color from the indoor and outdoor vehicle images based on GPU (Graphics Processing Unit) acceleration. In the preprocessing step, we construct feature victors from the sample vehicle images with different colors. Then, we combine the feature vectors for each color and store them as a reference texture that would be used in the GPU. Given an input vehicle image, the CPU constructs its feature Hector, and then the GPU compares it with the sample feature vectors in the reference texture. The similarities between the input feature vector and the sample feature vectors for each color are measured, and then the result is transferred to the CPU to recognize the vehicle color. The output colors are categorized into seven colors that include three achromatic colors: black, silver, and white and four chromatic colors: red, yellow, blue, and green. We construct feature vectors by using the histograms which consist of hue-saturation pairs and hue-intensity pairs. The weight factor is given to the saturation values. Our algorithm shows 94.67% of successful color recognition rate, by using a large number of sample images captured in various environments, by generating feature vectors that distinguish different colors, and by utilizing an appropriate likelihood function. We also accelerate the speed of color recognition by utilizing the parallel computation functionality in the GPU. In the experiments, we constructed a reference texture from 7,168 sample images, where 1,024 images were used for each color. The average time for generating a feature vector is 0.509ms for the $150{\times}113$ resolution image. After the feature vector is constructed, the execution time for GPU-based color recognition is 2.316ms in average, and this is 5.47 times faster than the case when the algorithm is executed in the CPU. Our experiments were limited to the vehicle images only, but our algorithm can be extended to the input images of the general objects.