• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4389-4410
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• 2014

Network coding is a promising technology that increases system throughput by reducing the number of packet transmissions from the source node to the destination node in a saturated traffic scenario. Nevertheless, some packets can suffer from end-to-end delay, because of a queuing delay in an intermediate node waiting for other packets to be encoded with exclusive or (XOR). In this paper, we analyze the delay according to packet arrival rate and propose two network coding schemes, iXOR (Intelligent XOR) and oXOR (Optimal XOR) with Markov Decision Process (MDP). They reduce the average delay, even under an unsaturated traffic load, through the Holding-${\chi}$ strategy. In particular, we are interested in the unsaturated network scenario. The unsaturated network is more practical because, in a real wireless network, nodes do not always have packets waiting to be sent. Through analysis and extensive simulations, we show that iXOR and oXOR are better than the Distributed Coordination Function (DCF) without XOR (the general forwarding scheme) and XOR with DCF with respect to average delay as well as delivery ratio.

• Journal of KIISE:Information Networking
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• v.36 no.3
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• pp.251-255
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• 2009

Network coding is a promising technology that increases the system throughput via reducing the number of transmission for a packet delivered from the source node to the destination node. Nevertheless, it suffers from the metrics of end-to-end delay. Network Coding scheme takes more processing delay which occurs as coding node encodes (XOR) a certain number of packets that relayed by the coding node, and more queuing delay which occurs as a packet waits for other packets to be encoded with. Therefore, in this paper, we analyze the dependency of the queuing delay to the arrival rate of each packet. In addition, we analyze and compare the delay in DCF without XOR and DCF with XOR under dynamic traffic.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.1-8
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• 2011

In multimedia fingerprinting field, it is many used a code based on BIBD, which has a strong resiliency of anti-collusion. When a collusion-attack code is generated with a logical XOR operation using the code based on BIBD, then some cases are occurred that a colluded code could be generated to the same fingerprint of non-colluder on the other hand, the colluder is decided to the non-colluder so that he would be excepted in the colluder tracing. For solving the serious problem of the wrong decision of the colluder tracing in this paper, XOR-ACC is implemented using multi-layer perceptron neural network among (AND, OR, XOR and Averaging)-ACC by the measured correlation coefficient. Through the experiment, it confirms that XOR-ACC efficiency of multimedia fingerprinting code{7,3,1} based on BIBD is improved to 88.24% from the conventional 41.18%, so that a ratio of the colluder tracing is also improved to 100% from the conventional 53%. As a result, it could be traced and decided completely a sectional colluder and non-colluder about the collusion attacks.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.1
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• pp.20-27
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• 2008

In this paper, a nonlinear classifier of a feed-forward neural network is implemented on an FPGA chip. The feedforward neural network is implemented in hardware for fast parallel processing. After off line training of neural network, weight values are saved and used to perform forward propagation of neural processing. As an example, AND and XOR digital logic classification is conducted in off line, and then weight values are used in neural network. Experiments are conducted successfully and confirmed that the FPGA neural network hardware works well.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.193-198
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• 2020

The various uses of the Convolutional Neural Network technology are accelerating the evolution of the computing area, but the opposite is causing serious hardware performance shortages. Neural network accelerators, next-generation memory device technologies, and high-bandwidth memory architectures were proposed as countermeasures, but they are difficult to actively introduce due to the problems of versatility, technological maturity, and high cost, respectively. This study proposes DRAM-based main memory technology that enables read operations to be completed without waiting until the end of the refresh operation using pre-stored XOR bit values, even when the refresh operation is performed in the main memory. The results showed that the proposed technique improved performance by 5.8%, saved energy by 1.2%, and improved EDP by 10.6%.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.10
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• pp.149-157
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• 1994

A method of applying a recurrent backpropagation network to identifying or modelling a dynamic system is proposed. After the recurrent backpropagation network having both the characteristicsof interpolative network and associative network is applied to XOR problem, a new model of recurrent backpropagation network is proposed and compared with the original recurrent backpropagation network by applying them to XOR problem. based on the observation thata function can be approximated with polynomials to arbitrary accuracy, the new model is developed so that it may generate higher-order terms in the internal states Moreover, it is shown that the new network is succesfully applied to recognizing noisy patterns of numbers.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.11-16
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• 2005

Recently, there are a lot of endeavor to implement a fuzzy theory to artificial neural network. Goh proposed the fuzzy single layer perceptron algorithm and advanced fuzzy perceptron based on the generalized delta rule to solve the XOR Problem and the classical Problem. However, it causes an increased amount of computation and some difficulties in application of the complicated image recognition. In this paper, we propose an enhanced fuzzy single layer Perceptron using the dynamic adjustment of threshold. This method is applied to the XOR problem, which used as the benchmark in the field of pattern recognition. The method is also applied to the recognition of digital image for image application. In a result of experiment, it does not always guarantee the convergence. However, the network show improved the learning time and has the high convergence rate.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.9 s.339
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• pp.41-50
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• 2005

As the size of circuits becomes larger, the test method needs more test data volume and larger test application time. In order to reduce test data volume and test application time, a new test data compression/decompression method is proposed. The proposed method is based on an XOR network uses don't-care-bits to improve compression ratio during seed vectors generation. After seed vectors are produced seed vectors can be merged using two prefix codes. It only requires 1 clock time for reusing merged seed vectors, so test application time can be reduced tremendously. Experimental results on large ISCAS '89 benchmark circuits prove the efficiency of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.505-509
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• 2001

In this paper, we evaluate the learning ability of non-monotonic DMM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.3
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• pp.62-67
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• 2002

From the view point of VLSI implementation, a new teaming algorithm suited for network with quantized connection weights is desired. This paper presents a new teaming algorithm for the DBM(deterministic Boltzmann machine) network with quantized connection weight. The performance of proposed algorithm is tested with the 2-input XOR problem and the 3-input parity problem through computer simulations. The simulation results show that our algorithm is efficient for quantized connection neural networks.