• Journal of IKEEE
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• v.2 no.1 s.2
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• pp.150-159
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• 1998

This paper described the recognition of the Off-line handwritten Hangul based on neural network using a feature extraction method. Features of Hangul can be extracted by a $5{\times}5$ window method which is the modified $3{\times}3$ mask method. These features are coded to binary patterns in order to use neural network's inputs efficiently. Hangul character is recognized by the consonant, the vertical vowel, and the horizontal vowel, separately. In order to verify the recognition rate, three different coding methods were used for neural networks. Three methods were the fixed-code method, the learned-code I method, and the learned-code II method. The result was shown that the learned-code II method was the best among three methods. The result of the learned-code II method was shown 100% recognition rate for the vertical vowel, 100% for the horizontal vowel, and 98.33% for the learned consonants and 93.75% for the new consonants.

• The KIPS Transactions:PartC
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• v.16C no.3
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• pp.373-382
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• 2009

we present more efficient method of a medium access for real-time ubiquitous sensor networks. Proposed MAC protocol is like the randomized CSMA/CA protocol, but unlike previous legacy protocols, it does not use a time-varying contention window from which a node randomly picks a transmission slot. To reduce the latency for the delivery of event reports from sensor nodes, we carefully decide to select a fixed-size contention window with non-uniform probability distribution of transmitting in each slot. We show that the proposed method can offer up to several times latency reduction compared to legacy of IEEE 802.11 as the size of the sensor network scales up to 256 nodes using a widely-used network simulation package, NS-2. We finally show that proposed MAC scheme comes close to meet bounds on the best latency being achieved by a decentralized CSMA-based MAC protocol for real-time ubiquitous sensor networks which is sensitive to latency.

• Journal of KIISE:Information Networking
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• v.35 no.3
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• pp.192-206
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• 2008

Multihop is the main communication style for wireless sensor networks composed of tiny sensor nodes. Until now, most applications have treated the periodic small sized sensing data. Recently, the burst traffic with the transient and continuous nature is increasingly introduced due to the advent of wireless multimedia sensor networks. Therefore, the efficient communication protocol to support this trend is required. In this paper, we propose a novel PIGAB(Packet Interval Gap based on Adaptive Backoff) protocol to efficiently transmit the burst data in multihop wireless sensor networks. The contention-based PIGAB protocol consists of the PIG(Packet Interval Gap) control algorithm in the source node and the MF(MAC-level Forwarding) algorithm in the relay node. The PIGAB is on basis of the newly proposed AB(Adaptive Backoff), CAB(Collision Avoidance Backoff), and UB(Uniform Backoff). These innovative algorithms and schemes can achieve the performance of network by adjusting the gap of every packet interval, recognizing the packet transmission of the hidden node. Through the simulations and experiments, we identify that the proposed PIGAB protocol considerably has the stable throughput and low latency in transmitting the burst data in multihop wireless sensor networks.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.2
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• pp.191-195
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• 2008

This paper proposes a technique for improving IEEE 802.11e EDCA's performance by dynamically adjusting each class's contention window size based on each class's traffic amount. For providing differentiated service differently from 802.11, 802.11e EDCA maintains four classes each of which specifies different static minimum and maximum contention window sizes. Since the initial window sites significantly affect the 802.11e performance, several window adjustment schemes have been proposed. One of the schemes known as CWminAS (CWmin Adaptation Scheme) dynamically and synchronously determines the four windows' site based on the periodically measured collision rate. This method, however, can lower the send probability of high priority classes since it can't differentiate their collisions from those of low priority classes, leading to the channel underutilization. For solving this problem, we propose ACATICT(Adaptive Contention-window Adjustment Technique based on Individual Class Traffic) algorithm which adapts each class window size based on each individual collision rate rather than one average collision rate. Our simulation experiments show that ACATICT achieves better utilization by around 10% at maximum.