• Journal of Communications and Networks
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• v.14 no.2
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• pp.195-198
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• 2012

An efficient sliding window algorithm employing an adaptive-length guard window for turbo decoders is proposed. The proposed algorithm results in significant complexity reductions, especially for small sliding window lengths where the additional computational complexity required for the guard window is critical.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8A
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• pp.1218-1226
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• 1999

In decoding the turbo codes, the sliding window BCJR algorthm, derived from the BCJR algorithm, permits a continuous decoding of the coded sequence without requiring trellis fermination of the constituent codes and uses reduced memory span. However, the number of computation required is greater than that of BCJR algorithm and no study on the effect of the window length has been reported. In this paper, we propose an eddicient sliding window type scheme which maintains the advantages of the conventional sliding window algorithm, reduces its computational burdens, and improves is BER performance. A guideline is first presented to determine the proper window length and then a computationally efficient sliding window BCJR algorithm is obtained by allowing the window to be forwarded in multi-step. Simulation results show that the proposed scheme outperforms the conventional sliding window BCJR algorithm with reduced complexity. It gains 0.1dB SNR improvements over the conventional method for the constraint length 3 and BER $10^{-4}$

• Journal of Multimedia Information System
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• v.6 no.1
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• pp.1-6
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• 2019

In the field of industrial applications, the real-time performance of the target detection problem is very important. The most serious time consumption in the pedestrian detection process is the extraction phase of the candidate window. To accelerate the speed, in this paper, a fast extraction of pedestrian candidate window based on the BING (Binarized Normed Gradients) algorithm replaces the traditional sliding window scanning. The BING features are extracted with the positive and negative samples and input into the two-stage SVM (Support Vector Machine) classifier for training. The obtained BING template may include a pedestrian candidate window. The trained template is loaded during detection, and the extracted candidate windows are input into the classifier. The experimental results show that the proposed method can extract fewer candidate window and has a higher recall rate with more rapid speed than the traditional sliding window detection method, so the method improves the detection speed while maintaining the detection accuracy. In addition, the real-time requirement is satisfied.

• The KIPS Transactions:PartC
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• v.9C no.5
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• pp.743-748
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• 2002

In recent research for the Internet, many studies have investigated the Diffserv AS architecture that can provide Quality of Service. However, this architecture still lacks the qualification to provide full use of the bandwidth to the customer In this paper, we propose the TS2W3C (Time Sliding Two Window Three Color) marking algorithm to improve the fair share of bandwidth by enhancing the TSW (Time Sliding Window) marking algorithm. Our proposed mechanism provides the bandwidth relatively more fairly than the TSW mechanism.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.146-151
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• 2019

This paper reports a sliding window filtering approach for ground moving targets with cross-correlated sensor noise and uncertainty. In addition, the effect of uncertain parameters during a tracking error on the model performance is considered. A distributed fusion sliding window filter is also proposed. The distributed fusion filtering algorithm represents the optimal linear combination of local filters under the minimum mean-square error criterion. The derivation of the error cross-covariances between the local sliding window filters is the key to the proposed method. Simulation results of the motion of the ground moving target a demonstrate high accuracy and computational efficiency of the distributed fusion sliding window filter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1298-1310
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• 2019

Network coding (NC) is a promising technology that can improve available bandwidth and packet throughput in wireless sensor networks (WSNs). Sliding window is an improved technology of NC, which is a supplement of TCP/IP technology and can improve data throughput and network lifetime on WSNs. This paper proposes a network coding-based maximum lifetime algorithm for sliding window in WSNs (NC-MLSW) which improves the throughput and network lifetime in WSN. The packets on the source node are sent on the WSNs. The intermediate node encodes the received original packet and forwards the newly encoded packet to the next node. Finally, the destination node decodes the received encoded data packet and recovers the original packet. The performance of the NC-MLSW algorithm is studied using NS2 simulation software and the network packet throughput, network lifetime and data packet loss rate were evaluated. The simulations experiment results show that the NC-MLSW algorithm can obviously improve the network packet throughput and network lifetime.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5A
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• pp.426-432
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• 2007

The MAP algorithm is designed and implemented through the sliding window method for turbo decoding. First, the implementation issues, which are the length of the sliding window and the normalization method of state metrics are reviewed, and their optimal values are obtained by the simulation. All component schemes of the decoder including the branch metric evaluator are also presented. The proposed MAP architecture can be easily redesigned according to the size of sliding window, that is, sub-frame length because of its simplicity on buffer control.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.69-72
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• 2000

In CDMA systems, the performance of the typical multipath searcher degrades much according as the signal to noise ratio becomes low. In this paper, multipath searcher algorithm is proposed based on sliding window to overcome this drawback. In searcher systems, correlation values between incoming and local PN sequences are used to acquire multipath components. Therefore more accurate distributions of correlation values obtained through this proposed algorithm enables to get higher detection probability. In computer simulations, it is verified that proposed algorithm has better performances in Rayleigh fading channel and Gaussian channel.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.1
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• pp.81-88
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• 2004

Turbo code has been adopted in the 3GPP standard, since its performance is very close to the Shannon limit. However, the turbo decoder requires a lot of computations and the amount of the memory increases as the block size of turbo codes becomes larger. In order to reduce the complexity of the turbo decoder, the Log-MAP, the Max-Log-MAP and the sliding window algorithm have been proposed. In this paper, the performance of turbo codes adopted in the 3GPP standard is analyzed by using the floating point and the fixed point implementation. The efficient decoding method is also proposed. It is shown that the BER performance of the proposed method is close to that of the Log-MAP algorithm.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.912-919
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• 2005

When we apply binary classification to multi-class classification for text categorization, we use the One-Against-All method generally, However, this One-Against-All method has a problem. That is, documents of a negative set are not labeled by human. Thus, they can include many noisy documents in the training data. In this paper, we propose that the Sliding Window technique and the EM algorithm are applied to binary text classification for solving this problem. We here improve binary text classification through extracting noise documents from the training data by the Sliding Window technique and re-assigning categories of these documents using the EM algorithm.