• Journal of Communications and Networks
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• v.17 no.6
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• pp.609-621
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• 2015

Recently, a variety of reduced complexity soft-in soft-output detection algorithms have been introduced for iterative detection and decoding (IDD) systems. However, it is still challenging to implement soft-in soft-output detectors for MIMO systems due to heavy burden in computational complexity. In this paper, we propose a soft detection algorithm for MIMO systems which performs close to the full dimensional joint detection, yet offers significant complexity reduction over the existing detectors. The proposed algorithm, referred to as soft-input soft-output successive group (SSG) detector, detects a subset of symbols (called a symbol group) successively using a deliberately designed preprocessing to suppress the inter-group interference. In fact, the proposed preprocessor mitigates the effect of the interfering symbol groups successively using a priori information of the undetected groups and a posteriori information of the detected groups. Simulation results on realistic MIMO systems demonstrate that the proposed SSG detector achieves considerable complexity reduction over the conventional approaches with negligible performance loss.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1689-1694
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• 2005

Face detection plays an important role in face recognition, video surveillance, and human computer interface. In this paper, we present a face detection system using eye detection with progressive thresholding from a digital camera. The face candidate is detected by using skin color segmentation in the YCbCr color space. The face candidates are verified by detecting the eyes that is located by iterative thresholding and correlation coefficients. Preprocessing includes histogram equalization, log transformation, and gray-scale morphology for the emphasized eyes image. The distance of the eye candidate points generated by the progressive increasing threshold value is employed to extract the facial region. The process of the face detection is repeated by using the increasing threshold value. Experimental results show that more enhanced face detection in real time.

• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.235-240
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• 2009

This paper combines various detection techniques and analyzes their performances in detecting the transmission information of the D-STTD scheme that uses, in parallel, the STTD scheme known as the Alamouti code. The D-STTD scheme adopts one of the STTD schemes for transmission to acquire diverse effects and uses another form of STTD for multiplexing effects. Due to the multiplexing effect that transmits different data, it is difficult to apply D-STTD to the conventional STTD combining technique. This paper combines the D-STTD system with linear algorithm, SIC algorithm and OSIC algorithm known as multiplexing detection scheme based on MMSE scheme. And we propose the detection scheme of the D-STTD using MAP algorithm and analyze the performance of each system. The simulation results showed that the detector using iterative algorithm has better performance than Linear MMSE Detector. Especially, we can show that the detector using MAP algorithm outperforms conventional detector.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.9
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• pp.528-534
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• 2014

In this paper, we propose an iterative detection and decoding scheme for the LDPC coded multiuser uplink massive-MIMO systems. We consider the simple maximal ratio combining (MRC) detector and LDPC decoder. We formulate the soft output of MRC detector and the relation between the extrinsic informations of the detector and decoder. The performance improvement of the proposed iterative detection and decoding scheme is shown by computer simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.10
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• pp.815-820
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• 2012

We introduce two-dimensional soft output Viterbi algorithm (2D SOVA) and iterative 2D SOVA for holographic data storage. Since the holographic data storage is 2D intersymbol interference (ISI) channel, the 2D detection schemes have good performance at holographic data storage. The 2D SOVA and iterative 2D SOVA are 2D detection schemes. We introduce and compare the two 2D detection schemes. The 2D SOVA is approximately 2 dB better than one-dimensional (1D) detection scheme, and iterative 2D SOVA is approximately 1 dB better than the 2D SOVA. In contrast, the iterative 2D SOVA is approximately twice complex higher than 2D SOVA, and 2D SOVA is approximately twice complex higher than 1D detection scheme.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2086-2094
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• 2008

Finding a planar surface on 3D space is very important for efficient and safe operation of a mobile robot. In this paper, we propose a method using a plane detection cell (PDC) and iterative randomized Hough transform (IRHT) for finding the planar region from a 3D range image. First, the local planar region is detected by a PDC from the target area of the range image. Each plane is then segmented by analyzing the accumulated peaks from voting the local direction and position information of the local PDC in Hough space to reduce effect of noises and outliers and improve the efficiency of the HT. When segmenting each plane region, the IRHT repeatedly decreases the size of the planar region used for voting in the Hough parameter space in order to reduce the effect of noise and solve the local maxima problem in the parameter space. In general, range images have many planes of different normal directions. Hence, we first detected the largest plane region and then the remained region is again processed. Through this procedure, we can segment all planar regions of interest in the range image.

• Journal of Satellite, Information and Communications
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• v.10 no.1
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• pp.22-28
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• 2015

In this paper, we propose a new computationally efficient joint iterative multi-input multi-output (MIMO) detection scheme using a soft interference cancellation and minimum mean squared-error (SIC-MMSE) method. The critical computational burden of the SIC-MMSE scheme lies in the multiple inverse operations of the complex matrices. We find a new way which requires only a single matrix inversion by utilizing the Taylor series expansion of the matrix, and thus the computational complexity can be reduced. The computational complexity reduction increases as the number of antennas is increased. The simulation results show that our method produces almost the same performances as the conventional SIC-MMSE with reduced computational complexity.

• Journal of Communications and Networks
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• v.10 no.1
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• pp.44-54
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• 2008

In this paper, an iterative multiple symbol differential detection for turbo coded differential unitary space-time modulation using a posteriori probability (APP) demodulator is investigated. Two approaches of different complexity based on linear prediction are presented to utilize the temporal correlation of fading for the APP demodulator. The first approach intends to take account of all possible previous symbols for linear prediction, thus requiring an increase of the number of trellis states of the APP demodulator. In contrast, the second approach applies Viterbi algorithm to assist the APP demodulator in estimating the previous symbols, hence allowing much reduced decoding complexity. These two approaches are found to provide a trade-off between performance and complexity. It is shown through simulation that both approaches can offer significant BER performance improvement over the conventional differential detection under both correlated slow and fast Rayleigh flat-fading channels. In addition, when comparing the first approach to a modified bit-interleaved turbo coded differential space-time modulation counterpart of comparable decoding complexity, the proposed decoding structure can offer performance gain over 3 dB at BER of $10^{-5}$.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1131-1143
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• 2016

Differential detection schemes do not require any channel estimation, which can be employed under user mobility with low computational complexity. In this work, a soft-input soft-output (SISO) differential detection algorithm is proposed for amplify-and-forward (AF) over time-varying relaying channels based cooperative communications system. Furthermore, maximum-likelihood (ML) detector for M-ary differential Phase-shift keying (DPSK) is derived to calculate a posteriori probabilities (APP) of information bits. In addition, when the SISO is exploited in conjunction with channel decoding, iterative detection and decoding approach by exchanging extrinsic information with outer code is obtained. Finally, simulation results show that the proposed non-coherent approach improves detection performance significantly. In particular, the system can obtain greater performance gain under fast-fading channels.

• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.99-102
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• 2002

In this work, we evaluated the performance of soft iterative decoder with soft block decoder in optical storage system. Because optical storage system requires run- length limited code in general, adaptation of the soft decoders such as turbo code or LDPC(low density parity check code) is difficult without soft block decoders. The performance of the overall optical detection system is evaluated and the simplified channel detection is also proposed.