3,444 research outputs found
Visualization on colour based flow vector of thermal image for movement detection during interactive session
Recently thermal imaging is exploited in applications such as motion and face detection. It has drawn attention many researchers to build such technology to improve lifestyle. This work proposed a technique to detect and identify a motion in sequence images for the application in security monitoring system or outdoor surveillance. Conventional system might cause false information with the present of shadow. Thus, methods employed in this work are Canny edge detector method, Lucas Kanade and Horn Shunck algorithms, to overcome the major problem when using thresholding method, which is only intensity or pixel magnitude is considered instead of relationships between the pixels. The results obtained could be observed in flow vector parameter and the segmentation colour based image for the time frame from 1 to 10 seconds. The visualization of both the parameters clarified the movement and changes of pixel intensity between two frames by the supportive colour segmentation, either in smooth or rough motion. Thus, this technique may contribute to others application such as biometrics, military system, and surveillance machine
Near-Instantaneously Adaptive HSDPA-Style OFDM Versus MC-CDMA Transceivers for WIFI, WIMAX, and Next-Generation Cellular Systems
Burts-by-burst (BbB) adaptive high-speed downlink packet access (HSDPA) style multicarrier systems are reviewed, identifying their most critical design aspects. These systems exhibit numerous attractive features, rendering them eminently eligible for employment in next-generation wireless systems. It is argued that BbB-adaptive or symbol-by-symbol adaptive orthogonal frequency division multiplex (OFDM) modems counteract the near instantaneous channel quality variations and hence attain an increased throughput or robustness in comparison to their fixed-mode counterparts. Although they act quite differently, various diversity techniques, such as Rake receivers and space-time block coding (STBC) are also capable of mitigating the channel quality variations in their effort to reduce the bit error ratio (BER), provided that the individual antenna elements experience independent fading. By contrast, in the presence of correlated fading imposed by shadowing or time-variant multiuser interference, the benefits of space-time coding erode and it is unrealistic to expect that a fixed-mode space-time coded system remains capable of maintaining a near-constant BER
Nonlinear Channel Estimation for OFDM System by Complex LS-SVM under High Mobility Conditions
A nonlinear channel estimator using complex Least Square Support Vector
Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long
Term Evolution (LTE) downlink under high mobility conditions. The estimation
algorithm makes use of the reference signals to estimate the total frequency
response of the highly selective multipath channel in the presence of
non-Gaussian impulse noise interfering with pilot signals. Thus, the algorithm
maps trained data into a high dimensional feature space and uses the structural
risk minimization (SRM) principle to carry out the regression estimation for
the frequency response function of the highly selective channel. The
simulations show the effectiveness of the proposed method which has good
performance and high precision to track the variations of the fading channels
compared to the conventional LS method and it is robust at high speed mobility.Comment: 11 page
Iterative Joint Channel Estimation and Multi-User Detection for Multiple-Antenna Aided OFDM Systems
Multiple-Input-Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems have recently attracted substantial research interest. However, compared to Single-Input-Single-Output (SISO) systems, channel estimation in the MIMO scenario becomes more challenging, owing to the increased number of independent transmitter-receiver links to be estimated. In the context of the Bell LAyered Space-Time architecture (BLAST) or Space Division Multiple Access (SDMA) multi-user MIMO OFDM systems, none of the known channel estimation techniques allows the number of users to be higher than the number of receiver antennas, which is often referred to as a “rank-deficient” scenario, owing to the constraint imposed by the rank of the MIMO channel matrix. Against this background, in this paper we propose a new Genetic Algorithm (GA) assisted iterative Joint Channel Estimation and Multi-User Detection (GA-JCEMUD) approach for multi-user MIMO SDMA-OFDM systems, which provides an effective solution to the multi-user MIMO channel estimation problem in the above-mentioned rank-deficient scenario. Furthermore, the GAs invoked in the data detection literature can only provide a hard-decision output for the Forward Error Correction (FEC) or channel decoder, which inevitably limits the system’s achievable performance. By contrast, our proposed GA is capable of providing “soft” outputs and hence it becomes capable of achieving an improved performance with the aid of FEC decoders. A range of simulation results are provided to demonstrate the superiority of the proposed scheme. Index Terms—Channel estimation, genetic algorithm, multiple-input-multiple-output, multi-user detection, orthogonal frequency division multiplexing, space division multiple access
Low Complexity V-BLAST MIMO-OFDM Detector by Successive Iterations Reduction
V-BLAST detection method suffers large computational complexity due to its
successive detection of symbols. In this paper, we propose a modified V-BLAST
algorithm to decrease the computational complexity by reducing the number of
detection iterations required in MIMO communication systems. We begin by
showing the existence of a maximum number of iterations, beyond which, no
significant improvement is obtained. We establish a criterion for the number of
maximum effective iterations. We propose a modified algorithm that uses the
measured SNR to dynamically set the number of iterations to achieve an
acceptable bit-error rate. Then, we replace the feedback algorithm with an
approximate linear function to reduce the complexity. Simulations show that
significant reduction in computational complexity is achieved compared to the
ordinary V-BLAST, while maintaining a good BER performance.Comment: 6 pages, 7 figures, 2 tables. The final publication is available at
www.aece.r
A Novel Data-Aided Channel Estimation with Reduced Complexity for TDS-OFDM Systems
In contrast to the classical cyclic prefix (CP)-OFDM, the time domain
synchronous (TDS)-OFDM employs a known pseudo noise (PN) sequence as guard
interval (GI). Conventional channel estimation methods for TDS-OFDM are based
on the exploitation of the PN sequence and consequently suffer from intersymbol
interference (ISI). This paper proposes a novel dataaided channel estimation
method which combines the channel estimates obtained from the PN sequence and,
most importantly, additional channel estimates extracted from OFDM data
symbols. Data-aided channel estimation is carried out using the rebuilt OFDM
data symbols as virtual training sequences. In contrast to the classical turbo
channel estimation, interleaving and decoding functions are not included in the
feedback loop when rebuilding OFDM data symbols thereby reducing the
complexity. Several improved techniques are proposed to refine the data-aided
channel estimates, namely one-dimensional (1-D)/two-dimensional (2-D) moving
average and Wiener filtering. Finally, the MMSE criteria is used to obtain the
best combination results and an iterative process is proposed to progressively
refine the estimation. Both MSE and BER simulations using specifications of the
DTMB system are carried out to prove the effectiveness of the proposed
algorithm even in very harsh channel conditions such as in the single frequency
network (SFN) case
Semiblind Channel Estimation and Data Detection for OFDM Systems With Optimal Pilot Design
This paper considers semiblind channel estimation and data detection for orthogonal frequency-division multiplexing (OFDM) over frequency-selective fading channels. We show that the samples of an OFDM symbol are jointly complex Gaussian distributed, where the mean and covariance are determined by the locations and values of fixed pilot symbols. We exploit this distribution to derive a novel maximum-likelihood (ML) semiblind gradient-descent channel estimator. By exploiting the channel impulse response (CIR) statistics, we also derive a semiblind data detector for both Rayleigh and Ricean fading channels. Furthermore, we develop an enhanced data detector, which uses the estimator error statistics to mitigate the effect of channel estimation errors. Efficient implementation of both the semiblind and the improved data detectors is provided via sphere decoding and nulling-canceling detection. We also derive the Cramér-Rao bound (CRB) and design optimal pilots by minimizing the CRB. Our proposed channel estimator and data detector exhibit high bandwidth efficiency (requiring only a few pilot symbols), achieve the CRB, and also nearly reach the performance of an ideal reference receiver
CRLBs for Pilot-Aided Channel Estimation in OFDM System under Gaussian and Non-Gaussian Mixed Noise
The determination of Cramer-Rao lower bound (CRLB) as an optimality criterion for the problem of channel estimation in wireless communication is a very important issue. Several CRLBs on channel estimation have been derived for Gaussian noise. However, a practical channel is affected by not only Gaussian background noise but also non-Gaussian noise such as impulsive interference. This paper derives the deterministic and stochastic CRLBs for Gaussian and non-Gaussian mixed noise. Due to the use of the non-parametric kernel method to build the PDF of non-Gaussian noise, the proposed CRLBs are suitable for practical channel environments with various noise distributions
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