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

Performance analysis and optimization of DCT-based multicarrier system on frequency-selective fading channels

Regarded as one of the most promising transmission techniques for future wireless communications, the discrete cosine transform (DCT) based multicarrier modulation (MCM) system employs cosine basis as orthogonal functions for real-modulated symbols multiplexing, by which the minimum orthogonal frequency spacing can be reduced by half compared to discrete Fourier transform (DFT) based one. With a time-reversed pre-filter employed at the front of the receiver, interference-free one-tap equalization is achievable for the DCT-based systems. However, due to the correlated pre-filtering operation in time domain, the signal-to-noise ratio (SNR) is enhanced as a result at the output. This leads to reformulated detection criterion to compensate for such filtering effect, rendering minimum-mean-square-error (MMSE) and maximum likelihood (ML) detections applicable to the DCT-based multicarrier system. In this paper, following on the pre-filtering based DCT-MCM model that build in the literature work, we extend the overall system by considering both transceiver perfections and imperfections, where frequency offset, time offset and insufficient guard sequence are included. In the presence of those imperfection errors, the DCT-MCM systems are analysed in terms of desired signal power, inter-carrier interference (ICI) and inter-symbol interference (ISI). Thereafter, new detection algorithms based on zero forcing (ZF) iterative results are proposed to mitigate the imperfection effect. Numerical results show that the theoretical analysis match the simulation results, and the proposed iterative detection algorithms are able to improve the overall system performance significantly

Compressive sensing based Bayesian sparse channel estimation for OFDM communication systems: high performance and low complexity

In orthogonal frequency division modulation (OFDM) communication systems, channel state information (CSI) is required at receiver due to the fact that frequency-selective fading channel leads to disgusting inter-symbol interference (ISI) over data transmission. Broadband channel model is often described by very few dominant channel taps and they can be probed by compressive sensing based sparse channel estimation (SCE) methods, e.g., orthogonal matching pursuit algorithm, which can take the advantage of sparse structure effectively in the channel as for prior information. However, these developed methods are vulnerable to both noise interference and column coherence of training signal matrix. In other words, the primary objective of these conventional methods is to catch the dominant channel taps without a report of posterior channel uncertainty. To improve the estimation performance, we proposed a compressive sensing based Bayesian sparse channel estimation (BSCE) method which can not only exploit the channel sparsity but also mitigate the unexpected channel uncertainty without scarifying any computational complexity. The propose method can reveal potential ambiguity among multiple channel estimators that are ambiguous due to observation noise or correlation interference among columns in the training matrix. Computer simulations show that propose method can improve the estimation performance when comparing with conventional SCE methods.Comment: 24 pages,16 figures, submitted for a journa