The Novel PAPR Reduction Schemes for O‐OFDM‐Based Visible Light Communications

In this chapter, we propose two novel peak-to-average power ratio (PAPR) reduction schemes for the asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) scheme used in the visible light communications (VLC) system. In the first scheme, we implement the Toeplitz matrix based Gaussian blur method to reduce the high PAPR of ACO-OFDM at the transmitter and use the orthogonal matching pursuit algorithm to recover the original ACO-OFDM frame at the receiver. Simulation results show that for the 256-subcarrier ACO-OFDM system a ~6 dB improvement in PAPR is achieved compared with the original ACO-OFDM in terms of the complementary cumulative distribution function (CCDF), while maintaining a competitive bit-error rate performance compared with the ideal ACO-OFDM lower bound. In the second scheme, we propose an improved hybrid optical orthogonal frequency division multiplexing (O-OFDM) and pulse-width modulation (PWM) scheme to reduce the PAPR for ACO-OFDM. The bipolar O-OFDM signal without negative clipping is converted into a PWM format where the leading and trailing edges carry the frame synchronization and modulated information, respectively. The simulation and experimental results demonstrate that the proposed OFDM-PWM scheme offers a significant PAPR reduction compared to the ACO-OFDM with an improved bit error rate

An active contour model for medical image segmentation with application to brain CT image

Cerebrospinal fluid (CSF) segmentation in computed tomography (CT) is a key step in computer-aided detection (CAD) of acute ischemic stroke. Because of image noise, low contrast and intensity inhomogeneity, CSF segmentation has been a challenging task. A region-based active contour model, which is insensitive to contour initialization and robust to intensity inhomogeneity, was developed for segmenting CSF in brain CT images

Enhanced Terahertz Phase Retrieval Imaging by Unequal Spaced Measurement

Terahertz lensless phase retrieval imaging is a promising technique for non-destructive inspection applications. In the conventional multiple-plane phase retrieval method, the convergence speed due to wave propagations and measures with equal interval distance is slow and leads to stagnation. To address this drawback, we propose a nonlinear unequal spaced measurement scheme in which the interval space between adjacent measurement planes is gradually increasing, it can significantly increase the diversity of the intensity with a smaller number of required images. Both the simulation and experimental results demonstrate that our method enables quantitative phase and amplitude imaging with a faster speed and better image quality, while also being computationally efficient and robust to noise

Edge detection techniques assisted target tracking algorithm

This paper proposes an improved approach for target tracking. The new approach addresses the tracking failure issue of Mean-shift algorithm when the dimension of an object changes over time. Object edge detection is implemented into the tracking process. The target can be located more accurately with an adaptive correction system based on the information of object edges. In addition, an improved edge detection algorithm is also studied to overcome the problem of low tracking accuracy in traditional methods. The experiments demonstrate that, compared with the traditional Mean-shift algorithm, the proposed approach can significantly improve the performance in tracking accuracy.Xu Xu, Shuxu Guo, Yinhao Din

Joint Channel Coding based on LDPC Codes with Gaussian Kernel Reflecton and CS Redundancy

This paper proposes a new joint decoding algorithm frame based on compressed sensing CS and LDPC (Low-Density Parity-Check) codes. Redundant information can be effectively extracted and amplified by CS reconstruction as a compensation to correct decoding of LDPC codes.We adopt Gaussian kernel function of image segmentation as a reflection. Simulation results indicate, compared with LDPC algorithm, the algorithm presented in this paper can obviously make BER (bit error ratio) lower and improve system decoding performance, and different variance of Gaussian kernel function can obtain different results

Blind Compressed Sensing Parameter Estimation of Non-cooperative Frequency Hopping Signal

To overcome the disadvantages of a non-cooperative frequency hopping communication system, such as a high sampling rate and inadequate prior information, parameter estimation based on Blind Compressed Sensing (BCS) is proposed. The signal is precisely reconstructed by the alternating iteration of sparse coding and basis updating, and the hopping frequencies are directly estimated based on the results. Compared with conventional compressive sensing, blind compressed sensing does not require prior information of the frequency hopping signals; hence, it offers an effective solution to the inadequate prior information problem. In the proposed method, the signal is first modeled and then reconstructed by Orthonormal Block Diagonal Blind Compressed Sensing (OBD-BCS), and the hopping frequencies and hop period are finally estimated. The simulation results suggest that the proposed method can reconstruct and estimate the parameters of noncooperative frequency hopping signals with a low signal-to-noise ratio

Joint Channel Coding based on LDPC Codes with Gaussian Kernel Reflecton and CS Redundancy

Abstract: This paper proposes a new joint decoding algorithm frame based on compressed sensing CS and LDPC (Low-Density Parity-Check) codes. Redundant information can be effectively extracted and amplified by CS reconstruction as a compensation to correct decoding of LDPC codes. We adopt Gaussian kernel function of image segmentation as a reflection. Simulation results indicate, compared with LDPC algorithm, the algorithm presented in this paper can obviously make BER (bit error ratio) lower and improve system decoding performance, and different variance of Gaussian kernel function can obtain different results