Peng Qi-cong

Image completion algorithm based on texture synthesis

Abstract A new algorithm is proposed for completing the missing parts caused by the removal of foreground or background elements from an image of natural scenery in a visually plausible way. The major contributions of the proposed algorithm are:(1) for most natural images, there is a strong orientation of texture or color distribution. So a method is introduced to compute the main direction of the texture and complete the image by limiting the search to one direction to carry out image completion quite fast;(2) there exists a synthesis ordering for image completion. The searching order of the patches is deffned to ensure the regions with more known information and the structures should be completed beforefflling in other regions;(3) to improve the visual effect of texture synthesis, an adaptive scheme is presented to determine the size of the template window for capturing the features of various scales. A number of examples are given to demonstrate the effectiveness of the proposed algorithm.

Encryption based on reversible cellular automata

With the development of information technology, information security as well as the implementation of encryption systems becomes more and more complex, and therefore new methods are explored to simplify the complexity of the implementation. Cellular automata (CA) have the characteristics of simplicity of basic components, locality of CA interactions, massive parallelism of information processing, and exhibiting of complex global properties, which make them suitable for cryptography applications. Based on the reversible CA with input boundary, a new method of encryption is presented in this paper, the key of the new method consists of the reversible CA, the vectors input from the input boundary, and the number of iterations. Evidently, it has a larger key space than other methods with only the reversible CA itself as the key.

Broadband Robust Adaptive Beamforming in the Presence of Correlated interferences and DOA uncertainties

A novel method of broadband robust adaptive beamforming is proposed in this paper to deal with the signal cancellation caused by correlated interferences and DOA uncertainties. To achieve this goal, the classic techniques of spectral averaging and diagonal loading are integrated into the beamforming, with the former aiming at decorrelation and the latter at robustness against DOA uncertainties. Moreover, the optimal loading is selected based on worst-case performance optimization and a simple closed-form solution to it is suggested after some approximations. Besides its simplicity and low computational cost, this analytical solution reveals how different factors affect the optimal loading. Compared with many relevant methods, the proposed one achieves better robustness against DOA uncertainties, while suppressing both correlated and uncorrelated interferences effectively. Its excellent performance is confirmed via a number of numerical examples.

Adaptive bit and power loading algorithm with low complexity in MIMO-OFDM systems

Two adaptive power and bit loading algorithms to maximize the throughput of MIMO-OFDM systems in frequency selective fading environment are proposed. The two algorithms allocate bit based on maximizing the overall throughput. One algorithm allocates power based on guaranteeing that the bit error rate (BER) of each sub-carrier and the total allocated power remain below a target BER threshold and a power threshold, respectively; another one allocates power based on guaranteeing that the mean BER of sub-carriers and the total allocated power remain below a target BER threshold and a power threshold, respectively. The simulation results show that the proposed algorithms can achieve faster throughput with lower computational complexity, which indicates that the proposed algorithms are effective when compared to some existing algorithms.

3D object recognition using multi-moment and neural network

To improve the performance of appearance-based three-dimensional object recognition system, we propose to extract Hupsilas moment invariants, affine moment invariants and color moments from the 2D images of 3D objects. Hupsilas and affine moment invariants have the properties of rotation, scale, translation invariance and affine transformation invariance respectively for the objects in images, and color moments are used to distinguish objects of similar shape and different color. Then these moments compose a 1-dimensional feature vector of 11 components for each 2D image of 3D objects and presented to the BP neural network for training. The trained network can be used to recognize 3D objects when provided the feature vectors of unseen views. We assessed our method based on both the original and noise corrupted COIL-100 3D objects dataset and achieved 100% correct rate of recognition when training images were presented every 10 degrees.

The Application of High-Rate LDPC Codes in Image Transmission over Wireless Channel

It has been proved that low-density parity-check (LDPC) codes is good codes and have good distance. They have near Shannon limit performance when decoded using an iterative probabilistic algorithm. Moreover, high-rate LDPC codes have high data rate and high spectral efficiency. So, in this paper, we explored three high-rate LDPC codes in image transmission over Rayleigh fading channel, the simulation results show that high rate LDPC codes can obtain high information speed and bandwidth efficiency, they also can achieve high reliability and excellent performance in image communication over wireless channels. We also found that the performance is more sensitive to code rate than to code length

Study of adaptive modulation and LDPC coding in multicarrier systems

Abstract An adaptive modulation (AM) algorithm is proposed and the application of the adapting algorithm together with low-density parity-check (LDPC) codes in multicarrier systems is investigated. The AM algorithm is based on minimizing the average bit error rate (BER) of systems, the combination of AM algorithm and LDPC codes with different code rates (half and three-fourths) are studied. The proposed AM algorithm with that of Fischer et al is compared. Simulation results show that the performance of the proposed AM algorithm is better than that of the Fischers algorithm. The results also show that application of the proposed AM algorithm together with LDPC codes can greatly improve the performance of multicarrier systems. Results also show that the performance of the proposed algorithm is degraded with an increase in code rate when code length is the same.

Variational Image Deblurring Using Modified Hopfield Neural Network

We address the problem of variational image restoration by neural network in this paper. A variational image deblurring algorithm based on modified Hopfield neural network (MHNN) is proposed. Then we analyze the convergence of the deblurring algorithm and give a strategy to guarantee the algorithm to converge stable state. Comparative experimental results show that the proposed algorithm is favorable to other related algorithms

Image Transmission with High-Rate LDPC Codes in Multicarrier Systems

Low-density parity-check (LDPC) codes have near Shannon performance and easy to implement, multicarrier modulation can achieve high spectral efficiency and resist the inter-symbol interference (ISI). In this paper, we explored three high rate LDPC codes in image transmission with multicarrier modulation. The simulation results show that the combination of multicarrier modulation and high rate LDPC codes can obtain high information speed and high bandwidth efficiency with high reliability and excellent performance in image communication over wireless channels. This can solve the problems about transmission speed and efficiency caused by large quantity of image data. We also found that the performance is more sensitive to code rate than to code length

The Optimal Loading of Robust Adaptive Beamforming Based on Worst-Case Performance Optimization

In this paper the robust adaptive beamforming (RABF) based on worst-case performance optimization is investigated. This method belongs to the class of diagonal loading approaches and determines the loading level by optimizing the worst-case performance. Other than solving the optimal loading by iteration, as many existing methods in this field did, we derive a simple closed-form solution to the optimal loading after some approximations. In addition to reducing the computational complexity, this simple analytical solution reveals how different factors affect the optimal loading. Numerical examples confirm that the closed-form expression is a very accurate approximation to the actual optimal loading