Retinal vessel segmentation is a significant problem in the analysis of fundus images. A novel deep learning structure called the Gaussian net (GNET) model combined with a saliency model is proposed for retinal vessel segmentation. A saliency image is used as the input of the GNET model replacing the original image. The GNET model adopts a bilaterally symmetrical structure. In the left structure, the first layer is upsampling and the other layers are max-pooling. In the right structure, the final layer is max-pooling and the other layers are upsampling. The proposed approach is evaluated using the DRIVE database. Experimental results indicate that the GNET model can obtain more precise features and subtle details than the UNET models. The proposed algorithm performs well in extracting vessel networks, and is more accurate than other deep learning methods. Retinal vessel segmentation can help extract vessel change characteristics and provide a basis for screening the cerebrovascular diseases.

This letter proposes a novel reversible data hiding (RDH) method with an adaptive embedding capability by extending the generalized integer transformation (GIT). We modify the GIT algorithm to a further generalized form, and accordingly we propose an adaptive embedding algorithm. When hiding data into the original image, parameters of the extended GIT can be identified according to the content of each block. Along with a multi-level location map, a better embedding efficiency of the net payload can be achieved. On the decoding side, the original image can be losslessly recovered after extracting the hidden message. Experimental results show that the proposed method outperforms state-of-the-art integer-transformation based RDH methods.

In this paper, by modeling a narrowband interference (NBI) as a wide-sense stationary (WSS), zero-mean Gaussian bandpass random process, a systematic theoretical framework is developed for the first time to characterize the operation and performance improvement of energy detection (ED) based impulse radio ultra-wideband (IR-UWB) receivers that employ a nonlinear Teager-Kaiser operator (TKO) and a highpass filter. Furthermore, through both theoretical analysis and simulations, we have identified (1) that the TKO technique can effectively mitigate not only the performance degradation caused by an NBI but also that caused by additive white Gaussian noise and (2) that a larger bandwidth of the UWB signal leads to better mitigation. Therefore, the TKO technique is considered as a very promising technique to improve the overall performance of ED-based IR-UWB receivers no matter an NBI is present or not.

In this paper, a systematic framework is developed for the first time for theoretically analyzing performance of the nonlinear Teager-Kaiser operator (TKO) employed in energy detection (ED) based impulse radio ultra-wideband (IR-UWB) receivers. Closed-form formulations are derived for various nonlinear terms in the TKO output as well as for the performance gain of the IR-UWB systems with the TKO technique (over those without the TKO technique). Both theoretical analysis and computer simulations show that the TKO technique can effectively mitigate not only performance degradation caused by narrowband interference (NBI) but also that by additive white Gaussian noise. As a result, the TKO technique can be used to improve overall performance of ED based IR-UWB receivers regardless of an NBI being present or not.

We present a method to reduce the number of test patterns (TPs) decoded in the Chase-II algorithm for turbo product codes (TPCs) constructed with multi-error-correcting extended Bose-Chaudhuri-Hocquengem (eBCH) codes. We classify TPs into different conditions based on the relationship between syndromes and the number of errors so that TPs with the same codeword are not decoded except the one with the least number of errors. For eBCH with code length of 64, simulation results show that over 50% of TPs need not to be decoded without any performance degradation.

In this paper, Chase decoding algorithm for turbo product codes (TPCs) built with multi-error-correcting codes is investigated. A method is proposed to reduce the decoding of test patterns (TPs) as well as the calculation of syndromes and metrics. In the beginning, syndromes and metrics of the first half of TPs are calculated recursively. After decoding of one TP, according to the error bit positions, other TPs that will have the same decoded codeword are identified and rejected from decoding. In the mean time, if these TPs fall into the range of second half of TPs, their syndromes and metrics need not to be calculated either. Simulations results for TPCs built with eBCH(64,51,6,2) or eBCH(64,45,8,3) are presented to demonstrate the efficiency of the proposed method. Moreover, this method that reduces the inherent redundancy in Chase decoding does not cause any degradation in coding gain.

MIMO-OFDM is a promising technique for the broadband wireless communication system. In this paper, we propose a novel scheme that integrates multiple description coding (MDC), error resilient video coding, and unequal error protection strategy with various space time coding codes for robust video transmission over MIMO-OFDM system. The proposed MDC coder generates multiple bitstreams of equal importance which are very suitable for multiple antennas system. Furthermore, according to the contribution to the reconstructed video quality, we apply unequal error protection strategy using BLAST and STBC space time codes for each video bitstream. Experimental results have demonstrated that the proposed scheme can achieve desired tradeoff between the reconstructed video quality and the transmission efficiency