Progressive Image Transmission Based on Joint Source-Channel Decoding Using Adaptive Sum-Product Algorithm

A joint source-channel decoding method is designed to accelerate the iterative log-domain sum-product decoding procedure of LDPC codes as well as to improve the reconstructed image quality. Error resilience modes are used in the JPEG2000 source codec making it possible to provide useful source decoded information to the channel decoder. After each iteration, a tentative decoding is made and the channel decoded bits are then sent to the JPEG2000 decoder. The positions of bits belonging to error-free coding passes are then fed back to the channel decoder. The log-likelihood ratios (LLRs) of these bits are then modified by a weighting factor for the next iteration. By observing the statistics of the decoding procedure, the weighting factor is designed as a function of the channel condition. Results show that the proposed joint decoding methods can greatly reduce the number of iterations, and thereby reduce the decoding delay considerably. At the same time, this method always outperforms the nonsource controlled decoding method by up to 3 dB in terms of PSNR

Efficient vanishing point detection method in unstructured road environments based on dark channel prior

Vanishing point detection is a key technique in the fields such as road detection, camera calibration and visual navigation. This study presents a new vanishing point detection method, which delivers efficiency by using a dark channel prior‐based segmentation method and an adaptive straight lines search mechanism in the road region. First, the dark channel prior information is used to segment the image into a series of regions. Then the straight lines are extracted from the region contours, and the straight lines in the road region are estimated by a vertical envelope and a perspective quadrilateral constraint. The vertical envelope roughly divides the whole image into sky region, vertical region and road region. The perspective quadrilateral constraint, as the authors defined herein, eliminates the vertical lines interference inside the road region to extract the approximate straight lines in the road region. Finally, the vanishing point is estimated by the meanshift clustering method, which are computed based on the proposed grouping strategies and the intersection principles. Experiments have been conducted with a large number of road images under different environmental conditions, and the results demonstrate that the authors’ proposed algorithm can estimate vanishing point accurately and efficiently in unstructured road scenes

Anisotropic nanomechanical properties of bovine horn using modulus mapping

Bovine horns are durable that they can withstand an extreme loading force which with special structures and mechanical properties. In this paper, we apply quasi-static nanoindentation and modulus mapping techniques to research the nanomechanical properties of bovine horn in the transverse direction (TD) and longitudinal direction (LD). In quasi-static nanoindentation, the horn’s modulus and hardness in the inner layer and the outer layer demonstrated a gradual increase in both TD and LD. Laser scanning confocal microscopy (LSCM) revealed microstructure in the horn with wavy morphology in the TD cross-section and laminate in the LD cross-section. When using tensile tests or quasi-static nanoindentation tests alone, the anisotropy of the mechanical properties of bovine horn were not obvious. However, when using modulus mapping, storage modulus (E′), loss modulus (E″) and loss ratio (tan δ) are clearly different depending on the position in the TD and LD. Modulus mapping is proposed as accurately describing the internal structures of bovine horn and helpful in understanding the horn’s energy-absorption, stiffness and strength that resists forces during fighting

Delivering hydrophilic and hydrophobic chemotherapeutics simultaneously by magnetic mesoporous silica nanoparticles to inhibit cancer cells

Using nanoparticles to deliver chemotherapeutics offers new opportunities for cancer therapy, but challenges still remain when they are used for the delivery of multiple drugs, especially for the synchronous delivery of hydrophilic and hydrophobic drugs in combination therapies. In this paper, we developed an approach to deliver hydrophilic–hydrophobic anticancer drug pairs by employing magnetic mesoporous silica nanoparticles (MMSNs). We prepared 50 nm-sized MMSNs with uniform pore size and evaluated their capability for the loading of two combinations of chemotherapeutics, namely doxorubicin–paclitaxel and doxorubicin–rapamycin, by means of sequential adsorption from the aqueous solution of doxorubicin and nonaqueous solutions of paclitaxel or rapamycin. Experimental results showed that the present strategy successfully realized the co-loading of hydrophilic and hydrophobic drugs with high-loading content and widely tunable ratio range. We elaborate on the theory behind the molecular interaction between the silica hydroxyl groups and drug molecules, which underlie the controllable loading, and the subsequent release of the drug pairs. Then we demonstrate that the multidrug-loaded MMSNs could be easily internalized by A549 human pulmonary adenocarcinoma cells, and produce enhanced tumor cell apoptosis and growth inhibition as compared to single-drug loaded MMSNs. Our study thus realized simultaneous and dose-tunable delivery of hydrophilic and hydrophobic drugs, which were endowed with improved anticancer efficacy. This strategy could be readily extended to other chemotherapeutic combinations and might have clinically translatable significance

Near infrared spectroscopy coupled with radial basis function neural network for at-line monitoring of Lactococcus lactis subsp. fermentation

AbstractIn our previous work, partial least squares (PLSs) were employed to develop the near infrared spectroscopy (NIRs) models for at-line (fast off-line) monitoring key parameters of Lactococcus lactis subsp. fermentation. In this study, radial basis function neural network (RBFNN) as a non-linear modeling method was investigated to develop NIRs models instead of PLS. A method named moving window radial basis function neural network (MWRBFNN) was applied to select the characteristic wavelength variables by using the degree approximation (Da) as criterion. Next, the RBFNN models with selected wavelength variables were optimized by selecting a suitable constant spread. Finally, the effective spectra pretreatment methods were selected by comparing the robustness of the optimum RBFNN models developed with pretreated spectra. The results demonstrated that the robustness of the optimal RBFNN models were better than the PLS models for at-line monitoring of glucose and pH of L. lactis subsp. fermentation

Efficient photocatalytic hydrogen evolution over carbon supported antiperovskite cobalt zinc nitride

Photocatalytic solar to chemical energy conversion is an important energy conversion process but suffer from low efficiency. Thus, development of efficient photocatalytic system using earth-abundant elements with low costs is highly desirable. Here, antiperovskite cobalt zinc nitride has been synthesized and coupled with carbon black (Co3ZnN/C) for visible light driven hydrogen production in an Eosin Y-sensitized system. Replacement of cobalt atom by zinc atom leads to an improved charge transfer kinetics and catalytic properties compared with Co4N. Density functional theory (DFT) calculations further reveal the adjusted electronic structure of Co3ZnN by zinc atom introducing. The lower antibonding energy states of Co3ZnN are beneficial for the hydrogen desorption. Moreover, carbon black as support effectively reduces the particle size of Co3ZnN and benefits to the electron storage and adsorption capabilities. The optimal Co3ZnN/C catalysts exhibit the H-2 evolution rate of 15.4 mu mol mg(-1) h(-1),which is over 6 times higher than that of monometallic Co4N. It is even greater than those of most of Eosin Y-sensitized systems