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Microcrystalline cellulose (MCC) fibers are co mmonly used to reinforce polymers; however, their freeze‐drying process consumes a lot of energy. In this study, MCC dried at different temperatures, oven‐dried at 25, 50, 75, and 103°C and freeze‐dried, was evaluated based on their effect on the reinforced composites. The dried MCC was compounded with polylactic acid (PLA) by melt extrusion and compressed into an MCC/PLA composite film. The morphology, structure, and crystallinity of the dried MCCs and MCC/PLA composites were analyzed using scanning electron microscopy, laser particle size analysis, x‐ray diffraction, mechanical testing, and Fourier transform infrared spectroscopy. It was proved that MCC dried at 103°C was more uniformly dispersed and tightly bound to the PLA matrix than freeze‐dried MCC. Tensile measurements showed that the MCC dried at 103°C had nearly the same tensile strength (58.35 MPa) as that of the freeze‐dried MCC (58.7 MPa) and was higher than that of the other oven‐dried MCC. Energy consumption evaluation revealed that oven‐drying consumed much less energy than freeze‐drying MCC for 24 h to obtain the same final moisture content. MCC dried at 103°C has significantly better properties than MCC dried at 25, 50, and 75°C, similar to freeze‐drying. Concerning performance and energy consumption, drying at 103°C is determined the optimal choice to support the preparation of MCC‐reinforced PLA composites. Highlights: Drying temperature effects aggregation of MCC.MCC Dried at 103°C can present similar properties to freeze‐dried MCC but save more energy.Oven‐drying can be a good way to dry MCC for the preparation of MCC/PLA composite. [ABSTRACT FROM AUTHOR]

Hashing scheme is a high‐efficiency technique for processing massive images. Two critical metrics of the hashing scheme are discrimination and robustness, but most schemes do not get satisfied classification performance between them. This paper proposes a novel hashing scheme via image feature map and 2D PCA. First, the proposed scheme extracts local phase quantization (LPQ) features in the frequency domain and local ternary pattern (LTP) features in the spatial domain, and combines them to construct an image feature map. Second, the proposed scheme conducts dimension reduction via 2D PCA for learning features from the image feature map. Last, the learned features are compressed to generate the hash sequence. Performances are tested on open image datasets. The results demonstrate that the proposed scheme can make a good balance between discrimination and robustness. In addition, the classification and copy detection of the proposed scheme are both superior to those of some famous hashing schemes. [ABSTRACT FROM AUTHOR]

Video hashing is a useful technology for diverse video applications, such as digital watermarking, copy detection and content authentication. This paper proposes a novel efficient video hashing based on low‐rank frames. A key contribution is the low‐rank frame calculation using the low‐rank approximation of singular value decomposition (SVD). As the large singular values of SVD are stable to digital operations, video hash extraction using low‐rank frames can provide good robustness. Since most energy is contained within the large singular values, low‐rank frames also contribute to discrimination. Moreover, two‐dimensional discrete wavelet transform (DWT) is applied to every low‐rank frame and the mean of low‐frequency DWT coefficients is selected as a hash element. Since these coefficients can represent input data approximately, hash discrimination is thus ensured. Experiments with 16,850 videos are carried out to test performances of the proposed algorithm. The results show that the proposed algorithm outperforms some well‐known video hashing algorithms in computational time and classification about discrimination and robustness. [ABSTRACT FROM AUTHOR]

Reversible data hiding (RDH) is a useful technique of data security. Embedding capacity is one of the most important performance of RDH for encrypted image. Many existing RDH algorithms for encrypted image do not reach desirable embedding capacity yet. To address this problem, a new RDH algorithm is proposed for encrypted image based on adaptive prediction error coding. The proposed RDH algorithm uses a block‐based encryption scheme to preserve spatial correlation of original image in the encrypted domain and exploits a novel technique called adaptive prediction error coding to vacate room for data embedding. A key contribution of the proposed RDH algorithm is the adaptive prediction error coding. It can efficiently vacate room from encrypted image block by adaptively coding prediction errors according to block content and thus contributes to a large embedding capacity. Many experiments on benchmark image databases are done to validate performance of the proposed RDH algorithm. The results show that the average embedding rates on the open databases of UCID, BOSSBase and BOWS‐2 are 1.7081, 2.4437 and 2.3083 bpp, respectively. Comparison results illustrate that the proposed RDH algorithm outperforms some state‐of‐the‐art RDH algorithms in embedding capacity. [ABSTRACT FROM AUTHOR]

Image hashing is an efficient technique of multimedia processing for many applications, such as image copy detection, image authentication, and social event detection. In this study, the authors propose a novel image hashing with visual attention model and invariant moments. An important contribution is the weighted DWT (discrete wavelet transform) representation by incorporating a visual attention model called Itti saliency model into LL sub‐band. Since the Itti saliency model can efficiently extract saliency map reflecting regions of attention focus, perceptual robustness of the proposed hashing is achieved. In addition, as invariant moments are robust and discriminative features, hash construction with invariant moments extracted from the weighted DWT representation ensures good classification performance between robustness and discrimination. Extensive experiments with open image datasets are done to validate the performances of the proposed hashing. The results demonstrate that the proposed hashing is robust and discriminative. Performance comparisons with some hashing algorithms are also conducted, and the receiver operating characteristic results illustrate that the proposed hashing outperforms the compared hashing algorithms in classification performance between robustness and discrimination. [ABSTRACT FROM AUTHOR]

Abstract: Multifunctional bionanocomposite foams with 3D interconnected networks showing advantageous mechanical properties, thermal insulation, underwater oleophobicity, and biocompatibility, were made from a chitosan matrix reinforced with nanofibrillated cellulose (NFC). The density of the NFC–chitosan nanocomposite foams can be controlled by varying the NFC/chitosan weight ratio and solid content of the suspension in the fabrication process. The mechanical properties and thermal stability of the nanocomposite foams were significantly improved by increasing the ratio of NFC, and effective thermal insulating performance was exhibited for temperature extremes at 0 °C and 70 °C. Moreover, the NFC–chitosan nanocomposite foams showed a highly efficient oil/water separation capacity even at a severe temperature of 90 °C. In addition, the nanocomposite foams possessed good biocompatibility toward L929 mouse fibroblasts. Therefore, the bionanocomposite foams are available for a number of applications, including as disposable and high‐performance filtration media for water purification, packaging, and biological scaffolds. [ABSTRACT FROM AUTHOR]

Colour vector angle has been widely used in edge detection and image retrieval, but its investigation in image hashing is still limited. In this study, the authors investigate the use of colour vector angle in image hashing and propose a robust hashing algorithm combining colour vector angles with discrete wavelet transform (DWT). Specifically, the input image is firstly resized to a normalised size by bi‐cubic interpolation and blurred by a Gaussian low‐pass filter. Colour vector angles are then calculated and divided into non‐overlapping blocks. Next, block means of colour vector angles are extracted to form a feature matrix, which is further compressed by DWT. Image hash is finally formed by those DWT coefficients in the LL sub‐band. Experiments show that the proposed hashing is robust against normal digital operations, such as JPEG compression, watermarking embedding and rotation within 5°. Receiver operating characteristics curve comparisons are conducted and the results show that the proposed hashing is better than some well‐known algorithms. [ABSTRACT FROM AUTHOR]