Effect of Microcrystalline Cellulose on the Properties of PBAT/Thermoplastic Starch Biodegradable Film with Chain Extender.

Poly (butylene adipate-co-terephthalate) (PBAT) is a fully biodegradable polymer with toughness and ductility. It is usually compounded with thermoplastic starch (TPS) to balance the cost for manufacturing biodegradable films such as disposable plastic bags. However, blending with TPS reduces valuable tensile strength, which limits the bearing capacity of PBAT film. In this study, microcrystalline cellulose (MCC) was employed as a reinforcement to strengthen the PBAT/TPS biodegradable film. The effect of MCC content on the mechanical, thermal, and morphological properties of the composite film were investigated. The optimal tensile strength and elongation at break reached 5.08 MPa and 230% when 4% MCC was added. The thermal stability and thermal resistance were improved with the addition of MCC; for example, Tmax increased by 1 °C and Tonset increased by 2-8 °C. Moreover, good compatibility among PBAT, TPS, and MCC can be achieved when the MCC content was below 6%. Consequently, the optimal MCC content was found to be 4%. These results could provide experimental data and method support for preparing high-performance PBAT hybrid films.

Covert photo classification by fusing image features and visual attributes.

In this paper, we study a novel problem of classifying covert photos, whose acquisition processes are intentionally concealed from the subjects being photographed. Covert photos are often privacy invasive and, if distributed over Internet, can cause serious consequences. Automatic identification of such photos, therefore, serves as an important initial step toward further privacy protection operations. The problem is, however, very challenging due to the large semantic similarity between covert and noncovert photos, the enormous diversity in the photographing process and environment of cover photos, and the difficulty to collect an effective data set for the study. Attacking these challenges, we make three consecutive contributions. First, we collect a large data set containing 2500 covert photos, each of them is verified rigorously and carefully. Second, we conduct a user study on how humans distinguish covert photos from noncovert ones. The user study not only provides an important evaluation baseline, but also suggests fusing heterogeneous information for an automatic solution. Our third contribution is a covert photo classification algorithm that fuses various image features and visual attributes in the multiple kernel learning framework. We evaluate the proposed approach on the collected data set in comparison with other modern image classifiers. The results show that our approach achieves an average classification rate (1-EER) of 0.8940, which significantly outperforms other competitors as well as human's performance.

Enlarging the depth of focus by filtering in the phase-space domain.

The ambiguity function was employed as a merit function to design an optical system with a high depth of focus. The ambiguity function with the desired enlarged-depth-of-focus characteristics was obtained by using a properly designed joint filter to modify the ambiguity function of the original pupil in the phase-space domain. From the viewpoint of the filter theory, we roughly propose that the constraints of the spatial filters that are used to enlarge the focal depth must be satisfied. These constraints coincide with those that appeared in the previous literature on this topic. Following our design procedure, several sets of apodizers were synthesized, and their performances in the defocused imagery were compared with each other and with other previous designs.