Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

A block cipher based on a suitable use of the chaotic standard map

Abstract: Due to their features of ergodicity, sensitivity to initial conditions and sensitivity to control parameters, etc., chaotic maps have good potential for information encryption. In this paper, a block cipher based on the chaotic standard map is proposed, which is composed of three parts: a confusion process based on chaotic standard map, a diffusion function, and a key generator. The parameter sensitivity of the standard map is analyzed, and the confusion process based on it is proposed. A diffusion function with high diffusion speed is designed, and a key generator based on the chaotic skew tent map is derived. Some cryptanalysis on the security of the designed cipher is carried out, and its computational complexity is analyzed. Experimental results show that the new cipher has satisfactory security with a low cost, which makes it a potential candidate for encryption of multimedia data such as images, audios and even videos.

FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors

Abstract: Face Super-Resolution (SR) is a domain-specific superresolution problem. The facial prior knowledge can be leveraged to better super-resolve face images. We present a novel deep end-to-end trainable Face Super-Resolution Network (FSRNet), which makes use of the geometry prior, i.e., facial landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR) face images without well-aligned requirement. Specifically, we first construct a coarse SR network to recover a coarse high-resolution (HR) image. Then, the coarse HR image is sent to two branches: a fine SR encoder and a prior information estimation network, which extracts the image features, and estimates landmark heatmaps/parsing maps respectively. Both image features and prior information are sent to a fine SR decoder to recover the HR image. To generate realistic faces, we also propose the Face Super-Resolution Generative Adversarial Network (FSRGAN) to incorporate the adversarial loss into FSRNet. Further, we introduce two related tasks, face alignment and parsing, as the new evaluation metrics for face SR, which address the inconsistency of classic metrics w.r.t. visual perception. Extensive experiments show that FSRNet and FSRGAN significantly outperforms state of the arts for very LR face SR, both quantitatively and qualitatively.

Amorphous FeOOH Quantum Dots Assembled Mesoporous Film Anchored on Graphene Nanosheets with Superior Electrochemical Performance for Supercapacitors

Abstract: Previous research on iron oxides/hydroxides has focused on the crystalline rather than the amorphous phase, despite that the latter could have superior electrochemical activity due to the disordered structure. In this work, a simple and scalable synthesis route is developed to prepare amorphous FeOOH quantum dots (QDs) and FeOOH QDs/graphene hybrid nanosheets. The hybrid nanosheets possess a unique heterostructure, comprising a continuous mesoporous FeOOH nanofilm tightly anchored on the graphene surface. The amorphous FeOOH/graphene hybrid nanosheets exhibit superior pseudocapacitive performance, which largely outperforms the crystalline iron oxides/hydroxides-based materials. In the voltage range between −0.8 and 0 V versus Ag/AgCl, the amorphous FeOOH/graphene composite electrode exhibits a large specific capacitance of about 365 F g−1, outstanding cycle performance (89.7% capacitance retention after 20 000 cycles), and excellent rate capability (189 F g−1 at a current density of 128 A g−1). When the lower cutoff voltage is extended to −1.0 and −1.25 V, the specific capacitance of the amorphous FeOOH/graphene composite electrode can be increased to 403 and 1243 F g−1, respectively, which, however, compromises the rate capability and cycle performance. This work brings new opportunities to design high-performance electrode materials for supercapacitors, especially for amorphous oxides/hydroxides-based materials.

Multi-Scale Boosted Dehazing Network With Dense Feature Fusion

Abstract: In this paper, we propose a Multi-Scale Boosted Dehazing Network with Dense Feature Fusion based on the U-Net architecture. The proposed method is designed based on two principles, boosting and error feedback, and we show that they are suitable for the dehazing problem. By incorporating the Strengthen-Operate-Subtract boosting strategy in the decoder of the proposed model, we develop a simple yet effective boosted decoder to progressively restore the haze-free image. To address the issue of preserving spatial information in the U-Net architecture, we design a dense feature fusion module using the back-projection feedback scheme. We show that the dense feature fusion module can simultaneously remedy the missing spatial information from high-resolution features and exploit the non-adjacent features. Extensive evaluations demonstrate that the proposed model performs favorably against the state-of-the-art approaches on the benchmark datasets as well as real-world hazy images.

Metal–organic framework-derived one-dimensional porous or hollow carbon-based nanofibers for energy storage and conversion

Abstract: Metal organic framework (MOF)-derived nanoporous carbons (NPCs) have been proposed as promising electrode materials for energy storage and conversion devices. However, MOF-derived NPCs typically suffer from poor electrical conductivity due to the lack of connectivity between these particles and a micropore-dominated storage mechanism, which hinder mass and electron transfer, thereby leading to poor electrochemical performance. In recent years, one-dimensional (1D) MOF-derived carbon nanostructures obtained using an electrospinning method have emerged as promising materials for both electrochemical energy storage (EES) and energy conversion applications. In this mini review, the recent progress in the development of MOF-derived 1D porous or hollow carbon nanofibers using the electrospinning method and their application in energy storage (e.g., supercapacitors and rechargeable batteries) and conversion devices (e.g., fuel cells) is presented. The synthetic method, formation mechanism and the structure–activity relationship of such porous or hollow carbon nanofibers are also discussed in detail. Finally, future perspectives on the development of electrospun MOF-derived carbon nanomaterials for energy storage and conversion applications are provided. This review will provide some guidance for future derivations of 1D hollow carbon nanomaterials from MOFs using electrospinning technology.