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.

Host–Parasite: Graph LSTM-in-LSTM for Group Activity Recognition

Abstract: This article aims to tackle the problem of group activity recognition in the multiple-person scene. To model the group activity with multiple persons, most long short-term memory (LSTM)-based methods first learn the person-level action representations by several LSTMs and then integrate all the person-level action representations into the following LSTM to learn the group-level activity representation. This type of solution is a two-stage strategy, which neglects the “host–parasite” relationship between the group-level activity (“host”) and person-level actions (“parasite”) in spatiotemporal space. To this end, we propose a novel graph LSTM-in-LSTM (GLIL) for group activity recognition by modeling the person-level actions and the group-level activity simultaneously. GLIL is a “host–parasite” architecture, which can be seen as several person LSTMs (P-LSTMs) in the local view or a graph LSTM (G-LSTM) in the global view. Specifically, P-LSTMs model the person-level actions based on the interactions among persons. Meanwhile, G-LSTM models the group-level activity, where the person-level motion information in multiple P-LSTMs is selectively integrated and stored into G-LSTM based on their contributions to the inference of the group activity class. Furthermore, to use the person-level temporal features instead of the person-level static features as the input of GLIL, we introduce a residual LSTM with the residual connection to learn the person-level residual features, consisting of temporal features and static features. Experimental results on two public data sets illustrate the effectiveness of the proposed GLIL compared with state-of-the-art methods.

Achieving Covert Wireless Communications Using a Full-Duplex Receiver

Abstract: Covert communications hide the transmission of a message from a watchful adversary while ensuring a certain decoding performance at the receiver. In this paper, a wireless communication system under fading channels is considered where covertness is achieved by using a full-duplex receiver. More precisely, the receiver of covert information generates artificial noise with a varying power causing uncertainty at the adversary, Willie, regarding the statistics of the received signals. Given that Willie’s optimal detector is a threshold test on the received power, we derive a closed-form expression for the optimal detection performance of Willie averaged over the fading channel realizations. Furthermore, we provide guidelines for the optimal choice of artificial noise power range, and the optimal transmission probability of covert information to maximize the detection errors at Willie. Our analysis shows that the transmission of artificial noise, although causing self-interference, provides the opportunity of achieving covertness but its transmit power levels need to be managed carefully. We also demonstrate that the prior transmission probability of 0.5 is not always the best choice for achieving the maximum possible covertness when the covert transmission probability and artificial noise power can be jointly optimized.

Enhanced performance and selectivity of CO2 methanation over phyllosilicate structure derived Ni-Mg/SBA-15 catalysts

Abstract: Ni and Ni-Mg phyllosilicate mesoporous SBA-15 catalysts were successfully prepared via ammonia evaporation (AE) method as evidenced by XRD, H2-TPR, TEM, FTIR and EXAFS fitting results. The catalysts derived from phyllosilicate structure exhibit superior catalytic performance in CO2 methanation as compared to catalyst prepared via wetness impregnation (WI) method due to enhanced metal-support interaction and the presence of weakly basic sites provided by surface hydroxyl groups. Incorporation of Mg into phyllosilicate structure with optimum 5 wt% is also found to increase medium basic sites, which can promote monodentate formate formation as identified by DRIFTS analysis and improve CO2 methanation activity at lower temperatures. Additionally, the turnover frequency of CO2 conversion can be well correlated with the concentration of basic sites. The strong metal-support interaction derived from phyllosilicate structure along with confinement effect of SBA-15 can suppress metal sintering, resulting in good stability.

Joint Reconstruction and Anomaly Detection From Compressive Hyperspectral Images Using Mahalanobis Distance-Regularized Tensor RPCA

Abstract: Anomaly detection plays an important role in remotely sensed hyperspectral image (HSI) processing. Recently, compressive sensing technology has been widely used in hyperspectral imaging. However, the reconstruction from compressive HSI and detection are commonly completed independently, which will reduce the processing’s efficiency and accuracy. In this paper, we propose a framework for hyperspectral compressive sensing with anomaly detection which reconstruct the HSI and detect the anomalies simultaneously. In the proposed method, the HSI is composed of the background and anomaly parts in the tensor robust principal component analysis model. To characterize the low-dimensional structure of the background, a novel tensor nuclear norm is used to constrain the background tensor. As the anomaly part is formed by a few anomalous spectra, the anomaly part is assumed to be a tuber-wise sparse tensor. In addition, to enhance the separation of the background and anomaly, we minimize the sum of Mahalanobis distance of the background pixels. Experiments on four HSIs demonstrate that the proposed method outperforms several state-of-the-art methods on both reconstruction and anomaly detection accuracies.