A series of homoleptic Cr(III) complexes containing substituted anionic 1,3-bis(pyridin-2-ylimino)isoindolin-2-ide ligands are phosphorescent with max in the 777970 nm range in degassed fluid solutions. The energy gap law has been...

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Homoleptic Cr(III) complexes containing anionic tridentate 1,8-(bisoxazolyl)carbazolide ligands are phosphorescent in deareated fluid solutions with peak maxima at 813-845 nm. The ligand carbon-centred chirality has been transferred to the helical...

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With the rapid development and widespread applications of Internet of Things (IoT) systems, the corresponding security issues are getting more and more serious. This paper proposes a multistage asymmetric information attack and defense model (MAIAD) for IoT systems. Under the premise of asymmetric information, MAIAD extends the single-stage game model with dynamic and evolutionary game theory. By quantifying the benefits for both the attack and defense, MAIAD can determine the optimal defense strategy for IoT systems. Simulation results show that the model can select the optimal security defense strategy for various IoT systems.

A novel compact MIMO antenna for personal digital assistant (PDA) and pad computer is proposed. The proposed antenna is composed by two multipatch monopole antennas which are placed 90° apart for orthogonal radiation. To strengthen the isolation, a T-shaped ground branch with proper dimension is used to generate an additional coupling path to lower the mutual coupling (below −15 dB), especially at GSM850/900 band. The proposed MIMO antenna is fabricated and tested, both the simulated and the measured results are presented, and some parametric studies are also demonstrated. In addition, there are some advantages about the proposed antenna such as simple structure, easy fabrication, and low cost.

Scene text detection methods based on deep learning have recently shown remarkable improvement. Most text detection methods train deep convolutional neural networks with full masks requiring pixel accuracy for good quality training. Normally, a skilled engineer needs to drag tens of points to create a full mask for the curved text. Therefore, data labelling based on full masks is time consuming and laborious, particularly for curved texts. To reduce the labelling cost, a weakly supervised method is first proposed in this paper. Unlike the other detectors (e.g., PSENet or TextSnake) that use full masks, our method only needs coarse masks for training. More specifically, the coarse mask for one text instance is a line across the text region in our method. Compared with full mask labelling, data labelling using the proposed method could save labelling time while losing much annotation information. In this context, a network pretrained on synthetic data with full masks is used to enhance the coarse masks in a real image. Finally, the enhanced masks are fed back to train our network. Analysis of experiments performed using the model shows that the performance of our method is close to that of the fully supervised methods on ICDAR2015, CTW1500, Total-Text, and MSRA-TD5000.

Cognitive radio systems offer the opportunity to improve spectrum utilization while avoiding the interference to primary users. Secondary users must be aware of interference caused to primary users. Therefore cognitive radio network should take primary users into account and control their transmission power. This paper focuses on the coverage analysis of cognitive radio network and finds its coverage upper boundary. Based on cognitive network models, this study reveals several factors effecting cognitive network coverage and proposes possible ways to enlarge coverage area for secondary users with keeping the interference to primary users under the endurable threshold.