The encapsulation of Cu nanoclusters (Cu NCs) in metal-organic frameworks (MOFs) would improve the properties of Cu NCs. So far, these composites were reported by a two-step synthesis process. In this work, a facile one-pot synthesis of hybridization of glutathione (GSH) protected Cu NCs (Cu NCs@GSH) and MOF-5 (Cu NCs@GSH/MOFs) composites was reported for the first time. The results of UV-vis, TEM, XPS and SEM proved Cu NCs@GSH were distributed homogeneously over the entire MOF structure. The fluorescence intensity of Cu NCs encapsulated in MOF-5 was enhanced about 35-fold owing to the confining scaffold of the MOF and the stability was extended from 3 days to 3 months. Cu NCs@GSH/MOFs composites exhibited strong orange fluorescence and the emissions could change between blue, orange and red as they were partially reversible in different pH environments. This one-pot synthetic strategy could be extended for the encapsulation of fluorescent Ag NCs in MOFs as well. As-prepared Cu NCs@GSH/MOF-5 composites had high stability, and were easily recycled by centrifugation in aqueous solution, therefore, it would be utilized to develop a reusable sensor for detection of metal ions in the future.
The data analysis of visible-near infrared (Vis-NIR) spectroscopy is critical for precise information extraction and prediction of fiber morphology. The objectives of this study were to discuss the de-noising of Vis-NIR spectra, taken from wood, to improve the prediction accuracy of tracheid length in Dahurian larch wood. Methods based on lifting wavelet transform (LWT) and local correlation maximization (LCM) algorithms were developed for optimal de-noising parameters and partial least squares (PLS) was employed as the prediction method. The results showed that: (1) The values of tracheid length in the study were generally high and had a great positive linear correlation with annual rings (R = 0.881), (2) the optimal de-noising parameters for larch wood based Vis-NIR spectra were Daubechies-2 (db2) mother wavelet with 4 decomposition levels while using a global fixed hard threshold based on LWT, and (3) the Vis-NIR model based on the optimal LWT de-noising parameters ( R c 2 = 0.834, RMSEC = 0.262, RPD c = 2.454) outperformed those based on the LWT coupled with LCM algorithm (LWT-LCM) ( R c 2 = 0.816, RMSEC = 0.276, RPD c = 2.331) and raw spectra ( R c 2 = 0.822, RMSEC = 0.271, RPD c = 2.370). Thus, the selection of appropriate LWT de-noising parameters could aid in extracting a useful signal for better prediction accuracy of tracheid length.
Internet of Things (IoT) serves not only as an essential part of the new generation information technology but as an important development stage in the information era. IoT devices such as unmanned aerial vehicles, robots and wearable equipments have been widely used in recent years. For most organizations' inner networks, innumerable dynamic connections with Internet accessible IoT devices occur at many parts all the time. It is usually these temporal links that arise potential threats to the security of the whole intranet. In this paper, we propose a new system named IoT Eye, which automatically discovers the IoT devices in real time. The IoT Eye detects all the potential IoT target hosts using an innovative two-stage architecture: (1) Scanning suspicious IP segments with stateless TCP SYN scan model and zero copy TCP stack; (2) Identifying each IoT device on various protocols using PI-AC, which is a novel high-performance multi-pattern matching algorithm. The preceding model ensures the IoT Eye searching each newly connected device out in rather small time delay, which minimizes the missing and wrong detection rates. Related intelligence on the active IoT devices linked with the organization's intranets are of great importance to the professionals. Since it can help them: (1) re-examine the borders of large intranets; (2) reduce non-essential device access; (3) fix security vulnerabilities timely.
In this paper, a data lake architecture is proposed for a class of monitoring and diagnostic systems applied to power grid. The differences between data lake and data warehouse is studied to make an informed decision on how to manage a huge amount of data. To adapt to the characteristics and performances of historical data and real-time data of power grid equipment, a monitoring and diagnosis system based on data lake storage architecture is designed. The application of the framework indicates the applicability and effectiveness of data lake architecture.
Text-embedded images are popular in the mobile Internet to spread malicious information. A fast text-embedded image Chinese text extracting algorithm based on homogeneous space mapping is proposed. Image enhancement functions are used to highlight edge and texture features of images. Sobel operator is used to extract the edge feature and wavelet packet is used to extract the 24-dimensional texture feature vectors in the enhanced images. The texture features and edge features are used to describe the homogeneity of an image, which construct the homogeneous feature map of the image. The differences between the non-text and the text region homogeneity are used to distinguish them and reduce non-text region further. Thus the text regions are highlighted. Then, homogeneous text samples are used to train the text region detector, which greatly reduces the computational complexity. Finally, the characters are segmented and recognized. Some experiments to verify the validity and practicability of the proposed algorithm have been conducted. The recognition rate achieves 86%, which is higher than that of other methods in industry. The algorithm is verified on the operator's malicious information monitoring system, which provides secure malicious filtering performance.
Relative attributes learning provides a way to capture the strength of the attributes under consideration and it can provide a more specific and accurate information to describe images. But for computers, extracting low-level features is the foundation of understanding images. Thus there is no doubt that the features will have an significant influence on relative attribute models learning. %For example, local features don't have much positive effects on learning global attributes. In this paper, we propose a sparse feature preservation (SFP) method to preserve the most important features on the learning of each attribute model. SFP is formulated through using rearrangement inequality according to relative attribute models learning. We first train the relative attribute models according to the supervision information of attribute pairs. Then the sorting results are used to train the key feature preservation factors and the sparse features are utilized to retrain the relative attribute models. We demonstrate the approach on five datasets and show its significant improvement on the accuracy of relative attribute learning.
Image hashing is critical for large-scale image analytic-based applications, such as image retrieval. Although there have been dozens of hashing approaches, few of them take the hierarchical structure of the image categories into consideration. In this paper, we propose to incorporate the category taxonomy information in a deep reinforcement learning (DRL) model for image hashing. In particular, we learn an agent to predict the hashing codes sequentially under the DRL theme. Each coordinate of the hashing function can take the errors incurred by previous ones into consideration and hence more reliable hashing codes can be obtained than learning them independently. Besides, we design a novel level-specific reward function to gradually refine the hashing function according to the taxonomy information. Extensive experiments on two popular datasets demonstrate effectiveness of the proposed method.
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