A survey on 5G massive MIMO Localization

Massive antenna arrays can be used to meet the requirements of 5G, by exploiting different spatial signatures of users. This same property can also be harnessed to determine the locations of those users. In order to perform massive MIMO localization, refined channel estimation routines and localization methods have been developed. This paper provides a brief overview of this emerging field

Big-five personality and BIS/BAS traits as predictors of career exploration: The mediation role of career adaptability

Based on career construction theory, the current research examined whether career adaptability mediates the relations of the personality traits (Five-Factor Model personality traits and behavioral inhibition and activation systems (BIS/BAS)) to career exploration behavior. Results from a survey in Chinese university students (N = 264) showed that career exploration correlated negatively with neuroticism, and positively with openness to experience, extroversion, agreeableness, conscientiousness and BAS. Results of regression analyses further showed that openness to experience, agreeableness, conscientiousness and BAS served as the strongest predictors for career exploration. In addition, career adaptability was shown to be a key mediator for the relationships between personality traits and career exploration behavior. Career concern and career curiosity were the more important dimensions in the mediation model. These findings advance current understandings on how different personality traits predict career exploration behavio

Tensor decomposition based R-dimensional matrix pencil method

In this paper, we extend the standard matrix pencil (MP) method to R-dimensional (R-D) tensor based MP. Higher-order singular value decomposition (HOSVD) is used to obtain the signal subspace. Performance of tensor based MP method is evaluated by computer simulations. Comparing with the conventional matrix based MP methods, better performance is obtained for tensor based R-D MP methods by exploiting the structure of the measurement data. Furthermore, it is straightforward to extend the proposed R-D tensor MP to other MP type methods, such as R-D unitary tensor MP, R-D beamspace tensor MP.Published versio

Localization for mixed near-field and far-field sources using data supported optimization

Recently, localization for the coexistence of the far-field and near-field sources has received more attentions. In this paper, a maximum likelihood (ML) localization method using data supported optimization is considered. The range and direction of arrival (DOA) of the sources are estimated sequentially. Since a two step estimation method is used, the proposed method is applicable for the near-field sources, far-field sources or the mixture of these two kinds of sources. Furthermore, the proposed method is applicable for far-field and near-field source classification. Simulations are implemented to verify the performance of the proposed method.Published versio

Jointly Modeling Aspect Information and Ratings for Review Rating Prediction

Although matrix model-based approaches to collaborative filtering (CF), such as latent factor models, achieve good accuracy in review rating prediction, they still face data sparsity problems. Many recent studies have exploited review text information to improve the performance of predictions. The review content that they use, however, is usually on the coarse-grained text level or sentence level. In this paper, we propose a joint model that incorporates review text information with matrix factorization for review rating prediction. First, we adopt an aspect extraction method and propose a simple and practical algorithm to represent the review by aspects and sentiments. Then, we propose two similarity measures: aspect-based user similarity and aspect-based product similarity. Finally, aspect-based user and product similarity measures are incorporated into a matrix factorization to build a joint model for rating prediction. To this end, our model can alleviate the data sparsity problem and obtain interpretability for the recommendation. We conducted experiments on two datasets. The experimental results demonstrate the effectiveness of the proposed model

Intercalation Polymerization Approach for Preparing Graphene/Polymer Composites

The rapid development of society has promoted increasing demand for various polymer materials. A large variety of efforts have been applied in order for graphene strengthened polymer composites to satisfy different requirements. Graphene/polymer composites synthesized by traditional strategies display some striking defects, like weak interfacial interaction and agglomeration of graphene, leading to poor improvement in performance. Furthermore, the creation of pre-prepared graphene while being necessary always involves troublesome processes. Among the various preparation strategies, an appealing approach relies on intercalation and polymerization in the interlayer of graphite and has attracted researchers’ attention due to its reliable, fast and simple synthesis. In this review, we introduce an intercalation polymerization strategy to graphene/polymer composites by the intercalation of molecules/ions into graphite interlayers, as well as subsequent polymerization. The key point for regulating intercalation polymerization is tuning the structure of graphite and intercalants for better interaction. Potential applications of the resulting graphene/polymer composites, including electrical conductivity, electromagnetic absorption, mechanical properties and thermal conductivity, are also reviewed. Furthermore, the shortcomings, challenges and prospects of intercalation polymerization are discussed, which will be helpful to researchers working in related fields

An impulse radio ultrawideband system for contactless noninvasive respiratory monitoring

We design a impulse radio ultrawideband radar monitoring system to track the chest wall movement of a human subject during respiration. Multiple sensors are placed at different locations to ensure that the backscattered signal could be detected by at least one sensor no matter which direction the human subject faces. We design a hidden Markov model to infer the subject facing direction and his or her chest movement. We compare the performance of our proposed scheme on 15 human volunteers with the medical gold standard using respiratory inductive plethysmography (RIP) belts, and show that on average, our estimation is over 81% correlated with the measurements of a RIP belt system. Furthermore, in order to automatically differentiate between periods of normal and abnormal breathing patterns, we develop a change point detection algorithm based on perfect simulation techniques to detect changes in the subject's breathing. The feasibility of our proposed system is verified by both the simulation and experiment results.Accepted versio

Crop classification for UAV visible imagery using deep semantic segmentation methods

Unmanned aerial vehicle (UAV) has become a mainstream data collection platform in precision agriculture. For more accessible UAV–visible imagery, the high spatial resolution brings the rich geometric texture features triggered large differences in same crop image's features. We proposed an encoder–decoder's fully convolutional neural network combined with a visible band difference vegetation index (VDVI) to perform deep semantic segmentation of crop image features. This model ensures the accuracy and the generalization ability, while reducing parameters and the operation cost. A case study of crop classification was conducted in Chengdu, China, where classified four crops, namely, maize, rice, balsam pear, and Loropetalum chinese, it was shown more effective results. In addition, this study explores a fine crop classification method based on visible light features, which is feasible with low equipment cost, and has a prospect of application in crop survey based on UAV low altitude remote sensing