다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Massive MIMO is a promising technique for increasing the spectral efficiency (SE) of cellular networks, by deploying antenna arrays with hundreds or thousands of active elements at the base stations and performing coherent transceiver processing. A common rule-of-thumb is that these systems should have an order of magnitude more antennas $M$ than scheduled users $K$ because the users’ channels are likely to be near-orthogonal when $M/K > 10$ . However, it has not been proved that this rule-of-thumb actually maximizes the SE. In this paper, we analyze how the optimal number of scheduled users $K^\star$ depends on $M$ and other system parameters. To this end, new SE expressions are derived to enable efficient system-level analysis with power control, arbitrary pilot reuse, and random user locations. The value of $K^\star$ in the large- $M$ regime is derived in closed form, while simulations are used to show what happens at finite $M$ , in different interference scenarios, with different pilot reuse factors, and for different processing schemes. Up to half the coherence block should be dedicated to pilots and the optimal $M/K$ is less than 10 in many cases of practical relevance. Interestingly, $K^\star$ depends strongly on the processing scheme and hence it is unfair to compare different schemes using the same $K$ .

Massive MIMO for Maximal Spectral Efficiency: How Many Users and Pilots Should Be Allocated?

An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion

The role of utilitarian and hedonic aspects in the continuance intention to use social mobile apps

The user behavior characteristics of mobile social network services are of guiding significance to the evaluation of user experience, and test cases and test scenarios should be designed according to user behavior characteristics. Current studies have heavily addressed the action sequence and the frequency distribution of user behavior. However, there is little research on the amount of user action triggered by the communication angle and the fluctuation of the user’s action communication performance under different scenarios. This paper analyzes the distribution of data concerning different user actions and tests the waiting time and success rate of different user actions in different scenes. The results suggest that the complex scenarios can consist of some typical user behaviors.

User behavior and user experience analysis for social network services

Smart transportation is an essential component of the smart city. Traffic prediction is an important issue in smart transportation. The convolutional neural networks (GCN) are an effective approach for traffic prediction. However, the GCN meets some challenges, such as stability of prediction precision and computation cost, in traffic prediction. The hyperparameters significantly affect the performance of GCN. We conduct a regression analysis between hyperparameters and GCN performance. Our simulation results show that there is the obvious optimal point of hyperparameters. Some empirical suggestion is given to adjust the hyperparameters based on the simulation results.

A Hyperparameters automatic optimization method of time graph convolution network model for traffic prediction

A fractional calculus fluid model can be used to better explain the bursty data service traffic, which is long-range dependence and has a fractal like the feature of network data flow. The heavy-tailed delay distributions, the hyperbolic decay of the packet delay auto-covariance function and fractional differential equations are shown to be formally related. Effective capacity is a useful model to describe wireless networks with QoS constraints. This paper builds a fluid model to describe the traffic of multi-hop wireless networks under QoS constraints. The proposed method could analyze the relationship between latency and a complicated traffic model, which is more similar to the real scenario.

Bursty data service latency analysis under fractional calculus fluid model of Multi-hop Wireless Networks

At present, the scale and types of data collected by people have shown explosive growth. It is very difficult to obtain specific and effective classification labels for high-dimensional data. By using subspace clustering method with low rank representation, the linear representation matrix of the data with the lowest rank is found, and the global structure of the original data is preserved to achieve the purpose of optimizing clustering. By comparing K-means clustering, LRR clustering and the improved LRR clustering method of self-adapting graph regularization low rank representation, the experiment proves that the latter has better effect in clustering image data collected from different angles.

Lei Chen

Papers

User behavior and user experience analysis for social network services

A Hyperparameters automatic optimization method of time graph convolution network model for traffic prediction

Bursty data service latency analysis under fractional calculus fluid model of Multi-hop Wireless Networks

Research on Image Data Clustering Algorithm Based on Low Rank Subspace Clustering

A novel micro-expression detection algorithm based on BERT and 3DCNN