Human cognition and behavior can be unconsciously affected by personal avatars in the virtual world, a phenomenon known as the Proteus Effect. When using first-person non-human avatars, the characteristics of virtual hands may also induce relevant cognitive and even behavioral patterns in real human hands. Therefore, evaluating human-avatar gesture consistency may be a potentially effective method for objectively assessing the Proteus Effect when using non-human avatars. To explore this question, we first created human and non-human avatars, including three animals. Then, we constructed a dataset of hand gestures and trained a model to dynamically recognize real-hand gestures that were consistent with corresponding avatar hands. Next, we designed a virtual reality experimental task involving grasping objects with intuitive gestures and performed a 2 (avatar type: human/non-human) $* 2$ (virtual hand: presence/absence of spontaneous movement) within-subject experiment to examine the effects of avatar characteristics on self-illusion and human-avatar gesture consistency. The results showed that participants performed a significantly larger percentage of gestures that were consistent with their currently used avatars. Additionally, participants did experience self-illusion when using non-human avatars, although the levels were significantly lower than those when using human avatars. Therefore, self-illusion may serve as a perceptual antecedent of the Proteus Effect, even with non-human avatars, inadvertently altering the behavioral gestures of their real hands. In conclusion, detecting human-avatar gesture consistency can help evaluate the Proteus Effect.

In this paper, the Generative Adversarial Network (GAN) is applied to LiDAR data classification. Generative Adversarial Network usually includes a generating network and a discriminant network. In GAN, a convolutional neural network (CNN) is designed to distinguish inputs. Another CNN is used to generate so-called false inputs. Combining with the actual training samples, the discriminant CNN is fine-tuned to improve the final classification performance. The proposed classifier is implemented on real data sets. The results show that the accuracy of the proposed network is higher than that of the classification method based on CNN, which shows that the features extracted by the network have better discrimination and stronger competitiveness.

Light detection and ranging (LiDAR) data is a very important type of data used for terrain classification. The traditional convolutional neural network (CNN) has insufficient effective transmission of features and gradients in feature classification and can only set a fixed input size by experience. In this paper, spatial transformation network (STN) and densely connected convolutional network (DenseNet) are combined to form STN-DenseNet, which makes the input data adaptively deform according to the network needs, making full use of all information from the front layers of the network. Thus the transmission of features and gradients are more effective. The proposed framework performs experiments on two LiDAR-DSM datasets (i.e. Bayview Park and Recology datasets). The results show that, comparing with the traditional deep convolution model, STN-DenseNet can improve the classification accuracy of LiDAR-DSM data.

Due to the respective developments of various radio access technologies, some geographical areas may be covered simultaneously by different wireless networks. The cooperation of these networks will not only meet the diverse quality of service requirements of mobile users but also efficiently utilize the resources of systems. As an important component of radio resource management, call admission control (CAC) is to decide whether a new call or a handover request can be accepted into a resource-constrained network. In such heterogeneous wireless environment, it needs more accurate metrics to measure the performances of mobility especially inside a cellular. In this paper, we propose a new equivalent cost ring based model to represent the complex heterogeneous wireless environment in a simplified and unified way. The coverage of base station is divided into regions based on the basic data rate. Using this model, we derive the mobility parameters such as new call blocking and handover failure probabilities. The system performances of heterogeneous networks are validated by simulations.

In overlay heterogeneous wireless networks, many mobile terminals may operate vertical handover simultaneously which is defined as Group Vertical Handover (GVHO). Conventionally, handover decision schemes designed for a single user may cause network congestion and increasing handover blocking probability in GVHO scenario. To provide seamless handover with QoS guarantee, a fuzzy clustering method (FCM) is proposed by combining the concurrent traffic attributes of GVHO users and the status of available candidate networks. After the combination, users are able to select the proper network based on the dynamic diagram, which is generated by FCM. The collision probability can also be decreased through allocating handover request properly in a specific time window. Compared to the other two schemes, the simulation results show that the proposed scheme reduces the handover blocking probability about 17% and 25% (the maximum rate) respectively.

With the increase of science project in size and complexity, it is necessary to integrate and share global research resources for distributed experimentation and simulation. This paper presents an architecture of Cooperative Remote Laboratories (CRLab) with scalability, flexibility and heterogeneity. It consists of a central Web server, simulation servers, instrument servers and experiment devices, which provide four logical functions including Parallel Computing, Virtual Instrument, Collaborative Simulation and Web 2.0. Based on these functions with interactive online features, the research can be more efficient and convenient just via a simple Web browser.