We have proposed a multi-contextual model architecture with color and depth information considered independently in this work. To utilize the feature maps of different levels better, short connection structures are used to integrate the knowledge from color and depth data separately. A novel loss function considering three criteria is proposed to improve the detection accuracy and spatial coherence of the detected results. The training process of the proposed network is divided into two stages, a pre-training phase and a refinement phase to increase the efficiency of the network.

Summary form only given. In mobile agent research, agent-to-agent (A2A) communication often relies on an infrastructure of the discovery server to resolve the location of the agents. This not only induces overhead on managing, maintaining and deploying these discovery servers, but also complicates the development of agent applications. While considering scalability and reliability, such an agent discovery infrastructure becomes very sophisticated. We propose a novel A2A communication system, called Armada, which is autonomic and it does not require a discovery infrastructure. Armada is scalable and reliable, and suitable for a wide-area environment with dynamic entities. It does not pose mobile constrains to the agents. Each agent can be globally and uniquely identified without regarding its location. We evaluate Armada through large-scale simulation and prototype measurement in a cluster environment with 35-node desktop PCs. Results from both accesses presents an efficient design of Armada.