Movable frame hybrid MAC : a multi-MAC protocol for wireless software radios in multi-rate multimedia applications

We describe a new medium-access control (MAC) protocol for dynamic adjustment of the bandwidth requirement of multimedia applications. The purpose of this technique is handling multi-rate, multi-level traffic in an integrated wireless-access network (IWAN). A proposed mechanism divide total bandwidth in basic band and reservation band. Four conventional access techniques which are CDMA, CSMA, TDMA and FDMA are combined in the basic band. Reservation band choose these four techniques flexibly depending the traffic characteristics and quality of services (QoS) requirement. This Movable Frame Hybrid MAC (FMHMAC) is called software radios in third generation (3G) wireless network designing. A comparative evaluation of this access technique is done by simulation procedure Through simulations, the performances of the proposed access technique (e.g. call blocking probability, average delay and delay jitter) show that is both robust and suitable for the intended IWAN applications, this will results in high QoS guarantee for arbitrary traffic condition

Investigation and optimization of the cable force of a combined highway and railway steel truss cable-stayed bridge in completion state

In order to study the reasonable cable force of a highway and rail dual-purpose steel truss cable-stayed bridge in the completion state, this paper employs four methods, i.e. the rigid supported continuous beam method, bending minimum energy method, influence matrix method and BP neural network method combined with a genetic algorithm. The Baijusi Yangtze river bridge, with a main span of 660 m in the completion state, is chosen as the object of study. Through comparative analysis, it is found that the rigid supported continuous beam combined with the influence matrix method can determine the reasonable cable force of the highway and rail dual-purpose steel truss cable-stayed bridge more quickly and effectively

CasIL: Cognizing and Imitating Skills via a Dual Cognition-Action Architecture

Enabling robots to effectively imitate expert skills in longhorizon tasks such as locomotion, manipulation, and more, poses a long-standing challenge. Existing imitation learning (IL) approaches for robots still grapple with sub-optimal performance in complex tasks. In this paper, we consider how this challenge can be addressed within the human cognitive priors. Heuristically, we extend the usual notion of action to a dual Cognition (high-level)-Action (low-level) architecture by introducing intuitive human cognitive priors, and propose a novel skill IL framework through human-robot interaction, called Cognition-Action-based Skill Imitation Learning (CasIL), for the robotic agent to effectively cognize and imitate the critical skills from raw visual demonstrations. CasIL enables both cognition and action imitation, while high-level skill cognition explicitly guides low-level primitive actions, providing robustness and reliability to the entire skill IL process. We evaluated our method on MuJoCo and RLBench benchmarks, as well as on the obstacle avoidance and point-goal navigation tasks for quadrupedal robot locomotion. Experimental results show that our CasIL consistently achieves competitive and robust skill imitation capability compared to other counterparts in a variety of long-horizon robotic tasks