Abstract: A follow-hold switch circuit comprising: a follower; a sampling sub-circuit for voltage sampling; a bootstrap-control sub-circuit, which provides a bootstrap voltage to the sampling sub-circuit when the circuit is in a following state; a sampling-switch-control sub-circuit, which provides a common-mode voltage to a bootstrap capacitor in the bootstrap-control sub-circuit when the circuit is in a holding state; the follower is connected to an output of the sampling sub-circuit; the sampling sub-circuit is connected to the bootstrap-control sub-circuit and the sampling-switch-control sub-circuit respectively through a sampling switch; the present disclosure can effectively improve the linearity of sampling switches.

Abstract: The present invention discloses a method for predicting a tag arrival rate of a mobile Radio Frequency Identification (RFID) system. The method includes: first, establishing a dynamic tag arrival model for a mobile RFID system based on modeling data and a grey model GM(1, 1); improving a weight of an initial value of a differential equation of the grey model through weighting; and predicating a tag arrival rate based on a sliding window mechanism. A weighted grey model predication algorithm based on the sliding window mechanism can be obtained, to predicate a tag arrival rate of a mobile RFID system. The method for predicting the tag arrival rate of the mobile RFID system of the present invention can reduce a prediction error rate of the system, maintain a modeling length of 4 for the system through the sliding window mechanism, and update modeling data online.

Abstract: An energy-efficient traffic scheduling algorithm based on multiple layers of virtual sub-topologies is provided. First, a mathematical optimization model for an energy-efficient traffic scheduling problem is established, to minimize network energy consumption while ensuring the capability of bearing all network data flows. Then, the mathematical optimization model is resolved using an energy-efficient traffic scheduling algorithm based on a multi-layer virtual topology, to obtain an energy-efficient scheduling scheme of the data flows. The virtual topology and switch ports in an upper layer are made dormant to save energy. The method can dynamically adjust the working state of the virtual sub-topology in the upper layer according to current link utilization. A path with a minimum number of hops and lowest maximum link utilization can be found in the booted sub-topology, to route the data flow, solving the problem that a “rich-connection” data center network has low energy resource utilization at low load.

Abstract: The present invention discloses a method for predicting a tag arrival rate of a mobile Radio Frequency Identification (RFID) system. The method includes: first, establishing a dynamic tag arrival model for a mobile RFID system based on modeling data and a grey model GM(1, 1); improving a weight of an initial value of a differential equation of the grey model through weighting; and predicating a tag arrival rate based on a sliding window mechanism. A weighted grey model predication algorithm based on the sliding window mechanism can be obtained, to predicate a tag arrival rate of a mobile RFID system. The method for predicting the tag arrival rate of the mobile RFID system of the present invention can reduce a prediction error rate of the system, maintain a modeling length of 4 for the system through the sliding window mechanism, and update modeling data online. The present invention features a low prediction error rate, low computational complexity, and high system stability.

Abstract: The invention discloses a cloud task scheduling method based on phagocytosis-based Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA). In response to the task scheduling problem in cloud environment, update manners of position and speed in a standard particle swarm optimization are changed; a fitness function and a load balancing standard deviation are used to perform primary division and secondary division on particles in each generation of a particle swarm; and phagocytosis mutation and crossover mutation are respectively performed on different particle subpopulations obtained after division, so that a cloud task scheduling scheme is obtained. The method provided in the present invention obviously reduces total completion time of cloud tasks, and is of higher convergence accuracy. Therefore, the cloud task scheduling method based on phagocytosis-based hybrid of particle swarm optimization and genetic algorithm provided in the present invention proves to be effective.

Abstract: An energy-efficient traffic scheduling algorithm based on multiple layers of virtual sub-topologies is provided. First, a mathematical optimization model for an energy-efficient traffic scheduling problem is established, to minimize network energy consumption while ensuring the capability of bearing all network data flows. Then, the mathematical optimization model is resolved using an energy-efficient traffic scheduling algorithm based on a multi-layer virtual topology, to obtain an energy-efficient scheduling scheme of the data flows. The virtual topology and switch ports in an upper layer are made dormant to save energy. The method can dynamically adjust the working state of the virtual sub-topology in the upper layer according to current link utilization. A path with a minimum number of hops and lowest maximum link utilization can be found in the booted sub-topology, to route the data flow, solving the problem that a “rich-connection” data center network has low energy resource utilization at low load.