Magneto and Electro-Optic Intensity Modulator Based on Liquid Crystal and Magnetic Fluid Filled Photonic Crystal Fiber

We propose a novel light intensity modulator based on magnetic fluid and liquid crystal（LC） filled photonic crystal fibers（PCFs）. The influences of electric and magnetic fields on the transmission intensity are theoretically and experimentally analyzed and investigated. Both the electric and magnetic fields can manipulate the molecular arrangement of LC to array a certain angle without changing the refractive index of the LC. Therefore, light loss in the PCF varies with the electric and magnetic fields whereas the peak wavelengths remain constant. The experimental results show that the transmission intensity decreases with the increase of the electric and magnetic fields. The cut-off electric field is 0.899 V/um at 20 Hz and the cut-off magnetic field is 195 m T. This simple and compacted optical modulator will have a great prospect in sensing applications.

Passive tracking and size estimation of volume target based on acoustic vector intensity

The special sections of volume target are observed with acoustic vector intensity according to the difference among their radiated-noise characteristics, then three sections are tracked with Kalman filtering, and target size is estimated. Simulation results indicate that in ideal condition three sections of a ship can be tracked and ship's size can be estimated even though one of three sections can not be observed.

Adaptive digital backward propagation based on variance of intensity noise

An adaptive digital backward propagation(ADBP) algorithm is proposed and experimentally demonstrated based on the variance of the intensity noise. The proposed algorithm can self-determine the unknown nonlinear coefficient γ and the nonlinear compensation parameter ξ. Compared to the scheme based on the variance of phase noise, the proposed algorithm can avoid the repeated frequency offset compensation and carrier phase recovery. The simulation results show that the system’s performance compensated by the proposed method is comparable to conventional ADBP schemes. The performance of the proposed algorithm is simulated in40/112 Gb/s polarization-division multiplexing(PDM)-quadrature phase-shift keying(QPSK) and 224 Gb/s PDM-16-quadrature amplitude modulation(QAM) systems and further experimentally verified in a 40 Gb/s PDM-QPSK coherent optical communication system over a 720 km single-mode fiber.

Deadline based scheduling for data-intensive applications in clouds

Cloud computing emerges as a new computing pattern that can provide elastic services for any users around the world. It provides good chances to solve large scale scientific problems with fewer efforts. Application deployment remains an important issue in clouds. Appropriate scheduling mechanisms can shorten the total completion time of an application and therefore improve the quality of service（QoS） for cloud users. Unlike current scheduling algorithms which mostly focus on single task allocation, we propose a deadline based scheduling approach for data-intensive applications in clouds. It does not simply consider the total completion time of an application as the sum of all its subtasks＇ completion time. Not only the computation capacity of virtual machine（VM） is considered, but also the communication delay and data access latencies are taken into account. Simulations show that our proposed approach has a decided advantage over the two other algorithms.