Search

With the rapid increase of data, stream computation contributes to the improvement of data real time processing. One of characters of stream computing is in the pipeline to exploit parallelism in the MIMD way. Moreover, pipelined parallel implementation is a common problem and often yields much better parallel efficiency with respect to other commonly used methods. Plentiful physical modeling in the field of image processing and machine vision deals with the solution of partial differential equations, typical representative by Poisson's equation. The finite difference schemes as the main method for the solution of partial difference equation for physical modeling can be time-consuming and computationally expensive. Constructing highly parallel computation models can greatly solve the problem and it is important that quickly and efficiently carry out it. The paper mainly studies the parallel implementation of the mix of Jacobi iterative and Gauss-Seidel method for the finite difference schemes to solve Poisson equations in a pipelined fashion on FPGA.

With the development of communication technology and the rapid augment in data and multimedia services, the demand for indoor localization is increasing. Radio Frequency Identification (RFID) based real time localization system is an effective positioning system. However, due to the complex multi-path interference of indoor environment, most of the localization methods are lack of positioning accuracy. In this paper, we present a multi-frequency carrier phase difference based ranging measurement for passive RFID system in the real experiment scenario. Kalman filter is used to calibrate the phase difference value and a least square method is employed to estimate the environmental influencing factor. Real environment experiment results indicate high ranging accuracy of our approach, which lay a foundation for further two or three dimensions measurement. The paper explains the basic measurement principle, experimental setup and performance analysis.

Radio Frequency IDentification (RFID) system has been wildly used in recent ten years. A Handhold reader and some passive tags compose an efficient system for retail scenarios, such as storage management and item inventory. However, the heavily energy consumption of the reader battery, which due to the frequent tag collisions, is a main limitation for such a RFID system in large scale item-level scenario. To avoid the collisions, Dynamic Frame Slotted ALOHA anti-collision algorithms (DFSA) are popularly being used. But unreliable tag number estimation and non-optimized frame size selection may lead to more energy consumption. In this paper, we propose a novel tag population estimation and frame length selection method based on tag location-aware scheme to optimize the DFSA tag anti-collision algorithm. In our method, the handhold reader firstly collects the tags' location information and divides them into different clusters based on their distance from the reader. Then by estimating the tag population for each cluster, the reader utilizes this value to achieve an optimum frame size and automatically adjusting the transmission power to scan the tags in corresponding tag cluster. We also give a quantitative energy consumption model for the passive RFID system. According to the simulation results, the passive RFID system throughput and energy efficiency can be highly increased by using our scheme.