Fuzzy Logic Based Hardware Accelerator with Partially Reconfigurable Defuzzification Stage for Image Edge Detection

In this paper, the design and the implementation of a pipelined hardware accelerator based on a fuzzy logic approach for an edge detection system are presented. The fuzzy system comprises a preprocessing stage, a fuzzifier with four fuzzy inputs, an inference system with seven rules, and a defuzzification stage delivering a single crisp output, which represents the intensity value of a pixel in the output image. The hardware accelerator consists of seven stages with one clock cycle latency per stage. The defuzzification stage was implemented using three different defuzzification methods. These methods are the mean of maxima, the smallest of maxima, and the largest of maxima. The defuzzification modules are interchangeable while the system runs using partial reconfiguration design methodology. System development was carried out using Vivado High-Level Synthesis, Vivado Design Suite, Vivado Simulator, and a set of Xilinx 7000 FPGA devices. Depending upon the speed grade of the device that is employed, the system can operate at a frequency range from 83 MHz to 125 MHz. Its peak performance is up to 58 high definition frames per second. A comparison of this system’s performance and its software counterpart shows a significant speedup in the magnitude of hundred thousand times

Using fuzzy logic for bounded recovery of autonomous agents

The solution to the problem of application-independent fault recovery of autonomous agents requires a specification method for the agent's capacity to act outside of its normal operational limits. This paper presents a recovery method based upon the theory of a fuzzy finite state machine. A fuzzy specification is given for the bounds within which an autonomous agent is capable to recover after an unexpected situation has occurred in its environment. It has been shown that the three main components of the recovery problem: fault detection, fault recovery, and the properties of the actuator/sensor gear of an autonomous agent are interrelated. The suggested method can be implemented either by an application-independent software algorithm, or by fuzzy logic hardwar