No abstract

One of the important factors in increasing the productivity of the incubation industry is to be sure that the eggs placed in the incubators are fertile. In this research, a fertility detection machine vision system is developed and evaluated. To this end, a mechatronic machine is fabricated for acquiring accurate digital images of eggs without harming them. An appropriate and cheap light source is also introduced for illuminating the eggs, which potentially enables a CCD camera to obtain good quality and informative images from inner side of the eggs. Finally, a robust machine vision algorithm is developed to process the captured images and distinguish fertile eggs from infertile ones. In order to evaluate the system, a large egg image dataset is provided using 240 incubated eggs (including 190 fertile and 50 infertile eggs). The fertility detection accuracy of the system on the provided dataset reaches 47.13% at day 1 of incubation, 81.41% at day 2, 93.08% at day 3, 97.73% at day 4, and 98.25% at day 5. Comparisons with existing approaches show that the proposed method achieves a superior performance. The obtained results indicate that the proposed system is highly reliable and applicable in the incubation industry.

No abstract

No abstract

Detecting the deadlock is one of the important problems in distributed systems. In this paper we proposed a distributed deadlock detection algorithm. In our algorithm the chance of phantom deadlocks detection is minimized by using a new approach and some improvements to resolution of deadlocks. Our algorithm can manage the simultaneous execution of the algorithm by nodes involved in deadlocks, prevents the detection of same deadlocks and minimize the number of useless messages in simultaneous execution of the algorithm by giving the priorities to the processes. In our proposed algorithm deadlocks are resolved as soon as they detected by its unique characteristic without creating and propagating of token to erase the memories of processes.