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Much research and analysis in biomedicine involve image and video inspection using microscopes. Presently, scientists are dissatisfied with manual observations and assessments, when objective and enhanced data can be obtained by applying new technologies (such as image and video inspection) to biomedical fields, such as sperm analysis. Computer Assisted Sperm Analysis (CASA) systems, developed in the late 1980s, constitute third-generation methods of sperm analysis. This study aimed to develop a standalone medical image and video analysis system that is reconfigurable, flexible, reliable, deterministic, and robust. It proposed a new sperm motility analysis system running on a dual core Central Processing Unit (CPU) + field programmable gate arrays (FPGA) platform, under a real-time operating system (RTOS), which is a step ahead of the third-generation CASA systems. The system hardware and related sperm detection and tracking algorithms were the novelty of this work. The image processing functions mainly run on FPGA, image acquisition, and calculations run on CPU, parallel with FPGA. The result is a much faster, reliable, reconfigurable, and compact intelligent analyzer system. Our prototype system was applied to sperm motility analysis; however, other image processing systems can be applied to this architecture. Additionally, the proposed tracking method for sperm track determination is simple, effective, and does not exert a load on the system. We are thankful to Prof. Dr. Erol Tavmergen, Ege University Family Planning, Infertility Research and Treatment Center, and the laboratory people at the center for their support, help and for providing human semen samples and comments on motility analysis. We also thank Geli?im T?p (Medical) Laboratory for showing and introducing us to their CASA system, for taking semen sample videos and providing comments on sperm analysis. We are thankful to Assist. Prof. Dr. Kamber Demir, Dr. Ramazan Ar?c?, Dr. Ahmet Eser from ?stanbul University ? Cerrahpa?a Faculty of Veterinary Medicine, Department of Reproduction and Artificial Insemination, for opening the laboratories, their comments and their help in conducting the tests and providing the samples. Publisher's Version

This paper presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with fixed time delays. The results impose constraint conditions on the network parameters of neural system independent of the delay parameters. The results are applicable to all continuous non-monotonic neuron activation functions. The results are also compared with the previously reported results in the literature, implying that the results obtained in this paper provide one more set of criteria for determining the stability of bidirectional associative memory neural networks with time delays.