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Yayın Real-time video frame differentiator based on DDR3 SDRAM memory interface(IEEE Computer Society, 2018-08) Davutoğlu, Doğancan; Yıldız, Nerhun; Tavşanoğlu, Ahmet Vedat; Ayten, Umut EnginIn this paper, design of a real-time video frame differentiator based on an external memory interface is proposed. Furthermore, implementation and simulation processes of the design is discussed. The proposed design is capable of differentiating video frames over time, up to full-HD resolution at 60 Hz frame rate. An external SDRAM memory unit is used within the proposed design and drived by a memory interface. In order to improve the flexibility of the architecture, video resolution, video buffer size on memory and burst size of the memory interface are designed to be user defined and configurable.Yayın Edge detection of aerial images using artificial bee colony algorithm(Kırgızistan Türkiye Manas Üniversitesi, 2022-06-30) Yelmenoğlu, Elif Deniz; Akhan Baykan, NurdanEdge detection techniques are the one of the best popular and significant implementation areas of the image processing. Moreover, image processing is very widely used in so many fields. Therefore, lots of methods are used in the development and the developed studies provide a variety of solutions to problems of computer vision systems. In many studies, metaheuristic algorithms have been used for obtaining better results. In this paper, aerial images are used for edge information extraction by using Artificial Bee Colony (ABC) Optimization Algorithm. Procedures were performed on gray scale aerial images which are taken from RADIUS/DARPA-IU Fort Hood database. Initially bee colony size was specified according to sizes of images. Then a threshold value was set for each image, which related with images’ standard deviation of gray scale values. After the bees were distributed, fitness values and probability values were computed according to gray scale value. While appropriate pixels were specified, the other ones were being abandoned and labeled as banned pixels therefore bees never located on these pixels again. So the edges were found without the need to examine all pixels in the image. Our improved method’s results are compared with other results found in the literature according to detection error and similarity calculations’. All the experimental results show that ABC can be used for obtaining edge information from images.Yayın A discussion on spatiotemporal filtering on a third generation real-time cellular neural network processor(IEEE Computer Society, 2016) Yıldız, Nerhun; Cesur, Evren; Tavşanoğlu, Ahmet VedatA third generation Real-Time Cellular Neural Network (CNN) Processor (RTCNNP-v3) is a CNN emulator currently being implemented targeting FPGA devices. Thanks to the frame buffer support of the RTCNNP-v3 it will be possible to store and recall multiple frames which will extend the range of applications that can be implemented with RTCNNP, including spatiotemporal filters. In this paper, the implementation method of a velocity-tuned filter currently being implemented is disclosed with further discussion.Yayın Sperm morphology analysis with CNN based algorithms(IEEE Computer Society, 2014-08-29) Şavkay, Osman Levent; Cesur, Evren; Yalçın, Müştak Erhan; Tavşanoğlu, Ahmet VedatIn this paper Morphological Analysis part of our proposed computer-aided sperm analysis system (CASA) is simulated and the results beside the algorithm steps are presented. The morphology analysis is simply dealing with shape of the sperms and extracting the shape characteristics in medical parameters. The characteristics are obtained by image processing algorithms which utilizes Cellular Nanoscale Network (CNN) based and spatial image processing blocks. The following calculation of medical parameters are obtained from the outputs of image processing blocks. The algorithm is so designed to adapt the final SoC architecture such as Xilinx Zynq7000 device.Yayın New criteria for the existence of stable equilibrium points in nonsymmetric cellular neural networks(IEEE, 2003) Özcan, Neyir; Arık, Sabri; Tavşanoğlu, Ahmet VedatA new criteria for the existence of stable equilibrium points in nonsymmetric cellular neural networks (CNN) was presented. It was shown that the results obtained can be used to derive some complete stability conditions for some special classes of CNNs such as positive cell-linking CNNs, opposite-sign CNNs and dominant-template CNNs. The model of the CNN whose dynamical behavior was described by the state equations was discussed.Yayın Realization of preprocessing blocks of CNN based CASA system on FPGA(2013) Şavkay, Osman Levent; Yıldız, Nerhun; Cesur, Evren; Yalçın Müştak, Erhan; Tavşanoğlu, Ahmet VedatIn this paper, hardware optimization of the preprocessing part of a computer aided semen analysis (CASA) system is proposed, which is also implemented on an FPGA device as a working prototype. A real-time cellular neural network (CNN) emulator (RTCNNP-v2) is used for the realization of the image processing algorithms, whose regular, flexible and reconfigurable infrastructure simplifies the prototyping process. For future work, the post-processing part of the CASA system is proposed to be implemented on the same FPGA device as software, using either a soft or hard processor core. By the integration of the pre- and post-processing parts, the designed CASA system will be capable of processing full-HD 1080p@60 (1080×1920) video images in real-time.Yayın On the way to a third generation real-time cellular neural network processor(IEEE Computer Society, 2016) Yıldız, Nerhun; Cesur, Evren; Tavşanoğlu, Ahmet VedatIn this proceeding, the architecture of a third generation Real-Time Cellular Neural Network (CNN) Processor (RTCNNP-v3) is disclosed, which is a digital CNN emulator to be implemented on an FPGA device. The previous generation emulator, RTCNNP-v2, is the only CNN implementation reported to be capable of processing full-HD 1080p@60 (1080×1920 resolution at 60 Hz frame rate) video images in real-time. However, there are some weaknesses in both the design and implementation of RTCNNP-v2, like the inability to process different parts of the video images in parallel, lack of support for recording and recalling intermediate frames using external memory and it has some jitter issues at computation rates above 200 MHz. All of those issues are addressed in the next architecture of our CNN emulator, RTCNNP-v3, which is being implemented of an FPGA device.Yayın Cryptanalysis of a chaos-based image encryption algorithm(Elsevier Science BV, 2009-03-30) Çokal, Cahit; Solak, ErcanA chaos-based image encryption algorithm was proposed in [Z.-H. Guan, F. Huang, W. Guan, Phys. Lett. A 346 (2005) 153]. In this Letter, we analyze the security weaknesses of the proposal. By applying chosen-plaintext and known-plaintext attacks, we show that all the secret parameters can be revealed.Yayın Texture recognition for frog identification(ACM SIGMM, 2012-11-02) Cannavo, Flavio; Nunnari, Giuseppe; Kale, İzzet; Tek, Faik BorayThis paper describes a visual processing technique for automatic frog (Xenopus Laevis sp.) localization and identification. The problem of frog identification is to process and classify an unknown frog image to determine the identity which is recorded previously on an image database. The frog skin pattern (i.e. texture) provides a unique feature for identification. Hence, the study investigates three different kind of features (i.e. Gabor filters, granulometry, threshold set compactness) to extract texture information. The classifier is built on nearest neighbor principle; it assigns the query feature to the database feature which has the minimum distance. Hence, the study investigates different distance measures and compares their performance. The detailed results show that the most successful feature and distance measure is granulometry and weighted L1 norm for the frog identification using skin texture features.Yayın Realization of processing blocks of CNN based CASA system on CPU and FPGA(IEEE, 2014) Şavkay, Osman Levent; Cesur, Evren; Yıldız, Nerhun; Yalçın, Mustak Erhan; Tavşanoğlu, Ahmet VedatIn this paper, hardware optimization of the preprocessing and software implementation of the processing blocks of a computer aided semen analysis (CASA) system are proposed, which is also implemented on an FPGA and ARM device as a working prototype. The software implementation of the track initialization, track maintenance, data validation and classification blocks of the processing part are implemented on a Zynq7000 ARM Cortex-A9 processor. In the preprocessing part, a real-time cellular neural network (CNN) emulator (RTCNNP-v2) is used for the realization of the image processing algorithms, whose regular, flexible and reconfigurable infrastructure simplifies the prototyping process. The CASA system introduced in this paper is capable of processing full-HD 1080p@60 (1080 x 1920) video images in real-time.
