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It is shown that an (M× N)-node planar resistive grid can be decomposed into two sub-grids; one made up of M N-node horizontal and the other of N M-node vertical linear resistive grids which corresponds to decomposing its nodal conductance matrix (NCM) into the Kronecker sum of the NCMs of horizontal and vertical linear grids. This enables the analytical expressions of the eigenvalues and eigenvectors of the NCMs of the sub-grids as well as those of the planar resistive grid to be expressed in terms of those of the two linear grids, whose analytical expressions are well known. For a Cellular Neural Network (CNN) Gabor-type filter (GTF) we define generalized nodal conductance matrices (GNCMs) that correspond to the NCMs of the resistive sub-grids, show that each Kronecker decomposition has a counterpart in CNN GTF and prove that each GNCM, its counterpart NCM and the corresponding temporal state matrices are related through unitary diagonal similarity transformations. Consequently, we prove that the eigenvalues of the temporal state matrix of a spatial band-pass CNN GTF are the same as those of its counterpart spatial low-pass CNN image filter, hence their temporal transient behaviors are similar in settling to a forced response. Publisher's Version

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

In this paper, design of a real-time video frame buffer with an external memory interface is proposed. In addition, simulation and implementation processes of the design is described. The mentioned system is able to buffer video signals up to 1920×1080 full-HD resolution at 60 Hz frame rate. The memory interface is designed based on an external SDRAM memory and supports burst read/write operations. Input video resolution, video buffer size on memory and burst size of the memory interface are user defined and can be configured. Publisher's Version

In this brief, the details of the architecture of a previously introduced improved field-programmable gate array implementation of the cellular neural network (CNN)-based 2-D Gabor-type filter are given, and the implementation results are discussed. The proposed architecture is suitable for real-time applications with high pixel rates. The prototype is capable of processing video streams up to a pixel rate of 373.2 megapixels per second (MP/s), including full-high-definition (HD) 1080p@60 (1080 x 1920 resolution, 60-Hz frame rate, and 124.4-MP/s visible pixel rate). This brief also contains convergence rate analysis results, along with some discussions on FIR and CNN-based implementation methods.

A fast simulation method for time-derivative cellular neural networks (TDCNN) is proposed. Using forward Euler approximation (FEA) for the derivative of the cell state and the backward Euler approximation (BEA) for the derivatives of the neighboring cell states enables the recursive computation of the cell state and provides a speed advantage of orders of magnitude. The state equations are then packed into a vector-matrix form which enables the previously empirically given time constraint to be expressed as a matrix condition. It is shown that using both FEA and BEA leads to a second-order difference equation whose corresponding second-order differential equation is derived and shown to yield the same simulation results.

In this paper, architecture of a Real-Time Cellular Neural Network (CNN) Processor (RTCNNP-v2) is given and the implementation results are discussed. The proposed architecture has a fully pipelined structure, capable of processing full-HD 1080p@60 (1920 1080 resolution at 60 Hz frame rate, 124.4 MHz visible pixel rate) video streams, which is implemented on both high-end and low-cost FPGA devices, Altera Stratix IV GX 230, and Cyclone III C 25, respectively. Many features of the architecture are designed to be either pre-synthesis configurable or runtime programmable, which makes the processor extremely flexible, reusable, scalable, and practical. This research was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under project number 108E023. This paper was recommended by Associate Editor M. Frasca 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.

This paper presents new criteria for the existence of stable equilibrium points in the total saturation region for cellular neural networks (CNNs). It is 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. Our results are also compared with the previous results derived in the literature for the existence of stable equilibrium points for CNNs.

In this paper, the features of the next generation Real-Time Cellular Neural Network Processor (RTCNNP-v3) are discussed. The RTCNNP-v2 structure is the only CNN implementation that is reported to be capable of processing full-HD 1080p@60 (1920 x 1080 resolution at 60 Hz frame rate) video images in real-time, due to its fully-pipelined architecture, however, it has some weaknesses like the inability to divide the processing in spatial domain, record and recall intermediate results to an external memory and has some issues in its internal memory coding. Those shortcomings are to be addressed in the next design of our CNN emulator - RTCNNP-v3, which will increase the range of applications and enable the implementation to match the requirements of the cutting-edge movie production technologies like UHD (4K) and the future FUHD (8K). This study was supported by The Scientific and Technological Research Council of Turkey (TOBITAK) under project number 108E023 Publisher's Version

In 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. Publisher's Version

In 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. Publisher's Version

In this paper, a sufficient condition is presented for the uniqueness and global asymptotic stability of the equilibrium point for delayed cellular neural networks (DCNN's). This condition imposes constraints on the feedback matrices independently of the delay parameter. This result is also discussed from the point of view of its relationship to earlier results.

The authors present two new results on the global stability of cellular neural networks with delay (DCCNs). First, a sufficient condition for the uniqueness and global asymptotic stability of the equilibrium point is given. Then, a sufficient condition for the existence of a stable equilibrium point in the total saturation region is obtained. The significance of the conditions is that the stability properties of the equilibrium point are not affected by the delay parameter. These conditions are also weaker than some previously given for global stability of DCNNs.

In this paper, we study the equilibrium set of cellular neural networks (CNNs). We establish new conditions for the stability of equilibria in delayed cellular neural networks (DCNNs), and we also study the topology of the equilibrium set-whether equilibria are isolated.

In this paper, a 3-D spatio-temporal Gabor-type filter is implemented using Time-Derivative Cellular Neural Network (TDCNN) structure. To this end, the transfer function of the spatio-temporal filter previously constructed by cascading a CNN spatial filter with a temporal filter is shown to be made equal to that of a band-pass filter obtained using a TDCNN structure having first-order temporal derivative diffusion connections only. By equating the coefficients, we obtain the discrete-space Fourier transforms of the TDCNN templates of the Gabor-type band-pass filter. Taking the inverse Fourier transform yields the TDCNN templates. Simulation results are presented. Publisher's Version

21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUS WOS: 000325005300338 In this study, first, an Automated Target Recognition/Tracking (ATR, ATT) system is analyzed, developed and simulated on Matlab environment. Second, a new edge detection method is proposed which is obtained by combining two common edge detection algorithms. Finally, it is shown that Freeman chain codding can be used in feature extraction stage of an ATR system. The system is tested for many different image databases, except for its classification and decision making part, which is still under development, and the results are verified by human eye. Note that, with the development of the classification part, the results will be able to verified with an objective criteria. On the other hand, the designed ATR system is planed to be optimized for an hardware and implemented on an FPGA device in a future work.

Two useful results concerning the equilibrium analysis of non-symmetric cellular neural networks (CNNs) are presented. First a new sufficient condition ensuring the existence of a stable equilibrium point in the total saturation region is given. Then another condition which guarantees the uniqueness and global asymptotic stability of the equilibrium point is obtained.

In this paper 2-D discrete-space filters are generated from their analog counterparts and implemented by Cellular Neural Networks (CNN). To this end, first 2-D analog transfer functions are obtained from their 1-D counterparts. Then, the corresponding difference equations are obtained by discretization of 2-D analog filter differential equations, which are then implemented by CNN. Simulation results are presented.

This proceeding is compiled from our previous works, where architecture of the Second-Generation Real-Time Cellular Neural Network (CNN) Processor (RTCNNP-v2) was proposed. The system is designed for applications where highresolution and high-speed is desired. The structure is fully-pipelined and the processing is real-time. Proposed structure is coded in VHDL and realized on two FPGA devices: one high-end and one low-budget. The system is the only reported CNN implementation supporting real-time Full-HD video image processing, to date.