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The quality of multimedia communicated through the Internet is highly sensitive to packet loss. In this letter, we develop a time-series prediction model for the end-to-end packet loss rate (PLR). The estimate of the PLR is needed in several transmission control mechanisms such as the TCP-friendly congestion control mechanism for UDP traffic. In addition, it is needed to estimate the amount of redundancy for the forward error correction (FEC) mechanism. An accurate prediction would therefore be very valuable. We used a relatively novel prediction model called sparse basis prediction model. It is an adaptive nonlinear prediction approach, whereby a very large dictionary of possible inputs are extracted from the time series (for example, through moving averages, some nonlinear transformations, etc.). Only few of the very best inputs among the dictionary are selected and are combined linearly. An algorithm adaptively updates the input selection (as well as updates the weights) each time a new time sample arrives in a computationally efficient way. Simulation experiments indicate significantly better prediction performance for the sparse basis approach, as compared to other traditional nonlinear approaches.

In this paper, we implement the internet-based remote control system for intelligent robot. For remote control of the robot, it uses the socket communication of the TCP/IP. It consists of the user interface and the robot control interface. Robot control interface transmits the navigation and environmental informations of the robot into the user interface. In order to transmit the large environmental images, a JPEG compression algorithm is used. User interface displays the navigation status of the robot and transmits the navigation order into the robot control interface. Also, we propose the design method of the fuzzy controller using navigation data acquired by expert`s knowledge or experience. To do this, we use virus-evolutionary genetic algorithm(VEGA). Finally, we have shown the proposed system can be operated through the real world experimentations.

The following paper introduces an application of Cellular Neural Networks for the generation of predetermined stochastic textures. The key element for the task realization is an appropriate selection of template elements, which should provide a transformation of initial, random CNN state into a stable equilibrium, featuring desired perceptual properties. A template derivation procedure comprises two steps: linear CNN design, followed by a template-refinement procedure that involves nonlinear optimization. In addition, a procedure that extends CNN texture rendition capabilities into a realm of non-pure stochastic textures is proposed.

A very-high-performance Viterbi decoder with a circularly connected two-dimensional analog cellular neural network (CNN) cell array is disclosed. In the proposed Viterbi decoder, the CNN cells with nonlinear unilateral connections are implemented with electronic circuits at nodes on a trellis diagram. The circuits are circularly connected, forming a cylindrical shape so that the cells of the last stage are connected to those of the first stage. Unilateral connections guide the information to flow circularly around the cylindrical surface. Such configuration enables the conceptually infinite length of the trellis diagram to be reduced to a circuit of limited size. The analog circuits does not require any analog-digital converters, which is the major cause of high power consumption and the quantization error. With the parallel analog processing structure, its decoding speed becomes very high. Also, the decoding mechanism using triggering wave of the CNN circuit does not require the path memory. Circuits for the proposed structure have been designed with HSPICE. Features of the proposed Viterbi decoder are compared with those of the conventional digital Viterbi decoder.

A Cellular Neural Networks (CNN)-based nonlinear pattern classification algorithm utilizing the most likely path-finding feature of dynamic programming is proposed. Dynamic programming for the most likely path-finding algorithm can be implemented with CNN. If exemplars and test patterns are assigned as the goals and the start positions, respectively for our CNN-based dynamic programming, the paths from the test patterns to their closest exemplars are found with the optimality feature of CNN-based dynamic programming. Such paths are utilized as aggregating keys for classification. Our algorithm is suitable for patterns with nonlinear pattern boundaries. Simulation results are included.

The cellular-neural-network universal machine (CNN-UM) technique which performs analog addition/subtraction between image frames has been developed. The equivalent circuit of the uncoupled CNN without self feedback is reduced to a simple RC circuit. If two inputs are presented to the circuit one after another during a very short time period, the voltages that are proportional to their input signals are superimposed on the state capacitor. The output of such superimposition is a reduced version of the addition/subtraction between the two signals. Simple amplification of the output can recover the actual output. The characteristics of analog addition/subtraction with the proposed algorithm are shown via on-chip experiment. Application of the proposed algorithm to moving target detection is also presented.

A high speed target detection and tracking algorithm for a CNN-UM chip is presented in this paper. The target confidence value is computed based on the fusion of target existence probabilities of features using products of weighted sums. The target decision is done with such a confidence value and target initiation is done through the temporal accumulation of the confidence. The probability of the target existence for each feature is created in the region of influence depending on the reliability and the strength of the feature. By virtue of the analogic parallel processing structure of the CNN-UM (Roska T, Chua LO. The CNN universal machine: an analogic array computer. IEEE Trans. Circuits Systems II 1993; CAS-40: 163–173), real time tracking can be achieved with presently available technologies with the speed of several kilo-frames per second. Due to the utilization of multiple features of target, robust target detection is possible via the proposed algorithm. On-chip experiments of the proposed target-tracking algorithm have been done and properties of the proposed approach are disclosed through the various experiments. Copyright © 2003 John Wiley & Sons, Ltd.

This paper presents a detailed analysis of various oscillatory behaviors observed in relation to the calcium and potassium ions in the third-order Morris–Lecar model of giant barnacle muscle fiber. Since, both the calcium and potassium ions exhibit all of the characteristics of memristor fingerprints, we claim that the time-varying calcium and potassium ions in the third-order Morris–Lecar model are actually time-invariant calcium and potassium memristors in the third-order memristive Morris–Lecar model. We confirmed the existence of a small unstable limit cycle oscillation in both the second-order and the third-order Morris–Lecar model by numerically calculating the basin of attraction of the asymptotically stable equilibrium point associated with two subcritical Hopf bifurcation points. We also describe a comprehensive analysis of the generation of oscillations in third-order memristive Morris–Lecar model via small-signal circuit analysis and a subcritical Hopf bifurcation phenomenon.

In this paper, we present a device to be a memristor should exhibit three characteristic fingerprints: (a) Pinched hysteresis loop in the voltage v vs. current i plane, namely, the v-i loci always passes through origin for any bipolar periodic input voltage v(t). (b) Beyond a certain critical frequency ω*, the area of the pinched hysteresis lobe decreases monotonically as the frequency of the periodic input signal increases and (c) the shape of the pinched hysteresis loop varies with frequency f and shrinks to a single-valued function through the origin, as the frequency tends to infinity. Examples of memristors exhibiting these three fingerprints are presented.

A circuit with multiple memristors can have various configurations including serial and parallel connections like R, L and C. When input voltage/current is supplied to a circuit with multiple memristors, the composite behavior of the memristor circuit goes through a transition state period before it enters a steady state period. During the transient state period, the behavior is very complex and not predictable due to each memristor's different action depending upon its connection polarity and initial state. In this paper, the transient characteristics of a composite memristor are analyzed via the relationships of charge, flux and memristance of each memristor.

A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems.

We implemented a memcapacitor emulator with off-the-shelf electronic devices. The memcapacitor is an element whose capacitance can be altered with external current or voltage. The implementation of the emulator is very important since commercial versions are not yet available. The proposed memcapacitor emulator has a simple dedicated memcapacitor architecture that does not require a mutator. It is the first memcapacitor emulator implemented with commercially available devices. The operation as a memcapacitor has been proven via both breadboard experiments and PSPICE simulations.

An adaptive control technique, using system identification based on Self-Organized Distributed Networks (SODNs), is presented for a class of discrete nonlinear dynamic systems having unknown dynamics. The SODN belongs to the category of distributed local learning networks and is composed of two main networks called the learning network and the distribution network. The learning network consists of subnets each responsible for a subproblem. The distribution network is responsible for input space decomposition. The learning of the SODN is fast and precise because of the local learning mechanism. In this paper, methods for identification and indirect adaptive control of nonlinear systems using the SODN are presented. Through extensive simulation, the SODN is shown to be effective both for identification and adaptive control of nonlinear dynamic systems.

A nationwide survey on the prevalence and risk factors for perennial allergic rhinitis in Korea was conducted by means of physical examination by otolaryngologists from July to October 1991. The total number of subjects examined was 9069 and they were drawn from 2899 households in 60 different districts throughout the country. The overall prevalence of perennial allergic rhinitis was 1.14%. For the evaluation of risk factors for perennial allergic rhinitis, a case-control analysis was carried out. The prevalence was influenced by age and urban or rural province. Risk factors such as current urban residence, urban birth place, overcrowding, higher educational attainment, nasal septal deformity and chronic sinusitis with nasal polyposis, indicated an increased risk of perennial allergic rhinitis. Other factors such as smoking and personal factors showed little influence on prevalence.

When multiple memristors are connected to each other, the composite behavior of the devices becomes complicated and is difficult to predict, due to the polarity dependent nonlinear variation in the memristance of individual memristor. In this paper, we investigate the relationships among flux, charge and memristance of diverse composite memristors, using the HP TiO2 model, and analyze the characteristics of complex memristor circuits. It is assumed that all memristor circuits operate at a stable composite memristance state, in which the composite flux curve does not vary and the memristor circuits act as a single memristive system, regardless of input current or voltage. Such study will be conducted for serial and parallel memristor circuits.

An electronic oscillator circuit is designed by connecting an inductor in series with a locally-active PTC Memristor and a battery. The PTC Memristor is locally active on the negative resistance region of its DC [Formula: see text]–[Formula: see text] curve. A DC operating point [Formula: see text] is chosen on the locally-active region of the PTC Memristor and a small-signal equivalent circuit at [Formula: see text] is derived via Taylor series. The small-signal admittance [Formula: see text] of the composite one-port in Fig. 1 is derived using the small-signal equivalent circuit at [Formula: see text], in series with an inductor whose value is chosen such that [Formula: see text] at some [Formula: see text]. The sinusoidal oscillation computed numerically from this circuit is shown to emerge from a supercritical Hopf bifurcation.

This paper demonstrates that the Chua Corsage Memristor, when connected in series with an inductor and a battery, oscillates about a locally-active operating point located on the memristor’s DC [Formula: see text]–[Formula: see text] curve. On the operating point, a small-signal equivalent circuit is derived via a Taylor series expansion. The small-signal admittance [Formula: see text] is derived from the small-signal equivalent circuit and the value of inductance is determined at a frequency where the real part of the admittance [Formula: see text] of the small-signal equivalent circuit of Chua Corsage Memristor is zero. Oscillation of the circuit is analyzed via an in-depth application of the theory of Local Activity, Edge of Chaos and the Hopf-bifurcation.

An adaptive critic learning (ACL) structure consists of two modules: the action and the critic ones. Learning occurs in both modules. The critic module learns to evaluate the system status. It transforms occasionally occurred failure signals into useful evaluation information. Utilizing such information, the action module can learn the control technique. In this paper, we investigate the technique of using basis functions (BFs) in ACL. One difficulty in the scheme is on selection of learning parameters. Without a guideline, the best set of learning parameters must be obtained from a large number of test simulations. This study investigated the effects of parameters through analysis and verified the analytical results by simulations. In addition to the problem of parameter selection, effects of measurement errors on the CMAC-based (one basis function technique) ACL have been also examined and reported.

The generation of action potentials (oscillations) in biological systems is a complex, yet poorly understood nonlinear dynamical phenomenon involving ions. This paper reveals that the time-varying calcium ion and the time-varying potassium ion, which are essential for generating action potentials in Barnacle giant muscle fibers are in fact generic memristors in the perspective of electrical circuit theory. We will show that these two ions exhibit all the fingerprints of memristors from the equations of the Morris–Lecar model of the Barnacle giant muscle fibers. This paper also gives a textbook reference to understand the difference between memristor and nonlinear resistor via analysis of the potassium ion-channel memristor and calcium ion-channel nonlinear resistor. We will also present a comprehensive in-depth analysis of the generation of action potentials (oscillations) in memristive Morris–Lecar model using small-signal circuit model and the Hopf bifurcation theorem.

A memristor emulator circuit which is designed with off-the-shelf solid state components is presented. As the memristors are not commercially available so far, some circuit replacements which behave like memristors are needed to develop application circuits. In this paper, the variable resistance of a memristor is built utilizing the input resistance of the closed loop circuit of an OP amp. The memristor emulator circuit has been implemented on breadboard with off-the-shelf solid state components. The experimental results of the proposed memristor emulator circuit show a memristor behavior that can be utilized as an alternative of hp TiO2 memristor model.

A simple and compact memristor-based bridge circuit which is able to perform signed synaptic weighting in neuron cells is proposed. The proposed memristor-based synapse is composed of four memristors which makes a bridge type configuration. By programming different values on each memristor of the memristor bridge circuit, weighting values can be set on the memristor bridge synapses. Various simulation results are included.

This paper presents a memristor based new synaptic circuit, consisting of five memristor in a bridge structure together with one differential amplifier. The circuit is able to perform positive and negative weighting for pulse type inputs in neural cells. Processing is conducted with applied pulses at a common terminal in different time slots. It is compact, non-volatile and low power efficient. Simulations of sign setting, weight setting and synaptic multiplication are performed with hp TiO 2 memristor models.

This paper presents the autonomous technologies of global vision-based golf ball collection robot. Since the autonomous golf ball collection robot is the very complex system which is composed of mechanical systems, power systems, and electronics systems, it requires many times of trial and errors for the development, which requires very high cost. Our strategy is the derivation of the optimal system architecture through the development of diverse functions on the emulator with a simple robot. For this, we use a global camera, which takes images of the whole ground and transfers to the server in real time. A special mark is installed on the robot to indicate the robot's position and orientation. The optimal paths to scan the dense ball regions are calculated using Traveling Salesman (TS) algorithm. Also, the robot collects balls in each dense ball region along with a spiral scanning trace. Development details and their experimental results are included in this paper.

Heading direction of robots is proposed using vanishing points of parallel lines contained in the facility buildings. In robot navigation, the straight navigation is often needed, which requires computation of the heading angle of the robot. In this study, we found that the relative heading direction of the robot to the surrounding facility buildings can be computed using the vanishing points of parallel lines of the buildings. The inter-stair parallel lines of facility buildings are extracted and the vanishing points are computed. As the vanishing points are computed in far away, the heading direction of the robot is close to normal angle to the facility building. The proposed vanishing point method is a novel robot orientation computing method and is proved to be time efficient and accurate via experiments. The experimental results of the proposed method are included in this paper.

We present an application-specific integrated circuit (ASIC) CMOS chip that implements a synchronized oscillator cellular neural network with a matrix size of 32 × 32 for object sensing and labeling in binary images. Networks of synchronized oscillators are a recently developed tool for image segmentation and analysis. Its parallel network operation is based on a “temporary correlation” theory that attempts to describe scene recognition as if performed by the human brain. The synchronized oscillations of neuron groups attract a person’s attention if he or she is focused on a coherent stimulus (image object). For more than one perceived stimulus, these synchronized patterns switch in time between different neuron groups, thus forming temporal maps that code several features of the analyzed scene. In this paper, a new oscillator circuit based on a mathematical model is proposed, and the network architecture and chip functional blocks are presented and discussed. The proposed chip is implemented in AMIS 0.35 μm C035M-D 5M/1P technology. An application of the proposed network chip for the segmentation of insulin-producing pancreatic islets in magnetic resonance liver images is presented.

Reconfigurability and flexibility are often desired characteristics in an automatic system whose capability needs to be changed from time to time to meet the need of its operation. In this study, a Hardware Description Language (HDL) software core for joint torques/forces computation for an n-joint general purpose manipulator is designed. This HDL core can be used in a reconfigurable robot control design. The inverse dynamics computation determines required joint torques for creating desired accelerations. Our study results in a state machine with forty-seven states. A complete torque computation can be finished in 167 clock cycles in microseconds if Xilinx XC2V4000 Field Programmable Gate Array (FPGA) device is used. This manipulator-structure independent core design can be integrated with other components on FPGAs. There could be a significant reduction in the load on the microprocessor by shifting heavy-load computation to FPGAs.

This paper exploits an interesting property of a certain type of elementary cellular automata, namely hybrid cellular automata (HCA) defined by rule 101 and its negate. Such HCA may be easily implemented on any CNN platform, and has some interesting properties. It provides a chaotic counting automaton instead of the traditional raster scan counter addressing the multiple sensing elements. Replacing raster scan with chaotic scan provided by the HCA allows implementation of both progressive image compression and spread spectrum transmission with a very low complexity of the transmitting sensor. The clear advantages of our system in terms of implementation complexity, makes it a valuable candidate for low power sensor networks applications.

A method to utilize the memristor as a multilevel memory has been proposed. There are several roadblocks in the practical use of memristors for multilevel memory. A difficulty comes from the nonlinearity in the ? vs. q curve which makes it difficult to determine the proper pulse width for desired resistance values. Another one comes from the property of the memristor which integrates any kind of signals including noise that appeared at the memristor and causes memristors to be perturbed from their original values. The proposed method enables the memristor to be used as multilevel memory using a reference resistance array by forcing the memristor to stick at a set of predetermined fixed reference resistance values. We propose the write-in (programming) circuit and the readout/restoration circuit which share the information storing technique using the reference resistance array.

The Cellular Neural Network (CNN) based analog Viterbi decoder with a circular-buffered architecture is proposed for decoding partial response maximum likelihood (PRML) signals. The Viterbi decoder is an error correcting method utilizing the dynamic programming which is an efficient algorithm for finding the optimal path with the identical local computation performed at each node. In the previous CNN-based analog Viterbi decoder, a circularly connected cylindrical structure was presented. In this paper, a multiplexer-based cellular 2D structure is presented in which positions of its decoding and output stages are fixed and a multiplexer which distributes input data sequence to appropriate CNN trellis stages is employed. The proposed CNN-based Viterbi decoder is simpler, requires less silicon area, higher speed and has better performance than the previous one. The principle of the new architecture is uncovered and its decoding performance is compared with that of the previous architecture in this paper.

Computer-based analysis of textures in magnetic resonance images provides a higher sensitivity to textural changes that cannot be recognized by the naked human eye. Thus, there is a better potential for identifying pathophysiological processes at an earlier stage or of a different character than even a trained radiologist can find. In the present study, the potential of texture analysis for in vivo identification of the administering effect of an anti-inflammatory drug in cerebral stroke in rats was evaluated. Twenty-seven Wistar rats underwent middle cerebral artery occlusion resulting in local ischemic brain infarct. One group of rats received alpha-melanocyte stimulating hormone (alpha-MSH) and a control group received saline only. T2-weighted images, apparent diffusion maps, and T2 maps were recorded by MR. Texture features were calculated in the T2-weighted images and correlated to the apparent diffusion coefficient (ADC) and the T2 values. From an array of tested texture features three independent features were tested further. Two of which were found to provide a significant discriminative classification between the control and the alpha-MSH groups. Furthermore, the same two texture features were significantly correlated to the ADCs. Thus, quantification of texture features can be helpful in detecting the effects of stroke therapy.

A novel spread spectrum communication system is proposed, being characterized by a very compact yet efficient implementation. A particular elementary cellular automaton with rule 45 is used as the generator of spreading sequences while it also has synchronization capabilities. Simulations demonstrate that generated sequences are orthogonal and the performances of the proposed system are comparable to those of more sophisticated CDMA systems.

In this paper, we propose a novel approach based on centroid distance signature and Dynamic Programming (DP), which can be used to guide a contour tracking execution. Edges are detected by an improved canny method; however, they still contain many noises as well as interferential information. So an edge-based contour tracking method is proposed to link the optimal object's outline. First, we apply centroid distance function to the obtained edges to get the rough signature graph. Second, the optimal path ofthe signature graph is acquired based on DP algorithm. Last, we resume the optimal contour using the acquired signature trace after inverse transform. The superiority of the proposed model comes from 1) the accurate acquisition of object's outline based on DP algorithm, and 2) scale and rotational invariant expression of contours with the centroid distance function.

An image frame of an interlaced camera is composed of two subframes (half-frames or fields). If the camera moves when it captures video images, two subframes become misaligned and this lowers the image quality. In this paper, a new algorithm that removes this undesired effect from such misalignment is presented.

An algorithm for tracking the trace of structured light is proposed to obtain depth information accurately. The technique is based on the fact that the pixel location of light in an image has a unique association with the object depth. However, sometimes the projected light is dim or invisible due to the absorption and reflection on the surface of the object. A dynamic programming approach is proposed to solve such a problem. In this paper, necessary mathematics for implementing the algorithm is presented and the projected laser light is tracked utilizing a dynamic programming technique. Advantages include 1) the trace remains integrity while many parts of the laser beam are dim or invisible. 2) the dynamic programming basically exploits the parallel processing mechanism; the most likely trace of the laser light can be computed very fast if the proposed structure is implemented in hardware. Experimental results as well as the 3-D restoration are reported.

A new road boundary detection algorithm based on the double filtering is proposed. This approach employs two filters, such as the filter with edge distribution function (EDF) and the dynamic programming (DP). Only the pixels with similar orientation to the road direction are kept using the EDF filter. Then the filter of the DP is performed. In the DP processing, all the edge candidates which are distant from the DP lines are removed. Experimental results for road images acquired under various situations are presented.

This paper describes the design process of Car Black Box system IC. As x by wire is introduced to the several part in vehicle, the demand of automotive semiconductors are increasing. Because it will be mandatory for every car to be equipped with Car Black Box, it is expected that many ICs for Car Black Box also will be integrated. Unlike general electronic control units (ECUs) as PCB or module, car black box implemented on a single chip can reduce its size, power consumption and the cost. So the topic of this paper is to develop the Embedded controller for Car Black Box using SoC (System on Chip) technique. System on Chip (SoC) is the effective method to implement embedded system like car black box, which consists of processor, memory, I/O peripheral and several interfaces. SoC for Car Black Box system consists of 8051 processor, CAN (Controller Area Network) controller, JPEG compressor, SD controller for dumping the data from memory buffer to SD card, ROM for programming and SRAM acting as memory buffer. Describing the design process in the hardware and software, it was shown in the verification using MFC(Microsoft Foundation Class) program whether the required data such as image, location and other vehicle control status information was recorded and recovered.

In this paper, we develop a novel approach to model the Internet round trip time using a recently proposed symbolic type neural network model called symbolic function network. The developed predictor is shown to have good generalization performance and simple representation compared to the multilayer perceptron based predictors.

A new vision-based road boundary detection algorithm combining dynamic programming and Hough transform is proposed. The dynamic programming (DP) is known to be a powerful algorithm for optimal path finding on a cost field. Utilizing the intensities of edge image as costs of the DP, most likely road boundaries are obtained in this paper. The DP is further utilized as a spatial filter which removes unrelated edge candidates as road boundaries. Randomized Hough transform is applied on the filtered edge image to detect practically useful road boundaries with straight line segments. Experimental results for road images acquired under situations with shadow, unmarked road painting and illumination variations in rural area are presented.

Solid single-walled carbon nanotube (SWCNT)/silica composites were prepared by a sol-gel method. Their third-order susceptibilities were estimated by using z-scan technique at 532 nm and 1064 nm, respectively. The results reveal negative nonlinear refraction and saturable absorption, which magnitudes depend on kinds of used SWCNTs prepared by different techniques.

This paper describes automatic analysis of microscopic mast cell images using network of synchronized oscillators. This network allows for detection of image objects and their boundaries along with evaluation of some geometrical object parameters, like their area and perimeter. Estimation of such mast cells' parameters is very important in description of both physiological and pathological processes in the human organism. It was also demonstrated, that oscillator networks is able to perform basic morphological operations along with binary image segmentation. Analysis of sample mast cell image was presented and discussed.

The following paper presents a method that allows for a parallel implementation of the most computationally expensive element of the deformable template paradigm, which is a grid-matching procedure. Cellular Neural Network Universal Machine has been selected as a framework for the task realization. A basic idea of deformable grid matching is to guide node location updates in a way that minimizes dissimilarity between an image and grid-recorded information, and that ensures minimum grid deformations. The proposed method provides a parallel implementation of this general concept and includes a novel approach to grid's elasticity modeling. The method has been experimentally verified using two different analog hardware environments, yielding high execution speeds and satisfactory processing accuracy.

An updated expressway lane departure identification which based on a combination of the lane boundaries detection method and lane following with searching of the region of interest on Hough domain were proposed in this paper. After preprocessing captured road images, we employed mathematical morphology technique to obtain edge map and Hough Transform (HT) to detect lane candidates. To estimate lane boundaries among these candidates, we use some information such as lane orientation (angle), the symmetrical property, boundary length, etc. on Hough domain. And then, lane following with prior of knowledge of lane detection showed a good performance. The robustness and the low compute cost of the proposed algorithm had been shown with experiment results.

Contrary to the traditional belief that at least two energy-storage elements (capacitor and/or inductor) and a locally-active nonlinearity are needed to build an electronic oscillator, this paper presents an oscillator made with only two circuit elements, namely, a memristor and a battery. This simplest of all physical oscillators also serves as a textbook example for explaining the intimate relationship between the super-critical Hopf bifurcation phenomenon and the edge of chaos.

An analog Viterbi decoder for the PRML signal is designed with the analog parallel processing circuits of the CNN. The PRML technology which is normally used for the high density storage device is incorporated with the multi-level coding and with the optimization technique of the Viterbi decoder. Since the PRML requires heavy computational load, the parallel processing structure of the CNN can be an efficient solution. In this paper, a CNN-based analog Viterbi decoder which is associated with a trellis diagram of PR(1,2,2,1) signal is fabricated into a chip. The test result of the chip is reported; its power consumption of the proposed one is 1/3 and its silicon area is 1/2 of its digital counter part while similar error correction performance is maintained.

The following paper presents an application of Cellular Neural Networks for spontaneous generation of patterns with predefined statistical properties. The considered pattern statistical characteristics included coarseness, directionality and edge sharpness distribution. A purpose of this choice was to verify CNN capabilities in generating images that comprise structural elements significantly larger than a range of cell interconnections. Resulting CNN templates, derived using the SQP optimization algorithm, were capable of meeting majority of formulated design requirements.

The presented paper introduces a method that allows for detecting complex patterns in spectrograms by means of a CNN. The method is based on two object recognition ideas adopted from the deformable grid paradigm: a distributed representation of object prototypes and a prototype-image matching for establishing a class membership. A proposed CNN pattern detection algorithm exploits specific properties of spectrogram images by differentiating rules for processing of temporal and frequency information. Isolated utterance detection has been used as a sample application for evaluating the proposed algorithm performance.

This paper propose an algorithm by which to achieve robust outlier detection without fitting camera models. This algorithm is applicable for cases in which the outlier rate is over 85%. If the outlier rate of optical flows is over 45%, then discarding outliers with conventional algorithms in real-time applications is very difficult. The proposed algorithm overcomes conventional difficulties by using a three-step algorithm: 1) construct a two-dimensional histogram with two axes having the lengths and directions of the optical flows; 2) sort the number of optical flows in each bin of the two-dimensional histogram in descending order, and remove bins having a lower number of optical flows than the given threshold: 3) increase the resolution of the two-dimensional histogram if the number of optical flows grouped in a specific bin is over 20%, and decrease the resolution if the number of optical flows is less than 10%. This process is repeated until the number of optical flows falls into a range of 10%-20%. The proposed algorithm works well on different kinds of images having many outliers. Experimental results are reported.

A novel algorithm to determine the optimal seam-line in image mosacking is proposed to establish the best borderline among neighboring images. The algorithm is based on the utilization of the dynamic programming algorithm on the converted cost field. The converting function is created adaptively based on the distribution of the pixel difference values. The path found with this algorithm runs along the smaller cost regions regardless its length.

In this paper, we propose a memristor emulator that embraces most of features of a real memristor. The important features that a memristor emulator should include are a sufficiently wide range of memristance, bimodal operability of pulse and continuous signal inputs, a long period of nonvolatility, floating operation, operability with other devices, and the ability to be implemented with off-the-shelf devices. The proposed memristor emulator circuit contains all of these features. Specifically, the small variation range of memristance and the nonfloating operation that limit conventional memristor emulators are improved significantly. It is designed to be built with off-the-shelf electronics devices.