Changju Yang

Chonbuk National University | cbnu · Department of Electrical Engineering

Nonlinear memristor-based neural network and a circuit-based learning system is addressed in this work. The weights of the neural network are based on the memristor bridge synapse and the learning architecture is designed in analog-digital mixed circuits by adopting a simple learning algorithm called random weight change algorithm. Though the memri...

Cell cytotoxicity assays, such as cell viability and lactate dehydrogenase (LDH) activity assays, play an important role in toxicological studies of pharmaceutical compounds. However, precise modeling for cytotoxicity studies is essential for successful drug discovery. The aim of our study was to develop a computational modeling that is capable of...

A nonlinear modeling of the protective effect of Quercetin (QCT) against various Mycotoxins (MTXs) has a high complexity and is conducted using artificial neural networks (ANNs). QCT is known to possess strong anti-oxidant, anti-inflammatory activity that can prevent many diseases. MTXs are highly toxic secondary metabolites that are capable of cau...

A novel generic memristor, dubbed the 6-lobe Chua corsage memristor , is proposed with its nonlinear dynamical analysis and physical realization. The proposed corsage memristor contains four asymptotically stable equilibrium points on its complex and diversified dynamic routes which reveals a 4-state nonlinear memory device . The higher degree of v...

A hybrid learning algorithm suitable for hardware implementation of multi-layer neural networks is proposed. Though backpropagation is a powerful learning method for multilayer neural networks, its hardware implementation is difficult due to complexities of the neural synapses and the operations involved in error backpropagation. We propose a learn...

A general solution for the construction of Cellular Neural Network (CNN) weights (cloning template) with Random Weight Change (RWC) algorithm is proposed. A target image for each input image is prepared via a sketch or any other kind of image processing technique for learning of Cellular Neural Network templates. A vector of randomly generated smal...

In this paper we propose a new first-order generic memristor, namely, a 4-lobe Chua corsage memristor, as an extension of the 2-lobe chua corsage memristor. the newly designed cor-sage memristor exhibits more complex dynamical routes with three stable equilibrium points for which it reveals a higher degree of versatility that the dynamic route chan...

This paper presents a vision sensor-based solution to the challenging problem of detecting and following trails in highly unstructured natural environments like forests, rural areas and mountains, using a combination of a deep neural network and dynamic programming. The deep neural network (DNN) concept has recently emerged as a very effective tool...

A development method of neural network with software-based learning and circuit-based fine tuning is proposed. The backpropagation is known as one of the most efficient learning algorithms. A weakness is that the hardware implementation is extremely difficult. The RWC algorithm which is very easy to implement its hardware circuits takes too many it...

In this study, we describe a battery balancing charge technology using the combined method of serial charging and selective supplementary charging to resolve the unbalanced charge problem common among battery cells for electric vehicles. In this method, the major charging is performed using serial charging while the balancing is carried out with su...

A hybrid charging method with serial and parallel architecture has been developed to resolve the unbalanced charge problem among battery cells for Electric Vehicles. In this method, the major charging is performed with serial part and the balancing is carried out with the parallel part, where the serial part is big and heavy but the parallel part i...

Ahybrid learningmethod of a software-based backpropagation learning and a hardware-based RWClearning 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 ver...

A linearized programming method of memristor-based neural weights is proposed. Memristor is known as an ideal element to implement a neural synapse due to its embedded functions of analog memory and analog multiplication. Its resistance variation with a voltage input is generally a nonlinear function of time. Linearization of memristance variation...

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 VM–IM curve. A DC operating point Q is chosen on the locally-active region of the PTC Memristor and a small-signal equivalent circuit at Q...

A floating type of memristor emulator which acts like the behavior of   memristor has been developed. Most of existing memristor emulators are grounded type which is built disregarding the connectivity with other memristor or other devices. The developed memristor emulator is a floating type whose output does not need to be grounded. Therefore,...

Implementation of memristor-based multilayer neural networks and their hardware-based learning architecture is investigated in this paper. Two major functions of neural networks which should be embedded in synapses are programmable memory and analog multiplication. "Memristor", which is a newly developed device, has two such major functions in it....

Memristor is a new kind of memory device whose resistance varies depending upon applied charge and whose previous resistance state is preserved even when its power is off. Ordinary memristor has a nonlinear programming characteristics about time when a constant voltage is applied. For the easiness of programming, it is desirable that resistance is...

In this paper, a generic model of memristive systems, which can emulate the behavior of real memristive devices is proposed. Non-ideal pinched hysteresis loops are sometimes observed in real memristive devices. For example, the hysteresis loops may deviate from the origin over a broad range of amplitude and frequency of the input signal. This devia...

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...

Memristor-based circuit architecture for multilayer neural networks is proposed. It is a first of its kind demonstrating successful circuit-based learning for multilayer neural network built with memristors. Though back-propagation algorithm is a powerful learning scheme for multilayer neural networks, its hardware implementation is very difficult...

Emulations of memristor-family elements are very important, since their physical realizations are very difficult to achieve with recent technologies. Although some previous studies succeeded in designing memristor and memcapacitor emulators, no significant contribution towards meminductor emulator has been presented so far. The implementation of a...

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 transien...

A learning architecture for memristor-based multilayer neural networks is proposed in this paper. A multilayer neural network is implemented based on memristor bridge synapses and its learning is performed with Random Weight Change architecture. The memristor bridge synapses are composed of bridge type architectures of back-to-back connected 4 memr...

An efficient method to build a mutator-based meminductor (ML) emulator whose inductance can be varied by an external current source is proposed. Implementation of a meminductor emulator is very important since real meminductors are not physically realizable with current technology. In this paper, a meminductor emulator has been designed using the p...

Amutator-based meminductor emulator whose inductance can be varied by an external current source is proposed. The implementation of a meminductor emulator is very important, since real meminductors are not physically realizable with current technology. Though there is active research on memristor or memcapacitor emulators, no significant contributi...

Memristor, a new electrical element, can have various configurations of multiple memristors, including serial and parallel connections like previous elements R, L and C. When input voltage/current is supplied to a circuit with multiple memristors, the composite behavior of the memristor circuit exhibits transient states before it enters a steady st...

A novel memristor bridge circuit which is able to perform zero, negative and positive synaptic weightings in neuron cells is proposed. It is composed of four memristors and three transistors for weighting operation and voltage-to-current conversion, respectively. It is compact as it can be fabricated in nano meter scale. It is power efficient since...

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 a...

An efficient method to build the expandable circuits of memcapacitor (MC) emulator in various configurations is proposed using our expandable memristor (MR) emulator. Most of the previous studies succeeded in designing only a stand-alone memcapacitor emulator. In this study, the expandable architecture of memcapacitor emulator is addressed, where t...

This study is an extension of our previous memristor bridge synapses advancing one step toward its circuit implementation. The architecture of the memristor bridge synapse is designed with memristor emulator circuits. Neural cells are built by combining memristor emulators-based synapses and differential amplifier circuits. Various simulations are...

A memristor bridge neural circuit which is able to perform signed synaptic weighting was proposed in our previous study, where the synaptic operation was verified via software simulation of the mathematical model of the HP memristor. This study is an extension of the previous work advancing toward the circuit implementation where the architecture o...

A memristor emulator which imitates the behavior of a TiO2 memristor is presented. Our emulator is built from off-the-shelf solid state components. To develop real world memristor circuit applications, the emulator can be used for breadboard experiments in real time. Two or more memristor emulators can be connected in serial, in parallel, or in hyb...

Analog hardware architecture of a memristor bridge synapse-based multilayer neural network and its learning scheme is proposed. The use of memristor bridge synapse in the proposed architecture solves one of the major problems, regarding nonvolatile weight storage in analog neural network implementations. To compensate for the spatial nonuniformity...

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 se...

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 resistan...

In this paper, we propose a memristor bridge circuit consisting of four identical memristors that is able to perform zero, negative, and positive synaptic weightings. Together with three additional transistors, the memristor bridge weighting circuit is able to perform synaptic operation for neural cells. It is compact as both weighting and weight p...

A novel doublet generator circuit for memristor-based analog memories or artificial synapses is presented. In memristor-based analog memories or artificial synapses, the read-out pulses cause a drifting problem in the programmed resistance of the memristor. Use of doublet pulse is known to be an efficient solution for preventing resistance variatio...

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...

A pulse-based programmable memristor circuit for implementing synaptic weights for artificial neural networks is proposed. In the memristor weighting circuit, both positive and negative multiplications are performed via a charge-dependent Ohm's law ($ v = M(q) \times i $). The circuit is composed of five memristors with bridge-like connections and...

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 prope...

A method for the detection of on-road succeeding vehicles using visual characteristic features like horizontal edges, shadow, symmetry and intensity is proposed. The proposed method uses the prominent horizontal edges along with the shadow under the vehicle to generate an initial estimate of the vehicle-road surface contact. Fast symmetry detection...

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 co...