Koichi Tanno

Four-quadrant CMOS current-mode multiplier independent of device parameters

In this work, we present a four-quadrant CMOS current-mode multiplier based on the square-law characteristics of an MOS transistor operated in the saturation region. One advantage of this multiplier is that the output current is independent of MOS transistor device parameters; another, that the input resistance is independent of the input current. Simulations of the multiplier demonstrate a linearity error of 1.22%, a THD of 1.54%, a -3 dB bandwidth of 22.4 MHz, and a maximum power consumption of 0.93 mW. Operation of the multiplier was also confirmed through an experiment using CMOS 4007 ICs.

Multiple-valued immune network model and its simulations

This paper describes a new model of multiple-valued immune network based on biological immune response network. The model of multiple-valued immune network is formulated based on the analogy with the interaction between B cells and T cells in immune system. The model has a property that resembles immune response quite well. The immunity of the network is simulated and makes several experimentally testable predictions. Simulation results are given to a letter recognition application of the network and compared with binary ones. The simulations show that, beside the advantages of less categories, improved memory pattern and good memory capacity, the multiple-valued immune network produces a stronger noise immunity than binary one.

Realization of NMAX and NMIN functions with multi-valued voltage comparators

In this paper, realization of three fundamental functions, NOT, negated MAX and negated MIN functions, in the voltage-mode quaternary logic is presented. First, the high-performance NOT circuits with the down literal circuits are composed. The proposed NOT circuits have the quantified effect to realize high noise margins in the voltage-mode quaternary logic circuits. Next, we propose the voltage comparator with the NOT circuit, and, as applications of the voltage comparator, NMAX and NMIN circuits are designed. They can realize the negated MAX and the negated MIN functions, respectively. The advantages of these proposed circuits are fabrication with a conventional CMOS process, high noise margins of more than 0.46[V] and low power consumption with peak of less than 350[/spl mu/W] under 3.0[V] of the supply voltage in verification using HSPICE simulations.

Multi-valued flip-flop with neuron-CMOS NMIN circuits

In this paper, the implementation and verification of the fundamental flip-flops for the voltage-mode multi-valued logic circuits on a conventional CMOS VLSI chip are presented. Using the quantized NMIN circuits and the analog NMIN circuits, two types of the multi-valued R-S flip-flop are designed like a wide-use R-S flip-flop with NAND circuits and are applied to the D flip-flop for multi-valued memory. In verification through HSPICE simulation, the proposed flip-flops perform with good results such as high noise margins and low power consumption.

A study of the electric wheelchair hands-free safety control system using the surface-electromygram of facial muscles

The goal of Human-Computer Interface (or called Human-Robot interface) research is to provide humans with a new communication channel that allows translating peoples intention states via a computer into performing specific actions. This paper presents a novel hands-free control system for controlling the electric wheelchair, which is based on Bio-signals as surface electromyogram signals. The Bioelectric signals are picked up from facial muscles then the Bio-signals are passed through an amplifier and a high pass filter. Motion control commands (Forward, Left, Right, Forward to the Right, Forward to the left and Stop) are classified by simple rule. These commands are used for controlling the electric wheelchair.

Online learning method using support vector machine for surface-electromyogram recognition

Research surface electromyogram (s-EMG) signal recognition using neural networks is a method which identifies the relation between s-EMG patterns. However, it is not sufficiently satisfying for the user because s-EMG signals change according to muscle wasting or to changes in the electrode position, etc. A support vector machine (SVM) is one of the most powerful tools for solving classification problems, but it does not have an online learning technique. In this article, we propose an online learning method using SVM with a pairwise coupling technique for s-EMG recognition. We compared its performance with the original SVM and a neural network. Simulation results showed that our proposed method is better than the original SVM.

Recurrent type ANFIS using local search technique for time series prediction

This paper presents an improved adaptive neuro-fuzzy inference system (ANFIS) for the application of time series prediction. Because ANFIS is based on a feedforward network structure, it is limited to static problem and cannot effectively cope with dynamic properties such as the time series data. To overcome this problem, an improved version of ANFIS is proposed by introducing self-feedback connections that model the temporal dependence. A batch type local search is suggested to train the proposed system. The effectiveness of the proposed system is tested by using two benchmark time series examples and comparison with the various models in time series prediction is also shown. The results obtained from the simulation show an improved performance.

Learning multiple-valued logic networks based on backpropagation

This paper describes a learning multiple-valued logic (MVL) network based on back propagation. The learning MVL network is derived directly from a canonical realization of MVL functions and therefore its functional completeness is guaranteed. We extend traditional back propagation to include the prior human knowledge on the MVL networks, for example, the architecture and the number of hidden units and layers. The prior knowledge from the MVL canonical form can be used as initial parameters of the learning MVL network in its learning process. As a result, the prior knowledge can guide the back propagation learning process to get started from a point in the parameter space that is not far from the optimal one, thus, back propagation can fine-tune the prior knowledge for achieving a desired output. This cooperative relation between the prior knowledge and the back propagation learning process is not always present in neural networks. Simulation results are also given to confirm the effectiveness of the methods.

High-linear four-quadrant multiplier based on MOS weak-inversion region translinear principle with adaptive bias technique

In this paper, we propose the high-linear four-quadrant analog multiplier based on MOS weak-inversion region. Because the proposed multiplier is based on the translinear principle, it is insensitive to the absolute accuracy of the devices and temperature variation. Furthermore, the proposed multiplier has very high linearity by adopting the adaptive bias technique. Simulations of the multiplier demonstrate a nonlinearity of 0.88%, a THD of 1.3%, a −3dB bandwidth of 768kHz, and a power consumption of 1.12µW under the condition that VDD = 1.0V.

Development of the electric wheelchair hands-free semi-automatic control system using the surface-electromyogram of facial muscles

The goal of Human–Computer Interface (or called Human–Robot interface) research is to provide humans with a new communication channel that allows translating people’s intention states via a computer into performing specific actions. This paper presents a novel hands-free control system for controlling the electric wheelchair, which is based on Bio-signals just as surface electromyogram signals. The Bioelectric signals are picked up from facial muscles, then the Bio-signals are passed through an amplifier and a high pass filter. Motion control commands (Forward, Left, Right, Forward to the Right, Forward to the left and Stop) are classified by simple rule, and these commands are used for controlling the electric wheelchair. However, it is difficult to realize safety control and fine control using the biological signal only. In addition, we introduce the semi-automatic control system using the laser range scanner. In this paper, we report the introduction of our proposal systems and our experimental results.