Tadanobu Misawa

Neural representation of preference relationships

This paper indicates that the human product-preference relationship can, using a product selection task, be predicted to an extent on the basis of changes in the oxygenated hemoglobin concentration in the ventromedial prefrontal cortex and that functional near-infrared spectroscopy allows this prediction despite the shallow depth at which brain information is measured. A Bayesian three-layer perceptron was used as a predictive model. Results of this work help to lay the foundations for the concept of utility in economics and marketing theories from the perspective of neuroscience and have important significance from a practical standpoint as well.

Noise reduction for periodic signals using high-resolution frequency analysis

The spectrum subtraction method is one of the most common methods by which to remove noise from a spectrum. Like many noise reduction methods, the spectrum subtraction method uses discrete Fourier transform (DFT) for frequency analysis. There is generally a trade-off between frequency and time resolution in DFT. If the frequency resolution is low, then the noise spectrum can overlap with the signal source spectrum, which makes it difficult to extract the latter signal. Similarly, if the time resolution is low, rapid frequency variations cannot be detected. In order to solve this problem, as a frequency analysis method, we have applied non-harmonic analysis (NHA), which has high accuracy for detached frequency components and is only slightly affected by the frame length. Therefore, we examined the effect of the frequency resolution on noise reduction using NHA rather than DFT as the preprocessing step of the noise reduction process. The accuracy in extracting single sinusoidal waves from a noisy environment was first investigated. The accuracy of NHA was found to be higher than the theoretical upper limit of DFT. The effectiveness of NHA and DFT in extracting music from a noisy environment was then investigated. In this case, NHA was found to be superior to DFT, providing an approximately 2 dB improvement in SNR.

Motion Analysis Using 3D High-Resolution Frequency Analysis

The spatiotemporal spectra of a video that contains a moving object form a plane in the 3D frequency domain. This plane, which is described as the theoretical motion plane, reflects the velocity of the moving objects, which is calculated from the slope. However, if the resolution of the frequency analysis method is not high enough to obtain actual spectra from the object signal, the spatiotemporal spectra disperse away from the theoretical motion plane. In this paper, we propose a high-resolution frequency analysis method, described as 3D nonharmonic analysis (NHA), which is only weakly influenced by the analysis window. In addition, we estimate the motion vectors of objects in a video using the plane-clustering method, in conjunction with the least-squares method, for 3D NHA spatiotemporal spectra. We experimentally verify the accuracy of the 3D NHA and its usefulness for a sequence containing complex motions, such as cross-over motion, through comparison with 3D fast Fourier transform. The experimental results show that increasing the frequency resolution contributes to high-accuracy estimation of a motion plane.

Image Denoising With Edge-Preserving and Segmentation Based on Mask NHA

In this paper, we propose a zero-mean white Gaussian noise removal method using a high-resolution frequency analysis. It is difficult to separate an original image component from a noise component when using discrete Fourier transform or discrete cosine transform for analysis because sidelobes occur in the results. The 2D non-harmonic analysis (2D NHA) is a high-resolution frequency analysis technique that improves noise removal accuracy because of its sidelobe reduction feature. However, spectra generated by NHA are distorted, because of which the signal of the image is non-stationary. In this paper, we analyze each region with a homogeneous texture in the noisy image. Non-uniform regions that occur due to segmentation are analyzed by an extended 2D NHA method called Mask NHA. We conducted an experiment using a simulation image, and found that Mask NHA denoising attains a higher peak signal-to-noise ratio (PSNR) value than the state-of-the-art methods if a suitable segmentation result can be obtained from the input image, even though parameter optimization was incomplete. This experimental result exhibits the upper limit on the value of PSNR in our Mask NHA denoising method. The performance of Mask NHA denoising is expected to approach the limit of PSNR by improving the segmentation method.

Image Inpainting on the Basis of Spectral Structure From 2-D Nonharmonic Analysis

The restoration of images by digital inpainting is an active field of research and such algorithms are, in fact, now widely used. Conventional methods generally apply textures that are most similar to the areas around the missing region or use a large image database. However, this produces discontinuous textures and thus unsatisfactory results. Here, we propose a new technique to overcome this limitation by using signal prediction based on the nonharmonic analysis (NHA) technique proposed by the authors. NHA can be used to extract accurate spectra, irrespective of the window function, and its frequency resolution is less than that of the discrete Fourier transform. The proposed method sequentially generates new textures on the basis of the spectrum obtained by NHA. Missing regions from the spectrum are repaired using an improved cost function for 2D NHA. The proposed method is evaluated using the standard images Lena, Barbara, Airplane, Pepper, and Mandrill. The results show an improvement in MSE of about 10-20 compared with the examplar-based method and good subjective quality.

Forecast of stock market based on nonharmonic analysis used on NASDAQ since 1985

Although research involving economic time series forecasting based on virtual market models is frequently conducted, long-term forecasting is difficult due to many factors that affect actual markets. However, as exemplified by the business cycle and Elliot Wave theories in economics, it is assumed that fluctuations in economic time series forecasting have various periodicities, ranging from short-term to long-term. Accordingly, we used a new high-resolution frequency analysis (Non-Harmonic Analysis (NHA)) method, which we have recently developed, to conduct analysis of the periodicity of economic time series forecasting. We also attempted a long-term economic time series forecast by combining multiple periodic signals. In the verification experiment, we analysed the National Association of Securities Dealers Automated Quotations (NASDAQ) closing price data for a time period of approximately 20 years using nonharmonic analysis with an analysis window of the previous 2 years, and forecasted price fluctuations for the following 2 years.

Predicting investment behavior: An augmented reinforcement learning model

The goal of this paper is to augment the ordinal temporal-difference type (TD-type) reinforcement learning model in order to detect the most suitable learning model of the human decision-making process in financial investment tasks. The simplicity and robustness of the TD-type learning model is fascinating. However, the available evidence and our observation suggest the necessity of introducing the nonlinear effect in learning and the possibility that additional factors might play important roles in the investment decision-making process. To extend the ordinal TD-type learning model, we adopt a three-layered perceptron as the basis function and the hierarchical Bayesian method to calibrate the parameter values. The result of the predictive test suggests that the augmented TD-type learning model constructed in this paper can evade the overfitting and can predict peoples investment behavior well as compared to other familiar learning models.

An Agent-Based Approach to Option Pricing Anomalies

Psychological studies on decision making under uncertainty, which have been inspired by Kahneman and Tverskys study, have attracted considerable interest in financial research as key factors to solve anomalies that cannot be explained by the traditional models. Recently, we proposed an agent-based prospect theoretical model and demonstrated that the loss-aversion feature of investors is capable of explaining a large number of financial stylized facts. This paper aims to extend the previous work to the field of option pricing. Two important anomalies in the field-the implied volatility smile and the skewness premium-will be analyzed. This paper can be considered as an attempt to integrate the behavioral financial theory and the option pricing theory by using the agent-based approach.

Proposition of sensor agent for estimation of air-pollution direction and its experimental simulation

Air pollutants appear at various areas on the earth due to economic growth, and harm the health of the residents of those regions. Various countries around those areas are also polluted. Many kinds of air-polluting factors must be grasped, for example, the place of origin, kind of pollutant, its direction and speed. These factors are also grasped quickly over a wide area. Various kinds of chemical sensors have been investigated and developed to detect air pollutants. In this study, metal oxide odor sensors are installed at 12 points in an experimental residence and experimental simulations are carried out. Each sensor with a microprocessor has the ability of an agent, which can autonomously make many advanced decisions and estimations. This sensor system is named sensor agent. The sensor agents can derive pollution direction by cooperation and collaboration with other sensor agents. Derived directions are compared with daily resident behaviors and reliability is examined. Results show that polluting direction is estimated with good reliability and a clue to practical use is achieved.

Numerical simulation validation of nonuniform, nonharmonic analysis of spectral-domain optical coherence tomography

Abstract. In spectral-domain optical coherence tomography (SD-OCT), the limited resolution of the spectrometer causes nonuniformity of the interference signal. The latter, in turn, causes the sensitivity of SD-OCT to decrease, thereby limiting the imaging range and decreasing the axial resolution. We addressed this problem by applying nonuniform, nonharmonic analysis (NUNHA) with software that features high-frequency resolution without interpolation. We demonstrate the application of NUNHA in SD-OCT and compare it with conventional frequency analysis methods by simulating nonuniform interference signals. The results suggest that application of NUNHA in SD-OCT can provide acquisition of a clearer tomographic image, accurate analysis of fine and complex structures, and preservation of resolution and sensitivity at regions deep within a sample. This is because it reduces the influence of nonuniformity caused by the spectrometer and is unaffected by distortion due to interpolation.