Masaya Ohta

A chaotic neural network for reducing the peak-to-average power ratio of multicarrier modulation

A novel method for the peak-to-average power ratio reduction on multicarrier modulation systems is proposed. The reduction problem is formulated as a combinatorial optimization, and is solved by using the chaotic neural network (CNN), which has easy structure and suitable for real time optimization. Experimental results show that CNN has better performance than conventional methods. Finally we consider relationship between a parameter of CNN and the chaotic behavior.

A photo-based augmented reality system with HTML5/JavaScript

Augmented Reality (AR) is applied in different fields, such as entertainment, medicine, manufacturing and repair, and training. Due to the increase of interest in AR, simple frameworks for developing AR system are required. This paper proposes a photo-based AR system that uses photo images taken from different perspectives around an object. The proposed system just pastes a suitable photo image on the display instead of rendering 3DCGs.

Improvement of the error characteristics of N-continuous OFDM system by SLM

N-continuous OFDM is a modulation technique that has a lower sidelobe than the original OFDM as a result of the continuous connection with its higher-order derivatives between the OFDM symbols. However, N-continuous OFDM has a high symbol error rate. In the present paper, we improve N-continuous OFDM without increasing the symbol error rate by using a selected mapping technique.

Receiver Iteration Reduction of an N-Continuous OFDM System with Cancellation Tones

N-continuous orthogonal frequency division multiplexing (OFDM) is a modulation technique that produces smaller sidelobes than standard OFDM as a result of continuous connections with higher-order derivatives between OFDM symbols. In this method, the transmitter inserts symbols into the signal such that inter-symbol connections are continuous through Nth-order derivatives. On the receiving side, the inserted symbols are deleted using an iterative algorithm and data symbols are demodulated. The conventional method has required numerous iterations, which is a problem due to the calculation load on the receiver. This paper therefore proposes a method by which the power of high frequency insertion symbols is canceled, and cancellation tones are used to allow a reduction in the number of receiver iterations on the receiving side. Numerical experimentation shows that by increasing transmit power by 1%, the number of receiver iterations can be reduced from 8 to 2.

N-continuous symbol padding OFDM for sidelobe suppression

N-continuous orthogonal frequency division multiplexing (OFDM) is a modulation technique that has a lower sidelobe than the original OFDM as a result of the continuous connection with its higher-order derivatives between the OFDM symbols. However, N-continuous OFDM requires an iterative algorithm for removing correction symbols from received signals at the receiver. In this paper, a method called N-continuous symbol padding is proposed, which inserts N-continuous OFDM correction symbols only into the guard interval and seamlessly connects each OFDM symbol, thereby suppressing the sidelobes. It has been demonstrated by numerical experiments that the N-continuous symbol padding method provides power spectral density and SER characteristics equal to or better than those of the conventional method, without using an iterative algorithm. This method is particularly effective for a system with a small number of subcarriers.

Photo-based Desktop Virtual Reality System Implemented on a Web-browser

Abstract : This paper proposes a novel desktop virtual reality system. Based on the position of the user’s face, the proposed system selects the most appropriate image of an object from a set of photographs taken at various angles, and simply “pastes” it onto the display at the appropriate location and scale. Using this system, the users can intuitively feel the presence of the object. Keywords : Virtual reality, Desktop VR, Photo AR, E-Commerce, Digital signage, HTML5/JavaScript 1. Introduction Virtual Reality (VR) has been evolving constantly since its early days, and is now a fundamental technology in different application areas, including scientific and medical visualization, entertainment, video games, education, and training. One of the aims of this study was to apply VR technology to the display of products on electronic commerce (EC) sites and digital signage. Object VR [1] is one of the notable methods that use the photographs of an object taken at various angles and allows the users to observe it from the angle they desire. On the other hand, Object VR requires dragging the mouse of a computer to change the angle. Therefore, the user does not feel the presence of the object in the display during mouse operation. Previously, a photo-based augmented reality (Photo AR) system that uses the photo images of an object captured at various angles instead of rendering a 3D model corresponding to the object during runtime was proposed [2]. With this system, an appropriate photo image for the position and orientation of the users camera was selected from previously captured and stored images, and then adjusted and simply “pasted” into the camera view. Because Photo AR displays the object as if it exists right in front of the user, it allows the user to intuitively recognize the object better than Object VR. On the other hand, the user needs to move the camera or marker when he/she wants to check the object at a different angle. Although this operation is more intuitive than dragging the mouse, it is not effective enough. Desktop VR, which is commonly referred to as fish tank VR, is a technology that involves the use of a display of a desktop computer coupled with a head tracker that estimates the user’s head position and updates a 3D projection matrix in real-time [3-5]. This system allows the users to observe virtual images through the display as if they were looking into an actual fish tank. He/she feels the presence of a rendered object in the display because no mouse operation is needed. Although, desktop VR does not provide strong immersion, it is suitable for visualization systems because of its ease of use and ability to present high-quality images. The main obstacle encountered when applying desktop VR to EC sites is that 3D models of all viewable products must be prepared in advance. A typical desktop VR system uses three dimensional computer graphics (3DCG) to render the objects required for a given users viewpoint. These objects are typically authored using 3D modeling programs. These tools are highly expressive but they also tend to be complex and time-consuming. If the number of viewable products is large, the cost of creating these models will in most cases be prohibitive. 3D reconstruction methods have been proposed [6-8] as an alternative to

An FPGA implementation of 1,024-neuron system for PAPR reduction of OFDM signal

The aim of this paper is to reduce computational complexity of the neural network for PAPR reduction of OFDM signal, and to implement the neural network including 1,024 neurons by FPGA for practical OFDM transmitter of the terrestrial digital broadcast. A couple of IDFTs reduce computational complexity of the neuron updating from O(N/sup 2/) to O(N log N). This neural network is designed using VHDL for Xilinx FPGA device, XC2V6000, and 1,024-neuron system is implemented by less than 30% of resources of the device.

Improvement of the Error Characteristics of an N-Continuous OFDM System with Low Data Channels by SLM

N-continuous OFDM is a modulation technique that produces a lower sidelobe than the original OFDM as a result of the continuous connection with its higher-order derivatives between the OFDM symbols. However, when there are low data channels, N-continuous OFDM has a high symbol error rate. In this paper, the authors propose a method for improving the symbol error rate of N-continuous OFDM using SLM and evaluate the effectiveness of this approach by means of numerical experiments. The experimental results showed that this method significantly improves the symbol error rate in systems with low data channels, and the authors confirmed the effectiveness of the method in systems with a scalable bandwidth, such as LTE.

Complexity suppression of neural networks for PAPR reduction of OFDM signal and its FPGA implementation

In this paper, a neural network (NN) for peak power reduction of orthogonal frequency-division multiplexing (OFDM) signals is improved in order to suppress its computational complexity. Numerical experiments show that the proposed NN has less computational complexity than the conventional one. The number of IFFT in NN can be reduced to half, and the computational time can be suppressed by 32.7%. From the HDL simulation for FPGA implementation, hardware resouces are approximately suppressed by about 30%.

A PAPR reduction of OFDM signal using neural networks with tone injection scheme

This paper proposes a novel peak-to-average power ratio (PAPR) reduction method for OFDM signals. The novelty of the method is the use of Hopfield type neural network (NN) with tone injection (TI) scheme to reduce PAPR. The proposed NN is suitable for global search and PAPR is sufficiently reduced, and side information of parameters for PAPR reduction transmitted to the receiver is not required. By pruning several EFFTs for neuron state updating, the proposed NN has less computational complexity than that of the conventional NNs.