Anomalous environmental electromagnetic (EM) radiation waves have been reported as the portents of earthquakes. Our study's goal is predicting earthquakes using EM radiation waves by detecting some anomalies. We have been measuring the Extremely Low Frequency (ELF) range EM radiation waves all over Japan. However, the recorded data contain signals unrelated to earthquakes. These signals, as noise, confound earthquake prediction efforts. In this paper, we propose an efficient method of global signal elimination and enhancement local signals using Independent Component Analysis (ICA). We evaluated the effectiveness of this method.

This paper proposes a novel signal estimation method that uses a tensor product expansion. When a bivariable function, which is expressed by two-dimensional matrix, is subjected to conventional tensor product expansion, two single variable functions are calculated by minimizing the mean square error between the input vector and its outer product. A tensor product expansion is useful for feature extraction and signal compression, however, it is difficult to separate global noise from other signals. This paper shows that global noise, which is observed in almost all input signals, can be estimated by using a tensor product expansion where absolute error is used as the error function.

The tectonic activities that precede significant earthquakes release electromagnetic (EM) waves that can be used as earthquake precursors. We have been observing EM radiation in the ELF (extremely low frequency) band at about 40 observation stations in Japan for predicting significant earthquakes. The recorded signals contain, however, several noise components generated from the ionosphere, human activity, and so on. Most background noise in observed signal is attributed to lightning in the tropics. This paper proposes method based on PCA (principal component analysis) to extract signals from large data sets. The good performance of the proposed method is confirmed.

In this paper, we show that the principle of quantum cryptography can be applied not only to a key distribution scheme but also to a data transmission scheme. We propose a secure data transmission scheme in which an eavesdropping can be detected based on sharing the bases Alice (the sender) and Bob (the receiver) have. We also show properties of this scheme.

In quantum communication theory, a realization of the optimum quantum receiver that minimizes the error probability is one of fundamental problems. A quantum receiver is described by detection operators. Therefore, it is very important to derive the optimum detection operators for a realization of the optimum quantum receiver. In general, it is difficult to derive the optimum detection operators, except for some simple cases. In addition, even if we could derive the optimum detection operators, it is not trivial what device corresponds to the operators. In this paper, we show a realization method of a quantum receiver which is described by a projection-valued measure (PVM) and apply the method to 3-ary phase-shift-keyed (3PSK) coherent-state signals.

The hardware implementation of a proposed high dimensional discrete torus knot code was successfully realized on an ASIC chip. The code has been worked on for more than a decade since then at Aichi Prefectural University and Nagoya Institutes of Technology, both in Nagoya, Japan. The hardware operation showed the ability to correct the errors about five to ten times the burst length, compared to the conventional codes, as expected from the code configuration and theory. The result in random error correction was also excellent, especially at a severely degraded error rate range of one hundredth to one tenth, and also for high grade characteristic exceeding 10-6. The operation was quite stable at the worst bit error rate and realized a high speed up to 50 Mbps, since the coder-decoder configuration consisted merely of an assemblage of parity check code and hardware circuitry with no critical loop path. The hardware architecture has a unique configuration and is suitable for large scale ASIC design. The developed code can be utilized for wider applications such as mobile computing and qualified digital communications, since the code will be expected to work well in both degraded and high grade channel situations.

We have developed a signal processing method that is appropriate for detecting electromagnetic radiation due to earthquake activities. The radiation is usually accompanied by a background noise that is mainly caused by atmospheric discharges in the tropical regions. Data representing the seismic radiation is presented as sound via the concept of sonification. This is useful for immediately finding out anomalous seismic radiations, which are often followed by a disastrous earthquake, from the massive data collected from over forty observation stations. It is illustrated that the auditory display is valuable for future earthquake prediction systems.

We have researched high dimensional parity-check (HDPC) codes that give good performance over a channel that has a very high error rate. HDPC code has a little coding overhead because of its simple structure. It has hard-in, maximum detected bit flipping (MDBF) decoding that has reasonable decoding performance and computational cost. In this paper, we propose a modified algorithm for MDBF decoding and compare the proposed MDBF decoding with conventional hard-in decoding.

Recently, the quantum information theory attracts much attention. In quantum information theory, the existence of superadditivity in capacity of a quantum channel was foreseen conventionally. So far, some examples of codes which show the superadditivity in capacity have been clarified. However in present stage, characteristics of superadditivity are not still clear up enough. The reason is as follows. All examples were shown by calculating the mutual information by quantum combined measurement, so that one had to solve the eigenvalue and the eigenvector problems. In this paper, we construct a simplification algorithm to calculate the mutual information by using square-root measurement as decoding process of quantum combined measurement. The eigenvalue and the eigenvector problems are avoided in the algorithm by using group covariancy of binary linear codes. Moreover, we derive the analytical solution of the mutual information for parity check codes with any length as an example of applying the simplification algorithm.

This paper proposes a background noise estimation method using an outer product expansion with non-linear filters for ELF (extremely low frequency) electromagnetic (EM) waves. We proposed a novel source separation technique that uses a tensor product expansion. This signal separation technique means that the background noise, which is observed in almost all input signals, can be estimated using a tensor product expansion (TPE) where the absolute error (AE) is used as the error function, which is thus known as TPE-AE. TPE-AE has two problems: the first is that the results of TPE-AE are strongly affected by Gaussian random noise, and the second is that the estimated signal varies widely because of the random search. To solve these problems, an outer product expansion based on a modified trimmed mean (MTM) is proposed in this paper. The results show that this novel technique separates the background noise from the signal more accurately than conventional methods.

Given the importance of the traffic on modern communication networks, advanced error correction methods are needed to overcome the changes expected in channel quality. Conventional countermeasures that use high dimensionality parity codes often fail to provide sufficient error correction capability. We propose a parity code with high dimensionality that is iteratively decoded. It provides better error correcting capability than conventional decoding methods. The proposal uses the steepest descent method to increase code bit reliability and the coherency between parities and code bits gradually. Furthermore, the quantization of the decoding algorithm is discussed. It is found that decoding with quantization can keep the error correcting capability high.

Improvement of the convergence characteristics of the NLMS algorithm has received attention in the area of adaptive filtering. A new variable stepsize NLMS method, in which the stepsize is updated optimally by using variances of the measured error signal and the estimated noise, is proposed. The optimal control equation of the stepsize has been derived from a convergence characteristic approximation. A new condition to judge convergence is introduced in this paper to ensure the fastest initial convergence speed by providing precise timing to start estimating noise level. And further, some adaptive smoothing devices have been added into the ADF to overcome the saturation problem of the identification error caused by some random deviations. By the simulation, The initial convergence speed and the identification error in precise identification mode is improved significantly by more precise adjustment of stepsize without increasing in computational cost. The results are the best ever reported performanced. This variable stepsize NLMS-ADF also shows good effectiveness even in severe conditions, such as noisy or fast changing circumstances.