Nowadays, technologies for information security have become essential in the cyberspace world. A lot of PC users know their values, but they do not have a good impression on the technologies. As the CPU of PCs is more powerful, the load caused by security technologies becomes comparably small. However, as you know, another powerful cyber-attack appears. This means, another security technology has to be installed in the PC, and the users do not feel that the CPU load caused by security technologies is relieved. To remove this feeling of disgust even if only slightly, what kind of technology should be developed? In this paper, the developments against such issues are explained.

In this article, convolutional dictionary learning (CDL) and its evolution as an effective and systematic data-driven sparse modeling for high-dimensional signals, especially image data, are outlined. With the development of measurement technology, it has become possible to acquire a large amount of diverse data. At the same time, demands for high-performance signal estimation and prediction are increasing. In order to meet such demands, a generative model is necessary for the effective representation of the data of interest. This article describes A CDL method that applies filter-bank theory is described as an example of a signal generation model that can easily reflect domain knowledge. The theory allows us to construct convolutional networks that preserve unitarity and filter kernel symmetry by combining several primitive local operations such as Givens rotation, shift, and butterfly operations. The significance of the convolutional dictionary is demonstrated through an example of CDL and an example of image approximation using learned dictionaries.

In view of sustainable development goals (SDGs), future wireless communications should be aimed at not only high bandwidth efficiency but also high power efficiency. In general, the modulated signals with high spectral efficiency have high peak power, which leads to low power amplifier efficiency. Therefore, there is an essential trade-off between spectral efficiency and power efficiency. In this article, discusses the distribution of instantaneous power for modern high-spectral-efficiency wireless communication signals is discussed with particular emphasis on the use of high-order moments that can readily be expressed in closed-form mathematical formulas. It is demonstrated that these formulas are useful for not only analyzing the instantaneous power of various signals, but also evaluating how superposed signals with various modulation formats, as in the case with downlink non-orthogonal multiple access (NOMA), approach the Gaussian distribution.

In this article, we present particle models of classical dissipative wave and quantum dispersive wave systems. A particle model of classical waves is designed such that the probability density function in terms of the position of a particle moving in discrete space evolves equivalently to the wave propagation of the original system. The particle model of dissipationless wave systems is applied to quantum digital wave filters in which binary signals are represented by single-electrons or single-flux quanta. Wave systems with dissipation are considered as diffusion systems with damped oscillation. Since a diffusion system is a set of random walkers, the model is utilized also as a parallel pseudorandom sequence generator. The autocorrelation of the sequences is set to be positive or negative depending on the model parameter. The quantum waves to be modeled in this article are nonrelativistic and described by the Schrödinger or Pauli equation. The probability density function of the particle models is governed by the Fokker-Planck equation, which is stochastically equivalent to the Schrödinger or Pauli equation. Then, the Langevin equation possessing the same drift term and diffusion coefficient as the Fokker-Planck equation is obtained. Since the Langevin equation, a stochastic ordinary differential equation, describes the behavior of the modeled particles, the modeling provides the basis for constructing circuit simulator models of quantum devices.

Optical access networks are undergoing a transformation from conventional dedicated equipment to configurations using general-purpose hardware, as the requirements are diversifying owing to the emergence of various advanced applications. By applying software-defined networks (SDN) and network function virtualization (NFV) to the access system, SDN-enabled broadband access (SEBA), a virtualized access platform that abstracts various access systems and can be commonly controlled by software on general-purpose hardware, has been commercially deployed by some overseas carriers. As a future form, an architecture is being considered, in which lower layer network applications share resources on general-purpose hardware with edge applications by expanding the software domain of the system. In this paper, we touch on the virtualization of optical access networks, trends in the standardization of NFV/SDNs, the status of open source software (OSS) development, and trends in research to expand the domain of softwarization and their technologies.