The maritime transport industry is increasingly reliant on computing and communication technologies, and the need for cyber risk management of critical systems and assets on vessels is becoming critically important. In this paper, a comprehensive cyber risk assessment of a ship is presented. An experimental process consisting of assessment preparation activities, assessment conduct and results communication has been developed. The assessment conduct relies on a survey developed and performed by interviewing a ship's crew. Computational vulnerability scanning of the ship's Electronic Chart Display and Information System (ECDIS) is introduced as a specific part of this cyber security assessment. The assessment process presented has been experimentally tested by evaluating the cyber security level of Kobe University's training ship Fukae-maru. For computational vulnerability scanning, an industry-leading software tool has been used, and a quantitative cyber risk analysis has been conducted to evaluate cyber risks on the ship.
Novel ionic amphotropic liquid crystals, in which a hydrophilic poly(ethyleneimine) chain with hydroxy side-groups was attached to an azobenzene mesogenic core through an alkylene chain, showed a thermotropic smectic A mesophase and lyotropic smectic A and C mesophases.
Ionic LC-dendrimers were obtained by proton-transfer reaction of alkanoic acid and primary amino groups of poly(amidoamine) dendrimer in a 1:1 molar ratio. The ion complexes containing stearate, palmitate or myristate ions showed a smectic A phase. However, the ion complexes containing laurate and decanoate ions exhibited no liquid-crystalline phase. Oilystreak, maltese, and homeotropic textures were observed in the smectic A phase. The formation of the homeotropic structure is characterized by conoscopic observation. The Xray diffraction pattern in the smectic A temperatures consists of sharp inner reflections at the small angles and the wide-angle diffuse halo. Mixtures of the dendrimer and stearic acid [a molar ratio (x) of stearic acid against the primary amino groups of the dendrimer] were prepared; mixtures with x ¼ 0.11-1.5 showed a smectic A phase. However, the mixture with x ¼ 2.33 formed a disordered hexagonal columnar mesophase, characterized by an optical texture and X-ray diffraction measurement.
This paper presents an energy-efficient spectral analysis method for the Internet of things. The objective of this work is to reduce the energy consumption of edge devices. The proposed method uses an autoregressive (AR) model for spectral analysis instead of the discrete Fourier transform, and its calculation process is distributed to the edge device and a base station by considering the energy consumption tradeoff of the data processing and the data communication. In this work, the Yule-Walker method is employed for the AR coefficient calculation. The calculation process of Yule-Walker method can be divided into two parts: an autocorrelation calculation and an AR coefficient calculation. The autocorrelation calculation is implemented in the edge devices, and its dedicated hardware is designed using Verilog HDL. Meanwhile, the AR coefficient is calculated in the base station and is used for the spectral analysis. According to this distributed processing approach, the energy consumption of the edge device can be reduced compared with conventional DFT approaches using the fast Fourier transform (FFT). The system level energy consumption is evaluated assuming the IoT edge device, which has a wireless transceiver using Bluetooth low energy. The evaluation results show that the proposed method can reduce 79% of the edge device energy consumption for spectral analysis in a practical application. Index Terms-Autoregressive (AR) model, data compression, edge computing, energy efficient devices, low power devices and circuits, spectral analysis, Yule-Walker method
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