High-accuracy wideband signal transmission is essential for 5G and Beyond wireless communication systems. Memory nonlinearity in transmitters is a serious issue for the goal, because it deteriorates the quality of signal and lowers the system performance. This paper studies a post-reception nonlinear compensation (PRC) schemes consisting of frequency domain equalizers (FDEs) and a blind nonlinear compensator (BNLC). A frequency-domain memory nonlinearity modeling approach is employed, and several PRC configurations with FDEs and BNLC are evaluated through computer simulations. It is concluded that the proposed PRC schemes can effectively compensate memory nonlinearity in wideband transmitters via frequency-selective propagation channel. By implementing the PRC in a base station, uplink performance will be enhanced without any additional cost and power consumption in user terminals.

The hydroxyl loss increase characteristics for antimony oxide-doped silica fibers were investigated to ensure long-term reliability. The loss increase was measured for non-irradiated and γ-irradiated fibers with hydrogen molecule diffusion and heat treatments. The loss increase characteristics for these experiments were similar to those for germanium oxide-doped silica fibers.

We report the generation of deeply-modulated optical pulse trains at high-repetition-rates through modulational instability in optical fibers. The optical pulse train can be generated over the wide repetition-rate-range from sub-THz to a few THz by controlling the anomalous group velocity dispersion of optical fibers. Very-high-repetition-rate of more than 4 THz is attained by the minimization of the absolute value of the anomalous dispersion. The deep modulation of the high-repetition-rate optical pulse train is achieved by inducing modulational instability with pump-probe mixing or external optical feedback. Both inducing methods also enable us to tune the pulse repetition rate precisely. The high repetition rate optical pulse trains seem to go far toward applications such as high-bit-rate optical communication and high-speed optical processing.

Natural quartz jacketed fibers showed a larger OH loss increase due to diffused H2 than synthesized silica jecketed fibers. This additional OH loss increased with an increase in the drawing temperature and a decrease in the drawing speed in the former, although it was not changed by the drawing conditions in the latter. From this result, it was estimated that impurities which diffused from the natural quartz into the core glass during the drawing process were related to the large OH loss increase.

Low temperature excess loss of fusion-spliced fibers packaged by a new packaging method is investigated. The package consists of a heat-shrinkable tube, an adhesive tube and a resistance heating rod. Based on the theoretical analysis using the theory of elasticity, the packaging structure using a rigid resistance rod, which has a high compression modulus, low linear expansion coefficient and large cross-sectional area, is recommended for reducing the loss increase at low temperatures. By using carbon fiber-reinforced carbon (CFRC) for such a resistance rod, the loss increase at -40 is found to be less than 0.03 dB per package.

The influence of impurities in performs and of the drawing condition on the hydrogen-induced loss increase in the OH absorption band and the radiation-induced loss increase is investigated. The loss increase is much larger in fibers contaminated by sodium. Moreover, the loss increase in fibers with natural SiO2 clad, which is contaminated by alkali ions, increases with increasing maximum temperature in the drawing furnace and with decreasing drawing speed. By thermodynamically analyzing these results, the origin of the loss increase is identified as follows:(1) The OH loss increase and the radiation-induced loss increase are ascribed to the same origin.(2) The structural reorientation from GeO4 tetrahedrons to GeO6 groups thermally occurs in the drawing process, when the perform is contaminated by alkali ions.(3) The loss increase is induced through the reaction of H2 and radiation with GeO6 groups.

In this paper, we propose an access control method with a secret key for object detection models for the first time so that unauthorized users without a secret key cannot benefit from the performance of trained models. The method enables us not only to provide a high detection performance to authorized users but to also degrade the performance for unauthorized users. The use of transformed images was proposed for the access control of image classification models, but these images cannot be used for object detection models due to performance degradation. Accordingly, in this paper, selected feature maps are encrypted with a secret key for training and testing models, instead of input images. In an experiment, the protected models allowed authorized users to obtain almost the same performance as that of non-protected models but also with robustness against unauthorized access without a key.

The growing threat of Hardware Trojans (HT) in the System-on-Chips (SoC) industry has given way to the embedded systems researchers to propose a series of detection methodologies to identify and detect the presence of Trojan circuits or logics inside a host design in the various stages of the chip design and manufacturing process. Many state of the art works propose different techniques for HT detection among which the popular choice remains the Side-Channel Analysis (SCA) based methods that perform differential analysis targeting the difference in consumption of power, change in electromagnetic emanation or the delay in propagation of logic in various paths of the circuit. Even though the effectiveness of these methods are well established, the evaluation is carried out on simplistic models such as AES coprocessors and the analytical approaches used for these methods are limited by some statistical metrics such as direct comparison of EM traces or the T-test coefficients. In this paper, we propose two new detection methodologies based on Machine Learning algorithms. The first method consists in applying the supervised Machine Learning (ML) algorithms on raw EM traces for the classification and detection of HT. It offers a detection rate close to 90% and false negative smaller than 5%. In the second method, we propose an outlier/novelty algorithms based approach. This method combined with the T-test based signal processing technique, when compared with state-of-the-art, offers a better performance with a detection rate close to 100% and a false positive smaller than 1%. In different experiments, the false negative is nearly the same level than the false positive and for that reason the authors only show the false positive value on the results. We have evaluated the performance of our method on a complex target design: RISC-V generic processor. Three HTs with their corresponding sizes: 0.53%, 0.27% and 0.09% of the RISC-V processors are inserted for the experimentation. In this paper we provide elaborative details of our tests and experimental process for reproducibility. The experimental results show that the inserted HTs, though minimalistic, can be successfully detected using our new methodology.