In various studies of attacks on autonomous vehicles (AVs), a phantom attack in which advanced driver assistance system (ADAS) misclassifies a fake object created by an adversary as a real object has been proposed. In this paper, we propose F-GhostBusters, which is an improved version of GhostBusters that detects phantom attacks. The proposed model uses a new feature, i.e, frequency of images. Experimental results show that F-GhostBusters not only improves the detection performance of GhostBusters but also can complement the accuracy against adversarial examples.

This paper describes a fast and effective algorithm for refining the parameter estimates of multicomponent third-order polynomial phase signals (PPSs). The efficiency of the proposed algorithm is accompanied by lower signal-to-noise ratio (SNR) threshold, and computational complexity. A two-step procedure is used to estimate the parameters of multicomponent third-order PPSs. In the first step, an initial estimate for the phase parameters can be obtained by using fast Fourier transformation (FFT), k-means algorithm and three time positions. In the second step, these initial estimates are refined by a simple moving average filter and singular value decomposition (SVD). The SNR threshold of the proposed algorithm is lower than those of the non-linear least square (NLS) method and the estimation refinement method even though it uses a simple moving average filter. In addition, the proposed method is characterized by significantly lower complexity than computationally intensive NLS methods. Simulations confirm the effectiveness of the proposed method.

We demonstrate a single-channel 1.28 Tbit/s-525 km transmission using OTDM of subpicosecond DQPSK signals. In order to cope with transmission impairments due to time-varying higher-order PMD, which is one of the major limiting factors in such a long-haul ultrahigh-speed transmission, we newly developed an ultrafast time-domain optical Fourier transformation technique in a round-trip configuration. By applying this technique to subpicosecond pulses, transmission impairments were greatly reduced, and BER performance below FEC limit was obtained with increased system margin.

We propose a novel Bragg grating filter synthesis method using a Fourier transform of the target scattering matrix. Multiple scattering processes are taken into account by iteration to improve the synthesis accuracy.

Network monitoring is one of the most significant functions in network management to understand the state of a network in real-time. In SNMP (Simple Network Management Protocol), polling is used for this purpose. If the time interval for two consecutive polling requests is too long, then we cannot understand the state of the network in real-time. Conversely, if it is too short, then the polling message traffic increases and imposes a heavy load on the network. Many dynamic polling algorithms have been proposed for controlling the increase in the polling message traffic. However, they cannot keep track of the time variations of management information values, since their main objectives are to check whether or not a network node is active and the next polling interval is determined being independent of the time variations of the values. The existing polling algorithms are thereby not applicable to the case where monitoring the time variation of management information values is critical. This paper proposes a new dynamic polling algorithm which, by making use of Discrete Fourier Transformation, enables not only to control the increase in the polling message traffic but also to keep track of the time variations of network management information values. We show the availability of the proposed algorithm by evaluating it through both simulations and experiments in actual network environment.