In this paper, we treat the problem of constructing secure communication systems using chaotic synchronization of distributed parameter systems. In particular, we concentrate on the chaotic vibration of a wave equation with a van der Pol boundary condition. In general, a secure communication system consists of three components, a synchronization system, and modulation and demodulation components. In the present work, a wave equation generating non-isotropic spatiotemporal chaotic vibration is used. The non-isotropic characteristic makes it harder for the mechanism behind the synchronization system to be detected. To simulate the chaotic vibration accurately, a numerical algorithm is also developed. Finally, through security analysis by numerical simulations, it is shown that the introduction of non-isotropy makes one of the system parameters more sensitive.

Diagnostic medical imaging has become more sophisticated in recent years owing to technological advances and improved diagnostic techniques. In addition, diagnostic support systems have been introduced to deep learning methods for digitalized image diagnosis and analysis such as convolutional neural network (CNNs) and fully convolutional network (FCNs). We propose a radiological technical support system based on a prelearned CNN that informs the radiological technologist of the required correction of the beam direction after establishing that re-exposure is required. In this study, a recognition system was developed by merging prelearned CNN with a classifier and semantic segmentation techniques (Faster R-CNN). The CNN with classifier was applied to identify the positional relationship among knee and ankle X-ray images. The Faster R-CNN was utilized to segment the target area in a lateral image of the knee joint X-ray images. In the CNN classification, “pass” and “NG”, two classes, and “pass”, “adduction”, “abduction”, “internal rotation” and “external rotation”, five classes were defined for the knee joint, and “pass”, “internal rotation”, “external rotation”, “cranio-caudal” and “caudo-cranial” five classes, were defined for the ankle joint. For the overall performance evaluation of the system, from Faster R-CNN segmentation to CNN classification, only the lateral knee joint images were applied. For a lateral image of the knee joint, the batch size of 6 and input image size of 256 in ResNet101 models with a support vector machine (SVM-ResNet101) resulted in a high performance of classification for two and five class. Among all investigated CNN models high accuracy of 0.9495 provided by SVM-ResNet101 model for among two and five class. In addition, the accuracy of the overall system was 0.7000. This value was compared with the results of visual evaluation and found to be equivalent to that of a radiological technologist with more than 10 years of clinical experience. Verification revealed that these results are dependent on the contrast of the image.

This paper presents a method for detecting replay attacks in an automatic speaker verification system. The replay attack is of interest because it is the most straightforward, effective attack, and difficult to detect. Even though many speech features and classifiers have been proposed, the detection performance, such as an equal error rate (EER), accuracy, and balanced accuracy, need to be improved. Therefore, we propose a method for replay attack detection that applies the Gammatone cepstral coefficients with a ResNet-based model. The proposed method was evaluated and compared with existing methods and baselines in the ASVspoof 2019 challenge. The results indicated that the proposed method outperforms our previous method and the baselines in which the EER was 8.4%. In addition, the accuracy and balanced accuracy of the spoofing detection were improved.

Bone conduction devices that can deliver a sound directly to the inner ear through the skull are roughly classified into three types: direct-drive, skin-drive, and tooth-drive types. In this paper, we develop a three-dimensional (3D) head anatomical model based on the elastic finite-difference time-domain (EFDTD) method and evaluate the propagation efficiency of bone conduction sound (BCS) to the inner ear using the three types of devices. Through the comparison of the excitation positions in the tooth-drive device, we confirmed that the acceleration responses of the inner ear when a back tooth was vibrated were about 20 dB higher than that when a front tooth was vibrated. In addition, by the comparison of direct-drive, skin-drive, and tooth-drive devices, we found that the propagation efficiency of BCS was, in order of increasing efficiency, as follows: direct drive > tooth drive > skin drive.

In this paper, we propose a two-stage network for real image denoising with filter response normalization, named as two-stage filter response normalization network (TFRNet). In TFRNet, we propose a filter response normalization(FRN) block to extract features and accelerate the training of the network. TFRNet consists of two stages, at each stage of which we use the encoder-decoder structure based on U-Net. We also use the coordinate attention block(CAB), double channel downsampling module, double skip connection module, and convolutional (Conv) block in our TFRNet. With the help of these modules, TFRNet provides excellent results on both SIDD and DND datasets for real image denoising.

COVID-19 has spread all over the world, and the cumulative number of infected people is still increasing dairy. Therefore, interventions to limit the spread of COVID-19 should be considered for each social situation. Effective interventions to minimize COVID-19 transmission vary for each situation in accordance with a quantitative framework called event R. Of those various events, the example of a school has the highest possibility of infection, but distancing as an intervention cuts the number of new infections in half. Therefore, perform we multi-agent simulation of COVID-19 transmission without measures and with social distancing. To perform simulation under the same conditions as those in the event R calculation, our simulation is performed under the following conditions: a single infected person enters the classroom with the first set of multiple uninfected people and a certain amount of time passes. After that, the infected person enters another classroom with the same number of new uninfected people, and the process is repeated eight times. The simulation results show that there is a relationship between social distancing and the spread of infection, but the rate of decrease is not constant.