This study proposes a low-complexity permittivity estimation for ground penetrating radar applications based on a contrast source inversion (CSI) approach, assuming multilayered ground media. The homogeneity assumption for each background layer is used to address the ill-posed condition while maintaining accuracy for permittivity reconstruction, significantly reducing the number of unknowns. Using an appropriate initial guess for each layer, the post-CSI approach also provides the dielectric profile of a buried object. The finite difference time domain numerical tests show that the proposed approach significantly enhances reconstruction accuracy for buried objects compared with the traditional CSI approach.

This paper proposes an enhanced model of Random Projection Outlyingness (RPO) for unsupervised outlier detection. When datasets have multiple modalities, the RPOs have frequent detection errors. The proposed model deals with this problem via unsupervised clustering and a local score weighting. The experimental results demonstrate that the proposed model outperforms RPO and is comparable with other existing unsupervised models on benchmark datasets, in terms of in terms of Area Under the Curves (AUCs) of Receiver Operating Characteristic (ROC).

Our group has developed terahertz(THz)-waves emitting devices utilizing single crystals of high temperature superconductor Bi2Sr2CaCu2O8+δ (Bi2212). The working principle of the device is based on the AC Josephson effect which is originated in the intrinsic Josephson junctions (IJJs) constructed in Bi2212 single crystals. In principle, based on the superconducting gap of the compound and the AC Josephson effect, the emission frequency range from 0.1 to 15 THz can be generated by simply adjusting bias voltages to the IJJs. In order to improve the device performances, we have performed continuous improvement to the device structures. In this paper, we present our recent approaches to high performance Bi2212 THz-waves emitters. Firstly, approaches to the reduction of self Joule heating of the devices is described. In virtue of improved device structures using Bi2212 crystal chips, the device characteristics, such as the radiation frequency and the output power, become better than previous structures. Secondly, developments of THz-waves emitting devices using IJJs-mesas coupled with external structures are explained. The results clearly indicate that the external structures are very useful not only to obtain desired radiation frequencies higher than 1 THz but also to control radiation frequency characteristics. Finally, approaches to further understanding of the spontaneous synchronization of IJJs is presented. The device characteristics obtained through the approaches would play important roles in future developments of THz-waves emitting devices by use of Bi2212 single crystals.

We propose a non-photorealistic rendering method to automatically generate reaction-diffusion-pattern-like images from photographic images. The proposed method uses smoothing filter with a circular window, and changes the size of the circular window depending on the position in photographic images. By partially changing the size of the circular window, the size of reaction-diffusion patterns can be changed partially. To verify the effectiveness of the proposed method, experiments were conducted to apply the proposed method to various photographic images.

BLS signature is an elliptic curve cryptography with an attractive feature that signatures can be aggregated and shortened. We have designed two ASIC architectures for hashing to the elliptic curve and pairing to minimize the latency. Also, the designs are optimized for BLS12-381, a relatively new and safe curve.

An electroactive supercoiled polymer artificial muscle, which is made from a conductive sewing thread using self-coiling caused by inserting a twist with a hanged appropriate weight, is 1/4-1/3 of the thread in length. Therefore, it is necessary to move the weight vertically about two or three times as long as the desired electroactive supercoiled polymer artificial muscle, resulting in a large vertical dimension of the fabrication equipment. This study has attempted to solve this problem by using constant-load springs that enable horizontal table-top fabrication equipment. It has been also demonstrated that inserting a twist into the bundled threads results in a strong electroactive supercoiled polymer artificial muscle.

We have developed multilayered polymer-based inverted organic light emitting diodes (iOLED) using transfer-printing and push-coating techniques. We obtained the higher efficiency and lower operation voltage with push-coated blue light emitting polymer and hole transporting polymer than the devices with spin-coated film. The β-phase obtained for blue emitting layer is attributable to the improved performance of relatively efficient bule and white iOLEDs with an external quantum efficiency (EQE) of above 2%.

Studies on intrinsic Josephson junctions (IJJs) of cuprate superconductors are reviewed. A system consisting of a few IJJs provides phenomena to test the Josephson phase dynamics and its interaction between adjacent IJJs within a nanometer scale, which is unique to cuprate superconductors. Quasiparticle density of states, which provides direct information on the Cooper-pair formation, is also revealed in the system. In contrast, Josephson plasma emission, which is an electromagnetic wave radiation in the sub-terahertz frequency range from an IJJ stack, arises from the synchronous phase dynamics of hundreds of IJJs coupled globally. This review summarizes a wide range of physical phenomena in IJJ systems having capacitive and inductive couplings with different nanometer and micrometer length scales, respectively.

Direct-current superconducting quantum interference device (dc-SQUID) based on intrinsic Josephson junction (IJJ) has been fabricated using Bi2Sr2CaCu2O8+δ (Bi-2212) films grown on MgO substrates with surface steps. The superconducting loop parallel to the film surface across the step edge contains two IJJ stacks along the edge. The number of crystallographically stacked IJJ for each SQUIDs were 40, 18 and 3. Those IJJ SQUIDs except for one with 40 stacked IJJs revealed clear periodic modulation of the critical current for the flux quanta through the loops. It is anticipated that phase locking of IJJ has an effect on the modulation depth of the IJJ dc-SQUID.

Intrinsic Josephson junctions (IJJs) in the high-Tc cuprate superconductors have several fascinating properties, which are superior to the usual Josephson junctions obtained from conventional superconductors with low Tc, as follows; (1) a very thin thickness of the superconducting layers, (2) a strong interaction between junctions since neighboring junctions are closely connected in an atomic scale, (3) a clean interface between the superconducting and insulating layers, realized in a single crystal with few disorders. These unique properties of IJJs can enlarge the applicable areas of the superconducting qubits, not only the increase of qubit-operation temperature but the novel application of qubits including the macroscopic quantum states with internal degree of freedom. I present a comprehensive review of the phase dynamics in current-biased IJJs and argue the challenges of superconducting qubits utilizing IJJs.

In this paper, we compare performances of train localization schemes by the dynamic programming of various sensor information obtained from a smartphone attached to a train, and further discuss the most superior sensor information and scheme in this localization system. First, we compare the localization performances of single sensor information schemes, such as 3-axis acceleration information, acoustic information, 3-axis magnetic information, and barometric pressure information. These comparisons reveal that the lateral acceleration information input scheme has the best localization performance. Furthermore, we optimize each data fusion scheme and compare the localization performances of the data-fusion schemes using the optimal ratio of coefficients. The results show that the hybrid scheme has the best localization performance, with a root mean squared error (RMSE) of 12.2 m. However, there are no differences between the RMSEs of the input fusion scheme and 3-axis acceleration input scheme in the most significant three digits. Consequently, we conclude that the 3-axis acceleration input fusion scheme is the most reasonable in terms of simplicity.

This work presents the thermal noise analysis results of ring amplifiers in the MDAC of cyclic ADC. Ring amplifier is an alternative closed-loop structure for residual signal amplification with MDAC, and two types of ring amplifiers: pseudo-differential and fully-differential ring-amplifiers are considered for the implementation of MDAC in cyclic ADC. Theoretical analysis results show that power of thermal noise in MDAC with a pseudo-differential amplifier is much higher than that with a fully-differential ring-amplifier. SPICE simulation results with transient noise analyses also show the similar trend. Experimental prototype cyclic ADCs in 65nm CMOS technology are implemented with the same architecture and the same circuit components except for amplifiers. Comparison of the measured results of the two ADCs confirms the validity of the theoretical analysis results.

The problem caused by fake news continues to worsen in today's online social networks. Intuitively, it seems effective to issue corrections as a countermeasure. However, corrections can, ironically, strengthen attention to fake news, which worsens the situation. This paper proposes a model for describing the interaction between fake news and the corrections as a reaction-diffusion system; this yields the mechanism by which corrections increase attention to fake news. In this model, the emergence of groups of users who believe in fake news is understood as a Turing pattern that appears in the activator-inhibitor model. Numerical calculations show that even if the network structure has no spatial bias, the interaction between fake news and the corrections creates groups that are strongly interested in discussing fake news. Also, we propose and evaluate a basic strategy to counter fake news.

In this paper, a metadata-based quality-estimation model is proposed for tile-based omnidirectional video streaming services, aiming to realize quality monitoring during service provision. In the tile-based omnidirectional video (ODV) streaming services, the ODV is divided into tiles, and the high-quality tiles and the low-quality tiles are distributed in accordance with the user's viewing direction. When the user changes the viewing direction, the user temporarily watches video with the low-quality tiles. In addition, the longer the time (delay time) until the high-quality tile for the new viewing direction is downloaded, the longer the viewing time of video with the low-quality tile, and thus the delay time affects quality. From the above, the video quality of the low-quality tiles and the delay time significantly impact quality, and these factors need to be considered in the quality-estimation model. We develop quality-estimation models by extending the conventional quality-estimation models for 2D adaptive streaming. We also show that the quality-estimation model using the bitrate, resolution, and frame rate of high- and low-quality tiles and that the delay time has sufficient estimation accuracy based on the results of subjective quality evaluation experiments.

Visual Question Answering (VQA) usually uses deep attention mechanisms to learn fine-grained visual content of images and textual content of questions. However, the deep attention mechanism can only learn high-level semantic information while ignoring the impact of the low-level semantic information on answer prediction. For such, we design a High- and Low-Level Semantic Information Network (HLSIN), which employs two strategies to achieve the fusion of high-level semantic information and low-level semantic information. Adaptive weight learning is taken as the first strategy to allow different levels of semantic information to learn weights separately. The gate-sum mechanism is used as the second to suppress invalid information in various levels of information and fuse valid information. On the benchmark VQA-v2 dataset, we quantitatively and qualitatively evaluate HLSIN and conduct extensive ablation studies to explore the reasons behind HLSIN's effectiveness. Experimental results demonstrate that HLSIN significantly outperforms the previous state-of-the-art, with an overall accuracy of 70.93% on test-dev.

Tool condition monitoring is one of the core tasks of intelligent manufacturing in digital workshop. This paper presents an intelligent recognize method of tool condition based on deep learning. First, the industrial microphone is used to collect the acoustic signal during machining; then, a central fractal decomposition algorithm is proposed to extract sensitive information; finally, the multi-scale convolutional recurrent neural network is used for deep feature extraction and pattern recognition. The multi-process milling experiments proved that the proposed method is superior to the existing methods, and the recognition accuracy reached 88%.

With the widespread use of indoor positioning technology, the need for high-precision positioning services is rising; nevertheless, there are several challenges, such as the difficulty of simulating the distribution of interior location data and the enormous inaccuracy of probability computation. As a result, this paper proposes three different neural network model comparisons for indoor location based on WiFi fingerprint - indoor location algorithm based on improved back propagation neural network model, RSSI indoor location algorithm based on neural network angle change, and RSSI indoor location algorithm based on depth neural network angle change - to raise accurately predict indoor location coordinates. Changing the action range of the activation function in the standard back-propagation neural network model achieves the goal of accurately predicting location coordinates. The revised back-propagation neural network model has strong stability and enhances indoor positioning accuracy based on experimental comparisons of loss rate (loss), accuracy rate (acc), and cumulative distribution function (CDF).

An attention residual network with triple feature as input is proposed to predict the remaining useful life (RUL) of bearings. First, the channel attention and spatial attention are connected in series into the residual connection of the residual neural network to obtain a new attention residual module, so that the newly constructed deep learning network can better pay attention to the weak changes of the bearing state. Secondly, the “triple feature” is used as the input of the attention residual network, so that the deep learning network can better grasp the change trend of bearing running state, and better realize the prediction of the RUL of bearing. Finally, The method is verified by a set of experimental data. The results show the method is simple and effective, has high prediction accuracy, and reduces manual intervention in RUL prediction.

In recent years, the applications of deep learning have facilitated the development of green intelligent transportation system (ITS), and carbon dioxide estimation has been one of important issues in green ITS. Furthermore, the carbon dioxide estimation could be modelled as the fuel consumption estimation. Therefore, a clustering-based neural network is proposed to analyze clusters in accordance with fuel consumption behaviors and obtains the estimated fuel consumption and the estimated carbon dioxide. In experiments, the mean absolute percentage error (MAPE) of the proposed method is only 5.61%, and the performance of the proposed method is higher than other methods.

QoS of applications is essential for content providers, and it is required to improve the end-to-end communication quality from a content provider to users. Generally, a content provider's data center network is connected to multiple ASes and has multiple egress paths to reach the content user's network. However, on the Internet, the communication quality of network paths outside of the provider's administrative domain is a black box, so multiple egress paths cannot be quantitatively compared. In addition, it is impossible to determine a unique egress path within a network domain because the parameters that affect the QoS of the content are different for each network. We propose a “Performance Aware Egress Path Discovery” method to improve QoS for content providers. The proposed method uses two techniques: Egress Peer Engineering with Segment Routing over IPv6 and Passive End-to-End Measurement. The method is superior in that it allows various metrics depending on the type of content and can be used for measurements without affecting existing systems. To evaluate our method, we deployed the Performance Aware Egress Path Discovery System in an existing content provider network and conducted experiments to provide production services. Our findings from the experiment show that, in this network, 15.9% of users can expect a 30Mbps throughput improvement, and 13.7% of users can expect a 10ms RTT improvement.