We propose an augmented-reality-based method for arranging furniture using natural markers extracted from the edges of the walls of rooms. The proposed method extracts natural markers and estimates the camera parameters from single images of rooms using deep neural networks. Experimental results show that in all the measurements, the superimposition error of the proposed method was lower than that of general marker-based methods that use practical-sized markers.

In recent years, the development of visual tracking is getting better and better, but some methods cannot overcome the problem of low accuracy and success rate of tracking. Although there are some trackers will be more accurate, they will cost more time. In order to solve the problem, we propose a reinforced tracker based on Hierarchical Convolutional Features (HCF for short). HOG, color-naming and grayscale features are used with different weights to supplement the convolution features, which can enhance the tracking robustness. At the same time, we improved the model update strategy to save the time costs. This tracker is called RHCF and the code is published on https://github.com/z15846/RHCF. Experiments on the OTB2013 dataset show that our tracker can validly achieve the promotion of the accuracy and success rate.

This paper proposes individual computation processes of the partial demodulation reference signal (DM-RS) sequence in a synchronization signal (SS)/physical broadcast channel (PBCH) block to be used to detect the radio frame timing based on SS/PBCH block index detection for New Radio (NR) initial access. We present the radio frame timing detection probability using the proposed partial DM-RS sequence detection method that is applied subsequent to the physical-layer cell identity (PCID) detection in five tapped delay line (TDL) models in both non-line-of-sight (NLOS) and line-of-sight (LOS) environments. Computer simulation results show that by using the proposed method, the radio frame timing detection probabilities of almost 100% and higher than 90% are achieved for the LOS and NLOS channel models, respectively, at the average received signal-to-noise power ratio (SNR) of 0dB with the frequency stability of a local oscillator in a set of user equipment (UE) of 5ppm at the carrier frequency of 4GHz.

Platform-mounted small antennas increase dielectric loss and conductive loss and decrease the radiation efficiency. This paper proposes a novel antenna design method to improve radiation efficiency for platform-mounted small antennas by characteristic mode analysis. The proposed method uses mapping of modal weighting coefficient (MWC) and infinitesimal dipole and evaluate the metal casing with 100mm × 55mm × 23mm as a platform excited by an inverted-F antenna. The simulation and measurement results show that the radiation efficiency of 5% is improved with the whole system from 2.5% of the single antenna.

We experimentally evaluated random number sequences generated by a superconducting hardware random number generator composed of a Josephson-junction oscillator, a rapid-single-flux-quantum (RSFQ) toggle flip-flop (TFF), and an RSFQ AND gate. Test circuits were fabricated using a 10 kA/cm2 Nb/AlOx/Nb integration process. Measurements were conducted in a liquid helium bath. The random numbers were generated for a trigger frequency of 500 kHz under the oscillating Josephson-junction at 29 GHz. 26 random number sequences of 20 kb length were evaluated for bias voltages between 2.0 and 2.7 mV. The NIST FIPS PUBS 140-2 tests were used for the evaluation. 100% pass rates were confirmed at the bias voltages of 2.5 and 2.6 mV. We found that the Monobit test limited the pass rates. As numerical simulations suggested, a detailed evaluation for the probability of obtaining “1” demonstrated the monotonical dependence on the bias voltage.

In this paper, we propose a new training framework named the INmfCA algorithm for nonparallel voice conversion (VC) systems. To train conversion models, traditional VC frameworks require parallel corpora, in which source and target speakers utter the same linguistic contents. Although the frameworks have achieved high-quality VC, they are not applicable in situations where parallel corpora are unavailable. To acquire conversion models without parallel corpora, nonparallel methods are widely studied. Although the frameworks achieve VC under nonparallel conditions, they tend to require huge background knowledge or many training utterances. This is because of difficulty in disentangling linguistic and speaker information without a large amount of data. In this work, we tackle this problem by exploiting NMF, which can factorize acoustic features into time-variant and time-invariant components in an unsupervised manner. The method acquires alignment between the acoustic features of a source speaker's utterances and a target dictionary and uses the obtained alignment as activation of NMF to train the source speaker's dictionary without parallel corpora. The acquisition method is based on the INCA algorithm, which obtains the alignment of nonparallel corpora. In contrast to the INCA algorithm, the alignment is not restricted to observed samples, and thus the proposed method can efficiently utilize small nonparallel corpora. The results of subjective experiments show that the combination of the proposed algorithm and the INCA algorithm outperformed not only an INCA-based nonparallel framework but also CycleGAN-VC, which performs nonparallel VC without any additional training data. The results also indicate that a one-shot VC framework, which does not need to train source speakers, can be constructed on the basis of the proposed method.

A Josephson parametric oscillator (JPO) is an interesting system from the viewpoint of quantum optics because it has two stable self-oscillating states and can deterministically generate quantum cat states. A theoretical proposal has been made to operate a network of multiple JPOs as a quantum annealer, which can solve adiabatically combinatorial optimization problems at high speed. Proof-of-concept experiments have been actively conducted for application to quantum computations. This article provides a review of the mechanism of JPOs and their application as a quantum annealer.

The adiabatic quantum-flux-parametron (AQFP) is an energy-efficient superconductor logic element based on the quantum flux parametron. AQFP circuits can operate with energy dissipation near the thermodynamic and quantum limits by maximizing the energy efficiency of adiabatic switching. We have established the design methodology for AQFP logic and developed various energy-efficient systems using AQFP logic, such as a low-power microprocessor, reversible computer, single-photon image sensor, and stochastic electronics. We have thus demonstrated the feasibility of the wide application of AQFP logic in future information and communications technology. In this paper, we present a tutorial review on AQFP logic to provide insights into AQFP circuit technology as an introduction to this research field. We describe the historical background, operating principle, design methodology, and recent progress of AQFP logic.

Salient ship detection plays an important role in ensuring the safety of maritime transportation and navigation. However, due to the influence of waves, special weather, and illumination on the sea, existing saliency methods are still unable to achieve effective ship detection in a complex marine environment. To solve the problem, this paper proposed a novel saliency method based on an attention nested U-Structure (AU2Net). First, to make up for the shortcomings of the U-shaped structure, the pyramid pooling module (PPM) and global guidance paths (GGPs) are designed to guide the restoration of feature information. Then, the attention modules are added to the nested U-shaped structure to further refine the target characteristics. Ultimately, multi-level features and global context features are integrated through the feature aggregation module (FAM) to improve the ability to locate targets. Experiment results demonstrate that the proposed method could have at most 36.75% improvement in F-measure (Favg) compared to the other state-of-the-art methods.

In this study, we investigate outdoor propagation measurements performed in an industrial park environment at 28.3GHz band. The propagation characteristics were evaluated with the measurement result regarding the path loss characteristics. Ray tracing simulation was also studied and compared with the measurement data to evaluate the quantitative accuracy of ray tracing in millimeter-wave band wireless propagations. Ray tracing, whose accuracy was evaluated based on a comparison with the measurement results, can aid in the theoretical design of the coverage area and deterministic channel modeling.

The adiabatic quantum flux parametron (AQFP) is an energy-efficient, high-speed superconducting logic device. To observe the tiny output currents from the AQFP in experiments, high-speed voltage drivers are indispensable. In the present study, we develop a compact voltage driver for AQFP logic based on a Josephson latching driver (JLD), which has been used as a high-speed driver for rapid single-flux-quantum (RSFQ) logic. In the JLD-based voltage driver, the signal currents of AQFP gates are converted into gap-voltage-level signals via an AQFP/RSFQ interface and a four-junction logic gate. Furthermore, this voltage driver includes only 15 Josephson junctions, which is much fewer than in the case for the previously designed driver based on dc superconducting quantum interference devices (60 junctions). In measurement, we successfully operate the JLD-based voltage driver up to 4 GHz. We also evaluate the bit error rate (BER) of the driver and find that the BER is 7.92×10-10 and 2.67×10-3 at 1GHz and 4GHz, respectively.

The adiabatic quantum-flux-parametron (AQFP) is an energy-efficient superconductor logic device. In a previous study, we proposed a low-latency clocking scheme called delay-line clocking, and several low-latency AQFP logic gates have been demonstrated. In delay-line clocking, the latency between adjacent excitation phases is determined by the propagation delay of excitation currents, and thus the rising time of excitation currents should be sufficiently small; otherwise, an AQFP gate can switch before the previous gate is fully excited. This means that delay-line clocking needs high clock frequencies, because typical excitation currents are sinusoidal and the rising time depends on the frequency. However, AQFP circuits need to be tested in a wide frequency range experimentally. Hence, in the present study, we investigate AQFP circuits adopting delay-line clocking with square excitation currents to apply delay-line clocking in a low frequency range. Square excitation currents have shorter rising time than sinusoidal excitation currents and thus enable low frequency operation. We demonstrate an AQFP buffer chain with delay-line clocking using square excitation currents, in which the latency is approximately 20ps per gate, and confirm that the operating margin for the buffer chain is kept sufficiently wide at clock frequencies below 1GHz, whereas in the sinusoidal case the operating margin shrinks below 500MHz. These results indicate that AQFP circuits adopting delay-line clocking can operate in a low frequency range by using square excitation currents.

Extremely energy-efficient logic devices are required for future low-power high-performance computing systems. Superconductor electronic technology has a number of energy-efficient logic families. Among them is the adiabatic quantum-flux-parametron (AQFP) logic family, which adiabatically switches the quantum-flux-parametron (QFP) circuit when it is excited by an AC power-clock. When compared to state-of-the-art CMOS technology, AQFP logic circuits have the advantage of relatively fast clock rates (5 GHz to 10 GHz) and 5 - 6 orders of magnitude reduction in energy before cooling overhead. We have been developing extremely energy-efficient computing processor components using the AQFP. The adder is the most basic computational unit and is important in the development of a processor. In this work, we designed and measured a 16-bit parallel prefix carry look-ahead Kogge-Stone adder (KSA). We fabricated the circuit using the AIST 10 kA/cm2 High-speed STandard Process (HSTP). Due to a malfunction in the measurement system, we were not able to confirm the complete operation of the circuit at the low frequency of 100 kHz in liquid He, but we confirmed that the outputs that we did observe are correct for two types of tests: (1) critical tests and (2) 110 random input tests in total. The operation margin of the circuit is wide, and we did not observe any calculation errors during measurement.

In the state estimation of steady-state power networks, a cyber attack that cannot be detected from the residual (i.e., the estimation error) is called a false data injection (FDI) attack. In this letter, to enforce the security of power networks, we propose a method of detecting an FDI attack. In the proposed method, an FDI attack is detected by randomly choosing sensors used in the state estimation. The effectiveness of the proposed method is presented by two examples including the IEEE 14-bus system.

Incremental Learning, a machine learning methodology, trains the continuously arriving input data and extends the model's knowledge. When it comes to unlabeled data streams, incremental learning task becomes more challenging. Our newly proposed incremental learning methodology, Data Augmented Incremental Learning (DAIL), learns the ever-increasing real-time streams with reduced memory resources and time. Initially, the unlabeled batches of data streams are clustered using the proposed clustering algorithm, Clustering based on Autoencoder and Gaussian Model (CLAG). Later, DAIL creates an updated incremental model for the labelled clusters using data augmentation. DAIL avoids the retraining of old samples and retains only the most recently updated incremental model holding all old class information. The use of data augmentation in DAIL combines the similar clusters generated with different data batches. A series of experiments verified the significant performance of CLAG and DAIL, producing scalable and efficient incremental model.

In this paper, we study the number of failed components in a consecutive-k-out-of-n:G system. The distributions and expected values of the number of failed components when system is failed or working at a particular time t are evaluated. We also apply them to the optimization problems concerned with the optimal number of components and the optimal replacement time. Finally, we present the illustrative examples for the expected number of failed components and give the numerical results for the optimization problems.

This paper presents improvement of port-to-port isolation characteristics of a linearly dual-polarized dual-band and wideband multi-ring microstrip antenna (MR-MSA) fed by two L-probes. The linearly dual-polarized dual-band and wideband MR-MSA consists of two circular ring patches and two L-probes arranged in a multi-layered dielectric substrate. By using a thick substrate for the L-probe and arranging two ring patches as radiation elements, the proposed antenna operates wideband and dual-band characteristics. Furthermore, by arranging two L-probes at the orthogonal positions, the proposed antenna can radiate dual linear polarizations. In this paper, for improving port-to-port isolation characteristics of the linearly dual-polarized dual-band and wideband MR-MSA fed by two L-probes, a via connected to the ground plane at the center of the radiation elements is arranged. The fractional bandwidths below -10dB reflection obtained by the simulation of the MR-MSA with the via were 17.0% and 14.4%. Furthermore, the simulated isolation characteristics were more than 21.0dB and 17.0dB in the two bands. Improvement of the isolation characteristics between two ports as well as the dual-band and wideband performance of the proposed MR-MSA with the via were confirmed by the simulation and the measurement.

Rain attenuation characteristics due to typhoon passage are discussed using the Ku-band BS satellite signal observations conducted by Osaka Electro-Communication University in Neayagawa from 1988 to 2019. The degree of hourly rain attenuation due to rainfall rate is largely enhanced as typhoon passes the east side of the station, while it becomes smaller in the case of west side passage. Compared to hourly ground wind velocities of nearby AMeDAS, the equivalent path lengths of rain attenuation become larger as the wind directions approach the same angle to the satellite, while they become smaller as the wind directions approach the opposite angle to the satellite. The increase and decrease of the equivalent path lengths are confirmed in other Ku-band and Ka-band satellite paths with different azimuth angles, such as CS, SKP, and SBC. Modified equivalent path lengths calculated by a simple propagation path model including horizontal wind speeds along the same direction to the satellite agree well with the equivalent path lengths observed by each satellite. The equivalent path lengths are, for the first time, proved to be largely affected by the direction of typhoon passage and the horizontal wind velocities.

Recently, the performances of discriminative correlation filter (CF) trackers are getting better and better in visual tracking. In this paper, we propose spatial-temporal regularization with precise state estimation based on discriminative correlation filter (STPSE) in order to achieve more significant tracking performance. First, we consider the continuous change of the object state, using the information from the previous two filters for training the correlation filter model. Here, we train the correlation filter model with the hand-crafted features. Second, we introduce update control in which average peak-to-correlation energy (APCE) and the distance between the object locations obtained by HOG features and hand-crafted features are utilized to detect abnormality of the state around the object. APCE and the distance indicate the reliability of the filter response, thus if abnormality is detected, the proposed method does not update the scale and the object location estimated by the filter response. In the experiment, our tracker (STPSE) achieves significant and real-time performance with only CPU for the challenging benchmark sequence (OTB2013, OTB2015, and TC128).

The objective of critical nodes problem is to minimize pair-wise connectivity as a result of removing a specific number of nodes in the residual graph. From a mathematical modeling perspective, it comes the truth that the more the number of fragmented components and the evenly distributed of disconnected sub-graphs, the better the quality of the solution. Basing on this conclusion, we proposed a new Cluster Expansion Method for Critical Node Problem (CEMCNP), which on the one hand exploits a contraction mechanism to greedy simplify the complexity of sparse graph model, and on the other hand adopts an incremental cluster expansion approach in order to maintain the size of formed component within reasonable limitation. The proposed algorithm also relies heavily on the idea of multi-start iterative local search algorithm, whereas brings in a diversified late acceptance local search strategy to keep the balance between interleaving diversification and intensification in the process of neighborhood search. Extensive evaluations show that CEMCNP running on 35 of total 42 benchmark instances are superior to the outcome of KBV, while holding 3 previous best results out of the challenging instances. In addition, CEMCNP also demonstrates equivalent performance in comparison with the existing MANCNP and VPMS algorithms over 22 of total 42 graph models with fewer number of node exchange operations.