Analyzing aging-induced delay degradations of ring oscillators (ROs) is an effective way to detect recycled field-programmable gate arrays (FPGAs). However, it requires a large number of RO measurements for all FPGAs before shipping, which increases the measurement costs. We propose a cost-efficient recycled FPGA detection method using a statistical performance characterization technique called virtual probe (VP) based on compressed sensing. The VP technique enables the accurate prediction of the spatial process variation of RO frequencies on a die by using a very small number of sample RO measurements. Using the predicted frequency variation as a supervisor, the machine-learning model classifies target FPGAs as either recycled or fresh. Through experiments conducted using 50 commercial FPGAs, we demonstrate that the proposed method achieves 90% cost reduction for RO measurements while preserving the detection accuracy. Furthermore, a one-class support vector machine algorithm was used to classify target FPGAs with around 94% detection accuracy.

This paper proposes a computational complexity-reduced algorithm for an adaptive peak-to-average power ratio (PAPR) reduction method previously developed by members of our research group that uses the null space in a multiple-input multiple-output (MIMO) channel for MIMO-orthogonal frequency division multiplexing (OFDM) signals. The proposed algorithm is an extension of the peak cancellation (PC) signal-based method that has been mainly investigated for per-antenna PAPR reduction. This method adds the PC signal, which is designed so that the out-of-band radiation is removed/reduced, directly to the time-domain transmission signal at each antenna. The proposed method, referred to as PCCNC (PC with channel-null constraint), performs vector-level signal processing in the PC signal generation so that the PC signal is transmitted only to the null space in the MIMO channel. We investigate three methods to control the beamforming (BF) vector in the PC signal, which is a key factor in determining the achievable PAPR performance of the algorithm. Computer simulation results show that the proposed PCCNC achieves approximately the same throughput-vs.-PAPR performance as the previous method while dramatically reducing the required computational cost.

We propose two algorithms for the gathering of k mobile agents in asynchronous Byzantine environments. For both algorithms, we assume that graph topology is arbitrary, each node is equipped with an authenticated whiteboard, agents have unique IDs, and at most f weakly Byzantine agents exist. Here, a weakly Byzantine agent can make arbitrary behavior except falsifying its ID. Under these assumptions, the first algorithm achieves a gathering without termination detection in O(m+fn) moves per agent (m is the number of edges and n is the number of nodes). The second algorithm achieves a gathering with termination detection in O(m+fn) moves per agent by additionally assuming that agents on the same node are synchronized, $f

Since the owner's data might be leaked from the centralized server storage, the distributed storage schemes with the server storage have been investigated. To ensure the owner's data in those schemes, they use Reed Solomon code. However, those schemes occur the burden of data capacity since the parity data are increased by how much the disconnected data can be restored. Moreover, the calculation time for the restoration will be higher since many parity data are needed to restore the disconnected data. In order to reduce the burden of data capacity and the calculation time, we proposed the server-based distributed storage using Secret Sharing with AES-256 for lightweight safety restoration. Although we use Secret Sharing, the owner's data will be safely kept in the distributed storage since all of the divided data are divided into two pieces with the AES-256 and stored in the peer storage and the server storage. Even though the server storage keeps the divided data, the server and the peer storages might know the pair of divided data via Secret Sharing, the owner's data are secure in the proposed scheme from the inner attack of Secret Sharing. Furthermore, the owner's data can be restored by a few parity data. The evaluations show that our proposed scheme is improved for lightweight, stability, and safety.

Driver drowsiness estimation is one of the important tasks for preventing car accidents. Most of the approaches are binary classification that classify a driver is significantly drowsy or not. Multi-level drowsiness estimation, that detects not only significant drowsiness but also moderate drowsiness, is helpful to a safer and more comfortable car system. Existing approaches are mostly based on conventional temporal measures which extract temporal information related to eye states, and these measures mainly focus on detecting significant drowsiness for binary classification. For multi-level drowsiness estimation, we propose two temporal measures, average eye closed time (AECT) and soft percentage of eyelid closure (Soft PERCLOS). Existing approaches are also based on a time domain convolutional neural network (CNN) as deep neural network models, of which layers are linked sequentially. The network model extracts features mainly focusing on mono-temporal resolution. We found that features focusing on multi-temporal resolution are effective to multi-level drowsiness estimation, and we propose a parallel linked time-domain CNN to extract the multi-temporal features. We collected an own dataset in a real environment and evaluated the proposed methods with the dataset. Compared with existing temporal measures and network models, Our system outperforms the existing approaches on the dataset.

The spectral efficiency of photonic networks can be enhanced by the use of higher modulation orders and narrower channel bandwidth. Unfortunately, these solutions are precluded by the margins required to offset uncertainties in system performance. Furthermore, as recently highlighted, the disaggregation of optical transport systems increases the required margin. We propose here highly spectrally efficient networks, whose margins are minimized by transmission-quality-aware adaptive modulation-order/channel-bandwidth assignment enabled by optical performance monitoring (OPM). Their effectiveness is confirmed by experiments on 400-Gbps dual-polarization quadrature phase shift keying (DP-QPSK) and 16-ary quadrature amplitude modulation (DP-16QAM) signals with the application of recently developed Q-factor-based OPM. Four-subcarrier 32-Gbaud DP-QPSK signals within 150/162.5/175GHz and two-subcarrier 32-Gbaud DP-16QAM signals within 75/87.5/100GHz are experimentally analyzed. Numerical network simulations in conjunction with the experimental results demonstrate that the proposed scheme can drastically improve network spectral efficiency.

In this paper, we propose homomorphic encryption based device owner equality verification (HE-DOEV), a new method to verify whether the owners of two devices are the same. The proposed method is expected to be used for credential sharing among devices owned by the same user. Credential sharing is essential to improve the usability of devices with hardware-assisted trusted environments, such as a secure element (SE) and a trusted execution environment (TEE), for securely storing credentials such as private keys. In the HE-DOEV method, we assume that the owner of every device is associated with a public key infrastructure (PKI) certificate issued by an identity provider (IdP), where a PKI certificate is used to authenticate the owner of a device. In the HE-DOEV method, device owner equality is collaboratively verified by user devices and IdPs that issue PKI certificates to them. The HE-DOEV method verifies device owner equality under the condition where multiple IdPs can issue PKI certificates to user devices. In addition, it can verify the equality of device owners without disclosing to others any privacy-related information such as personally identifiable information and long-lived identifiers managed by an entity. The disclosure of privacy-related information is eliminated by using homomorphic encryption. We evaluated the processing performance of a server needed for an IdP in the HE-DOEV method. The evaluation showed that the HE-DOEV method can provide a DOEV service for 100 million users by using a small-scale system in terms of the number of servers.

Collaborative filtering (CF) is one of the most popular approaches to building Recommender systems (RS) and has been extensively implemented in many online applications. But it still suffers from the new user cold start problem that users have only a small number of items interaction or purchase records in the system, resulting in poor recommendation performance. Thus, we design a new similarity model which can fully utilize the limited rating information of cold users. We first construct a new metric, Popularity-Mean Squared Difference, considering the influence of popular items, average difference between two user's common ratings and non-numerical information of ratings. Moreover, the second new metric, Singularity-Difference, presents the deviation degree of favor to items between two users. It considers the distribution of the similarity degree of co-ratings between two users as weight to adjust the deviation degree. Finally, we take account of user's personal rating preferences through introducing the mean and variance of user ratings. Experiment results based on three real-life datasets of MovieLens, Epinions and Netflix demonstrate that the proposed model outperforms seven popular similarity methods in terms of MAE, precision, recall and F1-Measure under new user cold start condition.

Assurance cases are documents for arguing that systems satisfy required properties such as safety and security in the given environment based on sufficient evidence. As systems become complex and networked, the importance of assurance cases has become significant. However, we observe that creating assurance cases has some essential difficulties, and unfortunately it seems that assurance cases have not been widely used in industries. For this problem, we have been developing assurance cases creation methods and opening workshops based on the creation methods. This paper presents an assurance cases creation method called “D-Case Steps” which is based on d* framework[1], an agent-based assurance case method, and reports the results of workshops. The results indicate that our workshops have been improved and our activities on assurance cases facilitates use of them in Japan. This paper is an extended version of [2]. We add detailed background and related works, workshops results and evaluation, and lessons learned from our a decade experiences.

In this paper, the influence of high-temperature sputtering on the nitrogen-doped (N-doped) LaB6 thin film formation utilizing RF sputtering was investigated. The N-doped LaB6/SiO2/p-Si(100) MOS diode and N-doped LaB6/p-Si(100) of Schottky diode were fabricated. A 30 nm thick N-doped LaB6 thin film was deposited from room temperature (RT) to 150°C. It was found that the resistivity was decreased from 1.5 mΩcm to 0.8 mΩcm by increasing deposition temperature from RT to 150°C. The variation of work function was significantly decreased in case that N-doped LaB6 thin film deposited at 150°C. Furthermore, Schottky characteristic was observed by increasing deposition temperature to 150°C. In addition, the crystallinity of N-doped LaB6 thin film was improved by increasing deposition temperature.

This work reports a new technique to suppress the undesirable multiple-triggering effect in the typical diode triggered silicon controlled rectifier (DTSCR), which is frequently used as an ESD protection element in the advanced CMOS technologies. The technique is featured by inserting additional N-Well areas under the N+ region of intrinsic SCR, which helps to improve the substrate resistance. As a consequence, the delay of intrinsic SCR is reduced as the required triggering current is largely decreased and multiple-triggering related higher trigger voltage is removed. The novel DTSCR structures can alter the stacked diodes to achieve the precise trigger voltage to meet different ESD protection requirements. All explored DTSCR structures are fabricated in a 65-nm CMOS process. Transmission-line-pulsing (TLP) and Very-Fast-Transmission-line-pulsing (VF-TLP) test systems are adopted to confirm the validity of this technique and the test results accord well with our analysis.

In recent years, cognition and use of manga pervade, and people who use manga for various purposes such as entertainment, study, marketing are increasing more and more. However, when people who do not specialize in it create it for these purposes, they can write plots expressing what they want to convey but the technique of the composition which arranges elements in manga such as characters or balloons corresponding to the plot create obstacles to using its merits for comprehensibility based on high flexibility of its expression. Therefore, we consider that support of this composition technique is necessary for amateurs to use manga while taking advantage of its benefits. We propose a method of generating composition proposal to support manga creation by amateurs. For the method, we also define new manga metadata model which summarize and extend metadata models by earlier studies. It represents the compostion and the plot in manga. We apply a neural machine translation mechanism for learing the relation between the composition and the plot. It considers that the plot annotation is the source of the composition annotation that is the target, and learns from the annotation dataset based on the metadata model. We conducted experiments to evaluate how the composition proposal generated by our method helps amateur manga creation, and demonstrated that it is useful.

The conventional digital video encoding and transmission are inefficient for crowdsourced multi-view video uploading due to its high power consumption, and undesirable quality degradation in unstable wireless channel. Soft video delivery scheme known as SoftCast skips digital video encoding and transmission to decrease power consumption in video encoding and transmission. In addition, it achieves graceful quality improvement with the improvement of wireless channel quality by directly sending linear-transformed video signals. However, there are two typical issues to apply conventional soft video delivery to crowdsourced multi-view video uploading. First, since soft video delivery has been designed for direct path between each contributor and the access point (AP), it may suffer low video quality when a contributor uploads its video to the AP over unstable direct wireless path. Second, conventional soft video delivery may suffer low video quality due to the redundant transmission of correlated videos because it does not exploit inter-camera correlations existed in multi-view videos. In this paper, we propose a cluster-based redirect video uploading scheme for high-quality and low-power crowdsourced multi-view video streaming. The proposed scheme integrates the four approaches of network clustering, delegate selection, soft video delivery, and four-dimensional discrete consine transform (4D-DCT) to redirectly upload the captured videos to the AP. Specifically, network clustering and delegate selection leverage the redirect path between the contributors and the AP. Soft video delivery removes power-hungry digital encoding and transmission by directly sending frequency-domain coefficients using multi-dimensional DCT and near-analog modulation. 4D-DCT exploits the content correlations between the contributors to reduce redundant transmissions. Evaluation results show that our proposed scheme outperforms the conventional soft video delivery scheme when the channel quality difference between the direct and redirect paths increases. In addition, our scheme outperforms the digital-based video uploading schemes in terms of both video quality and power consumption. For example, the proposed scheme yields graceful quality improvement with the improvement of wireless channel quality, however, the digital-based schemes suffer from sudden quality degradation due to synchronization errors in decoding.

This manuscript discusses a new indoor positioning method and proposes a multi-distance function trilateration over k-NN fingerprinting method using radio signals. Generally, the strength of radio signals, referred to received signal strength indicator or RSSI, decreases as they travel in space. Our method employs a list of fingerprints comprised of RSSIs to absorb interference between radio signals, which happens around the transmitters and it also employs multiple distance functions for conversion from distance between fingerprints to the physical distance in order to absorb the interference that happens around the receiver then it performs trilateration between the top three closest fingerprints to locate the receiver's current position. An experiment in positioning performance is conducted in our laboratory and the result shows that our method is viable for a position-level indoor positioning method and it could improve positioning performance by 12.7% of positioning error to 0.406 in meter in comparison with traditional methods.

To improve the cutting skills of learners, we developed a method for improving the skill involved in creating paper cuttings based on a steering task in the field of human-computer interaction. TaWe made patterns using the white and black boundaries that make up a picture. The index of difficulty (ID) is a numerical value based on the width and distance of the steering law. First, we evaluated novice and expert pattern-cutters, and measured their moving time (MT), error rate, and compliance with the steering law, confirming that the MT and error rate are affected by pattern width and distance. Moreover, we quantified the skills of novices and experts using ID and MT based models. We then observed changes in the cutting skills of novices who practiced with various widths and evaluated the impact of the difficulty level on skill improvement. Patterns considered to be moderately difficult for novices led to a significant improvement in skills.

In partially depleted SOI (PD-SOI) technology, the SCR-based protection device is desired due to its relatively high robustness, but be restricted to use because of its inherent low holding voltage (Vh) and high triggering voltage (Vt1). In this paper, the body-tie side triggering diode inserting silicon controlled rectifier (BSTDISCR) is proposed and verified in 180 nm PD-SOI technology. Compared to the other devices in the same process and other related works, the BSTDISCR presents as a robust and latchup-immune PD-SOI ESD protection device, with appropriate Vt1 of 6.3 V, high Vh of 4.2 V, high normalized second breakdown current (It2), which indicates the ESD protection robustness, of 13.3 mA/µm, low normalized parasitic capacitance of 0.74 fF/µm.

The outcome of document clustering depends on the scheme used to assign a weight to each term in a document. While recent works have tried to use distributions related to class to enhance the discrimination ability. It is worth exploring whether a deviation approach or an entropy approach is more effective. This paper presents a comparison between deviation-based distribution and entropy-based distribution as constraints in term weighting. In addition, their potential combinations are investigated to find optimal solutions in guiding the clustering process. In the experiments, the seeded k-means method is used for clustering, and the performances of deviation-based, entropy-based, and hybrid approaches, are analyzed using two English and one Thai text datasets. The result showed that the deviation-based distribution outperformed the entropy-based distribution, and a suitable combination of these distributions increases the clustering accuracy by 10%.

This paper presents a two-dimensional (2D) DOA algorithm for double L-shaped arrays. The algorithm is applied to the underwater environment for eliminating the performance error caused by the sound speed uncertainty factor. By introducing the third dimensional array, the algorithm eliminates the sound velocity variable in the depression angle expression, so that the DOA estimation no longer considering the true value of unknown sound velocity. In order to determine the parameters of a three-dimensional array, a parameter matching method with the double L-shaped array is also proposed. Simulations show that the proposed algorithm outperforms the conventional 2D-DOA estimation algorithm in unknown sound velocity environment.

This paper analyzes and compares two methods to estimate electromagnetically coupled noises introduced to an antenna due to the nearby circuits at a circuit design stage. One of them is to estimate the power spectrum, and the other one is to estimate the active S11 parameter at the victim antenna, respectively, and both of them use simulated standard S-parameters for the electromagnetic coupling in the circuit. They also need the assumed or measured excitation of noise sources. To confirm the validness of the two methods, an evaluation board consisting of an antenna and noise sources were designed and fabricated in which voltage controlled oscillator (VCO) chips are placed as noise sources. The generated electromagnetic noises are transferred to an antenna via loop-shaped transmission lines, degrading the performance of the antenna. In this paper, detailed analysis procedures are described using the evaluation board, and it is shown that the two methods are equivalent to each other in terms of the induced voltages in the antenna. Finally, a procedure to estimate antenna performance degradation at the design stage is summarized.

Three-dimensional (3D) reconstruction and scene depth estimation from 2-dimensional (2D) images are major tasks in computer vision. However, using conventional 3D reconstruction techniques gets challenging in participating media such as murky water, fog, or smoke. We have developed a method that uses a continuous-wave time-of-flight (ToF) camera to estimate an object region and depth in participating media simultaneously. The scattered light observed by the camera is saturated, so it does not depend on the scene depth. In addition, received signals bouncing off distant points are negligible due to light attenuation, and thus the observation of such a point contains only a scattering component. These phenomena enable us to estimate the scattering component in an object region from a background that only contains the scattering component. The problem is formulated as robust estimation where the object region is regarded as outliers, and it enables the simultaneous estimation of an object region and depth on the basis of an iteratively reweighted least squares (IRLS) optimization scheme. We demonstrate the effectiveness of the proposed method using captured images from a ToF camera in real foggy scenes and evaluate the applicability with synthesized data.