In this study, we use a vision-based driving monitoring sensor to track drivers’ visual scanning behavior, a key factor for preventing traffic accidents. Our system evaluates driver’s behaviors by referencing the safety knowledge of professional driving instructors, and provides real-time voice-guided safety advice to encourage safer driving. Our system’s evaluation of safe driving behaviors matched the instructor’s evaluation with accuracy over 80%.

We have proposed autonomous network diagnosis platform for operation of future large capacity and virtualized network, including 5G and beyond 5G services. As for the one candidate of information collection and analyzing function blocks in the platform, we proposed novel optical sensing techniques that utilized tapped raw signal data acquired from digital coherent optical receivers. The raw signal data is captured before various digital signal processing for demodulation. Therefore, it contains various waveform deformation and/or noise as it experiences through transmission fibers. In this paper, we examined to detect two possible failures in transmission lines including fiber bending and optical filter shift by analyzing the above-mentioned raw signal data with the help of machine learning. For the purpose, we have implemented Docker container applications in WhiteBox Cassini to acquire real-time raw signal data. We generated CNN model for the detections in off-line processing and used them for real-time detections. We have confirmed successful detection of optical fiber bend and/or optical filter shift in real-time with high accuracy. Also, we evaluated their tolerance against ASE noise and invented novel approach to improve detection accuracy. In addition to that, we succeeded to detect them even in the situation of simultaneous occurrence of those failures.

We present our data-collection and deep neural network (DNN)-training scheme for extracting the optical status from signals received by digital coherent optical receivers in fiber-optic networks. The DNN is trained with unlabeled datasets across multiple administrative network domains by combining federated learning and unsupervised learning. The scheme allows network administrators to train a common DNN-based encoder that extracts optical status in their networks without revealing their private datasets. An early-stage proof of concept was numerically demonstrated by simulation by estimating the optical signal-to-noise ratio and modulation format with 64-GBd 16QAM and quadrature phase-shift keying signals.

The optical phased array (OPA) is an emerging non-mechanical device that enables high-speed beam steering by emitting precisely phase-controlled lightwaves from numerous optical antennas. In practice, however, it is challenging to drive all phase shifters on an OPA in a deterministic manner due to the inevitable fabrication-induced phase errors and crosstalk between the phase shifters. In this work, we fabricate a 16-element silicon photonic non-redundant OPA chip with integrated phase monitors and experimentally demonstrate accurate monitoring of the relative phases of light from each optical antenna. Under the beam steering condition, the optical phase retrieved from the on-chip phase monitors varies linearly with the steering angle, as theoretically expected.

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.

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%.

As a key technology of 5G, NFV has attracted much attention. In addition, monitoring plays an important role, and can be widely used for virtual network function placement and resource optimisation. The existing monitoring methods focus on the monitoring load without considering they own resources needed. This raises a unique challenge: jointly optimising the NFV monitoring systems and minimising their monitoring load at runtime. The objective is to enhance the gain in real-time monitoring metrics at minimum monitoring costs. In this context, we propose a novel NFV monitoring solution, namely, iMon (Monitoring by inferring), that jointly optimises the monitoring process and reduces resource consumption. We formalise the monitoring process into a multitarget regression problem and propose three regression models. These models are implemented by a deep neural network, and an experimental platform is built to prove their availability and effectiveness. Finally, experiments also show that monitoring resource requirements are reduced, and the monitoring load is just 0.6% of that of the monitoring tool cAdvisor on our dataset.

End-to-end loss and delay are both fundamental metrics in network performance evaluation, and accurate measurements for these end-to-end metrics are one of the keys to keeping delay/loss-sensitive applications (e.g., audio/video conferencing, IP telephony, or telesurgery) comfortable on networks. In our previous work [1], we proposed a parallel flow monitoring method that can provide accurate active measurements of end-to-end delay. In this method, delay samples of a target flow increase by utilizing the observation results of other flows sharing the source/destination with the target flow. In this paper, to improve accuracy of loss measurements, we propose a loss measurement method by extending our delay measurement method. Additionally, we improve the loss measurement method so that it enables to fully utilize information of all flows including flows with different source and destination. We evaluate the proposed method through theoretical and simulation analyses. The evaluations show that the accuracy of the proposed method is bounded by theoretical upper/lower bounds, and it is confirmed that it reduces the error of loss rate estimations by 57.5% on average.

This brief proposes a solar-cell-assisted wireless biosensing system that operates using a biofuel cell (BFC). To facilitate BFC area reduction for the use of this system in area-constrained continuous glucose monitoring contact lenses, an energy harvester combined with an on-chip solar cell is introduced as a dedicated power source for the transmitter. A dual-oscillator-based supply voltage monitor is employed to convert the BFC output into digital codes. From measurements of the test chip fabricated in 65-nm CMOS technology, the proposed system can achieve 99% BFC area reduction.

This letter presents a new framework for synchronous remote online exams. This framework proposes new monitoring of notebooks in remote locations and limited messaging only enabled between students and their instructor during online exams. This framework was evaluated by students as highly effective in minimizing cheating during online exams.

This paper proposes a fast and scalable traffic monitoring system called Fast xFlow Proxy. For efficiently provisioning and operating networks, xFlow such as IPFIX and NetFlow is a promising technology for visualizing the detailed traffic matrix in a network. However, internet protocol (IP) packets in a large carrier network are encapsulated with various outer headers, e.g., layer 2 tunneling protocol (L2TP) or multi-protocol label switching (MPLS) labels. As native xFlow technologies are applied to the outer header, the desired inner information cannot be visualized. From this motivation, we propose Fast xFlow Proxy, which explores the complicated carrier's packet, extracts inner information properly, and relays the inner information to a general flow collector. Fast xFlow Proxy should be able to handle various packet processing operations possible (e.g., header analysis, header elimination, and statistics) at a wire rate. To realize the processing speed needed, we implement Fast xFlow Proxy using the data plane development kit (DPDK) and field-programmable gate array (FPGA). By optimizing deployment of processes between DPDK and FPGA, Fast xFlow Proxy achieves wire rate processing. From evaluations, we can achieve over 20 Gbps performance by using a single server and 100 Gbps performance by using scale-out architecture. We also show that this performance is sufficiently practical for monitoring a nationwide carrier network.

OpenFlow is a widely adopted implementation of the Software-Defined Networking (SDN) architecture. Since conventional network monitoring systems are unable to cope with OpenFlow networks, researchers have developed various monitoring systems tailored for OpenFlow networks. However, these existing systems either rely on a specific controller framework or an API, both of which are not part of the OpenFlow specification, and thus limit their applicability. This article proposes a transparent and low-overhead monitoring system for OpenFlow networks, referred to as Opimon. Opimon monitors the network topology, switch statistics, and flow tables in an OpenFlow network and visualizes the result through a web interface in real-time. Opimon monitors a network by interposing a proxy between the controller and switches and intercepting every OpenFlow message exchanged. This design allows Opimon to be compatible with any OpenFlow switch or controller. We tested the functionalities of Opimon on a virtual network built using Mininet and a large-scale international OpenFlow testbed (PRAGMA-ENT). Furthermore, we measured the performance overhead incurred by Opimon and demonstrated that the overhead in terms of latency and throughput was less than 3% and 5%, respectively.

Active network monitoring based on Boolean network tomography is a promising technique to localize link failures instantly in transport networks. However, the required set of monitoring trails must be recomputed after each link failure has occurred to handle succeeding link failures. Existing heuristic methods cannot compute the required monitoring trails in a sufficiently short time when multiple-link failures must be localized in the whole of large-scale managed networks. This paper proposes an approach for computing the required monitoring trails within an allowable expected period specified beforehand. A random walk-based analysis estimates the number of monitoring trails to be computed in the proposed approach. The estimated number of monitoring trails are computed by a lightweight method that only guarantees partial localization within restricted areas. The lightweight method is repeatedly executed until a successful set of monitoring trails achieving unambiguous localization in the entire managed networks can be obtained. This paper demonstrates that the proposed approach can compute a small number of monitoring trails for localizing all independent dual-link failures in managed networks made up of thousands of links within a given expected short period.

Predictive spatial-monitoring, which predicts spatial information such as road traffic, has attracted much attention in the context of smart cities. Machine learning enables predictive spatial-monitoring by using a large amount of aggregated sensor data. Since the capacity of mobile networks is strictly limited, serious transmission delays occur when loads of communication traffic are heavy. If some of the data used for predictive spatial-monitoring do not arrive on time, prediction accuracy degrades because the prediction has to be done using only the received data, which implies that data for prediction are ‘delay-sensitive’. A utility-based allocation technique has suggested modeling of temporal characteristics of such delay-sensitive data for prioritized transmission. However, no study has addressed temporal model for prioritized transmission in predictive spatial-monitoring. Therefore, this paper proposes a scheme that enables the creation of a temporal model for predictive spatial-monitoring. The scheme is roughly composed of two steps: the first involves creating training data from original time-series data and a machine learning model that can use the data, while the second step involves modeling a temporal model using feature selection in the learning model. Feature selection enables the estimation of the importance of data in terms of how much the data contribute to prediction accuracy from the machine learning model. This paper considers road-traffic prediction as a scenario and shows that the temporal models created with the proposed scheme can handle real spatial datasets. A numerical study demonstrated how our temporal model works effectively in prioritized transmission for predictive spatial-monitoring in terms of prediction accuracy.

Advanced information-processing services such as computer vision require a high-performance digital circuit to perform high-load processing at high speed. To achieve high-speed processing, several image-processing applications use an approximate computing technique to reduce idle time of the circuit. However, it is difficult to design the high-speed image-processing circuit while controlling the error rate so as not to degrade service quality, and this technique is used for only a few applications. In this paper, we propose a method that achieves high-speed processing effectively in which processing time for each task is changed by roughly detecting its completion. Using this method, a high-speed processing circuit with a low error rate can be designed. The error rate is controllable, and a circuit design method to minimize the error rate is also presented in this paper. To confirm the effectiveness of our proposal, a ripple-carry adder (RCA), 2-dimensional discrete cosine transform (2D-DCT) circuit, and histogram of oriented gradients (HOG) feature calculation circuit are evaluated. Effective clock periods of these circuits obtained by our method with around 1% error rate are improved about 64%, 6%, and 12%, respectively, compared with circuits without error. Furthermore, the impact of the miscalculation on a video monitoring service using an object detection application is investigated. As a result, more than 99% of detection points required to be obtained are detected, and it is confirmed the miscalculation hardly degrades the service quality.

In recent years, checking sleep quality has become essential from a healthcare perspective. In this paper, we propose a respiratory rate (RR) monitoring system that can be used in the bedroom without wearing any sensor devices directly. To develop the system, passive radio-frequency identification (RFID) tags are introduced and attached to a blanket, instead of attaching them to the human body. The received signal strength indicator (RSSI) and phase values of the passive RFID tags are continuously obtained using an RFID reader through antennas located at the bedside. The RSSI and phase values change depending on the respiration of the person wearing the blanket. Thus, we can estimate the RR using these values. After providing an overview of the proposed system, the RR estimation flow is explained in detail. The processing flow includes noise elimination and irregular breathing period estimation methods. The evaluation demonstrates that the proposed system can estimate the RR and respiratory status without considering the user's body posture, body type, gender, or change in the RR.

A noise-robust and accuracy-enhanced microwave imaging algorithm is presented for microwave ablation monitoring of cancer treatment. The ablation impact of dielectric change can be assessed by microwave inverse scattering analysis, where the dimension and dielectric drop of the ablation zone enable safe ablation monitoring. We focus on the distorted Born iterative method (DBIM), which is applicable to highly heterogeneous and contrasted dielectric profiles. As the reconstruction accuracy and convergence speed of DBIM depend largely on the initial estimate of the dielectric profile or noise level, this study exploits a prior estimate of the DBIM for the pre-ablation state to accelerate the convergence speed and introduces the matched-filter-based noise reduction scheme in the DBIM framework. The two-dimensional finite-difference time-domain numerical test with realistic breast phantoms shows that our method significantly enhances the reconstruction accuracy with a lower computational cost.

Due to the legal reform on the protection of personal information in US/Japan and the enforcement of the General Data Protection Regulation (GDPR) in Europe, service providers are obliged to more securely manage the sensitive data stored in their server. In order to protect this kind of data, they generally employ a cryptographic encryption scheme and secure key management schemes such as a Hardware Security Module (HSM) and Trusted Platform Module (TPM). In this paper, we take a different approach based on the space-hard cipher. The space-hard cipher has an interesting property called the space hardness. Space hardness guarantees sufficient security against the adversary who gains a part of key data, e.g., 1/4 of key data. Combined with a simple network monitoring technique, we develop a practical leakage resilient scheme Virtual Vault, which is secure against the snapshot adversary who has full access to the memory in the server for a short period. Importantly, Virtual Vault is deployable by only a low-price device for network monitoring, e.g. L2 switch, and software of space-hard ciphers and packet analyzer, while typical solutions require a dedicated hardware for secure key managements such as HSM and TPM. Thus, Virtual Vault is easily added on the existing servers which do not have such dedicated hardware.

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.