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.

This paper proposes a sampling strategy for bandlimited graph signals over perturbed graph, in which we assume the edge between any pair of the nodes may be deleted randomly. Considering the mismatch between the true graph and the presumed graph, we derive the mean square error (MSE) of the reconstructed bandlimited graph signals. To minimize the MSE, we propose a greedy-based algorithm to obtain the optimal sampling set. Furthermore, we use Neumann series to avoid the pseudo-inverse computing. An efficient algorithm with low-complexity is thus proposed. Finally, numerical results show the superiority of our proposed algorithms over the other existing algorithms.

This paper proposes a method to create various training images for instance segmentation in a semi-supervised manner. In our proposed learning scheme, a few 3D CG models of target objects and a large number of images retrieved by keywords from the Internet are employed for initial model training and model update, respectively. Instance segmentation requires pixel-level annotations as well as object class labels in all training images. A possible solution to reduce a huge annotation cost is to use synthesized images as training images. While image synthesis using a 3D CG simulator can generate the annotations automatically, it is difficult to prepare a variety of 3D object models for the simulator. One more possible solution is semi-supervised learning. Semi-supervised learning such as self-training uses a small set of supervised data and a huge number of unsupervised data. The supervised images are given by the 3D CG simulator in our method. From the unsupervised images, we have to select only correctly-detected annotations. For selecting the correctly-detected annotations, we propose to quantify the reliability of each detected annotation based on its silhouette as well as its textures. Experimental results demonstrate that the proposed method can generate more various images for improving instance segmentation.

In this study, simulations are performed to design an optimal device for thinning the GaN channel layer on the semi-insulating layer in HEMT. When the gate length is 50nm, the thickness of the undoped channel must be thinner than 300nm to observe the off state. When the GaN channel layer is an Fe-doped, an on/off ratio of ~300 can be achieved even with a gate length of 25nm, although the transconductance is slightly reduced.

When we collect vital data from exercisers by putting wireless sensor nodes to them, the reliability of the wireless data collection is dependent on the position of node on the body of exerciser, therefore, in order to determine the suitable body position, it is essential to evaluate the data collection performances by changing the body positions of nodes in experiments involving human subjects. However, their fair comparison is problematic, because the experiments have no repeatability, that is, we cannot evaluate the performances for multiple body positions in an experiment at the same time. In this paper, we predict the performances by a software network simulator. Using two main functions such as a channel state function and a mobility function, the network simulator can repeatedly generate the same channel and mobility conditions for nodes. Numerical result obtained by the network simulator shows that when collecting vital data from twenty two footballers in a game, among three body position such as waist, forearm and calf, the forearm position gives the highest data collection rate and the predicted data collection rates agree well with the ones obtained by an experiment involving real subjects.

This paper presents a real-time body motion classification system using the radio channel characteristics of a wearable body area network (BAN). We developed a custom wearable BAN radio channel measurement system by modifying an off-the-shelf ZigBee-based sensor network system, where the link quality indicator (LQI) values of the wireless links between the coordinator and four sensor nodes can be measured. After interpolating and standardizing the raw data samples in a pre-processing stage, the time-domain features are calculated, and the body motion is classified by a decision-tree based random forest machine learning algorithm which is most suitable for real-time processing. The features were carefully chosen to exclude those that exhibit the same tendency based on the mean and variance of the features to avoid overfitting. The measurements demonstrated successful real-time body motion classification and revealed the potential for practical use in various daily-life applications.

Massive MIMO is known as a promising technology for multiuser multiplexing in the fifth generation mobile communication system to accommodate the rapidly-increasing traffic. It has a large number of antenna elements and thus provides very sharp beams. As seen in hybrid beamforming, there have already been many papers on the concatenation of two precoders (beamformers). The inner precoder, i.e., a multi-beam former, performs a linear transformation between the element space and the beam space. The outer precoder forms nulls in the limited beam space spanned by selected beams to suppress the inter-user interference. In this two-step precoder, the beam shape is expected to determine the system performance. In this paper, we evaluate the achievable throughput performance for different beam-shaping schemes: a discrete Fourier transform (DFT) beam, Chebyshev weighted beams, and Taylor weighted beam. Simulations show that the DFT beam provides the best performance except the case of imperfect precoding and cell edge SNR of 30dB.

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.

We analyze the cost of networks consisting of optical cross-connect nodes with different architectures for realizing the next generation large bandwidth networks. The node architectures include wavelength granular and fiber granular optical routing cross-connects. The network cost, capital expenditure (CapEx), involves link cost and node cost, both of which are evaluated for different scale networks under various traffic volumes. Numerical experiments demonstrate that the subsystem modular architecture with wavelength granular routing yields the highest cost effectiveness over a wide range of parameter values.

In this paper, the effects of the TiN encapsulating layer and the dopant segregation process on the interface properties and the Schottky barrier height reduction of PdEr-silicide/n-Si(100) were investigated. The results show that controlling the initial location of the boron dopants by adding the TiN encapsulating layer lowered the Schottky barrier height (SBH) for hole to 0.20 eV. Furthermore, the density of interface states (Dit) on the order of 1011eV-1cm-2 was obtained indicating that the dopant segregation process with TiN encapsulating layer effectively annihilated the interface states.

This paper presents the feasibility of a capacitive coupler utilizing an electric double layer for wireless power transfer under seawater. Since seawater is an electrolyte solution, an electric double layer (EDL) is formed on the electrode surface of the coupler in direct current. If the EDL can be utilized in radio frequency, it is possible that high power transfer efficiency can be achieved under seawater because a high Q-factor can be obtained. To clarify this, the following steps need taking; First, measure the frequency characteristics of the complex permittivity in seawater and elucidate the behaviors of the EDL from the results. Second, clarify that EDL leads to an improvement in the Q-factor of seawater. It will be shown in this paper that capacitive coupling by EDL occurs using two kinds of the coupler models. Third, design a coupler with high efficiency as measured by the Q-factor and relative permittivity of EDL. Last, demonstrate that the designed coupler under seawater can achieve over 85% efficiency at a transfer distance of 5 mm and feasibility of the coupler with EDL.

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.

This study presents a method for improving the heartbeat interval accuracy of photoplethysmographic (PPG) sensors at ultra-low sampling rates. Although sampling rate reduction can extend battery life, it increases the sampling error and degrades the accuracy of the extracted heartbeat interval. To overcome these drawbacks, a sampling-error compensation method is proposed in this study. The sampling error is reduced by using linear interpolation and autocorrelation based on the waveform similarity of heartbeats in PPG. Furthermore, this study introduces two-line approximation and first derivative PPG (FDPPG) to improve the waveform similarity at ultra-low sampling rates. The proposed method was evaluated using measured PPG and reference electrocardiogram (ECG) of seven subjects. The results reveal that the mean absolute error (MAE) of 4.11ms was achieved for the heartbeat intervals at a sampling rate of 10Hz, compared with 1-kHz ECG sampling. The heartbeat interval error was also evaluated based on a heart rate variability (HRV) analysis. Furthermore, the mean absolute percentage error (MAPE) of the low-frequency/high-frequency (LF/HF) components obtained from the 10-Hz PPG is shown to decrease from 38.3% to 3.3%. This error is small enough for practical HRV analysis.

A laser imaging detection and ranging (LIDAR) is one of the key sensors for autonomous driving. In order to improve its performance of the measurable distance, especially toward the front-side direction of the vehicle, this paper presents rapid revolution speed control of a brushless DC (BLDC) motor with a cyclostationary command signal. This enables the increase of the signal integration time for the designated direction, and thus improves the signal-to-noise ratio (SNR), while maintaining the averaged revolution speed. We propose the use of pre-emphasis circuits to accelerate and decelerate the revolution speed of the motor rapidly, by modifying the command signal so as to enhance the transition of the speed. The adaptive signal processing can adjust coefficients of the pre-emphasis filter automatically, so that it can compensate for the decayed response of the motor and its controller. Experiments with a 20-W BLDC motor prove that the proposed technique can achieve the actual revolution speed output to track the designated speed profile ranging from 600 to 1400 revolutions per minute (rpm) during one turn.

Wireless medical telemetry service (WMTS) is an important wireless communication system in healthcare facilities. Recently, the potential for electromagnetic interference by noise emitted by switching regulators installed in light-emitting diode (LED) lamps has been a serious problem. In this study, we evaluated the characteristics of the electromagnetic noise emitted from LED lamps and its effect on WMTS. Switching regulators generally emit wide band impulsive noise whose bandwidth reaches 400MHz in some instances owing to the switching operation, but this impulsive nature is difficult to identify in the reception of WMTS because the bandwidth of WMTS is much narrower than that of electromagnetic noise. Gaussian approximation (GA) can be adopted for band-limited electromagnetic noise whose characteristics have no repetitive variation. On the other hand, GA with the impulsive correction factor (ICF) can be adopted for band-limited electromagnetic noise that has repetitive variation. We investigate the minimum receiver sensitivity of WMTS for it to be affected by electromagnetic noise emitted from LED lamps. The required carrier-to-noise power ratio (CNR) of Gaussian noise and electromagnetic noise for which GA can be adopted was approximately 15dB, but the electromagnetic noise for which GA with the ICF can be adopted was 3 to 4dB worse. Moreover, the spatial distribution of electromagnetic noise surrounding an LED lamp installation was measured. Finally, we roughly estimated the offset distance between the receiving antenna of WMTS and LED lamps when a WMTS signal of a certain level was added in a clinical setting using our experimental result for the required CNR.

Rapid development of modern communications has initiated essential requirements for providing efficient algorithms that can solve the routing and wavelength assignment (RWA) problem in satellite optical networks. In this paper, the bee colony algorithm optimization based on link cost for RWA (BCO-LCRWA) is tailored for satellite networks composed of intersatellite laser links. In BCO-LCRWA, a cost model of intersatellite laser links is established based on metrics of network transmission performance namely delay and wavelengths utilization, with constraints of Doppler wavelength drift, transmission delay, wavelength consistency and continuity. Specifically, the fitness function of bee colony exploited in the proposed algorithm takes wavelength resources utilization and communication hops into account to implement effective utilization of wavelengths, to avoid unnecessary over-detouring and ensure bit error rate (BER) performance of the system. The simulation results corroborate the improved performance of the proposed algorithm compared with the existing alternatives.

A method for estimating circuit performance variation caused by packaging-induced mechanical stress is proposed. The developed method is based on the stress distribution chart for the target integrated circuit (IC) and the stress sensitivity characteristics of individual devices. This information is experimentally obtained using a specially designed test chip and a cantilever bending calibration system. A post-packaging analysis and simulation tool, called Stress Netlist Generator (SNG), is developed for conducting the proposed method. Based on the stress distribution chart and the stress sensitivity characteristics, SNG modifies the SPICE model parameters in the target netlist according to the impact of the packaging-induced stress. The netlist generated by SNG is used to estimate packaging-induced performance variation with high accuracy. The developed method is remarkably effective even for small-scale ICs with chip sizes of roughly 1 mm2, such as power management ICs, which require higher precision.

Metric learning aims to generate similarity-preserved low dimensional feature vectors from input images. Most existing supervised deep metric learning methods usually define a carefully-designed loss function to make a constraint on relative position between samples in projected lower dimensional space. In this paper, we propose a novel architecture called Naive Similarity Discriminator (NSD) to learn the distribution of easy samples and predict their probability of being similar. Our purpose lies on encouraging generator network to generate vectors in fitting positions whose similarity can be distinguished by our discriminator. Adequate comparison experiments was performed to demonstrate the ability of our proposed model on retrieval and clustering tasks, with precision within specific radius, normalized mutual information and F1 score as evaluation metrics.

This paper proposes two variants of improved Compression by Substring Enumeration (CSE) with a finite alphabet. In previous studies on CSE, an encoder utilizes inequalities which evaluate the number of occurrences of a substring or a minimal forbidden word (MFW) to be encoded. The inequalities are derived from a contingency table including the number of occurrences of a substring or an MFW. Moreover, codeword length of a substring and an MFW grows with the difference between the upper and lower bounds deduced from the inequalities, however the lower bound is not tight. Therefore, we derive a new tight lower bound based on the contingency table and consequently propose a new CSE algorithm using the new inequality. We also propose a new encoding order of substrings and MFWs based on a sorted contingency table such that both its row and column marginal total are sorted in descending order instead of a lexicographical order used in previous studies. We then propose a new CSE algorithm which is the first proposed CSE algorithm using the new encoding order. Experimental results show that compression ratios of all files of the Calgary corpus in the proposed algorithms are better than those of a previous study on CSE with a finite alphabet. Moreover, compression ratios under the second proposed CSE get better than or equal to that under a well-known compressor for 11 files amongst 14 files in the corpus.

In this letter, an efficient hybrid direction-of-arrival (DOA) estimation scheme is devised for massive uniform rectangular array. In this scheme, the DOA estimator based on a two-dimensional (2D) discrete Fourier transform is first applied to acquire coarse initial DOA estimates for single data snapshot. Then, the fine DOA is accurately estimated through using the iterative search estimator within a very small region. Meanwhile, a Nyström-based method is utilized to correctly compute the required noise-subspace projection matrix, avoiding the direct computation of full-dimensional sample correlation matrix and its eigenvalue decomposition. Therefore, the proposed scheme not only can estimate DOA, but also save computational cost, especially in massive antenna arrays scenarios. Simulation results are included to demonstrate the effectiveness of the proposed hybrid estimate scheme.