Homotopy algorithm provides a very powerful approach to select the best regularization term for the l1-norm minimization problem, but it is lack of provision for handling singularities. The singularity problem might be frequently encountered in practical implementations if the measurement matrix contains duplicate columns, approximate columns or columns with linear dependent in kernel space. The existing method for handling Homotopy singularities introduces a high-dimensional random ridge term into the measurement matrix, which has at least two shortcomings: 1) it is very difficult to choose a proper ridge term that applies to several different measurement matrices; and 2) the high-dimensional ridge term may accumulatively degrade the recovery performance for large-scale applications. To get around these shortcomings, a modified ridge-adding method is proposed to deal with the singularity problem, which introduces a low-dimensional random ridge vector into the l1-norm minimization problem directly. Our method provides a much simpler implementation, and it can alleviate the degradation caused by the ridge term because the dimension of ridge term in the proposed method is much smaller than the original one. Moreover, the proposed method can be further extended to handle the SVMpath initialization singularities. Theoretical analysis and experimental results validate the performance of the proposed method.

This Letter proposes a autoencoder model supervised by semantic similarity for zero-shot learning. With the help of semantic similarity vectors of seen and unseen classes and the classification branch, our experimental results on two datasets are 7.3% and 4% better than the state-of-the-art on conventional zero-shot learning in terms of the averaged top-1 accuracy.

A unified decision scheme for the classification and localization of cable faults is proposed based on a two-step procedure. Having basis in the time domain reflectometry (TDR), the proposed scheme is capable of determining not only the locations but also types of faults in a cable without an excessive additional computational burden compared to other TDR-based schemes. Results from simulation and experiments with measured real data demonstrate that the proposed scheme exhibits a higher rate of correct decision than the conventional schemes in localizing and classifying faults over a wide range of the location of faults.

Ferroelectric gate field-effect transistors (MFSFETs) were investigated utilizing nondoped HfO2 deposited by RF magnetron sputtering utilizing Hf target. After the post-metallization annealing (PMA) process with Pt top gate at 500°C/30s, ferroelectric characteristic of 10nm thick nondoped HfO2 was obtained. The fabricated MFSFETs showed the memory window of 1.7V when the voltage sweep range was from -3 to 3V.

We have investigated the in-situ N2-plasma nitridation for high-k HfN gate insulator formed by electron cyclotron resonance (ECR) plasma sputtering to improve the electrical characteristics. It was found that the increase of nitridation gas pressure for the deposited HfN1.1 gate insulator, such as 98 mPa, decreased both the hysteresis width in C-V characteristics and leakage current. Furthermore, the 2-step nitiridation process with the nitridation gas pressure of 26 mPa followed by the nitridation at 98 mPa realized the decrease of equivalent oxide thickness (EOT) to 0.9 nm with decreasing the hysteresis width and leakage current. The fabricated metal-insulator-semiconductor field-effect transistor (MISFET) with 2-step nitridation showed a steep subthreshold swing of 87 mV/dec.

This paper proposes a framework for automatically annotating the keypoints of a human body in images for learning 2D pose estimation models. Ground-truth annotations for supervised learning are difficult and cumbersome in most machine vision tasks. While considerable contributions in the community provide us a huge number of pose-annotated images, all of them mainly focus on people wearing common clothes, which are relatively easy to annotate the body keypoints. This paper, on the other hand, focuses on annotating people wearing loose-fitting clothes (e.g., Japanese Kimono) that occlude many body keypoints. In order to automatically and correctly annotate these people, we divert the 3D coordinates of the keypoints observed without loose-fitting clothes, which can be captured by a motion capture system (MoCap). These 3D keypoints are projected to an image where the body pose under loose-fitting clothes is similar to the one captured by the MoCap. Pose similarity between bodies with and without loose-fitting clothes is evaluated with 3D geometric configurations of MoCap markers that are visible even with loose-fitting clothes (e.g., markers on the head, wrists, and ankles). Experimental results validate the effectiveness of our proposed framework for human pose estimation.

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.

The joint representations of knowledge graph have become an important approach to improve the quality of knowledge graph, which is beneficial to machine learning, data mining, and artificial intelligence applications. However, the previous work suffers severely from the noise in text when modeling the text information. To overcome this problem, this paper mines the high-quality reference sentences of the entities in the knowledge graph, to enhance the representation ability of the entities. A novel framework for joint representation learning of knowledge graphs and text information based on reference sentence noise-reduction is proposed, which embeds the entity, the relations, and the words into a unified vector space. The proposed framework consists of knowledge graph representation learning module, textual relation representation learning module, and textual entity representation learning module. Experiments on entity prediction, relation prediction, and triple classification tasks are conducted, results show that the proposed framework can significantly improve the performance of mining and fusing the text information. Especially, compared with the state-of-the-art method[15], the proposed framework improves the metric of H@10 by 5.08% and 3.93% in entity prediction task and relation prediction task, respectively, and improves the metric of accuracy by 5.08% in triple classification task.

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.

Pedestrian detection is a critical problem in computer vision with significant impact on many real-world applications. In this paper, we introduce an fast dual-task pedestrian detector with integrated segmentation context (DTISC) which predicts pedestrian location as well as its pixel-wise segmentation. The proposed network has three branches where two main branches can independently complete their tasks while useful representations from each task are shared between two branches via the integration branch. Each branch is based on fully convolutional network and is proven effective in its own task. We optimize the detection and segmentation branch on separate ground truths. With reasonable connections, the shared features introduce additional supervision and clues into each branch. Consequently, the two branches are infused at feature spaces increasing their robustness and comprehensiveness. Extensive experiments on pedestrian detection and segmentation benchmarks demonstrate that our joint model improves the performance of detection and segmentation against state-of-the-art algorithms.

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.

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.

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 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.

This letter studies the physical layer security of an unmanned aerial vehicle (UAV)-enabled multicasting system, where a UAV serves as a mobile transmitter to send a common confidential message to a group of legitimate users under the existence of multiple eavesdroppers. The worst situation in which each eavesdropper can wiretap all legitimate users is considered. We seek to maximize the average secrecy rate by jointly optimizing the UAV's transmit power and trajectory over a given flight period. The resulting optimization problem is nonconvex and intractable to solve. To circumvent the nonconvexity, we propose an iterative algorithm to approximate the solution based on the alternating optimization and successive convex approximation methods. Simulation results validate the convergence and effectiveness of our proposed algorithm.

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.

An offline sensor gain-phase errors calibration method for a linear array using a source in unknown location is proposed. The proposed method is realized through three steps. First, based on the observed covariance matrix, we construct a function related to direction, and it is proved that when the function takes the minimum value, the corresponding value should be the direction of the calibration source. Second, the direction of calibration source is estimated by locating the valley from the constructed function. Third, the gain-phase errors are obtained based on the estimated direction. The proposed method offers a number of advantages. First, the accurate direction measurement of the calibration source is not required. Second, only one calibration source needs to be arranged. Third, it does not require an iterative procedure or a two-dimensional (2D) spectral search. Fourth, the method is applicable to linear arrays, not only to uniform linear arrays (ULAs). Numerical simulations are presented to verify the efficacy of the proposed method.

This paper reviews our developed wide band human body communication technology for wearable and implantable robot control. The wearable and implantable robots are assumed to be controlled by myoelectric signals and operate according to the operator's will. The signal transmission for wearable robot control was shown to be mainly realized by electrostatic coupling, and the signal transmission for implantable robot control was shown to be mainly determined by the lossy frequency-dependent dielectric properties of human body. Based on these basic observations on signal transmission mechanisms, we developed a 10-50MHz band impulse radio transceiver based on human body communication technology, and applied it for wireless control of a robotic hand using myoelectric signals in the first time. In addition, we also examined its applicability to implantable robot control, and evaluated the communication performance of implant signal transmission using a living swine. These experimental results showed that the proposed technology is well suited for detection and transmission of biological signals for wearable and implantable robot control.

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 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.