This paper addresses the problem of developing an efficient fault-tolerant routing method for 2D mesh Network-on-Chips (NoCs) to realize dependable and high performance many core systems. Existing fault-tolerant routing methods have two critical problems of high communication latency and low node utilization. Unlike almost all existing methods where packets always detour faulty nodes, we propose a novel and unique approach that packets can pass through faulty nodes. For this approach, we enhance the common NoC architecture by adding switches and links around each node and propose a fault-tolerant routing method with no virtual channels based on the well-known simple XY routing method. Simulation results show that the proposed method reduces average communication latency by about 97.1% compared with the existing method, without sacrificing fault-free nodes.

An R-peak detection method with a high noise tolerance is presented in this paper. This method utilizes a customized deep convolution neural network (DCNN) to extract morphological and temporal features from sliced electrocardiogram (ECG) signals. The proposed network adopts multiple parallel dilated convolution layers to analyze features from diverse fields of view. A sliding window slices the original ECG signals into segments, and then the network calculates one segment at a time and outputs every point's probability of belonging to the R-peak regions. After a binarization and a deburring operation, the occurrence time of the R-peaks can be located. Experimental results based on the MIT-BIH database show that the R-peak detection accuracies can be significantly improved under high intensity of the electrode motion artifact or muscle artifact noise, which reveals a higher performance than state-of-the-art methods.

To the best of our knowledge, there are a few researches on air-handwriting character-level writer identification only employing acceleration and angular velocity data. In this paper, we propose a deep learning approach to writer identification only using inertial sensor data of air-handwriting. In particular, we separate different representations of degree of freedom (DoF) of air-handwriting to extract local dependency and interrelationship in different CNNs separately. Experiments on a public dataset achieve an average good performance without any extra hand-designed feature extractions.

The distinctive characteristics of unmanned aerial vehicle networks (UAVNs), including highly dynamic network topology, high mobility, and open-air wireless environments, may make UAVNs vulnerable to attacks and threats. Due to the special security requirements, researching in the high reliability of the power and communication network in drone monitoring system become special important. The reliability of the communication network and power in the drone monitoring system has been studied. In order to assess the reliability of the system power supply in the drone emergency monitoring system, the accelerated life tests under constant stress were presented based on the exponential distribution. Through a comparative analysis of lots of factors, the temperature was chosen as the constant accelerated stress parameter. With regard to the data statistical analysis, the type-I censoring sample method was put forward. The mathematical model of the drone monitoring power supply was established and the average life expectancy curve was obtained under different temperatures through the analysis of experimental data. The results demonstrated that the mathematical model and the average life expectancy curve were fit for the actual very well. With overall consideration of the communication network topology structure and network capacity the improved EED-SDP method was put forward in drone monitoring. It is concluded that reliability analysis of power and communication network in drone monitoring system is remarkably important to improve the reliability of drone monitoring system.

We propose a method for preventing smartphone theft when the owner dozes off. The owner of the smartphone wears a wristwatch type device that has an acceleration sensor and a vibration mode. This device detects when the owner dozes off. When the acceleration sensor in the smartphone detects an accident while dozing, the device vibrates. We implemented this function and tested its usefulness.

A variety of all-new systems such as a massive machine type communication (mMTC) system will be supported in 5G and beyond. Although each mMTC device occupies quite narrow bandwidth, the massive number of devices expected will generate a vast array of traffic and consume enormous spectrum resources. Therefore, it is necessary to proactively gather up and exploit fractional spectrum resources including guard bands that are secured but unused by the existing Long Term Evolution (LTE) systems. The guard band is originally secured as a margin for high out-of-band emission (OOBE) caused by the discontinuity between successive symbols in the cyclic prefix-based orthogonal frequency division multiplexing (CP-OFDM), and new-waveforms enabling high OOBE suppression have been widely researched to efficiently allocate narrowband communication to the frequency gap. Time-domain windowing is a well-known signal processing technique for reducing OOBE with low complexity and a universal time-domain windowed OFDM (UTW-OFDM) with a long transition duration exceeding the CP length has demonstrated its ability in WLAN-based systems. In this paper, we apply UTW-OFDM to the LTE downlink system and comprehensively evaluate its performance under the channel models defined by 3GPP. Specifically, we evaluate OOBE reduction and block error rate (BLER) by computer simulation and clarify how far OOBE can be reduced without degrading communication quality. Furthermore, we estimate the implementation complexity of the proposed UTW-OFDM, the conventional CP-OFDM, and the universal filtered-OFDM (UF-OFDM) by calculating the number of required multiplications. These evaluation and estimation results demonstrate that the proposed UTW-OFDM is a practical new-waveform applicable to the 5G and beyond.

In full duplex (FD), which improves the system capacity (or cell throughput) and reduces the transmission delay (or latency) through simultaneous transmission and reception in the same frequency band, self-interference (SI) from the transmitter should be suppressed using antenna isolation, an analog SI canceler, and digital SI canceler (DSIC) to a level such that the data or control channel satisfies the required block error rate (BLER). This paper proposes a structure of iterative DSIC with alternating estimate subtraction (AES-IDSIC) for orthogonal frequency division multiplexing (OFDM) using FD. We first present the required SI suppression level considering SI, quantization noise of an analog-to-digital converter, and nonlinear distortion of a power amplifier and RF receiver circuit for a direct conversion transceiver using FD. Then, we propose an AES-IDSIC structure that iterates the generation of the SI estimate, the downlink symbol estimate, and then alternately removes one of the estimates from the received signal in the downlink including SI. We investigate the average BLER performance of the AES-IDSIC for OFDM using FD in a multipath fading channel based on link-level simulations under the constraint that the derived required signal-to-SI ratio must be satisfied.

It becomes possible to prevent accidents beforehand by predicting dangerous riding behavior based on recognition of bicycle behaviors. In this paper, we propose a bicycle behavior recognition method using a three-axis acceleration sensor and three-axis gyro sensor equipped with a smartphone when it is installed on a bicycle handlebar. We focus on the periodic handlebar motions for balancing while running a bicycle and reduce the sensor noises caused by them. After that, we use machine learning for recognizing the bicycle behaviors, effectively utilizing the motion features in bicycle behavior recognition. The experimental results demonstrate that the proposed method accurately recognizes the four bicycle behaviors of stop, run straight, turn right, and turn left and its F-measure becomes around 0.9. The results indicate that, even if the smartphone is installed on the noisy bicycle handlebar, our proposed method can recognize the bicycle behaviors with almost the same accuracy as the one when a smartphone is installed on a rear axle of a bicycle on which the handlebar motion noises can be much reduced.

This paper describes transmission line couplers for non-contact connecters. Their characteristics are formulated in closed forms and design methodologies are presented. As their applications, three different types of transmission line couplers, two-fold transmission line coupler, single-ended to differential conversion transmission line coupler, and rotatable transmission line coupler are reviewed.

A concurrent system consists of multiple processes that are run simultaneously. The execution orders of these processes are defined by a scheduler. In model checking techniques, the scheduling policy is closely related to the search algorithm that explores all of the system states. To ensure the correctness of the system, the scheduling policy needs to be taken into account during the verification. Current approaches, which use fixed strategies, are only capable of limited kinds of policies and are difficult to extend to handle the variations of the schedulers. To address these problems, we propose a method using a domain-specific language (DSL) for the succinct specification of different scheduling policies. Necessary artifacts are automatically generated from the specification to analyze the behaviors of the system. We also propose a search algorithm for exploring the state space. Based on this method, we develop a tool to verify the system with the scheduler. Our experiments show that we could serve the variations of the schedulers easily and verify the systems accurately.

This new design uses a low power embedded controller (EC) in cooperation with the BIOS of a notebook (NB) computer, both to accomplish dynamic adjustment and to maintain a required performance level of the battery mode of the notebook. In order to extend the operation time at the battery mode, in general, the notebook computer will directly reduce the clock rate and then reduce the performance. This design can obtain the necessary balance of the performance and the power consumption by using both the EC and the BIOS cooperatively to implement the dynamic control of both the CPU and the GPU frequency to maintain the system performance at a sufficient level for a high speed and high resolution video game. In contrast, in order to maintain a certain notebook performance, in terms of battery life it will be necessary to make some trade-offs.

This paper introduces a method that identifies human activity from the height and Doppler Radar Cross Section (RCS) information detected by Multiple-Input Multiple-Output (MIMO) radar. This method estimates the three-dimensional target location by applying the MUltiple SIgnal Classification (MUSIC) method to the observed MIMO channel; the Doppler RCS is calculated from the signal reflected from the target. A gesture recognition algorithm is applied to the trajectory of the temporal transition of the estimated human height and the Doppler RCS. In experiments, the proposed method achieves over 90% recognition rate (average).

This paper proposes an improved closed-form method for moving source localization using time difference of arrival (TDOA), frequency difference of arrival (FDOA) and differential Doppler rate measurements. After linearizing the measurement equations by introducing three additional parameters, a rough estimate is obtained by using the weighted least-square (WLS) estimator. To further refine the estimate, the relationship between additional parameters and source location is utilized. The proposed method gives a final closed-form solution without iteration or the extra mathematics operations used in existing methods by employing the basic idea of WLS processing. Numerical examples show that the proposed method exhibits better robustness and performance compared with several existing methods.

This paper describes a non-contact and noise-tolerant heart rate monitoring system using a 24-GHz microwave Doppler sensor. The microwave Doppler sensor placed at some distance from the user's chest detects the small vibrations of the body surface due to the heartbeats. The objective of this work is to detect the instantaneous heart rate (IHR) using this non-contact system in a car, because the possible application of the proposed system is a driver health monitoring based on heart rate variability analysis. IHR can contribute to preventing heart-triggered disasters and to detect mental stress state. However, the Doppler sensor system is very sensitive and it can be easily contaminated by motion artifacts and road noise especially while driving. To address this problem, time-frequency analysis using the parametric method and template matching method are employed. Measurement results show that the Doppler sensor, which is pasted on the clothing surface, can successfully extract the heart rate through clothes. The proposed method achieves 13.1-ms RMS error in IHR measurements conducted on 11 subjects in a car on an ordinary road.

This paper presents the mode matching (MM)/finite element method (FEM) hybrid analysis for a short-slot 2-plane coupler, and an optimization process for a wideband design based on a genetic algorithm (GA). The method of the analysis combines a fast modal analysis of the MM which reduces the computation time, with the flexibility of an FEM which can be used with an arbitrary cross-section. In the analysis, the model is reduced into the one-eighth model by using the three-dimensional structural symmetry. The computed results agree well with those by the simulation and the computation time is reduced. The bandwidth is improved by the optimization based on the GA from 2.4% to 6.9% for the 2-plane hybrid coupler and from 5.4% to 7.5% for the 2-plane cross coupler. The measured results confirm the wideband design.

Smartphone users often want to customize the positions and functions of physical buttons to accommodate their own usage patterns; however, this is unfeasible for electronic mobile devices based on COTS (Commercial Off-The-Shelf) due to high production costs and hardware design constraints. In this letter, we present the design and implementation of customized virtual buttons that are localized using only common built-in sensors of electronic mobile devices. We develop sophisticated strategies firstly to detect when a user taps one of the virtual buttons, and secondly to locate the position of the tapped virtual button. The virtual-button scheme is implemented and demonstrated in a COTS-based smartphone. The feasibility study shows that, with up to nine virtual buttons on five different sides of the smartphone, the proposed virtual buttons can operate with greater than 90% accuracy.

This letter proposes a fast superpixel segmentation method based on boundary sampling and interpolation. The basic idea is as follow: instead of labeling local region pixels, we estimate superpixel boundary by interpolating candidate boundary pixel from a down-sampling image segmentation. On the one hand, there exists high spatial redundancy within each local region, which could be discarded. On the other hand, we estimate the labels of candidate boundary pixels via sampling superpixel boundary within corresponding neighbour. Benefiting from the reduction of candidate pixel distance calculation, the proposed method significantly accelerates superpixel segmentation. Experiments on BSD500 benchmark demonstrate that our method needs half the time compared with the state-of-the-arts while almost no accuracy reduction.

We propose a recursive algorithm to reduce the computational complexity of the r-order nonlinearity of n-variable Boolean functions. Applying the algorithm and using the sufficient and necessary condition put forward by [1] to cut the vast majority of useless search branches, we show that the covering radius of the Reed-Muller Code R(3, 7) in R(5, 7) is 20.

In this letter, we propose a new blind source separation method, independent low-rank matrix analysis based on generalized Kullback-Leibler divergence. This method assumes a time-frequency-varying complex Poisson distribution as the source generative model, which yields convex optimization in the spectrogram estimation. The experimental evaluation confirms the proposed method's efficacy.

In order to provide an alternative solution of human machine interfaces, this paper proposed to recognize 10 human hand gestures regularly used in the consumer electronics controlling scenarios based on a three-dimensional radar array. This radar array was composed of three low cost 24GHz K-band Doppler CW (Continuous Wave) miniature I/Q (In-phase and Quadrature) transceiver sensors perpendicularly mounted to each other. Temporal and spectral analysis was performed to extract magnitude and phase features from six channels of I/Q signals. Two classifiers were proposed to implement the recognition. Firstly, a decision tree classifier performed a fast responsive recognition by using the supervised thresholds. To improve the recognition robustness, this paper further studied the recognition using a two layer CNN (Convolutional Neural Network) classifier with the frequency spectra as the inputs. Finally, the paper demonstrated the experiments and analysed the performances of the radar array respectively. Results showed that the proposed system could reach a high recognition accurate rate higher than 92%.