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The wireless-powered communication paradigm brings self-sustainability to the on-vehicle sensors by harvesting the energy from radiated radio frequency (RF) signals. This paper proposes a novel transmission and resource allocation strategy for the scenario where multiple wireless powered vehicle area networks (VAN) co-existed with high density. The considered multi-VAN system consists of a remote master access point (MAP), multiple on-vehicle hybrid access points (HAPs) and sensors. Unlike previous works, we consider that the sensors can recycle the radiated radio frequency energy from all the HAPs when HAPs communicate with MAP, so the dedicated signals for energy harvesting (EH) are unnecessary. The proposed strategy can achieve simultaneous wireless information and power transfer (SWIPT) without complex receiver architecture requirements. The extra EH and interference caused by the dense distribution of VANs, which are rarely explored, are fully considered. To maximize the sum throughput of all the sensors while guaranteeing the transmission from HAPs to the MAP, we jointly optimize the time allocation, system energy consumption, power allocation, and receive beamforming. Due to the non-convexity of the formulated problem, we address the sub-problems separately through the Rayleigh quotient, Frobenius norm minimization and convex optimization. Then an efficient iterative algorithm to obtain sub-optimal solutions. The simulation results and discussions illustrate the proposed scheme's effectiveness and advantages. [ABSTRACT FROM AUTHOR]

This article investigates using optical fibers with enhanced backscattering profiles to improve distributed fiber sensor performance and reduce instrumentation costs. Using a femtosecond (fs) laser direct writing technique, the Rayleigh backscattering profile of a standard telecom fiber was enhanced by more than 40 dB to improve the signal-to-noise ratio (SNR) for optical frequency domain reflectometry (OFDR). The enhanced backscattering signals enable effectively distributed strain measurements using a low-cost tunable laser (TL). Median filtering is applied to denoise cross correlation results to further improve measurement outcomes. Results presented in this article show that a TL with a 1-nm tuning range, which is far less than the tuning range used in commercial OFDR interrogators, can perform effectively distributed strain measurements using sensing fibers with enhanced backscattering profiles. The sensing fiber with over 40-dB backscattering enhancement achieved 4.8-cm spatial resolution in strain measurements with a root mean square accuracy of less than 2.70 $\mu \varepsilon $ when 10–50 $\mu \varepsilon $ were exerted to the sensing fiber. Results presented in this article reveal both the potential and limitations of sensing fibers with enhanced backscattering for OFDR-based distributed fiber sensors. [ABSTRACT FROM AUTHOR]

The rapid development of autonomous driving in recent years presents many challenges for scene understanding. As an essential step towards scene understanding, semantic segmentation has received increased attention in the past few years. Although deep learning based approaches have achieved great success in improving the segmentation accuracy, most of them suffer from an inefficiency problem and can hardly be applied to real-time applications. In this paper, we analyze the computational cost of Convolutional Neural Network (CNN) and find that the inefficiency of CNNs is mainly caused by their wide structure rather than deep structure. In addition, the success of pruning based model compression methods proves that there are many redundant channels in CNNs. Thus, we design a narrow while deep backbone network to improve the efficiency of semantic segmentation. By casting our network to fully convolutional network (FCN32) segmentation architecture, the basic structure of most segmentation methods, we achieve 61.5% mIoU on Cityscapes validation dataset with only 4.2G floating-point operations (FLOPs) on 1024 × 2048 inputs, which already outperforms one of the earliest real-time deep learning based segmentation methods: ENet (58.3% mIoU, 3.8G FLOPs on 640 × 360 inputs). By further refining the output resolution of our network to the 1/8 of the input resolution with a simple encoder-decoder structure, we achieve 65.3% mIoU on Cityscapes test set with 14.0G FLOPs and 39.9 frames per second (FPS) on Titan X card. We have made our model publicly available at https://github.com/zgyang-hnu/NDNet. [ABSTRACT FROM AUTHOR]

Multilabel feature extraction (FE) is an effective preprocessing step to cope with some possible irrelevant, redundant, and noisy features, to reduce computational costs and even improve classification performance. Original normalized cross-covariance operator represents a kernel-based nonlinear dependence measure between features and labels, whose empirical estimator is formulated as a trace operation including two inverse matrices of feature and label kernels with a regularization constant. Due to such a complicated expression, it is impossible to derive an eigenvalue problem for linear FE directly. In this paper, we approximate this measure using Moore–Penrose inverse matrix, linear kernel for feature space, and delta kernel for label space, and then symmetrize the entire matrix in the trace operation, resulting in an effective approximated and symmetrized representation. According to orthonormal projection direction constraints, maximizing such a modified form induces a novel eigenvalue problem for multilabel linear FE. Experiments on 12 data sets illustrate that our proposed method works the best, compared with seven existing FE techniques, according to eight multilabel classification performance metrics and three statistical tests. [ABSTRACT FROM AUTHOR]

A large circular transmitter (power pad) is often utilized for inductively wireless charging so that the power receiver can position itself freely within the power pad. However, the uneven magnetic field distribution leads to the impedance mismatch and the variable transferred power due to the misalignment between a receiving coil and a relatively large transmitter coil. In this paper, the effects of the turn numbers, trace spacing, and coil size on the uniformity of magnetic field distribution over a planar spiral coil (PSC) are investigated based on an analytical model. Then, the genetic algorithm is used to optimize the trace width and spacing of a ten-turn PSC with the fixed outer and inner diameters, whereas the magnetic field is numerically calculated by HFSS software. The geometrically optimal PSC is obtained with the trace width approximately equal to trace spacing for each turn by minimizing the coefficient of variation (COV) of magnetic field within an effective charging area over the coil. The results showed that the simulated and measured COVs of magnetic field were 0.130 and 0.121, respectively, which are obviously less than that of the uniformly spaced regular coil and show a little less than that of the optimally spaced coil with fixed trace width. Therefore, the trace spacing is a major consideration to achieve the uniform magnetic field, whereas the trace width variation is applied to refine the evenness of field. It is also expected that the proposed GA-based optimization is well applied to design other Txs with conformable shape in inductively WPT systems for stable power delivery regardless of the receiver positions. [ABSTRACT FROM AUTHOR]

Polyphase permanent magnet synchronous motors exhibit outstanding advantages over the traditional three-phase traction motors in electrified transportation applications. This article introduces a novel direct torque model predictive control technique aiming at developing a highly reliable, loss-minimizing, and energy-efficient five-phase permanent magnet synchronous motor drive. Leveraging the underlying constrained optimization process, the direct torque model predictive control approach optimizes the torque delivery and speed regulation quality, improves the flux and torque ripples, reduces higher order current harmonics, minimizes motor drive losses, and boosts power efficiency of the electric powertrain. dSPACE DS1104 hardware-in-the-loop and computer simulation studies are jointly utilized to verify the effectiveness and robustness of the proposed direct torque model predictive control approach in polyphase permanent magnet synchronous motor drive applications. [ABSTRACT FROM AUTHOR]

In this paper, we investigate two classification methods based on the significance and fairness of sensors in virtual multi-input multi-output (MIMO) wireless body area network (WBAN), where the access point (AP) with multi-antenna provides energy to classified sensors placed on different parts of human body by broadcasting radio frequency (RF). In order to better characterize RF broadcasting channel, we use path loss model and model the link fading by a log-normal distribution. For two kinds of classified sensors., we adopt maximal ratio transmission (MRT) beamforming during wireless power transfer (WPT) phase, and zero-forcing (ZF) decoding for wireless information transfer (WIT) phase, respectively. Since that objective function is a non-convex optimization problem, we convert it to a convex function and solve it by CVX tool. Finally, simulation results demonstrate the reliability of the optimal solution. [ABSTRACT FROM AUTHOR]

Semantic segmentation is a key step in scene understanding for autonomous driving. Although deep learning has significantly improved the segmentation accuracy, current high-quality models such as PSPNet and DeepLabV3 are inefficient given their complex architectures and reliance on multi-scale inputs. Thus, it is difficult to apply them to real-time or practical applications. On the other hand, existing real-time methods cannot yet produce satisfactory results on small objects such as traffic lights, which are imperative to safe autonomous driving. In this paper, we improve the performance of real-time semantic segmentation from two perspectives, methodology and data. Specifically, we propose a real-time segmentation model coined Narrow Deep Network (NDNet) and build a synthetic dataset by inserting additional small objects into the training images. The proposed method achieves 65.7% mean intersection over union (mIoU) on the Cityscapes test set with only 8.4G floating-point operations (FLOPs) on $1024\times 2048$ inputs. Furthermore, by re-training the existing PSPNet and DeepLabV3 models on our synthetic dataset, we obtained an average 2% mIoU improvement on small objects. [ABSTRACT FROM AUTHOR]

Social anxiety is a negative emotion which may impair the health of the heart and social functioning of an individual. This work analyzes the influence of social anxiety on the autonomic nerve control of the heart in two social exposure events: public speaking and thesis defending. In an experiment of public speaking, 59 human subjects were tested, and 11 conventional heartbeat measures and a heartbeat measure named the range of local Hurst exponents (RLHE) were evaluated for their capabilities to reveal the onset of social anxiety. Two-sample t-test between the baseline data and high anxiety data shows that social anxiety significantly reduces the complexity of the heartbeats. In an experiment of thesis defense, heartbeats data were acquired from nine graduate students. With the combination of three conventional features and the RLHE feature, a support vector machine classifier obtained true positive rate and true negative rate of 84.88 and 97.29 percent in the five-fold cross validation process of binary classification between high anxiety status and low anxiety status; the classifier also realized a generalization accuracy of 81.82 percent in detecting the high anxiety status in the thesis defense. A real-time anxiety monitoring system was established based on the above anxiety detecting method. [ABSTRACT FROM AUTHOR]

Efficient implementation of finite field multipliers based on a reordered normal basis (RNB) is highly desirable in the current/emerging cryptosystems since it offers almost free realization of squaring operation. Therefore, in this paper, we propose novel bit-parallel and digit-serial finite field multipliers over $GF(2^{m})$ based on RNB. By efficient transformation of the core multiplication algorithm using a unique circular shifting feature, we have derived an efficient algorithm for low-complexity systolic mapping. Both bit-parallel and digit-serial structures of the multipliers are then obtained and optimized to enhance the area–time efficiency. We have also utilized the unique feature of the proposed multiplication algorithm to obtain the systolic multipliers by Karatsubalike decomposition. Detailed analysis and comparison show the superior performance of the proposed implementation. For example, the proposed regular and Karatsuba-based bit-parallel designs involve at least 48.4% less area-delay product (ADP) and 42.2% less power-delay product (PDP) than the best existing ones (37.7% and 55.3% less ADP and PDP on field-programmable gate array platform), respectively. The proposed multipliers, because of their lower area–time complexities, can be used for efficient realization of cryptographic applications. [ABSTRACT FROM AUTHOR]

According to the special working condition in the commutation region of the traction grid, a novel technique of online core-loss measurement for the electric locomotive onboard traction transformer is proposed to noninvasively monitor the status of the magnetic core in real time. Meanwhile, a new modeling approach is also put forward for the equivalent nonlinear magnetizing inductance LM of the magnetic core. First, a no-load test under 31 kV is implemented on a healthy traction transformer, and the measured transient terminal voltages and currents are then filtered and downsampled by the comprehensive test and fault diagnosis system of electric locomotive traction transformer. Next, the magnetizing inductance LM is modeled as the ratio of “the derivative of main flux linkage λp with respect to time” over “the derivative of magnetizing current im with respect to time.” After that the proposed LM model is verified by comparing with the experimental results and two other models. Finally, the proposed technique for online estimation of core loss is applied on a normal onboard traction transformer and a downscaled testing transformer with different severity of interlaminar fault. The experimental results approximately match the theoretical calculation, and the online estimated values of core loss show sharply rising trend under incipient fault. [ABSTRACT FROM AUTHOR]