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Bayesian optimization (BO) is popular for a analog circuit sizing problem recently. However, BO can only work well in small-scale circuit. Scaling BO to common circuit optimization (typically dimension > 10) is difficult due to the curse of dimensionality. In this article, we proposed a new BO algorithm (cBO), which can scale BO to more larger scale circuit and improve the optimization result. First, according to the fact that a specification of the analog integrated circuit is mainly determined by a few transistors, dropout strategy is adopted in our algorithm. At each iteration, only a subset of selected variables to be optimized. A variables selection strategy based on mutual information analysis and a new fill-in strategy are proposed. Second, the $g_{m}/I_{D}$ methodology-based optimization strategy is proposed. Optimization variables are not width and the length of transistors, but $g_{m}/I_{D}$ , $I_{D}$ and length. $g_{m}$ , $I_{D}$ and $g_{m}/I_{D}$ have a direct relationship to circuit performance. This is equivalent to search in circuit feature space, which could achieve better performance and handle constraints more easily. Four commonly used circuits, including low dropout regulator, two stage amplifiers, Bandgap voltage reference (VR), and VR are used for validation. Compared with other high-dimensional BO, common BO, and commercial tools (Cadence ADE_GXL), the proposed algorithm is found to produce the best optimization result and best stability. And, the ablation study shows the effectiveness and efficiency of two ingredients of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Community detection is one of the most fundamental problems in social network analysis, while most existing research focuses on unsigned graphs. In real applications, social networks involve not only positive relationships but also negative ones. It is important to exploit the signed information to identify more stable communities. In this paper, we propose a novel model, named stable $k$ k -core, to measure the stability of a community in signed graphs. The stable $k$ k -core model not only emphasizes user engagement, but also eliminates unstable structures. We show that the problem of finding the maximum stable $k$ k -core is NP-hard. To scale for large graphs, novel pruning strategies and searching methods are proposed. We conduct extensive experiments on 6 real-world signed networks to verify the efficiency and effectiveness of proposed model and techniques. [ABSTRACT FROM AUTHOR]

The time required to restore distribution systems following an extreme event is highly dependent on damage assessment. Waiting for field assessors patrolling the feeders to identify fault locations is a bottleneck in improving restoration efficiency. This paper proposes an optimal service restoration model for resilient distribution systems considering the coordination with damage assessment, as a contribution to earlier studies. The restoration actions such as fault isolation, network reconfiguration, crew mobilization and fault repair are brought forward to the damage assessment stage and the restoration schedules are dynamically updated with the reveal of the damage status. The relationship between fault location, switch status and node status is established to optimize the network topology and guarantee crew operation safety under the condition that the network has multiple faults or unchecked potential faulted areas. Moreover, the crew routing formulations are modified to enable fault isolation and load island reconnection by manual switches during the restoration process. Case studies validate the effectiveness of the proposed model in reducing load shedding and restoration duration. [ABSTRACT FROM AUTHOR]

A comprehensive analysis is derived to understand the mechanism of phase errors in cascode CMOS variable gain amplifiers (VGAs). According to the analysis, source–drain parasitic capacitors ($C_{\mathrm {ds}}$) of common gate (CG) transistors in off-region are the main causes for phase errors. An independent cascode current canceling (ICCC) VGA is proposed to relax phase errors by minimizing the effect of $C_{\mathrm {ds}}$ within a broadband. The proposed VGA achieves a 16-dB gain control with a 1.6° phase error and a 0.2-dB gain error from 15 to 25 GHz by 40-nm CMOS technology. Owing to the cascode structure, the peak gain of the VGA is 12 dB with only one stage including input–output pads consuming 16-mW power from 1.1-V voltage. Besides, the VGA can also serve as a 180° phase shifter (PS) due to ICCC structure with a 1.5° phase error and a 0.2-dB gain error from 15 to 25 GHz. The measured −3-dB bandwidth is 5 GHz, and output 1-dB compression point (OP1dB) is over 2.5 dBm in the maximum gain state. The core area is 0.065 mm2 excluding input–output pads. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) has been proven to be an efficient way to diagnose Alzheimer’s disease (AD). Recent dramatic progress on deep learning greatly promotes the MRI analysis based on data-driven CNN methods using a large-scale longitudinal MRI dataset. However, most of the existing MRI datasets are fragmented due to unexpected quits of volunteers. To tackle this problem, we propose a novel Temporal Recurrent Generative Adversarial Network (TR-GAN) to complete missing sessions of MRI datasets. Unlike existing GAN-based methods, which either fail to generate future sessions or only generate fixed-length sessions, TR-GAN takes all past sessions to recurrently and smoothly generate future ones with variant length. Specifically, TR-GAN adopts recurrent connection to deal with variant input sequence length and flexibly generate future variant sessions. Besides, we also design a multiple scale & location (MSL) module and a SWAP module to encourage the model to better focus on detailed information, which helps to generate high-quality MRI data. Compared with other popular GAN architectures, TR-GAN achieved the best performance in all evaluation metrics of two datasets. After expanding the Whole MRI dataset, the balanced accuracy of AD vs. cognitively normal (CN) vs. mild cognitive impairment (MCI) and stable MCI vs. progressive MCI classification can be increased by 3.61% and 4.00%, respectively. [ABSTRACT FROM AUTHOR]

Maglev train is a new vehicle of rail transit. The time-varying load caused by large capacity is an important test of suspension system. If the state variable matrix and parameter matrix caused by time-varying mass cannot be estimated effectively, it is very likely to lead to the decline of suspension performance and instability. Firstly, an NNs state observer based on neural network is proposed to effectively estimate the system state and parameter matrix. Secondly, based on the NNs observer model, an inverse control algorithm with stronger regulation ability is designed by integrating the output limited characteristics and Lyapunov function, which can ensure the stability of the system when disturbed. The simulation results show that the control algorithm proposed in this paper can have faster response speed and stability in the flotation process of 16mm-10mm, and has better anti-disturbance ability in the face of time-varying mass disturbance. Finally, based on the single point suspension experimental platform, the effectiveness and practicability of the proposed control algorithm are verified, and the correctness of the proposed NNs observer model is also verified. [ABSTRACT FROM AUTHOR]

In supervisory control of Petri nets (PNs), the place-invariant ($P$ -invariant) control principle is the most typical and principal method to deal with the siphon control problem. Although it has a relatively narrow application, this principle is widely acknowledged due to its simplicity and efficiency. In this article, we first propose the extended $P$ -invariant control principle in order to extend the application of $P$ -invariants and provide a general methodology for the control of siphons. Second, three types of $P$ -invariants, from the special to the general, are developed to implicitly or explicitly invariant control the siphons. In the most general case, the virtual $P$ -invariants are constructed in the PNs. Third, the extended principle is further applied to the supervisor simplification. In the paradigm of the extended principle, it presents the redundancy from a structural perspective in contrast to several typical methods, and shows the significant importance of structural analysis in PNs, especially the important role of $P$ -invariants. As a consequence, the extended $P$ -invariant control principle can be considered as the fundamental principle of siphon control as well as its supervisor simplification. [ABSTRACT FROM AUTHOR]

Objective: Mathematical modelling of surface electromyographic (EMG) signals has been proven a valuable tool to interpret experimental data and to validate signal processing techniques. Most analytical EMG models only consider muscle fibers with specific arrangements. However, the fiber orientation may change along the fiber paths and differ from fiber to fiber. Here we propose a subject-specific EMG model that simulates the fiber trajectories in muscles of the upper arm and analytically derives the action potentials assuming an approximate conductivity tensor. Methods: Magnetic Resonance (MR) images were acquired to generate muscle fiber paths and to build the volume conductor. While the action potentials propagated along the identified curvilinear fibers, the conductivity tensor was approximated to be cylindrically anisotropic. Single fiber action potentials (SFAPs) were computed by simulating the generation, propagation, and extinction of membrane current sources. To validate the assumption of the approximate conductivity tensor, two numerical models with approximate or exact conductivity tensors were implemented. Results: The motor unit action potentials generated by the proposed analytical model and the two numerical models were highly similar (cross-correlation $>$ 0.98, normalized root mean square error, nRMSE $\leq$ 0.04, relative error in the median frequency of the simulated waveforms of approximately 3%). The proposed analytical model was also evaluated by comparing simulated and experimentally recorded compound muscle action potentials (CMAPs). Finally, the proposed model was used to test the accuracy of an EMG decomposition algorithm, providing a realistic benchmark. Conclusions and Significance: The proposed analytical model generates action potentials that reflect the spatial distributions of muscle fibers with curvilinear paths. [ABSTRACT FROM AUTHOR]

Simultaneous and proportional control (SPC) using surface electromyography (sEMG) signals has become a prevailing solution for the intuitive control of prosthesis and human–robot interaction. However, only time and frequency domain features are generally involved in conventional SPC algorithms, ignoring the globally spatial information across channels. In this article, we presented an instantaneous sEMG-image-based model for continuous estimation on multiple degrees of freedom wrist torques. Specifically, a convolutional neural network was used to extract features from sEMG-images, and then to estimate wrist torques. The results demonstrated that the sEMG-image-based model outperformed three conventional regression methods and a deep-learning-based method with superior performance on estimation accuracy and smoothness. Furthermore, we found a strong linear correlation between the first two principal components of extracted features and the recorded wrist torques, validating the effectiveness of the proposed approach. The outcomes open up a new perspective for SPC and potentially enhance the performance of myoelectric control in practical application. [ABSTRACT FROM AUTHOR]

The traditional signalized intersection is no longer able to meet the increasing demands of transportation. A method combining autonomous intersection management (AIM) and connected and automated vehicles (CAVs) is suggested for solving existing traffic problems. This study proposes a control method for unsignalized intersections based on cooperative grouping, which discretizes the continuous traffic flow into a series of small-scale vehicle groups. In addition, a Petri Net model with a progressive structure is proposed, for which the time domain sequence of each group is strictly defined. To realize the global control of CAVs, the whole process of vehicle movement is decomposed into two processes: i) adjustment on branches and ii) coordination in the junction. All grouping conditions in the junction are clearly defined, and time windows are used for the discrete traffic flow modeling. Furthermore, a cooperative adaptive cruise control (CACC) model with bifurcations is proposed for grouping and following the control of vehicles on each branch. Simulation results show that vehicles are automatically grouped according to their initial positions, and no collision occurs during the whole process. Applied with the proposed method, the traffic capacity of a four-branch intersection exceeds 10000 veh/h under ideal conditions. The vehicles run more smoothly and show a better fuel economy than the other two methods. [ABSTRACT FROM AUTHOR]

Rail transportation, especially, high-speed rails (HSR), is an important infrastructure for the development of national economy and the promotion of passenger experience. Due to the large bandwidth, millimeter wave (mmWave) communication is regarded as a promising technology to meet the demand of high data rates. However, since mmWave communication has the characteristic of high attenuation, mobile relay (MR) is considered in this paper. Also, full-duplex (FD) communications have been proposed to improve the spectral efficiency. However, because of the high speed, as well as the problem of penetration loss, passengers on the train have a poor quality of service. Consequently, an effective user association scheme for HSR in mmWave band is necessary. In this paper, we investigate the user association optimization problem in mmWave mobile relay systems where the MRs operate in the FD mode. To maximize the system capacity, we propose a cooperative user association approach based on coalition formation game, and develop a coalition formation algorithm to solve the challenging NP-hard problem. We also prove the convergence and Nash-stable property of the proposed algorithm. Extensive simulations are done to show the system performance of the proposed scheme under various network settings. It is demonstrated that the proposed distributed low complexity scheme achieves a near-optimal performance and outperforms two baseline schemes in terms of average system throughput. [ABSTRACT FROM AUTHOR]

Small-cell networks (SCNs), especially those operating in millimeter-wave bands, are sensitive to blockages. In this letter, we develop a three-dimensional (3D) SCN model considering blockages to investigate the impact of base-station (BS) height, BS density and blockage density on the downlink coverage probability. More specifically, we model the blockages as cylinders whose locations follow a Poisson point process and model the locations of BSs as a Poisson hole process. We assume that all the BSs are of the same height and the blockage height follows an exponential distribution. Based on the 3D SCN model, we derive the exact integral expression of coverage probability for general SCNs and the closed-form expression of coverage probability for ultra-dense SCNs. Our analytical results are verified to be reliable through simulations. The numerical results quantify the impact of the blockage density and the BS height on the coverage probability. For a small blockage density, elevated BSs always degrade the coverage probability, while the coverage probability first increases and then decreases with the BS height when the blockage density becomes sufficiently large. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has been widely used in breast cancer diagnosis, and many high-performance models have emerged. However, most of the existing deep learning models are mainly based on static breast ultrasound (US) images. In actual diagnostic process, contrast-enhanced ultrasound (CEUS) is a commonly used technique by radiologists. Compared with static breast US images, CEUS videos can provide more detailed blood supply information of tumors, and therefore can help radiologists make a more accurate diagnosis. In this paper, we propose a novel diagnosis model based on CEUS videos. The backbone of the model is a 3D convolutional neural network. More specifically, we notice that radiologists generally follow two specific patterns when browsing CEUS videos. One pattern is that they focus on specific time slots, and the other is that they pay attention to the differences between the CEUS frames and the corresponding US images. To incorporate these two patterns into our deep learning model, we design a domain-knowledge-guided temporal attention module and a channel attention module. We validate our model on our Breast-CEUS dataset composed of 221 cases. The result shows that our model can achieve a sensitivity of 97.2% and an accuracy of 86.3%. In particular, the incorporation of domain knowledge leads to a 3.5% improvement in sensitivity and a 6.0% improvement in specificity. Finally, we also prove the validity of two domain knowledge modules in the 3D convolutional neural network (C3D) and the 3D ResNet (R3D). [ABSTRACT FROM AUTHOR]

Probabilistic amplitude shaping (PAS) has proved to be a promising way to achieve the shaping gain for an additive white Gaussian noise channel with a distribution matcher (DM). However, the DM schemes may suffer a rate loss and increase both complexity and latency, when they are not suited for the input bit stream with redundancy. In this paper, a novel PAS strategy is proposed for a joint source and channel coded modulation system, where the residual source redundancy after source coding can be exploited to obtain both shaping and coding gains. It is shown that the residual source redundancy can be controlled by choosing the row weight distribution for source code, thus making the probability distribution of the modulated symbols be optimized under an appropriate interleaver design. To guarantee the error-floor performance, the source code is constrained by predicting the probability distribution of source bits within target finite-length frames. By jointly designing source and channel codes, the residual source redundancy can also be exploited to achieve the coding gain. Compared with the state-of-the-art code pairs, the proposed code pairs have better error-floor performance, and achieve higher coding and shaping gains without suffering from the rate-loss and the latency caused by DM. [ABSTRACT FROM AUTHOR]

Multipass cell (MPC), with a long effective optical path length (OPL), is an effective method to improve measurement sensitivity in laser absorption spectroscopy for trace gas sensing. We present a space equation (SE) method to design MPC, which can solve inaccuracy caused by using paraxial-matrix (PM) method. The location and dimension of light spot pattern can be calculated accurately and intuitively through SE method. Meanwhile, actual effective OPL can be accurately calculated, SE method can reduce the interference effect, the direction of each ray can be obtained accurately, and, hence, designing the angle of outgoing ray is simple. By adjusting the mirror curvature, mirror space, incident angle, and incident position, we realized four dense spot patterns with different multiple reflections in MATLAB simulation. We calculated the shape changes of light spot after multiple reflection and its influence on the design of outgoing ray hole size accurately. We also considered the evolution of dense pattern and found a method easy to obtain multiple ring pattern. We verified the validity of the proposed model and built a compact MPC with seven rings dense spot pattern. Such MPC offers an effective OPL of 36.48 m with 198 times of reflection. We verified the tunable diode laser absorption spectroscopy (TDLAS) technology to measure 610-ppmv standard methane. In contrast to PM method, the improvement of the signal-to-noise ratio (SNR) of 1.36 times is achieved when the volume of MPC is reduced by 3 times. In addition, the SNR can be improved to 6.6 times, while the volume of MPC is the same. Results validated the ability of the SE method to design MPC. [ABSTRACT FROM AUTHOR]

This article proposes an isolated LLC resonant converter with MHz-level switching frequency that can achieve zero-voltage switching in the full-load range. In the proposed converter, conventional silicon devices are replaced with wide band-gap gallium nitride devices to reduce switching losses in the power device. A planar transformer structure with reduced winding length on the secondary side is proposed, which utilizes flux cancellation and reduces core loss while maintaining a low core volume. The path of the secondary winding is a fraction of a full turn, which reduces secondary-side losses while maintaining a turns ratio of 16:1. This planar transformer achieves the goals of decreasing core loss and volume while increasing power density. Within a limited circuit layout area, optimal points for the efficiency of core loss and copper loss were calculated. The magnetic simulation software ANSYS Maxwell was employed to verify whether the actual circuit behavior of this planar transformer conformed to the design theory. Finally, a resonant converter was achieved with a switching frequency operating at 1 MHz, input voltage of 380 V, output voltage of 12 V, output power of 800 W, power density of 55 W/cm3, and maximum efficiency of 96.5%. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a deep learning aided constraint encoding method to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by using a neural network, which admits an exact mixed-integer formulation (MIP). This formulation is then integrated with the scheduling problem to encode the frequency constraint. With the stronger representation power of the neural network, the resulting commands can ensure adequate frequency response in a realistic setting in addition to islanding success. The proposed method is validated on a modified 33-node system. Successful islanding with a secure response is simulated under the scheduled commands using a detailed three-phase model in Simulink. The advantages of our model are particularly remarkable when the inertia emulation functions from wind turbine generators are considered. [ABSTRACT FROM AUTHOR]

In this paper, we consider a light fidelity (LiFi)-enabled bidirectional Internet of Things (IoT) communication system, where visible light and infrared light are used in the downlink and uplink, respectively. In order to efficiently improve the energy efficiency (EE) of the bidirectional LiFi-IoT system, non-orthogonal multiple access (NOMA) with a quality-of-service (QoS)-guaranteed optimal power allocation (OPA) strategy is applied to maximize the EE of both downlink and uplink channels. We derive closed-form OPA sets based on the identification of the optimal decoding orders in both downlink and uplink channels, which can enable low-complexity power allocation. Moreover, we propose an adaptive channel and QoS-based user pairing approach by jointly considering users’ channel gains and QoS requirements. We further analyze the EE and the user outage probability (UOP) performance of both downlink and uplink channels in the bidirectional LiFi-IoT system. Extensive analytical and simulation results demonstrate the superiority of NOMA with OPA in comparison to orthogonal multiple access (OMA) and NOMA with typical channel-based power allocation strategies. It is also shown that the proposed adaptive channel and QoS-based user pairing approach greatly outperforms individual channel/QoS-based approaches, especially when users have diverse QoS requirements. [ABSTRACT FROM AUTHOR]

Objective: Estimation of the discharge pattern of motor units by electromyography (EMG) decomposition has been applied for neurophysiologic investigations, clinical diagnosis, and human-machine interfacing. However, most of the methods for EMG decomposition are currently applied offline. Here, we propose an approach for high-density surface EMG decomposition in real-time. Methods: A real-time decomposition scheme including two sessions, offline training and online decomposition, is proposed based on the convolutional kernel compensation algorithm. The estimation parameters, separation vectors and the thresholds for spike extraction, are first computed during offline training, and then they are directly applied to estimate motor unit spike trains (MUSTs) during the online decomposition. The estimation parameters are updated with the identification of new discharges to adapt to non-stationary conditions. The decomposition accuracy was validated on simulated EMG signals by convolving synthetic MUSTs with motor unit action potentials (MUAPs). Moreover, the accuracy of the online decomposition was assessed from experimental signals recorded from forearm muscles using a signal-based performance metrics (pulse-to-noise ratio, PNR). Main results: The proposed algorithm yielded a high decomposition accuracy and robustness to non-stationary conditions. The accuracy of MUSTs identified from simulated EMG signals was $>$ 80% for most conditions. From experimental EMG signals, on average, 12 $\pm$ 2 MUSTs were identified from each electrode grid with PNR of 25.0 $\pm$ 1.8 dB, corresponding to an estimated decomposition accuracy $>$ 75%. Conclusion and Significance: These results indicate the feasibility of real-time identification of motor unit activities non-invasively during variable force contractions, extending the potential applications of high-density EMG as a neural interface. [ABSTRACT FROM AUTHOR]

This paper presents an improved method for detecting methanol and the concentration of methanol in transformer oil-paper. Acetonitrile is used as an extractant, and methanol is detected by gas chromatography-mass spectrometry. Experiments show a linear correlation coefficient of this method that reaches 0.99 and the minimum detected concentration of methanol in transformer oil can be as low as 5.57 μg/L. In order to investigate the relationship between the methanol concentration and the degree of aging of insulating paper, an oil-paper insulation simulation system is designed. The experiment shows that there is a linear relationship between the logarithm of methanol concentration and the degree of polymerization, the linear correlation coefficient is greater than 0.94. An actual oil sample from a transformer is used to show that the method can be used to further study the methanol concentration. [ABSTRACT FROM AUTHOR]

For deep-sea natural gas hydrate (NGH) exploration, the highly sensitive detection of dissolved gas in seawater near the seabed is important. A prototype of a midinfrared (MIR) carbon dioxide (CO2) sensing system based on a wavelength modulation spectroscopy (WMS) technique is developed using a thermoelectrically cooled continuous-wave interband cascade laser at 4319.2 nm and an optical multipass cell (20 × 7.6 × 10.5 cm3) with a 24-m effective optical path. A MIR HgCdTe detector and a lock-in amplifier are used to detect and demodulate light source signals. The reported sensing system utilizes a linear optical structure to achieve a highly precision detection performance. The minimum detection limit (MDL) of 48.2 parts per billion (ppb) (at 74 s) at a gas pressure of 20 Torr for the 2f-WMS technique was achieved. The MDL was improved to 38.6 ppbv (at 89 s) at a gas pressure of 20 Torr using the 2f/1f-WMS technique, which reduces the effects of laser power drift. The CO2 sensor was deployed with a gas–liquid separator for CO2 detection in the gas extracted from water. Results validated the reported sensor system’s potential application to deep-sea NGH exploration. [ABSTRACT FROM AUTHOR]

The design and measurement of a power detector circuit based on the 0.15- $\mu \text{m}$ GaAs pHEMT technology and its usage in complex cross correlation measurement are presented in this article. The six-port technology is employed for the calculation of the cross correlation, and the design equations from the requirements of the correlation measurement are provided to guide the power detector design. The relation between the signal-to-noise ratio (SNR) of the correlation results and the load resistor is deduced, which can restrict the resistor value choice for an acceptable SNR. By properly choosing the load resistor and the biasing of the transistor, a power detector circuit is designed with a suppressed output dc voltage variation resulting from the statistic feature of monolithic microwave integrated circuit (MMIC) production. A fabricated detector chip has been mounted onto a printed circuit board and has been characterized. The measurement results show a flat responsivity over 3.5–8 GHz and a 3-nV/ $\sqrt {\mathrm{ Hz}}$ output noise voltage spectrum. The complex cross correlation measurement is implemented using two power detector modules with carefully chosen bias currents. Across the frequency range of 3.5–8 GHz, the measured correlation circles have an amplitude imbalance of 3.5 dB and an axial ratio from 0.85 to 1.15. With an input power of −5 dBm, the measured SNR of the correlator output is above 26 dB. [ABSTRACT FROM AUTHOR]

In zero-shot learning, attributes play as a bridge from original images to class labels. Therefore, to achieve accurate zero-shot image classification, we mainly focus on improving attribute prediction accuracy by taking full advantage of prior information about attribute from two aspects. First, we present a new attribute classifier called deep attribute prediction (DeepAP) model by using supervised deep convolutional neural networks (DCNNs), where the attribute label information participates in the training of DCNNs. Unlike common DCNNs that are usually used to extract image features, the constructed DCNNs are used to directly predict attribute values from the original input images. Thus, the designed DeepAP model can serve as the mapping from low-level image features to high-level semantic attributes in the traditional direct attribute prediction (DAP) model. Second, another prior information about attribute, i.e., class-attribute matrix is used to mine the attribute-class correlation with sparse representation coefficients. Since the attribute-class correlation can reflect different contributions of attributes to classification, we use it to define attribute weights and incorporate the idea of weighted attributes into DeepAP to form the deep weighted attribute prediction (DWAP) model. Experiments on three real datasets show that DWAP outperforms the deep attribute network and DAP on attribute prediction and zero-shot image classification. [ABSTRACT FROM AUTHOR]

Ambient backscatter devices (tags and readers) use existing radio frequency (RF) signals to transmit data. Most prior works consider single-antenna tags, but this paper investigates the case of multiple-antenna tags, which are capable of simultaneous energy harvesting and data transmission. However, the multi-antenna channel between the tag and the reader, and the unpredictable nature of RF signals due to uncontrollable RF sources (e.g., location and transmit power), make signal detection highly challenging. Thus, the detection process becomes a hypothesis testing problem with unknown parameters. Consequently, we design a blind detector based on the generalized likelihood ratio test (GLRT) without using channel state information (CSI), signal power and noise variance. The decision threshold and detection probability of it are also analyzed in detail. Furthermore, to maximize its detection performance, we develop the optimal backscatter antenna selection scheme. Interestingly, we show that the detector performs best when only two backscatter antennas are selected. Finally, extensive simulation results validate the analysis and illustrate the effectiveness of the proposed detector. [ABSTRACT FROM AUTHOR]

As sugar is an important intermediate in the early stage in the degradation of cellulose, this paper proposes to use the concentration of total sugar as an indicator of the degree of aging of insulating paper in an oil-paper insulation system. The oil-paper insulation system was simulated at 90, 100 and 110°C, and total sugar concentration in insulating paper and in transformer oil was measured by spectroscopy. The spectrum of sugar reaction was obtained by spectrometer, and TQ Analyst software was used to process the data. Our experiments have shown that the total sugar concentration increases significantly in the early stage of aging, and there is a linear relationship between the logarithm of total sugar concentration and the degree of polymerization. We obtained the relationship between absorbance and total sugar concentration using the partial least square method and the regression goodness of fit is better than 0.91. [ABSTRACT FROM AUTHOR]

For deep-sea natural gas hydrate (NGH) exploration, the highly sensitive detection of dissolved gas in seawater near the seabed is important. A prototype of a midinfrared (MIR) carbon dioxide (CO2) sensing system based on a wavelength modulation spectroscopy (WMS) technique is developed using a thermoelectrically cooled continuous-wave interband cascade laser at 4319.2 nm and an optical multipass cell ($20\times 7.6\times 10.5$ cm3) with a 24-m effective optical path. A MIR HgCdTe detector and a lock-in amplifier are used to detect and demodulate light source signals. The reported sensing system utilizes a linear optical structure to achieve a highly precision detection performance. The minimum detection limit (MDL) of 48.2 parts per billion (ppb) (at 74 s) at a gas pressure of 20 Torr for the 2f-WMS technique was achieved. The MDL was improved to 38.6 ppbv (at 89 s) at a gas pressure of 20 Torr using the 2f/1f-WMS technique, which reduces the effects of laser power drift. The CO2 sensor was deployed with a gas–liquid separator for CO2 detection in the gas extracted from water. Results validated the reported sensor system’s potential application to deep-sea NGH exploration. [ABSTRACT FROM AUTHOR]

In multi-user multi-cell multiple-input multiple-output (MIMO) visible light communication (VLC) systems, inter-cell interference (ICI) and inter-user interference (IUI) are the two key factors that could severely degrade the system performance. In this paper, for the first time, a novel joint precoder and equalizer design based on interference alignment is proposed to mitigate both IUI and ICI in multi-user multi-cell MIMO VLC systems under imperfect channel state information (CSI). The proposed approach aims to choose proper precoder and receiving equalizer to minimize the mean square error (MSE) under the unique optical power constraints in VLC. Furthermore, we consider optical channel estimation error in our formulated joint optimization problem when designing the joint precoder and receiving equalizer in multi-user multi-cell MIMO VLC systems. Numerical results show that the proposed design achieves a better performance under different users locations, channel estimation errors, and transmitter/receiver spacing, compared to the existing minimum mean square error (MMSE) and max-rate designs. Especially, when the transmit optical power is 12 W per light emitting diode (LED) lamp, our proposed design achieves a sum rate improvement of up to 18.2% and 28.7% as compared with the MMSE and max-rate designs, respectively. In addition, the proposed design can save about 1.4 and 4.3 W optical power per LED lamp as compared with the MMSE and max-rate designs, respectively, at a target bit error rate (BER) of 10 $^{-3}$. [ABSTRACT FROM AUTHOR]

Similarity measure is an essential component in image registration. In this article, we propose a novel similarity measure for registration of two or more images. The proposed method is motivated by the fact that optimally registered images can be sparsified hierarchically in the gradient domain and frequency domain with the separation of sparse errors. One of the key advantages of the proposed similarity measure is its robustness in dealing with severe intensity distortions, which widely exist on medical images, remotely sensed images and natural photos due to differences of acquisition modalities or illumination conditions. Two efficient algorithms are proposed to solve the batch image registration and pair registration problems in a unified framework. We have validated our method on extensive and challenging data sets. The experimental results demonstrate the robustness, accuracy and efficiency of our method over nine traditional and state-of-the-art algorithms on synthetic images and a wide range of real-world applications. [ABSTRACT FROM AUTHOR]

In steerable filters, a filter of arbitrary orientation can be generated by a linear combination of a set of “basis filters.” Steerable properties dominate the design of the traditional filters, e.g., Gabor filters and endow features the capability of handling spatial transformations. However, such properties have not yet been well explored in the deep convolutional neural networks (DCNNs). In this paper, we develop a new deep model, namely, Gabor convolutional networks (GCNs or Gabor CNNs), with Gabor filters incorporated into DCNNs such that the robustness of learned features against the orientation and scale changes can be reinforced. By manipulating the basic element of DCNNs, i.e., the convolution operator, based on Gabor filters, GCNs can be easily implemented and are readily compatible with any popular deep learning architecture. We carry out extensive experiments to demonstrate the promising performance of our GCNs framework, and the results show its superiority in recognizing objects, especially when the scale and rotation changes take place frequently. Moreover, the proposed GCNs have much fewer network parameters to be learned and can effectively reduce the training complexity of the network, leading to a more compact deep learning model while still maintaining a high feature representation capacity. The source code can be found at https://github.com/bczhangbczhang. [ABSTRACT FROM AUTHOR]

Double protograph low-density parity-check code has been shown to be a good solution for joint source-channel coding. Moreover, the source code and channel code (as one code pair) can be jointly optimized to achieve a good bit error rate (BER) performance. In this paper, first, an optimal design criterion of this code pair based on an extended curve-fitting algorithm is proposed. Second, a joint optimization procedure which searches for both the optimal decoding threshold and the optimal code pair is presented. In addition, for short-to-moderate-length codewords, the finite-length effect cannot be ignored and the procedure has to be modified with a new notion named length-N decoding threshold. The protograph extrinsic information transfer analysis and the BER simulation have shown that our optimized code pairs acquire lower decoding thresholds than conventional code pairs. Moreover, the optimized code pairs with short-to-moderate-length codewords can mitigate the finite-length effect and obtain low length-N decoding thresholds. [ABSTRACT FROM AUTHOR]

This paper presents a DC coefficient estimation algorithm for intra-predicted residual blocks in the High-Efficiency Video Coding (HEVC) standard. Discarding the DC coefficient directly in each transform block leads to substantial bit-saving, but at the same time produces strong discontinuities between neighboring blocks. To overcome this problem, we propose an estimation algorithm for the DC coefficient, which solves an optimal offset in a closed-form to recover the corresponding block edges. Then, we embed this algorithm into HEVC in its rate-distortion optimized quantization and sign bit hiding steps. Furthermore, a flag is signaled to decide whether the DC estimation strategy is used for each transform block. Test results under the common test condition show that our algorithm achieves 1.5% and 1.6% BD-rate reduction on average for luma and chroma, respectively, under all intra configuration. In the meantime, our simulation results show that both encoding time and decoding time increase only slightly (about 10%, without any special optimization on programming our proposed algorithm). When testing the proposed DC estimation algorithm on inter coding configurations, including low delay with P pictures, low delay with B pictures, and random access, we can also achieve 0.5%–1.1% bit-rate savings on average, while nearly no extra encoding and decoding time is needed. [ABSTRACT FROM AUTHOR]

Large antenna array systems are favored in next-generation wireless communications, as it can offer multiplexing and array gains that enhance the system sum-rate. However, the large antenna array systems often necessitate the use of high-cost and power-hungry radio frequency (RF) devices. To reduce the hardware complexity and avoid the explicit high-dimensional channel estimation, we propose a joint iterative training based hybrid precoding using low-resolution phase shifters (PSs). Different from the existing works based on the predefined codebook, the iterative training is applied for the hybrid architectures. The iterative training converges to the dominant steering vectors that align with the direction of the largest channel gain, thus it can harvest more array gains than the predefined codebook method. In addition, the performance loss induced by the finite phase quantization is analytically investigated for multiple RF chains. Simulation results show that the developed joint iterative training method having a fast convergence can achieve similar array gain compared with the systems equipped with the continuous PSs. Furthermore, the proposed hybrid precoding utilizing low-resolution PSs can offer a sum-rate comparable to the fixed-rank fully-digital multiple-input multiple-output systems, but with limited hardware cost and energy consumption. [ABSTRACT FROM AUTHOR]

The torus cryopumps in China Fusion Engineering Test Reactor (CFETR) are used to provide required pumping speeds and maintain specified partial pressures for various gases pumped during plasma operation. To begin with, a special structure of the cryopanel cooling system is proposed. However, thermal-hydraulic calculation displayed by computational fluid dynamics X codes shows that the temperature distribution of the structure does not meet the design requirements proposed by the cryogenic properties of the gases pumped. Therefore, an optimized structure relying on hydraulic inflation is adopted to uniform the surface temperature dramatically. Simulation results indicate that the temperature distribution and the stress distribution fulfill the design requirements well. The conceptual design of the cryopanel cooling system provides methods and experience for the overall design of a CFETR torus cryopump in the future. [ABSTRACT FROM AUTHOR]

With the emergence of the multicore architecture and the increase of chip operating frequency, power optimization has become a key step of circuit logic synthesis. Aiming at the XNOR/OR circuits, with the goal of minimizing power, construct the optimal polarity fixed-polarity Reed–Muller (FPRM) circuits power optimization scheme. However, the power optimization for FPRM circuits is a multipeak combinatorial optimization problem, we first propose a metaheuristic optimization algorithm (MOA), which includes the global exploration optimizer, local deep exploitation optimizer, and initial population and uses the proposed differential evolution optimization, fierce wolf siege algorithm-based tabu search, and improved skew tent map to make the population evolve. Based on the proposed Huffman tree construction algorithm and MOA, we propose an efficient power optimization approach (EPOA) to find the minimum power FPRM circuit. Experimental results on the benchmark circuits confirm the effectiveness of EPOA. [ABSTRACT FROM AUTHOR]

Given a large graph, like a computer communication network, which k nodes should we immunize (or monitor, or remove), to make it as robust as possible against a computer virus attack? This problem, referred to as the node immunization problem, is the core building block in many high-impact applications, ranging from public health, cybersecurity to viral marketing. A central component in node immunization is to find the best k bridges of a given graph. In this setting, we typically want to determine the relative importance of a node (or a set of nodes) within the graph, for example, how valuable (as a bridge) a person or a group of persons is in a social network. First of all, we propose a novel `bridging' score Dλ, inspired by immunology, and we show that its results agree with intuition for several realistic settings. Since the straightforward way to compute Dλ is computationally intractable, we then focus on the computational issues and propose a surprisingly efficient way (O(nk2 + m)) to estimate it. Experimental results on real graphs show that (1) the proposed `bridging' score gives mining results consistent with intuition; and (2) the proposed fast solution is up to seven orders of magnitude faster than straightforward alternatives. [ABSTRACT FROM PUBLISHER]

The encoder-decoder architecture is widely used as a lightweight semantic segmentation network. However, it struggles with a limited performance compared to a well-designed Dilated-FCN model for two major problems. First, commonly used upsampling methods in the decoder such as interpolation and deconvolution suffer from a local receptive field, unable to encode global contexts. Second, low-level features may bring noises to the network decoder through skip connections for the inadequacy of semantic concepts in early encoder layers. To tackle these challenges, a Global Enhancement Method is proposed to aggregate global information from high-level feature maps and adaptively distribute them to different decoder layers, alleviating the shortage of global contexts in the upsampling process. Besides, aLocal Refinement Module is developed by utilizing the decoder features as the semantic guidance to refine the noisy encoder features before the fusion of these two (the decoder features and the encoder features). Then, the two methods are integrated into a Context Fusion Block, and based on that, a novel Attention guided Global enhancement and Local refinement Network (AGLN) is elaborately designed. Extensive experiments on PASCAL Context, ADE20K, and PASCAL VOC 2012 datasets have demonstrated the effectiveness of the proposed approach. In particular, with a vanilla ResNet-101 backbone, AGLN achieves the state-of-the-art result (56.23% mean IOU) on the PASCAL Context dataset. The code is available at https://github.com/zhasen1996/AGLN. [ABSTRACT FROM AUTHOR]

Fully-supervised deep learning segmentation models are inflexible when encountering new unseen semantic classes and their fine-tuning often requires significant amounts of annotated data. Few-shot semantic segmentation (FSS) aims to solve this inflexibility by learning to segment an arbitrary unseen semantically meaningful class by referring to only a few labeled examples, without involving fine-tuning. State-of-the-art FSS methods are typically designed for segmenting natural images and rely on abundant annotated data of training classes to learn image representations that generalize well to unseen testing classes. However, such a training mechanism is impractical in annotation-scarce medical imaging scenarios. To address this challenge, in this work, we propose a novel self-supervised FSS framework for medical images, named SSL-ALPNet, in order to bypass the requirement for annotations during training. The proposed method exploits superpixel-based pseudo-labels to provide supervision signals. In addition, we propose a simple yet effective adaptive local prototype pooling module which is plugged into the prototype networks to further boost segmentation accuracy. We demonstrate the general applicability of the proposed approach using three different tasks: organ segmentation of abdominal CT and MRI images respectively, and cardiac segmentation of MRI images. The proposed method yields higher Dice scores than conventional FSS methods which require manual annotations for training in our experiments. [ABSTRACT FROM AUTHOR]

As more and more people choose high-speed rail (HSR) as a means of transportation for short trips, there is ever growing demand of high quality of multimedia services. With its rich spectrum resources, millimeter wave (mm-wave) communications can satisfy the high network capacity requirements for HSR. Also, it is possible for receivers (RXs) to be equipped with antenna arrays in mm-wave communication systems due to its short wavelength. However, as HSRs run with high speed, the received signal power (RSP) varies rapidly over a cell and it is the lowest at the edge of the cell compared to other locations. Consequently, it is necessary to conduct research on RX beamforming for HSR in mm-wave band to improve the quality of the received signal. In this paper, we focus on RX beamforming for a mm-wave train-ground communication system. To improve the RSP, we propose an effective RX beamforming scheme based on deep reinforcement learning (DRL), and develop a deep Q-network (DQN) algorithm to train and determine the optimal RX beam direction with the purpose of maximizing average RSP. Through extensive simulations, we demonstrate that the proposed scheme has better performance than the four baseline schemes in terms of average RSP at most positions on the railway. [ABSTRACT FROM AUTHOR]

Electrostatic actuators are being widely investigated to convert electrical energy into mechanical deformation in a broad variety of microrobotic applications. Both electrostatic gap closing and comb drive actuators require high excitation voltages for high performance operation. In the majority of applications, external bulky and heavy power supplies are used to provide such high voltages, which inevitably bring limitations on autonomous operation. One of the major challenges toward enabling operation of these systems without a bulky external power source is the development of ultralight and high power density power electronic interfaces (PEIs) with large step-up gains. In this paper, a special circuit topology, controlled by a pulse frequency modulation scheme, is introduced to meet stringent electrical and drive voltage requirements of electrostatic actuators. An ultracompact 63 mg (excluding PCB mass), 31.5 mm 2, and 500 mW PEI has been designed and fabricated. Experimental validations have been carried out to verify the circuit's ability to drive an 110-V-input electrostatic inchworm motor at 1-kHz actuating frequency. [ABSTRACT FROM AUTHOR]

Reconstruction of hyperspectral imagery from spectral random projections is considered. Specifically, multiple predictions drawn for a pixel vector of interest are made from spatially neighboring pixel vectors within an initial non-predicted reconstruction. A two-phase hypothesis-generation procedure based on partitioning and merging of spectral bands according to the correlation coefficients between bands is proposed to fine-tune the hypotheses. The resulting prediction is used to generate a residual in the projection domain. This residual being typically more compressible than the original pixel vector leads to improved reconstruction quality. To appropriately weight the hypothesis predictions, a distance-weighted Tikhonov regularization to an ill-posed least-squares optimization is proposed. Experimental results demonstrate that the proposed reconstruction significantly outperforms alternative strategies not employing multihypothesis prediction. [ABSTRACT FROM AUTHOR]

This study presents a smart homecare surveillance system, which utilizes sound-steered cameras to identify behavior of interest. First of all, to detect multiple source locations, a new direction-of-arrival (DOA) algorithm is proposed by introducing cascaded frequency filters, which can quickly calculate directions without creating much complexity. This method can also locate and separate different signals at the same time. Second, after the camera points in the direction of the estimated angle, the proposed state-transition support vector machine is used to provide favorable discriminability for human behavior identification. A new Markov random field (MRF) function based on the localized contour sequence (LCS) is also presented while the system computes transition probabilities between states. Such LCS-based MRF functions can effectively smooth transitions and enhance recognition. The experimental results show that the average error of DOA decreases to around 7^\circ, which is better than those of the baselines. Also, our proposed behavior identification system can reach an 88.3% accuracy rate. The aforementioned results have therefore demonstrated the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

The running environment of a magnetic levitation (maglev) vehicle is complex, with problems such as track irregularity, external disturbance, varying system parameters, and time delays, which bring great challenges to the design of a high-performance levitation controller. In this article, an adaptive neural network controller with input delay compensation and a control parameter optimization scheme is proposed for the electromagnetic levitation system of a maglev vehicle, which can solve the key engineering problems of external disturbance, input time delay, and time-varying mass. Aiming at the problem of input time delay, a sliding-mode surface with time-delay compensation is constructed, and a double-layer neural network and adaptive law are utilized to approximate the uncertain dynamics; thus, a finite time adaptive tracking control law is proposed. Based on the Lyapunov method, the stability of the proposed controller in finite time is analyzed. The proposed method does not only update the input and output weights of the neural network online, but also introduces reinforcement learning (Actor–Critic network) to optimize the key controller parameter in real time and enhance system robustness. Simulation and experimental results show that the proposed controller can effectively suppress the air gap vibration with time delay and uncertain dynamics, and significantly improve the performance of levitation control. [ABSTRACT FROM AUTHOR]

In this paper, we study the magnetization canting of the free layer in a synthetic spin valve at ultrahigh density data recording up to 10 Tb/in^2. Our micromagnetic simulations show that the canting problem deteriorates dramatically as the sensor size shrinks further to match higher data-recording density. Our systematic investigation on the magnetic field distribution reveals that the increasing net magnetostatic coupling field from the synthetic antiferromagnet (SAF) at smaller sensor dimensions is responsible for the observed effect. Further studies show that the stray field experienced by the free layer was dominated by the reference layer, and both the field profile and magnitude change markedly with the closer proximity of the magnetic charges. In order to restore the sensor to the appropriate operating point, affects of adjusting the thickness and magnetization ratio in the SAF, and tapering angle of the sensor stack, have been investigated and it was found that they were able to relieve the canting problem to certain extent. The findings in this work help to optimize the sensor properties for ultrahigh density data storage in the hard disk. [ABSTRACT FROM AUTHOR]

User selection plays a crucial role in multiple-access channels (e.g., uplink channels of cellular systems) to exploit the multiuser diversity. Although the achievable rate can be adopted for a performance indicator in user selection, it may not be proper if a suboptimal detector or decoder is employed. In particular, for multiuser multiple-input–multiple-output (MIMO) systems, a low-complexity suboptimal MIMO detector can be used instead of optimal MIMO detectors, which require prohibitively high computational complexity. Under this practical circumstance, it may be desirable to derive user selection criteria based on the error probability for a given low-complexity MIMO detector. In this paper, we propose a low-complexity greedy user selection scheme with an iterative lattice reduction (LR) updating algorithm when an LR-based MIMO detector is used. We also analyze the diversity gain for combinatorial user selection approaches with various MIMO detectors. Based on the simulation results, we can confirm that the proposed greedy user selection approach can provide a comparable performance with the combinatorial approaches with much lower complexity. [ABSTRACT FROM PUBLISHER]

A poly-Si nanowire (NW) thin-film transistor configured with the double-gated scheme was fabricated and characterized. The fabrication process features the clever use of selective plasma etching to form a rectangular NW underneath a hard mask. In this paper, we show that replacing the original ion-implanted poly-Si with in situ doped poly-Si for the source/drain significantly enhances the device performance, including steeper subthreshold swing (SS), larger on/off current ratio, and reduced series resistance. In particular, the SS is improved to a record-breaking low value of 73 mV/dec, which, to the best of our knowledge, is the most ideal ever reported for a poly-Si based device. The new NW transistors with such excellent switching properties are highly promising for reducing power consumption and operational voltage in practical circuit applications. [ABSTRACT FROM AUTHOR]

The two coupled optical cavities within a vertical-cavity surface-emitting laser have the unique ability to modulate the spatial distribution of the longitudinal optical mode, without changing the total photon density in the laser cavities, by simultaneously directly modulating the two optical cavities exactly out-of-phase. A rate-equation analysis predicts that this condition, which we term push-pull modulation, exhibits a superior modulation response than that of conventional direct modulation. The push-pull modulation can enable high-speed operation with low power consumption, as a large modulation bandwidth can be achieved independent of the total photon density and/or the injection dc current. Experimental evidence of spatially changing the longitudinal mode is presented, and push-pull modulation at 2.5 Gb/s is demonstrated for the first time. [ABSTRACT FROM AUTHOR]

Due to the subwavelength lithography, manufacturing sub-90-nm feature sizes require intensive use of resolutionenhancement techniques, among which optical proximity correction (OPC) is the most popular technique in industry. Considering the OPC effects during routing can significantly alleviate the cost of postlayout OPC operations. In this paper, we present an efficient, accurate, and economical analytical formula for intensity computation and develop the first modeling of postlayout OPC based on a quasi-inverse lithography technique. The technique provides key insights into a new direction for postlayout OPC modeling during routing. Extensive simulations with SPLAT, the golden lithography simulator in academia and industry, show that our intensity formula has high fidelity. Incorporating the OPC costs computed by the quasi-inverse lithography technique for our postlayout OPC modeling into a router, the router can be guided to maximize the effects of the correction. Compared with a rule-based OPC method, the experimental results show that our approach can achieve 15% and 16% reductions in the maximum and average layout distortions, respectively [ABSTRACT FROM AUTHOR]

In a semiconductor foundry, equipment failure has long been recognized as a major cause of unpredicted breakdown which results in an excessive production loss. Equipment maintenance management is one of the essential tasks in semiconductor manufacturing. Due to the prosperity of Internet and IT, e-diagnostics and e-maintenance through the web have become important applications in the semiconductor industry. Web services (WSs) can assist the data integration in heterogeneous e-manufacturing systems to support faster and remote maintenance functions. In this paper, a WS-based collaborative planning, forecasting and replenishment (CPFR) framework, namely WS-CPFR, is developed to collaboratively manage the logistics activities of spare parts in semiconductor equipment between equipment suppliers and semiconductor factories. [ABSTRACT FROM AUTHOR]