In this letter, we present an adaptive weighted transfer subspace learning (AWTSL) method for cross-database speech emotion recognition (SER), which can efficiently eliminate the discrepancy between source and target databases. Specifically, on one hand, a subspace projection matrix is first learned to project the cross-database features into a common subspace. At the same time, each target sample can be represented by the source samples by using a sparse reconstruction matrix. On the other hand, we design an adaptive weighted matrix learning strategy, which can improve the reconstruction contribution of important features and eliminate the negative influence of redundant features. Finally, we conduct extensive experiments on four benchmark databases, and the experimental results demonstrate the efficacy of the proposed method.

The application of compressed sensing (CS) theory to non-orthogonal multiple access (NOMA) systems has been investigated recently. As described in this paper, we propose a quality-of-service (QoS)-aware, low-complexity, CS-based user selection and power allocation scheme with adaptive resource block selection for downlink NOMA systems, where the tolerable interference threshold is designed mathematically to achieve a given QoS requirement by being relaxed to a constrained l1 norm optimization problem. The proposed scheme adopts two adaptive resource block (RB) selection algorithms that assign proper RB to user pairs, i.e. max-min channel assignment and two-step opportunistic channel assignment. Simulation results show that the proposed scheme is more effective at improving the user rate than other reference schemes while reducing the required complexity. The QoS requirement is approximately satisfied as long as the required QoS value is feasible.

This paper presents a novel blind signal separation method for the measurement of pulse waves at multiple body positions using an array radar system. The proposed method is based on a mathematical model of pulse wave propagation. The model relies on three factors: (1) a small displacement approximation, (2) beam pattern orthogonality, and (3) an impulse response model of pulse waves. The separation of radar echoes is formulated as an optimization problem, and the associated objective function is established using the mathematical model. We evaluate the performance of the proposed method using measured radar data from participants lying in a prone position. The accuracy of the proposed method, in terms of estimating the body displacements, is measured using reference data taken from laser displacement sensors. The average estimation errors are found to be 10-21% smaller than those of conventional methods. These results indicate the effectiveness of the proposed method for achieving noncontact measurements of the displacements of multiple body positions.

Intelligent transportation systems (ITSs) have been extensively studied in recent years to improve the safety and efficiency of transportation. The use of a radar system to enable the ITSs monitor the environment is robust to weather conditions and is less invasive to user privacy. Moreover, equipping the roadside units (RSUs) with radar modules has been deemed an economical and efficient option for ITS operators. However, because the detection and tracking parameters can significantly influence the radar system performance and the best parameters for different scenarios are different, the selection of appropriate parameters for the radar systems is critical. In this study, we investigated radar parameter selection and consequently proposes a parameter selection approach capable of automatically choosing the appropriate detection and tracking parameters for radar systems. The experimental results indicate that the proposed method realizes appropriate selection of parameters, thereby significantly improving the detection and tracking performance of radar systems.

Combinatorial testing is an effective testing technique for detecting faults in a software or hardware system with multiple factors using combinatorial methods. By performing a test, which is an assignment of possible values to all the factors, and verifying whether the system functions as expected (pass) or not (fail), the presence of faults can be detected. The failures of the tests are possibly caused by combinations of multiple factors assigned with specific values, called faulty interactions. Martínez et al. [1] proposed the first deterministic adaptive algorithm for discovering faulty interactions involving at most two factors where each factor has two values, for which graph representations are adopted. In this paper, we improve Martínez et al.'s algorithm by an adaptive algorithmic approach for discovering faulty interactions in the so-called “non-2-locatable” graphs. We show that, for any system where each “non-2-locatable factor-component” involves two faulty interactions (for example, a system having at most two faulty interactions), our improved algorithm efficiently discovers all the faulty interactions with an extremely low mistaken probability caused by the random selection process in Martínez et al.'s algorithm. The effectiveness of our improved algorithm are revealed by both theoretical discussions and experimental evaluations.

This paper proposes two accurate source-number estimation methods for array antennas and multi-input multi-output radar. Direction of arrival (DOA) estimation is important in high-speed wireless communication and radar imaging. Most representative DOA estimation methods require the source-number information in advance and often fail to estimate DOAs in severe environments such as those having low signal-to-noise ratio or large transmission-power difference. Received signals are often bandlimited or narrowband signals, so the proposed methods first involves denoising preprocessing by removing undesired components then comparing the original and denoised signal information. The performances of the proposed methods were evaluated through computer simulations.

A hybrid memory architecture (HMA) that consists of some distinct memory devices is expected to achieve a good balance between high performance and large capacity. Unlike conventional memory architectures, the HMA needs the metadata for data management since the data are migrated between the memory devices during the execution of an application. The memory controller caches the metadata to avoid accessing the memory devices for the metadata reference. However, as the amount of the metadata increases in proportion to the size of the HMA, the memory controller needs to handle a large amount of metadata. As a result, the memory controller cannot cache all the metadata and increases the number of metadata references. This results in an increase in the access latency to reach the target data and degrades the performance. To solve this problem, this paper proposes a metadata prefetching mechanism for HMAs. The proposed mechanism loads the metadata needed in the near future by prefetching. Moreover, to increase the effect of the metadata prefetching, the proposed mechanism predicts the metadata used in the near future based on an address difference that is the difference between two consecutive access addresses. The evaluation results show that the proposed metadata prefetching mechanism can improve the instructions per cycle by up to 44% and 9% on average.

In 2017, Tang et al. provided a complete characterization of generalized bent functions from ℤ2n to ℤq(q = 2m) in terms of their component functions (IEEE Trans. Inf. Theory. vol.63, no.7, pp.4668-4674). In this letter, for a general even q, we aim to provide some characterizations and more constructions of generalized bent functions with flexible coefficients. Firstly, we present some sufficient conditions for a generalized Boolean function with at most three terms to be gbent. Based on these results, we give a positive answer to a remaining question proposed by Hodžić in 2015. We also prove that the sufficient conditions are also necessary in some special cases. However, these sufficient conditions whether they are also necessary, in general, is left as an open problem. Secondly, from a uniform point of view, we provide a secondary construction of gbent function, which includes several known constructions as special cases.

The electromagnetic environment is increasingly complex and changeable, and radar needs to meet the execution requirements of various tasks. Modern radars should improve their intelligence level and have the ability to learn independently in dynamic countermeasures. It can make the radar countermeasure strategy change from the traditional fixed anti-interference strategy to dynamically and independently implementing an efficient anti-interference strategy. Aiming at the performance optimization of target tracking in the scene where multiple signals coexist, we propose a countermeasure method of cognitive radar based on a deep Q-learning network. In this paper, we analyze the tracking performance of this method and the Markov Decision Process under the triangular frequency sweeping interference, respectively. The simulation results show that reinforcement learning has substantial autonomy and adaptability for solving such problems.

High Frequency Surface Wave Radar (HFSWR) can achieve over-the-horizon detection, which can effectively detect and track the ships and ultra-low altitude aircrafts, as well as the acquisition of sea state information such as icebergs and ocean currents and so on. However, HFSWR is seriously affected by the clutters, especially sea clutter and ionospheric clutter. In this paper, we propose a deep learning image semantic segmentation method based on optimized Deeplabv3+ network to achieve the automatic detection of sea clutter and ionospheric clutter using the measured R-D spectrum images of HFSWR during the typhoon as experimental data, which avoids the disadvantage of traditional detection methods that require a large amount of a priori knowledge and provides a basis for subsequent the clutter suppression or the clutter characteristics research.

The prediction of the malfunction timing of wind turbines is essential for maintaining the high profitability of the wind power generation industry. Studies have been conducted on machine learning methods that use condition monitoring system data, such as vibration data, and supervisory control and data acquisition (SCADA) data to detect and predict anomalies in wind turbines automatically. Autoencoder-based techniques that use unsupervised learning where the anomaly pattern is unknown have attracted significant interest in the area of anomaly detection and prediction. In particular, vibration data are considered useful because they include the changes that occur in the early stages of a malfunction. However, when autoencoder-based techniques are applied for prediction purposes, in the training process it is difficult to distinguish the difference between operating and non-operating condition data, which leads to the degradation of the prediction performance. In this letter, we propose a method in which both vibration data and SCADA data are utilized to improve the prediction performance, namely, a method that uses a power curve composed of active power and wind speed. We evaluated the method's performance using vibration and SCADA data obtained from an actual wind farm.

In the upcoming Internet of Things era, reducing energy consumption of embedded processors is highly desired. Minimum Energy Point Tracking (MEPT) is one of the most efficient methods to reduce both dynamic and static energy consumption of a processor. Previous works proposed a variety of MEPT methods over the past years. However, none of them incorporate their algorithms with practical real-time operating systems, although edge computing applications often require low energy task execution with guaranteeing real-time properties. The difficulty comes from the time complexity for identifying an MEP and changing voltages, which often prevents real-time task scheduling. The conventional Dynamic Voltage and Frequency Scaling (DVFS) only scales the supply voltage. On the other hand, MEPT needs to adjust the body bias voltage in addition. This additional tuning knob makes MEPT much more complicated. This paper proposes an approximate MEPT algorithm, which reduces the complexity of identifying an MEP down to that of DVFS. The key idea is to linearly approximate the relationship between the processor frequency, supply voltage, and body bias voltage. Thanks to the approximation, optimal voltages for a specified clock frequency can be derived immediately. We also propose a task scheduling algorithm, which adjusts processor performance to the workload and then provides a soft real-time capability to the system. The operating system stochastically adjusts the average response time of the processor to be equal to a specified deadline. MEPT will be performed as a general task, and its overhead is considered in the calculation of the frequency. The experiments using a fabricated test chip and on-chip sensors show that the proposed algorithm is a maximum of 16 times more energy-efficient than DVFS. Also, the energy loss induced by the approximation is only 3% at most, and the algorithm does not sacrifice the fundamental real-time properties.

At Eurocrypt 2011, Lindell presented practical static and adaptively UC-secure commitment schemes based on the DDH assumption. Later, Blazy et al. (at ACNS 2013) improved the efficiency of the Lindell's commitment schemes. In this paper, we present static and adaptively UC-secure commitment schemes based on the same assumption and further improve the communication and computational complexity, as well as the size of the common reference string.

In this letter, firstly, a novel adaptive beamformer using independent component analysis (ICA) algorithm is proposed. By this algorithm, the ambiguity of amplitude and phase resulted from blind source separation is removed utilizing the special structure of array manifolds matrix. However, there might exist great calibration error when the powers of interferences are far larger than that of desired signal at many applications such as sonar, radio astronomy, biomedical engineering and earthquake detection. As a result, this will lead to a significant reduction in separation performance. Then, a new method based on the combination of ICA and primary component analysis (PCA) is proposed to recover the desired signal's amplitude under strong interference. Finally, computer simulation is carried out to indicate the effectiveness of our methods. The simulation results show that the proposed methods can obtain higher SNR and more accurate power estimation of desired signal than diagonal loading sample matrix inversion (LSMI) and worst-case performance optimization (WCPO) method.

Heart rate measurement for mm-wave FMCW radar based on phase analysis comprises a variety of noise. Furthermore, because the breathing and heart frequencies are so close, the harmonic of the breathing signal interferes with the heart rate, and the band-pass filter cannot solve it. On the other hand, because heart rates vary from person to person, it is difficult to choose the basic function of WT (Wavelet Transform). To solve the aforementioned difficulties, we consider performing time-frequency domain analysis on human skin surface displacement data. The PA-LI (Phase Accumulation-Linear Interpolation) joint ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) approach is proposed in this paper, which effectively enhances the signal's SNR, estimates the heart rate, and reconstructs the heartbeat signal. The experimental findings demonstrate that the proposed method can not only extract heartbeat signals with high SNR from the front direction, but it can also detect heart rate from other directions (e.g., back, left, oblique front, and ceiling).

Unsupervised domain adaptation (DA) is a challenging machine learning problem since the labeled training (source) and unlabeled testing (target) sets belong to different domains and then have different feature distributions, which has recently attracted wide attention in micro-expression recognition (MER). Although some well-performing unsupervised DA methods have been proposed, these methods cannot well solve the problem of unsupervised DA in MER, a. k. a., cross-domain MER. To deal with such a challenging problem, in this letter we propose a novel unsupervised DA method called Joint Patch weighting and Moment Matching (JPMM). JPMM bridges the source and target micro-expression feature sets by minimizing their probability distribution divergence with a multi-order moment matching operation. Meanwhile, it takes advantage of the contributive facial patches by the weight learning such that a domain-invariant feature representation involving micro-expression distinguishable information can be learned. Finally, we carry out extensive experiments to evaluate the proposed JPMM method is superior to recent state-of-the-art unsupervised DA methods in dealing with cross-domain MER.

Cross-project defect prediction (CPDP) is a hot research topic in recent years. The inconsistent data distribution between source and target projects and lack of labels for most of target instances bring a challenge for defect prediction. Researchers have developed several CPDP methods. However, the prediction performance still needs to be improved. In this paper, we propose a novel approach called Joint Domain Adaption and Pseudo-Labeling (JDAPL). The network architecture consists of a feature mapping sub-network to map source and target instances into a common subspace, followed by a classification sub-network and an auxiliary classification sub-network. The classification sub-network makes use of the label information of labeled instances to generate pseudo-labels. The auxiliary classification sub-network learns to reduce the distribution difference and improve the accuracy of pseudo-labels for unlabeled instances through loss maximization. Network training is guided by the adversarial scheme. Extensive experiments are conducted on 10 projects of the AEEEM and NASA datasets, and the results indicate that our approach achieves better performance compared with the baselines.

In this paper, we propose rate adaptation mechanisms for robust and low-latency video transmissions exploiting multiple access points (Multi-AP) wireless local area networks (WLANs). The Multi-AP video transmissions employ link-level broadcast and packet-level forward error correction (FEC) in order to realize robust and low-latency video transmissions from a WLAN station (STA) to a gateway (GW). The PHY (physical layer) rate and FEC rate play a key role to control trade-off between the achieved reliability and airtime (i.e., occupancy period of the shared channel) for Multi-AP WLANs. In order to finely control this trade-off while improving the transmitted video quality, the proposed rate adaptation controls PHY rate and FEC rate to be employed for Multi-AP transmissions based on the link quality and frame format of conveyed video traffic. With computer simulations, we evaluate and investigate the effectiveness of the proposed rate adaptation in terms of packet delivery rate (PDR), airtime, delay, and peak signal to noise ratio (PSNR). Furthermore, the quality of video is assessed by using the traffic encoded/decoded by the actual video encoder/decoder. All these results show that the proposed rate adaptation controls trade-off between the reliability and airtime well while offering the high-quality and low-latency video transmissions.

This letter proposes an adaptive beamforming switch algorithm for realistic massive multiple-input multiple-output (MIMO) systems through prototypes. It is analyzed and identified that a rigid single-mode beamforming regime is hard to maintain superior performance all the time due to no adaption to the inevitable channel variation in practice. In order to cope with this practical issue, the proposed systematic beamforming mechanism is investigated to enable dynamic selection between minimum mean-squared error and grid-of-beams beamforming algorithms, which improves system downlink performance, including throughput and block error rate. The significant performance benefits and realistic feasibility have been validated through the field tests in live networks and theoretical analyses. Meanwhile, the adaptive beamforming switch algorithm is applicable to both fourth and fifth generation time-division duplexing cellular communication system using massive-MIMO technology.

In this paper, we propose a new method based on copula theory to evaluate the detection performance of a distributed-processing multistatic radar system (DPMRS). By applying the Gaussian copula to model the dependence of local decisions in a DPMRS as well as data fusion rules of AND, OR, and K/N, the performance of a DPMRS for detecting Swerling fluctuating targets can be easily evaluated even under non-Gaussian clutter with a nonuniform dependence matrix. The reliability and flexibility of this method are validated by applying the proposed method to a previous problem by other authors, and our other investigation results indicate its high potential for evaluating DPMRS performance in various cases involving different models of target and clutter.