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This paper focuses on the design of the optimal or near-optimal detector resorting to extreme eigenvalues. A general framework for detector design involving model-driven and data-driven approaches is introduced. Specifically, the extreme eigenvalues based likelihood ratio test (LRT) is derived via the model-driven approach. Merging the model-driven and data-driven approaches, the Naive Bayesian detector is proposed based on the extreme eigenvalues, which converts the design of test statistic into a two-class decision boundary construction problem, and a solution is provided by the Naive Bayesian classifier. To render the detectors more practical, two near-optimal detectors called α-sum and α-product of maximum and minimum eigenvalues (α-SMME, α-PMME) are further designed, in which α is a weight coefficient. Furthermore, the theoretical performance analysis of the α-SMME and α-PMME algorithms is provided, and the optimal weight selection is further obtained by solving an optimization problem under the Neyman-Pearson criterion. Finally, simulation experiments demonstrate that the proposed detectors achieve performance improvements over the state-of-the-art detectors using extreme eigenvalues, and almost coincide with the detection performance of the LRT detector.

Modern complex chemical process operations produce a large amount of process monitoring and control time series data, which are potentially useful to extract valuable operating experiences and manipulating rules to improve the intelligence of process operations. Previous researches show that time series clustering is an effective method to mine historical control sequences. However, the actual working conditions often deviate from the historical data, making it difficult to reconstruct accurate process regulation manipulating strategies. In response to this problem, this paper proposes a manipulation strategies reconstruction method using a convolutional neural network model (MSR-CNN). Therein, the time series are hierarchically clustered according to the Levenshtein distance to obtain different classes of process disturbance states, and the corresponding manipulated sequences fragments obtained are treated with a symbolic aggregation approximation. Then the improved convolutional neural network is used for deep learning and manipulation strategies reconstruction of the process disturbance states and the corresponding manipulating sequences strings. Finally, applying to a numerical example and an ethanol–water distillation tower unit, the proposed method is proved to be effective. Compared with traditional supervisory control methods, our contributions can help well construct complex chemical process control strategies less dependent on human operators’ operating experiences.

The eigenvalues of the sample covariance matrix can capture signal correlations and noise characteristics well, which are widely used for spectrum sensing in cognitive radio networks. Some totally blind spectrum sensing algorithms were proposed, such as maximum eigenvalue-to-arithmetic mean (ME–AM), maximum eigenvalue-to-geometric mean (ME–GM), and arithmetic-to-geometric mean of eigenvalues (AGM) methods. This article makes full use of the advantages of these algorithms and proposes a universal spectrum sensing algorithm based on maximum eigenvalue, arithmetic mean, and geometric mean of eigenvalues. The proposed algorithm takes the weighted geometric mean of the test statistics of the ME–AM and AGM algorithms as new test statistic and includes the ME–AM, ME–GM, and AGM algorithms as special cases. Following the random matrix theory framework, in a similar vein to the ME–AM and ME–GM algorithms, we derive the analytical expressions of false alarm probability, threshold, and detection probability of the proposed method using the Tracy–Widom distribution of maximum eigenvalue. In addition, we discuss the problem of designing the optimal weighting factor through theoretical analysis and simulation experiments. Finally, simulation results verify that the universal algorithm can effectively improve the detection performance.

In this letter, a cascade algorithm combined subband decomposition with an eigenvalue-based detection scheme is proposed to detect moving targets in sea clutter for the radar system with short pulses. Using a discrete Fourier transform-modulated filter bank, on the one hand, subband decomposition can effectively suppress clutter as well as increase the coherent integration time. On the other hand, it transforms the scene where the target spectrum is separated from the clutter spectrum into the scene where the target spectrum overlaps with the clutter spectrum. In the target subband, the noncoherent method using amplitude difference is more favorable for detection due to the nonobvious phase difference caused by the overlap of the target spectrum and the clutter spectrum. Considering that the maximum eigenvalue can reflect the signal intensity and capture the signal correlations well, the maximum eigenvalue of the covariance matrix is adopted to discriminate the target from the clutter. Finally, the simulation results show that the proposed algorithm achieves superior performance.

Searching is one of the most fundamental operations in many complex systems. However, the complexity of the search process would increase dramatically in high-dimensional space. K-dimensional (KD) tree, as a classical data structure, has been widely used in high-dimensional vital data search. However, at present, common methods proposed for KD tree construction are either unstable or time-consuming. This paper proposed a new algorithm to construct a balanced KD tree based on presorted results. Compared with previous similar method, the new algorithm could reduce the complexity of the construction process (excluding the presorting process) from O (KNlog2N) level to O (Nlog2N) level, where K is the number of dimensions and N is the number of data. In addition, with the help of presorted results, the performance of the new method is no longer subject to the initial conditions, which expands the application scope of KD tree.

Fast time-varying channel parameters and large penetration losses for signals passing through train carriages are two well-known challenges for wireless communications on high speed rails (HSRs). In this paper, we introduce, for the first time, backscatter technology into HSR wireless communications, which can address these two challenges and yet have low complexity of signal processing and low cost of circuit implementation compared with traditional solutions such as relaying or beamforming. Specifically, we propose a backscatter aided wireless transmission (BAWT) scheme and demonstrate that it outperforms the existing direct wireless transmission (DWT) scheme. We derive the upper and lower bounds of channel capacity for BAWT and prove that it exceeds that of DWT on certain conditions. We also propose the transceiver design for both BAWT and DWT, including joint carrier frequency offset and channel estimator, and signal detector. We show that BAWT, rather than DWT, can obtain the channel statistical information in practical applications due to fixed train antennas and unchanged tracks, which can be utilized to facilitate channel estimation. Finally, simulation results are provided to corroborate the proposed solutions.

The existing Goodness of fit (GoF) test based spectrum sensing algorithms mostly use samples or energies as observations to make decisions, which can hardly achieve satisfactory performance especially when the Primary user (PU) signals are highly correlated. Meanwhile, the eigenvalue of covariance matrix can reflect signal correlations well. Motivated by this, we study the distribution of eigenvalue and propose an eigenvalue based GoF spectrum sensing algorithm. In the proposed scheme, we use the ratios of maximum to minimum eigenvalue as observations and thus it can bring performance improvements through capturing correlation of PU signals. We also provide the related theoretical analysis for the proposed method. Simulation results show that the proposed method overcomes the problem of noise uncertainty and achieves performance improvement over the classical samples-based GoF test.

Background Variable message signs (VMS) have been widely applied to alleviate traffic congestion by providing spatiotemporal information related to traffic and network conditions to drivers. There is a debate on whether the VMS information would lead to the less-alleviation of congestion or congestion transfer to the adjacent roads in the different traffic congestion situations due to the inappropriate VMS information. Additionally, it is still unknown how to find a practically applicable method to design the most effective VMS information for drivers. Objective In order to design and display the appropriate VMS information for drivers under different traffic conditions, this paper aims to identify the effects of VMS on individuals’ route choice behavior by exploring the different effects of VMS on the several separate groups. Drivers in these groups are homogenous in gender, driving experience, education level, occupation, and monthly income. Methods In the hypothetical scenario, a stated preference (SP) questionnaire was conducted to build the dataset consisting of 3462 samples from local private and taxi drivers. The decision tree via the chi-squared automatic interactive detector (CHAID) algorithm is utilized to split drivers into several groups, and further two fixed-parameter logistic regression models considering intra-heterogeneity between drivers (i.e., considering and not considering systematic taste variations), and random-parameter logistic regression models are employed to analyze the factors affecting driver route choice behavior of each group. Results Five groups with different driver characteristics are identified. Besides, the modeling results indicate that the effect of the VMS information on individual route choice behavior is heterogeneous. These findings include: (i) random-parameter logistic regression models have a significantly improved goodness-of-fit because of the lower AIC and BIC; (ii) compared with the stated cause information on road construction and congestion, the accident information had a larger effect on driver route choice behaviors; (iii) the route choice behaviors of drivers with more than 6-year driving experience are affected by the VMS information on road constructions, accidents, and the quantitative delay time; (V) the route choice behaviors of less-experienced female drivers were affected by display formats and display frequencies of VMS information. Conclusions The findings from this study can assist traffic authorities in designing the most appropriate VMS for different traffic congestions based on driver characteristic distribution and the road capacity for improving the practicability of VMS information and further provide the theoretical evidence for the design of in-vehicle personalized information service systems.

This letter considers integrating a backscatter link with a reconfigurable intelligent surface to enhance backscatter communication while assisting the direct communication. We derive the probability that the backscatter channel dominates in the composite channel. This probability is a useful performance measure to determine the number of reflectors. Since the exact probability lacks a closed-form solution, we develop two approximations by modeling the gain of the backscatter link with a Gaussian or Gamma distribution. We found that these approximations match well with the exact value. Importantly, with a well-designed number of reflectors, the channel gain of the backscatter link may be always stronger than that of the direct one.

Channel estimation is a well-known challenge for wireless orthogonal frequency division multiplexing (OFDM) communication systems with massive antennas on high speed rails (HSRs). This paper investigates this problem and design two practicable uplink and downlink channel estimators for orthogonal frequency division multiplexing (OFDM) communication systems with massive antenna arrays at base station on HSRs. Specifically, we first use pilots to estimate the initial angle of arrival (AoA) and channel gain information of each uplink path through discrete Fourier transform (DFT), and then refine the estimates via the angle rotation technique and suggested pilot design. Based on the uplink angel estimation, we design a new downlink channel estimator for frequency division duplexing (FDD) systems. Additionally, we derive the Cramer-Rao lower bounds (CRLBs) of the AoA and channel gain estimates. Finally, numerical results are provided to corroborate our proposed studies.

A parameterized controller design approach is proposed to solve the problem of multi-objective control for vehicle active suspension systems by using symbolic computation. The considered model is a quarter-vehicle model of the active suspension system. The multi-objective robust control performances include the sprung mass acceleration, suspension deflection, and tire deflection. Based on dissipative Hamiltonian systems and Lyapunov function, a multi-objective $H_{\infty }$ controller design approach is developed, which can avoid solving Hamilton-Jacobi-Issacs equations. Then, an algorithm of solving semi-positive definite polynomial with tuning parameters is proposed by using symbolic computation. Furthermore, a method of parameter optimization is proposed. Simulations and comparation show that the control performance is significantly improved comparing with passive controlled systems and existing other control systems for active suspension systems.

The deep learning methods in the field of computer vision and big data are becoming more and more mature. Through the application of big data and deep learning technology, the diagnosis of artificial intelligence medical image can be realized, which provides a new opportunity for the automatic analysis of obstetrics medical image and the assistance of doctors to realize high-precision intelligent diagnosis of diseases. The current medical obstetric image diagnosis platform mainly targets low-resolution medical obstetric image files, and does not consider the data-sharing problem of the distributed file system in different storage nodes, which greatly reduces the efficiency of obstetric image storage and diagnosis. Based on this, this article designs an obstetric image diagnostic platform based on cloud computing technology. First, a medical imaging platform was designed by combining cloud computing technology, caching technology, and a distributed file system. Secondly, the use of contrast-enhanced ultrasound technology provides a more accurate ultrasound image for assessing the structure, size, location, and developmental abnormalities of the placenta. Finally, the effectiveness of the obstetric imaging diagnostic platform proposed in this paper is verified by experiments. The results show that the platform has fast data processing speed and convenient use, which greatly reduces the cost of medical equipment and improves efficiency. The hospital only needs to collect the obstetric image of the patient at the front end, transfer it to the cloud for image processing, and finally diagnose the disease.

This paper considers the problem of spectrum sensing in multi-antenna cognitive radio networks. Energy detection (ED) method for spectrum sensing does not require any information of the source signal and channel, as well as it is suitable for detecting independent identically distributed signals. Since covariance matrix catches the signal correlations well, the maximum eigenvalue detection (MED) method is more competitive than the ED method for correlated signals. Under the framework of random matrix theory, this paper firstly proposes two enhanced detection algorithms based on the maximum eigenvalue and energy of the signal to achieve performance improvement while preserving the advantages of the two algorithms. The proposed algorithms are a generalization of the ED and MED methods. To render the proposed algorithms more practical, we propose two other new blind spectrum sensing algorithms based on the maximum likelihood estimate of unknown noise variance. Using random matrix theory, the theoretical analysis on detection probability, false alarm probability and threshold are given. Finally, simulation results show the effectiveness and robustness of the proposed algorithms.

The improvement of color similarity between stereo images can bring better performance to stereo matching algorithms. For this purpose, we present a color correction method to alleviate the color discrepancy between a pair of stereo images, so that the color appearance of one image, i.e., the target image, is consistent with the other image, i.e., the source image. Our method starts with decomposing both the target image and the source image into two intrinsic layers, i.e., the reflectance layer and the shading layer, using intrinsic decomposition. The purpose of intrinsic decomposition is to distinguish and then process different color discrepancies caused by different factors (shading and reflectance) separately and appropriately. Then, a practical and effective consistent segmentation algorithm, which applied to the original stereo images, is proposed to establish the region correspondences. Next, luminance correction method and color correction method, based on the local region correspondences, are adopted to correct the shading layer and the reflectance layer in the target image, respectively, making them as similar as possible to those of the source image. Eventually, the two corrected layers of the target image are combined to yield the final corrected image. The experimental results demonstrate that the proposed method, which using intrinsic decomposition to handle color discrepancy caused by different factors, not only enhances the visual color similarity between the stereo images but also improves the accuracy of their stereo matching.

Matrix constant false alarm rate (M-CFAR) detector based on information geometry provides a novel scheme to the detection of target in sea clutter environment for pulse Doppler radar with small bunch of pulses. The detection performance of the existing M-CFAR detectors is greatly influenced by the geometric measures on the matrix manifold. Different geometrical measures lead to different detection performance, some of which are not satisfactory. Motivated by this, the authors utilise Root-Euclidean, Power-Euclidean, Cholesky-Euclidean and new Riemannian measure to design several kinds of detection schemes, which improve the detection performance. Finally, simulation results verify that the designed detectors achieve a better detection performance with a relatively lower computational complexity.

Background Recent advance in variable message signs (VMS) technology has made it viable to provide spatio-temporal information on traffic and network conditions to drivers. There is a debate whether VMS diverts drivers’ attention away from the road and may cause unnecessary distraction in their driving tasks due to inconsistent VMS contents and formats. There are also other external factors such as weather conditions, visibility and time of day that may affect the integrity and reliability of the VMS. In China, only about 23% drivers were persuaded by VMS to follow route diversion. Objective In order to capture the full benefits of VMS, the aim of this paper is therefore to identify the factors affecting VMS by examining what kinds of VMS contents, formats and their interactions are more preferable to drivers, specifically in China. Methods A revealed preference (RP) questionnaire and stated preference (SP) survey consisting of 1154 samples from private and taxi drivers was conducted and analyzed using discrete choice model. Results The results revealed that the information showed by amber-on-black on text format, white-on-blue on graph format or the suggested route diversion information showed by single line are preferred by drivers in fog weather. In addition, highly educated drivers or drivers with no occupation are more prone to the qualitative delay time on a text-graph format in fog weather. In normal weather, drivers with working trip purpose are mostly preferred to receive the information on a congested traffic condition with a reason on a text-only format. However, the congested traffic condition along with the information on the apparent causes shown by red-on-black or green-on-black on a text-only format was least preferred by drivers. Regarding current and adjacent road traffic information, drivers prefer to receive the suggested route diversion on a graph-only format in fog weather and the qualitative delay time on a text-graph format in normal weather. Irrespective to weather conditions, male drivers incline to the qualitative delay time on a text-graph format. Conclusions The findings of this study could assist traffic authorities to design the most acceptable VMS for displaying traffic information for the purpose of improving road traffic efficiency and provide the theory evidence for the design of in-vehicle personalized information service system.

For the pulse Doppler radar system with short pulse sequence, maximum eigenvalue-based matrix CFAR detector (MEMD) provides an effective solution to the problem of detecting moving targets in the background of K distribution sea clutter when the target Doppler frequency is within clutter spectrum. However, its performance is inferior to adaptive normalized matched filter method (ANMF) when the target Doppler frequency heavily deviates from the clutter spectrum. In essence, the reason for the better performance of ANMF method is the clutter is effectively suppressed by whitening and matched filter. Motivated by this, the fusion idea of eigenvalue detection and pre-processing via fast Fourier transform is considered taking advantage of the priori information of target steering vector. A pre-processing-based maximum eigenvalue matrix CFAR detection (P-MEMD) method is proposed to suppress the influence of clutter on detection performance. Finally, the numerical experimental results based on simulated data and real sea clutter data show that the proposed method achieves better detection performance and robustness than some conventional methods.

Color constancy refers to the ability to make the color of an object not change with the change of the light color of the scene. The use of convolutional neural networks improves the accuracy and stability of color constancy. But the traditional convolutional neural network structure cannot apply the semantic features in the image to the color constancy, which makes the illumination estimation fuzzy, greatly reduces the quality of network training. In order to solve this problem, in this work we propose a new network structure called multi-path feature fusion weighted fully convolutional neural network structure. The network structure contains three convolutional neural network branches for fully extracting semantic features in images. After that, the extracted features are fused and weighted to accurately estimate the light source. Experimental results show that the multi-path feature fusion weighted network structure can make fully use of the semantic feature information in the image to accurately estimate the scene light source. At the same time, it is a more lightweight network, which achieves the simultaneous improvement of the accuracy and speed of light source estimation, meets the requirements of mobile devices for lightweight network models.

Since the energy detection method (ED) is suitable for detecting independent signals and easy to implement, it is widely used in spectrum sensing. While maximum eigenvalue-based detection (MED) method outperforms the ED method in correlated signals scenarios. However, both algorithms are greatly affected by the uncertainty of the noise power. To render the detection algorithms more practical, two totally-blind methods, estimated-noise-power based energy detection (ENP-ED) method and the ratio of maximum eigenvalue to trace detection (MET) method are considered in this paper. According to meta-analysis, the ENP-ED method and MET method are combined by using Fisher's method and the weighted z-transform method to derive two totally-blind mixture detection methods. Furthermore, for simplicity, we directly combine ENP-ED and sphericity test by weight to derive an improved mixture detection method. Finally, simulation results show the effectiveness of the proposed algorithms.

Fast time-varying channel parameters and large penetration loss for signals passing through train carriages are two well-known challenges for wireless communications on high speed rails (HSRs). In this paper we introduce, for the first time, backscatter technology into wireless communication systems on HSRs which can address the two challenges and meanwhile have low complexity of signal processing and low cost of circuit implementation compared with traditional solutions such as relay or beamforming technology. Specifically, we propose a backscatter aided wireless transmission (BAWT) scheme and compare its performance with existing direct wireless transmission (DWT) scheme. We also propose the transceiver design for both BAWT and DWT, including joint carrier frequency offset (CFO) and channel estimator, and signal detector. We show that BAWT, instead of DWT, can obtain the channel statistical information in practical applications due to fixed train antennas and unchanged tracks, which can be utilized to facilitate channel estimation. Finally simulation results are provided to corroborate the proposed studies.

KD (K-dimension) tree is widely used in solving multi-dimensional space search problems, such as the nearest neighbor search, ray tracing, etc. During the process of KD tree construction, the median point in a specified dimension is generally chosen as the split point so as to build a balanced tree. As the performance of building a KD tree is strongly influenced by the efficiency of the selection of the median point, a variety of methods have been developed to select the median point quickly and accurately. However, the performance of methods introduced in most previous literature is either uncertain or time consuming. This paper proposes an improved method to build the KD tree based on presorted results. Compared with previous similar work, the new algorithm not only reduces unnecessary comparisons during presorting and construction, but also could handle problems with duplicate data, which expands the application of such methods.

Signal processing on graphs offers a potential framework for signal features extraction by transforming the received signal into graph. Graph based information representation framework is explored and applied to radar target detection problem. The existing algorithm using graphs considers the case of long pulses and needs to perform eigenvalue decomposition, which requires high implementation complexity in practical scenarios. To render this algorithm practical, for the case of short pulses, this paper continues to explore the framework of signal processing on graphs, and designs an enhanced detection algorithm by using the ratio of maximum and minimum connectivity of graphs among the cell under test and reference cells as test statistic. Simulation results show that the proposed algorithm with a low computational complexity achieves superior detection performance than the existing algorithm based on graphs.

In this paper we introduce backscatter technology into wireless communication systems on high speed rails (HSRs). Specifically, we propose a backscatter aided wireless transmission (BAWT) scheme and demonstrate its outperformance over the existing direct wireless transmission (DWT) scheme. We derive the upper and lower bounds of channel capacity for BAWT and prove that the channel capacity for BAWT is larger than that of DWT on certain conditions. Finally, simulation results are provided to corroborate the proposed studies.

Grid remapping is an important mechanism for data transfer between different grids in the Earth modeling system, and searching for relevant points is one of the most complex and time-consuming operations. At present, there are few efficient and robust searching algorithms for unstructured grids. However, with the rise of unstructured grid based model and the improvement of model resolution, providing an efficient search algorithm for unstructured grid is in urgent need in engineering practice. In order to improve the performance of searching process between unstructured grid and other grids when remapping, this paper implements two search algorithms based on KD (K-dimension) tree (the nearest neighbor search and range search). Compared with the performance of brute force search, it is found that the efficiency of search algorithms based on KD tree are much higher than that of brute force search, including the nearest neighbor search and range search. The experimental results provide practical experience for the development of remapping software in the future.

A robust structural control method is proposed to solve the problems of structured uncertainties and multi-objective control for vehicle active suspension systems. The considered model is a quarter model of vehicle subject to unknown ground disturbance and structured uncertainties. The proposed method is based on robust structured control well supported by singular value decomposition (SVD) method, yielding a multi-objective control design approach, applicable to almost any active suspension systems. Some experiments show that the control performance of robust structural control for vehicle active suspension systems with structured uncertainties is significantly improved comparing with passive controlled system and existing other controlled active suspension systems.

A novel fusion spectrum sensing algorithm using energy and eigenvalues is proposed, which employs the energy, maximum eigenvalue and minimum eigenvalue of the sample covariance matrix to construct test statistic. The proposed algorithm includes the MET, MME and EME algorithms as special cases, and it can be seen as a fusion of the test statistics of the MET and EME algorithms. In addition, the false alarm probability and threshold of the proposed method are derived using random matrix theory. The proposed algorithm is a more general algorithm. Simulation results show the effectiveness of the new algorithm.

Effective surface defect detection are of great significance for the production of high quality products. Aiming at real-time detection of surface defect, we propose a reusable and high-efficiency Inception-based MobileNet-SSD method for surface defect inspection in industrial environment. First, convolutional layers for feature extraction used in SSD were replaced by depthwise separable convolutions utilized in MobileNet so that the speed of the network can be faster. Then, the layer in the base network as convolutional feature layer is constructed as Inception which can extract more rich features through multiple convolution combinations of different scales. Finally, predictions from multiple feature maps with different resolutions are combined by the network to naturally handle objects of various sizes. Experimental results on a surface defect dataset containing 2750 images of 5 classes we established confirm that our network has competitive accuracy and is much faster. For 300 × 300 input, ours network achieves 96.1% mAP on DAGM 2007 test at 73FPS on a NVIDIA GTX 1080Ti, outperforming a comparable state-of-the-art FCN model.

Accurate classification of power quality disturbance is the premise and basis for improving and governing power quality. A method for power quality disturbance classification based on time-frequency domain multi-feature and decision tree is presented. Wavelet transform and S-transform are used to extract the feature quantity of each power quality disturbance signal, and a decision tree with classification rules is then constructed for classification and recognition based on the extracted feature quantity. The classification rules and decision tree classifier are established by combining the energy spectrum feature quantity extracted by wavelet transform and other seven time-frequency domain feature quantities extracted by S-transform. Simulation results show that the proposed method can effectively identify six types of common single disturbance signals and two mixed disturbance signals, with fast classification speed and adequate noise resistance. Its classification accuracy is also higher than those of support vector machine (SVM) and k-nearest neighbor (KNN) algorithms. Compared with the method that only uses S-transform, the proposed feature extraction method has more abundant features and higher classification accuracy for power quality disturbance.

This paper deals with controller parameterisation method of adaptive H∞ control for polynomial Hamiltonian systems (PHSs) with disturbances and unknown parameters. We design a simplified co...

Ambient backscatter is an emerging green communication technology that exploits the environmental radio frequency (RF) signals to enable passive devices to communicate with each other. This paper investigates channel capacity and outage performance of ambient backscatter communication systems. Specifically, a calculation method is proposed to facilitate capacity analysis, and the ambient backscatter system capacities are derived in the case of four different RF signals. It is surprisingly found that the channel capacity is obtained when the RF signals are not equiprobably backscattered by tag, and that the capacity with complex Gaussian RF signals is not exactly twice that with real ones for the ambient backscatter communication systems. Then, the outage probability and its asymptotic value in the high signal-to-noise ratio regime are obtained. Since the exact outage expression consists of an infinite number of terms, a tight truncation error bound is derived to reasonably estimate the number of effective terms for numerical simulation. Finally, simulation results are provided to corroborate theoretical analysis.

Single-pixel imaging (SPI) recently has made significant progress, however, it is still an open issue to adaptively tradeoff its imaging fidelity and speed. Here, we present an adaptive sampling method for SPI to reduce the sampling rate for improving the imaging speed adaptively, sacrificing as little as possible of its imaging fidelity. The proposed method only requires setting an error band to realize adaptive under-Nyquist sampling, based upon the fact that the coefficient matrix consisted of the frequency components obtained by the orthogonal transforms is sparse. First, an appropriately elaborated sampling path, which can be divided into multiple segments, can be set according to this coefficient matrix measured by a single-pixel detector. Then the variance of each segment is calculated and expressed in logarithmic form for the real-time polynomial curve fitting. When the variation of the curve’s slope is small enough and reaches an error band, the sampling is adaptively stopped. Finally, the object image can be reconstructed via the adaptive sampling method following the elaborated sampling path for improving the imaging speed adaptively. The effectiveness of the adaptive sampling SPIs based on the orthogonal transforms, such as Hadamard transform, discrete cosine transform, Fourier transform, and Krawtchouk moments transform, is theoretically and experimentally demonstrated. It should be noted the proposed method can be also applied in SPIs using other orthogonal transforms, in which only an error band is set by the users.

This paper investigates the problem of adaptive robust simultaneous stabilization (ARSS) of two dissipative Hamiltonian systems (DHSs), and proposes a number of results on the controller parameterization design. Firstly, an adaptiveH∞control design approach is presented by using the dissipative Hamiltonian structural for the case that there are both external disturbances and parametric uncertainties in two DHSs. Secondly, an algorithm for solving tuning parameters of the controller is proposed using symbolic computation. The proposed controller parameterization method avoids solving Hamilton-Jacobi-Issacs (HJI) equations and the obtained controller is easier as compared to some existing ones. Finally, an illustrative example is presented to show that the ARSS controller obtained in this paper works very well.

The calibration of multi-projector display with extra-large field of view (FOV) and quantitative registration analysis for realizing perfect visual splicing is crucial and difficult. In this paper, we present a novel calibration method to realize the seamless splicing for a multi-projector display system with extra-large FOV. The display consists of 24 projectors, covering the range of 360 degrees in the longitude direction and 210 degrees in the latitude direction. A wide-angle camera fixed on a rotating optical system is used to scan the entire display scene and establish point-to-point correspondence between projector pixels and spatial points using the longitude and latitude information. Local longitude table and latitude table are established on the target of the wide-angle camera. A deterministic method is proposed to locate the North Pole of the display. The local tables corresponding to different camera views can be unified based on the image of the North Pole to form global longitude and latitude tables of arbitrary free-form surface. The mapping between the projector pixels and the camera pixels is established by inverse projection technique, and then each pixel of each projector can be appointed a pair of unique longitude and latitude values. A quantitative registration accuracy analysis method is proposed for multi-projector display system, in which, three-frequency temporal unwrapping method based on coded longitude and latitude values is applied to calculate the registration accuracy. Experiments prove that the registration error of the multi-projector system is less than 0.4 pixels.

Energy detection (ED) method for spectrum sensing does not require any information of the source signal and channel, as well as it is suitable for detecting independent identically distributed signals. Covariance matrix catches the signal correlations and the maximum eigenvalue detection (MED) method is better than the ED for correlated signals. In this paper, taking the advantages of the two algorithms, a combined method of the maximum eigenvalue and energy of the signal is proposed to achieve detection performance improvements. The proposed method is a generalization of the ED and MED methods, which takes the ED and MED algorithms as special cases. The theoretical analysis on detection probability, false alarm probability and threshold are given, and the optimum parameter selection of the proposed method is discussed. Finally, simulation results show the effectiveness of the proposed algorithm.

The maximum eigenvalue captures the signal correlation well, and the minimum eigenvalue also captures the noise characteristics well, thus the spectrum sensing algorithm based on the maximum and minimum eigenvalue gets better detection performance. This paper considers various combination algorithms based on maximum and minimum eigenvalues, and proposes some new spectrum sensing algorithms based on maximum and minimum eigenvalue which includes well known algorithm as its special case. Simulation result for multi-user, multi-antenna and multi-path scenarios shows the effectiveness of the proposed algorithms. In particular, the algorithm based on the product and sum of the maximum and minimum eigenvalues (α-MMEP and α-MMES) showed the best detection performance.

Ambient backscatter, one type of batteryless backscatter, is a new technology that utilizes environmental wireless signals to enable battery-free devices to communicate with each other. Most existing studies on ambient backscatter are based on the assumption of a single antenna at each tag. In this paper, we investigate signal detection in ambient backscatter communication systems with multiple-antenna tags. The use of multiple antennas results in multiple channel parameters between the tag and the reader, which constitutes the main challenge for signal detection because the tag has limited power and can transmit few training symbols. We show that traditional detection algorithms can not be directly applied into these Batteryless Backscatter systems with multiple-antenna tags. New detectors are to be designed for such systems, which is a good topic for future research.

In order to improve the utilization efficiency of motor train-sets, a Randomized Swap Differential Evolution algorithm (RSDE) is proposed. For the motor train-sets scheduling model with multiple hub stations, the “adjacent node” is taken as the selection range of the connected trains, and the optimization model of the motor train-sets based on the directed graph is established. To satisfy the continuity, an improved priority decoding method, applicable to any situation where trains run in pair, is proposed for generating the initial population and for decoding the individual into the operational tasks that motor train-sets undertakes in turn. In order to apply the differential evolution algorithm to the combinatorial optimization problem, a random permutation operator is proposed based on group theory. Considering the difference of evolutionary information carried by individuals, the adaptive regulatory shrinkage factor is proposed. A novel selection strategy is adopted to keep trial subjects as much as possible. The Wuhan-Guangzhou passenger line is taken as the research object, and the performance of the proposed algorithm is tested. The simulation results show that the algorithm can achieve better optimization performance compared with other several algorithms listed.

This paper provides a low complexity dispersive selective mapping (LDSLM) algorithm to the problem of high peak-to-average power ratio (PAPR) problem in filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) system. The proposed algorithm considers the overlapping characteristics of signals, and employs the average power distribution of the signals to shorten search range and reduces the system complexity. Compared with the existing clipping and filtering (CAF) algorithm, the LDSLM algorithm ensures the non-distortion property, while the PAPR is not be significantly reduced. Furthermore, considering the advantages of LDSLM and CAF algorithms, a hybrid algorithm combining CAF and LDSLM is proposed to effectively improve the PAPR performance of the system without affecting the bit error rate (BER).

Ambient backscatter, an emerging green communication technology, has aroused great interest from both academia and industry. One open problem for ambient backscatter communication (AmBC) systems is channel estimation for a massive-antenna reader. In this paper, we focus on channel estimation problem in AmBC systems with uniform linear array (ULA) at the reader which consists of large number of antennas. We first design a two-step method to jointly estimate channel gains and direction of arrivals (DoAs), and then refine the estimates through angular rotation. Additionally, Cramer-Rao lower bounds (CRLBs) are derived for both the modulus of the channel gain and the DoA estimates. Simulations are then provided to validate the analysis, and to show the efficiency of the proposed approach., 5 figures, submitted to IEEE Transactions on Vehicular Technology, 29 March, 2019

For an orthogonal transform based single-pixel imaging (OT-SPI), to accelerate its speed while degrading as little as possible of its imaging quality, the normal way is to artificially plan the sampling path for optimizing the sampling strategy based on the characteristic of the orthogonal transform. Here, we propose an optimized sampling method using a Deep Q-learning Network (DQN), which considers the sampling process as decision-making, and the improvement of the reconstructed image as feedback, to obtain a relatively optimal sampling strategy for an OT-SPI. We verify the effectiveness of the method through simulations and experiments. Thanks to the DQN, the proposed single-pixel imaging technique is capable of obtaining an optimal sampling strategy directly, and therefore it requires no artificial planning of the sampling path there, which eliminates the influence of the imperfect sampling path planning on the imaging performance.

Non-commercial resources provided by employers (NCRPE) are the imperative factor affecting the progress of engineering projects. However, the NCRPE with limited supply ability is rarely considered in the schedule optimization models in previous studies from the perspective of work. This paper analyzes the characteristics of the non-commercial single resource provided by employers (NCSRPE), the optimization principle, characteristics, and tasks. With the initial network, this paper researches the optimization model of the NCSRPE to minimize the supply capacity of the NCSRPE before the implementation and to reduce the actual demand of the NCSRPE to a level slightly below the supply capacity of the production system. NCSRPE optimization and re-optimization models are built based on this, and the Hybrid Particle Swarm Algorithm (HPSA) is used to solve the problem. The results show that the two models can effectively solve the optimization problem of program planning and adjustment under multiple independent stakeholders. Finally, resource balance before the implementation and the re-optimization of resource cutting during the implementation are carried out for artificial aggregate in Program X of the South-to-North Water Diversion Project (SNWDP) based on the HPSA. A reasonable and practical program planning and adjustment within different stakeholders can be obtained.

Scattering media are generally regarded as an obstacle in optical imaging. However, the scattering of a diffuser can be exactly taken as an advantage to act as random phase masks in the field of optical encryption to enhance information security. Here, we propose and demonstrate a dynamic diffuser based optical encryption method, which increases the ciphering strength by exploiting the uncorrelated characteristics of the dynamic diffuser as well as randomly sampling the plaintext multiple times. The light emitted from a randomly sampled plaintext passing through the dynamic diffuser generates noise-like speckles, and then SNR of the recorded speckles is further reduced for obtaining the ciphertexts, which makes COA using PRA almost impossible. The specific uncorrelated characteristics of the dynamic diffuser make the ciphertexts and the PSF keys of the optical encryption unique. Therefore, only authorized users who mastered the keys can decrypt the plaintext. The proposed method is very simple and flexible since it can also achieve the encryption offline by performing convolutions on partial-plaintexts with pre-recorded uncorrelated PSFs to generate speckle patterns and then reducing their SNR to obtain the ciphertexts. This type of encryption technique has a promising prospect in applications involving images and/or videos information encryption owing to its simplicity and flexibility.

This paper evaluates the potential of wavelet in low signal-to-noise ratio (SNR) speech classification. A wavelet feature extraction method is proposed, which includes wavelet analysis, wavelet denoising and sliding variance. The potential of wavelet feature extraction method is evaluated on two different datasets with Convolutional Neural Network. The results of our model and comparation show that the performance is significantly improving comparing with other state-of-the-art approaches.

Employing backscatter communication is a promising solution for Internet of Things (IoT). The novel large intelligent surface (LIS) concept can achieve reliable communication by establishing line-of-sight like channels. This letter thus considers an LIS-aided backscatter system to support high-reliable communications for IoT applications. In this letter, the symbol error probability (SEP) for both intelligent and random phase adjustments at the LIS reflectors is analytically investigated. In particular, we calculate the SEP based on the moment generating function approach and also provide tight SEP upper bounds for either fully correlated or mutually independent channels. Insightful observations of SEP outcomes reveal that having a large number of reflective elements on the LIS has a significantly positive impact on the SEP performance where high reliability can be achieved in moderate signal-to-noise.

Ambient backscatter exploits radio frequency (RF) signals to enable passive devices such as tags or sensors to communicate with readers, which has fascinating application for Internet of Things (IoT). The majority of the existing studies assume that channel state information (CSI) is perfectly acquired. Nevertheless, in ambient backscatter systems, the environmental RF signals are unknown at reader and thus may not serve as pilots. Therefore, the traditional channel estimators that routinely require pilots are not acceptable. In this paper, we focus on the problem of channel estimation with no pilots in ambient backscatter systems with multiple-antenna reader. Specifically, a blind channel estimator based on the eigenvalue decomposition (EVD) of the covariance matrix of the received signals is proposed, and the corresponding Cramer-Rao lower bounds (CRLBs) are derived. Simulation results are also provided to corroborate theoretical analysis.

Riemannian distance based matrix constant false alarm rate (CFAR) detector under small number of pulses provides a novel mechanism for detecting radar targets against the background of sea clutter. However, the computational com­plexity of this detector is heavy. In this paper, using the maximum eigenvalue, we propose two blind algorithms for rank one signal. The proposed methods achieve high detection rates with low computational complexity in which the maximum eigenvalue is employed as the test statistic to modify the Riemannian method. Furthermore, the CFAR property is derived by the group invariant theory. The computational complexity is also analyzed and simulation results verify the effectiveness of the proposed detection methods.