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During the operation of many real-time safety-critical systems, there are often strong needs for adapting to a dynamic environment or evolving mission objectives, e.g., increasing sampling and control frequencies of some functions to improve their performance under certain situations. However, a system's ability to adapt is often limited by tight resource constraints and rigid periodic execution requirements. In this work, we present a cross-layer approach to improve system adaptability by allowing proactive skipping of task executions, so that the resources can be either saved directly or re-allocated to other tasks for their performance improvement. Our approach includes three novel elements: (1) formal methods for deriving the feasible skipping choices of control tasks with safety guarantees at the functional layer, (2) a schedulability analysis method for assessing system feasibility at the architectural layer under allowed task job skippings, and (3) a runtime adaptation algorithm that efficiently explores job skipping choices and task priorities for meeting system adaptation requirements while ensuring system safety and timing correctness. Experiments demonstrate the effectiveness of our approach in meeting system adaptation needs.

Optical fiber sensor networks (OFSNs) provide powerful tools for large-scale buildings or long-distance sensing, and they can realize distributed or quasi-distributed measurement of temperature, strain, and other physical quantities. This article provides some optical fiber sensor network technologies based on the white light interference technology. We discuss the key issues in the fiber white light interference network, including the topology structure of white light interferometric fiber sensor network, the node connection components, and evaluation of the maximum number of sensors in the network. A final comment about further development prospects of fiber sensor network is presented.

Forward-looking imaging and target detection are highly desirable in many military and civilian fields, such as search and rescue, sea surface surveillance, airport surveillance, and guidance. However, there is a blind zone of forward-looking imaging for conventional Doppler beam sharpening and synthetic aperture radar. Scanning radar can be utilized to obtain a real beam image of a forward-looking area and implement target detection, while its azimuth resolution is poor due to the limitation of antenna size. Besides, during the processing procedure, imaging and target detection are usually regarded as two independent parts, which means that the imaging result will directly affect the detection performance. In this article, an integrated algorithm of super-resolution imaging and target detection for forward-looking scanning radar is proposed. In this algorithm, first of all, low-rank and sparse constraints as regularization norms are incorporated into the forward-looking scanning radar imaging and the objective function is established. Subsequently, the convex theory is utilized to solve the objective function and transform the problem of simultaneous super-resolution imaging and target detection into an optimization problem. Lastly, the super-resolution imaging and the target detection results are obtained simultaneously by solving the optimization problem using the alternating direction method of multipliers. In addition, simulation and experiment results are given to verify the effectiveness of the proposed algorithm.

Real-aperture radar can be utilized to realize forward-looking imaging by antenna scanning the imaging region. However, low azimuth resolution seriously affects its practical application. Although traditional super-resolution methods could enhance azimuth resolution to a certain extent, effective preservation of contour information for important targets still remains to be a problem. In this article, a method of total variation-sparse (TV-sparse) multiconstraint deconvolution is proposed to improve azimuth resolution of forward-looking imaging as well as preserve contour information of important targets. Since our interested targets usually appear to be sparse, the sparse constraint of the target is introduced first to achieve high resolution of forward-looking images, which may cause the loss of target contour information in the meantime. Second, total variation (TV) constraint is introduced based on the sparse constraint, converting traditional single-constraint super-resolution problem to a multiconstraint problem. We then use the split Bregman algorithm (SBA) to solve the multiconstraint problem, whose solution is the super-resolution image of radar forward-looking region. Compared with traditional super-resolution methods, the proposed method can improve the azimuth resolution of radar forward-looking imaging as well as better restore target contour information by adjusting respective weights of sparse constraint and TV constraint. Finally, the performance of the proposed method is validated with the simulation and measured data.

Scanning radar can be used to obtain images of targets in forward-looking area, and has attracted much attention in many fields, such as ocean monitoring, air-to-ground attack, navigation, and so on. However, its azimuth resolution is extremely poor due to the limitation of the antenna size. In order to break through the limitation, many superresolution algorithms have been proposed, and iterative shrinkage-thresholding algorithm (ISTA) is one of the most famous methods because of its antinoise ability and simplicity. In the meantime, the slow convergence of iterative shrinkage-thresholding algorithm is also known to all. In this article, a second-order accelerated ISTA for scanning radar forward-looking superresolution imaging is proposed. In this algorithm, a prediction vector is constructed before each iteration by using the first and the second-order difference information of iteration vectors to reduce the number of iterations and get a faster convergence speed. In the end, simulations and experimental results are given to illustrate the effectiveness of the accelerated imaging algorithm.

Applying neural networks as controllers in dynamical systems has shown great promises. However, it is critical yet challenging to verify the safety of such control systems with neural-network controllers in the loop. Previous methods for verifying neural network controlled systems are limited to a few specific activation functions. In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i.e., as long as they ensure that the neural networks are Lipschitz continuous. Specifically, we consider abstracting feedforward neural networks with Bernstein polynomials for a small subset of inputs. To quantify the error introduced by abstraction, we provide both theoretical error bound estimation based on the theory of Bernstein polynomials and more practical sampling based error bound estimation, following a tight Lipschitz constant estimation approach based on forward reachability analysis. Compared with previous methods, our approach addresses a much broader set of neural networks, including heterogeneous neural networks that contain multiple types of activation functions. Experiment results on a variety of benchmarks show the effectiveness of our approach.

Forward-looking scanning radar is capable of obtaining the real beam image of terrain in front of the flight platform, and can be used in military and civilian fields, such as sea surface surveillance, search and rescue, etc. However, it is difficult to extract multiple targets from the real beam image due to its poor azimuth resolution. A multiple-target extraction scheme based on low-rank and sparse matrix decomposition is proposed for forward-looking scanning radar. In the scheme, an image with good azimuth resolution is obtained first by the deconvolution preprocessing, and then it is used to map a patch-image. Second, using the low-rank property of the patch-image and the sparse property of the targets of interest, the target extraction is converted into an optimization problem of low-rank and sparse matrix decomposition. Third, the extraction results are obtained by solving this optimization problem using the alternating direction method of multipliers. Finally, simulation and experiment results are given to verify the effectiveness of the proposed scheme.

Synthetic aperture radar (SAR) imaging has always been an area of concern and exploration by researchers. The restoration of elevation information is a core issue of SAR imaging. Therefore, this paper proposes a method of three dimensional (3D) surface reconstruction with multistatic SAR. The presence of elevation information makes the target shift in the SAR image which is related to the configuration of SAR. This method utilizes the range history of the scattering point, combining the quantitative relationship between the receivers and the transmitters, to realize the reconstruction of the three-dimensional surface. Simulations prove the effectiveness of the proposed algorithm.

In the age of information explosion, image classification is the key technology of dealing with and organizing a large number of image data. Currently, the classical image classification algorithms are mostly based on RGB images or grayscale images, and fail to make good use of the depth information about objects or scenes. The depth information in the images has a strong complementary effect, which can enhance the classification accuracy significantly. In this paper, we propose an image classification technology using principal component analysis based on multi-view depth characters. In detail, firstly, the depth image of the original image is estimated; secondly, depth characters are extracted from the RGB views and the depth view separately, and then the reducing dimension operation through the PCA is implemented. Eventually, the SVM is applied to image classification. The experimental results show that the method has good performance.

Fifth generation mobile networks (5G) is coming into our life and it will not only provide an increase of 1000 times in Internet traffic in the next decade but will also offer “smarter” user experience. With the commercial uses of 5G, online social networks and smart phones will spring up again. Then cross-modality data will play a more important role in the daily information dissemination. As an effective way of content analysis, topic detection has attracted much research interest, but conventional topic analysis is undergoing the limitations from the cross-modality heterogenous data. This paper proposes a sentimental image dominant graph topic model, that can detect the topic from the heterogenous data and mine the sentiment of each topic. In details, we design a topic model to transfer both the low-level visual modality and the high-level text modality into a semantic manifold, and improve the discriminative power of CNN feature by jointly optimizing the output of both convolutional layer and fully-connected layer. Furthermore, since the sentimental impact is very significant for understanding the intrinsic meaning of topics, we introduce a semantic score of subjective sentences to calculate the sentiment on the base of the contextual sentence structure. The comparison experiments on the public cross-modality benchmark show the promising performance of our model. So our method using AI technology will facilitate the intellectualization of 5G.

The convenience of the cloud-assisted Internet of Things has led to the need for improved protections for the large volumes of data collected from devices around the world and stored on cloud-based servers. Proxy re-encryption (PRE) has been presented as a suitable mechanism for secure transmission and sharing of files within the cloud. However, existing PRE schemes do not support unidirectional data transformation, fine-grained controls, multiple hops, and identity-based encryption simultaneously. To solve these problems, we propose a unidirectional multi-hop identity based-conditional PRE scheme that meets all of the above requirements. Our proposal has the additional benefits of a constant ciphertext size, non-interactivity, and collusion resistance. We also prove that our scheme is secure against adaptive identity chosen-ciphertext attacks in the standard model.

Background: Gene Regulatory Network (GRN) inference algorithms aim to explore casual interactions between genes and transcriptional factors. High-throughput transcriptomics data including DNA microarray and single cell expression data contain complementary information in network inference. Objective: To enhance GRN inference, data integration across various types of expression data becomes an economic and efficient solution. Method: In this paper, a novel E-alpha integration rule-based ensemble inference algorithm is proposed to merge complementary information from microarray and single cell expression data. This paper implements a Gradient Boosting Tree (GBT) inference algorithm to compute importance scores for candidate gene-gene pairs. The proposed E-alpha rule quantitatively evaluates the credibility levels of each information source and determines the final ranked list. Results: Two groups of in silico gene networks are applied to illustrate the effectiveness of the proposed E-alpha integration. Experimental outcomes with size50 and size100 in silico gene networks suggest that the proposed E-alpha rule significantly improves performance metrics compared with single information source. Conclusion: In GRN inference, the integration of hybrid expression data using E-alpha rule provides a feasible and efficient way to enhance performance metrics than solely increasing sample sizes.

As a novel network architecture, information-centric networking (ICN) has a good performance in facilitating content sharing among users. Although in-network cache used in ICN can effectively solve the problem of network congestion. Meanwhile, it also brings a lot of challenges such as security and privacy issues during data transformation. Some of the current solutions are based on the traditional encryption technology, but these solutions introduce significant overhead in the client side and have a high requirement to the memory and computing power of end user. In this paper, we use an efficient proxy re-encryption (PRE) scheme in ICN framework to help reduce the overhead on the user-side while guaranteeing flexible data sharing between subscribers and even their cooperator. Our proposal has the additional benefits of a non-interactivity and collusion resistance. We also prove that our scheme is secure against adaptive replayable adaptive chosen ciphertext attack (RCCA) in re-encryption and chosen ciphertext attack (CCA) secure in complete ICN encryption. Our analysis of this program also shows that the scheme has a relatively good performance in computation cost and communication complexity aspects.

The problem of low azimuth resolution has restricted the applicability for radar forward-looking imaging in practice. In this paper, a sparse with fast majorization-minimization (SFMM) superresolution algorithm was proposed to realize fast superresolution imaging of sparse targets in radar forward-looking area. First, we analyzed the azimuth signal of the radar forward-looking area and modeled the azimuth signal as a convolution of antenna pattern and targets distribution. Second, the superresolution problem was converted into an L1 regularization issue by introducing the L1 norm to represent the distribution of the targets under the regularization framework. Third, according to the principle of majorization-minimization (MM) algorithm, a simple L2 regularization issue was obtained to replace the difficult L1 one, and the real target distribution was obtained by solving the L2 regularization problem (We named it sparse with MM (SMM) superresolution algorithm for convenience). Then, in order to improve the computational efficiency of the algorithm, we adopted the second-order vector extrapolation idea to accelerate the conventional MM algorithm and solve the L2 regularization problem. The simulation and real data verified that the proposed SFMM algorithm not only improves the azimuth resolution in radar forward-looking imaging but also increases convergence speed on the basis of SMM superresolution algorithm.

In general, autofocus methods integrated with frequency-domain imaging algorithms are instrumental to obtain a well-focused bistatic forward-looking synthetic aperture radar (BFSAR) image in the presence of motion errors. Nevertheless, before applying autofocus methods to correct the azimuth phase errors, range cell migration (RCM) should be eliminated by the RCM correction (RCMC) procedure in frequency-domain imaging algorithms. With motion errors being taken into account, there always exists some residual nonsystematic RCM (NsRCM), which refers to the residual migration components after the RCMC procedure. For the conventional side-looking SAR, NsRCM is caused by motion errors. On the other hand, NsRCM of BFSAR is originated from motion errors before RCMC and the NsRCM amplified by the RCMC procedure. In this paper, we analyze the different types of NsRCM in BFSAR imaging and their relationship. Based on the analyses, we propose an autofocus NsRCM correction scheme for BFSAR imagery using frequency-domain imaging algorithms that can eliminate the range-dependent NsRCM. The proposed scheme consists of three steps. First, for the BFSAR data after range compression and RCMC, a division procedure is carried out in the azimuth direction. The subblocks with the highest signal-to-clutter ratio along the range direction are selected after the azimuth segmentation procedure. Second, for the selected subblocks, the total NsRCM is estimated based on the minimum-entropy criterion. Based on the estimation results, different parts of the NsRCM are obtained by solving an ordinary differential equation. Third, a two-step compensation of the NsRCM is executed to reach the spatially variant correction. Simulations and experimental results are provided to demonstrate that our proposed scheme is effective for BFSAR imaging.

Absolute localization of a flying UAV on its own in a global-navigation-satellite-system (GNSS)-denied environment is always a challenge. In this paper, we present a landmark-based approach where a UAV is automatically locked into the landmark scene shown in a georeferenced image via a feedback control loop, which is driven by the output of an aerial image registration. To pursue a real-time application, we design and implement a speeded-up-robust-features (SURF)-based image registration algorithm that focuses efficiency and robustness under a 2D geometric transformation. A linear UAV controller with signals of four degrees of freedom is derived from the estimated transformation matrix. The approach is validated in a virtual simulation environment, with experimental results demonstrating the effectiveness and robustness of the proposed UAV self-localization system.

This paper considers the two-dimensional (2D) anchorless localization problem for sensor networks in global positioning system (GPS)-denied environments. We present an efficient method, based on the multidimensional scaling (MDS) algorithm, in order to estimate the positions of the nodes in the network using measurements of the inter-node distances. The proposed method takes advantage of the mobility of the nodes to address the location ambiguity problem, i.e., rotation and flip ambiguity, which arises in the anchorless MDS algorithm. Knowledge of the displacement of the moving node is used to produce an analytical solution for the noise-free case. Subsequently, a least squares estimator is presented for the noisy scenario and the associated closed-form solution derived. The simulations show that the proposed algorithm accurately and efficiently estimates the locations of nodes, outperforming alternative methods.

Future autonomous systems will employ complex sensing, computation, and communication components for their perception, planning, control, and coordination, and could operate in highly dynamic and uncertain environment with safety and security assurance. To realize this vision, we have to better understand and address the challenges from the "unknowns" - the unexpected disturbances from component faults, environmental interference, and malicious attacks, as well as the inherent uncertainties in system inputs, model inaccuracies, and machine learning techniques (particularly those based on neural networks). In this work, we will discuss these challenges, propose our approaches in addressing them, and present some of the initial results. In particular, we will introduce a cross-layer framework for modeling and mitigating execution uncertainties (e.g., timing violations, soft errors) with weakly-hard paradigm, quantitative and formal methods for ensuring safe and time-predictable application of neural networks in both perception and decision making, and safety-assured adaptation strategies in dynamic environment.

In this article, we present a layer-wise refinement method for neural network output range analysis. While approaches such as nonlinear programming (NLP) can directly model the high nonlinearity brought by neural networks in output range analysis, they are known to be difficult to solve in general. We propose to use a convex polygonal relaxation (overapproximation) of the activation functions to cope with the nonlinearity. This allows us to encode the relaxed problem into a mixed-integer linear program (MILP), and control the tightness of the relaxation by adjusting the number of segments in the polygon. Starting with a segment number of 1 for each neuron, which coincides with a linear programming (LP) relaxation, our approach selects neurons layer by layer to iteratively refine this relaxation. To tackle the increase of the number of integer variables with tighter refinement, we bridge the propagation-based method and the programming-based method by dividing and sliding the layer-wise constraints. Specifically, given a sliding number $s$ , for the neurons in layer $l$ , we only encode the constraints of the layers between $l-s$ and $l$ . We show that our overall framework is sound and provides a valid overapproximation. Experiments on deep neural networks demonstrate significant improvement on output range analysis precision using our approach compared to the state-of-the-art.

Total variation (TV) method has been utilized to realize super-resolution and preserve contour information of target in radar forward-looking imaging. However, its real-time ability is restricted to matrix inversion. In this paper, a fast TV (FTV) superresolution method is proposed to improve the real-time superresolution ability of traditional TV method. The proposed FTV method utilizes the low displacement rank features of Toplitz matrix and realizes fast matrix inversion by Gohberg-Semencul (GS) representation. It not only effectively improves the azimuth resolution and preserve the contour information of target, but also reduced the computational complexity of traditional TV method to improve its real-time superresolution ability. The superior performance of the proposed FTV method is verified by simulation and measured data processing.

In this paper, a weighted dense connected convolutional network(WDCCN) is proposed for SAR image super-resolution. In the network, to enhance feature propagation and the super-resolution performance, each layer will receive the output from all the previous layers in a different weight proportion. At last, the experimental results indicate that weighted dense connected convolutional network can realize SAR image super-resolution well.

Sparse regularization method has been widely used to realize super-resolution imaging in radar forward-looking imaging. However, most of existed methods directly minimize a nondifferentiable L1 regularization problem. In this paper, a Majorize-Minimization (MM) based super-resolution method is proposed to realize super-resolution for radar forward-looking imaging. According to MM principle, the proposed method converts the non-differentiable L1 regularization problem into a differentiable L2 regularization problem, and the real target distribution is obtained by solving the L2 regularization problem. Due to the introduction of the sparse prior, the proposed method can better improve the azimuth resolution of radar forward-looking imaging. In addition, the application of MM principle makes the non-differentiable L1 regularization easier to be solved. Finally, the superior performance of the proposed method is verified by simulation.

An efficient echo simulation algorithm considering topography in time domain for bistatic SAR is proposed in this paper. By using subaperture processing and equivalent scatter, this method can implement efficient echo simulation. At last, the effectiveness of this method is verified by numerical simulation.

In this paper, an improved target extraction scheme is proposed for forward-looking scanning radar. In the scheme, an image with good azimuth resolution is obtained firstly by deconvolution technique, and then it is used to map a patch-image. Secondly, low rank and sparse matrix decomposition is conducted on the patch image. Thirdly, constant false-alarm rate (CFAR) detection method is used to extract the targets from the sparse matrix. Finally, simulation results are given to verify the effectiveness of the proposed scheme.

Animated visualization is becoming increasingly popular as a compelling way to illustrate changes in time series data. However, maintaining the viewer’s focus throughout the entire animation is difficult because of its time-consuming nature. Viewers are likely to become bored and distracted during the ever-changing animated visualization. Informed by the role of foreshadowing that builds the expectation in film and literature, we introduce visual foreshadowing to improve the engagement of animated visualizations. In specific, we propose designs of visual foreshadowing that engage the audience while watching the animation. To demonstrate our approach, we built a proof-of-concept animated visualization authoring tool that incorporates visual foreshadowing techniques with various styles. Our user study indicates the effectiveness of our foreshadowing techniques on improving engagement for animated visualization.

We present TrojDRL, a tool for exploring and evaluating backdoor attacks on deep reinforcement learning agents. TrojDRL exploits the sequential nature of deep reinforcement learning (DRL) and considers different gradations of threat models. We show that untargeted attacks on state-of-the-art actor-critic algorithms can circumvent existing defenses built on the assumption of backdoors being targeted. We evaluated TrojDRL on a broad set of DRL benchmarks and showed that the attacks require only poisoning as little as 0.025% of the training data. Compared with existing works of backdoor attacks on classification models, TrojDRL provides a first step towards understanding the vulnerability of DRL agents.

Control schemes for autonomous systems are often designed in a way that anticipates the worst case in any situation. At runtime, however, there could exist opportunities to leverage the characteristics of specific environment and operation context for more efficient control. In this work, we develop an online intermittent-control framework that combines formal verification with model-based optimization and deep reinforcement learning to opportunistically skip certain control computation and actuation to save actuation energy and computational resources without compromising system safety. Experiments on an adaptive cruise control system demonstrate that our approach can achieve significant energy and computation savings., Accepted by Design Automation Conference (DAC) 2020

Recent successes of wireless networked systems in advancing industrial automation and in spawning the Internet of Things paradigm motivate the adoption of wireless networked systems in current and future safety-critical applications. As reliability is key in safety-critical applications, in this work we present NetDAG, a scheduler design and implementation suitable for real-time applications in the wireless setting. NetDAG is built upon the Low-Power Wireless Bus, a high-performant communication abstraction for wireless networked systems, and enables system designers to directly schedule applications under specified task-level real-time constraints. Access to real-time primitives in the scheduler permits efficient design exploration of tradeoffs between power consumption and latency. Furthermore, NetDAG provides support for weakly hard real-time applications with deterministic guarantees, in addition to heretofore considered soft real-time applications with probabilistic guarantees. We propose novel abstraction techniques for reasoning about conjunctions of weakly hard constraints and show how such abstractions can be used to handle the significant scheduling difficulties brought on by networked components with weakly hard behaviors.

We study the problem of policy repair for learning-based control policies in safety-critical settings. We consider an architecture where a high-performance learning-based control policy (e.g. one trained as a neural network) is paired with a model-based safety controller. The safety controller is endowed with the abilities to predict whether the trained policy will lead the system to an unsafe state, and take over control when necessary. While this architecture can provide added safety assurances, intermittent and frequent switching between the trained policy and the safety controller can result in undesirable behaviors and reduced performance. We propose to reduce or even eliminate control switching by ‘repairing’ the trained policy based on runtime data produced by the safety controller in a way that deviates minimally from the original policy. The key idea behind our approach is the formulation of a trajectory optimization problem that allows the joint reasoning of policy update and safety constraints. Experimental results demonstrate that our approach is effective even when the system model in the safety controller is unknown and only approximated.

We introduce ReachNN*, a tool for reachability analysis of neural-network controlled systems (NNCSs). The theoretical foundation of ReachNN* is the use of Bernstein polynomials to approximate any Lipschitz-continuous neural-network controller with different types of activation functions, with provable approximation error bounds. In addition, the sampling-based error bound estimation in ReachNN* is amenable to GPU-based parallel computing. For further improvement in runtime and error bound estimation, ReachNN* also features optional controller re-synthesis via a technique called verification-aware knowledge distillation (KD) to reduce the Lipschitz constant of the neural-network controller. Experiment results across a set of benchmarks show \(7\times \) to \(422\times \) efficiency improvement over the previous prototype. Moreover, KD enables proof of reachability of NNCSs whose verification results were previously unknown due to large overapproximation errors. An open-source implementation of ReachNN* is available at https://github.com/JmfanBU/ReachNNStar.git.

Due to the limitation of antenna size, the azimuth resolution of radar forward-looking imaging is limited. Conventional technologies improved the azimuth resolution and obtained good effects, but they are poor in contour recovery of targets. In this paper, we employed the total variation (TV) superresolution method to improve the azimuth resolution and recover the contour of the targets in radar forward-looking imaging. Firstly, the signal model of radar forward-looking imaging was deduced. Secondly, the TV operator was employed as the penalty term under regularization framework. Thirdly, the TV regularization model was solved by split Bregman algorithm. The TV regularization method not only improved the azimuth resolution, but also better recovered the contour of the targets. The performance was verified by simulations lastly.

Neural networks are widely used in many applications ranging from classification to control. While these networks are composed of simple arithmetic operations, they are challenging to formally verify for properties such as reachability due to the presence of nonlinear activation functions. In this paper, we make the observation that Lipschitz continuity of a neural network not only can play a major role in the construction of reachable sets for neural-network controlled systems but also can be systematically controlled during training of the neural network. We build on this observation to develop a novel verification-aware knowledge distillation framework that transfers the knowledge of a trained network to a new and easier-to-verify network. Experimental results show that our method can substantially improve reachability analysis of neural-network controlled systems for several state-of-the-art tools.

Deconvolution techniques provide efficient implementations for super-resolution imaging for forward-looking scanning radar. However, deconvolution is normally an ill-posed problem, and the solution is extremely sensitive to noise. From a statistical perspective, maximum likelihood (ML) methods are able to condition the ill-posed problem into a well-posed one. Nevertheless, traditional ML methods only consider the amplitude of the echo and by ignoring the phase that do not adequately model the radar imaging system. In this letter, an I/Q-channel modeling ML method is proposed for forward-looking scanning radar. First, the probability model of the echo is deduced by jointly considering noise in the I and Q channels. Then, a probability density function of the received data is deduced and used to formulate the likelihood function. Finally, the targets can be precisely estimated by maximizing this likelihood function. The results of simulations and experiments are provided to illustrate the effectiveness of the proposed method.

Ultra-dense networks (UDN) can provide extremely high throughput and data rate. However, there are severe interference due to dense and random deployment of femto base stations (FBSs). To mitigate interference and allocate network resource efficiently while ensuring quality of service (QoS) of user equipments (UEs), a cluster-based resource allocation scheme for UDN is proposed in this paper. Two stages, clustering and resource allocation, are involved in the scheme. In clustering stage, a modified K-means clustering algorithm is advanced to divide FBSs into different disjoint clusters dynamically according to the density of FBSs. Thus the number of clusters can be adjusted flexibly to fit for the dynamic network topology. In resource allocation stage, a greedy-based compensatory resource allocation algorithm (GCRAA) is further proposed to maximize the throughput of UDN. Herein the orthogonal resource blocks (RBs) are initially assigned among the UEs with a greedy algorithm. In order to ensure the fairness and QoS of UEs, a compensatory resource allocation algorithm is further proposed to allocate the remaining RBs. The simulated results show that the proposed resource allocation scheme can mitigate the interference in UDN effectively, and improve the system throughput while ensuring the QoS for UEs.

Preventive maintenance and production scheduling are two important and interactive activities in production systems. In this work, the integrated optimization problem of production scheduling for multi-state single-machine production systems experiencing degradation processes is investigated. Preventive maintenance tasks and jobs scheduling are jointly considered to find the optimal production policy by considering the processing costs, the maintenance costs, and the completion rewards, simultaneously. We formulate the integrated optimization problem as Markov decision process framework. R-learning algorithm is introduced to maximize the long-run expected average rewards per time unit over infinite horizon. On the basis of the analysis of the optimal stationary policy, the appropriate condition to perform preventive maintenance following optimal stationary policy is presented. This provides the basis for the improvement in R-learning algorithm. Furthermore, a novel heuristic reinforcement learning method is proposed to deal with the integrated model more efficiently. Finally, we present the simulation results and analysis of the proposed algorithm's performance in terms of the number of job types and machine states. The simulation results and analysis show the effectiveness of the proposed approach for solving the integrated problems.

Echo State Network (ESN) is often used for time series prediction and chaotic series prediction.But in the training process of ESN model, the reserve pool will involve a large number of calculations, so the reserve pool may have node redundancy, resulting in inaccurate training model. It is very important to select the active nodes in the reserve pool because the predicted nodes are very few compared with all the nodes in the reserve pool. Based on the characteristics of the original vector, compressed sensing can reduce the dimension of the vector so as to obtain the activated nodes. In this paper, the ESN model is added with compressed sensing, which simplifies the reserve pool part of the ESN model. The simulation results show that our method can not only reduce the calculation amount of training effectively, but also improve the accuracy.

We introduce a method for learning a model for the mobility of parts in 3D objects. Our method allows not only to understand the dynamic functionalities of one or more parts in a 3D object, but also to apply the mobility functions to static 3D models. Specifically, the learned part mobility model can predict mobilities for parts of a 3D object given in the form of a single static snapshot reflecting the spatial configuration of the object parts in 3D space, and transfer the mobility from relevant units in the training data. The training data consists of a set of mobility units of different motion types. Each unit is composed of a pair of 3D object parts (one moving and one reference part), along with usage examples consisting of a few snapshots capturing different motion states of the unit. Taking advantage of a linearity characteristic exhibited by most part motions in everyday objects, and utilizing a set of part-relation descriptors, we define a mapping from static snapshots to dynamic units. This mapping employs a motion-dependent snapshot-to-unit distance obtained via metric learning. We show that our learning scheme leads to accurate motion prediction from single static snapshots and allows proper motion transfer. We also demonstrate other applications such as motion-driven object detection and motion hierarchy construction.

This paper presents a method for the extraction of modal parameters for identification of time-varying systems using Data-Driven Stochastic Subspace Identification (SSI-DATA). In practical applications of SSI-DATA, both the modal parameters and computational ones are mixed together in the identified results. In order to differentiate the structural ones from computational ones, a new method based on the eigen-decomposition of the state matrix constructed in SSI-DATA is proposed. The efficiency of the proposed method is demonstrated through numerical simulation of a lumped-mass system and experimental test of a moving robot for extracting excited natural frequencies of the system.

The paper presents a passive auto-focusing technology by combining the discrete cosine transform(DCT) algorithm and small univalve segment assimilating nucleus(SUSAN) algorithm with evaluation function. The new method has the advantage of unimodal, local extremum and sensitivity of the focus curve. For the focusing search algorithm, the improved mountain climbing method which combined the large step and small step has been proposed, it makes the micro-vision system could find the focal plane more quickly and accurately.

In bistatic forward-looking synthetic aperture radar (BFSAR), conventional autofocus algorithms cannot estimate the phase errors accurately when the range walk is compensated in the azimuthal time domain. This problem stems from the influence of the azimuth-variant Doppler coefficients after linear range cell migration correction in azimuthal time domain. In principle, nonlinear chirp scaling processing can be carried out to remove the azimuth variance. Nevertheless, Doppler azimuth variations of the Doppler parameters induced by the motion errors are not taken into consideration and serious defocus would emerge in the final image. To cope with such a problem, an estimation–evaluation–equalization scheme is proposed before conventional autofocus for BFSAR. Different from the conventional autofocus method, the azimuth-variant Doppler coefficients are additionally estimated and equalized before autofocus, and as a consequence, phase errors can be precisely estimated. The BFSAR data processing results demonstrate the validity of the proposed method on the improvement of autofocus in BFSAR.

Bistatic forward-looking synthetic aperture radar (BFSAR) is a kind of bistatic SAR system that can image forward-looking terrain in the flight direction of a moving platform. In BFSAR, compensation of the flight trajectory errors is of great significance to get a well-focused image. To accomplish an accurate motion compensation in image processing, a high-precision navigation system is needed. However, in many cases, due to the accuracy limit of such systems, flight trajectory errors are hard to be compensated correctly, causing mainly the resolution decrease in final images. In order to cope with such a problem, we propose a rise-dimensional modeling and estimation for flight trajectory error based on raw BFSAR data in this paper. To apply this method, we first carry out a preprocessing named azimuth-slowtime decoupling to deal with the spatially variant flight trajectory error before estimation. Then, an optimization model for flight trajectory estimation under the criterion of maximum image intensity is built. The solution to the optimization model is the accurate flight trajectory. Then, block coordinate descent technique is used to solve this optimization model. The processing of BFSAR data shows that the algorithm can obtain a more accurate estimation results, and generate better focused images compared with the existing trajectory estimation method.

With appropriate geometry configurations, bistatic synthetic aperture radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Owing to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications. In BFSAR, the compensation of the spatially variant motion errors is of great significance to get a well-focused image. In this paper, first, the spatial-variance properties of motion errors are analyzed analytically and quantitatively. Different from the side-looking monostatic and bistatic SAR, 2-D space-variant motion errors should be taken into consideration in BFSAR. The 2-D spatial variance of the motion errors can be categorized into two parts, range-variant motion errors of the transmitter and azimuth-variant motion errors of the receiver. Moreover, these two parts are independent of each other. Based on this property analysis, second, a motion compensation (MoCo) approach with cubic-order processing is proposed to deal with the spatially variant motion errors in BFSAR. In the cubic-order processing, the first-order MoCo is performed to correct the spatially independent motion errors on the raw data. The second-order MoCo is accomplished on the non-range-cell-migration (RCM) data to deal with the range-variant errors. After the second-order MoCo, since the signal direction of the non-RCM data coincides with the variant direction of the uncompensated phase errors, the azimuth-variant motion errors and slow time signal are coupled together. To cope with such a problem, the slow time signal is transformed into the direction perpendicular to the azimuth by a novel procedure named azimuth–slow time decoupling. At this stage, the coupling between the azimuth-variant motion errors and slow time signal has been eliminated. Azimuth-variant motion errors can be corrected precisely. Simulation and experimental results verify the effectiveness of the proposed method.

Biological network inference has always been one of the central topics in systems biology. Network inference can be regarded as a process of determining relations between nodes with efficient measurements. For gene regulatory networks, transcriptomic data such as single cell RNA sequencing (sc RNA-seq) have increasingly act as the main information source in reconstructing network structures. Although many methods have been proposed towards this challenge, most of them do not focus on sing-cell data and omit the characteristics of gene regulatory networks. Here, we presented a new method names ICGNI to solve these problems about gene functional clustering, network inference with single-cell data and hub genes finding. Three single cell datasets were used to evaluate the performance of our method with satisfying results.

Target detection is an important function of forward-looking scanning radar search and tracking applications. However, it is difficult to detect the targets using the real beam image with low azimuth resolution. In this paper, a target detection scheme is proposed for forward-looking scanning radar. First, an image with better resolution is obtained by deconvolution technique, and it is used to map a patch-image. Then, according to the low rank characteristic of the patch-image and the sparse characteristic of the targets, the target detection is converted into an optimization problem of low rank and sparse matrix decomposition. Finally, the targets are obtained by solving this optimization problem. Simulations are given to verify its effectiveness.

In this paper, an accelerated maximum a posteriori (AMAP) algorithm is proposed to realize fast and effective super resolution imaging of real beam scanning radar. The main idea of this algorithm is to construct a prediction vector based on the first and the second order of difference information before iteration. By using Taylor expansion series and second-order vector extrapolation technique, it aims to enhance the convergence speed of maximum a posteriori algorithm. Finally, the proposed algorithm is verified by simulations.

Regularization technology can be utilized to improve the azimuth resolution for forward-looking scanning radar. In this paper, low-rank and sparse constraints as regularization norms are incorporated into the forward-looking scanning radar imaging. This method can achieve azimuth superresolution and noise suppression. Simulations are given to verify the effectiveness of the method.

Time and frequency synchronization is a crucial problem for Bistatic SAR systems. In this paper, frequency reference error of bistatic SAR system is analyzed firstly. Then the time and frequency synchronization errors are deduced and the intrinsic relationship between them is revealed. At last the effect of the frequency reference error is studied. Simulation and experiment results are given to verify the effectiveness of the analysis.

The clear contour is required when realize azimuth superresolution of forward-looking radar imaging in many applications. Traditional deconvolution methods achieve the azimuth superresolution but are limited in contour recovery. Although the total variation (TV) method can be used to keep the contour information, it’s sensitive to noise because of derivation. In this paper, we propose an improved total variation (ITV) method to realize azimuth superresolution of forward-looking radar imaging and recover the contour information. Firstly, the TV norm and L 2 norm are combined as the penalties under regularization framework. Then the regularization problem is solved by split Bregman algorithm. The proposed ITV method achieves higher azimuth resolution and better contour recovery performance than traditional methods, and the super performance is verified by simulations lastly.