Improved Capon Estimator for High-Resolution DOA Estimation and Its Statistical Analysis

Despite some efforts and attempts have been made to improve the direction-of-arrival(DOA)estimation performance of the standard Capon beamformer(SCB)in array processing,rigorous statistical performance analyses of these modified Capon estimators are still lacking.This paper studies an improved Capon estimator(ICE)for estimating the DOAs of multiple uncorrelated narrowband signals,where the higherorder inverse(sample)array covariance matrix is used in the Capon-like cost function.By establishing the relationship between this nonparametric estimator and the parametric and classic subspace-based MUSIC(multiple signal classification),it is clarified that as long as the power order of the inverse covariance matrix is increased to reduce the influence of signal subspace components in the ICE,the estimation performance of the ICE becomes equivalent to that of the MUSIC regardless of the signal-to-noise ratio(SNR).Furthermore the statistical performance of the ICE is analyzed,and the large-sample mean-squared-error(MSE)expression of the estimated DOA is derived.Finally the effectiveness and the theoretical analysis of the ICE are substantiated through numerical examples,where the Cramer-Rao lower bound(CRB)is used to evaluate the validity of the derived asymptotic MSE expression.

Human-Like Decision-Making of Autonomous Vehicles in Dynamic Traffic Scenarios

With the maturation of autonomous driving technology, the use of autonomous vehicles in a socially acceptable manner has become a growing demand of the public. Human-like autonomous driving is expected due to the impact of the differences between autonomous vehicles and human drivers on safety.Although human-like decision-making has become a research hotspot, a unified theory has not yet been formed, and there are significant differences in the implementation and performance of existing methods. This paper provides a comprehensive overview of human-like decision-making for autonomous vehicles. The following issues are discussed: 1) The intelligence level of most autonomous driving decision-making algorithms;2) The driving datasets and simulation platforms for testing and verifying human-like decision-making;3) The evaluation metrics of human-likeness;personalized driving;the application of decisionmaking in real traffic scenarios;and 4) The potential research direction of human-like driving. These research results are significant for creating interpretable human-like driving models and applying them in dynamic traffic scenarios. In the future, the combination of intuitive logical reasoning and hierarchical structure will be an important topic for further research. It is expected to meet the needs of human-like driving.

Parallel Learning:a Perspective and a Framework

The development of machine learning in complex system is hindered by two problems nowadays.The first problem is the inefficiency of exploration in state and action space,which leads to the data-hungry of some state-of-art data-driven algorithm.The second problem is the lack of a general theory which can be used to analyze and implement a complex learning system.In this paper,we proposed a general methods that can address both two issues.We combine the concepts of descriptive learning,predictive learning,and prescriptive learning into a uniform framework,so as to build a parallel system allowing learning system improved by self-boosting.Formulating a new perspective of data,knowledge and action,we provide a new methodology called parallel learning to design machine learning system for real-world problems.

A Correntropy-based Affine Iterative Closest Point Algorithm for Robust Point Set Registration

The iterative closest point(ICP)algorithm has the advantages of high accuracy and fast speed for point set registration,but it performs poorly when the point set has a large number of noisy outliers.To solve this problem,we propose a new affine registration algorithm based on correntropy which works well in the affine registration of point sets with outliers.Firstly,we substitute the traditional measure of least squares with a maximum correntropy criterion to build a new registration model,which can avoid the influence of outliers.To maximize the objective function,we then propose a robust affine ICP algorithm.At each iteration of this new algorithm,we set up the index mapping of two point sets according to the known transformation,and then compute the closed-form solution of the new transformation according to the known index mapping.Similar to the traditional ICP algorithm,our algorithm converges to a local maximum monotonously for any given initial value.Finally,the robustness and high efficiency of affine ICP algorithm based on correntropy are demonstrated by 2D and 3D point set registration experiments.

A Co-Point Mapping-Based Approach to Drivable Area Detection for Self-Driving Cars

The randomness and complexity of urban traffic scenes make it a difficult task for self-driving cars to detect drivable areas, Inspired by human driving behaviors, we propose a novel method of drivable area detection for self-driving cars based on fusing pixel information from a monocular camera with spatial information from a light detection and ranging （LIDAR） scanner, Similar to the bijection of collineation, a new concept called co-point mapping, which is a bijection that maps points from the LIDAR scanner to points on the edge of the image segmentation, is introduced in the proposed method, Our method posi- tions candidate drivable areas through self-learning models based on the initial drivable areas that are obtained by fusing obstacle information with superpixels, In addition, a fusion of four features is applied in order to achieve a more robust performance, In particular, a feature called drivable degree （DD） is pro- posed to characterize the drivable degree of the LIDAR points, After the initial drivable area is characterized by the features obtained through self-learning, a Bayesian framework is utilized to calculate the final probability map of the drivable area, Our approach introduces no common hypothesis and requires no training steps; yet it yields a state-of-art performance when tested on the ROAD-KITTI benchmark, Experimental results demonstrate that the proposed method is a general and efficient approach for detecting drivable area,

Delay-dependent H-infinity control for continuous time-delay systems via state feedback

The delay-dependent H-infinity analysis and H-infinity control problems for continuous time-delay systems are studied. By introducing an equality with some free weighting matrices, an improved criterion of delay-dependent stability with H-infinity performance for such systems is presented, and a criterion of existence and some design methods of delay-dependent H-infinity controller for such systems are proposed in term of a set of matrix inequalities, which is solved efficiently by an iterative algorithm. Further, the corresponding results for the delay-dependent robust H-infinity analysis and robust H-infinity control problems for continuous time-delay uncertain systems are given. Finally, two numerical examples are given to illustrate the efficiency of the proposed method by comparing with the other existing results.

Robust monocular SLAM towards motion disturbance

The standard extended Kalman filter-based simultaneously localization and mapping(EKF-SLAM)algorithm has a drawback that it could not handle the sudden motion caused by the motion disturbance.This prevents the SLAM system from real applications.Many techniques have been developed to make the system more robust to the motion disturbance.In this paper,we propose a robust monocular SLAM algorithm.First,when the motion model-based system failed to track the features,a KLT tracker will be activated for each feature.Second,the KLT tracked features are used to update the camera states.Third,the difference between the camera states and the predictions is used to adjust the input motion noise.Finally,we do the standard EKF-SLAM with the new input motion noise.In order to make the system more reliable,a joint compatibility branch and bound algorithm are used to check the outliers,and an IEKF filter is used to make the motion estimation smoother when the camera encounters sudden movement.The experiments are done on an image sequence caught by a shaking hand-held camera,which show that the proposed method is very robust to large motion disturbance.

Fractal image encoding with flexible classification sets

Fractal image compression(FIC)technology is an interesting attempt at structure similarity-based image compression.It has been widely applied in many fields such as image encryption,image retrieval,image sharpening,and pattern recognition.However,overlong encoding time is the main difficulty for the application of FIC.In this paper,a new FIC speedup algorithm is proposed with two steps.Firstly,the simplified statistical variable expressions can speed up encoding twice more than the baseline fractal compression(BFC)without loss of image quality corresponding to BFC.Secondly,based on the fact that the affine self-similarity is equivalent to the absolute value of Pearson’s correlation coefficient,a new block classification strategy with flexible classification sets is proposed to speed up encoding further.The experiment results and theoretical analysis show that the proposed scheme achieves high performance in both image quality preservation and encoding efficiency.

Hybrid hard-/soft-decision LDPC decoding strategy for NAND flash memory

This paper concerns a decoding strategy to improve the throughput in NAND flash memory using lowdensity parity-check(LDPC) codes. As the reliability of NAND flash memory continues degrading, conventional error correction codes have become increasingly inadequate.LDPC code is highly desirable, due to its powerful correction strength. However, in order to maximize the correction strength, LDPC codes demand fine-grained memory sensing,leading to a significant read latency penalty. To address the drawbacks caused by soft-decision LDPC decoding, this paper proposes a hybrid hard-/soft-decision LDPC decoding strategy. Simulation results show that the proposed approach could reduce the read latency penalty and hence improve the decoding throughput up to 30 %, especially in early lifetime of NAND flash memory, compared with the conventional decoding with equivalent area.

A new implementation of image-processing engine for 3D visualization and stereo video stream display

This paper presents a new implementation of a high-definition image-processing engine,which mainly targets the 3-dimensional(3D)visualization and stereo video stream display of binocular display equipment.The engine is compatible with the mainstream analog and digital stereo videos in component format and is able to receive stereo composite video broadcast signals using an integrated analog stereo video decoder.The four modules include a spatiotemporal scaling transform engine,a 2D–3D converter,an image animating engine,and a 2D scalar operating in pipeline architecture to implement the video format conversion and the stereo effect enhancement.Furthermore,the data access,hardware structure,and system-level configurations are optimized.Finally,the proposed architecture is realized by 0.18 lm CMOS technology.The application-specific integrated circuit verification results show that the engine can generate a strong feeling of 3D immersion and highdefinition image quality with minimal flicker.The chip has wide compatibility and an uppermost 1080P-processing capacity,which has approximately 3.5 million gates with about 43 mm2 die size.