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Deep images can provide rich spatial structure information, which can effectively exclude the interference of illumination and road texture in road scene segmentation and make better use of the prior knowledge of road area. This paper first proposes a new cross‐modal feature maintenance and encouragement network. It includes a quantization statistics module as well as a maintenance and encouragement module for effective fusion between multimodal data. Meanwhile, for the problem that if the road segmentation is performed directly using a segmentation network, there will be a lack of supervised guidance with clear physical meaningful information and poor interpretability of learning features, this paper proposes two road segmentation models based on prior knowledge of deep image: disparity information and surface normal vector information. Then, a two‐branch neural network is used to process the colour image and the processed depth image separately, to achieve the full utilization of the complementary features of the two modalities. The experimental results on the KITTI road dataset and Cityscapes dataset show that the method in this paper has good road segmentation performance and high computational efficiency. [ABSTRACT FROM AUTHOR]

To eliminate blind spots in the field of vision and achieve a safe and collision‐free path, this paper proposes a path planning method based on multivehicle collaborative mapping in the context of vehicle networking. First, a multi vehicle map merging strategy based on the fireworks algorithm is proposed. In this strategy, a dissimilarity objective function based on the concept of grid map similarity is established and an improved fireworks algorithm is used to quickly search for the maximum overlap between local maps, achieving multivehicle collaborative mapping. Second, a real‐time path planning method based on artificial potential field theory is proposed. The information obtained from multivehicle collaborative mapping is first combined with the potential field model to form a multifield coupled road environment model. Then, the obstacle repulsion potential field model is improved to address the issues of traditional artificial potential field methods that target unreachability and poor dynamic response. The feasibility and effectiveness of the collaborative path planning method and single vehicle path planning method are tested through simulation analysis. This paper demonstrates through simulation analysis that the proposed path planning method can effectively achieve beyond line of sight perception and safely and comfortably guide vehicles to complete path planning. [ABSTRACT FROM AUTHOR]

Blind watermark extraction in discrete cosine transform (DCT) domain has a wide application prospect as well as a challenging subject. The imperceptibility of watermark signal makes watermark extraction a weak signal reception issue in essence. For DCT coefficients of host image generally disobey Gaussian distribution, at which the performance of linear correlated reception is no longer optimal. Aiming at this, a novel blind watermark extraction scheme combining the uncorrelated reception with adaptive bistable stochastic resonance (ABSR) technique is proposed. First, by block DCT transformation for host image, an additive watermark embedding algorithm is introduced, in which the watermarked image can be converted to one dimensional time domain weak signal (binary watermark image) reception under additive Laplacian noise (selected DCT coefficients). On this basis, through the key technology research on quantitatively cooperative resonance relationship under Laplacian noise, the ABSR system can be implemented by bistable system parameters self‐adaptive adjustment, in which the ABSR system output signal will be enhanced rather than be weakened by random noise. Finally, the ABSR‐based watermark extraction scheme is investigated, and both the visual effect, bit error ratio performance and robustness of proposed scheme are testified to be superior to that of traditional uncorrelated extraction. [ABSTRACT FROM AUTHOR]

This article is aimed at organisations and researchers to urge them to adopt more systemic ways to deal with energy justice issues in renewable energy projects being built around the world to help meet the United Nations Sustainable Development Goal (UNSDG) 7. It will focus on solar and wind farms. While these projects positively contribute towards achieving UNSDG 7 (viz., affordable clean energy), they have also created a variety of justice issues, which need to be addressed. While measures have been taken more recently to redress these issues, we make the case that the application of systemic thinking and practice could maximise the positives and minimise negative impacts of creating short‐term fixes without addressing the underlying root causes of the issues. Using two case studies, we will show how working systemically with indigenous populations and considering indigenous knowledge systems could help in dealing with justice issues. [ABSTRACT FROM AUTHOR]

This letter addresses the problem of grid map merging in multi‐robot systems without knowledge of inter‐robot observations, common landmarks and initial relative poses. Several map merging methods have been proposed to solve the problem while overcoming the lack of features or local minima. This letter proposes a selective spectral correlation method for more efficient map merging which can reduce computation times while maintaining the accuracy. The performance of the proposed method was tested with experimental datasets and compared with other existing methods. [ABSTRACT FROM AUTHOR]

Steganography is used for secret or covert communication. A graph wavelet transform‐based steganography using graph signal processing (GSP) is presented, which results in better visual quality stego image as well as extracted secret image. In the proposed scheme, graph wavelet transforms of both the cover image and transformed secret image (using Arnold cat map) are taken followed by alpha blending operation. The GSP‐based inverse wavelet transform is performed on the resulting image, to get the stego image. Here, the use of GSP increases the inter‐pixel correlation that results in better visual quality stego and extracted secret image as shown in simulation results. Simulation results show that the proposed scheme is more robust than other existing steganography techniques. [ABSTRACT FROM AUTHOR]

Often similar structures need to be compared to reveal local differences throughout the entire model or between related copies within the model. Therefore, a program to compare multiple structures and enable correction any differences not supported by the density map was written within the Phenix framework (Adams et al., Acta Cryst 2010; D66:213-221). This program, called Structure Comparison, can also be used for structures with multiple copies of the same protein chain in the asymmetric unit, that is, as a result of non-crystallographic symmetry (NCS). Structure Comparison was designed to interface with Coot(Emsley et al., Acta Cryst 2010; D66:486-501) and PyMOL(DeLano, PyMOL 0.99; 2002) to facilitate comparison of large numbers of related structures. Structure Comparison analyzes collections of protein structures using several metrics, such as the rotamer conformation of equivalent residues, displays the results in tabular form and allows superimposed protein chains and density maps to be quickly inspected and edited (via the tools in Coot) for consistency, completeness and correctness. [ABSTRACT FROM AUTHOR]

Copyright of Papers in Regional Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Self‐similarity is an attractive image property that has been successfully applied to object recognition due to its robustness to severe target appearance variations. However, less attention has been paid to explore self‐similarity for visual tracking, mainly because it is difficult to learn self‐similarity information between different features suitable for visual tracking. To address this issue, a simple, yet effective approach is presented to learn self‐similarity information among the local features extracted from the different regions of the target. The target is first divided into some non‐overlapping regions, in which each region is described by the histogram of gradient (HOG) features. Then, an explicit polynomial kernel feature map is constructed, which is capable of characterising the self‐similarity information among all the local regions in the targets. Finally, based on the feature maps, a linear support vector machine (SVM) is learnt via an online dual coordinate descent method that offers fast convergence guarantee. Experiments on a large tracking benchmark dataset with 50 sequences demonstrate the superiority of the proposed method over state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ABSTRACT. This paper considers two types of chaotic map time series models, including the well-known tent, logistic and binary-shift maps as special cases; these are called curved tent and curved binary families. Deterministic behaviour is investigated by invariant distributions, Lyapunov exponents, and by serial dependency. Stochastic time reversal of the families is shown to produce models which have a broader range of stochastic and chaotic properties than their deterministic counterparts. The marginal distributions may have concentrations and restricted supports and are shown to be a non-standard class of invariant distribution. Dependency is generally weaker with the reversed stochastic models. The work gives a broad statistical account of deterministic and stochastically reversed map models, such as are emerging in random number generation, communication systems and cryptography. [ABSTRACT FROM AUTHOR]

A new grid map‐merging technique which consists of virtual emphasis by one‐way observation, curvature‐based map matching and particle swarm optimisation is proposed. The proposed technique can improve not only the accuracy of map merging, but also the flexibility of multi‐robot systems. The improved performance is verified by showing higher similarities than the existing map‐merging techniques in experiments. [ABSTRACT FROM AUTHOR]

Self-similarity is an attractive image property that has been successfully applied to object recognition due to its robustness to severe target appearance variations. However, less attention has been paid to explore self-similarity for visual tracking, mainly because it is difficult to learn self-similarity information between different features suitable for visual tracking. To address this issue, a simple, yet effective approach is presented to learn self-similarity information among the local features extracted from the different regions of the target. The target is first divided into some non-overlapping regions, in which each region is described by the histogram of gradient (HOG) features. Then, an explicit polynomial kernel feature map is constructed, which is capable of characterising the self-similarity information among all the local regions in the targets. Finally, based on the feature maps, a linear support vector machine (SVM) is learnt via an online dual coordinate descent method that offers fast convergence guarantee. Experiments on a large tracking benchmark dataset with 50 sequences demonstrate the superiority of the proposed method over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Multi-robot map merging without inter-robot observations, common landmarks, and the knowledge of initial relative poses is a challenging problem due to lack of features or local maxima. This Letter proposes a multi-hypothesis map merging algorithm with sinogram-based particle swarm optimisation to solve the problem. Experimental results show that the proposed algorithm can merge individual robot maps much more accurately than the existing algorithms. [ABSTRACT FROM AUTHOR]

A new grid map-merging technique which consists of virtual emphasis by one-way observation, curvature-based map matching and particle swarm optimisation is proposed. The proposed technique can improve not only the accuracy of map merging, but also the flexibility of multi-robot systems. The improved performance is verified by showing higher similarities than the existing map-merging techniques in experiments. [ABSTRACT FROM AUTHOR]

The article focuses on the study conducted by researchers from Korea which develops a map merging technique for multi-robot systems (MRS). The study uses particle swarm optimisation (PSO), virtual emphasis by one-way observation, and curvature-based map matching to develop the technique. Results show that the technique provides more accurate and efficient path planning for multiple robots.