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Relocation is one of the most common problems in Simultaneous Localization and Mapping (SLAM). This paper presents a novel relocation method, using unsupervised deep learning algorithm to extract the feature of Light Detection and Ranging (LiDAR) data, and narrows the scope of relocation by classifying these features to reduce the randomness of the relocation. Compared with the other methods which is based on matching the manual feature points, this method avoids some limitations of manual features. We modify the Particle Filter SLAM (PF-SLAM), and use our relocation method to replace the original method for experimentation. The experimental results demonstrate that this method can be relocation whit high success rate only use a small amount of computational resource in a short time. Training neural network will consume a lot of computing resources, we also propose a cloud computing framework to the implementation of this method for the mobile robot which computational resources are limited. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we have addressed the problem of finding a path through a maze of a given size. The traditional ways of finding a path through a maze employ recursive algorithms in which unwanted or non-paths are eliminated in a recursive manner. Neural networks with their parallel and distributed nature of processing seem to provide a natural solution to this problem. We present a biologically inspired solution using a two level hierarchical neural network for the mapping of the maze as also the generation of the path if it exists. For a maze of size S the amount of time it takes would be a function of S (O(S)) and a shortest path (if more than one path exists) could be found in around S cycles where each cycle involves all the neurons doing their processing in a parallel manner. The solution presented in this paper finds all valid paths and a simple technique for finding the shortest path amongst them is also given. The results are very encouraging and more applications of the network setup used in this report are currently being investigated. These include synthetic modeling of biological neural mechanisms, traversal of decision trees, modeling of associative neural networks (as in relating visual and auditory stimuli of a given phenomenon) and surgical micro-robot trajectory planning and execution. [Copyright &y& Elsevier]

• Proposing a novel hybrid SVR based model for the market price forecasting considering the risk error in the analysis and evaluation. • Proposing the firefly algorithm as a swarm based solution for optimizing the SVR hyperparameters according to the market price. • Proposing a two-stage modification approach for the FA (MFA) to enhance the search capabilities and boost the diversity of solutions. This paper proposes a novel artificial intelligence based approach for the accurate forecasting of the market cost incorporating the error risk. The proposed model makes use of a hybrid evolving solution based on firefly algorithm and support vector regression (SVR) for getting to the highest accuracy and precision. In contrast to the artificial neural network models which face the overfitting problem, the SVR would keep a limit on the forecast model complexity and thus would escape from the overfitting concerns. In order to get into the maximum performance of the SVR, one need to adjust the hyperparameters optimally. This article proposes the firefly algorithm which mimics the social behavior of these insects in their colony. In addition, a new modification method is introduced which would add up the algorithm population diversity and thus enhance the search results. The appropriate performance of the proposed hybrid AI base model is assessed on the typical market price datasets. [Display omitted] [ABSTRACT FROM AUTHOR]

As an essential subproblem in the face recognition field, occluded face recognition has received considerable attention in recent years. However, satisfactory recognition accuracy for occluded faces has not been achieved. Therefore, in order to solve this problem, this paper proposes a convolutional neural network that has multidimensional serial feature extraction modules for occluded faces and uses the deep learning method to improve the recognition rate. In order to improve the expression ability of the network, the method extracts features from two dimensions — space and channel — and built a multidimensional serial feature extraction module. By "multiscale" and "dependence" processing features, the generalization ability was significantly improved. Through the experimental test, the recognition accuracy of the multidimensional feature network (MFNet) reached 90.35%. This accuracy is 17.89% (72.36%) better than that of the traditional algorithm. Meanwhile, compared with other convolutional neural networks, this network has different degrees of improvement, which are 0.68% (ArcFace), 2.14% (Visual Geometry Group Face), and 4.83% (DeepID3). [ABSTRACT FROM AUTHOR]

Abstract: Artificial neural networks are some kind of data processing systems, which try to simulate features of the human brain and its learning process. So, they are widely used by researchers to solve different problems in optimization, classification, pattern recognition, associative memory and control. In this paper, an educational tool, which can be used to work on different kinds of neural network models and learn fundamentals of the artificial neural network, is described. At this point, the whole tool environment provides an advanced system to ensure mentioned functions. The developed system supports using MLP, LVQ and SOM models and related learning algorithms. It employs some visual, interactive tools, which enable users to compose their own neural networks and work on the developed networks easily. By using these tools, users can also understand and learn working mechanism of a typical artificial neural network, using features of different models and related learning algorithms. [Copyright &y& Elsevier]

Abstract: Voice over IP (VoIP) applications requires a buffer at the receiver to minimize the packet loss due to late arrival. Several algorithms are available in the literature to estimate the playout buffer delay. Classic estimation algorithms are non-adaptive, i.e. they differ from more recent approaches basically due to the absence of learning mechanisms. This paper introduces two new formulations of adaptive algorithms for online learning and prediction of the playout buffer delay, the first one being based on the standard Box–Jenkins autoregressive model, while the second one being based on the feedforward and recurrent neural networks. The obtained results indicate that the proposed algorithms present better overall performance than the classic ones. [Copyright &y& Elsevier]