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With fully optical gridless networks (also called, with some conservation, Elastic or Flexible-grid) considered hard to manage by its deploying channels that have different assigning spectrum widths. Optimization or defragmentation exercise is required because of the sequential Routing and Spectrum Assignment (RSA) used methods. A challenging problem produced after creating the new optimized Spectrum Map (SM) which is how to transform the existing SM to it with the minimum number of operations. In this paper, we propose a novel heuristic algorithm to execute spectrum map transformation once a defragmentation map is determined.

We present a general framework for fusing pre-trained object detection networks for multiple sensor modalities in autonomous cars at an intermediate stage. The key innovation is an autoencoder-inspired Transformer module which transforms perspective as well as feature activation characteristics from one sensor modality to another. Transformed feature maps can be combined with those of a modality-native feature extractor to enhance performance and reliability through a simple fusion scheme. Our approach is not limited to specific object detection network types. Compared to other methods, our framework allows fusion of pre-trained object detection networks and fuses sensor modalities at a single stage, resulting in a modular and traceable architecture. We show effectiveness of the proposed scheme by fusing camera and Lidar information to detect objects using our own as well as the KITTI dataset.

In this article, an optical image encryption algorithm based on chaotic baker’s map is presented. The stego-image is encrypted with the help of double random phase encoding algorithm and then produced disorder with the help of chaotic transformation. Security test shows that the reading of proposed algorithm is very close to the optimal values.

Based on our observations of the working principles of the archetypal hierarchical neural network, Neocognitron, we propose a simplified model which we call the Map Transformation Cascade. The least complex Map Transformation Cascade can be understood as a sequence of filters, which maps and transforms the input pattern into a space where patterns in the same class are close. The output of the filters is then passed to a simple classifier, which yields a classification for the input pattern. Instead of a specifically crafted learning algorithm, the Map Transformation Cascade separates two different learning needs: Information reduction, where a clustering algorithm is more suitable (e.g., K-Means) and classification, where a supervised classifier is more suitable (e.g., nearest neighbor method). The performance of the proposed model is analyzed in handwriting recognition. The Map Transformation Cascade achieved performance similar to that of Neocognitron.

It is well known that high-resolution 3D movie contents frequently do not deliver the identical 3D perception to low-resolution 3D images. For solving this problem, we propose a novel method that produces a new stereoscopic image based on depth map transformation using the spatial complexity of an image. After analyzing the depth map histogram, the depth map is decomposed into multiple depth planes that are transformed based upon the spatial complexity. The transformed depth planes are composited into a new depth map. Experimental results demonstrate that the lower the spatial complexity is, the higher the perceived video quality and depth perception are. As well, visual fatigue test showed that the stereoscopic images deliver less visual fatigue.

This paper presents a novel method to convert a grayscale image to a colored image for quality image analysis. The grayscale IP operations are very challenging and limited. The information extracted from such images is inaccurate. Therefore, the input image is transformed using a reference color image by reverse engineering. The gray levels of grayscale image are mapped with the color image in all the three layers (red, green, blue). These mapped pixels are used to reconstruct the grayscale image such that it is represented in a 3 dimensional color matrix. The algorithm is very simple and accurate that it can be used in any domain such as medical imaging, satellite imaging and agriculture/environment real-scene. The algorithm is implemented and tested on low cost mobile devices too and the results are found appreciable.

The distribution of central places is theoretically investigated by using Rushton's map transformation algorithm, MAPTRANS, for the case of the Kanto region in preindustrial Japan. The recovered hierarchical spacing reveals that central places were located according to the marketing principle to some extent, as well as being influenced by population density. Their locations were also locally distorted by traffic, administrative, and topographical conditions. These results suggest that Christaller's three principles did generally determine the distribution of central places. Application of the general hierarchical model to the actual frequency distribution of centers further confirms that the hierarchical structure is well approximated by a variable k-system rather than by the Christallerian k-system, which implies that a structural change in the hierarchy had taken place in the course of modernization.

RoboEarth aims at providing a distributed cloud-based web platform from robots for robots that is publicly accessible and enables robots to autonomously share knowledge among each other and to generate new knowledge from previously stored data. As a result robots don’t have to gain the same knowledge over and over again, but can build upon it right from the start. Currently, shareable data are abstract task descriptions, object models and environment maps. In this paper we describe RoboEarth’s approach to automatically and transparently generate 2D maps for localization and navigation, which are extracted from shared 3D maps and suited for a specific robot configuration. The parameters of the map generation process get inferred from a robot’s semantic self-description. Using RoboEarth for knowledge generation enables simple platforms with low computational power to execute complex tasks in complex environments. Furthermore the approach effectively simplifies the time consuming process of generating new maps every time a new robot platform with different specifications is used.

In this paper, the algorithms for interactive objects tracing on grey-scale images are proposed that is based on a pseudo-distance map (PDM). The PDM is a simplified distance map of gray-scale image and uses only that features of image and objects that are necessary to build a optimal contour. The algorithms wors fast for large gray-scale images and allows constructing a high quality contour as well as central line.