Ground robotics in tunnels: Keys and lessons learned after 10 years of research and experiments

The work reported in this article describes the research advances and the lessons learned by the Robotics, Perception and Real‐Time group over a decade of research in the field of ground robotics in confined environments. This study has primarily focused on localization, navigation, and communications in tunnel‐like environments. As will be discussed, this type of environment presents several special characteristics that often make well‐established techniques fail. The aim is to share, in an open way, the experience, errors, and successes of this group with the robotics community so that those that work in such environments can avoid (some of) the errors made. At the very least, these findings can be readily taken into account when designing a solution, without needing to sift through the technical details found in the papers cited within this text.     

USAD: undetectable steganographic approach in DCT domain

In this work, we propose a new adaptive chaotic steganographic method based on the Discrete Cosine Transform (DCT) and a reversible mapping function. The mapping function is used to map the secret bits into their corresponding symbols. This mapping technique has to preserve the same dynamics, properties and distribution of the original DCT coefficients. The novelty of our approach is based on the adaptive selection phase of embedding spots. This selection is established through a blindness condition which is applied over each image of the database. The proposed embedding scheme within the middle DCT coefficients shows lower probability of detection and higher flexibility in extraction. We evaluate the detection of our method using the Ensemble Classifiers and a set of frequency and spatial domain feature extractors such as the Spatial domain Rich Model (SRM) features, Chen et al.'s 486-dimensional both inter- and intra-block Markov-based features and Liu's 216-dimensional adaptive steganography-based features.     

Characterization system based on image mapping for field emission devices

Field Emission devices (FE) have been proposed as efficient electron sources for several applications such as electron microscopy and vacuum sensors. Evidently, characterization methods applied during development phase of FE devices are crucial to evaluate aspects related with their working stability, homogeneity, and efficiency. However, the traditional methods provide only overall information about such characteristics, which difficult to improve the performance of these devices and their integration with electronics. To overcome this problem, this work presents an alternative system to characterize FE devices through electron emission imaging in real-time. The proposed system acquires I-V features of FE devices, while a video camera captures the emission image from a phosphor screen. Virtual instrumentation based on LabVIEW manages the whole system including measurement instruments, image capture, and data processing. As a result, histograms, 3D maps, and other FE analyses provide information about emitting characteristics of selected regions of interest. The main contribution of this work is to offer an important tool for the analyses of electron emission, by the association of captured images with the localized emission current. The extracted information from our system can efficiently support the characterization and the development of FE devices.     

Exploration of connectivity with SEM: An fMRI study of resting state

In this era, the neuroscientists get attention to find a network of brain which is activated in resting state of mind. It is challenging for any analytical technique to find out the connectivity among Brodmann areas of brain in resting state. To find network, a study was conducted in which brains are scan by functional magnetic resonance imaging of four healthy right handed individuals. Exploratory factor analysis was used to explore that which Brodmann areas are related to each other and then structural equation model has applied on explored factors (lobes) to find a network of activated brain in resting state. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 264–269, 2016     

食物致敏原表位定位技术的研究进展

食物致敏原是引起食物过敏的元凶，多为蛋白质。抗原表位是在抗原分子中与抗体反应或被抗原受体识别，并引发机体免疫应答的特殊化学基团。从表位水平认识食物致敏原，能揭示食物过敏的物质基础，为解决食物过敏问题提供精准的靶标。本文基于表位结构和定位方法的不同，介绍了食物致敏原表位定位技术的发展，并进一步展望了致敏原表位信息对于改进食物过敏鉴定技术和低致敏食物加工方法的应用前景。     

Knowledge structures in a knowledge base

Rough set theory is a useful tool for dealing with imprecise knowledge. One of the advantages of rough set theory is the fact that an unknown target concept can be approximately characterized by existing knowledge structures in a knowledge base. This paper explores knowledge structures in a knowledge base. Knowledge structures in a knowledge base are firstly described by means of set vectors and relationships between knowledge structures divided into four classes. Then, properties of knowledge structures are discussed. Finally, group, lattice, mapping, and soft characterizations of knowledge structures are given.     

Applying an anchor based patent mapping approach: Basic conception and the case of carbon fiber reinforcements

Patent mapping is an important method for analyzing technological patterns both for scientific research and strategic tasks in companies. In this paper we focus on a specific type of technological pattern, namely the analysis of patents' positions in relation to predefined positions of application fields. For this purpose we use an anchoring approach. We apply semantic patent measurement and discuss RadViz as a powerful method to visualize the measurement's results and to provide insightful motion patterns for monitoring technology change. Moreover, we present an algorithm to define so called anchor points as high dimensional reference points by using textual elements of patents. By the example of carbon fiber reinforcements we demonstrate the usefulness of our approach. Thus, our approach enables academics to analyze important types of technological patterns like convergence or divergence by means of a new instrument and gives practitioners like the R&D management of companies the opportunity to build a reliable strategic business decision support.     

Mixing tone mapping operators on the GPU by differential zone mapping based on psychophysical experiments

In this paper, we present a new technique for displaying High Dynamic Range (HDR) images on Low Dynamic Range (LDR) displays in an efficient way on the GPU. The described process has three stages. First, the input image is segmented into luminance zones. Second, the tone mapping operator (TMO) that performs better in each zone is automatically selected. Finally, the resulting tone mapping (TM) outputs for each zone are merged, generating the final LDR output image. To establish the TMO that performs better in each luminance zone we conducted a preliminary psychophysical experiment using a set of HDR images and six different TMOs. We validated our composite technique on several (new) HDR images and conducted a further psychophysical experiment, using an HDR display as the reference that establishes the advantages of our hybrid three-stage approach over a traditional individual TMO. Finally, we present a GPU version, which is perceptually equal to the standard version but with much improved computational performance.     

测绘技术应用于现代化煤矿

煤炭是中国能源的主体和重要的化工原料,在国民经济发展中占有及其重要的战略地位。煤矿生产是一个涉及多学科系统性的工作,其中矿山测量起着重要的作用。矿山测量作为一门交叉性学科,其进步和发展关系到矿业工程和采矿技术的快速发展,在煤矿勘探、矿井设计、采矿工艺布置、煤矿开采和运输等多个环节中都需要测绘技术,先进的测绘技术支撑着煤矿现代化的进程。因此,开展测绘技术在煤矿应用中的研究具有重要的意义。     

Mapping commuter cycling risk in urban areas

Cycling is becoming an increasingly important transportation option for commuters. Cycling offers exercise opportunities and reduces the burden of motor vehicle travel on society. Mapping the risk of collision between cyclists and motor vehicles in urban areas is important to understanding safe cyclist route opportunities, making informed transportation planning decisions, and exploring patterns of injury epidemiology. To date, many geographic analyses and representations of cyclist risk have not taken the concept of exposure into account. Instead, risk is either expressed as a rate per capita, or as a count of events. Using data associated with the City of Hamilton, Canada, we illustrate a method for mapping commuter cyclist collision risk per distance travelled. This measure can be used to more realistically represent the underlying geography of cycling risk, and provide more geographically and empirically meaningful information to those interested in understanding how cycling safety varies over space.