[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

/pdf/remote-sensing-of-the-environment-4yfxjaaswe.pdf

Remote sensing of the environment

This paper provides a comprehensive review of the domain of physical layer security in multiuser wireless networks. The essential premise of physical layer security is to enable the exchange of confidential messages over a wireless medium in the presence of unauthorized eavesdroppers, without relying on higher-layer encryption. This can be achieved primarily in two ways: without the need for a secret key by intelligently designing transmit coding strategies, or by exploiting the wireless communication medium to develop secret keys over public channels. The survey begins with an overview of the foundations dating back to the pioneering work of Shannon and Wyner on information-theoretic security. We then describe the evolution of secure transmission strategies from point-to-point channels to multiple-antenna systems, followed by generalizations to multiuser broadcast, multiple-access, interference, and relay networks. Secret-key generation and establishment protocols based on physical layer mechanisms are subsequently covered. Approaches for secrecy based on channel coding design are then examined, along with a description of inter-disciplinary approaches based on game theory and stochastic geometry. The associated problem of physical layer message authentication is also briefly introduced. The survey concludes with observations on potential research directions in this area.

/pdf/principles-of-physical-layer-security-in-multiuser-wireless-3w4jvwpgy4.pdf

Principles of Physical Layer Security in Multiuser Wireless Networks: A Survey

In this paper, we propose a unified framework to generate a pleasant and high-quality street-view panorama by stitching multiple panoramic images captured from the cameras mounted on the mobile platform. Our proposed framework is comprised of four major steps: image warping, color correction, optimal seam line detection and image blending. Since the input images are captured without a precisely common projection center from the scenes with the depth differences with respect to the cameras to different extents, such images cannot be precisely aligned in geometry. Therefore, an efficient image warping method based on the dense optical flow field is proposed to greatly suppress the influence of large geometric misalignment at first. Then, to lessen the influence of photometric inconsistencies caused by the illumination variations and different exposure settings, we propose an efficient color correction algorithm via matching extreme points of histograms to greatly decrease color differences between warped images. After that, the optimal seam lines between adjacent input images are detected via the graph cut energy minimization framework. At last, the Laplacian pyramid blending algorithm is applied to further eliminate the stitching artifacts along the optimal seam lines. Experimental results on a large set of challenging street-view panoramic images captured form the real world illustrate that the proposed system is capable of creating high-quality panoramas.

A Unified Framework for Street-View Panorama Stitching.

The pulsed eddy current (PEC) technique, as an emerging technique of the eddy current technique, has been used in engineering, such as aircrafts, oil/gas pipelines, nuclear steam pipes and high-speed rails, due to its richer information in time domain and frequency domain. However, the lift-off noise, introduced by varying coating thicknesses, irregular sample surface or movement of transducers, has a serious influence on the accuracy of the detection for the defects in these key structures. It greatly limits the application of PEC in quantitative nondestructive testing. In order to reduce the effect of the lift-off, the lift-off effect is analyzed theoretically and experimentally; based on the investigation of the relationship between the peak value of the difference signal and the lift-off, an approach to reduce the lift-off noise for detection the defect depth or width is proposed. In this approach, the defect depth and width are determined by the slope of the linear curve of the peak value of the difference signal and the lift-off. The proposed approach is verified by experiment and the results indicate that it can highly reduce the lift-off noise in the PEC technique. Therefore, it can be applied in characterization of the surface defects in sample with non-ferrous material.

An approach to reduce lift-off noise in pulsed eddy current nondestructive technology

In this paper, we present an Intrusion Detection System (IDS) using the hybridization of the deep learning technique and the multi-objective optimization method for the detection of Distributed Denial of Service (DDoS) attacks in the Internet of Things (IoT) networks is proposed in this paper. IoT networks consist of different devices with unique hardware and software configurations communicating over different communication protocols, which produce huge multidimensional data that make IoT networks susceptible to cyber-attacks. In a network the IDS is a vital tool for securing it from cyber-attacks. Detection of new emerging cyber threats are becoming difficult for existing IDS, and therefore advanced IDS is required. A DDoS attack is a cyber-attack that has posed substantial devastating losses in IoT networks recently. In this paper, we propose an IDS founded on the fusion of a Jumping Gene adapted NSGA-II multi-objective optimization method for data dimension reduction and the Convolutional Neural Network (CNN) integrating Long Short-Term Memory (LSTM) deep learning techniques for classifying the attack. The experimentation is conducted using a High-Performance Computer (HPC) on the latest CISIDS2017 datasets on DDoS attacks and achieved an accuracy of 99.03% with a 5-fold reduction in training time. We evaluated our proposed method by comparing it with other state-of-the-art algorithms and machine learning algorithms, which confirms that the proposed method surpasses other approaches.

An Intrusion Detection System Against DDoS Attacks in IoT Networks

This paper reviews recent developments in the area of concealed weapon detection using largely electromagnetic methods including metal detection, magnetic field distortion, electromagnetic resonance, acoustic and ultrasonic inspection, millimetre waves, Terahertz imaging, Infrared, X-ray etc. The advantages and disadvantages of these approaches are discussed. Research challenges are presented. Future research perspectives are presented and our joint research project has also been introduced.

A Review of Concealed Weapon Detection and Research in Perspective

Eddy current pulsed thermography (ECPT) is an emerging non-destructive testing technology and has an increasing span of application with capabilities of rapid contactless and large surface area detection. The close process loop of ECPT that contains pre-processing, post-processing, and objective quantitative assessment is rarely presented. This paper proposed a complete strategy aims to perform pre- and post- processing for surface crack detection based on ECPT platform. In addition, the quantitative evaluation is involved to objectively evaluate detectability. Specially, a new post-image segmentation algorithm is proposed which based on the idea of grouping histogram and iterative adaption approach that leads to better performance for quantitatively identifying and sizing the defect. Experimental tests on man-made metal and natural defects have been conducted to show the reliability of the proposed strategy. This paper can be further applied for other types of defects detection, quantitative evaluation, and aid in the development of machine vision industry for automated visual inspection.

Quantitative Surface Crack Evaluation Based on Eddy Current Pulsed Thermography

There is a need for fast and efficient techniques to inspect engineering structures and complex components such as aircraft turbine blades to identify potential sites of failure. Pulsed eddy current (PEC) thermography is a new inspection technique which allows the user to capture the eddy current distribution in a component or structure using infrared imaging and detect defects over a relatively wide area. The technique is applicable to materials with a reasonable level of electrical conductivity and has the ability to detect defects under coatings. However, PEC thermography has received relatively little attention compared to other thermographic inspection techniques. In this paper, the design, development and optimisation of a PEC thermography inspection system is detailed, including coil design for global and local heating of samples, optimisation of excitation parameters (frequency, power, pulse duration etc) and camera selection. The system is used to inspect several real-world samples, using different coil designs, and the results are assessed using newly developed feature extraction techniques. The work shows that with judicious coil design and selection of excitation parameters, PEC thermography can be used to obtain quantitative information for defect characterisation through analysis of the surface heating pattern and the transient temperature change.

Gui Yun Tian

Papers

An approach to reduce lift-off noise in pulsed eddy current nondestructive technology

An Intrusion Detection System Against DDoS Attacks in IoT Networks

A Review of Concealed Weapon Detection and Research in Perspective

Quantitative Surface Crack Evaluation Based on Eddy Current Pulsed Thermography

Pulsed eddy current thermography: System development and evaluation