MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

The Human Microbiome.

Wearable electronics is a rapidly growing field that recently started to introduce successful commercial products into the consumer electronics market. Employment of biopotential signals in wearable systems as either biofeedbacks or control commands are expected to revolutionize many technologies including point of care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMIs), and brain–computer interfaces (BCIs). Since electrodes are regarded as a decisive part of such products, they have been studied for almost a decade now, resulting in the emergence of textile electrodes. This study presents a systematic review of wearable textile electrodes in physiological signal monitoring, with discussions on the manufacturing of conductive textiles, metrics to assess their performance as electrodes, and an investigation of their application in the acquisition of critical biopotential signals for routine monitoring, assessment, and exploitation of cardiac (electrocardiography, ECG), neural (electroencephalography, EEG), muscular (electromyography, EMG), and ocular (electrooculography, EOG) functions.

https://www.mdpi.com/2079-9292/8/5/479/pdf

Wearable and Flexible Textile Electrodes for Biopotential Signal Monitoring: A review

The main goal of the research presented in this paper was to develop a refined machine learning algorithm for industrial tomography applications. The article presents algorithms based on logistic regression in relation to image reconstruction using electrical impedance tomography (EIT) and ultrasound transmission tomography (UST). The test object was a tank filled with water in which reconstructed objects were placed. For both EIT and UST, a novel approach was used in which each pixel of the output image was reconstructed by a separately trained prediction system. Therefore, it was necessary to use many predictive systems whose number corresponds to the number of pixels of the output image. Thanks to this approach the under-completed problem was changed to an over-completed one. To reduce the number of predictors in logistic regression by removing irrelevant and mutually correlated entries, the elastic net method was used. The developed algorithm that reconstructs images pixel-by-pixel is insensitive to the shape, number and position of the reconstructed objects. In order to assess the quality of mappings obtained thanks to the new algorithm, appropriate metrics were used: compatibility ratio (CR) and relative error (RE). The obtained results enabled the assessment of the usefulness of logistic regression in the reconstruction of EIT and UST images.

https://www.mdpi.com/1424-8220/19/15/3400/pdf

Logistic Regression for Machine Learning in Process Tomography.

In-situ deposition of reduced graphene oxide layers on textile surfaces by the reactive inkjet printing technique and their use in supercapacitor applications

*Department of Clothing Technology and Textronics, Technical University of Lodz, ul. Żeromskiego 116 90-924 Łodź, Poland E-mail: stemp@p.lodz.pl Abstract Work associated with obtaining an electroconductive layer on the surface of selected textile structures using vacuum deposition technology is described in the paper. The vacuum deposition method gives the opportunity to receive products if the metal layers obtained have good electrical conductivity with just the adhesion of particles to the textile substrate. The experimental work on the application of metallic layers on fabric samples were divided into three stages. At each stage, the conductivity of the layers obtained was investigated. A microscopic examination was carried out using optical microscopy and scanning electron microscopy in order to evaluate the continuity of the electroconductive layers deposited. Based on the work, it was found that the sputtered layers are characterised by an electrical conductivity depending on the process parameters.

http://fibtex.lodz.pl/pliki/Fibtex_(2lwtn18o2d426x3i).pdf

Using Vacuum Deposition Technologyfor the Manufacturing of Electro-Conductive Layers on the Surface of Textiles

The most well-known method for producing conductive elements for flexible electronics is to print the paths of electronic circuits directly onto polymer materials. The authors of the paper propose an alternative solution for creation of conductive elements such as using PVD vacuum deposition technology and laser shaping. The resistors and conductors obtained in this way have good electrical and mechanical properties. In the paper the results of the possibility of achieving thin-film structures of a small width by using laser ablation of the vaporized metal layer are also presented.

Elements of elastic electronics created on textile substrate

Abstract In this paper, the results of mechanical strength tests of thin conductive Ag and Au layers created on Cordura composite substrate using the thermal vapor deposition method are presented. The resistance of the conductive layers to the bending and tensile stresses was tested and changing the surface resistance of the test structures was accepted as a criterion. The layers created on unmodified and plasma-treated surfaces have been examined. As a result of the surface modification, the electrical and mechanical properties of the thin Ag and Au metal structures have been improved. The results of measurements of surface resistance changes during strength tests and SEM microscopic studies of stressed samples indicate the high mechanical strength of the electroconductive layers deposited on Cordura, which may be the basis for the application of such technology in textronics applications.

Ewa Korzeniewska

Papers

In-situ deposition of reduced graphene oxide layers on textile surfaces by the reactive inkjet printing technique and their use in supercapacitor applications

Using Vacuum Deposition Technologyfor the Manufacturing of Electro-Conductive Layers on the Surface of Textiles

Elements of elastic electronics created on textile substrate

Properties Of Thin Metal Layers Deposited On Textile Composites By Using The Pvd Method For Textronic Applications

Classification algorithms to identify changes in resistance