L. Van Langenhove

A Textile Antenna for Off-Body Communication Integrated Into Protective Clothing for Firefighters

The introduction of intelligent textile systems to increase the wearers level of protection has exposed the necessity of wearable communication tools and has led to research in textile antennas. However, most textile fabrics are quite thin (0.5 mm), making it challenging for antenna designers to provide an antenna which operates adequately and resiliently in the 2.4-2.4835-GHz industrial-scientific-medical bandwidth. Flexible pad foam is commonly available in protective clothing and overcomes these constraints by providing a uniform, stable, and sufficient thickness. Moreover, its cellular structure and properties, such as flame retardance and water repellence, make it an excellent substrate material for the integration of antennas into protective garments. In this paper, we describe the design, manufacture, and performance of the first textile planar antenna to be implemented on flexible protective foam, suitable for firefighter garments. We employed shock absorbing foam with a thickness of 3.94 mm and achieved a nearly circularly polarized antenna with a bandwidth of more than 180 MHz even when the antenna was compressed or bent. These outstanding substrate and antenna characteristics result in an antenna that is highly appropriate for garment integration.

Aperture-Coupled Patch Antenna for Integration Into Wearable Textile Systems

The emergence of wearable textile systems in recent years exhibited the need for wireless communication tools integratable into garments. In literature, several planar antenna designs based on textile materials have been presented, however, without an adapted feeding structure for wearable applications. An aperture-coupled patch antenna (ACPA) meets this requirement since the rigid coaxial feed is replaced by a microstrip feed line that couples its power into the antenna through an aperture in the ground plane. This letter presents the first ACPA entirely made out of textile material. The result is a highly efficient, fully flexible, and wearable antenna that is integratable into garments.

OPTIMIZING THE FIBER-TO-YARN PRODUCTION PROCESS WITH A COMBINED NEURAL NETWORK/GENETIC ALGORITHM APPROACH

An important aspect of the fiber-to-yam production process is the quality of the resulting yarn. The yarn should have optimal product characteristics (and minimal faults). In theory, this objective can be realized using an optimization algorithm. The complexity of a fiber-to-yarn process is very high, however, and no mathematical function is known to exist that represents the whole process. This paper presents a method to simulate and optimize the fiber-to-yam production process using a neural network combined with a genetic algorithm. The neural network is used to model the process, with the machine settings and fiber quality parameters as input and the yarn tenacity and elongation as output. The genetic algorithm is used afterward to optimize the input parameters for obtaining the best yarns. Since this is a multi-objective optimization, the genetic algorithm is enforced with a sharing function and a Pareto optimization. The paper shows that simultaneous optimization of yarn qualities is easily achieved as a function of the necessary (optimal) input parameters, and that the results are considerably better than current manual machine intervention. The last part of the paper is dedicated to finding an optimal mixture of available fiber qualities based on the predictions of the genetic algorithm.

Use of Neural Nets for Determining the Spinnability of Fibres

It is very important for a spinner to be able to predict the degree of spinnability of a given fibre quality. Certain process conditions must be taken into account here. This paper describes how the spinnability or a given fibre quality on a rotor and ring spinning machine can be predicted with a reliability of 95% by means of a neural network. The structure and the characteristics or the neural net used will be considered in greater depth, and a simple method of implementation of such a neural net will be described.

The Use of Neural Nets to Simulate the Spinning Process

In a previous paper, a description was given of how the spinnability of a given fibre quality on a rotor- or a ring-spinning machine can be predicted with a reliability of 95% by means of a neural network. This paper goes further. It describes how yarn properties can be deduced from fibre properties and spinning-machine settings. In other words, a description is given of how to construct, train, and use a neural network in order to simulate the spinning process (predict yarn properties) on both rotor- and ring-spinning machines with an accuracy of over 95%.

Using genetic algorithms to design a control strategy of an industrial process

Abstract In this paper a methodology is presented to design a control strategy to optimise a complex spinning (fibre-yarn) production process, using a neural network combined with genetic algorithms. The neural network is used to model the process, with the machine setpoints and raw fibre quality parameters as input, and with the yarn tenacity and elongation as output. Genetic algorithms are used in two ways: • to optimise the architecture and the underlying parameters of the neural network, in order to achieve the most effective model of the production process; • to obtain setpoint values and raw material characteristics for an optimal quality of the spinned yarns.

EGG SAC STRUCTURE OF ZYGIELLA X-NOTATA (ARACHNIDA, ARANEIDAE)

Abstract A detailed examination of the egg sac of Zygiella x-notata (Clerck 1757) revealed its structure, composition and different fibers. All egg sacs were composed of a basic layer, an insulation layer and an outer layer. The insulation layer consisted of two layers of cylindrical (or tubuliform) fibers with different diameters and probably with different mechanical properties. Knowing the complete structure of the egg sac allows us to locate and extract the needed fibers for further research and to observe how the egg sac composition alters in relation to the habitat.

Performance study of screen-printed textile antennas after repeated washing

Abstract The stability of wearable textile antennas after 20 reference washing cycles was evaluated by measuring the reflection coefficient of different antenna prototypes. The prototypes’ conductive parts were screen-printed on several textile substrates using two different silver-based conductive inks. The necessity of coating the antennas with a thermoplastic polyurethane (TPU) coating was investigated by comparing coated with uncoated antennas. It is shown that covering the antennas with the TPU layer not only protects the screen-printed conductive area but also prevents delamination of the multilayered textile fabric substrates, making the antennas washable for up to 20 cycles. Furthermore, it is proven that coating is not necessary for maintaining antenna operation and this up to 20 washing cycles. However, connector detachment caused by friction during the washing process was the main problem of antenna performance degradation. Hence, other flexible, durable methods should be developed for establishing a stable electrical connection.

Electrochemical impedance spectroscopy as an objective method for characterization of textile electrodes

Textile electrodes are being developed for monitoring, eg, heart and respiration rate. Textile materials are comfortable and thus enable long-term monitoring. This flexibility, however, leads to many artefacts in the signals. One of the main problems in testing such sensors on a human body is the huge variability of the latter. This paper describes and objective method that enables accurate and reproducible characterization of textile electrodes using electrochemical impedance spectroscopy. The effect of textile structure, presence of sweat, sensor size and long-term stability are being studied.

A comparative study of two different regression methods for radiographs in Polish youngsters estimating chronological age on third molars

AIM The aim of this study was to establish a third molar developmental database to model dental age of Polish youngsters, to investigate the rating level of the scores when dividing a year interval into a quarter of a year and to examine sex differences, left-right and upper-lower jaw asymmetry. MATERIAL AND METHODS A cross-sectional sample of 1048 orthopantomograms of 644 females and 404 males aged between 12 and 26 years was investigated using the scoring system of Gleiser and Hunt modified by Köhler. Reference tables according to age were split in a whole year and in quarters of a year using descriptive statistics. The various developmental stages between males and females were analyzed with a paired t-test and the cusum method. Differences in mineralization between the quadrants were analyzed with a two-factor ANOVA and the Duncan post hoc test. The single quadratic and support vector regression were performed to describe the relationship between score and age. RESULTS Dividing age classes in quarters of a year discriminated better between individuals provided that there is a sufficient sampling size for all age classes. The mineralization tempo occurred significantly at a faster rate in males. The maturational events in the upper arch developed significantly at earlier ages for both genders. Obtained chronological age had nearly the same standard error of estimate when calculated with both regression methods. DISCUSSION AND CONCLUSION Comparing the results of the present study with those of other population groups suggests that there are differences in the ageing process of the wisdom tooth. This is the first database of Polish youngsters (15-24 years) with their respective regression equations to yield age estimations.