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1 – 10 of over 12000A. Yao and M. Soleimani
Electrical impedance measurement and imaging are techniques that are widely used in a range of applications. Electro‐conductive knitted structure is a major new development in…
Abstract
Purpose
Electrical impedance measurement and imaging are techniques that are widely used in a range of applications. Electro‐conductive knitted structure is a major new development in wearable computing. The purpose of this paper is to carry out a preliminary investigation of applying electrical impedance analysis to predict the behavior of electro‐conductive knitted structure. This can potentially pave the way for a low‐cost solution for pressure mapping imaging.
Design/methodology/approach
Electrical impedance tomography (EIT) has been used as a mapping technique for deformation imaging in conductive knitted fabric. EIT is an imaging system used to generate a map of electrical conductivity. Pressure and deformation mapping scanner is being developed based on electrical conductivity imaging of the conductive area generated in a fabric. The results are presented using these new sensors with various deformations.
Findings
Experimental results show the feasibility of qualitative deformation imaging. In particular, it is promising that multiple deformations can be mapped using the proposed technique. The paper also demonstrates preliminary results regarding quantitative pressure and deformation mapping using EIT technique.
Research limitations/implications
The results presented in the paper are laboratory‐based experiments for proof of principle and will be evaluated in specific application areas in future.
Originality/value
The paper shows, for the first time, detection of multiple pressure points as well as quantifying the pressure map using the new imaging sensor. The sensor proposed here can be used for robotic touch sensing application, as well as some biomechanical observations.
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C.L. Yang, A. Mohammed, Y Mohamadou, T. I. Oh and M. Soleimani
The aim of this paper is to introduce and to evaluate the performance of a multiple frequency complex impedance reconstruction for fabric-based EIT pressure sensor. Pressure…
Abstract
Purpose
The aim of this paper is to introduce and to evaluate the performance of a multiple frequency complex impedance reconstruction for fabric-based EIT pressure sensor. Pressure mapping is an important and challenging area of modern sensing technology. It has many applications in areas such as artificial skins in Robotics and pressure monitoring on soft tissue in biomechanics. Fabric-based sensors are being developed in conjunction with electrical impedance tomography (EIT) for pressure mapping imaging. This is potentially a very cost-effective pressure mapping imaging solution in particular for imaging large areas. Fabric-based EIT pressure sensors aim to provide a pressure mapping image using current carrying and voltage sensing electrodes attached on the boundary of the fabric patch.
Design/methodology/approach
Recently, promising results are being achieved in conductivity imaging for these sensors. However, the fabric structure presents capacitive behaviour that could also be exploited for pressure mapping imaging. Complex impedance reconstructions with multiple frequencies are implemented to observe both conductivity and permittivity changes due to the pressure applied to the fabric sensor.
Findings
Experimental studies on detecting changes of complex impedance on fabric-based sensor are performed. First, electrical impedance spectroscopy on a fabric-based sensor is performed. Secondly, the complex impedance tomography is carried out on fabric and compared with traditional EIT tank phantoms. Quantitative image quality measures are used to evaluate the performance of a fabric-based sensor at various frequencies and against the tank phantom.
Originality/value
The paper demonstrates for the first time the useful information on pressure mapping imaging from the permittivity component of fabric EIT. Multiple frequency EIT reconstruction reveals spectral behaviour of the fabric-based EIT, which opens up new opportunities in exploration of these sensors.
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The paper aims to present an innovative method for imaging the pressure distribution between two interface surfaces. The physical principles behind the design of the pressure…
Abstract
Purpose
The paper aims to present an innovative method for imaging the pressure distribution between two interface surfaces. The physical principles behind the design of the pressure imaging system are explained, and some case studies involving the use of this technology in diverse applications are described.
Design/methodology/approach
The XSENSOR pressure sensor is comprised of a matrix of capacitive sensing elements. Pressure applied to the surface of the sensing element causes a change in capacitance that is correlated to a change in pressure. Proprietary Windows based software compensates for sensor non‐linearity, hysteresis, and creep over time, resulting in enhanced accuracy.
Findings
XSENSOR's capacitive based pressure imaging sensors can graphically display pressure distributions in real time between virtually any two surfaces in contact. The sensor element is accurate, thin, flexible, and robust. These physical characteristics minimize any artificial influences created by the presence of the sensor during data collection.
Practical implications
Pressure imaging technology can be used in industrial and engineering environments for product design and verification, process control, or quality assurance.
Originality/value
This paper will be useful to the engineer or business manager interested in applying sensor technology to solve engineering or design problems.
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Riyaz Ali Shaik and Elizabeth Rufus
This paper aims to review the shape sensing techniques using large area flexible electronics (LAFE). Shape perception of humanoid robots using tactile data is mainly focused.
Abstract
Purpose
This paper aims to review the shape sensing techniques using large area flexible electronics (LAFE). Shape perception of humanoid robots using tactile data is mainly focused.
Design/methodology/approach
Research papers on different shape sensing methodologies of objects with large area, published in the past 15 years, are reviewed with emphasis on contact-based shape sensors. Fiber optics based shape sensing methodology is discussed for comparison purpose.
Findings
LAFE-based shape sensors of humanoid robots incorporating advanced computational data handling techniques such as neural networks and machine learning (ML) algorithms are observed to give results with best resolution in 3D shape reconstruction.
Research limitations/implications
The literature review is limited to shape sensing application either two- or three-dimensional (3D) LAFE. Optical shape sensing is briefly discussed which is widely used for small area. Optical scanners provide the best 3D shape reconstruction in the noncontact-based shape sensing; here this paper focuses only on contact-based shape sensing.
Practical implications
Contact-based shape sensing using polymer nanocomposites is a very economical solution as compared to optical 3D scanners. Although optical 3D scanners can provide a high resolution and fast scan of the 3D shape of the object, they require line of sight and complex image reconstruction algorithms. Using LAFE larger objects can be scanned with ML and basic electronic circuitory, which reduces the price hugely.
Social implications
LAFE can be used as a wearable sensor to monitor critical biological parameters. They can be used to detect shape of large body parts and aid in designing prosthetic devices. Tactile sensing in humanoid robots is accomplished by electronic skin of the robot which is a prime example of human–machine interface at workplace.
Originality/value
This paper reviews a unique feature of LAFE in shape sensing of large area objects. It provides insights from mechanical, electrical, hardware and software perspective in the sensor design. The most suitable approach for large object shape sensing using LAFE is also suggested.
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Oduetse Matsebe, Khumbulani Mpofu, John Terhile Agee and Sesan Peter Ayodeji
The purpose of this paper is to present a method to extract corner features for map building purposes in man-made structured underwater environments using the sliding-window…
Abstract
Purpose
The purpose of this paper is to present a method to extract corner features for map building purposes in man-made structured underwater environments using the sliding-window technique.
Design/methodology/approach
The sliding-window technique is used to extract corner features, and Mechanically Scanned Imaging Sonar (MSIS) is used to scan the environment for map building purposes. The tests were performed with real data collected in a swimming pool.
Findings
The change in application environment and the use of MSIS present some important differences, which must be taken into account when dealing with acoustic data. These include motion-induced distortions, continuous data flow, low scan frequency and high noise levels. Only part of the data stored in each scan sector is important for feature extraction; therefore, a segmentation process is necessary to extract more significant information. To deal with continuous flow of data, data must be separated into 360° scan sectors. Although the vehicle is assumed to be static, there is a drift in both its rotational and translational motions because of currents in the water; these drifts induce distortions in acoustic images. Therefore, the bearing information and the current vehicle pose corresponding to the selected scan-lines must be stored and used to compensate for motion-induced distortions in the acoustic images. As the data received is very noisy, an averaging filter should be applied to achieve an even distribution of data points, although this is partly achieved through the segmentation process. On the selected sliding window, all the point pairs must pass the distance and angle tests before a corner can be initialised. This minimises mapping of outlier data points but can make the algorithm computationally expensive if the selected window is too wide. The results show the viability of this procedure under very noisy data. The technique has been applied to 50 data sets/scans sectors with a success rate of 83 per cent.
Research limitations/implications
MSIS gives very noisy data. There are limited sensorial modes for underwater applications.
Practical implications
The extraction of corner features in structured man-made underwater environments opens the door for SLAM systems to a wide range of applications and environments.
Originality/value
A method to extract corner features for map building purposes in man-made structured underwater environments is presented using the sliding-window technique.
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Saba Gharehdash, Bre-Anne Louise Sainsbury, Milad Barzegar, Igor B. Palymskiy and Pavel A. Fomin
This research study aims to develop regular cylindrical pore network models (RCPNMs) to calculate topology and geometry properties of explosively created fractures along with…
Abstract
Purpose
This research study aims to develop regular cylindrical pore network models (RCPNMs) to calculate topology and geometry properties of explosively created fractures along with their resulting hydraulic permeability. The focus of the investigation is to define a method that generates a valid geometric and topologic representation from a computational modelling point of view for explosion-generated fractures in rocks. In particular, extraction of geometries from experimentally validated Eulerian smoothed particle hydrodynamics (ESPH) approach, to avoid restrictions for image-based computational methods.
Design/methodology/approach
Three-dimensional stabilized ESPH solution is required to model explosively created fracture networks, and the accuracy of developed ESPH is qualitatively and quantitatively examined against experimental observations for both peak detonation pressures and crack density estimations. SPH simulation domain is segmented to void and solid spaces using a graphical user interface, and the void space of blasted rocks is represented by a regular lattice of spherical pores connected by cylindrical throats. Results produced by the RCPNMs are compared to three pore network extraction algorithms. Thereby, once the accuracy of RCPNMs is confirmed, the absolute permeability of fracture networks is calculated.
Findings
The results obtained with RCPNMs method were compared with three pore network extraction algorithms and computational fluid dynamics method, achieving a more computational efficiency regarding to CPU cost and a better geometry and topology relationship identification, in all the cases studied. Furthermore, a reliable topology data that does not have image-based pore network limitations, and the effect of topological disorder on the computed absolute permeability is minor. However, further research is necessary to improve the interpretation of real pore systems for explosively created fracture networks.
Practical implications
Although only laboratory cylindrical rock specimens were tested in the computational examples, the developed approaches are applicable for field scale and complex pore network grids with arbitrary shapes.
Originality/value
It is often desirable to develop an integrated computational method for hydraulic conductivity of explosively created fracture networks which segmentation of fracture networks is not restricted to X-ray images, particularly when topologic and geometric modellings are the crucial parts. This research study provides insight to the reliable computational methods and pore network extraction algorithm selection processes, as well as defining a practical framework for generating reliable topological and geometrical data in a Eulerian SPH setting.
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Carl Senior, Hannah Smyth, Richard Cooke, Rachel L. Shaw and Elizabeth Peel
To describe the utility of three of the main cognitive neuroscientific techniques currently in use within the neuroscience community, and how they can be applied to the emerging…
Abstract
Purpose
To describe the utility of three of the main cognitive neuroscientific techniques currently in use within the neuroscience community, and how they can be applied to the emerging field of neuromarket research.
Design/methodology/approach
A brief development of functional magnetic resonance imaging, magnetoencephalography and transcranial magnetic stimulation are described, as the core principles are behind their respective use. Examples of actual data from each of the brain imaging techniques are provided to assist the neuromarketer with subsequent data for interpretation. Finally, to ensure the neuromarketer has an understanding of the experience of neuroimaging, qualitative data from a questionnaire exploring attitudes about neuroimaging techniques are included which summarize participants' experiences of having a brain scan.
Findings
Cognitive neuroscientific techniques have great utility in market research and can provide more “honest” indicators of consumer preference where traditional methods such as focus groups can be unreliable. These techniques come with complementary strengths which allow the market researcher to converge onto a specific research question. In general, participants considered brain imaging techniques to be relatively safe. However, care is urged to ensure that participants are positioned correctly in the scanner as incorrect positioning is a stressful factor during an imaging procedure that can impact data quality.
Originality/value
This paper is an important and comprehensive resource to the market researcher who wishes to use cognitive neuroscientific techniques.
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Huigang Xiao, Min Liu and Jinbao Jiang
The purpose of this paper is to study the effect of alignment of conductive particles on the piezoresistivity of composite based on a theoretical model. The piezoresistivity of…
Abstract
Purpose
The purpose of this paper is to study the effect of alignment of conductive particles on the piezoresistivity of composite based on a theoretical model. The piezoresistivity of composite is associated with the characteristics of conductive network formed by the conductive particles distributed in the composite, which can be changed through aligning the conductive particles.
Design/methodology/approach
The orientations of the tunnel resistors formed by each two adjacent conductive particles are dependent on the aligned level of the conductive particles, and different orientations induce different deformations for a tunnel resistor under external strain, which determines the piezoresistivity of the composites. To investigate the resistance behavior of composites with various characteristics of conductive networks, a piezoresistivity model is developed in this paper by considering the aligned level of conductive particles.
Findings
The results obtained from the proposed piezoresistivity model indicate that the sensitivity and stability of composites can be enhanced through aligning the conductive particles. Also, the piezoresistivity of composites filled with randomly distributed conductive particles is isotropic, and it turns to be anisotropic when the conductive particles are aligned.
Originality/value
The change and its mechanism of the piezoresistivity upon the aligned level of conductive particles have been pointed out in this paper based on the proposed model. The achievement of this paper will help the people understand, predict and optimize the piezoresistivity of composites, and provide a new approach to design a strain sensor based on the piezoresistivity.
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Mert Gülçür, Kevin Couling, Vannessa Goodship, Jérôme Charmet and Gregory J. Gibbons
The purpose of this study is to demonstrate and characterise a soft-tooled micro-injection moulding process through in-line measurements and surface metrology using a…
Abstract
Purpose
The purpose of this study is to demonstrate and characterise a soft-tooled micro-injection moulding process through in-line measurements and surface metrology using a data-intensive approach.
Design/methodology/approach
A soft tool for a demonstrator product that mimics the main features of miniature components in medical devices and microsystem components has been designed and fabricated using material jetting technique. The soft tool was then integrated into a mould assembly on the micro-injection moulding machine, and mouldings were made. Sensor and data acquisition devices including thermal imaging and injection pressure sensing have been set up to collect data for each of the prototypes. Off-line dimensional characterisation of the parts and the soft tool have also been carried out to quantify the prototype quality and dimensional changes on the soft tool after the manufacturing cycles.
Findings
The data collection and analysis methods presented here enable the evaluation of the quality of the moulded parts in real-time from in-line measurements. Importantly, it is demonstrated that soft-tool surface temperature difference values can be used as reliable indicators for moulding quality. Reduction in the total volume of the soft-tool moulding cavity was detected and quantified up to 100 cycles. Data collected from in-line monitoring was also used for filling assessment of the soft-tool moulding cavity, providing about 90% accuracy in filling prediction with relatively modest sensors and monitoring technologies.
Originality/value
This work presents a data-intensive approach for the characterisation of soft-tooled micro-injection moulding processes for the first time. The overall results of this study show that the product-focussed data-rich approach presented here proved to be an essential and useful way of exploiting additive manufacturing technologies for soft-tooled rapid prototyping and new product introduction.
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