Abstract3D fabric preforms are used as reinforcements in composite applications. 3D woven preforms have a huge demand in ballistic applications, aircraft industry, automobiles and structural reinforcements. A variety of 3D woven fabric reinforced composites and two dimensional woven fabric reinforced laminates can be found in the literature. However, the majority of the said products lack in delamination resistance and possess poor out-of-plane mechanical characteristics, due to the absence or insufficiency of through-thickness reinforcement. 3D fully interlaced preform weaving introduces a method of producing fully interlaced 3D woven fabric structures with through-thickness reinforcement, which enhances the delamination resistance as well as out-of-plane mechanical characteristics. 3D woven fabric preforms made from 3D fully interlaced preform weaving, using high-performance fiber yarns such as Dyneema, Carbon, Kevlar and Zylon, have exceptional mechanical properties with light-weight characteristics, which make them suitable candidates for high-end technical composite applications. In this work, a brief introduction is given to the history of weaving followed by an introduction to 3D woven fabrics. In the existing literature, an emphasis is given to the 3D fully interlaced preform weaving process, distinguishing it from other 3D woven fabric manufacturing methods. Subsequently, a comprehensive review is made on the existing literature on 3D fully interlaced preform weaving devices, such as primary and secondary mechanisms as well as modelling of 3D woven fabric structures produced by 3D fully interlaced preform weaving. Finally, the authors attempted to discuss the existing research gaps with potential directions for future research.

A sign language is a language which uses visually transmitted sign patterns, instead of acoustically conveyed sound patterns, to deliver the meaning. Sign languages are typically constructed by simultaneous combination of hand shapes, orientations and movements of the hands, arms or body, with facial expressions to fluidly express a speaker's thoughts. This paper presents a less costly approach to develop a computer vision based sign language recognition application in real time context with motion recognition. We explore new concepts of breaking down motion gestures to sub components for parallel processing and mapping motion data into static data representations. This concept can be used to identify sign language gestures, without performing computational intensive tasks of each and every frame captured. Moreover, sign language gestures can be evaluated with minimal image processing and map the motion to linear/non-linear equations using functionalities proposed in this paper.

The detection of lane boundaries on suburban streets using images obtained from video constitutes a challenging task. This is mainly due to the difficulties associated with estimating the complex geometric structure of lane boundaries, the quality of lane markings as a result of wear, occlusions by traffic, and shadows caused by road-side trees and structures. Most of the existing techniques for lane boundary detection employ a single visual cue and will only work under certain conditions and where there are clear lane markings. Also, better results are achieved when there are no other onroad objects present. This paper extends our previous work and discusses a novel lane boundary detection algorithm specifically addressing the abovementioned issues through the integration of two visual cues. The first visual cue is based on stripe-like features found on lane lines extracted using a two-dimensional symmetric Gabor filter. The second visual cue is based on a texture characteristic determined using the entropy measure of the predefined neighbourhood around a lane boundary line. The visual cues are then integrated using a rulebased classifier which incorporates a modified sequential covering algorithm to improve robustness. To separate lane boundary lines from other similar features, a road mask is generated using road chromaticity values estimated from CIE L*a*b* colour transformation. Extraneous points around lane boundary lines are then removed by an outlier removal procedure based on studentized residuals. The lane boundary lines are then modelled with Bezier spline curves. To validate the algorithm, extensive experimental evaluation was carried out on suburban streets and the results are presented.

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Purpose Woven fabrics have been popularised in use owing to their superior properties and functionality. Today, weavers strive to add value to their product to be competitive and to secure profit in performance fabrics such as technical fabrics, smart fabrics and sportswear fabrics. Over the years, fabrics with special properties such as moisture management have gained higher demand. In this context, multi-layer fabrics provide a reasonable solution to the demand. Design/methodology/approach An attempt was made to develop two-layer fabrics with different compositions and properties. A two-layer woven fabric was produced using handloom weaving, with a hydrophobic inner layer and hydrophilic outer layer, the two layers being attached together using different stitching methods. Different fabric structures and yarn counts were used to achieve the objectives. Findings Experiments carried out verified the suitability of the developed fabric for effective moisture management. It was found that a fabric with a 100% cotton outer layer and 100% polyester inner layer, both layers of 2 × 2 matt weave, showed the best properties. Practical implications In the present COVID-19 pandemic situation, the use of masks in public has become mandatory in many countries. This research will help handloom manufacturers meet the need using simple methods. Originality/value This research uses handloom fabric. As such it provides an opportunity for small and medium enterprises to use available low-cost technology to develop fabric with superior properties.