Adinkra Symbol Recognition using Classical Machine Learning and Deep Learning

Artificial intelligence (AI) has emerged as a transformative influence, engendering paradigm shifts in global societies, spanning academia and industry. However, in light of these rapid advances, addressing the underrepresentation of black communities and African countries in AI is crucial. Boosting enthusiasm for AI can be effectively accomplished by showcasing straightforward applications around tasks like identifying and categorizing traditional symbols, such as Adinkra symbols, or familiar objects within the community. In this research endeavor, we dived into classical machine learning and harnessed the power of deep learning models to tackle the intricate task of classifying and recognizing Adinkra symbols. The idea led to a newly constructed ADINKRA dataset comprising 174,338 images meticulously organized into 62 distinct classes, each representing a singular and emblematic symbol. We constructed a CNN model for classification and recognition using six convolutional layers, three fully connected (FC) layers, and optional dropout regularization. The model is a simpler and smaller version of VGG, with fewer layers, smaller channel sizes, and a fixed kernel size. Additionally, we tap into the transfer learning capabilities provided by pre-trained models like VGG and ResNet. These models assist us in both classifying images and extracting features that can be used with classical machine learning models. We assess the model's performance by measuring its accuracy and convergence rate and visualizing the areas that significantly influence its predictions. These evaluations serve as a foundational benchmark for future assessments of the ADINKRA dataset. We hope this application exemplar inspires ideas on the various uses of AI in organizing our traditional and modern lives.Comment: 15 pages, 6 figures, 5 table

Study of Biometric Identification Method Based on Naked Footprint

The scale of deployment of biometric identity-verification systems has recently seen an enormous increase owing to the need for more secure and reliable way of identifying people. Footprint identification which can be defined as the measurement of footprint features for recognizing the identity of a user has surfaced recently. This study is based on a biometric personal identification method using static footprint features viz. friction ridge / texture and foot shape / silhouette. To begin with, naked footprints of users are captured; images then undergo pre processing followed by the extraction of two features; shape using Gradient Vector Flow （GVF） snake model and minutiae extraction respectively. Matching is then effected based on these two features followed by a fusion of these two results for either a reject or accept decision. Our shape matching feature is based on cosine similarity while the texture one is based on miniature score matching. The results from our research establish that the naked footprint is a credible biometric feature as two barefoot impressions of an individual match perfectly while that of two different persons shows a great deal of dissimilarity. Doi: 10.12777/ijse.5.2.29-35 How to cite this article: King, R.R. and Xiaopeng, W. (2013). Study of Biometric Identification Method Based on Naked Footprint . International Journal of Science and Engineering, 5(2),18-24. Doi: 10.12777/ijse.5.2.29-35

Review of Printed Fabric Pattern Segmentation Analysis and Application

Image processing of digital images is one of the essential categories of image transformation in the theory and practice of digital pattern analysis and computer vision. Automated pattern recognition systems are much needed in the textile industry more importantly when the quality control of products is a significant problem. The printed fabric pattern segmentation procedure is carried out since human interaction proves to be unsatisfactory and costly. Hence, to reduce the cost and wastage of time, automatic segmentation and pattern recognition are required. Several robust and efficient segmentation algorithms are established for pattern recognition. In this paper, different automated methods are presented to segregate printed patterns from textiles fabric. This has become necessary because quality product devoid of any disturbances is the ultimate aim of the textile printing industry

Three-Dimensional Deformation of Warp-Knitted Spacer Fabrics Under Tensile Loading

This paper puts forward a new method for measuring the three-dimensional deformation of warp-knitted spacer fabrics under tensile stress. The three-dimensional deformation mechanisms of warp-knitted spacer fabrics have been analyzed using stress–strain curves. Poisson’s ratio of the three-dimensional deformation has also been analyzed. The stress–strain curves obtained for tests in the warp-ward direction and weft-ward direction show a characteristic initial large deformation, followed by minimal-to-no deformation. The stress–strain curves obtained for tests in the thickness direction exhibit different characteristics due to the differences in stretch directions. In the weft-ward direction, the curve shows an approximate linear change with minimal strain. In the warp-wise direction, the curve shows a large stress with small strain, and subsequently, a small stress yielded a large strain. During the stretching process, the surface deformation perpendicular to the direction of tensile force is greater than the tensile deformation, and the deformation in the thickness direction is also minimal compared to that in the direction of the tensile deformation

Wearable Devices for Gait Measurement - A Case for Textile-Based Devices

Wearable devices for gait measurement are devices worn on the body to measure the gait of the wearers. During gait measurement, several parameters are measured and the choice of parameters is influenced by the application and by extension the gait index. Two approaches have been adopted in this research. One is the provision of an overview of wearable devices for gait measurement with a bias towards textile-based “soft” smart wearable systems using information from varied academic sources and databases. The second approach is to map out key scientific research trends within the wearable device classes using the Web of Science database. The focus is to make a case for textile-based gait measurement devices and systems while exploring the key determinants of wearable gait sensor placements and application efficiency. These soft smart wearable systems describe flexible material sensor-based systems which have their sensing mechanisms based on material deformation after being subjected to stress or pressure. This study could therefore serve as an apt reference for the development of soft smart wearable gait measurement systems as it throws light on the various soft wearable gait measurement applications, the bottlenecks in soft wearable device design, opportunities for developing new devices and the merit that soft gait analysis systems possess over their hard gait measurement counterparts

Optical Fibers in the Design and Fabrication of Smart Garments – a Review

Several publications and even commercial products showcasing the application of optical fibers for textile goods abound in literature. Optical fibers can be employed as sensors by making use of physical principles to sense strain, temperature, and other quantities by tailoring the fiber such that the quantity to be measured alters the intensity, phase, polarisation, and wavelength of light within the fiber. However, a paper directed at the development of textile based applications or smart garments using optical fibers is lacking. This review seeks to serve as apt reference material for the development of optical fiber based textile sensors or smart garments with a focus on the application of plastic optical fibers (POFs). Highlighted are the salient material properties of POFs and their importance in delivering satisfactory sensing results. Special treatment has also been given to their proposed feasibility for embedment within weft knitted structures