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1
Content available remote Predicting Cotton Fibre Maturity by Using Artificial Neural Network
Farooq A., Sarwar M. I., Ashraf M. A., Iqbal D., Hussain A., Malik S.
Autex Research Journal
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2018
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Vol. 18, no. 4
429--433
EN
Cotton fibre maturity is the measure of cotton’s secondary cell wall thickness. Both immature and over-mature fibres are undesirable in textile industry due to the various problems caused during different manufacturing processes. The determination of cotton fibre maturity is of vital importance and various methods and techniques have been devised to measure or calculate it. Artificial neural networks have the power to model the complex relationships between the input and output variables. Therefore, a model was developed for the prediction of cotton fibre maturity using the fibre characteristics. The results of predictive modelling showed that mean absolute error of 0.0491 was observed between the actual and predicted values, which show a high degree of accuracy for neural network modelling. Moreover, the importance of input variables was also defined.
2
Content available remote Investigation on Thermal Comfort Characteristics of Regenerated Bamboo and Cotton Woven Structured Fabrics
Abro Z. A., Chen N., Yifan Z., Cheng-Yu H., Abassi A. M. R., Simair A. A., Ahmed R., Hussain A.
Autex Research Journal
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2018
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Vol. 18, no. 4
323--329
EN
In this research work, thermal properties of plain woven fabrics generated from regenerated bamboo and cotton fiber blended yarns were investigated. Seven mixtures of fiber (100% bamboo, 100% cotton, 10:90 bamboo: cotton, 20:80 bamboo: cotton, 30:70 bamboo: cotton, 40:60 bamboo: cotton and 50:50 bamboo: cotton) were developed to create 60 Tex ring spun yarn. The warp yarns were used as 100% regenerated bamboo and the bamboo: cotton blends were used alternatively in weft to produce plain woven fabrics. The plain structured woven fabrics show eminent thermal comfort properties with the blending of regenerated bamboo fibers. The air permeability of 100% regenerated bamboo fiber was recorded higher than the compared blends; the increased key factor contents of bamboo changed the air properties of the fabric. Furthermore, plain woven fabric of bamboo/cotton (50/50) has shown greater thermal conductivity and heat retention properties. The work reported in this paper is ensuring highpoints of thermal comfort properties of regenerated bamboo (100%) and cotton (100%) with plain woven structured fabrics, and potentially, the fabrics can be used for winter suiting apparel products.
3
Content available remote Shape Matching and Color Segmentation Based Traffic Sign Detection System
Wali S. B., Hannah M.A., Abdullah S, Hussain A., Samad S. A.
Przegląd Elektrotechniczny
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2015
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R. 91, nr 1
36-40
EN
An automatic traffic sign detection system detects traffic signs from within images captured by an imaging sensor, and assists the driver to properly operate the vehicle. The idea presented here is through pixel value detection for hazard traffic signs containing red color background, computing in range regions and finally shape matching to choose the most appropriate traffic sign candidates to be drawn on the screen. The experimental result showed that, by comparing with the similar color segmentation based techniques, the proposed system has a higher accuracy of traffic sign detection rate with a lower computational time.
PL
W artykule opisano system automatycznego rozpoznawania znaków drogowych na podstawie sygnału czujnika obrazu. System rozpoznaje znaki na czerwonym tle, dopasowuje odpowiedni znak i wyświetla go na ekranie.
4
Content available remote Comparative Survey on Traffic Sign Detection and Recognition: a Review
Wali S. B., Hannan M. A., Hussain A., Samad S. A.
Przegląd Elektrotechniczny
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2015
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R. 91, nr 12
38-42
EN
Developing real-time Advanced Driver Assistance Systems (ADAS) based on video aiming to extract reliable vehicle state information has attracted a lot of attention during the past decades. This ADAS system includes inter-vehicle communication, driver behavioral monitoring, and human-machine interactions. In these systems, robust and reliable traffic sign detection and recognition (TSDR) technique is a critical step for ensuring vehicle safety. This paper provides a comprehensive survey on traffic sign detection and recognition system based on image and video data. Our main focus is to present the current trends and challenges in the field of developing an efficient TSDR system followed by a detail comparative study between different renowned methods used by various researchers. Finally, conclusion followed by some future suggestion is provided to develop an efficient TSDR system is provided. This survey will hopefully lead to develop an effective traffic sign detection and recognition system which will ensure driver safety in future.
PL
System ADAS (Advanced Driver Assistance System) obejmuje także metody rozpoznawania znaków drogowych. W artykule przedstawiono przegląd metod detekcji i rozpoznawania znaków drogowych bazujących na obrazie video. W artykule dokonano oceny istniejących metod oraz zaproponowano środki poprawy ich efektywności.
5
Content available remote Traffic Sign Classification based on Neural Network for Advance Driver Assistance System
Hannan M. A., Wali S. B., Pin T. J., Hussain A., Samad S. A.
Przegląd Elektrotechniczny
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2014
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R. 90, nr 11
169--172
EN
Traffic sign is utmost important information or rule in transportation. In order to ensure the transportation safety the automotive industry has developed Advance Driver Assistance System (ADAS). Among the ADAS system, development of TSDR is the most challenging to the researchers and developers due to unsatisfying performance. This paper deals with, automatic traffic sign classification and reduces the effect of illumination and variable lighting over the classification scheme by using neural network according to the traffic sign shape. There are three main phase of the classification scheme such as; pre-processing using image normalization, feature extraction using color information of 16-point pixel values and multilayer feed forward neural network for classification. An accuracy rate of 84.4% has been achieved by the proposed system. Overall processing time of 0.134s shows the system is a fast system and real-time application.
PL
W artykule opisano metodę automatycznego rozpoznawania I klasyfikacji znaków drogowych z przenaczeniem do inteligentnych systemów wspomagania kierowcy ADAS. Do tego celu wykorzystano sieci neuronowe przeprowadzając normalizację obrazu, ekstrakcję cech i klasyfikację. Osiągnieto dokładność rozpoznawania rzędu 84% przy przeciętnym czasie rozpoznawania około 0.13 s.
6
Urease Inhibitiory Flavone Glucosides from Marrubium anisodon
Hussain A., Perveen S., Malik A., Afza N., Iqbal L., Tareen R. B.
Polish Journal of Chemistry
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2009
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Vol. 83, nr 7
1329-1335
EN
Compounds 1–3, new flavonoid glucosides have been isolated from the ethyl acetate soluble fractions of Marrubium anisodon, along with apigenin 4'-O-beta-D-glucopyranoside 4, kaempferol 3-O-beta-D-glucopyranoside 5 and beta-sitosterol 3-O-beta-D-glucopyranoside 6, re - spectively. Their structures were as signed from 1H- and 13C-NMR spectra, DEPT and by COSY, HMQC, NOESY and HMBC experiments. Compounds 1–5 showed significant inhibitory activity against the enzyme urease.
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