Yarn periodical errors determination using three signal processing approaches

Carvalho, Vı́tor; Belsley, M.; Vasconcelos, Rosa; Soares, Filomena

doi:10.1016/j.dsp.2013.06.003

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Where T q is the quantization time factor and n a positive integer number. The TQ-RS in case of ARS scheme for T q =4 is illustrated in [7]. Circuit of Pseudorandom Signal Sampler.…”

Section: S S T T −mentioning

confidence: 99%

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Random Sampling Approaches for Implementation of FPGA in Signal Processing

2014

AMM

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Nowadays, digital front-end devices are widely used in large number of fields such as electronic appliances. Signals must be presented in an appropriate format, for example, the original analog signals must be converted into digital formats that can be recognized and used by digital equipment. In this paper, the random sampling technique is implemented by original design of a Pseudorandom Signal Sampler circuit for controlling ADC to relax constraints of receiver circuits supporting multiband signal processing. This new idea of using non-uniform sampling technique for multiband signals sampling allows the main advantage of suppressing spectral aliases at integer multiples of sampling frequency produced by conventional uniform sampling technique. This approach could reduce the constraints on the anti-aliasing filter, relax the automatic gain control dynamic range, and decrease the ADC dynamic power consumption. Experiments show that this approach achieve good performance and it could be implemented in FPGA in signal processing successfully.

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“…Where T q is the quantization time factor and n a positive integer number. The TQ-RS in case of ARS scheme for T q =4 is illustrated in [7]. Circuit of Pseudorandom Signal Sampler.…”

Section: S S T T −mentioning

confidence: 99%

“…Studies have indicated that randomness in sampling is not always harmful [5,6]. Random irregularities in sampling sometimes can even be beneficial [7]. These irregularities, if properly introduced, for instance, can provide useful effects such as the suppression of aliasing.…”

Section: Introductionmentioning

confidence: 99%

Random Sampling Approaches for Implementation of FPGA in Signal Processing

2014

AMM

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“…In [11][12][13][14][15] , the researchers developed an image processing-based system to measure the variation in yarn parameters. The performance of the developed system is free from any fluctuation of ambient temperature or humidity.…”

mentioning

confidence: 99%

Yarn tension control technique for improving polyester soft winding process

Ali¹,

Ahmed

Amer

2021

Sci Rep

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The textile industry has a great role in the improvement of any country’s economy. Moreover, the ready-made garments need different coloured high yarn quality, so yarn should be rewinded on plastic cones for dyeing. However, manufacturers are facing the problem of tension variation during soft winding process that mainly affects the yarn quality. Consequently, to overcome the tension variation drawbacks, the attainment of constant optimal tension values is required in order to: (1) Increase the winding speed while maintaining the yarn quality, (2) Improve the dyeing quality, and (3) Reduce the water consumption during the dyeing process. In this paper, a proposed yarn tension control technique is introduced to upgrade the soft winding machine, thus maintain the yarn quality and improve the manufacturing capacity. The proposed technique has been tested on Polyester yarn samples classified as; fine, medium and coarse yarn counts, to cover most yarn sizes used in the industry. Arduino Mega 2560 controller is utilized to implement the proposed tension control. The results are compared to the conventional system to advocate the effectiveness and capability of the proposed technique in overcoming the trade-off between tension control and machine speed that occurs in conventional system using variable tension levels.

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