AlphanumericJournal, The journal of operations research, statistics, econometrics and management information systems, Jul 12, 2023
Efficiently transferring information, which is of major importance for almost all engineering sys... more Efficiently transferring information, which is of major importance for almost all engineering systems, requires a data representative appropriate to maintain the density and consistency of the information dispatched. As such a machine-readable representation of data, barcodes are one of the most recognized and powerful instruments of the purpose in every domain. Likewise, from the perspective of engineering, colors are excellent sources of information exchange, since the transmission of color connotes the conveyance of its entire scalar attributes in the same spatial channel component. Exerting colors on barcodes as an effective way of bursting the data conveyance capacity has been an active area of research for over 50 years. Significant progress has been achieved through efforts in this regard. It is also envisagable that the evident evolution in related technologies exoterically empowers the enhancement of color barcode capabilities as capacity and reliability, thereby further encouraging prospected research in this direction. Herein, a comprehensive survey of the studies on this main area of interest is presented. To help better acquainted with the field, also a taxonomy of the peculiar interference sources and distortion effects is provided, besides, the 3D barcoding process itself and the research areas are described. Most of the relevant works from debut to the present are broadly examined. Rather than presenting a timeline, studies that pertain to similar issues are addressed together. Amongst those related, premising or pivotal ones are preferably cited as far as feasible. Moreover, all perused works are analyzed by the research areas and the results are presented. Also, the issues relatively more prominent as affecting the performance of the whole process are specified. In the conclusion, some of the research subjects that appear open, scarce, or require further elaboration were remarked on as well. It is anticipated this study to contribute to the efforts toward leveraging color in barcodes.
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Papers by Tanju Sirmen
instruments of data analytics. Despite clustering being realized under
uncertainty, validity indices do not deliver any quantitative evaluation of the
uncertainties in the suggested partitionings. Also, validity measures may be
biased towards the underlying clustering method. Moreover, neglecting a confidence
requirement may result in over-partitioning. In the absence of an error
estimate or a confidence parameter, probable clustering errors are forwarded
to the later stages of the system. Whereas, having an uncertainty margin of the
projected labeling can be very fruitful for many applications such as machine
learning. Herein, the validity issue was approached through estimation of the
uncertainty and a novel low complexity index proposed for fuzzy clustering.
It involves only uni-dimensional membership weights, regardless of the data
dimension, stipulates no specific distribution, and is independent of the
underlying similarity measure. Inclusive tests and comparisons returned that
it can reliably estimate the optimum number of partitions under different
data distributions, besides behaving more robust to over partitioning. Also,
in the comparative correlation analysis between true clustering error rates and
some known internal validity indices, the suggested index exhibited the highest
strong correlations. This relationship has been also proven stable through
additional statistical acceptance tests. Thus the provided relative uncertainty
measure can be used as a probable error estimate in the clustering as well.
Besides, it is the only method known that can exclusively identify data points
in dubiety and is adjustable according to the required confidence level.
instruments of data analytics. Despite clustering being realized under
uncertainty, validity indices do not deliver any quantitative evaluation of the
uncertainties in the suggested partitionings. Also, validity measures may be
biased towards the underlying clustering method. Moreover, neglecting a confidence
requirement may result in over-partitioning. In the absence of an error
estimate or a confidence parameter, probable clustering errors are forwarded
to the later stages of the system. Whereas, having an uncertainty margin of the
projected labeling can be very fruitful for many applications such as machine
learning. Herein, the validity issue was approached through estimation of the
uncertainty and a novel low complexity index proposed for fuzzy clustering.
It involves only uni-dimensional membership weights, regardless of the data
dimension, stipulates no specific distribution, and is independent of the
underlying similarity measure. Inclusive tests and comparisons returned that
it can reliably estimate the optimum number of partitions under different
data distributions, besides behaving more robust to over partitioning. Also,
in the comparative correlation analysis between true clustering error rates and
some known internal validity indices, the suggested index exhibited the highest
strong correlations. This relationship has been also proven stable through
additional statistical acceptance tests. Thus the provided relative uncertainty
measure can be used as a probable error estimate in the clustering as well.
Besides, it is the only method known that can exclusively identify data points
in dubiety and is adjustable according to the required confidence level.