Abstract: Title: Automated Evaluation of Surface Roughness using Machine Vision based Intelligent Systems Authors : Chebrolu, Varun; Koona, Ramji; Raju, R S Umamaheswara: Machine vision systems play a vital role in entirely automating the evaluation of surface roughness due to the hitches in
the conformist system. Machine vision systems significantly abridged the ideal time and human errors for evaluation of the
surface roughness in a nondestructive way. In this work, face milling operations are performed on aluminum and a total of
60 diverse cutting experiments are conducted. Surface images of machined components are captured for the development of
machine vision systems. Images captured are processed for texture features namely RGB (Red Green Blue), GLCM (Grey
Level Co-occurrence Matrix) and an advanced wavelet known as curvelet transforms. Curvelet transforms are developed to
study the curved textured lines present in the captured images and this module is capable to unite the discontinuous curved
lines present in images. The CNC machined components consists of visible lay patterns in the curved form, so this novel
machine vision technique is developed to identify the texture well over the other two extensively researched methods.
Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) intelligent models are developed to evaluate the
surface roughness from texture features. The model average error attained using RGB, GLCM, Curvelet transform-based
machine vision systems are 12.68, 7.8 and 3.57 respectively. In comparison, the results proved that computer vision system
based on curvelet transforms outperformed the other two existing systems. This curvelet based machine vision system can
be used for the evaluation of surface roughness. Here, image processing might be crucial in identifying certain information.
One crucial issue is that, even as performance improves, cameras continue to get smaller and more affordable. The
possibility for new applications in Industry 4.0 is made possible by this technological advancement and the promise of everexpanding
networking.Page(s): 11-25 PubDate: 2023-01-01T00:00:00Z
Abstract: Title: A Comprehensive Review on Audio based Musical Instrument Recognition: Human-Machine Interaction towards Industry 4.0 Authors : Dash, Sukanta Kumar; Solanki, S S; Chakraborty, Soubhik: Over the last two decades, the application of machine technology has shifted from industrial to residential use. Further,
advances in hardware and software sectors have led machine technology to its utmost application, the human-machine
interaction, a multimodal communication. Multimodal communication refers to the integration of various modalities of
information like speech, image, music, gesture, and facial expressions. Music is the non-verbal type of communication that
humans often use to express their minds. Thus, Music Information Retrieval (MIR) has become a booming field of research
and has gained a lot of interest from the academic community, music industry, and vast multimedia users. The problem in
MIR is accessing and retrieving a specific type of music as demanded from the extensive music data. The most inherent
problem in MIR is music classification. The essential MIR tasks are artist identification, genre classification, mood
classification, music annotation, and instrument recognition. Among these, instrument recognition is a vital sub-task in MIR
for various reasons, including retrieval of music information, sound source separation, and automatic music transcription. In
recent past years, many researchers have reported different machine learning techniques for musical instrument recognition
and proved some of them to be good ones. This article provides a systematic, comprehensive review of the advanced
machine learning techniques used for musical instrument recognition. We have stressed on different audio feature
descriptors of common choices of classifier learning used for musical instrument recognition. This review article emphasizes
on the recent developments in music classification techniques and discusses a few associated future research problems.Page(s): 26-37 PubDate: 2023-01-01T00:00:00Z
Abstract: Title: QODA – Methodology and Legislative Background for Assessment of Open Government Datasets Quality Authors : Spalević, Žaklina; Veljković, Nataša; Milić, Petar: In last few years, many open government data portals have been emerging in the world. These portals publish open
government datasets which can be accessed and used by everyone for their own needs. In this paper, we propose
methodology named QODA (Quality of Open government DAtasets) for assessment of quality of published datasets via two
aspects. First one is assessment of quality of pure open government datasets, and second is assessment of quality features on
the platforms which contributes to the publication of quality datasets. It provides a step-by-step dataset analysis guidance
and summarization of results. Research presented in this paper shows that open government dataset quality depends on data
provider as well as proper definition of metadata behind datasets. Our findings result in recommendations to open
government data (OGD) publishers, to constantly supervise the use of published datasets, with aim to have timely and
punctual information on OGD portals, with special attention on quality features.Page(s): 38-49 PubDate: 2023-01-01T00:00:00Z
Abstract: Title: An Integrated Secure Scalable Blockchain Framework for IoT Communications Authors : Sekhar, G Chandra; Aruna, R: The Internet of Things (IoT) has shown great promise in the years since its invention and widespread acceptance by
demonstrating its ability to adapt and improve manual processes while bringing them into the digital age. IoT's capacity to
do so has elevated it to the ranks of the most promising technologies of our time. Despite the fact that IPv4 and IPv6 are
being utilized to serve a growing number of devices in IoT connectivity, there are still issues with address space allocation
and other security concerns, including scalability and poor access control methods. It is necessary to go through these
difficulties and worries. Both of these organizations have spent a considerable amount of time in the vanguard of
advancement in the study of IoT and Blockchain technology. Since IoT devices are capable of efficient two-way
communication, integrating Blockchain technology is challenging. However, scalability is the biggest obstacle. The IoT
Blockchain Framework discussed in the research article has the potential to be a game-changing solution to the issues that
IoTs currently face, provided that it is used properly. Data access control and data interchange, transparency, and scalability
without compromising privacy or dependability are all issues with the IoT paradigm that Blockchain technology may be
able to efficiently address. Creating a local index that is scalable and does not interfere with either the local or global peer
validation procedures is one way to limit the number of transactions that contact the global Blockchain. According to the
findings, the blocks are significantly lighter and smaller than those seen in other parts of the world.Page(s): 50-62 PubDate: 2023-01-01T00:00:00Z
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