<p>Special Issue Industry 4.0: A Way Forward for Self-reliance and Sustainability—Part I.</p> http://nopr.niscpr.res.in/handle/123456789/61193 2026-10-09T20:57:58Z 2026-10-09T20:57:58Z Chebrolu, Varun Koona, Ramji Raju, R S Umamaheswara http://nopr.niscpr.res.in/handle/123456789/61208 2023-01-16T10:55:52Z 2023-01-01T00:00:00Z

Title: Automated Evaluation of Surface Roughness using Machine Vision based Intelligent Systems Authors: Chebrolu, Varun; Koona, Ramji; Raju, R S Umamaheswara Abstract: 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

2023-01-01T00:00:00Z Dash, Sukanta Kumar Solanki, S S Chakraborty, Soubhik http://nopr.niscpr.res.in/handle/123456789/61207 2023-01-16T10:52:28Z 2023-01-01T00:00:00Z

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 Abstract: 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

2023-01-01T00:00:00Z Spalević, Žaklina Veljković, Nataša Milić, Petar http://nopr.niscpr.res.in/handle/123456789/61206 2023-01-16T10:49:58Z 2023-01-01T00:00:00Z

Title: QODA – Methodology and Legislative Background for Assessment of Open Government Datasets Quality Authors: Spalević, Žaklina; Veljković, Nataša; Milić, Petar Abstract: 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

2023-01-01T00:00:00Z Sekhar, G Chandra Aruna, R http://nopr.niscpr.res.in/handle/123456789/61205 2023-01-16T10:46:56Z 2023-01-01T00:00:00Z

Title: An Integrated Secure Scalable Blockchain Framework for IoT Communications Authors: Sekhar, G Chandra; Aruna, R Abstract: 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

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