http://nopr.niscpr.res.in/handle/123456789/61643 2026-10-09T17:33:01Z 2026-10-09T17:33:01Z Asgar, Shakeel Chauhan, Manorama http://nopr.niscpr.res.in/handle/123456789/61660 2023-04-03T10:06:37Z 2023-04-01T00:00:00Z

Title: Scope of Technological Intervention in the Sector of Traditional Indian Milk Products Industry for Sustainable Rural Development Authors: Asgar, Shakeel; Chauhan, Manorama Abstract: The entrepreneurial activities related to the vast sector of traditional Indian milk products and milk sweets effectuate the holistic development module fulfilling the concept of economic viability, technological feasibility, social obligation, cultural necessity and above all ecological balance. Despite robust growth and plenteous milk production, this informal sector of dairy products and sweetmeats is facing unprecedented challenges on multiple fronts. The major identified problems include non-availability of the packaging system, lack of proper handling and processing of raw material and finished products, inconsistent product quality, shortage of modern equipment, and inadequate arrangement of effluent handling. Milk and traditional Indian milk products are extremely perishable. With the ever-increasing cost of production, this sector being vast and unorganized has become increasingly vulnerable and can have far-reaching prejudicial consequences if protective technological measures are not employed at grassroots level. The pressure driven membrane processing system can bring a sea change to this industry from higher yields to a reduction in the cost of handling, storage and transportation. Modified Atmosphere Packaging (MAP), solar energy and business digitization are ready to open new vistas. The urgent necessity indicates concentrated efforts to pool, evaluate, preserve and motivate traditional practices, knowledge and wisdom coupled with selective uses of modern technological advancement in order to bring paradigm shift and inclusive growth. Page(s): 397-406

2023-04-01T00:00:00Z Singh, Kamal Kant Mangal, Dharamvir http://nopr.niscpr.res.in/handle/123456789/61658 2023-04-03T10:04:58Z 2023-04-01T00:00:00Z

Title: Optimization of the Stirring Parameters of AZ91Mg-based Stir Casted Hybrid Composites using Taguchi and ANN Authors: Singh, Kamal Kant; Mangal, Dharamvir Abstract: In the current research, the fabricated Mg hybrid composites amalgamate with the vacuum-based squeezed stir casting process having TiC and Al2O3 reinforcement’s in which AZ91Mg-alloy is the base material. The study includes the processing parameters of the vacuum-based squeezed stir casting method (such as stirring time, stirrer depth, and stirring speed) that have been varied and then investigates their influential effect by using the L16 orthogonal Taguchi approach. By considering these parameters, the tribo-mechanical properties are also optimized like porosity, ultimate strength, and rate of wear loss of Mg-hybrid composites. The result reveals a significant association between processing parameters and optimized tribomechanical properties. The results signify that the processing parameters are best optimized at processing parameters of 500 rpm of stirring speed, 5 min of stirring time, and 50 mm of stirrer depth and the optimized tribo-mechanical properties are 0.4% porosity, 245 MPa of ultimate strength, and 0.0011 mm3 per min is the loss of wear rate. However, the ANOVA results contribute that the stirrer depth has the most significant processing parameter compared to other optimized parameters. This research also includes an artificially designed networking model which validates the designated set of empirical data. Thus due to their suitability for abrasion wear AZ91-hybrid composite gives a new divergence towards the designing parts of minuscule airplanes used in surveillance applications such as ailerons and wing flaps. Page(s): 407-417

2023-04-01T00:00:00Z Bonam, Janakiramaiah Kondapalli, Sai Sudheer V, Narasimha Prasad L Marlapalli, Krishna http://nopr.niscpr.res.in/handle/123456789/61657 2023-04-03T10:03:21Z 2023-04-01T00:00:00Z

Title: Lightweight CNN Models for Product Defect Detection with Edge Computing in Manufacturing Industries Authors: Bonam, Janakiramaiah; Kondapalli, Sai Sudheer; V, Narasimha Prasad L; Marlapalli, Krishna Abstract: Detecting product defects is one of the manufacturing industry's most essential processes in quality control. Human visual inspection for product defects is the traditional method employed in the industry. Nevertheless, it can be laborious, prone to human mistakes, and unreliable. Deep Learning-based Convolution Neural Networks (CNN) has been extensively used in fully automating product defect detection systems. However, real-time edge devices installed at the manufacturing site generally have limited computing capability and cannot run different CNN models. A lightweight CNN model is adopted in this scenario to find a balance between defect detection, model training time, memory consumption, computing time and efficiency. This work provides lightweight CNN models with transfer learning for product defect detection on fabric, surface, and casting datasets. We deployed the trained model to the NVIDIA Jetson Nano-kit edge device for detection speed with better simulation results in terms of accuracy, sensitivity rate, specificity rate, and F1 measure in the workplace context of the Manufacturing Industries. Page(s): 418-425

2023-04-01T00:00:00Z J, Hyma P, Rajamani http://nopr.niscpr.res.in/handle/123456789/61656 2023-04-03T10:00:43Z 2023-04-01T00:00:00Z

Title: A Two-Stage Image Frame Extraction Model -ISLKE for Live Gesture Analysis on Indian Sign Language Authors: J, Hyma; P, Rajamani Abstract: The new industry revolution focused on Smart and interconnected technologies along with the Robotics and Artificial Intelligence, Machine Learning, Data analytics etc. on the real time data to produce the value-added products. The ways the goods are being produced are aligned with the people’s life style which is witnessed in terms of wearable smart devices, digital assistants, self-driving cars etc. Over the last few years, an evident capturing of the true potential of Industry 4.0 in health service domain is also observed. In the same context, Sign Language Recognition- a breakthrough in the live video processing domain, helps the deaf and mute communities grab the attention of many researchers. From the research insights, it is clearly evident that precise extraction and interpretation of the gesture data along with an addressal of the prevailing limitations is a crucial task. This has driven the work to come out with a unique keyframe extraction model focusing on the preciseness of the interpretation. The proposed model ISLKE deals with a clustering-based two stage keyframe extraction process. It has experimented on daily usage vocabulary of Indian Sign Language (ISL) and attained an average accuracy of 96% in comparison to the ground-truth facts. It is also observed that with the two-stage approach, filtering of uninformative frames has reduced complexity and computational efforts. These key leads, help in the further development of commercial communication applications in order to reach the speech and hearing disorder communities. Page(s): 426-431

2023-04-01T00:00:00Z