http://nopr.niscpr.res.in/handle/123456789/65873 2026-10-09T21:53:51Z http://nopr.niscpr.res.in/handle/123456789/65886 Title: Superhydrophobic Silica-based Nano-Coatings for Anti-Reflective and Anti-Soiling Surface: Effect of HMDS and DIPEA Authors: Samadhiya, Abhineet; Jhinge, Pradeep Kumar; Kushwah, Kamal Kumar Abstract: This study presents a novel nano-silica-HMDS (hexamethyldisilazane) surface coating, synthesized through an efficient chemical route, exhibiting anti-reflective, superhydrophobic, and anti-soiling properties suitable for solar panel top covers working in dusty environments. The synthesized surface coating enhances optical transmission, mitigating incident light reflection and performance loss from soiling. The experiment results show a 5.8% average power improvement in the nano-silica-HMDS-coated solar photovoltaic cell compared to a manually cleaned, uncoated solar cell. The cost-effective dip-coating method applied to the substrate surface at room temperature offers a practical alternative to costly surface coatings. The surface coating exhibits a finely tuned refractive index of 1.14 and 96.1% transmittance for wavelengths ranging from 400–800 nm demonstrating exceptional transparency and optical effectiveness. The Field Emission Scanning Electron Microscopy (FESEM) and Atomic Force Microscopy (AFM) results show a highly rough HMDS-modified-nanosilica surface exhibiting anti-soiling and super-hydrophobicity characteristics confirmed by a water contact angle of 156° in test results. The synthesized surface coating also showed excellent stability over two months in a dusty environment consisting of abundant particulate particles pm 2.5 and pm 10 observed in environmental stability tests conducted over 2.5 months with the help of an experimental setup. The test results confirm the nano-silica-HMDS (0.3:1) surface coating as the most optimum concentration for simultaneously achieving superhydrophobicity, anti-soiling, and antireflection properties. Page(s): 509-519 2025-05-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/65885 Title: A Hybrid Framework for the Diagnosis of Parkinson’s Disease using Handwritten Drawings-Spiral and Wave Authors: Reddy, K Rasool; Rajesh, Kandala NVPS; Polinati, Srinivasu; Dhuli, Ravindra Abstract: Parkinson's disease is a progressive neurological disorder that significantly affects individuals worldwide. Early and accurate classification of the disease is crucial for timely intervention and improved patient outcomes. This study aims to develop an effective classification system using drawings of spirals and waves to discriminate between healthy individuals and those with Parkinson's disease, aiming to provide an early diagnostic method, leading to improved patient lifespan. The study utilizes two sets of drawings: spirals and waves. Data augmentation techniques are employed to increase the dataset size and enhance training data for deep neural networks. The Pyramid Histogram of Oriented Gradients (PHoG) algorithm is applied to compute shape descriptors from healthy and Parkinson's drawings. A Visual Geometry Group (VGG)-based deep learning model is used to extract significant features from the modified drawings, particularly from the fc6 and fc7 layers. Supervised classifiers, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), are employed individually and in combination to classify the extracted features. The results demonstrate that the fused features achieved the highest accuracy values: 98.6% for spiral drawings using SVM and 96.57% for wave drawings using KNN. These accuracy rates highlight the effectiveness of the proposed method in accurately classifying Parkinson's disease based on drawings of spirals and waves. The findings suggest that the proposed method has the potential to serve as a non-invasive and reliable tool for early diagnosis of Parkinson's disease. It can enable timely interventions and improved patient care. Page(s): 520-530 2025-05-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/65884 Title: Adaptive Hierarchical Clustering and Batch-free Top-K Sequential Pattern Mining for Data Streams Authors: Poongodi, K; Kumar, Dhananjay; Meenakshi, K Abstract: The Sequential Pattern Mining (SPM) is a challenging task in data streams due to huge memory and computational costs to meet accuracy in mined results. Sequential patterns mined from target stream in traditional batch-based processing results in pattern loss when the batches are processed independently, where the pattern frequency is determined local to the batch. However, if a pattern is frequent in the stream and its items appear in various batches, then this pattern never becomes frequent and hence requires pruning. To address this issue, the sequences are clustered by similarity using Adaptive Hierarchical Clustering (AHC) and Batch-Free Top-K Sequential Pattern Mining (BFTKSPM) algorithms proposed to mine approximate sequential patterns over data streams. The BFTKSPM algorithm targets data stream in a continuous and batch-free manner. The top-k sequential patterns are extracted from data streams and are maintained in an inverted tree structure. The experimental results of the proposed algorithm are carried out on benchmark datasets for data streams and it outperforms the existing batch-based methods in terms of execution time, memory, precision, recall, and F1-score. Page(s): 531-543 2025-05-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/65883 Title: Application of an Integrated Fog-IoT Framework to a Smart Traffic Surveillance Management System Authors: Yang, Jui-Pin Abstract: In addressing urban traffic management, a prevalent approach involves the installation of surveillance cameras along roadways. However, this approach raises two critical concerns. The primary issue lies in the potential for negligence caused by extended periods of manual monitoring. Hence, the prevailing trend in traffic management has shifted towards the adoption of intelligent traffic surveillance management systems. The second challenge pertains to the need for swift responses to traffic conditions, necessitating real-time and efficient management strategies. In this research paper, we present an integrated Fog-IoT framework for a Smart Traffic Surveillance Management System (STSMS). This framework leverages IoT devices and fog nodes for task processing, significantly enhancing overall system performance. The STSMS utilizes surveillance cameras to collect extensive traffic data. Subsequently, the collected data is rigorously analyzed to accurately assess congestion levels in adjacent areas. The system autonomously generates commands to control traffic signals, effectively coordinating neighboring signals, thereby achieving efficient and intelligent traffic management. Furthermore, the STSMS promptly communicates its findings to administrators, enabling proactive responses. For instance, it can dispatch notifications to nearby police stations, facilitating the allocation of personnel to alleviate traffic congestion. Clearly, the STSMS plays a pivotal role in the development of smart cities, not only by facilitating intelligent traffic management but also by optimizing resource utilization, including reductions in latency and network usage. To assess the effectiveness of the STSMS comprehensively, simulations were conducted using the iFogSim tool. The experimental results demonstrate unequivocally that the Fog-IoT-based STSMS significantly reduces latency and network usage compared to cloud-based frameworks. These findings underscore the transformative potential of the STSMS in revolutionizing urban traffic management, thereby advancing the vision of efficient and intelligent smart cities. Page(s): 544-555 2025-05-01T00:00:00Z