http://localhost:8080/xmlui/handle/123456789/7044 2026-04-22T21:49:30Z 2026-04-22T21:49:30Z Viswanath, Divya http://localhost:8080/xmlui/handle/123456789/7078 2024-12-19T03:39:00Z 2023-01-01T00:00:00Z

Title: A Thesis on Investigation on the Engineering Properties of Soil Stabilized by Biological Methods Authors: Viswanath, Divya Abstract: Ground improvement or soil stabilization helps in the modification of physical and engineering properties of the soil, thereby making it more suitable for various Civil engineering projects. Many research works have been reported in the past using conventional stabilizers such as lime, fly ash, chemical additives, GGBS, waste plastics etc. The use of conventional methods of stabilization poses several issues such as brittle failure, corrosive soil environment etc. The decreasing availability and increasing cost of construction materials together with the drawbacks confronted by the traditional methods have forced the researchers to focus on ground improvement using sustainable stabilizing agents. In recent years, bio-mediated soil improvement has been gaining significance due to its sustainable and eco-friendly nature. In this method, enzymes or microorganisms are used as catalysts in altering the soil properties. Bio-enzymes are chemical, organic and liquid concentrated substances which are formulated from vegetable extracts and found to be free from side effects.

2023-01-01T00:00:00Z K N, Sudhakar http://localhost:8080/xmlui/handle/123456789/7077 2024-12-19T03:42:28Z 2019-05-01T00:00:00Z

Title: Load balancing in self organized Wireless sensor networks Authors: K N, Sudhakar Abstract: The demand for wireless communication systems has gained huge attraction from the research community due to their utilization in several real-time applications. It has several advantages such as data sensing and actuating along with various real-world applications such as healthmonitoring, environment monitoring, and army applications. However, these networks contain limited resources such as memory, transmission capacity, and power which need to be utilized eciently. In order to overcome these issues, we propose novel approaches for sensor node localization using range-based localization methodology in which maximum likelihood function based problem is formulated and later RSSI based distance measurement model is developed to minimize the error and find the current coordinates of the nodes. Energy-aware load balancing and security protocol are formulated which is called PEAR, and energy harvesting and energy management protocol is formulated to improve the network lifetime of Wireless Sensor Networks. This complete simulation study is carried out using MATLAB simulation tool, and we present a comparative experimental analysis performance of proposed approaches. The proposed models achieve better performance when compared with the existing models on node localization, load-balancing, security, and power utilization on improving network lifetime

2019-05-01T00:00:00Z C, Sarika http://localhost:8080/xmlui/handle/123456789/7076 2024-12-19T03:44:42Z 2017-04-01T00:00:00Z

Title: Studies on enzyme based biosensor for analysis of phenolic compounds in industry COMPOUNDS IN INDUSTRY EFFLUENTS Authors: C, Sarika Abstract: Phenolic compounds (PCs) present in industry effluents badly affects human as well as aquatic life when it directly disposed off to water bodies as they are major pollutants capable of causing severe health hazards. PCs are carcinogenic in nature and they are equipped for meddling with the endocrine framework modulating it or imitating normal hormones hence are considered as dangerous compounds. So the determination of PCs in environmental samples and industry effluents has turned into a matter of concern. Conventional techniques used for the analysis of mono and poly phenols include HPLC, GC and spectrophotometry. However these instruments are highly advanced, costly and involve complex pre-treatment steps and tedious detection processes. These disadvantages can be overcome by the use of biosensors as they are simple to operate, easy to miniaturize, inexpensive and are highly specific and selective. Encouraged by the continuing research work on enzyme based biosensors to detect phenolics, it was felt to develop a cost effective amperometric principle based detector system and explore some new research work using various immobilizations and immobilization using different PBSAs on different support materials such as, polymer membranes and MONCs

2017-04-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/7075 2024-12-19T03:45:42Z 2022-02-01T00:00:00Z

Title: Performance analysis of supervised classification algorithms for non parametric skin detection Abstract: Skin segmentation is a pre-processing step in any application involving the detection of face, biometrics, gesture recognition, objectionable image blocking, human computer interaction and other recognition systems with pre-processing element being skin. The challenges in skin segmentation process are background blend, illumination variation, variation in skin tone and occlusion. The aim of this research is to analyse the performance parameters of supervised nonparametric skin classification algorithms. To achieve this, two algorithms are designed and twenty-two parameters are calculated in each case to qualitatively support the design. To address the challenges in skin detection system, Kendall Estimated Skin-Segmentation based on Heuristic Ashta Vyshistya Approach (KESHAVA) is designed. In this approach, 96 features are extracted from the input image and 12 sets are formed with initial set containing 8 features and subsequent set consisting of summation of 8 new features to the existing set. The 8 features are selected according to Kendall coefficient based on dominating performance providers. The KESHAVA algorithm is used in Classification of Human-skin Ensembles using Nonparametric necessary-feature based Neural network Algorithm (CHENNA). Supervised feature selection approach is used for classification of skin pixels and semantic segmentation network is used for training, testing and validation. Semantic segmentation network for segmenting the skin region in input images is proposed that involves minimum number of steps to arrive at the skin-segmented output. KESHAVA algorithm is tested on Pratheepan dataset and CHENNA is trained on Pratheepan dataset and tested on Pratheepan dataset, Compaq dataset, SFA dataset and Schummage dataset that are publicly available. The positive effect of the results on the qualitative and quantitative success parameters is promising

2022-02-01T00:00:00Z