Browsing by Author "Kumar, N.K."

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A novel optimal fuzzy system for color image enhancement using bacterial foraging
(Institute of Electrical and Electronics Engineers Inc., 2009) Hanmandlu, M.; Verma, O.P.; Kumar, N.K.; Kulkarni, M.
A new approach is presented for the enhancement of color images using the fuzzy logic technique. An objective measure called exposure has been defined to provide an estimate of the underexposed and overexposed regions in the image. This measure serves as the dividing line between the underexposed and overexposed regions of the image. The hue, saturation, and intensity (HSV) color space is employed for the process of enhancement, where the hue component is preserved to keep the original color composition intact. A parametric sigmoid function is used for the enhancement of the luminance component of the underexposed image. A power-law operator is used to improve the overexposed region of the image, and the saturation component of HSV is changed through another power-law operator to recover the lost information in the overexposed region. Objective measures like fuzzy contrast and contrast and visual factors are defined to make the operators adaptive to the image characteristics. Entropy and the visual factors are involved in the objective function, which is optimized using the bacterial foraging algorithm to learn the parameters. Gaussian and triangular membership functions (MFs) are chosen for the underexposed and overexposed regions of the image, respectively. Separate MFs and operators for the two regions make the approach universal to all types of contrast degradations. This approach is applicable to a degraded image of mixed type. On comparison, this approach is found to be better than the genetic algorithm (GA)-based and entropy-based approaches. © 2009 IEEE.
A novel optimal fuzzy system for color image enhancement using bacterial foraging
(2009) Hanmandlu, M.; Verma, O.P.; Kumar, N.K.; Kulkarni, M.
A new approach is presented for the enhancement of color images using the fuzzy logic technique. An objective measure called exposure has been defined to provide an estimate of the underexposed and overexposed regions in the image. This measure serves as the dividing line between the underexposed and overexposed regions of the image. The hue, saturation, and intensity (HSV) color space is employed for the process of enhancement, where the hue component is preserved to keep the original color composition intact. A parametric sigmoid function is used for the enhancement of the luminance component of the underexposed image. A power-law operator is used to improve the overexposed region of the image, and the saturation component of HSV is changed through another power-law operator to recover the lost information in the overexposed region. Objective measures like fuzzy contrast and contrast and visual factors are defined to make the operators adaptive to the image characteristics. Entropy and the visual factors are involved in the objective function, which is optimized using the bacterial foraging algorithm to learn the parameters. Gaussian and triangular membership functions (MFs) are chosen for the underexposed and overexposed regions of the image, respectively. Separate MFs and operators for the two regions make the approach universal to all types of contrast degradations. This approach is applicable to a degraded image of mixed type. On comparison, this approach is found to be better than the genetic algorithm (GA)-based and entropy-based approaches. � 2009 IEEE.
Pod Scheduling and Proactive Resource Management in an Edge Cluster using MCDM and Federated Learning
(Springer Science and Business Media B.V., 2025) Kumar, N.K.; B, A.; J, H.; Srinivasan, S.; Sand, S.S.
Edge computing, which locates computational resources closer to the data sources, has become crucial in meeting the demands of applications that need high bandwidth and low latency. To cater to edge computing scenarios, KubeEdge, an extension of Kubernetes(K8s), expands its capabilities to meet edge-specific requirements such as limited resources, irregular connections, and heterogeneous environments. Edge trace data cannot be shared between cloud providers because of privacy issues, which makes generic distributed training ineffective. However, even with edge computing’s potential advantages, the built-in scheduling algorithms have several drawbacks. A significant problem is the lack of efficient resource management and allocation mechanisms at the edge, which causes edge nodes to be underutilized or overloaded which leads to violation of Quality of Service(QoS) and inefficient utilization of resources leads to Service Level Agreement(SLA) violations. In this regard, VIKOR and ELECTRE III based pod scheduling strategy is proposed in this paper and evaluated using Wikipedia and NASA server workload. The experimental results shows that 50% reduction in standard deviation for ELECTRE III and 40% reduction in standard deviation for VIKOR against default scheduler of Kubernetes. The average response time of 30.6593ms and 31.8803ms is achieved for Electre III and VIKOR for Wikipedia dataset. A proactive resource management system is proposed for KubeEdge containerized services where it incorporates a federated learning framework to predict future workloads using the Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU). The experimental comparison of federated learning shows 99.65%, 98.64% reduction in MSE for CPU utilization % and 89.72%, 76.57% reduction in MSE for Memory utilization % with respect to GRU and BI-LSTM models in contrast to centralized learning. The proposed approach effectiveness is evaluated through statistical techniques and found significant. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.