RLPAS: Reinforcement Learning-based Proactive Auto-Scaler for Resource Provisioning in Cloud Environment

Mobile Networks and Applications - Public cloud system offers Infrastructure-as-a-Service (IaaS) to deliver the computational resources on demand. Resource requirements of a cloud environment are...     

A novel Rotational Symmetry Dynamic Texture (RSDT) based sub space construction and SCD (Similar-Congruent-Dissimilar) based scoring model for background subtraction in real time videos

Multimedia Tools and Applications - Background Subtraction (BS) plays an important role in video surveillance system because it provides a focus of attention for moving object detection. But there...     

A Novel Video Coding Framework with Tensor Representation for Efficient Video Streaming

Video compression is one among the pre-processes in video streaming. While capturing moving objects with moving cameras, more amount of redundant data is recorded along with dynamic change. In this paper, this change is identified using various geometric transformations. To register all these dynamic relations with minimal storage, tensor representation is used. The amount of similarity between the frames is measured using canonical correlation analysis (CCA). The key frames are identified by comparing the canonical auto-correlation analysis score of the candidate key frame with CCA score of other frames. In this method, coded video is represented using tensor which consists of intra-coded key frame, a vector of P frame identifiers, transformation of each variable sized block and information fusion that has three levels of abstractions: measurements, characteristics and decisions that combine all these factors into a single entity. Each dimension can have variable sizes which facilitates storing all characteristics without missing any information. In this paper, the proposed video compression method is applied to under-water videos that have more redundancy as both the camera and the underwater species are in motion. This method is compared with H.264, H.265 and some recent compression methods. Metrics like Peak Signal to Noise Ratio and compression ratio for various bit rates are used to evaluate the performance. From the results obtained, it is obvious that the proposed method performs compression with a high compression ratio, and the loss is comparatively less.

     

Novel directional patterns and a Generalized Supervised Dimension Reduction System (GSDRS) for facial emotion recognition

This paper presents two novel directional patterns, a Maximum Response-based Directional Texture Pattern (MRDTP) and a Maximum Response-based Directional Number Pattern (MRDNP), for recognizing the facial emotions in constrained as well as unconstrained situations. The intensity information obtained from the maximum of the edge responses, after applying eight Kirsch masks, is used for the calculation of facial features in MRDTP. In MRDNP, instead of intensity information, the direction number of the maximum response is used. After dividing MRDNP and MRDTP code images into grids, feature vectors are created from the concatenated histograms obtained from the grids. This paper also proposes an effective Generalized Supervised Dimension Reduction System (GSDRS) and uses Extreme Learning Machine with Radial Basis Function (ELM-RBF) classifier for rapid and efficient classification of emotions. Both the proposed patterns are more effective than the existing ones in removing random noise and providing good structural information using prominent edges which help to achieve high classification accuracy when tested with seven datasets.     

Efficient background subtraction for thermal images using reflectional symmetry pattern (RSP)

Multimedia Tools and Applications - Nowadays, thermal image processing has gained more attention. Thermal camera’s cost is decreasing, and so many real-time applications use thermal cameras...     

A novel framework for real-time diseased coral reef image classification

Multimedia Tools and Applications - The new challenge in image processing is in processing submarine coral reef images. The coral reef disease classification from such submarine coral reef images...     

Performance Improvement of MapReduce for Heterogeneous Clusters Based on Efficient Locality and Replica Aware Scheduling (ELRAS) Strategy

MapReduce is a parallel programming model for processing the data-intensive applications in a cloud environment. The scheduler greatly influences the performance of MapReduce model while utilized in heterogeneous cluster environment. The dynamic nature of cluster environment and computing workloads affect the execution time and computational resource usage in the scheduling process. Further, data locality is essential for reducing total job execution time, cross-rack communication, and to improve the throughput. In the present work, a scheduling strategy named efficient locality and replica aware scheduling (ELRAS) integrated with an autonomous replication scheme (ARS) is proposed to enhance the data locality and performs consistently in the heterogeneous environment. ARS autonomously decides the data object to be replicated by considering its popularity and removes the replica as it is idle. The proposed approach is validated in a heterogeneous cluster environment with various realistic applications that are IO bound, CPU bound and mixed workloads. ELRAS improves the throughput by a factor about 2 as compared with the existing FIFO and it also yields near optimal data locality, reduce the execution time, and effective utilization of resources. The simplicity of ELRAS algorithm proves its feasibility to adopt for a wide range of applications.