International Journal of Computer and Applications

In the multimedia era a large volume of video data can be recorded during a certain period of time by multiple cameras. Such a rapid growth of video data requires both effective and efficient multiview video summarization techniques. The users can quickly browse and comprehend a large amount of audiovisual data. It is very difficult in real-time to manage and access the huge amount of video-content-handling issues of interview dependencies significant variations in illumination and presence of many unimportant frames with low activity. In this paper we propose a local-alignment-based FASTA approach to summarize the events in multiview videos as a solution of the aforementioned problems. A deep learning framework is used to extract the features to resolve the problem of variations in illumination and to remove fine texture details and detect the objects in a frame. Interview dependencies among multiple views of video are then captured via the FASTA algorithm through local alignment. Finally object tracking is applied to extract the frames with low activity. Subjective as well as objective evaluations clearly indicate the effectiveness of the proposed approach. Experiments show that the proposed summarization method successfully reduces the video content while keeping momentous information in the form of events. A computing analysis of the system also shows that it meets the requirement of real-time applications.

F-DES: Fast and Deep Event Summarization

A survey of teaching–learning-based optimization

While software defined network (SDN) brings more innovation to the development of future networks, it also faces a more severe threat from DDoS attacks. In order to deal with the single point of failure on SDN controller caused by DDoS attacks, we propose a framework for detection and defense of DDoS attacks in the SDN environment. Firstly, we deploy a trigger mechanism of DDoS attack detection on data plane to screen for abnormal flows in the network. Then, we use a combined machine learning algorithm based on K-Means and KNN to exploit the rate characteristics and asymmetry characteristics of the flows and to detect the suspicious flows determined by the detection trigger mechanism. Finally, the controller will take corresponding actions to defense against the attacks. In this paper, we propose a new framework of cooperative detection methods of control plane and data plane, which effectively improve the detection accuracy and efficiency, and prevent DDoS attacks on SDN.

A New Framework for DDoS Attack Detection and Defense in SDN Environment

New-flow based DDoS attacks in SDN: Taxonomy, rationales, and research challenges

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. It is a herculean task to manage access to video content in real time where humongous amount of audiovisual recorded data is generated every second. In this paper we propose an Eratosthenes Sieve based key-frame extraction approach for video summarization (VS) which can work better for real-time applications. Here, Eratosthenes Sieve is used to generate sets of all Prime number frames and nonprime number frames up to total N frames of a video. k-means clustering procedure is employed on these sets to extract the key–frames quickly. Here, the challenge is to find the optimal set of clusters, achieved by employing Davies-Bouldin Index (DBI). DBI a cluster validation technique which allows users with free parameter based VS approach to choose the desired number of key-frames without incurring additional computational costs. Moreover, our proposed approach includes likes of both local and global perspective videos. The method strongly enhances clustering procedure performance trough engagement of Eratosthenes Sieve. Qualitative and quantitative evaluation and complexity computation are done in order to compare the performances of the proposed model and state-of-the-art models. Experimental results on two benchmark datasets with various types of videos exhibit that the proposed methods outperform the state-of-the-art models on F-measure.

Eratosthenes sieve based key-frame extraction technique for event summarization in videos

Nowadays, the video surveillance systems may be omnipresent, but essential for supervision everywhere, e.g., ATM, airport, railway station and other crowded situations. In the multi-view video systems, various cameras are producing a huge amount of video content around the clock which makes it difficult for fast browsing, retrieval, and analysis. Accessing and managing such huge data in real time becomes a real challenging task because of inter-view dependencies, illumination changes and the bearing of many inactive frames. The work highlights an accurate and efficient technique to detect and summarize the event in multi-view surveillance videos using boosting, a machine learning algorithm, as a solution to the above issues. Interview dependencies across multiple views of the video are captured via weak learning classifiers in boosting algorithm. The light changes and still frames are tackled with moving an object in the frame by Deep learning framework. It helps to reach the correct decision for the active frame and inactive frame, without any prior information about the number of issues in a video. Target, as well as subjective ratings, clearly indicate the potency of our proposed DELTA model, where it successfully reduces the video data, while keeping the important information as events.

Deep Event Learning boosT-up Approach: DELTA

Software-defined networking (SDN) is a networking scenario which changes the traditional network architecture by bringing all control functionalities to a single location and making centralized decisions. Controllers are the brain of SDN architecture, which perform the control decision tasks while routing the packets. Centralized decision capability for routing enhances the network performance. Through this paper, we presented a review report on various available SDN controllers. Along with the SDN introduction, we discuss the prior work in the field. The review states how the centralized decision capability of the controller changes the network architecture with network flexibility and programmability. We also discuss the two categories of the controller along with some popular available controller. For each controller, we discuss the architectural overview, design aspects, and so on. We also evaluate the performance characteristics by using various metrics, such as throughput, response time, and so on. This paper points to the major state-of-the-art controllers used in industry and academia. Our review work covers major popular controllers used in SDN paradigm.

Controllers in SDN: A Review Report

In wireless sensor networks, sensors are expected to work autonomously for a long time with a finite source of energy. Therefore, the sensors must be energy efficient in their duties to prolong the network lifetime. Cluster-based protocols are widely used in the literature to prolong the network lifetime. Clustering protocols use cluster-head rotation (CH-rotation) and re-clustering methods to rotate the energy-intensive load of CH nodes among other nodes of the network. Re-clustering is a global method, so its overhead cost is higher compared with CH-rotation. Existing clustering protocols employ an energy threshold on the residual energy of the CH for CH-rotation. This energy threshold can be static or dynamic. The static threshold does not consider the current energy load of the CH for CH-rotation. Thus, it increases premature death of CH nodes. In this paper, a load-aware rotation of CH (LAR-CH) approach is proposed, which sets a dynamic threshold for CH-rotation to reduce the premature death of CH nodes. The LAR-CH uses the current energy load of the CH to determine the dynamic threshold for CH-rotation. The simulation results show that LAR-CH reduces the premature death of CH nodes by 40% compared with the low-energy adaptive clustering hierarchy protocol.

Deepti D. Shrimankar

Papers

F-DES: Fast and Deep Event Summarization

Eratosthenes sieve based key-frame extraction technique for event summarization in videos

Deep Event Learning boosT-up Approach: DELTA

Controllers in SDN: A Review Report

LAR-CH: A Cluster-Head Rotation Approach for Sensor Networks