In this paper, we consider the problem of reactive collision avoidance for objects that have elongated and/or non-convex shapes. The literature largely models objects using spheres, but this can either make the avoidance maneuvers over conservative (when a single large sphere is used to approximate each object) or lead to increased computational complexity (when multiple smaller spheres are used to approximate each object). We therefore model such objects using quadric surfaces that may include suitable combinations of ellipsoids, paraboloids and one-sheeted/two-sheeted hyperboloids. We present a method to construct the 3D collision cones for such object shapes using tools from projective geometry, as well as provide an analytical characterization of the 3σ bounds of the collision cone that account for noisy measurements. These are then used to devise suitable avoidance strategies. Computational analysis and simulation results are presented to demonstrate the efficacy of the methodology.

Abstract The present era is at the peak of technological advancement in image and video processing techniques, with user-friendly accessible tools/techniques. This immersive technology development makes video forensics enormously challenging. Specifically, the passive approaches to object-based forgeries in videos are crucial for legal and judicial matters. Hence, to ensure the integrity of the videos, a scientific, statistical and passive investigation of videos is required to maintain the spatial and temporal information content. This paper aims to develop a passive approach for digging out the forgery traces by applying the motion residue windowing technique for object removal forgery in surveillance videos. The novel max averaging windowing techniques improve visual imprints of the object removal forgery in the videos from the existing methods in the literature. A deep learning approach is the next step for achieving forgery detection in surveillance videos. The proposed lightweight depth-separable layer-optimized CNN has fast execution speed, optimized in terms of parameters without compromising the desired accuracy. This network is trained at a frame level with 98.60% testing accuracy, followed by a pipeline architecture of the proposed model for detection of forgery at video level with 99.01% accuracy. The suggested model works better than current models regarding post-processing operations, compression rates, forged video detection accuracy, precision, recall and F1 score.

Abstract Nowadays, the whole world is covered with the revolution of digital information so that digital data are easy to transfer, access and process. Modern image processing tools are used to create forged images; by using these tools, forgers introduced forged region in the original image to spread the rumor in the public. Now there is a challenge for the forensic department to prove the authenticity of the original image. So there is required a new efficient copy-move tamper detection method. To fulfill this requirement, a new computational efficient method for copy-move forgery detection using firefly algorithm is proposed.

Abstract In this paper, we have proposed a computationally efficient algorithm to detect splicing (copy-create) forgery, our proposed method is developed by using differential evolution and wavelet decomposition, the differential evolution algorithm automatically generates customized parameter values of tampered images, and wavelet decomposition is used to process large-size images under block-based framework. Our proposed method is resilient to distortions, such as the addition of Gaussian noise, scaling and compression of the forged images.

Power efficient operation is very critical in energy constrained multi-hop wireless networks. One important technique is to intelligently assign transmission powers to nodes while maintaining network connectivity. Previous work has focused on assigning a single transmission power to each node. This often leads to "power imbalance", where some nodes use much more power than other nodes. This can reduce network lifetime. In this paper, we investigate the problem of two power assignments where nodes alternate the use of these assigned powers. We rigorously formulate the problem of two power assignment under the constraint that the network connectivity is maintained. The objective here is to minimize the maximum average power used by the nodes. We show that, in general, the problem is not just NP-hard but also hard to approximate. We then propose a distributed localized heuristic to compute the two power assignments. We perform extensive simulations to show that the algorithm can reduce the average power significantly when compared with algorithms that assign a single power. By assuming some properties on radio propagation, we also present a centralized algorithm with bounded worst case guarantees for the two power assignment problem.

In this paper, we present an algorithm for intra-domain traffic engineering. We assume that the traffic matrix, which specifies traffic load between every source-destination pair in the network, is unknown and varies with time, but that always lies inside an explicitly defined region. Our goal is to compute a fixed robust routing with best worst case performance for all traffic matrices inside the bounding region. We formulate this problem as a semi-infinite programming problem. Then, we focus on a special case with practical merits, where (1) the traffic matrix region is assumed to be a polytope specified by a finite set of linear inequalities, and (2) our objective is to find the routing that minimizes the maximum link utilization. Under these assumptions, the problem can be formulated as a polynomial size linear programming (LP) problem with finite number of constraints. We further consider two specific set of constraints for the traffic matrix region. The first set is based on the hose model and limits the total traffic rate of network point of presence (PoP) nodes. The second set is based on the pipe model and limits the traffic between source-destination pairs. We study the effectiveness of each set of constraints using extensive simulations.

We consider the problem of joint routing, scheduling and transmission power assignment in multi-hop wireless mesh networks with unknown traffic. We assume the traffic is unknown, but the traffic matrix, which specifies the traffic load between every source-destination pair in the network, always lies inside a polytope defined by hose model constraints. The objective is to minimize the maximum of the total transmission power in the network over all traffic matrices in a given polytope. We propose efficient algorithms that compute a two-phase routing, schedule and power assignment, and prove the solution to be 3-approximation with respect to an optimal two-phase routing, scheduling and power assignment. We show via extensive simulations that the proposed algorithm has good performance at its worst operating traffic compared to an algorithm optimized for that traffic.

We consider the problem of finding a single-path intra-domain routing for time-varying traffic. We characterize the traffic variations by a finite set of traffic profiles with given non-zero fractions of occurrence. Our goal is to optimize the average performance over all of these traffic profiles. We solve the optimal multi-path version of this problem using linear programming and develop heuristic single-path solutions using randomized rounding and iterated rounding. We analyze our single-path heuristic (finding the optimal single-path routing is NP-hard), and prove that the randomized rounding algorithm has a worst case performance bound of O(log(KN)/log(log(KN))) compared to the optimal multi-path routing with a high probability, where K is the number of traffic profiles, and N the number of nodes in the network. Further, our simulations show the iterated rounding heuristics perform close to the optimal multi-path routing on a wide range of measured ISP topologies, in both the average and the worst-case. Overall, these results are extremely positive since they show that in a wide-range of practical situations, it is not necessary to deploy multi-path routing; instead, an appropriately computed single-path routing is sufficient to provide good performance.

We propose a framework for providing instantaneous backup to traffic in a hybrid RF/FSO mesh network. Free space optical (FSO) links have high bandwidth and security, making them suitable for use in backbone networks. RF links have low bandwidth, but offer high reliability in conditions where FSO links are obscured. Thus, RF links are primarily used to provide instantaneous backup to traffic flowing on FSO links. We propose a framework to model FSO link failures due to obscuration, and propose algorithms for integrated topology control of RF links and routing for maximizing the backup provided. We do extensive simulations to demonstrate that the performance of RF topology and routing computed using our algorithms is much better than the case of having the same RF and FSO topologies and routes.

Sensors typically use wireless transmitters to communicate with each other. However, sensors may be located in a way that they cannot even form a connected network (e.g, due to failures of some sensors, or loss of battery power). In this paper we consider the problem of adding the smallest number of additional (relay) nodes so that the induced communication graph is 2-connected. The problem is NP -hard. In this paper we develop O(1)-approximation algorithms that find close to optimal solutions in time O((kn)) for achieving k-edge connectivity of n nodes. The worst case approximation guarantee is 10, but the algorithm produces solutions that are far better than this bound suggests. We also consider extensions to higher dimensions, and the scheme that we develop for points in the plane, yields a bound of 2dMST where dMST is the maximum degree of a minimum-degree Minimum Spanning Tree in d dimensions using Euclidean metrics. In addition, our methods extend with the same approximation guarantees to a generalization when the locations of relays are required to avoid certain polygonal regions (obstacles). We also prove that if the sensors are uniformly and identically distributed in a unit square, the expected number of relay nodes required goes to zero as the number of sensors goes to infinity.

Wireless optical networks are being increasingly used in the backbone of hierarchical ad hoc networks. We consider the problem of minimizing the congestion in wireless optical (FSO) backbone networks by placing controllable relay nodes. We propose algorithms for placement of relays in the network under node interface constraints. The interfaces at each backbone node are limited, thus limiting the number of neighbors a node can have. We come up with algorithms to formulate the problem as a constrained knapsack problem, and propose algorithms to solve it. We use the mathematical technique of rollout to achieve better performance than the heuristics. We show by simulations that our algorithms significantly outperform some greedy algorithms, and a small number of relay nodes (when placed using our algorithms) can lead to a significant reduction in the congestion in the network

We provide a routing framework for hybrid RF/FSO backbone networks, utilizing the characteristics of RF and free space optical (FSO) links. FSO links offer higher bandwidth and security, while RF links offer more reliability. We propose the concept of criticality index for different classes of traffic and providing obscuration-tolerant paths to the traffic in a weighted max-min fair way. We provide an optimal algorithm for the case where a traffic demand can be routed along multiple paths. The problem of routing unsplittable traffic is NP-hard, so we propose efficient heuristics for routing them. We do extensive simulations to demonstrate that our algorithms outperform the algorithms currently in use.

We consider a mobile backbone network with free space optical point-to-point links. Requests for aggregate bandwidth between pairs of backbone nodes arrive one-by-one, and a bandwidth guaranteed connection is established if there are sufficient network resources; otherwise, the request is rejected. In addition to the ordinary backbone nodes, there are a limited number of controllable mobile agents; these are nodes that may be located as desired to optimize the performance of the network. The specific problem we consider is that of determining the positions for the mobile agents to maximize the minimum max How over all source-destination pairs. We show that this problem is NP-hard. We then develop a heuristic algorithm to locate the agents based on the current network state. It is shown that by using the algorithm to strategically place a limited number of agents, a significant improvement in network performance can be achieved.

In networks with mobile nodes, power consumption is an important constraint due to the limited battery power available at the mobile nodes. Reduction of power consumption in mobile devices can be achieved by keeping these devices in a low power (standby) mode during periods of inactivity. This may, however, result in increased end-to-end delays leading to violation of the quality of service (QoS) parameters. We propose medium access control (MAC) scheduling policies for centrally controlled TDD wireless systems. The proposed policies reduce the power consumption of mobile devices by putting them into low power mode intelligently, using probabilistic estimates of inactivity based on the previous traffic arrival pattern. The policies ensure that the QoS parameters such as end-to-end packet delays are not violated.