An extended KCF tracking algorithm based on TLD structure in low frame rate videos

The KCF (Kernelized Correlation Filter) algorithm achieved a good performance on target tracking challenges. However, it still has some defects and problems of false tracking in low frame rate (LFR) scenarios, target scale variation, occlusion and out of view target, that exists in the correlation filter based methods. In this paper, we overcome the shortcomings of KCF tracking algorithm based on Tracking-Learning-Detection (TLD) framework. The proposed algorithm trained two classifiers simultaneously, based on semi supervised co-training learning algorithm. Then, we comparatively evaluate the proposed method on TB-100 datasets by other trackers. The experimental results demonstrate that the precision and robustness of the improved tracking algorithm is higher than traditional KCF, TLD and the other top state-of-the-art tracking algorithms in LFR videos.

     

An Improved UTD Based Model for the Multiple Building Diffraction of Plane Waves in Urban Environments by Using Higher Order Diffraction Coefficients

This paper describes an improved model for multiple building diffraction modeling based on the uniform theory of diffraction. A well-known problem in conventional uniform theory of diffraction (CUTD) is multiple edge transition zone diffraction. Here, higher order diffracted fields are used in order to improve the result. Hence, we use higher order diffraction coefficients to improve a hybrid physical optics (PO)-CUTD model, the results show that the new model can correct errors of the PO-CUTD model. Therefore, the proposed model can find application in the development of theoretical models to predict more realistic path loss in urban environments, when multiple building diffraction is considered.     

Optimized Reliable Data Combining Cooperative Spectrum Sensing Method in Cognitive Radio Networks

Spectrum sensing is one of the most important tasks of each cognitive radio network. Cooperation among secondary users, by increasing the sensing accuracy can improve the network throughput, but also increases the energy consumption of cognitive radio network. In this paper, we propose the reliable data combining method for cooperative spectrum sensing, according to which the fusion center by using two threshold values, makes the final decision only if it is confident enough in validity of received local data. Otherwise, an additional sensing will be performed. Throughput maximization problems under interference constraints are formulated for both soft and hard fusion schemes and the optimal sensing time and threshold values are obtained. Simulations show that for all SNRs, higher absolute throughput and also higher throughput per energy consumption are accessible, rather than conventional cooperative sensing. Moreover, for a large range of SNRs the less energy is consumed.     

Level-direction decomposition analysis with a focus on image watermarking framework

This research addresses the new level-direction decomposition in the area of image watermarking as the further development of investigations. The main process of realizing a watermarking framework is to generate a watermarked image with a focus on contourlet embedding representation. The approach performance is evaluated through several indices including the peak signal-to-noise ratio and structural similarity, whereby a set of attacks are carried out using a module of simulated attacks. The obtained information is analyzed through a set of images, using different color models, to enable the calculation of normal correlation. The module of the inverse of contourlet embedding representation is correspondingly employed to obtain the present watermarked image, as long as a number of original images are applied to a scrambling module, to represent the information in disorder. This allows us to evaluate the performance of the proposed approach by analyzing a complicated system, where a deci-sion making system is designed to find the best level and the corresponding direction regarding contourlet embedding represen-tation. The results are illustrated in appropriate level-direction decomposition. The key contribution lies in using a new integration of a set of subsystems, employed based upon the novel mechanism in contourlet embedding representation, in association with the decision making system. The presented approach is efficient compared with state-of-the-art approaches, under a number of serious attacks. A number of benchmarks are obtained and considered along with the proposed framework outcomes. The results support our ideas.     

An Agile Adaptive Clustering Algorithm for Wireless Sensor Networks Considering Energy Constraint

Wireless Personal Communications - Security in vehicular ad hoc network (VANET) is one of the major challenging topics and the secure key interchange between two legitimate vehicles is an important...     

Providing a New Way for Sustainable Communication on the VANET Network on Mountainous Roads

Wireless Personal Communications - VANET networks are classified as one of the applications of mobile case networks designed to improve road safety and provide passengers with comfort. Vehicle case...     

QoS-aware multi-path video streaming for urban VANETs using ACO algorithm

Drivers can be provided with several beneficial services associated with video streaming in a vehicular ad-hoc network (VANET). Given the dynamic topology and high mobility of VANETs, a single path cannot support the required quality of service (QoS). To maximize global QoS metrics, a two-path model is proposed based on a disjoint algorithm to forward sub-streams over diverse paths from the transmitter to the receiver vehicle. In this solution, the video information spread in separate paths is categorized based on their priority. For this purpose, the protocol for transmitting each kind of video data should be selected cautiously. The present study aims to propose an ant colony optimization-based technique to establish the primary and secondary paths and enhance the QoS of routing paths. To achieve this goal, the QoS routing issue is formulated mathematically as a problem of constrained optimization. Moreover, to achieve high-quality video streaming, inter-frames are transmitted over the user datagram protocol and intra-frames are transmitted over the transmission control protocol (TCP). TCP transmission delays are also minimized using a TCP-ETX algorithm for selecting appropriate paths. According to the simulation results, the proposed two-path solution can be used to improve the quality of video streaming and to enhance the performance in terms of end-to-end delay, packet delivery ratio, and overhead. In this way, the proposed method can outperform several prominent routing algorithms such as adaptive QoS-based routing for VANETs, geographic source routing (GSR), intersection-based geographical routing protocol, and efficient GSR.

     

A New QoS Adaptive Multi-path Routing for Video Streaming in Urban VANETs Integrating Ant Colony Optimization Algorithm and Fuzzy Logic

Wireless Personal Communications - The multi-path transmission is an appropriate transmission method for high data rate packets like video streaming. To provide video streaming with high quality,...     

Information-Based Node Selection for Joint PCA and Compressive Sensing-Based Data Aggregation

Wireless Personal Communications - Recently it has been shown that when Principal Component Analysis is applied as a dictionary learning technique to Compressive Sensing-based data aggregation,...     

A novel cost optimization method for mobile cloud computing by dynamic processing of demands based on their power consumption

Mobile cloud computing (MCC) is an emerging technology that is introduced to combat the existing limitations in mobile computing such as constrained energy and storage. MCC enables mobile users to perform their tasks in the operator cloud and benefit from the offered services. On the other hand, operators are required to decrease their costs to stay in the competitive market. In this paper, we propose a method to reduce the cost of power consumption and increase the profit of 4G/5G network operators delivering MCC services. We propose an online method that is based on dynamic processing of mobile users’ demands based on their power consumption in the cloud, called Dyn-PDPC. In this algorithm, the power consumption of demands is estimated based on event counters, and demands are classified and processed accordingly. Unlike the offline methods, the proposed online method can be implemented with the existing information and there is no need for prior knowledge. We also present an extended version of Dyn-SP algorithm, in which we had an unrealistic assumption about the energy consumption of demands. In Dyn-PDPC, by using control parameters, when the electricity price is low, demands with high power consumption are processed, and then the low power-consumption demands are processed. Similarly, when the electricity price is high, demands with low power consumption are processed at first. Simulation results demonstrate that the proposed algorithm has more accuracy, and more reduction in long-term cost compared to other online methods in MCC networks.