PSNR Analysis of video transmission in VANETS, using NS2 and Evalvid Framework

International audienceRecent developments in wireless technologies and the Internet of Things have led to the design of new communication systems, such as ad-hoc vehicle networks (VANETs) [1]. These networks are expected to offer new entertainment applications and traffic security services with intelligent equipment in a smart environment. A smart city is designed as a controlled and monitored environment using advanced technologies and new types of communication to improve the quality of life through innovative services. One of the key challenges of smart city communication is ensuring effective service delivery in economic, social and environmental conditions. As a result, multimedia communications, including streaming video, should be very beneficial for traffic management as well as for the provision of entertainment and advertising services. With video streaming services, real-time information will be used and provided by vehicle networks for safety and efficiency purposes. In this article, we focus on studying the effect of streaming low brightness video feeds on PSNR

DTMF and CLIP decoding in a noisy area using adaptive approach

Multi-frequency signals are greatly used in telecommunication fields. Signaling and speech are such an example of multi-frequency signals exchanging through the telecommunication networks. Extracting the frequencies embedded in these signals is very useful for a lot of operations: like filtering, decoding, compressing….We propose in this paper adaptive technique to process in real time multi-frequency signals and extracting the frequencies that they contain. Keywords: DTMF, CLIP, Noise, Adaptive, Real Tim

Multi-Objective Optimal Dispatching and Operation Control of a Grid Connected Microgrid Considering Power Loss of Conversion Devices

This paper proposes a novel daily energy management system for optimization dispatch and operation control of a typical microgrid power system. The multi-objective optimization dispatch problem is formulated to simultaneously minimize the operating cost, pollutant emission level as well as the power loss of conversion devices. While satisfying the system load and technical constraints, ensure high penetration of renewable energy and optimal scheduling of charging/discharging of battery storage system based on a fuzzy logic approach. The weighted sum method is adopted to obtain Pareto optimal solutions, then a fuzzy set theory is employed to find the best compromise solution. Ant lion optimizer method is considered to solve the formulated problem. To prove the efficacy and robustness of the proposed algorithm, a comparison of the performance of ant lion optimizer algorithm with other known heuristic optimization techniques has been investigated. The results obtained show that the proposed algorithm outperforms the other heuristic techniques in solving the multi-objective optimization dispatch problem. They also reveal that a better compromise between the considered contradictory objective functions is achieved when priority is given to the generation of the internal microgrid’s sources with an equivalent contribution rate of 68.45% of generated power from both fuel cell and micro-turbine, whereas the contribution rate of external grid is limited to 11.72%

Genetic Algorithm based Model Predictive Control of Fractional Order Systems

International audienceFractional calculus is a generalization of integration and derivative operators to no integer order. In the Last decades fractional systems receives a great attention in the research community, the reason lies in the fact that many real systems can be described accurately using dynamic models of fractional order. In our work a design of genetic algorithm based fractional order model predictive controller is simulated and applied to a given fractional order model plant

Impact of RPL objective functions on energy consumption in Ipv6 based wireless sensor networks

International audienceWith the arrival of the internet of things concept, a new vision of wireless sensor networks has been adopted allowing them to be addressed with ipv6 addresses, thus forming 6LowPAN networks. It is categorized as a new technology being developed and improved. This causes issues about the networks performance to create the communication path and collecting data. Therefore, IETF has proposed an IPv6 based routing protocol with low cost and power constraints RPL that builds a Destination Oriented Directed Acyclic Graph (DODAG) based on a set of metrics and constraints via a specific Objective Functions (OFs). This objective function selects the best parents and construct the routes. Our research is focus on performance analysis of two objective functions that are Minimum Rank with Hysteresis Objective Function (MRHOF) and Objective Function Zero (OFO) in a small area under a large scenarios and topologies. This comparison is focused on energy consumption of the network in the given scenrios to distinguish which objective function is the most optimal to guarantee long life expectancy of the sensor networks especially in static environment

A New Objective Function Based on Additive Combination of Node and Link Metrics as a Mechanism Path Selection for RPL Protocol

Since its development by IETF, the IPv6 routing protocol for low power and lossy networks (RPL) remains the subject of several researches. RPL is based on objective function as a mechanism selection of paths in the network. However, the default objective functions standardized selects the routes according to a single routing metric that leads to an unoptimized path selection and a lot of parent changes. Thus, we propose in this paper weighted combined metrics objective function (WCM-OF) and non-weighted combined metrics objective function (NWCM-OF) that are based both on additive link quality and energy metrics with equal weights or not to achieve a tradeoff between reliability and saved energy levels. The proposed objective functions were implemented in the core of Contiki operating system and evaluated with Cooja emulator. Results show that the proposed objective functions improved the network performances compared to default objective functions

Video retrieval with CNN features

International audienceConvolutional neural network features are becoming the norm in instance retrieval. This work investigate the relevance of using an of the shelf object detection network like Faster R-CNN as a feature extractor. We build an Image-to-video face retrieval pipeline composed of filtering and re-ranking that uses the objects proposals learned by a Region Proposal Network (RPN) and their associated representations taken from a CNN. Moreover we study the relevance of features from a finetuned network. The results obtained are very promisin

Street crossing pedestrian detection system A comparative study of descriptor and classification methods

International audiencein recent years, the number of people killed on roads has increased enormously, several pedestrian detection techniques in monocular images have been proposed to address this problem. We present our pedestrian protection system from moving vehicles using video cameras installed on the vehicle, this system combines pedestrian detection, trajectory estimation, risk evaluation, and driver alert. First, we focus on the pedestrian recognition task. Different combinations of image descriptors and classification methods have been evaluated on this task. Experiments are performed on a dataset captured on-board a vehicle driving through urban environments. Results show that the best model is HOG&RbfSVM

Hybrid multiple watermarking technique for securing medical images of modalities MRI, CT scan, and X-ray

In order to contribute to the security of sharing and transferring medical images, we had presented a multiple watermarking technique for multiple protections; it was based on the combination of three transformations: the discrete wavelet transform (DWT), the fast Walsh-Hadamard transform (FWHT) and, the singular value decomposition (SVD). In this paper, three watermark images of sizes 512x 512 were inserted into a single medical image of various modalities such as magnetic resonance imaging (MRI), computed tomography (CT), and X-Radiation (X-ray). After applying DWT up to the third level on the original image, the high-resolution sub-bands were being selected subsequently to apply FWHT and then SVD. The singular values of the three watermark images were inserted into the singular values of the cover medical image. The experimental results showed the effectiveness of the proposed method in terms of quality and robustness compared to other reported techniques cited in the literature

Performance analysis of direction of arrival algorithms for Smart Antenna

This paper presents the performance analysis of the direction of arrival estimation algorithms such as Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), Multiple Signal Classification (MUSIC), Weighted Subspace Fitting (WSF), The Minimum Variance Distortionless Response (MVDR or capon) and beamspace. These algorithms are necessary to overcome the problem of detecting the arrival angles of the received signals in wireless communication. Therefore, these algorithms are evaluated and compared according to several constraints required in smart antenna system parameters, as the number of array elements, number of samples (snapshots), and number of the received signals. The main purpose of this study is to obtain the best estimation of the direction of arrival, which can be perfectly implemented in a smart antenna system. In this context, the ROOT-Weighted Subspace Fitting algorithm provides the most accurate detection of arrival angles in each of the proposed scenarios