Adaptive moving shadow detection and removal by new semi-supervised learning technique

The efficient application of current methods of shadow detection in video is hindered by the difficulty in defining their parameters or models and/or their application domain dependence. This paper presents a new shadow detection and removal method that aims to overcome these inefficiencies. It proposes a semi-supervised learning rule using a new variant of co-training technique for shadow detection and removal in uncontrolled scenes. The new variant both reduces the run-time through a periodical execution of a co-training process according to a novel temporal framework, and generates a more generic prediction model for an accurate classification. The efficiency of the proposed method is shown experimentally on a testbed of videos that were recorded by a static camera and that included several constraints, e.g., dynamic changes in the natural scene and various visual shadow features. The conducted experimental study produced quantitative and qualitative results that highlighted the robustness of our shadow detection method and its accuracy in removing cast shadows. In addition, the practical usefulness of the proposed method was evaluated by integrating it in a Highway Control and Management System software called RoadGuard.     

On line background modeling for moving object segmentation in dynamic scenes

Fast and accurate moving object segmentation in dynamic scenes is the first step in many computer vision applications. In this paper, we propose a new background modeling method for moving object segmentation based on dynamic matrix and spatio-temporal analyses of scenes. Our method copes with some challenges related to this field. A new algorithm is proposed to detect and remove cast shadow. A comparative study by quantitative evaluations shows that the proposed approach can detect foreground robustly and accurately from videos recorded by a static camera and which include several constraints. A Highway Control and Management System called RoadGuard is proposed to show the robustness of our method. In fact, our system has the ability to control highway by detecting strange events that can happen like vehicles suddenly stopped in roads, parked vehicles in emergency zones or even illegal conduct such as going out from the road. Moreover, RoadGuard is capable of managing highways by saving information about the date and time of overloaded roads.     

Supervisory control based on minimal cuts and Petri net sub-controllers coordination

This paper addresses the synthesis of Petri net (PN) controller for the forbidden state transition problem with a new utilisation of the theory of regions. Moreover, as any method of control synthesis based on a reachability graph, the theory of regions suffers from the combinatorial explosion problem. The proposed work minimises the number of equations in the linear system of theory of regions and therefore one can reduce the computation time. In this paper, two different approaches are proposed to select minimal cuts in the reachability graph in order to synthesise a PN controller. Thanks to a switch from one cut to another, one can activate and deactivate the corresponding?PNcontroller. An application is implemented in a flexible manufacturing system to illustrate the present method. Finally, comparison with previous works with experimental results in obtaining a maximally permissive controller is presented.     

A four year study to determine the optimal harvesting period for Tunisian Chemlali olives

Due to the importance of the stage of maturity on several olive pomological parameters and oil quality indices, and to the interest that Tunisian olive oil production has recently received, the optimal harvesting period for the main Tunisian olive cultivar, Chemlali, was assessed. For the first time, a four‐season crops study, carried out in three representative geographical areas, was focused on both olive fruit pomological parameters and oil chemical composition at different stages of maturity. The stage of maturity was the factor showing the highest influence on the major part of these parameters, followed by the crop year. To guarantee a reasonable fat content and a good chemical quality of oil, in particular a harmonious acidic composition, acceptable UV absorbance, and a content high of antioxidants, unsaponifiable matter and sterols, the optimal harvesting period for Chemlali olives would appears to be between the end of November to the middle of December, which corresponds to a maturity index between 2.5 and 3.5.     

BER and Outage Probability Approximations for LMMSE Detectors on Correlated MIMO Channels

This paper is devoted to the study of the performance of the linear minimum mean-square error (LMMSE) receiver for (receive) correlated multiple-input multiple-output (MIMO) systems. By the random matrix theory, it is well known that the signal-to-noise ratio (SNR) at the output of this receiver behaves asymptotically like a Gaussian random variable as the number of receive and transmit antennas converge to +infin at the same rate. However, this approximation being inaccurate for the estimation of some performance metrics such as the bit error rate (BER) and the outage probability, especially for small system dimensions, Li proposed convincingly to assume that the SNR follows a generalized gamma distribution which parameters are tuned by computing the first three asymptotic moments of the SNR. In this paper, this technique is generalized to (receive) correlated channels, and closed-form expressions for the first three asymptotic moments of the SNR are provided. To obtain these results, a random matrix theory technique adapted to matrices with Gaussian elements is used. This technique is believed to be simple, efficient, and of broad interest in wireless communications. Simulations are provided, and show that the proposed technique yields in general a good accuracy, even for small system dimensions.     

Primary intraosseous meningioma of the vault

Primitive intra-osseous meningioma is a rare benign tumor. We report a case with a temporal localization and antro-attical extension into the petrous bone.     

Performance evaluation of the Over-The-Air activation procedure in a large scale LoRaWAN

LoRaWAN is a promising LPWAN technology for IoT connectivity. It offers long-range and wide-area communication at low-power, low cost and low data rate. LoRaWAN performance has been evaluated according to many features such as coverage, scalability, physical layer parameters, communication reliability and latency. Existing studies assume that the LoRaWAN end-devices are already connected to the LoRa NetServer. Therefore, the performance of LoRaWAN activation procedure has not been well investigated. In this work, we study the performance of LoRaWAN during the Over-The-Air activation procedure. This process enables a large number of end-devices to join the network before being able to exchange any kind of packets. Thus, we analyze the average activation delay and the average energy consumption for an end-device in a large scale LoRaWAN. To achieve this goal, we first implement the Over-The-Air activation procedure in ns-3, especially in the ’lorawan’ module and conduct extensive simulations. Then, we elaborate a mathematical model using Markov-chain to evaluate both the delay and the energy consumption analytically. Our study shows that in a LoRaWAN cell composed of 1000 end-devices, the average activation delay for an end-device is about 35 minutes and this activation requires an average of three join-packet transmissions and an average energy consumption of 0.0887J.