This paper proposes a compromise-tolerant (t,n)-threshold certification management scheme for MANETs. Our solution allows to mitigate the impact of compromised nodes that participate in the certification service. In our design, certification management is achieved anonymously by an Anonymous Certification Authority (ACA). The latter is fully distributed into multiple disjointed coalitions of nodes whose structure is made hidden. This prevents an adversary from taking the control of the ACA by arbitrarily compromising t or more nodes. In other words, our proposal enhances the compromise-tolerance to more than the threshold number t of nodes without breaking down the whole certification system. As a result, our scheme requires a very smaller threshold than traditional schemes, which improves considerably the service availability. The experimental study shows a clear advantage over traditional threshold-based certification schemes by ensuring a significant positive compromise between security and availability of certification service.

Let B(H) be the C* -algebra of all bounded linear operators acting on a complex separable Hilbert space H . We shall show that: 1. The class of all selfadjoint operators in B(H) multiplied by scalars is characterized by ∀X ∈ B(H), S2X +XS2 =>2||SXS||, (S ∈ B(H)). 2. The class of all normal operators in B(H) is characterized by each of the three following properties (where DS = S*S-SS* , for S ∈ B(H)), (i) ∀X ∈ B(H), S2X + XS2 =>2||SXS||,(S ∈ B(H)), (ii) S*DSS = 0 = SDSS*,(S ∈ B(H)), (iii) S*DSS=> 0 =>SDSS*,(S ∈ B(H)).  

Electronic health records (EHRs) replaced the old paper-based systems to make patient data more accurate, reliable, and more accessible. Yet, the EHRs system requires high transmission cost, energy, and waste of time for both doctors and patients. Furthermore, EHRs security presents a serious issue threatening the patient’s privacy. Most of the third-party hosting systems have some issues related to the users’ privacy and data security. Hence, it is necessary to restrict the access control policies and develop efficient mechanisms for cloud-based EHRs data. In this paper, a sensitive and energetic access control (SE-AC) mechanism is proposed for managing the cloud-hosted EHRs and providing a fine-grained access control even in critical situations. The proposed mechanism ensures the confidentiality of the patient’s data, where only authorized individuals to have permission to be able to edit or review certain of the patient’s data. Each EHR data is encrypted by the managing authority before submitting to the cloud storage. The requesting user can get dynamically changing permissions based on authentication and context attributes. In addition, seven major aspects have been quantified to assess the operation of any access control that could be deployed in the Internet-of-Thing (IoT). The security analysis indicates that the SE-AC mechanism is secure and will prevent any unauthorized access. The results show exceptional compatibility and performance with different setups and configuration.

This paper designs an indirect power control method for brushless doubly fed induction generator (BDFIG), in which the stator is attached to grid with back-to-back space vector modulation (SVM) converter that converts the generated wind power. Our control method is a sliding mode control based on the theory of variable structure control. Specifically, the active and reactive powers, which are exchanged between the stator of the BDFIG and the grid in a linear and decoupled manner, are subjected to decoupled, vector control. In addition, a proportional integral (PI) controller was implemented to keep the DC-voltage constant for the back-to-back SVM converter. The efficiency of our control strategy was validated through simulation. The research greatly promotes the control of renewable energy generators.

This article presents a computer vision system for real-time detection and robust recognition of speed limit signs, specially designed for intelligent vehicles. First, a new segmentation method is proposed to segment the image, and the CHT transformation (circle hog transform) is used to detect circles. Then, a new method based on local binary patterns is proposed to filter segmented images in order to reduce false alarms. In the classification phase, a cascading architecture of two linear support vector machines is proposed. The first is trained on the GTSRB dataset to decide whether the detected region is a speed limit sign or not, and the second is trained on the MNIST dataset to recognize the sign numbers. The system achieves a classification recall of 99.81% with a precision of 99.08% on the GTSRB dataset; in addition, the system is also tested on the BTSD and STS datasets, and it achieves a classification recall of 99.39% and 98.82% with a precision of 99.05% and 98.78%, respectively, within a processing time of 11.22 ms.