A minimal sensing and communication control strategy for adaptive platooning

Several cooperative driving strategies proposed in literature, sometimes known as cooperative adaptive cruise control strategies, assume that both relative spacing and relative velocity with preceding vehicle are available from on-board sensors (laser or radar). Alternatively, these strategies assume communication of both velocity states and acceleration inputs from preceding vehicle. However, in practice, on-board sensors can only measure relative spacing with preceding vehicle (since getting relative velocity requires additional filtering algorithms); also, reducing the number of variables communicated from preceding vehicle is crucial to save bandwidth. In this work we show that, after framing the cooperative driving task as a distributed model reference adaptive control problem, the platooning task can be achieved in a minimal sensing and communication scenario, that is, by removing relative velocity measurements with preceding vehicle and by removing communication from preceding vehicle of velocity states. In the framework we propose, vehicle parametric uncertainty is taken into account by appropriately designed adaptive laws. The proposed framework is illustrated and shown to be flexible to several standard architectures used in cooperative driving (one-vehicle look-ahead topology, leader-to-all topology, multivehicle look-ahead topology)

The Error Performance and Fairness of CUWB Correlated Channels

AbstractThe symbol period becomes smaller compared to the channel delay in multiband orthogonal frequency division multiplexing (MB-OFDM) cognitive ultra wideband (CUWB) wireless communications, the transmitted signals experiences frequency-selective fading and leads to performance degradation. In this paper, a new design method for space-time trellis codes in MB-OFDM systems with correlated Rayleigh fading channels is introduced. This method converts the single output code symbol into several STTC code symbols, which are to be transmitted simultaneously from multiple transmitter-antennas. By using Viterbi optimal soft decision decoding algorithm, we investigate both quasi-static and interleaved channels and demonstrate how the spatial fading correlation affects the performance of space–time codes over these two different MB-OFDM wireless channel models. Simulation results show that the performance of space–time code is to be robust to spatial correlation. When the system bandwidth increases, the long term fairness quality will gradually become better and finally converges to 1

On Distributed Implementation of Switch-Based Adaptive Dynamic Programming

Switch-based adaptive dynamic programming (ADP) is an optimal control problem in which a cost must be minimized by switching among a family of dynamical modes. When the system dimension increases, the solution to switch-based ADP is made prohibitive by the exponentially increasing structure of the value function approximator and by the exponentially increasing modes. This technical correspondence proposes a distributed computational method for solving switch-based ADP. The method relies on partitioning the system into agents, each one dealing with a lower dimensional state and a few local modes. Each agent aims to minimize a local version of the global cost while avoiding that its local switching strategy has conflicts with the switching strategies of the neighboring agents. A heuristic algorithm based on the consensus dynamics and Nash equilibrium is proposed to avoid such conflicts. The effectiveness of the proposed method is verified via traffic and building test cases

Unsupervised detection of botnet activities using frequent pattern tree mining

A botnet is a network of remotely-controlled infected computers that can send spam, spread viruses, or stage denial-of-serviceattacks, without the consent of the computer owners. Since the beginning of the 21st century, botnet activities have steadilyincreased, becoming one of the major concerns for Internet security. In fact, botnet activities are becoming more and moredifficult to be detected, because they make use of Peer-to-Peer protocols (eMule, Torrent, Frostwire, Vuze, Skype and manyothers). To improve the detectability of botnet activities, this paper introduces the idea of association analysis in the field ofdata mining, and proposes a system to detect botnets based on the FP-growth (Frequent Pattern Tree) frequent item miningalgorithm. The detection system is composed of three parts: packet collection processing, rule mining, and statistical analysisof rules. Its characteristic feature is the rule-based classification of different botnet behaviors in a fast and unsupervisedfashion. The effectiveness of the approach is validated in a scenario with 11 Peer-to-Peer host PCs, 42063 Non-Peer-to-Peerhost PCs, and 17 host PCs with three different botnet activities (Storm, Waledac and Zeus). The recognition accuracy of theproposed architecture is shown to be above 94%. The proposed method is shown to improve the results reported in literature

Application of medical gases in the field of neurobiology

Medical gases are pharmaceutical molecules which offer solutions to a wide array of medical needs. This can range from use in burn and stroke victims to hypoxia therapy in children. More specifically however, gases such as oxygen, helium, xenon, and hydrogen have recently come under increased exploration for their potential theraputic use with various brain disease states including hypoxia-ischemia, cerebral hemorrhages, and traumatic brain injuries. As a result, this article will review the various advances in medical gas research and discuss the potential therapeutic applications and mechanisms with regards to the field of neurobiology