Protocol misbehavior detection framework using machine learning classification in vehicular Ad Hoc networks

Wireless Networks - A novel approach is proposed to detect protocol misbehavior using state-of-the-art machine learning frameworks and entropy. Nodes in Vehicular Ad Hoc Networks (VANETs) use...     

Novel transmit antenna selection strategy for massive MIMO downlink channel

In this paper, we deal with the problem of acquiring the channel state information (CSI) at the transmitter in large-scale multiple input multiple output (MIMO) systems, so-called massive MIMO systems. Clearly, obtaining CSI plays a central role to provide high system performance. Even though, in frequency-division duplexed systems, acquiring this information requires a prohibitive amount of feedback, since it increases with the number of transmit antenna. In this work, we design an efficient transmit antenna selection strategy aware of the amount of required CSI for a point-to-multipoint transmission in massive MIMO systems. The proposed strategy provides high sum-rate with limited CSI feedback and limited computational complexity. Innovatively, the antenna selection in our strategy is performed in a decentralized fashion successively at the receiving users. Two schemes are proposed in this work to perform the antenna selection at each user. Next, taking into consideration that the large-scale MIMO transmitter suffers from imperfect knowledge of CSI, we design a new performance criterion. Computer simulations validate that, when the CSI is perfectly known, the proposed strategy is able to achieve high performance in terms of system sum-rate while a significant reduction in both CSI feedback overhead and computational complexity is observed. Moreover, assuming imperfect CSI, the new proposed criterion achieves higher performance when the estimation accuracy is low and at high SNR regime.     

Measurement and Explanation of DC/RF Power Loci of an Active Patch Antenna

A case study of an active transmitting patch antenna revealed a characteristic loop locus of DC power versus RF output power as drive frequency was varied, with an operational bandwidth substantially smaller than the impedance bandwidth of the radiator. An approximate simulation technique, based on separation of the output capacitance of the power transistor, yielded easily visualized plots of power dependence on internal load impedance, and a simple interpretation of the experimental results in terms of a near‐resonance condition between the output capacitance and output packaging inductance.     

Nifuroxazide Mitigates Angiogenesis in Ehlrich’s Solid Carcinoma: Molecular Docking,Bioinformatic and Experimental Studies on Inhibition of Il-6/Jak2/Stat3 Signaling

Nifuroxazide is an antidiarrheal medication that has promising anticancer activity against diverse types of tumors. The present study tested the anticancer activity of nifuroxazide against Ehrlich’s mammary carcinoma grown in vivo. Furthermore, we investigated the effect of nifuroxazide on IL-6/jak2/STAT3 signaling and the possible impact on tumor angiogenesis. The biological study was supported by molecular docking and bioinformatic predictions for the possible effect of nifuroxazide on this signaling pathway. Female albino mice were injected with Ehrlich carcinoma cells to produce Ehrlich’s solid tumors (ESTs). The experimental groups were as follows: EST control, EST + nifuroxazide (5 mg/kg), and EST + nifuroxazide (10 mg/kg). Nifuroxazide was found to reduce tumor masses (730.83 ± 73.19 and 381.42 ± 109.69 mg vs. 1099.5 ± 310.83) and lessen tumor pathologies. Furthermore, nifuroxazide downregulated IL-6, TNF-α, NFk-β, angiostatin, and Jak2 proteins, and it also reduced tumoral VEGF, as indicated by ELISA and immunohistochemical analysis. Furthermore, nifuroxazide dose-dependently downregulated STAT3 phosphorylation (60% and 30% reductions, respectively). Collectively, the current experiment shed light on the antitumor activity of nifuroxazide against mammary solid carcinoma grown in vivo. The antitumor activity was at least partly mediated by inhibition of IL-6/Jak2/STAT3 signaling that affected angiogenesis (low VEGF and high angiostatin) in the EST. Therefore, nifuroxazide might be a promising antitumor medication if appropriate human studies will be conducted.     

Power control and clustering in heterogeneous cellular networks

Recently, it is widely believed that significant coverage and performance improvement can be achieved through the deployment of small cells in conjunction with the well-established macro cells. However, it is expected that the high density of base stations in such heterogeneous cellular networks will give rise to multiple design problems related to both co-tier (small-to-small) and cross-tier (between small and macro cells) interference. Fortunately, cooperation between base stations will play a major role to cope with these problems and hence to enhance the users’ data rates. In this paper, we consider a two-tier cellular network comprised of a macro cell underlaid with multiple small cells where both co-tier and cross-tier interference are taken into account. We study the scenario where the small cell base stations seek to maximize a common objective by forming multiple clusters through cooperation. These base stations have also to allocate power to their associated users and, at the same time, control the total aggregate interference caused to the macro cell user which has to be kept below a threshold prefixed by the macro cell base station. We consider two utility functions: the overall sum rate of the small cell network and the minimum data rate of the small cell users. We formulate the studied problems as mixed integer nonlinear optimization problems and we discuss their NP hardness. Therefore, due to the complexity of finding the optimal solution, we design heuristic algorithms which resolves efficiently the tradeoff between computational complexity and performance. We show through simulations that the designed heuristics approach the optimal solution (obtained using the complex exhaustive search algorithm) with highly reduced computational complexity.