驱动X光二极管的级联Blumlein型脉冲网络过电压脉冲分析

为了提高装置容许的运行电压以提高辐射剂量产额,开展了放电过程中影响脉冲形成网络过电压幅值因素的研究。在引入开关导通不同步性和导通电阻条件下,建立了适用于任意电阻性负载的级联Blumlein型脉冲形成网络电压波过程理论模型,基于波过程模型进一步分析了影响脉冲形成网络过电压幅值的因素。研究表明开关不同步是产生过电压的主要因素,过电压峰值出现在开关闭合后的3倍形成网络电长度时刻。随着网络级联级数的增加,二极管阻抗与源阻抗匹配情况下最大过电压可达到-2倍充电电压,而二极管阻抗下降使得过电压幅值得以加强,阻抗过早崩溃可使过电压幅值接近充电电压的-3倍。     

利用可调谐的混沌Fabry-Perot激光器实现波分复用无源光网络的断点检测

波分复用无源光网络具有支路多、节点密的特征,为精确定位各支路的断点,提出了一种基于可调谐混沌Fabry-Perot激光器的检测方法.将光反馈多纵模Fabry-Perot半导体激光器作为混沌光源,在改变反馈光光波模式的条件下输出波长可调谐的混沌激光.以探测光的波长标记各被测支路,将探测信号和携带延时信息的回波信号进行互相关运算,根据相关曲线峰值的位置即可完成定位.分析了可调谐混沌源的特性,并以1×4的波分复用无源光网络为例,进行了初步的实验验证,结果表明该方法可以精确定位光网络支路中连接点及断点的位置,空间分辨率达4 cm,且与探测距离无关.     

On network form and function

Moving from the observation that drainage network configurations minimizing total energy dissipation are stationary solutions of the general equation describing landscape evolution, we review theoretical and observational evidence on river patterns and their scale-invariant structure. Exact results complemented by numerical annealing of the basic equation in the presence of additive noise suggest that configurations at (or very close to) the global minimum of energy dissipation differ from dynamically accessible states, which have rather different scaling properties and conform much better to natural forms. Thus we argue that, at least in the fluvial landscape, Nature works through imperfect searches for dynamically accessible optimal configurations. We also show that optimal networks are spanning loopless configurations only under precise physical requirements. This is stated in a form applicable to generic networks, suggesting that other branching structures occurring in Nature (e.g. scale-free and looping) may possibly arise through optimality to selective pressures. Indeed, we show that this is the case.     

Mobile sink-based data collection in event-driven wireless sensor networks using a modified ant colony optimization

The hotspot problem is one of the primary challenges in the wireless sensor networks (WSNs) because it isolates the sink node from the remaining part of the WSN. A mobile sink (MS)-based data acquisition strategy mitigates the hotspot problem, but the traditional MS-based data gathering approaches do not resolve the issue. However, the conventional techniques follow a fixed order of visits and static traversal of the MS. In this context, this paper uses a modified version of the ant colony optimization strategy for the data collected through a MS to mitigate the hotspot problem in the WSNs while improving the energy efficiency, network lifetime, throughput by reducing the packet loss and delay. In our work, we initially construct a forwarded load spanning tree to estimate the freight of each node in the WSN. Further, we choose RPs and their path simultaneously using the modified ACO algorithm by considering the forward loads, remaining energy, distance, etc. The proposed work also adopts the virtual RP selection strategy void unnecessary data exchanges between the nodes and RPs. Hence, it reduces the burden on relay nodes and optimize the energy usage among the nodes. We compare our approach with the recent ACO-based algorithms, and our approach outperforms them.     

Improvement of packet transmission scheduling and delivery rate in wireless Ad-Hoc networks

The principal purpose of Ad-Hoc wireless networks is to increase service efficiency in terms of transmission scheduling and packet transfer rate. The approaches that assume frame unicity to satisfy a given set of packets minimize the end-to-end delay. However, they do not guarantee a maximum packet delivery rate due to the difficulty of establishing robust paths for packet transfer across nodes deployed in the network, especially in a three-dimensional (3D) environment. The objective is to minimize the end-to-end delay by ensuring the maximum delivery of packets to their destinations. Furthermore, the signal-to-interference-and-noise-ratio (SINR) model is considered to optimize transmission scheduling. In this paper, an optimal node coordinates optimization approach is proposed to extend two recently investigated schemes in the literature (S-RDSP and I-RDSP). The developed algorithms, named S-MPDR and I-MPDR, seek to reduce the end-to-end delay by delivering a collection of inserted packets over a 3D environment while also maximizing the delivery rate of these packets. Desirability functions are used to evaluate the network’s performance in various scenarios involving two different environments, Level 0 and Level 1. Numerical results demonstrate that the developed algorithms outperform both schemes in terms of end-to-end delay and packet delivery rate. In the Level 0 environment, the overall minimum delay and packet delivery rate scores provided by S-MPDR are increased by 28% and 88% compared to S-RDSP, respectively. In comparison, those provided by I-MPDR are increased by 24% and 16% compared to I-RDSP. Similarly, in the Level 1 environment, the scores provided by S-MPDR are increased by 25% and 100% compared to S-RDSP, respectively, while those provided by I-MPDR are increased by 23% and 25% compared to I-RDSP.     

Secrecy sum rate maximization for a MIMO-NOMA uplink transmission in 6G networks

Non-orthogonal multiple access (NOMA), as a well-qualified candidate for sixth-generation (6G) mobile networks, has been attracting remarkable research interests due to high spectral efficiency and massive connectivity. The aim of this study is to maximize the secrecy sum rate (SSR) for a multiple-input multiple-output (MIMO)-NOMA uplink network under the maximum total transmit power and quality of service (QoS) constraints. Thanks to the generalized singular value decomposition method, the SSR of NOMA is compared with conventional orthogonal multiple access and other baseline algorithms in different MIMO scenarios. Due to the subtractive and non-convex nature of the SSR problem, the first-order Taylor approximation is exploited to transform the original problem into a suboptimal concave problem. Simulation results are provided and compared with some other benchmarks to evaluate the efficacy of the proposed method.     

RAT selection for IoT devices in HetNets: Reinforcement learning with hybrid SMDP algorithm

Due to the increasing deployment of heterogeneous networks (HetNets), the selection of which radio access technologies (RATs) for Internet of Things (IoT) devices such as user equipments (UEs) has recently received extensive attention in mobility management research. Most of existing RAT selection methods only optimize the selection strategies from the UE side or network side, which results in heavy network congestion, poor user experience and system utility degradation. In this paper the UE side and the network side are considered comprehensively, based on the game theory (GT) model we propose a reinforcement learning with assisted network information algorithm to overcome the crucial points. The assisted information is formulated as a semi-Markov decision process (SMDP) provided for UEs to make accurate decisions, and we adopt the iteration approach to reach the optimal policy. Moreover, we investigate the impacts of different parameters on the system utility and handover performance. Numerical results validate that our proposed algorithm can mitigate unnecessary handovers and improve system throughputs.     

Nonlinear aeroelastic reduced order modeling by recurrent neural networks

The paper develops a reduction scheme based on the identification of continuous time recursive neural networks from input–output data obtained through high fidelity simulations of a nonlinear aerodynamic model at hand. The training of network synaptic weights is accomplished either with standard or automatic differentiation integration techniques. Particular emphasis is given to using such a reduced system in the determination of aeroelastic limit cycles. The related solutions are obtained with the adoption of two different approaches: one trivially producing a limit cycle through time marching simulations, and the other solving a periodic boundary value problem through a direct periodic time collocation with unknown period. The presented formulations are verified for a typical section and the BACT wing.     

Chemical network algorithms for the risk assessment and management of chemical threats

A network of chemical threats: Current regulatory protocols are insufficient to monitor and block many short-route syntheses of chemical weapons, including those that start from household products. Network searches combined with game-theory algorithms provide an effective means of identifying and eliminating chemical threats. (Picture: an algorithm-detected pathway that yields sarin (bright red node) in three steps from unregulated substances.).