一种基于节点状态预测机制的高稳定性路由设计

该文旨在为拓扑动态变化的无线自组织网络设计一种稳定性路由机制。首先建模分析了动态变化网络中节点间通信路径存在的条件,以及数据包在该路径上可靠传输的条件,基于以上分析提出了保证路径有效的关键指标：链路持续时间、拥塞可能性因子。接下来提出基于节点当前运动情况计算链路持续时间,以及基于缓冲区队列长度与队列长度变化率计算节点拥塞可能性因子的算法。结合链路持续时间与节点拥塞可能性等信息,该文提出了一种面向路径可靠性及稳定性综合指标的按需路由机制(ODRP-DR),并利用NS2仿真平台对该机制性能进行分析,仿真结果表明,在网络拓扑结构动态变化的情况下能有效减小数据包丢包概率,路由重建次数。     

无人机用激光雷达跨阻电路改进设计

无人机采用激光雷达探测目标位置和障碍物,是一种新型的探测跟踪工具,但传统方法设计的跨阻放大器前端接收电路稳定性较差,导致无人机避障能力下降,因此,设计全新的无人机用激光雷达跨阻电路.此次设计根据得到的激光发射器脉冲飞行时间探测误差,设计外环与内环共同组成的控制回路,并根据跨阻放大器前段接收电路,重新设计一个自适应增益控...     

Effects of Grape Polyphenols on the Life Span and Neuroinflammatory Alterations Related to Neurodegenerative Parkinson Disease-Like Disturbances in Mice

Functional nutrition is a valuable supplementation to dietary therapy. Functional foods are enriched with biologically active substances. Plant polyphenols attract particular attention due to multiple beneficial properties attributed to their high antioxidant and other biological activities. We assessed the effect of grape polyphenols on the life span of C57BL/6 mice and on behavioral and neuroinflammatory alterations in a transgenic mouse model of Parkinson disease (PD) with overexpression of the A53T-mutant human α-synuclein. C57BL/6 mice were given a dietary supplement containing grape polyphenol concentrate (GPC—1.5 mL/kg/day) with drinking water from the age of 6–8 weeks for life. Transgenic PD mice received GPC beginning at the age of 10 weeks for four months. GPC significantly influenced the cumulative proportion of surviving and substantially augmented the average life span in mice. In the transgenic PD model, the grape polyphenol (GP) diet enhanced memory reconsolidation and diminished memory extinction in a passive avoidance test. Behavioral effects of GP treatment were accompanied by a decrease in α-synuclein accumulation in the frontal cortex and a reduction in the expression of neuroinflammatory markers (IBA1 and CD54) in the frontal cortex and hippocampus. Thus, a GP-rich diet is recommended as promising functional nutrition for aging people and patients with neurodegenerative disorders.     

Repair the faulty TSVs with the improved FNS-CAC codec

Through-silicon via (TSV) is a key enabling technology for the emerging 3-dimension (3D) integrated circuits (ICs). However, the crosstalk between the neighboring TSVs is one of the important sources of the soft faults. To suppress the crosstalk, the Fibonacci-numeral-system-based crosstalk avoidance code ( FNS-CAC) is an effective scheme. Meanwhile, the self-repair schemes are often used to deal with the hard faults, but the repaired results may change the mapping between signals to TSVs, thus may reduce the crosstalk suppression ability of FNS-CAC. A TSV self-repair technique with an improved FNS-CAC codec is proposed in this work. The codec is designed based on the improved Fibonacci numeral system (FNS) adders, which are adaptive to the health states of TSVs. The proposed self-repair technique is able to suppress the crosstalk and repair the faulty TSVs simultaneously. The simulation and analysis results show that the proposed scheme keeps the crosstalk suppression ability of the original FNS-CAC, and it has higher reparability than the local self-repair schemes, such as the signal-switching-based and the signal-shifting-based counterparts.     

基于单片机的智能小车行驶控制方案设计

以STC89C52单片机作为控制核心,提出了智能小车行驶控制方案,完成了硬件系统的设计,实现了小车的红外循迹和自动避障的功能.设计方案简单低廉、性能稳定、通用性强、控制精度高,具有较高的使用价值.     

“北斗”救生终端海上自适应功率退避算法

受海况、浸水、结冰、对星等因素影响,飞行员海上遇险后报位可靠性较低,工作时长较短。为此,提出一种自适应功率退避方法,在对报位影响因素分析基础上,利用深度神经网络,通过对经纬度、高度、俯仰角、方向、加速度、速度、浸水、信号、电量等多维度数据进行学习,拟合出覆冰、浪涌、液面等信号特征与环境特征联想模型,自适应控制信号发射时机,实现功耗的有效控制。试验表明,该方法将通信成功率及工作时长分别由36.3%、6.0 h提升至73.3%、8.6 h。     

基于单片机的智能小车避障循迹系统设计

本设计是以STC89C52为核心,根据超声波等感测模块传输的路面信息,检测信号识别障碍物并及时调整小车行驶走向,实现了小车自适应行驶、避障的功能。智能小车自适应行驶的成功研究有助于智能车辆的研制与开发,同时也为交通工具的智能化发展提供了一个合理可行的方向。     

面向不确定性环境的多无人机协同防碰撞

随着无人机碰撞事故的增加, 防碰撞已成为多无人机共空域飞行亟待解决的问题。首先, 进行多无人机协同防碰撞体系结构设计, 然后, 提出一种新的模型预测控制方法: 应用扩展卡尔曼滤波预测不确定性环境空间的障碍物和目标轨迹, 无人机通过机间通信实现环境信息共享, 基于模型预测控制方法进行多无人机协同防碰撞制导决策。仿真结果表明所提方法的有效性, 同时协同机制的效果明显。     

基于强化学习的多机器人避碰算法研究

采用强化学习解决多机器人避碰问题。然后针对表格式Q学习算法只能用于离散的状态并且学习时间过长,难以收敛的不足,提出了神经网络和Q学习相结合的算法。最后将该算法应用到多机器人避碰问题中,仿真实验表明该算法有效,能较好地解决多机器人避碰问题。     

基于最速下降法的车载网络功率控制研究

车载网络中的功率控制算法D-FPAV在功率值计算精度要求较高时,即计算功率值的步长取值较小时,存在计算量较大的问题。该文在D-FPAV算法的基础上提出了一种改进的功率控制算法最速下降功率控制SDPC算法,该算法运用了最速下降法快速收敛的原理。仿真结果表明,SDPC算法能够降低计算迭代次数,提高算法收敛速度,并且使得车辆发送状态信息的功率能够随车辆位置信息和环境的变化快速做出调整,从而能有效地进行信道拥塞控制,有利于提高车辆行驶的安全性。