In IEEE 802.11 wireless LAN, as the number of nodes increases, collisions will increase correspondingly, channel utilization will decline, and the total throughput will decline instead of increasing, leading to system...
详细信息
In IEEE 802.11 wireless LAN, as the number of nodes increases, collisions will increase correspondingly, channel utilization will decline, and the total throughput will decline instead of increasing, leading to system performance degradation. Through in-depth analysis of the root cause of this problem, this paper designs a simple and effective algorithm, which uses ARMA filtering algorithm to measure the conditional collision probability and then calculate the number of nodes in the network. The AP tells all nodes in the network in the form of broadcast when the number of nodes in the network changes, after all nodes receive the broadcast sent by the AP, they dynamically adjust the network parameters accordingly. Finally, NS2 simulation software is used to conduct simulation experiments in a variety of network scenarios, and the experimental results verify that the algorithm in this paper is simple and effective, with less changes to IEEE 802.11, and is suitable for use and promotion in wireless networks. It can optimize the network system performance based on the number of nodes, improve the system throughput, and significantly improve the network performance. According to the number and density of nodes, the algorithm reduces the collision probability by dynamically adjusting the timeslot, accordingly increases the total network throughput, and achieves the purpose of effectively improving the network performance.
In this paper,iterative learning control(ILC) is applied to network-based control problems in which communication channels are subject to random transport delay and data *** averaging ILC algorithm is used to overcome...
详细信息
In this paper,iterative learning control(ILC) is applied to network-based control problems in which communication channels are subject to random transport delay and data *** averaging ILC algorithm is used to overcome the random *** analysis,it is shown that ILC can perform well and achieve asymptotical convergence in ensemble average along the iteration axis,as far as the probability of the transmission delay and data dropout are known a priori.A unique contribution in this work is to illustrate the applicability of ILC to nonlinear systems while both the one-step delay and the data-dropout phenomena are taken into *** analysis and simulations validate the effectiveness of the ILC algorithm for network-based control tasks.
This study designs and simulates weather parameters using MEMS technology with suitable characteristics for integration into a single chip for environment monitoring. The sensing and heating filament of the temperatur...
详细信息
ISBN:
(数字)9798350391565
ISBN:
(纸本)9798350391572
This study designs and simulates weather parameters using MEMS technology with suitable characteristics for integration into a single chip for environment monitoring. The sensing and heating filament of the temperature sensor was designed using a thin film platinum resistor, examining its ability to experience stability, increase in resistivity, accuracy, and free-contamination. The temperature sensing and heating element are based on platinum RTD (Resistance Temperature Detector) with meander shape and aluminum as the bonding pad connecting the sensor to the external circuit. A capacitive humidity sensor is implemented in consolidation with a piezoresistive pressure sensor with a square membrane. The capacitive humidity sensor with hole pattern at the upper plate and a polyimide serves as water absorbent sandwiched between the electrodes. The simulation analysis was conducted using finite element analysis in COMSOL Multiphysics version 6.0 . It was observed that the temperature increases from -100oC to - 20oC, and 10oC to 50oC respectively as a result of deviation in relative humidity in the troposphere, and atmosphere. The average changes in capacitances were found at 0.0713/%RH, while the pressure was examined to vary from 1050hPa to 5hPa in a step of 200hPa with a sensitivity of 11.50mv/bar.
Accurate pitch extraction from speech is important but challenging problem for speech synthesis. However, the additive nature and long-term suprasegmental property of pitch features have not been fully exploited in mo...
详细信息
Accurate pitch extraction from speech is important but challenging problem for speech synthesis. However, the additive nature and long-term suprasegmental property of pitch features have not been fully exploited in most of the existing pitch estimators as they are operated frame by frame. As a result, they would cause some inherent discontinuities, such as double/half F0 errors and unvoiced/voiced(U/V) error. This would adversely affect the quality of synthetic speech as well as the expressiveness of the prosody information. In this paper, we explore the novel use of multi-tasks(Task 1: U/V;Task 2: Pitch) bidirectional long short-term memory recurrent neural network(BLSTM) to model the pitch and voicing decision simultaneously in a unified framework. The features used in this study are extracted from the frequency domain. We compute the log-frequency power spectrogram and then normalize to the long-term speech spectrum to attenuate noises. A filter is then used to enhance the harmonicity. Experiments show that the proposed approach substantially outperforms RAPT, which behaves the best in clean condition. Besides, our proposed approach can even work well with a certain level of background noise.
The ability to perceive and comprehend a traffic situation and to estimate the state of the vehicles and road-users in the surrounding of the ego-vehicle is known as situational awareness. Situational awareness for a ...
详细信息
Neuroimaging plays an significant role in diagnosing and pathological study of brain diseases. Considering that both functional and structural abnormalities may lead to brain dis-eases and disorders, single modal neur...
详细信息
ISBN:
(数字)9781665468190
ISBN:
(纸本)9781665468206
Neuroimaging plays an significant role in diagnosing and pathological study of brain diseases. Considering that both functional and structural abnormalities may lead to brain dis-eases and disorders, single modal neuroimaging approach may not fully characterize brain activities and working modes. Fusion of multimodal neuroimaging data is expected to provide more comprehensive characterization of brain diseases, given that the different modalities contain more complementary information. Recently, Graph Convolutional Networks (GCNs) is shown to have powerful capacity in representation learning for graph-structure data, which is considered to integrate both graph se-mantic structure and node information. Therefore, in this paper, we propose the Weighted Graph AutoEncoder (WGAE), a GCN- driven multimodal fusion model, to learn the combinational latent node representation of fMRI and DTI neuroimaging data, which are used as node features and graph structure respectively in the graph in unsupervised manner. Experimental results on two real-world datasets show the superiority of the proposed model over other existing single-modal or multi-modal methods in learning representations for disease prediction as the downstream task. Furthermore, ablation experiments also show the collaborative contribution of multimodal neuroimaging fusion in the proposed model, and also show the feasibility of assessing the respective importance of the two modalities during the disease prediction.
This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cu...
详细信息
Abstract We apply nonparametric techniques to identify nonlinear dynamic block-oriented systems of Hammerstein type. Hammerstein system consists of a memoryless nonlinear system followed by a dynamic, linear system. W...
Abstract We apply nonparametric techniques to identify nonlinear dynamic block-oriented systems of Hammerstein type. Hammerstein system consists of a memoryless nonlinear system followed by a dynamic, linear system. We introduce identification algorithms based on input-output observations for both systems and study their convergence and the rates. The performance of identification algorithms is validated in simulation studies. We apply Hammerstein system identification algorithms to identification of nonlinearities in a flexible robot manipulator.
This paper presents a new real-time architecture for motion control of industrial robots. The new control system obtained has two main advantages: first it provides a total open control architecture and the second adv...
详细信息
This paper presents a new real-time architecture for motion control of industrial robots. The new control system obtained has two main advantages: first it provides a total open control architecture and the second advantage is the simplicity and the interactivity of the platform developed. Experimental evaluation of a passivity-based control scheme shows the benefits of the architecture which is unique in the sense that open and advanced control can be combined with built-in safety logic as required in industrial applications.
暂无评论