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0.18μ m cmos low voltage power amplifier for wsn application

Telkomnika - Indonesian Journal of Electrical engineering 2013年第8期11卷 4470-4476页

作者： Chenjian, Wu Zhiqun, Li Nan, Yao Meng, Zhang Liang, Chen Jia, Cao Institute of RF- and OE-ICs Southeast University Engineering Research Center of RF-ICs and RF-Systems Ministry of Education Southeast University Nanjing 210096 China Jiangsu Provincial Key Laboratory of sensor network technology Wuxi 214135 China

This paper presents the design of a Class A/B power amplifier (PA) for 2.4-2.4835GHz Wireless sensor network (WSN) system in 0.18μm CMOS technology. The PA adopts the single-stage differential structure and the output power of the PA can be controlled by switching the sizes of transistors. Seven different level of output power can be obtained through a three-bit control code. The tested results shows that the proposed PA achieves power added efficiency (PAE) of 26.73% while delivering an output power of 6.35dBm at 1dB compression point. Its power gain is 15.87dB. With a low DC voltage supply of 1V, its power consumption is 15.3mW. The PA die size is 1070×610μm2. © 2013 Universitas Ahmad Dahlan.

关键词： Wireless sensor networks

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Optimization design of a new type of Halbach array motor with magnetic axis offset

Optimization design of a new type of Halbach array motor wit...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Shaoru Zhang Muyan Jiang Yihan Liu Zhanping Huang Xiuju Du Jielu Zhang Fanglin Luo College of Engineering Hebei Normal University Shijiazhuang China Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology Jiangsu Tailong Reducer Co. LTD Taizhou China Jiangsu Tailong Reducer Co. LTD Taizhou China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

This paper proposes a novel Halbach array structure based on the traditional radial and tangential magnetized Halbach array permanent magnet motor. This structure enables the magnetic field lines to be concentrated, thereby enhancing the motor torque. Concurrently, the magnetic axis offset is designed on this basis by combining ferrite with low magnetic conductivity and NdFeB with high magnetic conductivity. This design effectively reduces the gap between the electromagnetic torque and the peak current angle of the reluctance torque, and fully utilizes both the electromagnetic and reluctance torques to further improve the total output torque of the motor. The newly proposed motor, known as the Magnetic Axis Offset Halbach Motor (MAOHM), is a result of this design. Furthermore, this paper also employs a finite element method to study and compare the proposed motor with the traditional motor, serving to verify the rationality and effectiveness of the design.

关键词： Torque Shape Rotors Random access memory Conductivity Motors Permanent magnets

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Computation Offloading and Resource Allocation for MEC in C-RAN: A Deep Reinforcement Learning Approach 19

Computation Offloading and Resource Allocation for MEC in C-...

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19th IEEE International Conference on Communication technology, ICCT 2019

作者： Jin, Xiaoyan Zhang, Jun Sun, Xinghua Zhang, Ping Cai, Shu Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510006 China Key Laboratory of Wireless Sensor Network and Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Key Laboratory of Computer Application Technology Anhui Polytechnic University Wuhu241000 China

ISBN: (纸本)9781728105352

Mobile edge computing (MEC) technology has become a promising example for cloud radio access networks (CRAN) to provide close-range services, thereby reducing service delays and saving energy consumption. In this paper, we consider a multi-user MEC system and solve the problem of the computation offloading strategies and resource allocation policies. We set the total cost of delays and energy consumption as our optimization goal. However, getting an optimal strategy in a dynamic environment is challenging. Reinforcement learning (RL) aims at long-term cumulative rewards, which are essential for time-varying dynamic systems. Therefore, we propose an optimization framework based on deep RL to solve these problems. The deep neural network (DNN) is used to estimate the value function of the critics, thereby reducing the state space complexity of the optimization target. The actor part uses another DNN to represent a parametritis stochastic strategy and improve the strategy with the help of critics. Compared with other schemes, the simulation results show that the scheme significantly reduces the total cost. © 2019 IEEE.

关键词： Reinforcement learning

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Research on short-term wind farm output power prediction model based on meteorological data collected by WSN

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International Journal of Hybrid Information technology 2013年第5期6卷 219-226页

作者： Ma, Li Li, Bo Jie, Du Shen, Jian Wang, Jin Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China Key Laboratory of Meteorological Disaster of Ministry of Education Nanjing University of Information Science and Technology Nanjing 210044 China

The prediction of wind farm output power is considered as an effective way to increase the wind power capacity and improve the safety and economy of power system. It is one of the hot research topics on wind power. The wind farm output power is related to many factors such as wind speed, temperature, etc., which is difficult to be described by some mathematical expression. In this paper, Back Propagation (BP) neural network algorithm and genetic algorithm (GA) are combined to establish the prediction model of the short-term wind farm output power based on meteorological data collected by Wireless sensor network (WSN). The meteorological data is used to determine the input variables of the BP neural network. Meanwhile, the GA is used to adjust the value of BP's connection weight and threshold dynamically. Then the trained BP neural network is used to predict the wind power. The experiment results show that our method has better prediction capability compared with that using BP neural network alone or using wind power formulas. © 2013 SERSC.

关键词： Wireless sensor networks

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Hybrid neural network model application in annual precipitation forecast

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International Journal of u- and e- Service, Science and technology 2012年第4期5卷 21-30页

作者： Ma, Li Li, Xuelian Wang, Jin Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China Key Laboratory of Meteorological Disaster of Ministry of Education Nanjing University of Information Science and Technology Nanjing 210044 China

When applied to precipitation forecasting, the mean generating function - optimal subset regression (MGF-OSR) model is limited by its low accuracy and high error, while the back propagation (BP) neural network model has difficulty in learning for matrix selection. This paper proposes a new MGF-OSR-BP model, which uses a MGF to extend original data, an OSR to select the best series as the BP neural network input node and learning matrix, and the resultant data for training. The training procedure determines the number of hidden layers and uses an optimal number of hidden layers for model training. This paper uses the MGF-OSR-BP model to analyze precipitation data from Hangzhou, China, for 53 years, from 1956 to 2008. The 1956-2006 precipitation data are used as the training sample, and the 2007-2008 data are used as the test set data to verify the practicality of the forecast system. A fitting verification is performed using the forecasted data against field measurement data, and the results show that the forecast accuracy is better than that of the MGF-OSR model or the MGF stepwise multiple regression model.

关键词： Backpropagation

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Research on the Loading Method of Logistics Vehicle and Cargo Based on IPSO Algorithm

Research on the Loading Method of Logistics Vehicle and Carg...

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第42届中国控制会议

作者： Zewei Zhao Huqing Wang Zhe Sun Lei Xu Guangfu Gong Xiangpeng Xie Zhixin Sun Post Industry Technology Research and Development Center of the State Posts Bureau (Internet of Things Technology) Nanjing University of Posts and Telecommunications Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and Telecommunications

Logistics vehicle and cargo matching problem is a typical combination optimization problem,which has important theoretical and application value in the field of logistics *** proved particle swarm optimization(PSO) algorithm is one of the most extensive optimization methods studied in recent years,with excellent global search ability and convergence *** on the IPSO algorithm,this paper proposes an effective IPSO algorithm model for the matching problem of logistics vehicles and goods,and conducts the corresponding comparative *** final experimental results show that the algorithm has good results in practical application and can provide a feasible optimization method for solving the problem of logistics vehicles and goods.

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The short-term photovoltaic power generation forecasting based on Fruit Fly Optimization Algorithm optimized BP neural network

The short-term photovoltaic power generation forecasting bas...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Shaoru Zhang Haoxuan Zhu Likun Yang Zhanping Huang Xiuju Du Jielu Zhang Fang Lin Luo College of Engineering Hebei Normal University Shijiazhuang China Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology Jiangsu Tailong Reducer Co. LTD Taizhou China Jiangsu Tailong Reducer Co. LTD Taizhou China School of Electrical and Electronic Engineering Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Accurately predicting photovoltaic power generation is crucial for ensuring the safe operation of power grids and advancing solar energy development and utilization. For the issue of large errors in current prediction methods, this paper introduces a forecasting method for photovoltaic power generation. The method utilizes Fruit Fly Optimization Algorithm (FOA) to optimize Back Propagation (BP) neural network. This involves analyzing the correlations among factors impacting photovoltaic power generation and selecting relevant meteorological data via the Pearson correlation coefficient method. Fruit Fly Algorithm demonstrates rapid convergence, a minimal parameter set and ease of adjustment, rendering it applicable across various domains. Employing Fruit Fly Algorithm to optimize weights and thresholds within BP neural network leads to the final prediction outcomes. Simulation results confirm the superior prediction accuracy of the FOA-BP model for photovoltaic power generation, particularly during spring, autumn and winter, showcasing its practical utility.

关键词： Photovoltaic systems Correlation coefficient Atmospheric modeling Electricity Neural networks Predictive models Prediction algorithms

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Deep Learning-based Visual Recognition System for Fruit-Picking Robots

Deep Learning-based Visual Recognition System for Fruit-Pick...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Rui Ma Yihan Liu Qike Wu Shaoru Zhang Nan Wang Jielu Zhang Fang Lin Luo College of Engineering Hebei Normal University Shijiazhuang China Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology Jiangsu Tailong Reducer Co. LTD Taizhou China Jiangsu Tailong Reducer Co. LTD Taizhou China School of Electrical and Electronic Engineering Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Fruit recognition plays an important role in automated picking, in order to improve the accuracy and real-time picking, this study uses deep learning methods to design a fruit-picking robot visual recognition system. First, multiple preprocessing methods are used to expand the sample data, and the images are proportionally cropped and scaled to make the image dataset more complete; subsequently, to improve the accuracy of image recognition, a multilayer Convolutional Neural networks (CNN) is established; and the Adam algorithm is utilized to train the model parameters several times to determine the optimal hyperparameters, thus overcoming the defects of the multilayer neural network that has a local optimal search. The experimental results show that compared with the random forest method, this system has the characteristics of high-speed recognition and high accuracy in fruit picking, and can quickly and accurately recognize fruit images, with the recognition speed of a single image taking only 0.081 seconds, and the recognition accuracy reaching more than 97.35%. The method has important theoretical and application value and provides an effective means for automatic fruit recognition.

关键词： Visualization Image recognition Accuracy Random access memory Nonhomogeneous media Real-time systems Character recognition

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Crop-picking Robot Grasping System Based on Deep Learning

Crop-picking Robot Grasping System Based on Deep Learning

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Qike Wu Yihan Liu Rui Ma Shaoru Zhang Sai Gao Jielu Zhang Fang Lin Luo College of Engineering Hebei Normal University Shijiazhuang China Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology Jiangsu Tailong Reducer Co. LTD Taizhou China Jiangsu Tailong Reducer Co. LTD Taizhou China School of Electrical and Electronic Engineering Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

The crop-picking system plays a key role in automatic picking. In this study, a set of crop-picking robot grasping system based on convolutional neural network (CNN) was designed by using deep learning technology. Firstly, a variety of preprocessing methods are used to expand and process the image data to ensure the diversity and integrity of the sample data. Then, CNN and gradient descent (GD) algorithm were combined to optimize the training model to identify the types of crops accurately. Then, the lidar used ToF (Time of Flight) technology and AGglomerative NESting(AGNES) algorithm to obtain the key positioning points. After excluding invalid cluster centers, the identification results were accurately located. The experimental results show that compared with the 3D pose estimation method of the manipulator based on multi-view, the calculation error of the 3D coordinates of crops is smaller, and the reasoning speed for a single image is only 0.000316 seconds. Finally, the comprehensive accuracy of the system reaches 97.58%, which provides practical technical support for automatic crop picking.

关键词： Training Three-dimensional displays Accuracy Laser radar Robot kinematics Pose estimation Crops

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Traffic Matrix Prediction in SDN based on Spatial-Temporal Residual Graph Convolutional network

Traffic Matrix Prediction in SDN based on Spatial-Temporal R...

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第35届中国控制与决策会议

作者： Xintong Wang Yibo Sun Xuan Wang Enliang Wang Zhixin Sun Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Post Industry Technology Research and Development Center of the State Posts Bureau (Internet of Things Technology) Nanjing Universiry of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and Telecommunications School of Information Technology and Electrical Engineering The University of Queensland Brisbane

Traffic prediction is the basis for dynamic network services and resource *** Defined networking(SDN) provides a global view for network measurement and management,and a reliable way for network information collection and network traffic ***,SDN traffic prediction model enabled by deep learning mainly focuses on mining the temporal correlation of traffic data,while the modeling of its spatial correlation remains to be *** the perspective of the problem,a novel Spatial-Temporal Residual Graph Convolutional network(STRGCN) model for SDN Traffic Matrix(TM) prediction is *** with SDN network topology,TM prediction is defined as time series data prediction in non-Euclidean *** GatedConv Blocks are based on Gated Convolutional Layer,and Spatial Graph-Conv Blocks are based on Graph Convolutional network(GCN).Through identity mapping between SpatialTemporal Conv Blocks,the deep spatial and temporal features extraction is *** by TM data from CERNET and GEANT datasets,the accuracy and the timely performance of our model are *** results suggest that the general performance of STRGCN for TM prediction in SDN is superior to the current deep learning models.

关键词： software defined networking traffic prediction traffic matrix graph convolutional neural network spatialtemporal residual graph network

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