Energy efficiency aware dynamic rate and power adaptation in carrier sensing based WLANs under Rayleigh fading and shadowing

We consider the problem of energy efficiency aware dynamic adaptation of data transmission rate and transmission power of the users in carrier sensing based Wireless Local Area Networks(WLANs)in the presence of path loss,Rayleigh fading and log-normal shadowing.For a data packet transmission,we formulate an optimization problem,solve the problem,and propose a rate and transmission power adaptation scheme with a restriction methodology of data packet transmission for achieving the optimal energy efficiency.In the restriction methodology of data packet transmission,a user does not transmit a data packet if the instantaneous channel gain of the user is lower than a threshold.To evaluate the performance of the proposed scheme,we develop analytical models for computing the throughput and energy efficiency of WLANs under the proposed scheme considering a saturation traffic condition.We then validate the analytical models via simulation.We find that the proposed scheme provides better throughput and energy efficiency with acceptable throughput fairness if the restriction methodology of data packet transmission is included.By means of the analytical models and simulations,we demonstrate that the proposed scheme provides significantly higher throughput,energy efficiency and fairness index than a traditional non-adaptive scheme and an existing most relevant adaptive scheme.Throughput and energy efficiency gains obtained by the proposed scheme with respect to the existing adapting scheme are about 75%and 103%,respectively,for a fairness index of 0.8.We also study the effect of various system parameters on throughput and energy efficiency and provide various engineering insights.

Evaluating the Conservation Efforts of Multi-Projects Using Remote Sensing and Light Use Efficiency Model: A Case of Nyungwe Forest National Park, Rwanda

This paper investigates the effectiveness of conservation efforts in the Nyungwe Forest National Park (Nyungwe). The forest is one of the six key landscapes identified for conservation in the Albertine Rift because it hosts many threatened species. As such, a number of different stakeholders have been involved in its conservation since 1987;yet, studies that emphasize and evaluate the success of these conservation efforts are limited. We combined a rapid and relatively low cost remotely-sensed data and the Light Use Efficiency model to generate forest conservation indicators such as NDVI, forest canopy Net Primary Productivity and carbon sequestered from 1986 to 2010. The influence of topographic and climatic factors on these indicators was examined. The supervised classifier was used to catalogue the area into Forest, Wetland, and Bareland. The forest was the major category (above 90%) of Nyungwe relative to wetland and bareland. Based on degradation intensity, two distinctive periods were realised;the first period spans 8 years (1986-1994) whereas the second spans 16 years (1994-2010). The former degradation intensity period is 10 times higher than the latter period. Although the size of forest recovered up to 90%, the daily NPP and carbon sequestration capacity decreased by 37.1% (i.e. NPP 6.5 Mg tons in 1986 to 4.1 Mg tons in 2010). Areas of the forest that are physically constrained (high altitude) had a higher degradation. Guided by our indicators, there is an overall success in conservation efforts, but efforts were mostly concentrated in accessible areas. Therefore, conservation efforts that aim to respond to degradation of the inaccessible areas of the forest should be stressed in the management plan of the park.

Joint Optimal Energy-Efficient Cooperative Spectrum Sensing and Transmission in Cognitive Radio

In order to improve the energy efficiency(EE) in cognitive radio(CR), a joint optimal energy-efficient cooperative spectrum sensing(CSS) and transmission in multi-channel CR is proposed in this paper. EE is described as a tradeoff between the throughput and the entirely consumed power. A joint optimization problem is formulated to maximize EE by jointly optimizing local sensing time, number of cooperative sensing secondary users(SU), transmission bandwidth and power. A combined optimization algorithm of bi-level optimization, Polyblock optimization and Dinkelbach's optimization is proposed to solve the proposed non-convex optimization problem effectively. The simulation results show that, compared with throughput maximization model(TMM), the energy efficiency maximization model(EEMM) improves EE of the CR system and limits the excessive power consumption effectively.

Efficient Solution to Electromagnetic Scattering Problems of Bodies of Revolution by Compressive Sensing

Under the theory structure of compressive sensing （CS）, an underdetermined equation is deduced for describing the discrete solution of the electromagnetic integral equation of body of revolution （BOR）, which will result in a small-scale impedance matrix. In the new linear equation system, the small-scale impedance matrix can be regarded as the measurement matrix in CS, while the excited vector is the measurement of unknown currents. Instead of solving dense full rank matrix equations by the iterative method, with suitable sparse representation, for unknown currents on the surface of BOR, the entire current can be accurately obtained by reconstructed algorithms in CS for small-scale undetermined equations. Numerical results show that the proposed method can greatly improve the computgtional efficiency and can decrease memory consumed.

Energy Efficiency-Based Decision Making in Cognitive Radio Networks

As a constraint for smart devices,energy consumption has attract people's attention for a long time period. How to get higher resource utilization with less energy consumption is a challenge for cognitive radio networks. Secondary users have to participate in spectrum sensing at the cost of energy and access idle spectrum without interfering primary users. However,not all participating secondary users can access idle spectrum. How to ensure the participation users access spectrum efficiently with a larger probability is an urgent problem to be solved. We propose an Energy Efficiency-based Decision Making(EEDM) for cognitive radio networks,which fully considers residual energy and probability of obtaining spectrum resources. Simulation and analysis show that the proposed scheme can maximize proportion of allocated users under the premise of ensuring the accuracy of spectrum sensing,then balance users' energy consumption and access efficiency,so as to effectively improve the utilization of spectrum resources.

A More Efficient Approach for Remote Sensing Image Classification

Over the past decade,the significant growth of the convolutional neural network(CNN)based on deep learning(DL)approaches has greatly improved the machine learning(ML)algorithm’s performance on the semantic scene classification(SSC)of remote sensing images(RSI).However,the unbalanced attention to classification accuracy and efficiency has made the superiority of DL-based algorithms,e.g.,automation and simplicity,partially lost.Traditional ML strategies(e.g.,the handcrafted features or indicators)and accuracy-aimed strategies with a high trade-off(e.g.,the multi-stage CNNs and ensemble of multi-CNNs)are widely used without any training efficiency optimization involved,which may result in suboptimal performance.To address this problem,we propose a fast and simple training CNN framework(named FST-EfficientNet)for RSI-SSC based on an EfficientNetversion2 small(EfficientNetV2-S)CNN model.The whole algorithm flow is completely one-stage and end-to-end without any handcrafted features or discriminators introduced.In the implementation of training efficiency optimization,only several routine data augmentation tricks coupled with a fixed ratio of resolution or a gradually increasing resolution strategy are employed,so that the algorithm’s trade-off is very cheap.The performance evaluation shows that our FST-EfficientNet achieves new state-of-the-art(SOTA)records in the overall accuracy(OA)with about 0.8%to 2.7%ahead of all earlier methods on the Aerial Image Dataset(AID)and Northwestern Poly-technical University Remote Sensing Image Scene Classification 45 Dataset(NWPU-RESISC45D).Meanwhile,the results also demonstrate the importance and indispensability of training efficiency optimization strategies for RSI-SSC by DL.In fact,it is not necessary to gain better classification accuracy by completely relying on an excessive trade-off without efficiency.Ultimately,these findings are expected to contribute to the development of more efficient CNN-based approaches in RSI-SSC.

Characterization of Regional Combustion Efficiency usingΔXCO:ΔXCO_(2)Observed by a Portable Fourier-Transform Spectrometer at an Urban Site in Beijing

Measurements of column-averaged dry-air mole fractions of carbon dioxide and carbon monoxide,CO_(2)(XCO_(2))and CO(XCO),were performed throughout 2019 at an urban site in Beijing using a compact Fourier Transform Spectrometer(FTS)EM27/SUN.This data set is used to assess the characteristics of combustion-related CO_(2)emissions of urban Beijing by analyzing the correlated daily anomalies of XCO and XCO_(2)(e.g.,ΔXCO andΔXCO_(2)).The EM27/SUN measurements were calibrated to a 125HR-FTS at the Xianghe station by an extra EM27/SUN instrument transferred between two sites.The ratio ofΔXCO overΔXCO_(2)(ΔXCO:ΔXCO_(2))is used to estimate the combustion efficiency in the Beijing region.A high correlation coefficient(0.86)betweenΔXCO andΔXCO_(2)is observed.The CO:CO_(2)emission ratio estimated from inventories is higher than the observedΔXCO:ΔXCO_(2)(10.46±0.11 ppb ppm^(−1))by 42.54%-101.15%,indicating an underestimation in combustion efficiency in the inventories.DailyΔXCO:ΔXCO_(2)are influenced by transportation governed by weather conditions,except for days in summer when the correlation is low due to the terrestrial biotic activity.By convolving the column footprint[ppm(μmol m-2 s-1)-1]generated by the Weather Research and Forecasting-X-Stochastic Time-Inverted Lagrangian Transport models(WRF-X-STILT)with two fossil-fuel emission inventories(the Multi-resolution Emission Inventory for China(MEIC)and the Peking University(PKU)inventory),the observed enhancements of CO_(2)and CO were used to evaluate the regional emissions.The CO_(2)emissions appear to be underestimated by 11%and 49%for the MEIC and PKU inventories,respectively,while CO emissions were overestimated by MEIC(30%)and PKU(35%)in the Beijing area.

Polish Teachers in Changing Reality

Authors of the text present Polish context of teachers’ professional promotion and teachers’ competencies. The text also has its empirical dimension-a research about teachers’ sense of life quality has been done. There are teachers’ expressions, collected and quoted during qualitative research. Teachers’ thoughts are concerned about the sense of life quality in the context of working at lower secondary school.

On the Low-Efficiency in College English Teaching In China

Great changes have taken place in college English teaching in China in recent decades.Different teaching theories have been introduced into English teaching in many colleges and universities.However,college English,as one of the required courses,is faced with a lot of complaints and criticism from the students and teachers as well as the society.The low efficiency in college English teaching is one of the problems,which attract the attention of all those concerned.It is of great significance to find out why students spend so much time,money and energy on English learning but are still at loss how to use English for communication.Surely,teaching and learning English is not an easy job.It involves a number of complicated interrelated factors.The article is meant to discuss the factors that are closely related to the low-efficiency in college English teaching in china,such as education notion,usage of textbooks,teacher quality as well as students motivations,aiming to make people have a clear idea of the problem so that some things can be done to improve the quality and efficiency of college English teaching in China.

面向新时代地方高校师范生培养的“遥感概论”教学内容与方法创新

面向新时代背景下高素质专业化中学地理教师的培养需求,探讨了地理科学专业“遥感概论”课程的教学内容与方法创新。抽样调查了当前国内地理科学专业“遥感概论”课程的开设情况。从对中学地理教学的支持、师范生的学习困难和课程思政三个方面分析了开展教学创新的必要性。针对课程存在的问题,提出了一系列创新策略,包括重构课程目标和内容以加强对中学地理教学的支撑,采用直观教学法以提升教学效率以及课程思政的科学建设与合理融入等。

新农科背景下遥感地学分析课程混合式教学效果探究

新农科是以现代农业需求为导向,融合信息技术、生物技术、环境科学等领域,对传统农业科学进行创新改造的学科体系。而遥感技术通过精准数据分析提高农业生产效率和资源利用率,正是新农科的重要组成部分。在此背景下,该研究通过调查问卷探讨遥感地学分析课程的混合式教学效果,评估学生在知识掌握、实践能力提升和学习满意度方面的表现。结果表明,混合式教学模式不仅提高学生对遥感技术的理解与应用能力,还增强其自主学习能力,且学生对该模式表现出较高的满意度。该研究为新农科背景下课程教学改革提供有益参考。

Energy-aware cooperative spectrum sensingfor underground cognitive sensor networks

With the development of wireless technologies,multifarious standards are currently used in the underground coal mine communication systems.In this paper,the coexistence of 802.15.4 based wireless senser networks （WSNs） with other wireless networks using cognitive radio technique are discussed.Multiple sensor nodes are involved in the spectrum sensing to avoid the interference from other wireless users.The more the sensor nodes cooperate in the sensing,the better the detection performance can be obtained; however,more energy is consumed.How to get the tradeoff between energy efficiency and detection performance is a key problem.According to the requirements for detection,we first give the least required detection time of a single sensor node.Then,the voting fusion rule is adopted for the final decision making.Finally,the relationship between final detection performance and energy consumption is analyzed.

学校支持对中小学心理健康教师职业满意度的影响:被看重感与成就感的链式中介作用

目的:探讨学校支持对中小学心理健康教师职业满意度的影响以及被看重感和成就感的中介作用机制。方法:采用相关量表,对214名中小学心理健康教师进行调查。结果:①不同性别、年龄、学历的心理健康教师职业满意度不存在显著差异(P>0.05),但不同婚姻状况(t=-3.41,P<0.01)、职称(F=4.37,P<0.01)以及任职学校类型(t=2.72,P<0.01)的心理健康教师职业满意度呈现显著差异。②学校支持正向预测中小学心理健康教师职业满意度(B=0.49,P<0.001)。③被看重感与成就感在学校支持和中小学心理健康教师职业满意度之间起链式中介作用(效应值为0.04,95%置信区间下限和上限分别为0.02与0.07)。结论:学校支持不仅直接影响中小学心理健康教师职业满意度,也可以通过被看重感与成就感的链式中介间接作用于中小学心理健康教师职业满意度。

师范专业认证背景下地理科学专业“遥感概论”实验教学探索

“遥感概论”是地理科学专业的基础必修专业课程,在师范专业认证的标准和要求下,结合地方型院校的办学定位,以OBE理念为指导,分析了地理科学专业“遥感概论”实验教学中存在的教学内容、教学模式和课程考核机制不完善等问题,从课程教学目标、教学内容、教学模式和考核环节等方面提出了相应的优化措施,并就师范专业认证需要的课程目标与毕业要求的关联矩阵和毕业目标达成度进行了计算.最后通过问卷调查和同行评价的方式,对“遥感概论”实验教学改革的效果进行评价.结果表明,改革后的教学模式和内容更能够有效调动学生的学习积极性,进而提高学生的实践能力和创新能力.

A novel weighed cooperative bandwidth spectrum sensing for spectrum occupancy of cognitive radio network

In cognitive radio network(CRN), a secondary user(SU) may utilize the spectrum resource of the primary user(PU) and avoid causing harmful interference to the primary network(PN) via spectrum sensing. In the traditional time spectrum sensing, the SU cannot detect the PU's presence during its transmission, thus increasing interference to the PN. In this work, a novel weighed cooperative bandwidth spectrum sensing method is proposed, which allows multiple SUs to use part of the bandwidth to perform cooperative spectrum sensing throughout the whole frame in order to detect the PU's reappearance in time. The SU's spectrum efficiency is maximized by jointly optimizing sensing bandwidth proportion, number of cooperative SUs and detection probability, subject to the constraints on the SU's interference and the false alarm probability. Simulation results show significant decrease on the interference and improvement on the spectrum efficiency using the proposed weighed cooperative bandwidth spectrum sensing method.

Research on Efficient Seismic Data Acquisition Methods Based on Sparsity Constraint

In actual exploration,the demand for 3D seismic data collection is increasing,and the requirements for data are becoming higher and higher.Accordingly,the collection cost and data volume also increase.Aiming at this problem,we make use of the nature of data sparse expression,based on the theory of compressed sensing,to carry out the research on the efficient collection method of seismic data.It combines the collection of seismic data and the compression in data processing in practical work,breaking through the limitation of the traditional sampling frequency,and the sparse characteristics of the seismic signal are utilized to reconstruct the missing data.We focus on the key elements of the sampling matrix in the theory of compressed sensing,and study the methods of seismic data acquisition.According to the conditions that the compressed sensing sampling matrix needs to meet,we introduce a new random acquisition scheme,which introduces the widely used Low-density Parity-check(LDPC)sampling matrix in image processing into seismic exploration acquisition.Firstly,its properties are discussed and its conditions for satisfying the sampling matrix in compressed sensing are verified.Then the LDPC sampling method and the conventional data acquisition method are used to synthesize seismic data reconstruction experiments.The reconstruction results,signal-to-noise ratio and reconstruction error are compared to verify the seismic data based on sparse constraints.The LDPC sampling method improves the current seismic data reconstruction efficiency,reduces the exploration cost and the effectiveness and feasibility of the method.

Optimal Energy-Efficient Transmission for Hybrid Spectrum Sharing in Cooperative Cognitive Radio Networks

In order to improve the energy efficiency(EE)in cognitive radio(CR),this paper investigates the joint design of cooperative spectrum sensing time and the power control optimization problem for the secondary user systems to achieve the maximum energy efficiency in a cognitive network based on hybrid spectrum sharing,meanwhile considering the maximum transmit power,user quality of service(QoS)requirements,interference limitations,and primary user protection.The optimization of energy efficient sensing time and power allocation is formulated as a non-convex optimization problem.The Dinkelbach’s method is adopted to solve this problem and to transform the non-convex optimization problem in fractional form into an equivalent optimization problem in the form of subtraction.Then,an iterative power allocation algorithm is proposed to solve the optimization problem.The simulation results show the effectiveness of the proposed algorithms for energy-efficient resource allocation in the cognitive network.

Research on High Altitude Remote Sensing Building Segmentation Based on Improved U-Net Algorithm

Building extraction from high resolution remote sensing image is a key technology of digital city construction[14].In order to solve the problems of low efficiency and low precision of traditional remote sensing image segmentation,an improved U-Net network structure is adopted in this paper.Firstly,in order to extract efficient building characteristic information,FPN structure was introduced to improve the ability of integrating multi-scale information in U-Net model;Secondly,to solve the problem that feature information weakens with the deepening of network depth,an efficient residual block network is introduced;Finally,In order to better distinguish the target area and background area in the image and improve the precision of building target edge detection,the cross entropy loss and Dice loss were linearly combined and weighted.Experimental results show that the algorithm can improve the image segmentation effect and improve the image accuracy by 18%.

Measuring Teacher Sense of Efficacy: Insights and Recommendations Concerning Scale Design and Data Analysis from Research with Preservice and Inservice Teachers in China

In this study,issues concerning the design of scales for measuring teacher sense of efficacy(TSE)are first identified with particular attention to the Teacher Sense of Efficacy Scale(TSES).Psychometric issues concerning analysis and reporting of TSE data are subsequently identified.Recommendations are offered about all identified issues,and these recommendations are taken into account when obtaining and analyzing TSE data from Chinese mainland preservice and inservice teachers.Exploratory factor analyses yielded a single factor for both samples as well as for four subgroups within the inservice teacher sample.Results also provided insights about scale design as well as the TSES being limited for capturing the breadth of TSE.Suggestions are made for improvements in the assessment of TSE.

Phase type delay distribution with PU return and sensing error in CR networks

Many studies have been done in cognitive radios to maximize the network efficiency. There is always a trade-off between sensing and transmission time which has been addressed fully in the literature. There is also some missed idle times during the waiting phase when secondary user finds the channel busy. Therefore, the longer the transmission time is, the higher the successfully delivered bits would be and the higher the missed idle times in the waiting stage would be expected. In this work, we formulate these missed idle times to addressed this trade-off. Furthermore, interference due to PU re-occupancy is modelled in successful transmitted time and in collision probability. Moreover, we derive secondary user's packet delay distribution using phase type model at which retransmission due to collision results from both sensing error and PU re-occupancy is considered. Thanks to derived delay distribution, any statistical moments of delay could be found as a closed form mathematical expression. These moments can be used as constraints for applications with delay sensitive packets. Numerical results are given to clarify the network metrics. Finally, the optimized values for sensing and transmission time are computed using genetic algorithm in order to maximize access efficiency.