基于化学学科认知障碍突破的中考复习教学实践* ——以“二氧化碳的性质”为例

分析学生对二氧化碳的3个典型学科认知障碍,以“二氧化碳的捕捉”为情境,通过“尝试捕捉二氧化碳”“谁在捕捉二氧化碳”“捕捉背后的原理”等3个主要环节,实现对学科认知障碍的突破,展示了一种新的中考复习教学思路。     

A Review of Wavelets for Digital Wireless Communication

Wavelets have been favorably applied in almost all aspects of digital wireless communication systems including data compression, source and channel coding, signal denoising, channel modeling and design of transceivers. The main property of wavelets in these applications is in their flexibility and ability to characterize signals accurately. In this paper recent trends and developments in the use of wavelets in wireless communications are reviewed. Major applications of wavelets in wireless channel modeling, interference mitigation, denoising, OFDM modulation, multiple access, Ultra Wideband communications, cognitive radio and wireless networks are surveyed. The confluence of information and communication technologies and the possibility of ubiquitous connectivity have posed a challenge to developing technologies and architectures capable of handling large volumes of data under severe resource constraints such as power and bandwidth. Wavelets are uniquely qualified to address this challenge. The flexibility and adaptation provided by wavelets have made wavelet technology a strong candidate for future wireless communication. Madan Kumar Lakshmanan was born in Chennai, India, in 1979. He received the B.E. (with distinction) in electrical engineering from the University of Madras, Chennai, India, in 2000. He joined the Indian Software firm, Polaris Software Labs Ltd., in 2000 where he wrote software for Telecommunication applications. At Polaris, he was awarded the “On The Spot Of Excellence Award” for his efforts. In 2003, he moved to the Indian Institute of Technology-Madras, to develop and establish a wireless communications network for rural connectivity. In 2004, he was awarded the Royal Dutch/Shell Chevning scholarship to pursue a Master degree in Telecommunications at the Delft University of Technology (TUDelft). At TUDelft he is affiliated to the International Research Center for Telecommunications-Transmission and Radar (IRCTR) where he is undertaking research in the field of wavelets applications in Wireless Communications. Homayoun Nikookar received his Ph.D. in Electrical Engineering from Delft University of Technology (TUDelft), The Netherlands, in 1995. From 1995 to 1998 he was a postdoc researcher at the International Research Center for Telecommunications-Transmission and Radar, TUDelft, where since 1999 he has been an Assistant Professor. Dr. Nikookar has done research on different areas of wireless communications, including wireless channel modeling, UWB, MIMO, multicarrier transmission, Wavelet-based OFDM and CDMA. He is a senior member of the IEEE.     

Enhancing cognitive radio systems with robust reasoning

Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with an effective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright © 2007 John Wiley & Sons, Ltd.     

PLC-RF无线传输网络中的能效最优设计方法

针对电力线通信（PLC）和射频（RF）无线通信混合传输的室内通信场景,提出了一种基于角度信息的信道状态信息（AI-CSI）的能效优先传输方案。首先,Wi-Fi无线网络和PLC网络分别作为主网络和次级网络,并且采用认知无线电技术来提高频谱效率的情况下,建立次级网络总能效最大化为目标函数的优化问题。其次,为了求解该问题,通过基于AI-CSI的迫零波束成形方法,获得波束成形权矢量,并进一步提出Dinkelbach与拉格朗日乘子法相结合的优化方法,进行最优的功率分配。最后,计算机仿真结果不仅验证了所提方案的有效性和优越性,而且分析了中继天线数和用户个数等典型参数对系统能效带来的影响,从而为实际系统设计提供了参考和依据。     

Short packet communication in underlay cognitive network assisted by an intelligent reflecting surface

We propose short packet communication in an underlay cognitive radio network assisted by an intelligent reflecting surface (IRS) composed of multiple reconfigurable reflectors. This scheme, called the IRS protocol, operates in only one time slot (TS) using the IRS. The IRS adjusts its phases to give zero received cumulative phase at the secondary destination, thereby enhancing the end-to-end signal-to-noise ratio. The transmitting power of the secondary source is optimized to simultaneously satisfy the multi-interference constraints, hardware limitations, and performance improvement. Simulation and analysis results of the average block error rates (BLERs) show that the performance can be enhanced by installing more reconfigurable reflectors, increasing the blocklength, lowering the number of required primary receivers, or sending fewer information bits. Moreover, the proposed IRS protocol always outperforms underlay relaying protocols using two TSs for data transmission, and achieves the best average BLER at identical transmission distances between the secondary source and secondary destination. The theoretical analyses are confirmed by Monte Carlo simulations.     

Throughput analysis in censoring-based cooperative cognitive radio network with energy harvesting

An energy harvesting (EH) and cooperative cognitive radio (CR) network (CRN) is studied in this paper where CR users transmit data through a primary user (PU) channel if the channel remains idle, else an optimal number CRs helps in transmission of PU. To achieve the optimum number of CRs (ONCR) involved in cooperation, a novel scheme based on a combination of channel censoring and total error is proposed. The performance of the proposed scheme is investigated under RF harvesting scenario. The EH is dependent on sensing decision and a CR source harvests energy from PU's RF signal. The harvested energy (HE) is split into two parts: One part is used by the CR network (CRN) for its own transmission, and the other part is used for supporting PU. The effect of the energy allocation factor on total throughput is also investigated. New expressions for optimal number of CRs and throughput are developed. The effect of network parameters such as sensing time, censoring threshold, and energy allocation parameter (EAP) on throughput is investigated. Impact of distance between nodes is also studied.     

太赫兹辐射对小鼠脑神经发生及认知能力的影响

为探究太赫兹(THz)辐射对成年小鼠海马齿状回亚颗粒区(SGZ)神经发生及对老年小鼠认知能力的影响,用0.14 THz波照射实验小鼠头部,10 min/次,2次/d,连续21 d,对照组小鼠进行相同时间的麻醉。采用5-溴脱氧尿嘧啶核苷(BrdU)染色评价新生细胞存活、BrdU/神经元细胞核(NeuN)染色评价细胞分化、Morris水迷宫(MWM)实验分析认知能力变化。实验结果表明：与对照组相比,0.14 THz 10 min/次,2次/d,连续照射21 d后,可有效促进成年小鼠SGZ神经发生,成年小鼠SGZ新生细胞存活明显改善(P<0.05),新生细胞向神经元的分化无明显改变。老年小鼠逃避潜伏期,运动寻找轨迹无显著变化,THz辐射没有影响老年小鼠的空间认知能力。     

提升小波与能量检测联合优化下的频谱感知方法北大核心CSCD

传统的频谱感知方法易受噪声波动的干扰,而且在低信噪比的无线通信条件下检测精度较差。通过结合提升小波去噪与动态门限能量检测算法,能有效提高传统频谱感知方法的抗噪声性能和检测精度。首先对含噪信号进行奇偶抽样,分解信号,去除噪声部分,再重构为去噪新信号,然后通过能量检测方法来统计信号的能量积累,设置动态门限,最后以动态门限判断用户信号是否存在。提升小波去噪能够有效地去除采样信号中的噪声,减少噪声对能量检测法检测精度的影响,动态门限能根据噪声波动进行调整来适应复杂的噪声环境。仿真结果表明,提升小波去噪结合动态门限能量检测算法相比于传统的频谱感知要有更优的检测精度。此方法不但提高了其对不确定噪声的抵抗性,使之能适应复杂的通信环境,而且提高了频谱感知过程的可靠性。     

静息态脑功能连接在不同认知状态老年人中的应用

目的探讨不同认知状态老年人静息态脑功能连接情况。方法收集阿尔茨海默病患者（AD组）13例、轻度认知功能障碍患者（MCI组）12例、认知正常老年人（对照组）13例，进行磁共振扫描，应用局部一致性（ReHo）方法确定种子点，并进行静息态功能连接分析。结果在静息状态下，AD组和MCI组患者的后扣带回区域、额叶内侧、前额叶背外侧和顶叶部分区域的ReHo均较高。与对照组相比，MCI患者左侧颞叶（颞下回及颞中回）、海马旁回及右侧枕叶舌回、皮层下的部分脑区ReHo显著降低；而右侧额叶（额上回、额中回及中央前回）、顶上小叶及左侧梭状回、颞上回、额下回的部分脑区ReHo显著增高。AD患者变化更加明显，额叶-顶叶，颞叶-脑岛，颞叶-枕叶之间的长距离连接降低，但是额叶内部和枕颞间、丘脑及颞叶间的功能连接增高，3组间差异有统计学意义（P＜0.05）。结论3组不同认知状态老年人的功能连接不同，有助于AD、MCI患者及正常老年人的鉴别诊断。     

Generalized nets in neurology: an example of mathematical modelling

The purpose of this paper is to explain the basic theory of generalized nets (GNs) and their applications in the context of the differential diagnosis of neurological diseases. We define formally the concepts of a GN and transitions of a GN and also outline some remarks on their theory. The work here constructs an example which aims to trace the process of diagnosing different signs and symptoms in neurology. This will enable the interested reader to see the scope of nets in general as tools for the modelling, simulation, optimization and control of real processes.