The memristor is postulated by Chua (IEEE Trans Circuit Theory, 18(5):507–519, 1971) as the fourth fundamental passive circuit element and experimentally validated by HP labs in 2008. It is an emerging device in nano technology that provides low power consumption as well as high density. In this paper, the staircase memristor model is discussed and investigated with the HP memristor model. By connecting HP memristor models in series or parallel, a staircase memristor model could be constructed and demonstrate staircase behavior. By comparing the staircase memristor model to the general HP memristor model, distinctions between them are demonstrated and this lends themselves to different applications. Further to the memristor-based cellular neural network (CNN), the structure is modified and applied to the echo state network (ESN) where memristors are used as local connections. By this means, the ESN benefits from the simple structure, non-volatility and low power feature of memristors and therefore the complexity and size of the original ESN architecture can be reduced. Meanwhile, the simpler structure still has satisfactory performance in applications compared with the original ESN.

Memristor was initially introduced by Professor Leon Chua in 1971 as the fourth passive fundamental circuit element. In this chapter, we revise and extend the hard switching memristor model, that is developed based on the HP's memristor model. This model matches most of the memristor characteristics such as the pinched hysteresis loops (the fingerprint of memristive devices). Different materials of memristive devices could require different memristor models, which are sufficiently accurate and easily to do their simulations. The hard switching memristor model could be fit to specific materials of memristive devices and particular memristive systems. In some cases, this model is more reliable and flexible than the existing models of memristive devices.

Memristors are labelled as a significant candidate of building a better storage structure, higher capacity and more efficient performance. Research shows that the density of a memristor array could be 100 times higher compared to the dynamic random-access memory, hence it is possible to build a storage structure with high density. Tracing back to the year 1971, Prof. Chua firstly proposed the memristor as the fourth circuit element and defined it as memory-resistor, it is a new emerged passive device with two terminals that have the ability to respond to the history or past memory of the current that passes within it. We hereby, propose comprehensive numerical models based window functions. When the parameters of the models are varied, various dynamic behaviours can be obtained and discussed.

Memristor is a newly emerged device in nano-technology that provides low power consumption as well as high density. In this paper, the staircase memristor which has multiple stable resistive states is modelled and exploited, and its possible applications are investigated. Comparing with the analog memristor model proposed by HP, the staircase memristor model shows a significant delayed-switching effect which yields a range of more or less discrete memristance values rather than continuous values. We present using the staircase memristor to produce staircase waveforms which can be used in testing circuit or sampling oscilloscope. Besides, staircase memristors are used in cellular neural network (CNN) circuits to represent the connection weights between CNN cells. Benefit from the simple structure, non-volatility and low power properties, the complexity and size of CNN circuits can be reduced.

Content addressable memory is a novel storage device that can save data in its cells, which could be read, written and searched on the basis of their contents. This paper presents Memristor content addressable memory (M-CAM) structures that are formed of M-CAM cells, which compare searched data and stored data then give a cell output signal to be kept in its comparator. After the comparison in each cell, reading is enabled at each row of all comparators. The current of each row could be measured, if some comparators are high resistance (0) in a row, the current of that row could be lower than the current from another row where all comparators are low resistance (1), which means the corresponding row is a match. The main emphasis of this paper is to highlight the process of the M-CAM comparison and how to get the match entry. Our experimental results show that M-CAM is able to not only query accurately, but also fuzzy lookup through setting the memristor off-to-on resistance ratio.

Because of the rapid development of transgenic maize, the potential effect of transgene flow on seed purity has become a major concern in public and scientific communities. Setting a proper isolation distance in field experiments and seed production is a possible solution to meet seed-quality standards and ensure adventitious contamination of products is below a specific threshold. By using a Gaussian plume model as basis and data recorded by meteorological stations as input, we have established a simple regionally applicable maize gene-flow model for prediction of the maximum threshold distances (MTD) at which gene-flow frequency is equal to or lower than a threshold value of 1 or 0.1 % (MTD1%, MTD0.1%). After optimization of the model variables, simulated outcrossing rate was a good fit to data obtained from field experiments (y = 1.156x, R 2 = 0.8913, n = 30, P < P 0.01). In the process of model calibration, it was found that only 15.82 % of the total amount of the pollen released by each plant participated in the dispersal process. The variable “a” for genetic pollen competitiveness between donor and recipient was introduced into our model, for the “Zinuo18” and “Su608” used, “a” was 17.47. Finally, the model was successfully used in the spring maize-growing region of Northeast China. The range of MTD1% and MTD0.1% in this region varied from 10 m to 49 m and from 17 m to 125 m, respectively.

The memristor (short for memory resistor) was reasoned from symmetry arguments in 1971 by Leon Chua as the fourth fundamental circuit element. Memristors are novel nano-devices, which could be used in many applications, such as memories and neural networks. Among the emerging nanotechnologies options, memristors have become a very promising candidate during post-CMOS times because of its better nonvolatility, shorter switching time, higher capacity and lower power consumption and more compatibility with CMOS. A new memristor model has been developed and introduced in this paper. Different memristive systems may require different memristor models, which are sufficiently accurate and simple for computing. Hard Switching Memristor Model (HSMM) could be fit to specific memristive system and particular chemical materials. In some cases, HSMM is more authentic than the existing memristor models.

Memristor is an emerging topic that has experienced a great development in the last few years. The concept of memristor was introduced as the fourth fundamental circuit element by Prof. Chua in the year 1971. A memristor could be one kind of electronic devices of novel nano-scale structures, which could be used in many applications, such as associative memories and neural networks. Among the emerging nano-technologies, memristors have become a very promising candidate because of its shorter switching time, higher capacity and lower power consumption. In this paper, we introduce that a new window function can be used to model the memristor based content addressable memory. Different memristive applications may require different memristor models, which are sufficiently accurate and simple for computing. This model could fit specific chemical materials in content addressable memory. In some cases, this model is more authentic and flexible than those existing memristor models for content addressable memory.

The memristor is an emerging device in nanotech-nology, which provides nanoscale size, low power consumption and high density. It has been widely used in neural networks where memristors are utilised as synaptic weights (connection sites) between neurons. In particular, current research focuses on the spiking-time dependant plasticity (STDP) which is an important synaptic modification rule based on the timings of the pre- and postsynaptic spikes. It has been revealed that the memristor is intrinsically similar with a synapse and capable to mimic the long-term depression (LTD) and long-term potentiation (LTP) behaviours under the STDP rule. Further studies show that the associative learning can be mimicked by memristive neural networks through proper learning rules. However, such studies focus on demonstrating the process of building the association, which lacks the retention loss process of forgetting the association. In this paper, a rate-based term is proposed to improve the previous model, and therefore the retention loss process can be implemented in the given network. The results demonstrate that the network with the improved model can successfully reproduce the retention loss process meanwhile retaining the process of building the association.

Synaptic plasticity has been widely assumed to be the mechanism behind memory and learning, in which, synapse has a critical role. As a newer biologic update rule to hebbian learning, spiking-time dependent plasticity (STDP) concerns on the temporal order of presynaptic spike and postsynaptic spike which will change the strength of, the connection site of neurons, synapse. In this paper a different way is shown to utilise the novel element memristors to implement a supervised STDP. Because the resistance of memristor depends on its past states, researchers are particularly interested in using such functionality to mimic synaptic connection. Furthermore, benefit from the nano size of memristors and its crossbar structure, large scale neural networks could be implemented. In this supervised STDP, each spike arrival will be assumed to leave a trace which decays exponentially and spikes interact under all-to-all interaction. Depending on the temporal order, memristor synapse will weaken or strengthen the connection of presynaptic neuron and postsynaptic neuron. The temporal all-to-all interaction is implemented during the simulation with training samples. We show that, by combining the memristors, a supervised STDP neural network can be built and learn from the temporal order of presynaptic spike and postsynaptic spike of the training samples.

Memristor is short for memory resistor, which provides a functional relation between flux and charge. Professor Leon Chua named and formulated it in his paper "Memristor-The Missing Circuit Element" in 1971. The memristor has a special effect, 'the delayed switching effect', which is the memristor switching takes place with a time delay. Content addressable memory (CAM) is a type of associative memories that is adopted in high speed searching applications. The new Memristor-CAM cell has been proposed, which included two memristors; one is used as an important part of the comparator that is instead of the traditional logic gate; and another one is for storing the data as a storage element. In this paper, we report that applying the memristor delayed switching effect to the novel design of the Memristor Content Addressable Memory (M-CAM) cell. The delayed switching effect is used to control the changing time of the memristor's state, which can enhance the performance, decrease the searching time and save energy.

We designed Adaptive Neuromorphic Architecture (ANA) that self-adjusts its inherent parameters (for instance, the resonant frequency) naturally following the stimuli frequency. Such an architecture is required for brain-like engineered systems because some parameters of the stimuli (for instance, the stimuli frequency) are not known in advance. Such adaptivity comes from a circuit element with memory, namely mem-inductor or mem-capacitor (memristor’s sisters), which is history-dependent in its behavior. As a hardware model of biological systems, ANA can be used to adaptively reproduce the observed biological phenomena in amoebae.

Pollen dispersal from rice results in gene flows. The transgenic flow from transgenic rice can be risky for environmental safety. In order to investigate the pollen disposal processes,field experiments with a hybrid rice (Oryza sativa L. cv. indica) were conducted in Nanjing (32 01'N,118°52'E) and Lishui,(31°35'N,119°11'E) Jiangsu,China in 2007 and 2009. The concentration of pollen grain in the air was measured and the gradient data of the environment factors were obtained from a meteorological tower. A model for simulating rice pollen dispersal was developed based on the Gaussian plume model. Least square regression was used to estimate the mean pollen grain release rate. Independent experimental data were used to validate the model. The results showed that the peak of pollen release in the filed scale occurred from 09:00 to 11:00 during 1-5 day after anthesis. The amount of pollen releasing was significantly dependent on the flowering number (r=0.880**). The simulated rice pollen concentration agreed well with the measured one. The root mean square error and the relative error between the simulated and the measured values were,respectively,0.509 grain/(m2·d) and 3.47%. The results indicate that this pollen dispersal model can properly simulate pollen distribution in the air. Improvements,however,are needed for the model to predict the concentration of pollen grain,especially at the larger scale field.

我国水资源稀缺,人均淡水资源正以惊人速度减少,而咸水、微咸水量则不断增长.但目前这类水资源基本上未得到利用,浪费严重,若能加以利用、转化增值将对区域生态经济产生不可估量的效益[1].

We aimed to establish a rice gene flow model based on (i) the Gaussian plume model, (ii) data from a three-location x 3-yr field experiment on transgene flow to common rice cultivars (Oryza sativa), male sterile (ms) lines (O. sativa) and common wild rice (Oryza rufipogon), and (iii) 32-yr historical meteorological data collected from 38 meteorological stations in southern China during the rice flowering period. The concept of the gene flow coefficient (GFC) is proposed; that is, the ratio of the transgene flow frequency (G%) obtained from field experiments to the aggregated pollen dispersal frequency (P%) calculated based on the pollen dispersal model. The maximum distances of gene flow (MDGF) to traditional rice cultivars, ms lines, and common wild rice at a threshold value of either 1.0 or 0.1% were determined. The MDGF and its spatial distribution in southern China show that the gene flow pattern is significantly affected by the monsoon climate, the topography, and the outcrossing ability of recipients. We believe that the information provided in this study will be useful for the risk assessment of transgenic rice in other rice-growing regions.

A new method of atmospheric pollutant total emission control (APTEC) based on the genetic algorithm (GA) was developed; a case of total emission control (TEC) of SO2 for Liuzhou City is presented to demonstrate its effectiveness. This method does not require the difficult-to-estimate permitted regional total emission amount (PRTEA) which is required by conventional methods.GA, an effective self-adapted global searching optimization algorithm, is employed for estimating the source strength distribution from the concentration distribution of key grid points of the study area. The calculated source strengths meet the demands of TEC. Using this method, the source strengths are first coded as chromosomes which are then placed in the simulated evolution environment for model simulation. After generations of natural selection, the best chromosome will carry the information of the optimal source strength distribution pattern. The GA-base APTEC method is feasible, simple and effective.