用于微电极阵列和光同时记录的神经传感器平台设计

微电极阵列(MEA,Microelectrode Arrays)和光成像记录已成为从神经网络收集可靠数据的重要工具。然而,没有商业平台被设计用于微电极阵列和光成像的同时记录,由于商业平台的物理限制和高成本,无法对其进行定制。为了解决这个问题,开发了一个小体积的定制神经记录平台,它很容易集成到现有的显微镜环境中。该系统的特点是低输入参考噪声(<2μV)、紧凑的尺寸以及有同时执行微电极阵列和光成像记录的能力。使用该平台对微电极阵列上的神经元进行测量验证表明,该平台为研究钙动力学及其与神经元中动作电位的关系提供了有价值的工具。

导电聚合物聚吡咯在神经元传感器中的应用

利用电化学聚合的方法将聚吡咯聚合于神经元传感器的电极上 ,SEM表明该涂层的表面形貌为具有较高粗糙度的菜花状。聚吡咯导电涂层平均的表观模量为 1 .3GPa,介于神经元传感器晶体硅材料 (E =1 72GPa)和中枢神经组织 (E =1 0 0kPa )之间 。

神经形态视觉传感器及其应用研究

神经形态视觉传感器是一种模拟生物视觉系统工作机理的传感器,具有高时间分辨率、低时延、低功耗以及高动态范围等特点。首先,介绍了神经形态工程的研究背景、神经形态芯片以及神经形态视觉传感器的工作机理和主要优点。然后,详细介绍了神经形态视觉的主要计算方法,包括概率统计方法、脉冲神经网络方法以及深度神经网络方法等。最后,综述了神经形态视觉传感器在同步定位与地图构建、图像重建以及特征检测与跟踪等方面的应用研究。对神经形态视觉传感器从硬件、计算方法和应用等方面作了系统概述,为相关研究者提供了全面的参考。

神经生物传感器测量活细胞中的电活动

2003年2月,德国慕尼黑Infineon技术公司的研究人员宣称,他们已成功地将新研发的生物传感器芯片直接接到活的神经细胞上,这样就能读出由这种细胞产生的电信号。这种新研发的神经芯片将使得能察看神经元、神经组织和器官神经网络的生物功能。

目视检查自动化的神经视觉传感器

在很多产业领域,利用图象处理的目视检查(外观检查)都在向自动化发展。最近,又出现了感性测量这一新名词,而且愈来愈热门。分析了眼睛的功能和神经细胞的作用,并把它用于神经视觉传感器。

受体生物传感器的研究进展

综述了利用各种生物材料中的膜受体蛋白作为分子识别元件的受体生物传感器研究的最新进展。主要从离体受体传感器、细胞受体传感器和神经组织受体传感器 3个方面 ,讨论了它们的特点和存在问题 ,并展望了受体生物传感器未来的发展方向。共引用文献 4 2篇。

基于虚拟仪器的粮食水分检测系统神经网络温度补偿

在进行粮食水分检测过程中发现,温度变化对系统的输出值影响很大,从而影响系统性能和测量准确度。为消除温度对水分测量值的影响,在虚拟仪器软件平台上采用人工神经网络法对水分检测系统进行温度补偿,即通过对神经网络建模达到消除温度的影响。

人工神经网络在焊缝跟踪中的应用

本文将人工神经网络用于焊缝跟踪系统中，研制了一种新型的焊缝跟踪用传感器─神经电弧传感器．该传感器成本底，操作简单，工作可靠．将其用于焊缝跟踪系统中，能够得到满意的跟踪效果。

具有感存算一体化的新型神经形态视觉传感器

传统的数字图像处理系统包括图像传感器与图像处理单元,二者在物理空间上分离,图像信息在其间的传输造成了延时与能耗。此外,数字图像传感器基于“帧”的工作原理,可能丢失一些重要信息,或者造成数据冗余。人类视觉系统提供了一种高效并行的信息处理方式。神经形态视觉传感器能够模拟人类视网膜的功能,同时具备感知光信号、存储信号和进行信息预处理的功能。这类感存算一体化的神经形态视觉传感器简化了人工视觉系统的电路复杂性,提升了信息处理效率,节省了系统功耗。文章总结了传统的数字图像传感器存在的问题,介绍了几种重要的人工神经网络,讨论了新型神经形态视觉传感器的研究进展和存在的问题。

Computational analysis of genetic loci required for amphid structure and functions and their possibly corresponding microRNAs in C. elegans

Objective To examine the important roles of microRNAs （miRNAs） in regulating amphid structure and function, we performed a computational analysis for the genetic loci required for the sensory perception and their possibly corresponding miRNAs in C. elegans. Methods Total 55 genetic loci required for the amphid structure and function were selected. Sequence alignment was combined with E value evaluation to investigate and identify the possible corresponding miRNAs. Results Total 30 genes among the 55 genetic loci selected have their possible corresponding regulatory miRNA（s）, and identified genes participate in the regulation of almost all aspects of amphid structure and function. In addition, our data suggest that both the amphid structure and the amphid functions might be regulated by a series of network signaling pathways. Moreover, the distribution of miRNAs along the 3＇ untranslated region （UTR） of these 30 genes exhibits different patterns. Conclusion We present the possible miRNA-mediated signaling pathways involved in the regulation of chemosensation and thermosensation by controlling the corresponding sensory neuron and interneuron functions. Our work will be useful for better understanding of the miRNA-mediated control of the chemotaxis and thermotaxis in C. elegans.

Calibration Method Based on RBF Neural Networks for Soil Moisture Content Sensor

Temporal and spatial variation of soil moisture content is significant for crop growth,climate change and the other fields.In order to overcome shortage of non-linear output voltage of TDR3 soil moisture content sensor and increase soil moisture content data collection and computational efficiency,this paper presents a RBF neural network calibration method of soil moisture content based on TDR3 soil moisture sensor and wireless sensor networks.Experiment results show that the calibration method is effective...

A novel fully-integrated miniature six-axis force/torque sensor

This paper presents a new designed miniature six DOF （degree of freedom） force/torque sensor. This sensor is fully integrated with a micro DSP （digital signal processor）, so all the signal conditioning, A/D, decoupling, digital-signals serial output are performed in the sensor. Some experimental results are presented to demonstrate the capability of the proposed design. Finally, a neural network was used for decoupling the interacting signals, compared with the conventional method using the inverse matrix, this new method is more accurate.

Optical Fiber Type Slide Tactile Sensor Used for Underwater Robot

Because of the special underwater environment, many sensors used well in robots working in space or on the land can not be used in the underwater. So an optical fiber type slide tactile sensor is designed by the inner modulation mechanism of the intensity type optical fiber. The principle and structure of the sensor are introduced in detail. The static and dynamic characteristics are analyzed theoretically and experimentally. The dynamic characteristic model is built and the simulation is made by using genetic algorithm based on neural network. In order to use the sensor perfectly, the recognition model of the sensor is built on the basis of the principle of “inverse solution” using neural networks. The control precision and sensitivity of the manipulator are improved.

4-CBA Soft Sensor Based on Fuzzy CMAC Neural Networks

Soft sensor is attractive in dealing with online product quality measurement by virtue of other easily measured variables. In AMOCO PTA (purified terephthalic acid) production process, the unavailability of real-time measurement of 4-CBA makes it impossible for timely adjustment and thereby influences the product quality and the plant economy benefit. In this paper, a kind of FCMAC (fuzzy cerebellar model articulation controller) method is presented to solve the online measurement problem. Different from the conventional CMAC (cerebellar model articulation controller) networks, which has inferior smoothing ability because of its table look-up based technology. Integrating fuzzy model into CMAC networks, it becomes more accurate in functional mapping without weakening its generalization ability. Numerical example and industrial application results show the method proposed here is satisfactory and feasible.

Reconstruction based approach to sensor fault diagnosis using auto-associative neural networks

Fault diagnostics is an important research area including different techniques.Principal component analysis(PCA)is a linear technique which has been widely used.For nonlinear processes,however,the nonlinear principal component analysis(NLPCA)should be applied.In this work,NLPCA based on auto-associative neural network(AANN)was applied to model a chemical process using historical data.First,the residuals generated by the AANN were used for fault detection and then a reconstruction based approach called enhanced AANN(E-AANN)was presented to isolate and reconstruct the faulty sensor simultaneously.The proposed method was implemented on a continuous stirred tank heater(CSTH)and used to detect and isolate two types of faults(drift and offset)for a sensor.The results show that the proposed method can detect,isolate and reconstruct the occurred fault properly.

Soft sensor for ratio of soda to aluminate based on PCA-RBF multiple network

Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized variables were divided into 2 groups according to the original information and 2 corresponding neural networks were established. A radial basis function network was used to depict the relationship between the output variables and the first group input variables which contain main original information. An other single-layer neural network model was used to compensate the error between the output of radial basis function network and the actual output variables. At last, The multiple network was used as soft sensor for the ratio of soda to aluminate in the process of high-pressure digestion of alumina. Simulation of industry application data shows that the prediction error of the model is less than 3%, and the model has good generalization ability.

Data-driven intelligent monitoring system for key variables in wastewater treatment process

In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.

A novel speed sensor-less direct torque control system for mining locomotive haulage

A novel speed sensor-less direct torque control induction motor drive system for the mining locomotive haulage is presented in the paper. Rotor speed identification is based on the model reference adaptive control theory with neural network using back propagation algorithm. The system is implemented using a real-time TMS320F240 digital signal processor. The simulation study and experiment results indicate that the suggested system has good performance.

Structural Damage Assessment by an Embedded Fiberoptic Sensor Array and Its ANN Signal Processor

A novel type of two-dimensional damage assessment system for real time monitoring the structural health of composite structure is described. The configuration of the embedded fiberoptic sensor array and the model of the artificial neural network (ANN) are discussed. The experimental system and results show that the system can monitor a damage of structures with potential application in areas such as space aeronautics, civil engineering, etc.

Novel Fiberoptic Sensing Array and Fundamental Experiments

A suitable fiberoptic sensing array embedded in the smart structure to monitor quasi-distributed forces on the structure is presented in this paper.Artificial nearal networks are used in processing of fiberoptic sensing array signals.Fundamental experiments have been done and the results are also given.