MEMS Microrobot with Pulse-Type Hardware Neural Networks Integrated Circuit

2015 ◽  
pp. 18-35 ◽  
Author(s):  
Ken Saito ◽  
Minami Takato ◽  
Yoshifumi Sekine ◽  
Fumio Uchikoba
Keyword(s):  
Neural Networks ◽  
Integrated Circuit ◽  
Pulse Waveform ◽  
Biologically Inspired ◽  
Micro Electro Mechanical Systems ◽  
Controlling System ◽  
Pulse Type ◽  
The Neural Networks ◽  
Mechanical Components ◽  
Electrical Components

Hexapod locomotive Micro-Electro Mechanical Systems (MEMS) microrobot with Pulse-type Hardware Neural Networks (P-HNN) locomotion controlling system is presented in this chapter. MEMS microrobot is less than 5 mm width, length, and height in size. MEMS microrobot is made from a silicon wafer fabricated by micro fabrication technology to realize the small size mechanical components. The mechanical components of MEMS microrobot consists of body frames, legs, link mechanisms, and small size actuators. In addition, MEMS microrobot has a biologically inspired locomotion controlling system, which is the small size electrical components realized by P-HNN. P-HNN generates the driving pulses for actuators of the MEMS microrobot using pulse waveform such as biological neural networks. The MEMS microrobot emulates the locomotion method and the neural networks of an insect with small size actuator, link mechanisms, and P-HNN. As a result, MEMS microrobot performs hexapod locomotion using the driving pulses generated by P-HNN.

MEMS Microrobot with Pulse-Type Hardware Neural Networks Integrated Circuit

2016 ◽  
pp. 630-647
Author(s):  
Ken Saito ◽  
Minami Takato ◽  
Yoshifumi Sekine ◽  
Fumio Uchikoba
Keyword(s):  
Neural Networks ◽  
Integrated Circuit ◽  
Pulse Waveform ◽  
Biologically Inspired ◽  
Micro Electro Mechanical Systems ◽  
Controlling System ◽  
Pulse Type ◽  
The Neural Networks ◽  
Mechanical Components ◽  
Electrical Components

Hexapod locomotive Micro-Electro Mechanical Systems (MEMS) microrobot with Pulse-type Hardware Neural Networks (P-HNN) locomotion controlling system is presented in this chapter. MEMS microrobot is less than 5 mm width, length, and height in size. MEMS microrobot is made from a silicon wafer fabricated by micro fabrication technology to realize the small size mechanical components. The mechanical components of MEMS microrobot consists of body frames, legs, link mechanisms, and small size actuators. In addition, MEMS microrobot has a biologically inspired locomotion controlling system, which is the small size electrical components realized by P-HNN. P-HNN generates the driving pulses for actuators of the MEMS microrobot using pulse waveform such as biological neural networks. The MEMS microrobot emulates the locomotion method and the neural networks of an insect with small size actuator, link mechanisms, and P-HNN. As a result, MEMS microrobot performs hexapod locomotion using the driving pulses generated by P-HNN.

Silicon Micro-Robot with Neural Networks

2013 ◽  
pp. 1-10 ◽  
Author(s):  
Ken Saito ◽  
Minami Takato ◽  
Yoshifumi Sekine ◽  
Fumio Uchikoba
Keyword(s):  
Neural Networks ◽  
Switching Behavior ◽  
Robot System ◽  
Micro Electro Mechanical Systems ◽  
Controlling System ◽  
Micro Robot ◽  
Mems Technology ◽  
Pulse Type ◽  
Spatio Temporal ◽  
Rotary Type

Insect type 4.0, 2.7, 2.5 mm. width, length, height size silicon micro-robot system with active hardware neural networks locomotion controlling system is presented in this chapter. The micro-robot system was made from a silicon wafer fabricated by Micro-Electro Mechanical Systems (MEMS) technology. The mechanical system of the robot equipped with millimeter-size rotary type actuators, link mechanisms, and six legs to realize the insect-like switching behavior. In addition, the authors constructed the active hardware neural networks by analog CMOS circuits as a locomotion controlling system. Hardware neural networks consisted of pulse-type hardware neuron models as basic components. Pulse-type hardware neuron model has same basic features of biological neurons such as threshold, refractory period, spatio-temporal summation characteristics, and enables the generation of continuous action potentials. The hardware neural networks output the driving pulses using synchronization phenomena such as biological neural networks. Four output signal ports are extracted from hardware neural networks, and they are connected to the actuators. The driving pulses can operate the actuators of silicon micro-robot directly. Therefore, the hardware neural networks realize the robot control without using any software programs or A/D converters. The micro-robot emulates the locomotion method and the neural networks of an insect with rotary type actuators, link mechanisms, and hardware neural networks. The micro-robot performs forward and backward locomotion, and also changes direction by inputting an external trigger pulse. The locomotion speed was 26.4 mm/min when the step width was 0.88 mm.

Silicon Micro-Robot With Neural Networks

2019 ◽  
pp. 979-990
Author(s):  
Ken Saito ◽  
Minami Takato ◽  
Yoshifumi Sekine ◽  
Fumio Uchikoba
Keyword(s):  
Neural Networks ◽  
Switching Behavior ◽  
Robot System ◽  
Micro Electro Mechanical Systems ◽  
Controlling System ◽  
Micro Robot ◽  
Mems Technology ◽  
Pulse Type ◽  
Spatio Temporal ◽  
Rotary Type

Insect type 4.0, 2.7, 2.5 mm. width, length, height size silicon micro-robot system with active hardware neural networks locomotion controlling system is presented in this chapter. The micro-robot system was made from a silicon wafer fabricated by Micro-Electro Mechanical Systems (MEMS) technology. The mechanical system of the robot equipped with millimeter-size rotary type actuators, link mechanisms, and six legs to realize the insect-like switching behavior. In addition, the authors constructed the active hardware neural networks by analog CMOS circuits as a locomotion controlling system. Hardware neural networks consisted of pulse-type hardware neuron models as basic components. Pulse-type hardware neuron model has same basic features of biological neurons such as threshold, refractory period, spatio-temporal summation characteristics, and enables the generation of continuous action potentials. The hardware neural networks output the driving pulses using synchronization phenomena such as biological neural networks. Four output signal ports are extracted from hardware neural networks, and they are connected to the actuators. The driving pulses can operate the actuators of silicon micro-robot directly. Therefore, the hardware neural networks realize the robot control without using any software programs or A/D converters. The micro-robot emulates the locomotion method and the neural networks of an insect with rotary type actuators, link mechanisms, and hardware neural networks. The micro-robot performs forward and backward locomotion, and also changes direction by inputting an external trigger pulse. The locomotion speed was 26.4 mm/min when the step width was 0.88 mm.

A BIOLOGICALLY INSPIRED NEURAL NETWORK FOR DYNAMIC PROGRAMMING

2001 ◽  
Vol 11 (06) ◽  
pp. 561-572 ◽  
Author(s):  
ROSELI A. FRANCELIN ROMERO ◽  
JANUSZ KACPRYZK ◽  
FERNANDO GOMIDE
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Dynamic Programming ◽  
Optimization Problems ◽  
Direct Method ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Biologically Inspired ◽  
The Neural Network ◽  
The Neural Networks

An artificial neural network with a two-layer feedback topology and generalized recurrent neurons, for solving nonlinear discrete dynamic optimization problems, is developed. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. The neural network based algorithm is an advantageous approach for dynamic programming due to the inherent parallelism of the neural networks; further it reduces the severity of computational problems that can occur in methods like conventional methods. Some illustrative application examples are presented to show how this approach works out including the shortest path and fuzzy decision making problems.

Development of neural networks chip generating driving waveform for electrostatic motor

2021 ◽  
Author(s):  
Takuro Sasaki ◽  
Mika Kurosawa ◽  
Yu Usami ◽  
Shinya Kato ◽  
Arisa Sakaki ◽  
...  
Keyword(s):  
Neural Networks ◽  
Integrated Circuit ◽  
Synaptic Weight ◽  
Low Frequency ◽  
Waveform Generator ◽  
Two Phase ◽  
Body Model ◽  
The Neural Networks ◽  
Driving Waveform ◽  
Driving Circuit

AbstractThe authors are studying hardware neural networks (HNN) to control the locomotion of the microrobot. The neural networks chip is the integrated circuit chip of the HNN. We proposed the electrostatic motor that is the new actuator of the microrobot in our previous research. The electrostatic motor used the waveform generator to generate the driving waveform. In this paper, the authors will propose the driving circuit using neural networks chip. The cell body model is the basic component of the neural networks chip that outputs 3 MHz frequency of electrical oscillated pulse waveform. Therefore, large capacitors need to connect outside of the neural networks chip to generate the low-frequency driving waveform. The proposal neural networks chip generates a long delay without using large capacitors. In addition, the neural networks chip generated a two-phase anti-phase synchronized waveform by incorporating a mechanism for adjusting synaptic weight. As a result, the proposal neural networks chip can generate the electrostatic motor’s driving waveform with variable frequency. The frequency of the driving waveform could vary from 40 to 126 Hz.

An Efficient Geometric Algorithm for Extracting Mechanical Components: A Step Towards Developing an Automated Extraction Tool for MEMS

2001 ◽  
Author(s):  
Sashidhar Bellam ◽  
Satyandra K. Gupta
Keyword(s):  
Critical Role ◽  
Spatial Layout ◽  
Mems Devices ◽  
Micro Electro Mechanical Systems ◽  
Geometric Algorithm ◽  
Design Cycle ◽  
Extraction Algorithm ◽  
Mechanical Components ◽  
Electrical Components

Abstract Micro-Electro-Mechanical Systems (MEMS) are very small multi-domain devices and systems that contain both mechanical and electrical components with sizes in the order of microns. Design cycle time plays a critical role in the development and introduction of MEMS devices and systems in the market. VLSI designers often use extraction tools to compare the desired schematic with the reconstructed schematic extracted from the spatial layout of the design. Use of such tools in VLSI industry has significantly reduced the number of design iterations. In order to develop extraction tools for MEMS designs, we need geometric algorithms to analyze the spatial layout of the mechanical portion of the MEMS device and extract a net-list of mechanical components. Such extracted net-list of mechanical components can be combined with the electronic component net-list to provide the complete device schematic. A key step in the extraction of mechanical components is classification of various portions of the layout into various types of structural components. Because MEMS designs consist of a large number of components, computational efficiency of the underlying extraction algorithm is very important for it to work on complex devices. This paper describes an efficient geometric algorithm for extracting mechanical components from spatial layout of MEMS designs.

scholarly journals Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers

2019 ◽  
Vol 11 (4) ◽  
pp. 86 ◽  
Author(s):  
César Pérez López ◽  
María Delgado Rodríguez ◽  
Sonia de Lucas Santos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Income Tax ◽  
Fraud Detection ◽  
Personal Income ◽  
Personal Income Tax ◽  
Tax Returns ◽  
The Neural Networks ◽  
Tax Fraud ◽  
Efficiency Rate

The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud.

Neural networks trained with high-dimensional functions approximation data in high-dimensional space

2021 ◽  
pp. 1-12
Author(s):  
Jian Zheng ◽  
Jianfeng Wang ◽  
Yanping Chen ◽  
Shuping Chen ◽  
Jingjin Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Dimensional Space ◽  
Data Distribution ◽  
High Dimensional ◽  
Sufficient Information ◽  
Sufficient Data ◽  
High Dimensional Space ◽  
Positive Effects ◽  
The Neural Networks ◽  
Using Data

Neural networks can approximate data because of owning many compact non-linear layers. In high-dimensional space, due to the curse of dimensionality, data distribution becomes sparse, causing that it is difficulty to provide sufficient information. Hence, the task becomes even harder if neural networks approximate data in high-dimensional space. To address this issue, according to the Lipschitz condition, the two deviations, i.e., the deviation of the neural networks trained using high-dimensional functions, and the deviation of high-dimensional functions approximation data, are derived. This purpose of doing this is to improve the ability of approximation high-dimensional space using neural networks. Experimental results show that the neural networks trained using high-dimensional functions outperforms that of using data in the capability of approximation data in high-dimensional space. We find that the neural networks trained using high-dimensional functions more suitable for high-dimensional space than that of using data, so that there is no need to retain sufficient data for neural networks training. Our findings suggests that in high-dimensional space, by tuning hidden layers of neural networks, this is hard to have substantial positive effects on improving precision of approximation data.

Classification and Recognition of Plant Leaf Based on Neural Networks

2011 ◽  
Vol 464 ◽  
pp. 38-42 ◽  
Author(s):  
Ping Ye ◽  
Gui Rong Weng
Keyword(s):  
Neural Networks ◽  
Fractal Dimension ◽  
Recognition Rate ◽  
Image Binarization ◽  
Characteristic Parameters ◽  
Moment Invariant ◽  
Plant Leaf ◽  
The Neural Networks ◽  
Novel Method ◽  
The Moment

This paper proposed a novel method for leaf classification and recognition. In the method, the moment invariant and fractal dimension were regarded as the characteristic parameters of the plant leaf. In order to extract the representative characteristic parameters, pretreatment of the leaf images, including RGB-gray converting, image binarization and leafstalk removing. The extracted leaf characteristic parameters were further utilized as training sets to train the neural networks. The proposed method was proved effectively to reach a recognition rate about 92% for most of the testing leaf samples

scholarly journals Localization of Scattering Objects Using Neural Networks

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 11
Author(s):  
Domonkos Haffner ◽  
Ferenc Izsák
Keyword(s):  
Neural Networks ◽  
Plane Waves ◽  
Measurement Data ◽  
Scattered Wave ◽  
Training Data ◽  
Data Set ◽  
Main Compound ◽  
Incident Plane ◽  
The Neural Networks ◽  
Simulation Package

The localization of multiple scattering objects is performed while using scattered waves. An up-to-date approach: neural networks are used to estimate the corresponding locations. In the scattering phenomenon under investigation, we assume known incident plane waves, fully reflecting balls with known diameters and measurement data of the scattered wave on one fixed segment. The training data are constructed while using the simulation package μ-diff in Matlab. The structure of the neural networks, which are widely used for similar purposes, is further developed. A complex locally connected layer is the main compound of the proposed setup. With this and an appropriate preprocessing of the training data set, the number of parameters can be kept at a relatively low level. As a result, using a relatively large training data set, the unknown locations of the objects can be estimated effectively.

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