An analog silicon retina with multichip configuration

The neuromorphic silicon retina is a novel analog very large scale integrated circuit that emulates the structure and the function of the retinal neuronal circuit. We fabricated a neuromorphic silicon retina, in which sample/hold circuits were embedded to generate fluctuation-suppressed outputs in the previous study . The applications of this silicon retina, however, are limited because of a low spatial resolution and computational variability. In this paper, we have fabricated a multichip silicon retina in which the functional network circuits are divided into two chips: the photoreceptor network chip (P chip) and the horizontal cell network chip (H chip). The output images of the P chip are transferred to the H chip with analog voltages through the line-parallel transfer bus. The sample/hold circuits embedded in the P and H chips compensate for the pattern noise generated on the circuits, including the analog communication pathway. Using the multichip silicon retina together with an off-chip differential amplifier, spatial filtering of the image with an odd- and an even-symmetric orientation selective receptive fields was carried out in real time. The analog data transfer method in the present multichip silicon retina is useful to design analog neuromorphic multichip systems that mimic the hierarchical structure of neuronal networks in the visual system.     

Computer Vision Techniques Applied for Diagnostic Analysis of Retinal OCT Images: A Review

The retina is a tiny layer at the posterior pole of an eye and is made up of tissues sensitive to light, these tissues generate nerve signals that pass through the optic nerve to the brain. A retinal disorder occurs when the retina malfunctions; glaucoma, diabetic retinopathy and pathologic myopia are retinal disorders and principal causes of blindness worldwide. These retinal disorders are often diagnosed and treated by an ophthalmologist. However, to accurately assess a retinal disease, ophthalmologist would need qualitative and quantitative analysis of the disease, it’s early and current statistics, but acquisition of these measurements are not possible through manual techniques, there should be automated computer aided diagnosis (CAD) systems to assist ophthalmologists. In this comprehensive review, an analysis and evaluation has been performed of different computer vision and image processing approaches applied to OCT images for automatic diagnosis of retinal disorders. We also reported disease causes, symptoms and pathologies manifestations within OCT images, which can serve as baseline knowledge for development of an automated CAD system. Hence, this disease specific review offers a good understanding to analyze visual impairments from retinal OCT images which will help researcher to design enhanced therapeutic systems for retinal disorders.     

Visual information processing in primate cone pathways. I. A model

At the retinal level, the strategies utilized by biological visual systems allow them to outperform machine vision systems, serving to motivate the design of electronic or "smart" sensors based on similar principles. Design of such sensors in silicon first requires a model of retinal information processing which captures the essential features exhibited by biological retinas. In this paper, a simple retinal model is presented, which qualitatively accounts for the achromatic information processing in the primate cone system. The computer retina model exhibits many of the properties found in biological retinas such as data reduction through nonuniform sampling, adaptation to a large dynamic range of illumination levels, variation of visual acuity with illumination level, and enhancement of spatiotemporal contrast information. The main emphasis of the model presented here is to demonstrate how different adaptation mechanisms play a role in extending the operating range of the primate retina.     

Artificial realization of human skill for contour control of robot manipulator

A methodology for the artificial realization of expert human skill is described. Artificial human skill was realized in the problem of contour control of mechatronic servo systems including robot manipulators and machine tools. The merits of the artificial human skill thus obtained are discussed. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996.     

人工嗅觉系统及其在临床诊断中的应用

人工嗅觉系统是由气敏传感器阵列、信号处理和适当的模式识别方法组成的仪器,能模拟人的嗅觉来检测、识别复杂气味成分.本文介绍了人工嗅觉系统的发展历史,阐述了人工嗅觉系统的基本组成,详细介绍了人工嗅觉系统的研究现状及其在临床诊断中的应用,并展望了人工嗅觉系统在临床诊断中的发展趋势.     

Steps toward Parallel Intelligence

The origin of artificial intelligence is investigated, based on which the concepts of hybrid intelligence and parallel intelligence are presented. The paradigm shift in Intelligence indicates the "new normal" of cyber-social-physical systems (CPSS), in which the system behaviors are guided by Merton's Laws. Thus, the ACP-based parallel intelligence consisting of Artificial societies, Computational experiments and Parallel execution are introduced to bridge the big modeling gap in CPSS.      

U-net与Dense-net相结合的视网膜血管提取

目的 视网膜血管健康状况的自动分析对糖尿病、心脑血管疾病以及多种眼科疾病的快速无创诊断具有重要参考价值。视网膜图像中血管网络结构复杂且图像背景亮度不均使得血管区域的准确自动提取具有较大难度。本文通过使用具有对称全卷积结构的U-net深度神经网络实现视网膜血管的高精度分割。方法 基于U-net网络中的层次化对称结构和Dense-net网络中的稠密连接方式,提出一种改进的适用于视网膜血管精准提取的深度神经网络模型。首先使用白化预处理技术弱化原始彩色眼底图像中的亮度不均,增强图像中血管区域的对比度;接着对数据集进行随机旋转、Gamma变换操作实现数据增广;然后将每一幅图像随机分割成若干较小的图块,用于减小模型参数规模,降低训练难度。结果 使用多种性能指标对训练后的模型进行综合评定,模型在DRIVE数据集上的灵敏度、特异性、准确率和AUC（area under the curve）分别达到0.740 9、0.992 9、0.970 7和0.917 1。所提算法与目前主流方法进行了全面比较,结果显示本文算法各项性能指标均表现良好。结论 本文针对视网膜图像中血管区域高精度自动提取难度大的问题,提出了一种具有稠密连接方式的对称全卷积神经网络改进模型。结果表明该模型在视网膜血管分割中能够达到良好效果,具有较好的研究及应用价值。     

Retina disease prediction using modified convolutional neural network based on Inception-ResNet model with support vector machine classifier

Artificial intelligence and deep learning have aided ocular disease through experiments including automatic illness recognition from images of the iris, fundus, or retina. Automated diagnosis systems (ADSs) provide services for the benefit of humanity and are essential in the early detection of harmful diseases. In fact, early detection is essential to avoid total blindness. In real life, several diagnostic tests such as visual ocular tonometry, retinal exam, and acuity test are performed, but they are conclusively time demanding and stressful for the patient. To consume time and detect the retinal disease earlier, an efficient prediction method is designed. In this proposed model, the first process is data collection that consists of a retinal disease dataset for testing and training. The second process is pre-processing, which executes image resizing and noise filter for feature extraction. The third step is feature extraction, which extracts the image's form, size, color, and texture for classification with CNN based on Inception-ResNet V2. The classification process is done by using the SVM with the extracted features. The prediction of diseases is classified such as normal, cataract, glaucoma, and retinal disease. The suggested model's performance is assessed using performance indicators such as accuracy, error, sensitivity, precision, and so forth. The suggested model's accuracy, error, sensitivity, and precision are 0.96, 0.962, 0.964, and 0.04, respectively, higher than existing techniques such as VGG16, Mobilenet V1, ResNet, and AlexNet. Thus, the proposed model instantly predicts retinal disease.     

人工免疫系统研究的新进展

综述了人工免疫系统的最新研究成果。首先简述了生物免疫系统的信息处理机理，其次介绍了独特型人工免疫网络、多值免疫网络、免疫联想记忆等人工免疫模型，以及反向选择、免疫遗传、克隆选择等五类免疫学习算法，最后介绍了人工免疫系统的应用，并展望了该领域的进一步研究方向。     

Recent developments in computational color image denoising with PDEs to deep learning: a review

Artificial Intelligence Review - Image denoising methods are of fundamental importance in image processing and artificial intelligence systems. In this review, we analyze the traditional and state...     

Vision chip architecture with light adaptation mechanism

Light adaptive algorithms/architectures are proposed for regularization vision chips. The adaptation mechanisms allow the regularization parameters to change in an adaptive manner in accordance with the light intensity of given images. This is achieved by adaptively changing the conductance values associated with massively parallel resistive networks. The algorithms/architectures are inspired by the adaptation mechanisms of the retinal horizonal cells of the lower vertebrates. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

人工免疫系统及其应用

人工免疫系统是基于人类和其它高等动物免疫系统原理而提出的一种新的信息处理系统。简要介绍了生物免疫系统的特点，概述了当前几种主要的人工免疫系统和在计算机安全、优化、故障检测及处理、控制等方面的工程应用，并对其应用前景作了展望。     

Phrase-level speech simulation with an airway modulation model of speech production

Artificial talkers and speech synthesis systems have long been used as a means of understanding both speech production and speech perception. The development of an airway modulation model is described that simulates the time-varying changes of the glottis and vocal tract, as well as acoustic wave propagation, during speech production. The result is a type of artificial talker that can be used to study various aspects of how sound is generated by humans and how that sound is perceived by a listener. The primary components of the model are introduced and simulation of words and phrases are demonstrated.     

An educational tool for artificial neural networks

Artificial neural networks are some kind of data processing systems, which try to simulate features of the human brain and its learning process. So, they are widely used by researchers to solve different problems in optimization, classification, pattern recognition, associative memory and control. In this paper, an educational tool, which can be used to work on different kinds of neural network models and learn fundamentals of the artificial neural network, is described. At this point, the whole tool environment provides an advanced system to ensure mentioned functions. The developed system supports using MLP, LVQ and SOM models and related learning algorithms. It employs some visual, interactive tools, which enable users to compose their own neural networks and work on the developed networks easily. By using these tools, users can also understand and learn working mechanism of a typical artificial neural network, using features of different models and related learning algorithms.     

Visual System Based on Artificial Retina for Motion Detection

We present a bioinspired model for detecting spatiotemporal features based on artificial retina response models. Event-driven processing is implemented using four kinds of cells encoding image contrast and temporal information. We have evaluated how the accuracy of motion processing depends on local contrast by using a multiscale and rank-order coding scheme to select the most important cues from retinal inputs. We have also developed some alternatives by integrating temporal feature results and obtained a new improved bioinspired matching algorithm with high stability, low error and low cost. Finally, we define a dynamic and versatile multimodal attention operator with which the system is driven to focus on different target features such as motion, colors, and textures.     

航天员辅助操作系统中人工标识的选取

介绍了两种常用的人工标识系统ARToolKit和ARTag.通过实验对两个系统在最小识别尺寸、识别正确率、系统运行速度以及防遮挡等方面的表现进行了比较,并且从算法机制上阐述了表现差异的原因,为航天员辅助操作系统中人工标识的选取提供技术参考.     

Analog VLSI model of binaural hearing

The stereausis model of biological auditory processing is proposed as a representation that encodes both binaural and spectral information in a unified framework. A working analog VLSI chip that implements this model of early auditory processing in the brain is described. The chip is a 100000-transistor integrated circuit that computes the stereausis representation in real time, using continuous-time analog processing. The chip receives two audio inputs, representing sound entering the two ears, computes the stereausis representation, and generates output signals that can directly drive a color CRT display. Outputs from the chips for a variety of artificial and speech stimuli are shown.     

人工生命浅谈

1987年兰顿首次提出了人工生命的概念，定义“人工生命是研究人工系统来模拟自然生命系统行为特性的学科”，经过20年的发展，人工生命的独立学科地位已经得到国际学术界的广泛承认，正在逐渐成为学术研究的热点之一，人工生命的研究兴趣在于对生命系统行为特性的仿生，学科中应使用由下而上合成的方法，以使人工系统具有很好适应性、灵活性。本文将介绍人工生命起源、重要概念和发展趋势等方面的内容。     

A neural-network architecture for syntax analysis

Artificial neural networks (ANNs), due to their inherent parallelism, offer an attractive paradigm for implementation of symbol processing systems for applications in computer science and artificial intelligence. The paper explores systematic synthesis of modular neural-network architectures for syntax analysis using a prespecified grammar-a prototypical symbol processing task which finds applications in programming language interpretation, syntax analysis of symbolic expressions, and high-performance compilers. The proposed architecture is assembled from ANN components for lexical analysis, stack, parsing and parse tree construction. Each of these modules takes advantage of parallel content-based pattern matching using a neural associative memory. The proposed neural-network architecture for syntax analysis provides a relatively efficient and high performance alternative to current computer systems for applications that involve parsing of LR grammars which constitute a widely used subset of deterministic context-free grammars. Comparison of quantitatively estimated performance of such a system (implemented using current CMOS VLSI technology) with that of conventional computers demonstrates the benefits of massively parallel neural-network architectures for symbol processing applications.