2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)最新文献_第8页

A new architecture for Cellular Neural Network on reconfigurable hardware with an advance memory allocation method 一种新的基于可重构硬件的细胞神经网络结构，采用一种先进的内存分配方法

2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010) Pub Date : 2010-03-29 DOI: 10.1109/CNNA.2010.5430316

M. Tukel, M. Yalçin

{"title":"A new architecture for Cellular Neural Network on reconfigurable hardware with an advance memory allocation method","authors":"M. Tukel, M. Yalçin","doi":"10.1109/CNNA.2010.5430316","DOIUrl":"https://doi.org/10.1109/CNNA.2010.5430316","url":null,"abstract":"In this study a discretized version of Cellular Neural Network (CNN) was implemented with an Hardware Description Language using forward Euler approximation. When the designs in literature were reviewed, it was seen that registers of the designs significantly affect occupied chip area. The introduced design is focused on reducing chip area by reducing register occupation while not causing design complexity or lack in processing speed. Block Random Access Memories (Block-RAMs) in Field Programmable Gate Arrays (FPGA) were used instead of register arrays, which were designed to handle the relationship of the neighborhood and input-output communication in previous designs. The proposed design does not require additional memory to store input image and states of the CNN. Storing, reading and updating image, also providing neighbor relations of image were done with the proposed method which includes an advance memory allocation, image partitioning and supplementary blocks for the relationship of the neighborhood. In order to reduce the chip area of Cellular Processors, cellular control was simplified. Cellular Processors which have similar arithmetic units with previous designs occupy less combinatorial part and significantly less registers. The advantage of this design is presented by comparing the proposed designs in literature.","PeriodicalId":336891,"journal":{"name":"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)","volume":"234 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132587944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

DTCNN implementation in a LEGO Mindstorm NXT for infrared and ultrasonic sensor data processing DTCNN在LEGO Mindstorm NXT中的实现，用于红外和超声波传感器数据处理

2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010) Pub Date : 2010-03-29 DOI: 10.1109/CNNA.2010.5430335

J. Albó-Canals, S. Consul-Pacareu, J. Riera-Babures, X. Vilasís-Cardona

引用次数: 0

A new active wave computing based real time mobile robot navigation algorithm for dynamic environment 一种新的基于有源波计算的动态环境下移动机器人实时导航算法

2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010) Pub Date : 2010-03-29 DOI: 10.1109/CNNA.2010.5430279

V. Kilic, R. Yeniceri, M. Yalçin

{"title":"A new active wave computing based real time mobile robot navigation algorithm for dynamic environment","authors":"V. Kilic, R. Yeniceri, M. Yalçin","doi":"10.1109/CNNA.2010.5430279","DOIUrl":"https://doi.org/10.1109/CNNA.2010.5430279","url":null,"abstract":"This paper introduces an active wave computing based algorithm for real-time robot navigation problem in dynamically changing environment and presents a real test setup with a roving robot. Travelling waves propagated on Cellular Neural Network (CNN) were employed by the system to find the shortest path as proposed in our previous work. The setup has a ceiling camera which captures an image of the top view of the platform at almost every one second. After preprocessing of this image, it is loaded to the network and an active wave which is generated by this network is propagated until the robot is covered by the wave. This method reduces the simulation time and speeds up the system. Although the robot is navigated in a dynamically changing environment, the system does not need to check the moving obstacles. The algorithm proposed here always generates a solution for the current state of the platform without the information about its past states. At every repetition of the algorithm, the target position is updated by the system if it is changed. Any change of the target position is immediately applied to the location of the wave source. Hence the system becomes working in dynamic environment with dynamic target.","PeriodicalId":336891,"journal":{"name":"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116292440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Electromagnetic cluster reconstruction in LHCb LHCb中的电磁团重构

2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010) Pub Date : 2010-03-29 DOI: 10.1109/CNNA.2010.5430344

Pascal Perret

引用次数: 2

Cellular Neural Network (CNN) based control algorithms for omnidirectional laser welding processes: Experimental results 基于细胞神经网络(CNN)的全向激光焊接控制算法:实验结果

2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010) Pub Date : 2010-03-29 DOI: 10.1109/CNNA.2010.5430300

L. Nicolosi, R. Tetzlaff, F. Abt, A. Blug, H. Höfler

引用次数: 11

Self-organization in CNN-based Object Nets 基于cnn的对象网中的自组织

2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010) Pub Date : 2010-03-29 DOI: 10.1109/CNNA.2010.5430292

L. Werbos, P. Werbos

{"title":"Self-organization in CNN-based Object Nets","authors":"L. Werbos, P. Werbos","doi":"10.1109/CNNA.2010.5430292","DOIUrl":"https://doi.org/10.1109/CNNA.2010.5430292","url":null,"abstract":"Cellular Neural Network (CNN) chips containing a thousand times as many processors as conventional programmable chips can offer a huge improvement in computational throughput, for those applications they are able to address. The artificial neural network (ANN) community has developed new learning designs and topologies, consistent with CNN, which can provide very general capabilities, especially for tasks calling for optimal decision-making or control or for prediction. The Multilayer Perceptron (MLP), a conventional ANN, approximates smooth input-output relations or functions of many variables much better than traditional universal approximators like Taylor series or piecewise approximations; however, to cope with even larger systems such as megapixel image recognition or control of electric power grids, it is necessary to move to a family of more complex ANNs such as cellular Simultaneous Recurrent Networks (CSRN), Object Networks, and networks with object symmetry and small world connectivity, which can be emulated on CNNs. Kozma and Werbos have recently patented a new learning algorithm which achieved adequate learning speed in training such networks to handle tasks beyond the capacity of conventional ANNs. A new center at the FedEx Institute of Technology plans to use and extend these capabilities in many directions.","PeriodicalId":336891,"journal":{"name":"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114852413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Magnetic excitations for information processing 信息处理中的磁激励

2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010) Pub Date : 2010-03-29 DOI: 10.1109/CNNA.2010.5430308

G. Csaba, P. Lugli, M. Niemier, W. Porod

引用次数: 0

A chaotic memristor circuit 一个混沌忆阻电路

2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010) Pub Date : 2010-03-29 DOI: 10.1109/CNNA.2010.5430322

E. Lehtonen, M. Laiho, J. Poikonen

引用次数: 5

Autonomous long distance transfer on SIMD cellular processor arrays 基于SIMD蜂窝处理器阵列的自主长距离传输

2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010) Pub Date : 2010-03-29 DOI: 10.1109/CNNA.2010.5430266

M. Geese, P. Dudek

引用次数: 7

Pullback and forward attractors for dissipative cellular neural networks with additive noises 具有加性噪声的耗散细胞神经网络的前拉吸引子和后拉吸引子

2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010) Pub Date : 2010-03-29 DOI: 10.1109/CNNA.2010.5430258

Jung-Chao Ban, Cheng-Hsiung Hsu, Tzi-Sheng Yang

引用次数: 0