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

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A prototype for the Bionic Eyeglass 仿生眼镜的原型
K. Karacs, M. Radványi
{"title":"A prototype for the Bionic Eyeglass","authors":"K. Karacs, M. Radványi","doi":"10.1109/CNNA.2010.5430338","DOIUrl":"https://doi.org/10.1109/CNNA.2010.5430338","url":null,"abstract":"An experimental prototype for the Bionic Eyeglass will be demonstrated including three sample functions: pedestrian crosswalk detection, light detection and a basic color recognition. The prototype is built on the Bi-i visual computer, and uses a commercial cellular phone as a front-end towards the user. The visual flow is streamed from the camera of the cellular phone wirelessly to the Bi-i where it is processed, and the result is communicated through the speakers of the phone. Scalar or binary outputs are provided for the first two functions by beeping tones, whereas a basic color name is given for the last one.","PeriodicalId":336891,"journal":{"name":"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)","volume":"51 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":"129396443","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: 0
Dynamic feature and signature selection for robust tracking of multiple objects 多目标鲁棒跟踪的动态特征与签名选择
V. Szabo, C. Rekeczky
{"title":"Dynamic feature and signature selection for robust tracking of multiple objects","authors":"V. Szabo, C. Rekeczky","doi":"10.1109/CNNA.2010.5430270","DOIUrl":"https://doi.org/10.1109/CNNA.2010.5430270","url":null,"abstract":"The goal of this paper is to introduce a new tracking framework, which exploits dynamic feature and signature selection techniques for data association models. It performs robust multiple object tracking in a noisy, cluttered environment with closely spaced targets. This method extends the back-end processing capabilities of tracking systems by creating a hierarchy between the parallelly extracted features. These features are dynamically selected based on spatio-temporal consistency weight function, which maximizes the robustness of data association, and reduces the overall complexity of the algorithm.","PeriodicalId":336891,"journal":{"name":"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)","volume":"552 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":"116397114","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: 0
On the diffusion model for Autonomous Ratio-Memory Cellular Nonlinear Network for pattern recognition 模式识别中自主比记忆元胞非线性网络的扩散模型
Su-Yung Tsai, Chi-Hsu Wang, Chung-Yu Wu
{"title":"On the diffusion model for Autonomous Ratio-Memory Cellular Nonlinear Network for pattern recognition","authors":"Su-Yung Tsai, Chi-Hsu Wang, Chung-Yu Wu","doi":"10.1109/CNNA.2010.5430295","DOIUrl":"https://doi.org/10.1109/CNNA.2010.5430295","url":null,"abstract":"This paper proposes the diffusion circuit for Autonomous Ratio-Memory Cellular Nonlinear Networks (ARMCNNs). ARMCNNs can tolerate large variations of ratio weights which has been shown in our previous paper. However, in our previous circuit implementation, the synapse weight circuit between neighboring neurons was composed of two voltage to current converters (V/Is) and current mirrors. The layout area is still too large for a high density CNN array. Another issue is that for each subsystem of ARMCNNs, spurious memory points may exist besides two binary equilibrium points. The occurence of these spurious memory points will reduce the recognition rate (RR). So this paper proposes the diffusion circuit for synapse weights to extend the domain of attraction (DOA) and therefore eliminate these spurious memory points in comparison with our previous paper. In the literature, MOSFET transistors for the synapse weight circuit mostly either work in the weak inversion region, or in the strong inversion, but not both. Hence, the gate voltage has to be carefully desgined for MOSFET transistors working in the correct regions. On the contrary, in this paper, the synapse weight of a single MOSFET can work in either the weak inversion region or the strong inversion, making analog design more robust.","PeriodicalId":336891,"journal":{"name":"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)","volume":"61 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":"116915354","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: 0
GPU implementation of volume reconstruction and object detection in Digital Holographic Microscopy 数字全息显微镜中体重建和目标检测的GPU实现
L. Orzó, Z. Göröcs, István Szatmári, S. Tõkés
{"title":"GPU implementation of volume reconstruction and object detection in Digital Holographic Microscopy","authors":"L. Orzó, Z. Göröcs, István Szatmári, S. Tõkés","doi":"10.1109/CNNA.2010.5430246","DOIUrl":"https://doi.org/10.1109/CNNA.2010.5430246","url":null,"abstract":"Using Digital Holographic Microscopy (DHM) we can gather information from a whole volume and thus we can avoid the small depth of field constraint of the conventional microscopes. This way a volume inspection system can be constructed, which is capable to find, segment, collect, and later classify those objects that flow through an inspection chamber. Digital hologram reconstruction and processing, however, require considerable computational resources. We are developing volume reconstruction and object detection algorithms that can speed up considerably by parallel hardware implementation. Therefore, we put these tasks into operation on a GPU. As data transfer of the reconstructed planes would slow down the algorithm, all the reconstruction, object detection processes are to be completed on the parallel hardware, while fine tuning of object reconstruction and classification will be done on a CPU later. The actual speed up of the GPU implemented algorithm comparing to its conventional CPU realization depends on the applied hardware devices. So far we reached a 10 times acceleration value.","PeriodicalId":336891,"journal":{"name":"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)","volume":"2 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":"125297131","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
Robust focal-plane analog processing hardware for dynamic texture segmentation 用于动态纹理分割的鲁棒焦平面模拟处理硬件
J. Fernández-Berni, R. Carmona-Galán
{"title":"Robust focal-plane analog processing hardware for dynamic texture segmentation","authors":"J. Fernández-Berni, R. Carmona-Galán","doi":"10.1109/CNNA.2010.5430250","DOIUrl":"https://doi.org/10.1109/CNNA.2010.5430250","url":null,"abstract":"Cellular Nonlinear Networks (CNN) establish a theoretical framework in which programmable focal-plane image processing arrays can be developed. The conventional support for its analog programmability in VLSI is the implementation of transconductor-based multiplication of the input, output and state variables times the corresponding template elements. However, some distributions of weights can be greatly affected by the intrinsic nonidealities of the physical implementation. This is exactly the case when implementing linear diffusion within a transconductor-based CNN implementation. In this paper we propose an alternative implementation: a resistive grid based on MOSFETs operating in the triode region to realize linear diffusion of the input image, considered as the initial state of the network. In addition, these MOS-resistors can be employed as switches in order to sub-divide the image into bins, sized to track features on the appropriate scale. Thus, by simply controlling the size of the binning and for how long the pixel voltages will diffuse, it will be possible to segment and track dynamic textures along an image flow. Each frame of the flow is described by a smaller image in which each pixel represents the energy of the corresponding image bin, once the non-relevant spatial frequency components have been filtered out. We will demonstrate that the resulting low-resolution representation of the scene is very robust to the different sources of nonidealities in a standard CMOS technology.","PeriodicalId":336891,"journal":{"name":"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)","volume":"96 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":"122032155","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
CPU-GPU hybrid compiling for general purpose: Case studies 通用的CPU-GPU混合编译:案例研究
Á. Rák, G. Feldhoffer, B. G. Soós, G. Cserey
{"title":"CPU-GPU hybrid compiling for general purpose: Case studies","authors":"Á. Rák, G. Feldhoffer, B. G. Soós, G. Cserey","doi":"10.1109/CNNA.2010.5430340","DOIUrl":"https://doi.org/10.1109/CNNA.2010.5430340","url":null,"abstract":"In this demonstration, a new approach to GPU utilization has been demonstrated. The compilation of the standard C or C++ source code with our GCC based compiler results hybrid executable which uses GPU as well. Host CPU code is generated automatically. Case studies are presented.","PeriodicalId":336891,"journal":{"name":"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)","volume":"5 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":"128134931","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: 2
Extracting Local Binary Patterns with MIPA4k vision processor 利用MIPA4k视觉处理器提取局部二值模式
O. Lahdenoja, J. Poikonen, M. Laiho
{"title":"Extracting Local Binary Patterns with MIPA4k vision processor","authors":"O. Lahdenoja, J. Poikonen, M. Laiho","doi":"10.1109/CNNA.2010.5430265","DOIUrl":"https://doi.org/10.1109/CNNA.2010.5430265","url":null,"abstract":"In this paper we show how the MIPA4k vision processor can be used to extract and process local image features called Local Binary Patterns (LBPs). Using these low level image features more advanced higher level processing can be performed. LBP histograms can be extracted with wide field of potential applications. Also, directional thresholding results, which are part of LBP extraction process, can be used for higher level processing such as face localization. The MIPA4k vision processor consists of an array of 64 × 64 mixed-mode processing units with on-chip image capture sensors.","PeriodicalId":336891,"journal":{"name":"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)","volume":"56 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":"128374895","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: 4
Towards an automated seizure anticipation device based on Cellular Neural Networks (CNN) 基于细胞神经网络(CNN)的癫痫发作自动预测装置
G. Geis, F. Gollas, R. Tetzlaff
{"title":"Towards an automated seizure anticipation device based on Cellular Neural Networks (CNN)","authors":"G. Geis, F. Gollas, R. Tetzlaff","doi":"10.1109/CNNA.2010.5430287","DOIUrl":"https://doi.org/10.1109/CNNA.2010.5430287","url":null,"abstract":"The epileptic disorder, already mentioned in a Babylonian text dated from the middle of the first millenium BC, nowadays is known to be the most common chronical disorder of the nervous system. Epileptic seizures are phenonema of abnormal synchronization of neural activity with symptoms like convulsions and generally strike without warning. Epileptic drugs, taken in the most important therapy, have the disadvantage of adverse effects and possible habituation. A reliably, automated seizure warning system would not only provide valuable information to the patient, but also enable an efficient, event specific therapy. The problem of detecting a possible pre-seizure state in epilepsy from electroencephalogram (EEG) signals, has been addressed by many authors over the past decades but still remains unsolved. Provided that the transition between interictal state and the ictal event is not an abrupt phenomenon but a gradual change in dynamics [1], [2], precursors could be detected by analyzing brain electrical activity. Several publications report evidence that a preictal state can be detected in focal epilepsy by considering multi-variate measures [3], [4], [5], [6], [7], [8], [9] in particular, although seizures cannot be anticipated with necessary sensitivity and specificity up to now. In this contribution models based on CNN are considered in order to analyze signals from intracranial EEG, taking into account mutual dependencies between signals of neighboring electrodes. Due to their inherently parallel paradigm of computation and their high processing speed under real-time conditions combined with low power consumption, CNN are well suited to a great extent for the processing of multi-dimensional bio-electrical activity and a promising candidate for a future implantable seizure warning and preventing device. In the first proposed algorithm, solutions of Reaction-Diffusion CNN (RD-CNN) models are used in order to approximate short segments of EEG-signals. In a second algorithm, the behavior of linear spatio-temporal systems represented by discrete-time CNN (DT-CNN), are used for signal prediction of intracranial EEG. Results for the analysis of long-time recordings gained during presurgical diagnostics in temporal lobe epilepsy are given regardimg both algorithms and their predictive performance with respect to impending epileptic seizures is evaluated statistically. Additionally, the second above mentioned algorithm has been implemented on the Eyes-RIS 1.1 system [10], [11]. First results for the analysis of intracranial long-time recordings carried out on this system are given.","PeriodicalId":336891,"journal":{"name":"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)","volume":"2011 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":"129635239","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: 0
An annealing method for cellular neural networks 细胞神经网络的退火方法
T. Konishi, H. Aomori, T. Otake, N. Takahashi, I. Matsuda, S. Itoh, M. Tanaka
{"title":"An annealing method for cellular neural networks","authors":"T. Konishi, H. Aomori, T. Otake, N. Takahashi, I. Matsuda, S. Itoh, M. Tanaka","doi":"10.1109/CNNA.2010.5430261","DOIUrl":"https://doi.org/10.1109/CNNA.2010.5430261","url":null,"abstract":"The spurious minima in optimizing operation is one of the difficulty for Lyapunov function. In this paper, novel lossless image coding method based on lifting scheme using discrete-time cellular neural networks (DT-CNNs) with annealing approach is proposed. In the proposed, the image prediction of lifting scheme is implemented by DT-CNNs solving the nonlinear optimization problem of Lyapunov energy function. Since the stability point of DT-CNNs energy function is depends to the initial state value of cells, an annealing effect of adaptive chaotic noise is used to avoid the difficulty of global asymptotical stability of DT-CNNs dynamics. The experimental results show that the proposed method produces better results than those of conventional lossless image coding methods.","PeriodicalId":336891,"journal":{"name":"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)","volume":"1 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":"129740136","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: 0
Potential contribution of CNN-based solving of stiff ODEs & PDEs to enabling real-time Computational Engineering 基于cnn的刚性ode和pde求解对实现实时计算工程的潜在贡献
J. Chedjou, Cyrille Kalenga Wa Ngoy, Michel Matalatala Tamasala, K. Kyamakya
{"title":"Potential contribution of CNN-based solving of stiff ODEs & PDEs to enabling real-time Computational Engineering","authors":"J. Chedjou, Cyrille Kalenga Wa Ngoy, Michel Matalatala Tamasala, K. Kyamakya","doi":"10.1109/CNNA.2010.5430262","DOIUrl":"https://doi.org/10.1109/CNNA.2010.5430262","url":null,"abstract":"One of the most common approaches to avoid complexity while numerically solving stiff ordinary differential equations (ODEs) is approximating them by ignoring the nonlinear terms. While facing stiff partial differential equations (PDEs) the same is done by avoiding/suppressing the nonlinear terms from the Taylor's series expansion. By so doing, the traditional methods for solving stiff PDEs and ODEs do compromise on both efficiency and precision of the resulting computations. This does inevitably lead to less accurate results that consequently cannot provide the full insight that may be needed in diverse cutting-edge situations in the 'real' nonlinear dynamical behavior experienced by the various engineering and natural systems (generally modeled by nonlinear differential equations of the types ODE or PDE), which are analyzed in the frame of the novel discipline called Computational Engineering. For many of these systems, even a real-time simulation and/or control of the behavior is wished or needed; this sets evidently extremely high challenging requirements to the computing capability with regard to both computing speed and precision. This paper develops/proposes and validate through a series of presentable examples a comprehensive high-precision and ultra-fast computing concept for solving stiff ODEs and PDEs with Cellular Neural Networks (CNN). The core of this concept is a straight-forward scheme that we call 'Nonlinear Adaptive Optimization (NAOP)', which is used for a precise template calculation for solving any (stiff) nonlinear ODE through CNN processors. One of the key contributions of this work, this is a real breakthrough, is to demonstrate the possibility of mapping/transforming different types of nonlinearities displayed by various classical and well-known oscillators (e.g. van der Pol-, Rayleigh-, Duffing-, Ro?ssler-, Lorenz-, and Jerk- oscillators, just to name a few) unto first-order CNN elementary cells, and thereby enabling the easy derivation of corresponding CNN templates. Furthermore, in case of PDE solving, the same concept also allows a mapping unto first-order CNN cells while considering one or even more nonlinear terms of the Taylor's series expansion generally used in the transformation of a PDE in a set of coupled nonlinear ODEs. Therefore, the concept of this paper does significantly contribute to the consolidation of CNN as a universal and ultra-fast solver of stiff differential equations (both ODEs and ODEs). This clearly enables a CNN-based, realtime, ultra-precise, and low-cost Computational Engineering. As proof of concept some well-known prototypes of stiff equations (van der Pol, Lorenz, and Ro?ssler oscillators) have been considered; the corresponding precise CNN templates are derived to obtain precise solutions of corresponding equations. An implantation of the concept developed is possible even on embedded digital platforms (e.g. FPGA, DSP, GPU, etc.); this opens a broad range of applications. On-going works","PeriodicalId":336891,"journal":{"name":"2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)","volume":"210 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":"133068721","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: 6
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