Computational-complexity comparison of time- and frequency-domain artificial neural networks for optical nonlinearity compensation

2020 ◽  
Author(s):  
Takeru Kyono ◽  
Moriya Nakamura
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Complexity ◽  
Frequency Domain ◽  
Optical Nonlinearity ◽  
Nonlinearity Compensation ◽  
Artificial Neural

scholarly journals Polarization demultiplexing and optical nonlinearity compensation based on artificial neural networks

2019 ◽  
Vol 8 (12) ◽  
pp. 542-547 ◽  
Author(s):  
Yuichiro Kurokawa ◽  
Takeru Kyono ◽  
Yuta Fukumoto ◽  
Noriki Sumimoto ◽  
Moriya Nakamura
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optical Nonlinearity ◽  
Nonlinearity Compensation ◽  
Artificial Neural

scholarly journals Feasibility of Acoustic Print Head Monitoring for Binder Jetting Processes with Artificial Neural Networks

2021 ◽  
Vol 11 (22) ◽  
pp. 10672
Author(s):  
Philipp Lechner ◽  
Philipp Heinle ◽  
Christoph Hartmann ◽  
Constantin Bauer ◽  
Benedikt Kirchebner ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Frequency Domain ◽  
Typical Problem ◽  
Print Head ◽  
Acoustic Data ◽  
Artificial Neural ◽  
Binder Jetting

The clogging of piezoelectric nozzles is a typical problem in various additive binder jetting processes, such as the manufacturing of casting molds. This work aims at print head monitoring in these binder jetting processes. The structure-born noise of piezoelectric print modules is analyzed with an Artificial Neural Network to classify whether the nozzles are functional or clogged. The acoustic data are studied in the frequency domain and utilized as input for an Artificial Neural Network. We found that it is possible to successfully classify individual nozzles well enough to implement a print head monitoring, which automatically determines whether the print head needs maintenance.

scholarly journals Experimental Implementation of a Neural Network Optical Channel Equalizer in Restricted Hardware Using Pruning and Quantization

2022 ◽  
Author(s):  
Diego Argüello Ron ◽  
Pedro Jorge Freire De Carvalho Sourza ◽  
Jaroslaw E. Prilepsky ◽  
Morteza Kamalian-Kopae ◽  
Antonio Napoli ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Complexity ◽  
Communication Systems ◽  
Complexity Reduction ◽  
Edge Computing ◽  
Optical Channel ◽  
Raspberry Pi ◽  
Artificial Neural ◽  
The Impact

Abstract The deployment of artificial neural networks-based optical channel equalizers on edge-computing devices is critically important for the next generation of optical communication systems. However, this is a highly challenging problem, mainly due to the computational complexity of the artificial neural networks (NNs) required for the efficient equalization of nonlinear optical channels with large memory. To implement the NN-based optical channel equalizer in hardware, a substantial complexity reduction is needed, while keeping an acceptable performance level. In this work, we address this problem by applying pruning and quantization techniques to an NN-based optical channel equalizer. We use an exemplary NN architecture, the multi-layer perceptron (MLP), and address its complexity reduction for the 30 GBd 1000 km transmission over a standard single-mode fiber. We demonstrate that it is feasible to reduce the equalizer’s memory by up to 87.12%, and its complexity by up to 91.5%, without noticeable performance degradation. In addition to this, we accurately define the computational complexity of a compressed NN-based equalizer in the digital signal processing (DSP) sense and examine the impact of using different CPU and GPU settings on power consumption and latency for the compressed equalizer. We also verify the developed technique experimentally, using two standard edge-computing hardware units: Raspberry Pi 4 and Nvidia Jetson Nano.

Optical Nonlinearity Monitoring and Launch Power Optimization by Artificial Neural Networks

2020 ◽  
Vol 38 (9) ◽  
pp. 2637-2645 ◽  
Author(s):  
Matteo Lonardi ◽  
Jelena Pesic ◽  
Philippe Jenneve ◽  
Petros Ramantanis ◽  
Nicola Rossi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Optimization ◽  
Optical Nonlinearity ◽  
Artificial Neural

Selection for Wandering Behavior in a Small Robot

1994 ◽  
Vol 2 (1) ◽  
pp. 101-116 ◽  
Author(s):  
Orazio Miglino ◽  
Kourosh Nafasi ◽  
Charles E. Taylor
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Complexity ◽  
Random Noise ◽  
Effective Means ◽  
Selection For ◽  
Artificial Neural ◽  
Small Robot ◽  
Fixed Period

We have evolved artificial neural networks to control the wandering behavior of small robots. The task and environment were very simple—to touch as many squares in a grid as possible during a fixed period of time. A number of the simulated robots were embodied in a small Lego™ robot, controlled by a Motorola™ 6811 processor; and their performance was compared to the simulations. We observed that: (a) evolution was an effective means to program the robot's behavior; (b) progress was characterized by sharply stepped periods of improvement, separated by periods of stasis that corresponded to levels of behavioral/computational complexity; and (c) the simulated and realized robots behaved quite similarly, the realized robots in some cases outperforming the simulated ones. Introducing random noise to the simulations improved the fit somewhat (from r = 0.73 to 0.79). Hybrid simulated/embodied selection regimes for evolutionary robots are discussed.

STRING MATCHING ARTIFICIAL NEURAL NETWORKS

2001 ◽  
Vol 11 (05) ◽  
pp. 445-453 ◽  
Author(s):  
TATIANA TAMBOURATZIS
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Complexity ◽  
Building Block ◽  
String Matching ◽  
Matching Problems ◽  
Low Computational Complexity ◽  
Artificial Neural ◽  
Harmony Theory ◽  
Fast Match

Three artificial neural networks (ANNs) are proposed for solving a variety of on- and off-line string matching problems. The ANN structure employed as the building block of these ANNs is derived from the harmony theory (HT) ANN, whereby the resulting string matching ANNs are characterized by fast match-mismatch decisions, low computational complexity, and activation values of the ANN output nodes that can be used as indicators of substitution, insertion (addition) and deletion spelling errors.

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