A NEW CNN OSCILLATOR MODEL FOR PARALLEL IMAGE SEGMENTATION

2008 ◽  
Vol 18 (07) ◽  
pp. 1999-2015 ◽  
Author(s):  
MICHAŁ STRZELECKI ◽  
JACEK KOWALSKI ◽  
HYONGSUK KIM ◽  
SOOHONG KO
Keyword(s):  
Image Segmentation ◽  
Block Diagram ◽  
Scene Recognition ◽  
Oscillator Model ◽  
Parallel Structure ◽  
Oscillator Circuit ◽  
Wide Range ◽  
Network Operation ◽  
Parallel Network ◽  
Oscillator Network

Segmentation of the textured images into disjoint homogeneous regions is a very important aspect of visual perception. The texture represents properties of visualized objects; it may provide information about their structure. One of the recently developed tools used for texture segmentation is a network of synchronized oscillators. A parallel network operation is based on a "temporary correlation" theory, which attempts to explain scene recognition as performed by the human brain. This theory states that the synchronized oscillations of neuron groups attract attention if it is focused on a coherent stimulus (image object). For more than one perceived stimulus, these synchronized patterns switch in time between different neuron groups, thus forming temporal maps coding several features of the analyzed scene. Consequently, to implement this theory, a new oscillator network was proposed for image segmentation. The segmentation is obtained due to local interactions among neighboring cells. Such a network was successfully used for segmentation of the wide range of different images, including textured and biomedical ones. The network is very suitable for a hardware realization owing to its parallel structure. The realization provides a much faster image segmentation when compared to computer simulation techniques. The paper presents a new mathematical oscillator model suitable to be implemented in a CNN network chip. The model was used to design and simulate a CMOS oscillator circuit, which enables parallel network operation. The proposed oscillator model was analyzed and discussed from the point of view of its computer simulations. Furthermore, it was demonstrated that the oscillator network which implements the presented model is able to perform segmentation of the sample textured images. Oscillator circuit and block diagram of the proposed network chip were also presented and discussed.

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

A Novel Automatic Modulation Classification Method Using Attention Mechanism and Hybrid Parallel Neural Network

2021 ◽  
Vol 11 (3) ◽  
pp. 1327
Author(s):  
Rui Zhang ◽  
Zhendong Yin ◽  
Zhilu Wu ◽  
Siyang Zhou
Keyword(s):  
Neural Network ◽  
Communication Systems ◽  
Parallel Structure ◽  
Modulation Classification ◽  
Automatic Modulation Classification ◽  
Spatial Features ◽  
Temporal Features ◽  
Different Types ◽  
In Series ◽  
Parallel Network

Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When it comes to the output layer, softmax function is applied for classification to expand the inter-class distance. In this paper, we propose a hybrid parallel network for the AMC problem. Our proposed method designs a hybrid parallel structure which utilizes Convolution Neural Network (CNN) and Gate Rate Unit (GRU) to extract spatial features and temporal features respectively. Instead of superposing these two categories of features directly, three different attention mechanisms are applied to assign weights for different types of features. Finally, a cosine similarity metric named Additive Margin softmax function, which can expand the inter-class distance and compress the intra-class distance simultaneously, is adopted for output. Simulation results demonstrate that the proposed method can achieve remarkable performance on an open access dataset.

scholarly journals Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

2020 ◽  
Vol 12 (11) ◽  
pp. 1772
Author(s):  
Brian Alan Johnson ◽  
Lei Ma
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Online Survey ◽  
Accuracy Assessment ◽  
Learning Approaches ◽  
Special Issue ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

scholarly journals Bond-Graph model based stepper motor output characteristics simulation implemented in LabVIEW environment

2021 ◽  
Vol 7 (1.) ◽  
Author(s):  
Zsolt Molnár
Keyword(s):  
Block Diagram ◽  
Real Life ◽  
Graph Model ◽  
Bond Graph ◽  
Visual Programming ◽  
Stepper Motor ◽  
Intermediate Step ◽  
Virtual Device ◽  
Wide Range ◽  
Made In

In the industry, simulations are of great importance. They enable measurements to be made in different conditions about a virtual device, which are highly comparable to measurements made in a real life scenarios. Because of their wide range of usage in lower power drive systems, where precision and simplicity is a must, the subject of study is a permanent magnet stepper motor. For precise positioning purposes, it is essential to know the positioning behaviour of these devices. The model construction process involved an intermediate step, which consisted of creating the Bond-Graph of the motor based on pre-defined models available in the literature in this field. In the next step, the Bond-Graph model was converted to a block diagram of the motor. This permitted the direct implementation of the motor model in LabVIEW visual programming environment. The preliminary steps allows us to check and confirm the functionality and correctness of the model. This article covers in detail the model conversion and implementation steps of the simulation. At the end, the functionality of the simulation was tested.

All Digital Nanometer Subdivision Method to Process Coarse Grating Signal

2010 ◽  
Vol 108-111 ◽  
pp. 1199-1204
Author(s):  
Li Chang ◽  
Hui Xu ◽  
Ben Wei Liu ◽  
Jian Qiang Li
Keyword(s):  
High Resolution ◽  
Block Diagram ◽  
Measurement Range ◽  
Difficult Problem ◽  
Control Functions ◽  
Measurement Resolution ◽  
Wide Range ◽  
Moire Fringe ◽  
Moiré Fringe

The high resolution and wide measurement range are important for the displacement measurement based on the grating moiré fringe at present. 50lines/mm coarse grating scale is chosen often in order to carry out the wide range. Therefore, the realization of the high resolution is a difficult problem for coarse grating scale. The paper proposes a new nanometer subdivision method with CCD based on the disadvantages of previous moiré fringe collection method. CCD replaces the traditional photoelectric triode and converts moiré fringe to the pixels signal output, which can improve the moiré fringe quality of grating scale. The pixel is the basis unit of CCD; the pixel number decides directly the measurement resolution and precision. The paper chooses the liner array CCD TCD1501C that has 5000 pixel units and 7μm pixel unit，which ensures the nanometer resolution. The output signals of CCD are processed by amplifier, filter, 12 bit analog-digital converter and sent to the core processor of FPGA. The FPGA consisted of all digital system including CCD driver, subdivision, identification direction, displacement calculation, display and key control functions. The paper gave the principle of nanometer subdivision, block diagram of FPGA module and simulation waveforms.

Comparison of Modeling Techniques for a Landing Gear

2010 ◽  
Author(s):  
Rebecca Margetts ◽  
Roger F. Ngwompo
Keyword(s):  
Block Diagram ◽  
Differential Algebraic Equations ◽  
Landing Gear ◽  
Algebraic Equations ◽  
Bond Graphs ◽  
Aircraft Landing ◽  
Aircraft Landing Gear ◽  
Wide Range ◽  
Modeling Techniques ◽  
Computational Errors

A wide range of modeling techniques is available to the engineer. The objective of this paper is to compare some typical modeling techniques for the simulation of a multi-domain mechatronic system. Usual dynamic modeling methods, such as block diagrams and iconic diagrams, can cause problems for the engineer. Differential algebraic equations (DAEs) and algebraic loops can significantly increase simulation times and cause numeric errors. Bond graphs are less common in industry, and are presented here as a method which allows the engineer to easily identify causal loops and elements in differential causality. These can indicate DAEs in the underlying equations. An aircraft landing gear is given as an example of a multi-domain system, and is modeled as a block diagram, an iconic diagram and as a bond graph. The time to construct the model, time to solve and problems faced by the analyst are presented. Bond graphs offer distinct advantages in terms of the ease of implementing algebraic equations and visibility of causality. The time taken to model a system can be significantly reduced and the results appear free from computational errors. Bond graphs are therefore recommended for this type of multi-domain systems analysis.

Design and Application of an New Wireless Adaptive AP

2012 ◽  
Vol 203 ◽  
pp. 484-487
Author(s):  
Xiao Hui Bai
Keyword(s):  
Wireless Networks ◽  
Design Scheme ◽  
Wide Range ◽  
Network Operation ◽  
Design And Application

With the wide range of applications of wireless networks, Wireless AP in indoor use of limitation is very big. Reduce the quality of network operation in many cases, This paper analyzes its limitation, put forward several methods to optimize the use of of AP, also put forward a design scheme of an new wireless adaptive AP.

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