TUNNELING PHASE LOGIC CELLULAR NONLINEAR NETWORKS

2001 ◽  
Vol 11 (12) ◽  
pp. 2895-2911 ◽  
Author(s):  
TAO YANG ◽  
RICHARD A. KIEHL ◽  
LEON O. CHUA
Keyword(s):  
Image Segmentation ◽  
Cellular Automata ◽  
Edge Detection ◽  
Voltage Source ◽  
Sinusoidal Voltage ◽  
Simple Circuit ◽  
Dc Voltage ◽  
Nonlinear Networks ◽  
2D Arrays ◽  
Image Computation

Based on a simple circuit model of a tunneling phase logic (TPL) element that is driven by a sinusoidal voltage source and biased by a DC voltage source, we present simulations of operations in cellular nonlinear networks (CNN) that could potentially be used to perform general computations in 2D arrays of simple, locally connected nanoscale devices. Some examples are presented to demonstrate the image computation capability of TPL–CNN. In particular, we use a simple 2D TPL–CNN structure to perform edge detection, image enhancement and image segmentation. Some cellular automata (CA)-like behaviors of our 2D TPL-CNN are also presented.

An Improved Method for Edge Detection and Image Segmentation Using Fuzzy Cellular Automata

2016 ◽  
Vol 47 (3) ◽  
pp. 161-179 ◽  
Author(s):  
Reza Shahverdi ◽  
Madjid Tavana ◽  
Ali Ebrahimnejad ◽  
Khadijeh Zahedi ◽  
Hesam Omranpour
Keyword(s):  
Image Segmentation ◽  
Cellular Automata ◽  
Edge Detection ◽  
Improved Method ◽  
Fuzzy Cellular Automata

scholarly journals Detection of Tumor Cells in Brain using Cellular Automata with Image Segmentation and Edge Detection.

2019 ◽  
Vol 9 (4) ◽  
pp. 11-13
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Cellular Automata ◽  
Edge Detection ◽  
Human Life ◽  
Computation Time ◽  
Mri Scan ◽  
Magnetic Resonance Imaging Mri ◽  
Medical Examinations ◽  
Cancerous Cells

Tumor growth or, growth of cancerous cells is a big challenge in today’s medical word. When dealing with human life, the detection of tumors through computers has to be highly accurate. Thus we require the assistance of computer in medical examinations, so that we will get very low rate of false cases. Brain tumor, in today’s world, is seen as most threatening and life taking disease. In order to detect brain tumor more accurately in lesser time, many techniques have already been proposed using image segmentation and edge detection. In our paper we propose a technique which is more efficient to detect brain tumor where edge detection through cellular automata have been used from Magnetic Resonance Imaging (MRI) scan images. It processes these images, and determines the area affected by using segmentation and edge detection with cellular automata. Simulated work is completed with the help of Simulink in MATLAB. Regarding this particular topic there are many studies, however our proposal of combination of both segmentation and edge detection through cellular automata shows better results as compared to combining segmentation with classical edge detection in term of computation time and clarity. This will help in efficiency of detecting brain tumor and later in its removal.

scholarly journals Using Semi-Supervised Learning in Cellular Automata for Edge Detection of Image Segmentation

2018 ◽  
Vol 7 (02) ◽  
pp. 23613-23619
Author(s):  
Draiya A. Alaswad ◽  
Yasser F. Hassan
Keyword(s):  
Image Segmentation ◽  
Cellular Automata ◽  
Edge Detection ◽  
Supervised Learning ◽  
Unlabeled Data ◽  
Machine Learning Techniques ◽  
Natural Image ◽  
Clustering Method ◽  
Learning Techniques ◽  
Better Than

Semi-Supervised Learning is an area of increasing importance in Machine Learning techniques that make use of both labeled and unlabeled data. The goal of using both labeled and unlabeled data is to build better learners instead of using each one alone. Semi-supervised learning investigates how to use the information of both labeled and unlabeled examples to perform better than supervised learning. In this paper we present a new method for edge detection of image segmentation using cellular automata with modification for game of life rules and K-means algorithm. We use the semi-supervised clustering method, which can jointly learn to fusion by making use of the unlabeled data. The learning aim consists in distinguishing between edge and no edge for each pixel in image. We have applied the semi-supervised method for finding edge detection in natural image and measured its performance using the Berkeley Segmentation Dataset and Benchmark dataset. The results and experiments showed the accuracy and efficiency of the proposed method.

scholarly journals Detection of Tumor Cells in Brain using Cellular Automata with Image Segmentation and Edge Detection

2019 ◽  
Vol 9 (4) ◽  
pp. 11-13
Author(s):  
Dr Kumaravel A. ◽  
◽  
Jasmeena Tariq ◽  
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Cellular Automata ◽  
Edge Detection ◽  
Human Life ◽  
Computation Time ◽  
Mri Scan ◽  
Magnetic Resonance Imaging Mri ◽  
Medical Examinations ◽  
Cancerous Cells

Tumor growth or, growth of cancerous cells is a big challenge in today’s medical word. When dealing with human life, the detection of tumors through computers has to be highly accurate. Thus we require the assistance of computer in medical examinations, so that we will get very low rate of false cases. Brain tumor, in today’s world, is seen as most threatening and life taking disease. In order to detect brain tumor more accurately in lesser time, many techniques have already been proposed using image segmentation and edge detection. In our paper we propose a technique which is more efficient to detect brain tumor where edge detection through cellular automata have been used from Magnetic Resonance Imaging (MRI) scan images. It processes these images, and determines the area affected by using segmentation and edge detection with cellular automata. Simulated work is completed with the help of Simulink in MATLAB. Regarding this particular topic there are many studies, however our proposal of combination of both segmentation and edge detection through cellular automata shows better results as compared to combining segmentation with classical edge detection in term of computation time and clarity. This will help in efficiency of detecting brain tumor and later in its removal.

Battery Losses In a MMC for BEVS Application

2018 ◽  
Vol 12 (1) ◽  
pp. 98-109 ◽  
Author(s):  
Adolfo Dannier ◽  
Gianluca Brando ◽  
Ivan Spina ◽  
Diego Iannuzzi
Keyword(s):  
Mathematical Model ◽  
System Architecture ◽  
Electrochemical Cell ◽  
Voltage Source ◽  
Modular Multilevel Converter ◽  
Voltage Reference ◽  
Sinusoidal Voltage ◽  
Current Waveform ◽  
Voltage Source Inverter ◽  
Multilevel Converter

Objective:This paper analyses the Modular Multilevel Converter (MMC) topology, where each individual Sub Module (SM), in half bridge configuration, is directly fed by an elementary electrochemical cell.Methods:The aim is to investigate how the reference voltages influence the cells currents waveforms, determining how the active powers and the losses are distributed among the cells. Considering a 2-level Voltage Source Inverter (VSI) topology working in the same conditions, the ratio between the MMC total cells losses and VSI total cells losses is calculated. After showing the system architecture and mathematical model, the cells current waveform investigation is presented and detailed both for triangular and sinusoidal voltage reference waveform.Results:Finally, the results are critically discussed with particular focus on the comparison between the MMC and the VSI topologies.

scholarly journals Transient Current Density in a Pair of Long Parallel Conductors

2021 ◽  
Vol 11 (15) ◽  
pp. 6920
Author(s):  
Oldřich Coufal
Keyword(s):  
Steady State ◽  
Cross Section ◽  
Current Density ◽  
Circular Cross Section ◽  
Transient Current ◽  
Constant Current ◽  
Voltage Source ◽  
Sinusoidal Voltage ◽  
Steady State Current ◽  
Maximum Current

Two infinitely long parallel conductors of arbitrary cross section connected to a voltage source form a loop. If the source voltage depends on time, then due to induction there is no constant current density in the loop conductors. It is only recently that a method has been published for accurately calculating current density in a group of long parallel conductors. The method has thus far been applied to the calculation of steady-state current density in a loop connected to a sinusoidal voltage source. In the present article, the method is used for an accurate calculation of transient current using transient current density. The transient current is analysed when connecting and short-circuiting the sources of sinusoidal, constant and sawtooth voltages. For circular cross section conductors, the dependences of maximum current density, maximum current and the time of achieving steady state on the source frequency, the distance of the conductors and their resistivity when connecting the source of sinusoidal voltage are examined.

scholarly journals Novel Control Strategy for Enhancing Microgrid Operation Connected to Photovoltaic Generation and Energy Storage Systems

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1261
Author(s):  
Dina Emara ◽  
Mohamed Ezzat ◽  
Almoataz Y. Abdelaziz ◽  
Karar Mahmoud ◽  
Matti Lehtonen ◽  
...  
Keyword(s):  
Energy Storage ◽  
Control Strategy ◽  
Voltage Stability ◽  
Storage Systems ◽  
Voltage Source ◽  
Stable Operation ◽  
Energy Storage Systems ◽  
Dc Microgrid ◽  
Photovoltaic Generation ◽  
Dc Voltage

Recently, the penetration of energy storage systems and photovoltaics has been significantly expanded worldwide. In this regard, this paper presents the enhanced operation and control of DC microgrid systems, which are based on photovoltaic modules, battery storage systems, and DC load. DC–DC and DC–AC converters are coordinated and controlled to achieve DC voltage stability in the microgrid. To achieve such an ambitious target, the system is widely operated in two different modes: stand-alone and grid-connected modes. The novel control strategy enables maximum power generation from the photovoltaic system across different techniques for operating the microgrid. Six different cases are simulated and analyzed using the MATLAB/Simulink platform while varying irradiance levels and consequently varying photovoltaic generation. The proposed system achieves voltage and power stability at different load demands. It is illustrated that the grid-tied mode of operation regulated by voltage source converter control offers more stability than the islanded mode. In general, the proposed battery converter control introduces a stable operation and regulated DC voltage but with few voltage spikes. The merit of the integrated DC microgrid with batteries is to attain further flexibility and reliability through balancing power demand and generation. The simulation results also show the system can operate properly in normal or abnormal cases, thanks to the proposed control strategy, which can regulate the voltage stability of the DC bus in the microgrid with energy storage systems and photovoltaics.

scholarly journals Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India

2020 ◽  
Vol 30 (1) ◽  
pp. 273-286
Author(s):  
Kalyan Mahata ◽  
Rajib Das ◽  
Subhasish Das ◽  
Anasua Sarkar
Keyword(s):  
Land Use ◽  
Image Segmentation ◽  
Cellular Automata ◽  
Land Cover ◽  
Satellite Imagery ◽  
Current Model ◽  
Discrete Dynamical System ◽  
Land Use Land Cover ◽  
Data Set ◽  
Validity Indices

Abstract Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method.

Self-induction in two long parallel conductors connected to sinusoidal voltage source

2020 ◽  
Vol 95 (6) ◽  
pp. 065503
Author(s):  
Oldřich Coufal ◽  
Lukáš Radil
Keyword(s):  
Voltage Source ◽  
Sinusoidal Voltage
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