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Automatic Polyp Detection Using Microwave Endoscopy for Colorectal Cancer Prevention and Early Detection: Phantom Validation

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IEEE ACCESS 2021年 9卷 148048-148059页

作者： Garrido, Alejandra Sont, Roberto Dghoughi, Walid Marcoval, Sergi Romeu, Jordi Fernandez-Esparrach, Gloria Belda, Ignasi Guardiola, Marta MiWEndo Solut Barcelona 08014 Spain Univ Politecn Catalunya UPC CommSensLab Barcelona 08034 Spain Univ Barcelona IDIBAPS CIBEREHD Endoscopy UnitDept GastroenterolHosp Clin Barcelona 08036 Spain

A system to integrate microwave imaging with optical colonoscopy is presented. The overarching goal is to improve the prevention and early diagnosis of one of the main health and economic burdens of an increasingly aging population, i.e., colorectal cancer. For a colonoscopy, the gold standard for colorectal cancer diagnosis, 22% of polyps are not detected, and the risk of cancer after a negative colonoscopy can be up to 7.9%. To remedy this, a microwave imaging system able to generate an alarm when a polyp is detected is designed, manufactured and validated with a colon phantom composed of tissue-mimicking oil-gelatin materials reproducing the anatomy and dielectric properties of a human colon with a polyp. The acquisition was performed by a miniaturized ring-shaped switched array of 16 antennas attachable at the tip of a conventional colonoscope. This has been conceived to satisfy endoscopy size restrictions, patient safety and intercompatibility with current clinical practice. A Modified Monofocusing imaging method preceded by a previous frame average subtraction as a calibration technique shows a perfect detection of a 10-mm polyp (100% sensitivity and specificity) in the eight analyzed trajectories. The phantom results demonstrate the feasibility of the system in future preclinical trials.

关键词： Colonoscopy Colon Microwave imaging Microwave theory and techniques Imaging Endoscopes Cancer backpropagation algorithms endoscopes medical diagnostic imaging microwave antenna arrays microwave imaging

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The Reduction of Fully Connected Neural Network Parameters Using the Pre-training Technique

The Reduction of Fully Connected Neural Network Parameters U...

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IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications

作者： Aliaksandr Kroshchanka Vladimir Golovko Brest State Technical University Brest Belarus Państwowa Szkoła Wyższa im. Papieża Jana Pawła II Biala Podlaska Poland

ISBN: (纸本)9781665426060

This paper proposes an approach to the parameters reduction of fully connected neural networks using the conventional method of pre-training deep neural networks. It is shown that this approach makes it possible to reduce the number of trainable parameters without significant loss the generalization ability. The capabilities of the proposed technique are demonstrated using the standard dataset of handwritten digits MNIST.

关键词： Training Deep learning Conferences Neural networks Data acquisition Computer architecture backpropagation algorithms

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Neural Network Based Heart Disease Prediction

Neural Network Based Heart Disease Prediction

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： S N Shivappriya R Navaneethakrishnan K. Sangeetha Raj M. Abirami S. Chidhambaram Kumaraguru College of Technology Coimbatore

ISBN: (纸本)9781665428309

Heart attack is deadly disease which may result in sudden heart block, and it may lead to death. Every day many people are affected by a sudden myocardial infarction, and this may lead to sudden death. A heart attack is a medical emergency usually occurs when a blood coagulates or blood flow to the heart gets blocked and it may also occur due to poor contraction and expansion of cardiac muscles. Thus, we can able to understand that there must be a proper prediction method to predict the heart attack. The proposed system has been designed to predict the heart attack using Multi Layer Perceptron neural network-based classification approach. The objective of this project is to train a neural network and classify the type of heart attack into 17 categories such [Normal Sinus Rhythm, Auricular Premature beat, Atrial Flutter, Supraventricular Tachycardia, Atrial Fibrillation, Wolff-Parkinson-white syndrome, Premature ventricular contraction, Ventricular Bigeminy, Ventricular Trigeminy, Ventricular Tachycardia, Presbyopia, Succinate Dehydrogenase iron-sulfur subunit, Right bundle branch block, Fusion, Left bundle branch block]. Initially we have obtained an online data set [Physio net], by preprocessing and segmentation 4 different features are extracted. Then the extracted features are given as an input to neural network and classified into 17 classes.

关键词： backpropagation Heart Neural networks Cardiac arrest backpropagation algorithms Filtering algorithms Feature extraction

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Adaptive Learning Rate for Dealing with Imbalanced Data in Classification Problems

Adaptive Learning Rate for Dealing with Imbalanced Data in C...

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International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON)

作者： Ratanon Jantanasukon Arit Thammano Computational Intelligence Laboratory Faculty of Information Technology King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9781665446914

This research modified a backpropagation learning algorithm in order to increase its ability to deal with imbalanced data problems. We used the backpropagation algorithm and a concept of multiple adaptive learning rates to train the feedforward neural network. Using multiple adaptive learning rates allowed us to achieve a classification model that had fewer problems when dealing with an imbalanced dataset. The experimental results showed that the proposed method performed significantly better than the conventional backpropagation neural network in all tests.

关键词： backpropagation Training Adaptive learning Digital art backpropagation algorithms Media Distance measurement

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Plug-And-Play Learned Gaussian-mixture Approximate Message Passing

Plug-And-Play Learned Gaussian-mixture Approximate Message P...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Osman Musa Peter Jung Giuseppe Caire Communications and Information Theory Group Technische Universität Berlin Data Science in Earth Observation Technical University of Munich

Deep unfolding showed to be a very successful approach for accelerating and tuning classical signal processing algorithms. In this paper, we propose learned Gaussian-mixture AMP (L-GM-AMP) - a plug-and-play compressed sensing (CS) recovery algorithm suitable for any i.i.d. source prior. Our algorithm builds upon Borgerding's learned AMP (LAMP), yet significantly improves it by adopting a universal denoising function within the algorithm. The robust and flexible denoiser is a byproduct of modelling source prior with a Gaussian-mixture (GM), which can well approximate continuous, discrete, as well as mixture distributions. Its parameters are learned using standard backpropagation algorithm. To demonstrate robustness of the proposed algorithm, we conduct Monte-Carlo (MC) simulations for both mixture and discrete distributions. Numerical evaluation shows that the L-GM-AMP algorithm achieves state-of-the-art performance without any knowledge of the source prior.

关键词： Noise reduction Signal processing algorithms backpropagation algorithms Signal processing Approximation algorithms Numerical models Noise measurement

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Continuous-wave terahertz diffraction tomography

Continuous-wave terahertz diffraction tomography

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International Conference on Infrared and Millimeter Waves

作者： Xiaoyu Jin Dayong Wang Jie Zhao Yunxin Wang Lu Rong Faculty of Science Beijing University of Technology Beijing China Beijing Engineering Research Center of Precision Measurement Technology and Instruments Beijing China

We implement diffraction tomography using a continuous-wave terahertz (CW-THz) source. An off-axis digital holographic interferometry system was built to measure the complex amplitude distributions of a sample for various rotation angles. Based on the Rytov approximation, the three-dimensional (3D) refractive index (RI) distributions of the polystyrene foam spheres was reconstructed by the filtered backpropagation algorithm.

关键词： Three-dimensional displays Diffraction Refractive index backpropagation algorithms Tomography Filtering algorithms Rotation measurement

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Background Compensation of Static TI-ADC Nonlinearities in Coherent Optical Receivers

Background Compensation of Static TI-ADC Nonlinearities in C...

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Argentine Conference on Electronics (CAE)

作者： Álvaro Fernández Bocco Fredy Solis Benjamín T. Reyes Damián A. Morero Mario R. Hueda Fundación Fulgor Córdoba Argentina Universidad Nacional de Cordoba - CONICET Córdoba Argentina

ISBN: (数字)9781728175799

ISBN: (纸本)9781728175805

This paper presents a novel background compensation technique of nonlinear distortions in time-interleaved analog-to-digital converters (TI-ADC). The proposed scheme is based on the well-known error backpropagation algorithm, which has been extensively used in machine learning applications. The technique enables adaptive compensation using low-complexity hardware such as look-up tables. The effectiveness and intrinsic background characteristics of this proposal make it a well suited candidate for application in high-speed, high modulation order digital communication receivers. Simulation results in a coherent optical receiver demonstrate that the proposed background compensation algorithm is able to practically eliminate the penalties caused by the TI-ADC nonlinearity.

关键词： Simulation Nonlinear distortion backpropagation algorithms Optical receivers Table lookup Proposals Signal to noise ratio

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Classification of Honey as Genuine or Fake via Artificial Neural Network using Gradient Descent backpropagation Algorithm

Classification of Honey as Genuine or Fake via Artificial Ne...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Carlos C. Hortinela Jessie R. Balbin Patrick Jonas A. Tibayan John Myrrh D. Cabela Glenn V. Magwili School of Electrical Electronics and Computer Engineering Mapúa University Muralla St. Intramuros Manila Philippines

ISBN: (纸本)9781665419710;9781665429979

Honey is always among the lists for food fraud around the world. A whistleblower surfaced in a South African Honey manufacturer that claims their honey is revealed as passing off a sugar concoction. Also, in the Philippines, mid-2016 there's a manufacturer of honey named Cem's Honey that mislabels their product as real honey but in fact their product is a fake honey. The main objective of this study is to create a system that could classify a honey whether it is genuine or fake using Artificial Neural Network with Gradient Descent backpropagation as the training algorithm, and sensors (Electrical Conductivity and pH Sensor). Through testing, the system classified the presented samples at an accuracy rate of 87.5%. In conclusion, the researchers successfully developed a system that can classify a honey whether is it genuine or fake using Artificial Neural Network.

关键词： Training Temperature Artificial neural networks backpropagation algorithms Sensor systems Sensors Testing

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Evaluation of backpropagation Reconstruction Algorithm for 3D Microwave Imaging of Body Part Anomalies

Evaluation of Backpropagation Reconstruction Algorithm for 3...

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IEEE International Symposium on Antennas and Propagation

作者： Claire Migliaccio Christian Pichot Ian Platt Adrian Tan Ian Woodhead Universite Cote d'Azur CNRS LEAT Sophia Antipolis France Lincoln Agritech Ltd Lincoln Canterbury New Zealand

We investigate the performance of a fast reconstruction algorithm based a backpropagation algorithm for 3D Microwave Imaging of Body Part Anomalies.

ISBN: (数字)9781728166704

ISBN: (纸本)9781728166704;9781728166711

We investigate the performance of a fast reconstruction algorithm based a backpropagation algorithm for 3D Microwave Imaging of Body Part Anomalies.

关键词： Microwave measurement Microwave antennas Three-dimensional displays backpropagation algorithms Reconstruction algorithms Image reconstruction Microwave imaging

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backpropagation-Based Background Compensation of Frequency Interleaved ADC for Coherent Optical Receivers

Backpropagation-Based Background Compensation of Frequency I...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Leandro Passetti Agustin C. Galetto Diego J. Hernando Damian Morero Benjamin T. Reyes Mario R. Hueda Fundación Fulgor − Romagosa 518 − Córdoba (5000) − Argentina Laboratorio de Comunicaciones Digitales − Universidad Nacional de Cordoba − CONICET Av. Velez Sarsfield 1611 − Cordoba (X5016GCA) − Argentina

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

This paper introduces an adaptive background compensation technique to mitigate errors of the analog signal path of Frequency-Interleaved ADCs (FI-ADC). The key ingredients of the proposed technique are MIMO equalization and the backpropagation algorithm often used to train neural networks. The technique introduced here is applicable to digital receivers such as those used in coherent optical communications. Although compensation of FI-ADCs has been discussed in the technical literature, adaptive background techniques have not been reported so far. The latter are important to automatically compensate errors caused by process, voltage, and temperature variations in CMOS implementations of the data converters and therefore ensure high manufacturing yield. Simulations show that the impairments of the analog signal path are accurately compensated and their effect essentially eliminated, resulting in a high performance ADC system.

关键词： Optical receivers MIMO communication Equalizers Adaptive optics backpropagation algorithms Demodulation

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