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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

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Power control in d2d underlay distributed antenna systems 2nd

Power control in d2d underlay distributed antenna systems

引用

2nd international conference on Sensor Networks and signal processing, SNSP 2019

作者： Qian, Gongbin Zhang, Ce He, Chunlong Li, Xingquan Tian, Chu Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

ISBN: (纸本)9789811549168

A new scenario is considered that device-to-device (D2D) communication users underlay the spectrum resource of cellular user in distributed antenna systems (DAS) is discussed in this paper. We mainly focus on how to improve spectral efficiency (SE) and energy efficiency (EE) of the system. Under the maximum transmit power constraint per antenna unit, we propose two resource allocation algorithms to solve the optimal problems of maximum SE and EE. The first problem can be transformed into a difference of convex (DC) structure problem by function recombination, then the concave-convex procedure (CCCP) algorithm and the interior point method which are adopted to get the optimal solutions for the maximum SE. Subsequently, by using the Dinkelbach algorithm based on the parameter method, a power allocation algorithm for energy efficiency is developed to solve the maximum EE optimization problem. The optimal solutions are also obtained by the CCCP algorithm and the interior point method. Simulation results show that compared to co-located antenna systems (CAS) with D2D users, the SE and EE performances of the proposed system have a significant improvement. © Springer Nature Singapore Pte Ltd 2021.

关键词： Spectrum efficiency

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Aspect Ratio Estimation for MOS Amplifier using machine learning

Aspect Ratio Estimation for MOS Amplifier using Machine Lear...

引用

international conference on Applied Electromagnetics, signal processing and Communication (AESPC)

作者： Jeenang Shah Harshil Patel Ruchi Gajjar Dipesh Panchal Manish I. Patel Institute of Technology Nirma University Ahmedabad India

ISBN: (纸本)9781665443005

Conventional analog circuit design is complicated due to non-linearity. Due to which the design process was more complex in terms of hand calculations and was time-consuming. This paper estimate the aspect ratio for MOSFET based Common Source Amplifier with Diode connected load using machine learning techniques like Neural Network (NN), General Regression Neural Network (GRNN), Support Vector Regression (SVR) and Decision Tree Regression. The aim is to predict the size of the transistors of the circuit according to the corresponding design constraints or design parameters, without the knowledge of SPICE technology parameters. The designed models were trained using 180 nm and 250 nm technology nodes. These models take parameters like Voltage Gain, Power Dissipation, Power Supply, and Input Impedance as input and give the sizes of transistors as output. As the design constraints were applied to these techniques, the Neural Network provided the most accurate size of the transistors as an output with maximum accuracy of 97.49%.

关键词： Support vector machines machine learning algorithms Voltage machine learning Analog circuits Predictive models Prediction algorithms

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proceedings of the 2020 2nd international conference on Image, Video and signal processing, IVSP 2020

Proceedings of the 2020 2nd International Conference on Imag...

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2nd international conference on Image, Video and signal processing, IVSP 2020

ISBN: (纸本)9781450376952

The proceedings contain 26 papers. The topics discussed include: analyzing the compatibility of identifying emotions by facial expressions and text analytics when using mobile devices;fish species identification using a CNN-based multimodal learning method;study on the identification of irritability emotion based on the percentage change in pupil size;face recognition based on shallow convolutional neural network classifier;fractional active contour model for edge detector on medical image segmentation;automated hand hygiene compliance monitoring;circular template matching based on improved ring projection method;a hybrid approach for counting templates in images;no-reference image quality assessment based on a multi-feature extraction network;and disaster assessment from satellite imagery by analyzing topographical features using deep learning.

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Transfer learning based COVID-19 detection Using Radiological Images

Transfer learning based COVID-19 detection Using Radiologica...

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international conference on Applied Electromagnetics, signal processing and Communication (AESPC)

作者： Aurosweta Mahapatra Sasmita Pahadsingh T. Kar

ISBN: (纸本)9781665443005

Artificial Intelligence is a process that enables machines to imitate human behaviour. Both machine learning and Deep learning are subsets of AI. The basic difference between ML(machine learning) and DL(Deep learning) is that in machine learning manually defining of features is done to get the desired outcome whereas in deep learning the neural network learns of its own and publishes the result. In the present crisis due to COVID-19 pandemic the contagious power of virus has led to huge encounter of cases on daily basis. This stimulates the need for specialised and accurate methods to detect COVID-19 cases. The contribution of deep learning to this problem has been significant. The application of deep learning concepts has shown its emence importance and utility in medical domain for detection of COVID-19 cases using CT scan and X-Ray images of lungs. Our proposed method compares the accuracy of multiple pretrained models in predicting COVID-19 infected cases for a specific dataset of radiological images using three distinct optimizers for each model. This research aims to determine which model, together with its associated optimizer, is most suitable for identifying COVID-19 infected cases from radiological lungs images.

关键词： COVID-19 Deep learning Computed tomography Computational modeling Transfer learning Lung Predictive models

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A Framework For Single Image Dehazing Using DWT Based Cross Bilateral Filter Fusion of Generative and ASM Models

A Framework For Single Image Dehazing Using DWT Based Cross ...

引用

international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Banala Revanth Sanjay K. Dwivedi Manoj Kumar Department of Computer Science Babasaheb Bhimrao Ambedkar University Lucknow India

ISBN: (纸本)9781665474177

Due to the latest advancements in technologies like artificial intelligence, machine learning, and deep learning in image processing, computer vision, and their uses in various applications, there has been a lot of interest in the restoration of murky images. This paper gives a novel single-image dehazing (SID) framework for the restoration of single hazy image (SHI). The framework has three parts. The first part of the framework comprises three networks. A white-balanced auto-encoder network in the front, followed by a pair of sequential auto-encoder networks at the end for image dehazing (ID). A white balance auto-encoder network is used for correcting the white balance error in the input hazy image (HI). Pair of sequential auto- encoder networks gives haze-reduced output by taking corrected white balanced error image as input. The second part comprises an ASM-based model Dark Channel Prior (DCP) for ID. DCP produce haze-free images (HFI) by taking HI as input. The third part presents the fusion model for integrating the first and second parts of the framework. DWT-based cross bilateral filter model is the fusion model (FM) to get the final HFI. The first part of the framework is trained by using a hybrid loss function (perceptual loss function combined with MSE loss function) on O- HAZE, I-HAZE, and MRFID data sets. The proposed framework got considerably good result in terms of Structural Similarity Index (SSIM), Peak signal-to-noise ratio (PSNR), compared with cutting-edge methods.

关键词： Training Measurement learning systems PSNR Benchmark testing Discrete wavelet transforms Image restoration

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2nd international conference on machine learning, Image processing, Network Security and Data Sciences, MInd 2020

2nd International Conference on Machine Learning, Image Proc...

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2nd international conference on machine learning, Image processing, Network Security and Data Sciences, MInd 2020

ISBN: (纸本)9789811563171

The proceedings contain 83 papers. The special focus in this conference is on machine learning, Image processing, Network Security and Data Sciences. The topics include: An Empirical Study to Predict Myocardial Infarction Using K-Means and Hierarchical Clustering;a Robust Technique for End Point Detection Under Practical Environment;an Explainable machine learning Approach for Definition Extraction;Steps of Pre-processing for English to Mizo SMT System;efficient Human Feature Recognition Process Using Sclera;optimization of Local Ordering Technique for Nearest Neighbour Circuits;attention-Based English to Mizo Neural machine Translation;in Depth Analysis of Lung Disease Prediction Using machine learning Algorithms;improve the Accuracy of Heart Disease Predictions Using machine learning and Feature Selection Techniques;Evaluation of Multiplier-Less DCT Transform Using In-Exact Computing;convolutional Neural Network Based Sound Recognition Methods for Detecting Presence of Amateur Drones in Unauthorized Zones;comparison of Different Decision Tree Algorithms for Predicting the Heart Disease;dynamic Speech Trajectory Based Parameters for Low Resource Languages;identification and Prediction of Alzheimer Based on Biomarkers Using ‘machine learning’;solving Quadratic Assignment Problem Using Crow Search Algorithm in Accelerated Systems;speech signal Analysis for Language Identification Using Tensors;Effective Removal of Baseline Wander from ECG signals: A Comparative Study;Face Recognition Based on Human Sketches Using Fuzzy Minimal Structure Oscillation in the SIFT Domain;an Ensemble Model for Predicting Passenger Demand Using Taxi Data Set;a Novel Approach to Synthesize Hinglish Text to English Text;legal Amount Recognition in Bank Cheques Using Capsule Networks;comparative Analysis of Neural Models for Abstractive Text Summarization.

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Analysis of dictionary learning algorithms for image fusion using sparse representation 2

Analysis of dictionary learning algorithms for image fusion ...

引用

2nd international conference on Inventive Research in Computing Applications, ICIRCA 2020

作者： Saini, Lalit Kumar Mathur, Pratistha Manipal University Jaipur Department of Information Technology Jaipur302007 India Sobhasaria Group of Institutions Department of CSE Sikar332001 India

ISBN: (纸本)9781728153742

Sparse Representation is a useful signal modeling technique. In recent years, researchers have explored the applications of sparse representation in different domains. This representation technique has many applications in image processing like image fusion, noise removal, image impainting, image super-resolution, compress sensing, etc. and signal processing. A predefined dictionary is used in the traditional approach of sparse representation, but it has many drawbacks like visual artifacts and high computational cost. To overcome these conditions and drawbacks, instead of using a predefined and redundant dictionary another option is to generate a dictionary from the set of images or the input images itself. This paper analyses the dictionary learning algorithms for the process of image fusion with different image pairs using sparse approximation or representation. © 2020 IEEE.

关键词： Image fusion

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Compositional models: iterative structure learning from data 2nd

Compositional models: iterative structure learning from data

引用

2nd international conference on Sensor Networks and signal processing, SNSP 2019

作者： Kratochvíl, Václav Bína, Vladislav Jiroušek, Radim Lee, Tzong-Ru Institute of Information Theory and Automation Czech Academy of Sciences Prague Czech Republic Faculty of Management University of Economics Prague Czech Republic Department of Marketing National Chung Hsing University Taichung Taiwan

ISBN: (纸本)9789811549168

Multidimensional probability distributions that are too large to be stored in computer memory can be represented by a compositional model—a sequence of low-dimensional probability distributions that when composed together try to faithfully estimate the original multidimensional distribution. The decomposition to the compositional model is not satisfactorily resolved. We offer an approach based on search traversal through the decomposable model class using likelihood-test statistics. The paper is a work sketch of the current research. © Springer Nature Singapore Pte Ltd 2021.

关键词： Probability distributions

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Spectral Analysis of Lungs sounds for Classification of Asthma and Pneumonia Wheezing 2

Spectral Analysis of Lungs sounds for Classification of Asth...

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2nd international conference on Electrical, Communication and Computer Engineering, ICECCE 2020

作者： Naqvi, Syed Zohaib Hassan Arooj, Misha Aziz, Sumair Khan, Muhammad Umar Choudhary, Mohammad Ahmad Ul Hassan, Muhammad Nafees University of Engineering and Technology Department of Electronics Engineering Taxila Pakistan Shifa International Hospital Department of Anesthesia ICU and Pain Medicine Islamabad Pakistan

ISBN: (纸本)9781728171166

World Health Organization Statistics declares the pulmonic illness as the class of deadly illness. Wheezing is a key indicator for the diagnosis of pulmonic illnesses like Asthma and pneumonia. In this research article, the identification of wheeze sound in asthma and pneumonia subjects is done from breathing sound. The analysis is performed through signal processing and machine learning practices. Overall, data is acquired from 300 subjects. It includes 100 Asthma, 100 Pneumonia, and 100 Normal subjects This research work proposes a complete design for accurate classification of wheezing signals. It includes pre-processing by normalization, denoising by filtration, segmentation to remove the non-breathing and silent parts, feature extraction from the spectral domain, and classification by support vector machine (SVM) using Matlab 2019b. The system evidenced an accuracy greater than 96%. Further investigation can be done by analyzing the wheezing sound originates in other pulmonic diseases and exploring its role to identify the pulmonary illness. © 2020 IEEE.

关键词： Support vector machines

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Deep learning Approaches for Fall Detection Using Acoustic Information 2nd

Deep Learning Approaches for Fall Detection Using Acoustic I...

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2nd international conference on Power Engineering, Computing and Control, PECCON 2019

作者： Alex, John Sahaya Rani Abai Kumar, M. Swathy, D.V. School of Electronics Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789811572401

Senior citizens are prone to accidents due to their old age. The accidents may cause severe injuries and even to death if it is not identified and treated within a short period of time. Also, it is more risk if they stay alone in their homes. To mitigate the risk, an alert system is to be designed to alert the caretaker about the occurrence of the accident. By mounting three aerial microphones and one-floor acoustic sensor (FAS) in their room and monitoring the acoustic information received from the microphone and FAS, the acoustic information of the fall event is recorded. The acoustic features such as energy, spectral centroid, spectral flux, zero-crossing rate and Mel-frequency cepstral coefficients (MFCC) are extracted from the acoustic signal. Support vector machine (SVM) network and deep learning neural networks (DNN) with more than two hidden layers are trained with a reduced set of features obtained with principal component analysis (PCA) from the acoustic features. DNN classifier is proved to be better than SVM classifier. The obtained accuracy for DNN is 97%, the accuracy of the SVM classifier with MLP kernel and RBF kernel is 50% and 83%, respectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Accidents

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