فهرست مقالات compressed sensing

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  1 - یک اصلاح کاهشی مقیاس‌بندی شده از روش گرادیان مزدوج هستنس-اشتیفل با نگاه کاربردی در حسگری فشرده
  علی ابراهیم نژاد زهره امینی فرد سامان بابایی کفاکی
  10.30495/jnrm.2022.65570.2211
  به منظور بهبود روش گرادیان مزدوج کلاسیک هستنس-اشتیفل، شنگوی و همکاران یک روش گرادیان مزدوج موثر را پیشنهاد کردند که با استفاده از جستجوی خطی ولف قوی (با محدود کردن پارامترهای جستجوی خطی) در خاصیت کافی کاهشی ‎صدق می‌ کند. با الهام از توسیع مقیاس بندی شده ی روش هستنس- چکیده کامل

  به منظور بهبود روش گرادیان مزدوج کلاسیک هستنس-اشتیفل، شنگوی و همکاران یک روش گرادیان مزدوج موثر را پیشنهاد کردند که با استفاده از جستجوی خطی ولف قوی (با محدود کردن پارامترهای جستجوی خطی) در خاصیت کافی کاهشی ‎صدق می‌ کند. با الهام از توسیع مقیاس بندی شده ی روش هستنس-اشتیفل که اخیرا توسط دانگ و همکاران مطرح شده است، یک اصلاح مقیاس بندی شده از روش گرادیان مزدوج شنگوی و همکاران پیشنهاد می شود که قادر است شرط کافی کاهشی را مستقل از تکنیک جستجوی خطی و بدون فرض تحدب تابع هدف برقرار سازد. همچنین، همگرایی سراسری روش‌ مطرح شده بر اساس فرضیات استاندارد مورد بحث قرار می‌گیرد. به علاوه، یک تقریب هموار برای مساله بهینه‌سازی حسگری فشرده ارائه می شود. عملکرد عددی بر مجموعه‌ای از مسائل کلاسیک از کتابخانه CUTEr و نیز در حل مساله حسگری فشرده مورد ارزیابی قرار می گیرد. نتایج مقایسات برتری رویکرد پیشنهادی را به تصویر می کشند. پرونده مقاله
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  2 - Comparison of Reconstruction Algorithms for Sparse Signal Recovery from Noisy Measurement
  Murat Emre Erkoc , Gokcen Ozdemir Nurhan Karaboga
  Compressive sensing is a technique that can reconstruct sparse signals under Nyquist rate. This study is about comparison of widely used sparse signal reconstruction algorithms under noisy measurements. Three algorithms, Orthogonal Matching Pursuit, Compressive Sensing چکیده کامل

  Compressive sensing is a technique that can reconstruct sparse signals under Nyquist rate. This study is about comparison of widely used sparse signal reconstruction algorithms under noisy measurements. Three algorithms, Orthogonal Matching Pursuit, Compressive Sensing Matching Pursuit and Primal Dual Interior Point method are used to reconstruct sparse signal from noisy measurement and performance results are compared. Firstly, a sparse signal is sampled under Nyquist rate and observation vector is obtained. After that, white Gaussian noise is added to this observation vector. Then, sparse reconstruction algorithms are employed to reconstruct the original signal from noisy measurement. These algorithms are tested for various measurement number and sparsity levels. Test conditions are same for all algorithms. Finally some performance metrics results related to reconstructed signal are obtained. These performance metrics are mean squared error, correlation of the reconstructed signal and original signal, reconstruction time of the algorithms and iteration numbers. According to these metrics, when sparsity level is very smaller than measurement number, Orthogonal Matching Pursuit has better results than others. However, when sparsity level is increased and close to measurement number, Primal Dual Interior Point method has better performance than others in terms of reconstruction a sparse signal from noisy measurement. پرونده مقاله
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  3 - Compressed sensing: a review
  Razieh Keshavarzian
  Compressed sensing (CS) is a new and promising framework for simultaneous sampling and compression of signals at sub-Nyquist rates. Under certain conditions, the signal can be reconstructed exactly from a small set of measurements via solving an optimization problem. In چکیده کامل

  Compressed sensing (CS) is a new and promising framework for simultaneous sampling and compression of signals at sub-Nyquist rates. Under certain conditions, the signal can be reconstructed exactly from a small set of measurements via solving an optimization problem. In order to make this possible, compressed sensing is based on two principles of sparsity and incoherence. Compressed sensing takes advantage of the fact that most signals in nature are sparse or compressible, which means that when expressed in a suitable basis called as sparsifying basis, they will have a sparse representation. In the CS, the sparse signal is sampled by a non-adaptive linear sampling matrix. Then, based on the limited measurements obtained from the sampling matrix and using a non-linear algorithm, the original signal is reconstructed. The sparse signal reconstruction problem in the CS is an optimization problem that various algorithms have been proposed to solve it. The compressed sensing has a great application potential and can be used in a wide range of applications. Recently, deep learning has been used to solve the CS problem and its medical applications. In this paper, the generalities of compressed sensing are presented and CS reconstruction algorithms are reviewed. Also, the application of CS in magnetic resonance imaging (MRI) are investigated. پرونده مقاله
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  4 - Frames for compressed sensing using coherence
  L. Gavruta G. Zamani Eskandani P. Gavruta
  We give some new results on sparse signal recovery in the presence of noise, forweighted spaces. Traditionally, were used dictionaries that have the norm equal to 1, but, forrandom dictionaries this condition is rarely satisfied. Moreover, we give better estimationsthen چکیده کامل

  We give some new results on sparse signal recovery in the presence of noise, forweighted spaces. Traditionally, were used dictionaries that have the norm equal to 1, but, forrandom dictionaries this condition is rarely satisfied. Moreover, we give better estimationsthen the ones given recently by Cai, Wang and Xu. پرونده مقاله