scholarly journals COMPRESSIVE SENSING

2020 ◽  
Vol 5 (2) ◽  
pp. 249-259
Author(s):  
Abhilasha Sharma
Keyword(s):  
Signal Processing ◽  
Compressive Sensing ◽  
Basic Concept ◽  
Sampling Rate ◽  
New Technique ◽  
Maximum Frequency ◽  
Conventional System ◽  
Nyquist Rate

Compressive sensing is a relatively new technique in the signal processing field which allows acquiring signals while taking few samples. It works on two principles: sparsity, which pertains to the signals of interest, and incoherence, which pertains to the sensing modality. Since, in conventional system all signals follow the Nyquist criteria, in which the sampling rate must be at least twice the maximum frequency of modulating signal. But, in this new concept we can recover the signal below the Nyquist rate. This paper presents the basic concept of compressive sensing and area of applications, where we can apply this technique.                                      

Design of a Multi-Channel Random Demodulator for Wideband Signals

2014 ◽  
Vol 644-650 ◽  
pp. 4221-4224
Author(s):  
Jian Lin Zhao ◽  
Wei Qing Huang ◽  
Zhi Qiang Lv ◽  
Xue Jie Ding
Keyword(s):  
Compressive Sensing ◽  
Sampling Rate ◽  
Prior Work ◽  
Measurement Matrix ◽  
Switching Scheme ◽  
Wideband Signals ◽  
Reconstructed Signal ◽  
Random Demodulator ◽  
Tone Signal ◽  
Nyquist Rate

The wideband signals in most important applications are sparse or compressible in some sense. A multi-channel scheme for random demodulator without integrator is introduced in this paper. This architecture is based on compressive sensing (CS) and random demodulator (RD), and overcomes the problem of the integrator’s switching scheme injects noise into the signal and deteriorates the reconstructed signal of the RD, which has the same reconstruction guarantees by similar algorithms with the basic RD because the measurement matrix between their is identical, and which resolves some of the practical issues present in prior work. The results of simulation indicate that multi-tone signal can be successful reconstructed at sampling rate downs to 1/10 of the Nyquist-rate, which represents an up to 90% savings in the bandwidth and the storage memory.

scholarly journals Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Irena Orović ◽  
Vladan Papić ◽  
Cornel Ioana ◽  
Xiumei Li ◽  
Srdjan Stanković
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Signal Acquisition ◽  
Transform Domain ◽  
Hermite Transform ◽  
Time Frequency ◽  
Reconstruction Methods ◽  
Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

Intelligent Tracking and Positioning of Targets Using Passive Sensing Systems

2019 ◽  
pp. 286-305
Author(s):  
Saad Iqbal ◽  
Usman Iqbal ◽  
Syed Ali Hassan
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
System Performance ◽  
Target Localization ◽  
The Other ◽  
Conventional System ◽  
Sensing Systems ◽  
Passive Sensing ◽  
Indoor Scenarios ◽  
Localization And Tracking

Target localization and tracking has always been a hot topic in all eras of communication studies. Conventional system used radars for the purpose of locating and/or tracking an object using the classical methods of signal processing. Radars are generally classified as active and passive, where the former uses both transmitter and receivers simultaneously to perform the localization task. On the other hand, passive radars use existing illuminators of opportunity such as wi-fi or GSM signals to perform the aforementioned tasks. Although they perform detection using classical correlation methods and CFAR, recently machine learning has been used in various application of passive sensing to elevate the system performance. The latest developed models for intelligent RF passive sensing system for both outdoor and indoor scenarios are discussed in this chapter, which will give insight to the readers about their designing.

scholarly journals Signal-Processing Framework for Ultrasound Compressed Sensing Data: Envelope Detection and Spectral Analysis

2020 ◽  
Vol 10 (19) ◽  
pp. 6956
Author(s):  
Yisak Kim ◽  
Juyoung Park ◽  
Hyungsuk Kim
Keyword(s):  
Signal Processing ◽  
Compressed Sensing ◽  
Frequency Domain ◽  
Time Domain ◽  
Sampling Rate ◽  
Processing Technique ◽  
Original Form ◽  
Basis Matrix ◽  
Full Reconstruction ◽  
Processing Framework

Acquisition times and storage requirements have become increasingly important in signal-processing applications, as the sizes of datasets have increased. Hence, compressed sensing (CS) has emerged as an alternative processing technique, as original signals can be reconstructed using fewer data samples collected at frequencies below the Nyquist sampling rate. However, further analysis of CS data in both time and frequency domains requires the reconstruction of the original form of the time-domain data, as traditional signal-processing techniques are designed for uncompressed data. In this paper, we propose a signal-processing framework that extracts spectral properties for frequency-domain analysis directly from under-sampled ultrasound CS data, using an appropriate basis matrix, and efficiently converts this into the envelope of a time-domain signal, avoiding full reconstruction. The technique generates more accurate results than the traditional framework in both time- and frequency-domain analyses, and is simpler and faster in execution than full reconstruction, without any loss of information. Hence, the proposed framework offers a new standard for signal processing using ultrasound CS data, especially for small and portable systems handling large datasets.

HYCA: A New Technique for Hyperspectral Compressive Sensing

2015 ◽  
Vol 53 (5) ◽  
pp. 2819-2831 ◽  
Author(s):  
Gabriel Martin ◽  
Jose M. Bioucas-Dias ◽  
Antonio Plaza
Keyword(s):  
Compressive Sensing ◽  
New Technique ◽  
A New Technique

High Speed Under-Sampling Frequency Measurements on FPGA

2017 ◽  
Vol 7 (3) ◽  
pp. 1316
Author(s):  
Seyed Ehsan Yasrebi Naeini ◽  
Ali Maroosi
Keyword(s):  
Field Programmable Gate Array ◽  
High Speed ◽  
Sampling Rate ◽  
Sampling Frequency ◽  
Phase Changes ◽  
Frequency Measurements ◽  
Frequency Detection ◽  
Field Programmable ◽  
Under Sampling ◽  
Nyquist Rate

A Sampling rate is less than Nyquist rate in some applications because of hardware limitations. Consequently, extensive researches have been conducted on frequency detection from sub-sampled signals. Previous studies on under-sampling frequency measurements have mostly discussed under-sampling frequency detection in theory and suggested possible methods for fast under-sampling frequencies detection. This study examined few suggested methods on Field Programmable Gate Array (FPGA) for fast under-sampling frequencies measurement. Implementation of the suggested methods on FPGA has issues that make them improper for fast data processing. This study tastes and discusses different methods for frequency detection including Least Squares (LS), Direct State Space (DSS), Goertzel filter, Sliding DFT, Phase changes of Fast Furrier Transform (FFT), peak amplitude of FFT to conclude which one from these methods are suitable for fast under-sampling frequencies detection on FPGA. Moreover, our proposed approach for sub-sampling detection from real waveform has less complextity than previous approaches from complex waveform.

scholarly journals Compressed Detection for Pulse-Based Communications in the Terahertz Band

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Pankaj Singh ◽  
Byung-Wook Kim ◽  
Sung-Yoon Jung
Keyword(s):  
Channel Estimation ◽  
Matching Pursuit ◽  
Sampling Rate ◽  
Design Parameters ◽  
Hardware Complexity ◽  
Analog To Digital ◽  
High Data ◽  
Formidable Challenge ◽  
Reconstructed Signal ◽  
Nyquist Rate

Terahertz (THz) band (0.1-10 THz)-based electromagnetic communications are envisioned as a key technology to enable future high-data-rate short-range ultrabroadband communications. However, one of the fundamental bottlenecks is the efficiency of the analog-to-digital converters (ADCs) considering the formidable challenge of sampling the signal at the Nyquist rate eventually increasing transceiver design complexity. The Compressed sensing (CS) framework enables the successful reconstruction of sparse signals from a small set of projections onto a random vector which would lead to sub-Nyquist rate sampling. In this paper, THz band channel estimation based on the theory of CS is developed. The proposed approach exploits the fact that transmitting an ultrashort pulse through a multipath THz channel leads to a received THz signal that can be approximated by a linear combination of a few atoms from a predefined dictionary, yielding thus a sparse representation of the received signal. The fundamental idea is in the design of the dictionary of atoms that closely matches the transmitted pulse leading thus to a higher probability of CS reconstruction. The Orthogonal Matching Pursuit (OMP) algorithm is used to identify the strongest atoms in the projected signal. This reconstructed signal is subsequently used as a reference template in a correlator-based detector. The bit error rate (BER) performance of the proposed detector is analyzed and compared with the conventional CS-based channel estimation and reconstruction approach. Extensive simulations show that, for different design parameters, our proposed detector outperforms the traditional CS-based correlator receiver for the same sampling rate leading thus to a much-reduced use of analog-to-digital resources. Moreover, the proposed detector has been shown to reduce the hardware complexity of the receiver by significantly reducing the number of parallel mixer-integration branches.

scholarly journals Compressive sensing: A methodological approach to an efficient signal processing

DYNA ◽  
2015 ◽  
Vol 82 (192) ◽  
pp. 203-210 ◽  
Author(s):  
Evelio Astaiza Hoyos ◽  
Pablo Emilio Jojoa Gómez ◽  
Héctor Fabio Bermúdez Orozco
Keyword(s):  
Signal Processing ◽  
Dimensionality Reduction ◽  
Compressive Sensing ◽  
Methodological Approach ◽  
Signal Acquisition ◽  
Methodological Issues ◽  
New Paradigm ◽  
Signal Acquisition And Processing

Compressive Sensing (CS) is a new paradigm for signal acquisition and processing, which integrates sampling, compression, dimensionality reduction and optimization, which has caught the attention of a many researchers; SC allows the reconstruction of dispersed signals in a given domain from a set of measurements could be described as incomplete, due to that the rate at which the signal is sampled is much smaller than Nyquist's rate. This article presents an approach to address methodological issues in the field of processing signals from the perspective of SC.

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