Sampling-rate restrictions for exponential signal reconstruction

1972 ◽  
Vol 60 (6) ◽  
pp. 726-726 ◽  
Author(s):  
F.W. Fairman ◽  
R.D. Gupta
Keyword(s):  
Signal Reconstruction ◽  
Sampling Rate

scholarly journals Compressed sensing with continuous parametric reconstruction

2021 ◽  
Vol 11 (1) ◽  
pp. 851
Author(s):  
Imrich Andras ◽  
Linus Michaeli ◽  
Jan Saliga
Keyword(s):  
Signal Reconstruction ◽  
Sampling Rate ◽  
Quality Monitoring ◽  
Wireless Sensor ◽  
Model Parameters ◽  
Sampling Process ◽  
Continuous Approach ◽  
Continuous Models ◽  
Stable Performance ◽  
Unconventional Method

This work presents a novel unconventional method of signal reconstruction after compressive sensing. Instead of usual matrices, continuous models are used to describe both the sampling process and acquired signal. Reconstruction is performed by finding suitable values of model parameters in order to obtain the most probable fit. A continuous approach allows more precise modelling of physical sampling circuitry and signal reconstruction at arbitrary sampling rate. Application of this method is demonstrated using a wireless sensor network used for freshwater quality monitoring. Results show that the proposed method is more robust and offers stable performance when the samples are noisy or otherwise distorted.

Modified meta-heuristic-oriented compressed sensing reconstruction algorithm for bio-signals

2019 ◽  
Vol 17 (05) ◽  
pp. 1950031
Author(s):  
Ashok Naganath Shinde ◽  
Sanjay L. Lalbalwar ◽  
Anil B. Nandgaonkar
Keyword(s):  
Compressed Sensing ◽  
Signal Reconstruction ◽  
Sampling Rate ◽  
Reconstruction Algorithm ◽  
Haar Wavelet ◽  
Evaluation Procedure ◽  
Grey Wolf Optimizer ◽  
Reconstruction Algorithms ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization

In signal processing, several applications necessitate the efficient reprocessing and representation of data. Compression is the standard approach that is used for effectively representing the signal. In modern era, many new techniques are developed for compression at the sensing level. Compressed sensing (CS) is a rising domain that is on the basis of disclosure, which is a little gathering of a sparse signal’s linear projections including adequate information for reconstruction. The sampling of the signal is permitted by the CS at a rate underneath the Nyquist sampling rate while relying on the sparsity of the signals. Additionally, the reconstruction of the original signal from some compressive measurements can be authentically exploited using the varied reconstruction algorithms of CS. This paper intends to exploit a new compressive sensing algorithm for reconstructing the signal in bio-medical data. For this purpose, the signal can be compressed by undergoing three stages: designing of stable measurement matrix, signal compression and signal reconstruction. In this, the compression stage includes a new working model that precedes three operations. They are signal transformation, evaluation of [Formula: see text] and normalization. In order to evaluate the theta ([Formula: see text]) value, this paper uses the Haar wavelet matrix function. Further, this paper ensures the betterment of the proposed work by influencing the optimization concept with the evaluation procedure. The vector coefficient of Haar wavelet function is optimally selected using a new optimization algorithm called Average Fitness-based Glowworm Swarm Optimization (AF-GSO) algorithm. Finally, the performance of the proposed model is compared over the traditional methods like Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Firefly (FF), Crow Search (CS) and Glowworm Swarm Optimization (GSO) algorithms.

scholarly journals Wideband Noise Interference Suppression for Sparsity-Based SAR Imaging Based on Dechirping and Double Subspace Extraction

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1019 ◽  
Author(s):  
Li ◽  
Lu ◽  
Lao ◽  
Ye
Keyword(s):  
Signal Reconstruction ◽  
Sampling Rate ◽  
The Other ◽  
Synthetic Aperture ◽  
Noise Interference ◽  
Support Set ◽  
Wideband Noise ◽  
Sar Imaging ◽  
New Perspective ◽  
The Impact

Sparsity-based synthetic aperture radar (SAR) imaging has attracted much attention since it has potential advantages in improving the image quality and reducing the sampling rate. However, it is vulnerable to deliberate blanket disturbance, especially wideband noise interference (WBNI), which severely damages the imaging quality. This paper mainly focuses on WBNI suppression for SAR imaging from a new perspective—sparse recovery. We first analyze the impact of WBNI on signal reconstruction by deducing the interference energy projected on the real support set of the signal under different observation parameters. Based on the derived results, we propose a novel WBNI suppression algorithm based on dechirping and double subspace extraction (DDSE), where the signal of interest (SOI) is reconstructed by exploiting the known geometric prior and waveform prior, respectively. The experimental results illustrate that the DDSE-based WBNI suppression algorithm for sparsity-based SAR imaging is effective and outperforms the other algorithms.

Ultrasonic Phased Array Industrial Imaging with Sub-Nyquist Sampling Rate

2013 ◽  
Vol 347-350 ◽  
pp. 327-331
Author(s):  
Guang Zhi Dai ◽  
Guo Qiang Han ◽  
Xian Yue Ouyang
Keyword(s):  
High Speed ◽  
Phased Array ◽  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Sampling Rate ◽  
Sampling Frequency ◽  
Frequency Resolution ◽  
Signal Frequency ◽  
Beam Focusing ◽  
Ultrasonic Phased Array

this paper uses a new type of FRI (Finite Rate of Innovation) sampling pattern based Sub-Nyquist sampling model breaked through Shannon theorem that it can get accurate signal reconstruction based on signal information rate, which requires the sampling frequency lower than two times the max signal frequency. We apply the new model in the ultrasonic phased array industrial imaging. In the experiment, ultrasonic phased array realized dynamic focusing and the high speed scan by ultrasonic array transducer of various array time delays to get flexible controllable synthesis beam composed signals that received by 32 phased array elements . The results indicate that in the model it greatly reduces the signal sampling frequency and improves the signal-to-noise ratio, frequency resolution at the same of the beam focusing and steering flexible.

scholarly journals Application of Compressive Sampling in Computer Based Monitoring of Power Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Sarasij Das ◽  
Tarlochan Sidhu
Keyword(s):  
Power Systems ◽  
Power System ◽  
Signal Reconstruction ◽  
Relevant Literature ◽  
Sampling Rate ◽  
Compressive Sampling ◽  
Signal Acquisition ◽  
System Monitoring ◽  
Power System Monitoring ◽  
Computer Based

Shannon’s Nyquist theorem has always dictated the conventional signal acquisition policies. Power system is not an exception to this. As per this theory, the sampling rate must be at least twice the maximum frequency present in the signal. Recently, compressive sampling (CS) theory has shown that the signals can be reconstructed from samples obtained at sub-Nyquist rate. Signal reconstruction in this theory is exact for “sparse signals” and is near exact for compressible signals provided certain conditions are satisfied. CS theory has already been applied in communication, medical imaging, MRI, radar imaging, remote sensing, computational biology, machine learning, geophysical data analysis, and so forth. CS is comparatively new in the area of computer based power system monitoring. In this paper, subareas of computer based power system monitoring where compressive sampling theory has been applied are reviewed. At first, an overview of CS is presented and then the relevant literature specific to power systems is discussed.

Examples of Multirate Filter Banks

2009 ◽  
pp. 347-384
Author(s):  
Ljiljana Milic
Keyword(s):  
Filter Bank ◽  
Filter Banks ◽  
Signal Reconstruction ◽  
Sampling Rate ◽  
Perfect Reconstruction ◽  
Original Signal ◽  
Multichannel Filter ◽  
Analysis And Synthesis ◽  
Synthesis Filter ◽  
Channel Filter

The purpose of this chapter is to illustrate by means of examples the construction of the analysis and synthesis filter banks with the use of FIR and IIR two-channel filter banks as the basic building blocks. In Chapter VIII, we have discussed the design and properties of several types of complementary filter pairs, and in Chapters IX and X we have shown how those filter pairs are used in the synthesis of digital filters with sharp spectral constraints. In this chapter, we demonstrate the application of the complementary filter pairs as two-channel filter banks used to decompose the original signal into two channel signals and to reconstruct the original signal from the channel signals. Signal decomposition is referred to as the signal analysis, whereas the signal reconstruction is referred to as the signal synthesis. Thereby, the filter bank used for the signal decomposition is called the analysis filter bank, and the bank used for signal reconstruction is called the synthesis filter bank. The two-channel filter bank is usually composed of a pair of lowpass and highpass halfband filters, which satisfy some complementary properties. The bandwidth that occupies each of two channel signals is a half of the original signal bandwidth. Hence, the channel signals can be processed with the sampling rate which is a half of the original signal sampling rate. At the output of the analysis bank, the channel signals are down-sampled-by-two and then processed at the lower sampling rate. For the signal reconstruction, each of two channel signals has to be up-sampled-by-two first, and then fed into the synthesis bank. The sampling rate alteration in the two-channel filter bank causes the unwanted effects: the downsampling produces aliasing, and the up-sampling produces imaging. The essential feature of the two-channel filter bank is that the aliasing produced in the analysis side can be compensated in the synthesis side. This is achieved by choosing the proper combination of filters in the analysis and synthesis banks. The elimination of aliasing opens the possibility of the perfect (and nearly perfect) reconstruction of the original signal. The perfect reconstruction means that the signal at the output of the cascade connection of the analysis and synthesis bank is a delayed replica of the original input signal. Constructing perfect reconstruction and nearly perfect reconstruction analysis/synthesis filter banks is an unbounded area of research. An important and widely used application of the two-channel filter banks is the construction of multichannel filter banks based on the tree-structures where the two-channel filter bank is used as a building block. In this way, a multilevel multichannel filter bank can be obtained with either uniform or nonuniform separation between the channels. The two-channel filter banks are particularly useful in generating octave filter banks. Depending on applications, the filter bank can be requested to provide frequency-selective separation between the channels, or to preserve the original waveform of the signal. The example applications of the frequency-selective filter banks are audio and telecommunication applications. The importance of preserving the original waveform is related with the images. In the case of the discrete-time wavelet banks, the frequency-selectivity is less important. The main goal is to preserve the waveform of the signal. The purpose of this chapter is to illustrate by means of MATLAB examples the signal analysis and synthesis based on the two-channel filter banks. We give first a brief review of the properties of the two-channel filter banks with the conditions for aliasing elimination. We discuss the perfect reconstruction and nearly perfect reconstruction properties and show the solutions based on FIR and IIR QMF banks and the orthogonal two-channel filter banks. In the sequel, the tree-structured multichannel filter banks are considered. The process of signal decomposition and reconstruction is illustrated by means of examples.

Dual-band signal reconstruction based on periodic nonuniform sampling at optimal sampling rate

2021 ◽  
pp. 103252
Author(s):  
Liping Guo ◽  
Chi Wah Kok ◽  
Hing Cheung So ◽  
Wing Shan Tam
Keyword(s):  
Signal Reconstruction ◽  
Sampling Rate ◽  
Nonuniform Sampling ◽  
Dual Band ◽  
Optimal Sampling ◽  
Band Signal

scholarly journals A Sequential Compressed Spectrum Sensing Algorithm against SSDH Attack in Cognitive Radio Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zhuhua Hu ◽  
Yong Bai ◽  
Lu Cao ◽  
Mengxing Huang ◽  
Mingshan Xie
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
Signal Reconstruction ◽  
Sampling Rate ◽  
Radio Networks ◽  
Wideband Spectrum Sensing ◽  
Sensing Technology ◽  
Key Technologies ◽  
Simulation Results

Spectrum sensing is one of the key technologies in wireless wideband communication. There are still challenges in respect of how to realize fast and robust wideband spectrum sensing technology. In this paper, a novel nonreconstructed sequential compressed wideband spectrum sensing algorithm (NSCWSS) is proposed. Firstly, the algorithm uses a sequential spectrum sensing method based on history memory and reputation to ensure the robustness of the algorithm. Secondly, the algorithm uses the strategy of compressed sensing without reconstruction, which thus ensures the sensing agility of the algorithm. The algorithm is simulated and analyzed by using the centralized cooperative sensing. The theoretical analysis and simulation results reveal that, under the condition of ensuring the certain detection probability, the proposed algorithm effectively reduces complex computation of signal reconstruction, significantly reducing the wideband spectrum sampling rate. At the same time, in the cognitive wideband communication scenarios, the algorithm also achieves a better defense against the SSDF attack in spectrum sensing.

scholarly journals Reconstruction of Periodic Band Limited Signals from Non-Uniform Samples with Sub-Nyquist Sampling Rate

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6246
Author(s):  
Dongxiao Wang ◽  
Xiaoqin Liu ◽  
Xing Wu ◽  
Zhihai Wang
Keyword(s):  
Signal Reconstruction ◽  
Sampling Rate ◽  
Sampling Theorem ◽  
Drive System ◽  
Reconstruction Method ◽  
Robot Arm ◽  
Reconstructed Signal ◽  
Band Limited ◽  
Simulation Signal ◽  
Important State

Important state parameters, such as torque and angle obtained from the servo control and drive system, can reflect the operating condition of the equipment. However, there are two problems with the information obtained through the network from the control and drive system: the low sampling rate, which does not meet the sampling theorem and the nonuniformity of the sampling points. By combing equivalent sampling and nonuniform signal reconstruction theory, this paper proposes a reconstruction method for signal obtained from servo system in periodic reciprocating motion. Equivalent sampling combines the low rate and nonuniform samples from multiple periods into one single period, so that the equivalent sampling rate is far increased. Then, the nonuniform samples with high density are further resampled to meet the reconstruction conditions. This step can avoid the amplitude error in the reconstructed signal and increase the possibility of successful reconstruction. Finally, the reconstruction formula derived from basis theory is applied to recover the signal. This method has been successfully verified by the simulation signal of the robot swing process and the actual current signal collected on the robot arm testbed.

Effects of minimum sampling rate and signal reconstruction on surface electromyographic signals

2005 ◽  
Vol 15 (5) ◽  
pp. 474-481 ◽  
Author(s):  
Jennifer L. Durkin ◽  
Jack P. Callaghan
Keyword(s):  
Signal Reconstruction ◽  
Sampling Rate ◽  
Electromyographic Signals
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