Signal Compression Research Articles

Compression of Higher-Order Ambisonic Signals Using Directional Audio Coding

Compression of Higher-Order Ambisonic Signals Using Directional Audio Coding

Tensor decomposition-based compression and noise reduction of multichannel ECG signals

Tensor decomposition-based compression and noise reduction of multichannel ECG signals

The high/low frequency balance drives tactile perception of noisy vibrations.

Noisy vibrotactile signals transmitted during tactile explorations of an object provide precious information on the nature of its surface. Understanding the link between signal properties and how they are interpreted by the tactile sensory system remains challenging. In this paper, we investigated human perception of broadband, stationary vibrations recorded during exploration of textures and reproduced using a vibrotactile actuator. Since intensity is a well-established perceptual attribute, we here focused on the relevance of the spectral content. The stimuli were first equalized in perceived intensity and subsequently used to identify the most salient spectral features using dissimilarity estimations between pairs of successive vibration. Based on dimensionally reduced spectral representations, models of dissimilarity ratings showed that the balance between low and high frequencies was the most important cue. Formal validation of this result was achieved through a Mushra experiment, in which participants assessed the fidelity of resynthesized vibrations with various distorted frequency balances. These findings offer valuable insights into human vibrotactile perception and establish a computational framework for analyzing vibrations as humans do. Moreover, they pave the way for signal synthesis and compression based on sparse representations, holding significance for applications involving complex vibratory feedback.

Noisy One-Bit Compressed Sensing With Side-Information

We consider the problem of sparse signal reconstruction from noisy one-bit compressed measurements when the receiver has access to side-information (SI). We assume that compressed measurements are corrupted by additive white Gaussian noise before quantization and sign-flip error after quantization. A generalized approximate message passing-based method for signal reconstruction from noisy one-bit compressed measurements is proposed which is then extended for the case where the receiver has access to a signal that aids signal reconstruction, i.e., side-information. Two different scenarios of SI are considered-a) SI consisting of support information only, and b) SI consisting of support and amplitude information. SI is either a noisy version of the signal or a noisy estimate of the support of the signal. We develop reconstruction algorithms from one-bit measurements using noisy SI available at the receiver. Laplacian distribution and Bernoulli distribution are used to model the two types of noises which when applied to the signal and the support, yield the SI for the above two cases, respectively. The Expectation-Maximization algorithm is used to estimate the noise parameter using noisy one-bit compressed measurements and the SI. We show that one-bit compressed measurement-based signal reconstruction is quite sensitive to noise, and the reconstruction performance can be significantly improved by exploiting available side-information at the receiver.

Compressed Sensing Vs. Auto-Encoder: On the Perspective of Signal Compression and Restoration

Compressed Sensing Vs. Auto-Encoder: On the Perspective of Signal Compression and Restoration

Compressed Sensing Based on an Improved K-SVD for Vibration Signal Compression Reconstruction in Wireless Sensor Networks

Compressed Sensing Based on an Improved K-SVD for Vibration Signal Compression Reconstruction in Wireless Sensor Networks

Biomedical signal compression using deep learning based multi-task compressed sensing

<span>Real-time transmission of biomedical signals is immensely challenging and requires cloud and internet of things (IoT) infrastructure. Security is also an important factor; however, to accomplish this, a reconstruction method is developed in which the entire signal is supplied as an input, the primary portion is considered here, and the signal is further encoded and transmitted to the destination. Electrocardiogram (ECG) compression for the lightweight wireless network is quite challenging for long-term healthcare monitoring. Compressed sensing (CS) involves efficient encoding mechanisms for error rate estimation for reconstruction and energy consumption for wireless transmission of data. We propose a multi-task compressed sensing (MT-CS) reconstruction mechanism in this study for ECG compression of data is most chosen for a wireless network system that has various sensors embedded in it. This model further extracts the essential adaptive features for correlation existing in the ECG signals. The performance of the proposed MT-CS reconstruction mechanism is evaluated on the multiparameter intelligent monitoring in intensive care (MIMIC-II) dataset, which ensures its robustness and generalization. The results obtained upon simulation ensure that the proposed MT-CS based reconstruction approach ensures excellent reconstruction signal with fewer measurements in comparison with the existing state-of-art CS techniques.</span>

Graph-Based EEG Signal Compression for Human–Machine Interaction

Graph-Based EEG Signal Compression for Human–Machine Interaction

An energy efficient reconfigurable architecture for multi-lead ECG signal compression

An energy efficient reconfigurable architecture for multi-lead ECG signal compression

Adaptive bias control of optical IQ modulator with low LFM dither and strong fluctuation resistance.

An innovative approach has been proposed for adaptive bias control in optical IQ modulators. In contrast to traditional approaches that utilize sine dither, this method employs a linear frequency modulated (LFM) signal as the dither, associated with the fractional Fourier Transform (FrFT) to extract the bias point drift. The LFM signal, after undergoing FrFT, transforms into a compressed signal (CS) with energy concentration in the fractional domain. Utilizing this signal for bias point monitoring, the proposed method demonstrates robust bias control even in the presence of substantial interferences, as substantiated by comprehensive simulations and experimental investigations. Remarkably, in a 20-Gbaud 16QAM signal transmission, the proposed approach achieves stable control of the bias point for over 4 hours, even in the presence of voltage fluctuations, while effectively reducing the dither amplitude by half. Furthermore, it maintains a low bit error rate (BER) below 10-5 even under intentional external interference.

Interference fading suppression for distributed acoustic sensor using frequency-shifted delay loop

In order to suppress the interference fading of phase-sensitive optical time domain reflectometer (Φ-OTDR), a multi-frequency detection method based on frequency-shifted delay loop (FSDL) is proposed. A probe pulse train with up to five consistent frequency components are generated by periodic frequency-shifted, amplification, delay and filtering processing in FSDL. The piecewise aggregate approximation (PAA) is introduced to compress the amount of operational data. Wavelet energy spectrum analysis and support vector machine (SVM) are used to extract features and realize the fading classification. With the help of fading label making and restoration, the multi-frequency signals are intelligently aggregated using rotated-vector-sum (RVS). Experimental results show that PAA has applicability in data compression of beat signals. After 5-layer wavelet energy spectrum analysis, the fading binary classification accuracy can reach 96.77 %, and the fading signals identified after segmentation can be restored to the original data. The fading probability based on SVM output labels can be reduced to 1.89 %. The SVM-based classification output labels aggregation shows that the vibration positioning signal-to-noise ratio (SNR) can be increased to 13.52 dB, the phase demodulation curve is smoother, and the demodulation SNR can be up to 17.63 dB. Finally, the R-Square of 0.997 for the amplitude of demodulated vibration signal in repeatability experiment verifies the validity of this effective scheme for fading recognition and suppression.

Radio Receiver with Internal Compression of Input Signals Using a Dispersive Delay Line with Bandpass Filters

Radio Receiver with Internal Compression of Input Signals Using a Dispersive Delay Line with Bandpass Filters

A likelihood based method for compressive signal recovery under Gaussian and saturation noise

Practical sensing systems have a limited dynamic range, which gives rise to saturation noise in signal acquisition if the signal attains values outside this range at certain locations. Despite rapid advances in compressed sensing theory and systems over the years, the effect of saturation on compressed sensing measurements, especially in the presence of additive Gaussian noise, has often been overlooked in many existing approaches. Some methods for compressive recovery under saturation noise simply discard saturated measurements, while others impose some ad hoc hard constraints to ensure consistency with the known saturation threshold. In this paper, we propose a novel maximum likelihood based estimator for compressive reconstruction in the presence of both Gaussian and saturation noise. Our proposed method ensures probabilistic consistency between the estimated signal and the saturated measurements and helps account for both the saturation effect and the potentially unbounded Gaussian noise in the measurements. We also derive upper bounds on the reconstruction error for our estimator, and argue that they follow intuitive trends. We analyze an important curvature term in these bounds, and show that it is superior to methods such as saturation rejection, indicating better stability and robustness. Furthermore, we present extensive simulation results to demonstrate the effectiveness of our method over several other existing methods in reconstructing synthetic signals and images from compressive measurements with saturation and Gaussian noise. We also show simulation results in audio signal de-clipping.

Latent ODEs-based data compression method for power system

In order to effectively compress the power system data and reduce the space occupied by data storage and transmission, this paper proposes a data compression method based on Latent Segmented ODE (LatSegODE) for the large-scale steady-state data generated during the working process of the power system. By training the ODE model using a large amount of steady-state data, a latent ODE model is generated to characterize the steady-state data of the power system, thereby significantly reducing the storage space occupied by the data. During the experimental stage, compression testing is carried out on the static data of the power system of a power grid company. According to the quantitative evaluation results of the two indices of normalized mean square error and data compression ratio, the proposed method has obvious compression advantages, and the compressed signals have a high degree of fitting to the actual sampled signal waveforms.

A REAL STRUCTURE-PRESERVING ALGORITHM FOR THE LOW- RANK DECOMPOSITION OF PURE IMAGINARY QUATERNION MATRICES AND ITS APPLICATIONS IN SIGNAL PROCESSING

With the increasing use of quaternions in fields such as quantum mechanics, rigid body rotation, signal and color image processing, and aerospace engineering, three- dimensional signal models represented by pure imaginary quaternion matrices have been cre- ated. This model treats the 3D information as a whole, and the processing preserves the intrinsic connections between the different channels. However, the computational process of quaternion algebra often generates real parts, which are inevitable. How to ensure the pure imaginary properties of 3D signal models is also a research priority. In this paper, we inves- tigate the low-rank decomposition of pure imaginary quaternion matrices using least squares iteration and give a real structure-preserving algorithm for the low-rank decomposition based on the isomorphic real representation. In addition, this matrix decomposition algorithm is applied to color image compression and 3D wave signal denoising problems. Numerical ex- periments show the effectiveness of the algorithm in this paper.

Hartley Transform Signal Compression and Fast Power Quality Measurements for Smart Grid Application

Hartley Transform Signal Compression and Fast Power Quality Measurements for Smart Grid Application

Analysis and identification of transformer acoustic signal based on compressed sensing

The operation state of the transformer affects the operation of the whole power system. Aiming at the application of online monitoring of transformer operation, based on compressive sensing theory and wavelet packet analysis technology, a transformer acoustic fault diagnosis method based on compressive sensing is proposed. Firstly, the partial Hadamard matrix is constructed as the observation matrix to compress the acoustic signal of the transformer. The energy decomposition of the compressed signal is completed based on the wavelet packet. Finally, the feature selection is completed by considering the energy distribution characteristics using wavelet information entropy. The acoustic signal characteristics of transformer core fault simulation data are extracted by this method, and the fault diagnosis simulation is completed by using a particle swarm -SVM classifier. The results show that this method can obtain higher fault identification accuracy under the condition of a high compression ratio.

Depth Quality Improvement with a 607 MHz Time-Compressive Computational Pseudo-dToF CMOS Image Sensor

In this paper, we present a prototype pseudo-direct time-of-flight (ToF) CMOS image sensor, achieving high distance accuracy, precision, and robustness to multipath interference. An indirect ToF (iToF)-based image sensor, which enables high spatial resolution, is used to acquire temporal compressed signals in the charge domain. Whole received light waveforms, like those acquired with conventional direct ToF (dToF) image sensors, can be obtained after image reconstruction based on compressive sensing. Therefore, this method has the advantages of both dToF and iToF depth image sensors, such as high resolution, high accuracy, immunity to multipath interference, and the absence of motion artifacts. Additionally, two approaches to refine the depth resolution are explained: (1) the introduction of a sub-time window; and (2) oversampling in image reconstruction and quadratic fitting in the depth calculation. Experimental results show the separation of two reflections 40 cm apart under multipath interference conditions and a significant improvement in distance precision down to around 1 cm. Point cloud map videos demonstrate the improvements in depth resolution and accuracy. These results suggest that the proposed method could be a promising approach for virtually implementing dToF imaging suitable for challenging environments with multipath interference.

Optimum Vector Information Technologies Based on the Multidimensional Combinatorial Configurations

Paper devoiced to optimum vector information technologies based on the multi-dimensional combinatorial configurations, such as Ideal Ring Bundles (IRBs). One-dimensional IRBs are ring ordered positive integers that form finite set of integers from 1 to S using both these numbers and all its consecutive terms. Two- and multi-dimensional IRBs make available to configure intelligent information and telecommunication systems providing generate the maximum number of distinct vector sums of consecutive terms in the combinatorial configuration. Applications profiting from optimum vector information technologies based on the multi-dimensional combinatorial theory provide for example data mining technologies and big vector data processing, data analysis and system security, signal compression and reconstruction, vector computing and telecommunications, and other branches of sciences and advanced information technologies.

Semi-tensor product-based one-bit compressed sensing

The area of one-bit compressed sensing (1-bit CS) focuses on the recovery of sparse signals from binary measurements. Over the past decade, this field has witnessed the emergence of well-developed theories. However, most of the existing literature is confined to fully random measurement matrices, like random Gaussian and random sub-Gaussian measurements. This limitation often results in high generation and storage costs. This paper aims to apply semi-tensor product-based measurements to 1-bit CS. By utilizing the semi-tensor product, this proposed method can compress high-dimensional signals using lower-dimensional measurement matrices, thereby reducing the cost of generating and storing fully random measurement matrices. We propose a regularized model for this problem that has a closed-form solution. Theoretically, we demonstrate that the solution provides an approximate estimate of the underlying signal with upper bounds on recovery error. Empirically, we conduct a series of experiments on both synthetic and real-world data to demonstrate the proposed method’s ability to utilize a lower-dimensional measurement matrix for signal compression and reconstruction with enhanced flexibility, resulting in improved recovery accuracy.