Signal Compression Research Articles

A Deep-learning-based Auto Encoder-Decoder Model for Denoising Electrocardiogram Signals

Learning-based denoising techniques have become superior to the traditional assumption-based denoising methods in this modern era. Also, with the advancement of wearable technologies and remote electrocardiogram (ECG) monitoring systems, the requirement for optimal storage has increased due to the limited availability of hardware resources. Therefore, denoising and compression both are essential at the preprocessing stage of the ECG signal. Deep learning-based denoising auto encoder-decoder (DAED) models guarantee cutting-edge performance for these tasks. This article presents a lightweight, adaptive, hybrid Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) based DAED model that achieves a signal compression ratio of 64 with high signal-to-noise ratio improvement for the elimination of ECG noises. The novelty of this work lies in the customization of the CNN layers and utilization of the advantages of the GRU layer in a proper channel for compression and denoising ECG signals. The comparative study with other complex deep learning-based DAED arrangements and state-of-the-art denoising techniques shows the proposed model has simplicity in construction and an improved signal-to-noise ratio with minimum mean square error.

Design of Intelligent Control Under Machine Learning Supervision and Signal Compression Mechanism Design for NCSs Under DoS Attacks

Design of Intelligent Control Under Machine Learning Supervision and Signal Compression Mechanism Design for NCSs Under DoS Attacks

Automatic Classification of Anomalous ECG Heartbeats from Samples Acquired by Compressed Sensing.

In this paper, a method for the classification of anomalous heartbeats from compressed ECG signals is proposed. The method operating on signals acquired by compressed sensing is based on a feature extraction stage consisting of the evaluation of the Discrete Cosine Transform (DCT) coefficients of the compressed signal and a classification stage performed by means of a set of k-nearest neighbor ensemble classifiers. The method was preliminarily tested on five classes of anomalous heartbeats, and it achieved a classification accuracy of 99.40%.

Multilinear compressive learning and reconstruction of hyperspectral data with U-Net architecture for enhanced spectral imaging

Hyperspectral (HSI) data provide vast amounts of data which contain rich spatial and spectral information about each pixel of an image, such information is useful in detecting objects and identifying materials. However, the information provided by the HSI image is challenging to process. Hence, the compression of HSI makes it easier to utilize the data without the need for any heavy computation. Traditional compressive sensing techniques are adequate for lower dimensional signals, but they fail to preserve the essential spectral information provided by HSIs while performing compression. The proposed method aims to implement a Multilinear Compressive Learning (MCL) model for ideal compression of HSIs and a CNN based U-NET architecture is used for effective reconstruction of the compressed signal. The core of the algorithm involves two key blocks: (a) A multidimensional compressive sensing block for performing compression along every dimension, preserving spectral and spatial correlations. The employed sensing matrix is learned with respect to the given data for effective compression, (b) Reconstruction of the signal using a CNN based U-NET algorithm, which consists of encoders and decoders for effective reconstruction of the signal. Band selection is also performed in this technique to optimally select the most informative bands. The proposed model was implemented on the Indian Pines, Pavia University, and Salinas scenes datasets. The compression and reconstruction results were evaluated using performance metrics.

Experimental qualification and modelling of the non-linear response of the DEPFET active pixel sensor for the DSSC camera

A modified Depleted P-Channel Field Effect Transistor (DEPFET) is the key feature of the DEPFET Sensor with Signal Compression (DSSC), a 1 Mpixel X-ray camera aiming at ultra-fast imaging of soft X-rays at the European XFEL. Operation of large-area devices with a large number of DSSC-type DEPFET pixels requires the accurate knowledge of the sensitivity of the response to the relevant DEPFET parameters. The paper presents the experimental qualification of the response of DSSC-type DEPFET pixels in the space of 4 relevant parameters: drain current, source voltage, drain voltage and back side voltage. The obtained results allow estimation of the impact of the inevitable parameter fluctuations in large monolithic sensors and of the actual trimming range of the shape of signal compression in view of the pixelwise calibration of the 1 Mpixel DSSC camera.

Detection of human movement by combining supervised machine learning and an embroidered textile capacitance sensor

This study contributes to respiratory pattern detection by introducing a fabric sensor utilizing capacitance measurement and a semi-supervised machine learning algorithm known as an AI-based autoencoder. The sensor, consisting of two embroidered electrodes composed of silver-coated conductive nylon filaments, leverages the body as a dielectric material. In the research, a garment-type respiratory sensor was employed to continuously monitor respiratory data during both static (standing) and dynamic (walking, brisk walking, running) actions. The sparse autoencoder algorithm was particularly employed for individual static and dynamic actions, effectively distinguishing respiratory patterns corresponding to various movements. In addition, the sparse autoencoder helps prevent overfitting, fundamentally minimizing errors between the compression and reconstruction of signals. The maximum number of epochs was set to 2000, and the target error was set at 0.005. All data were compared against the static walking as the training baseline. Ultimately, the root mean squared error (RMSE) between static postures averaged 0.1, while the RMSEs between dynamic actions of walking, brisk walking, and running were 0.61, 0.91, and 2.78, respectively. These results suggest that movement detection through error detection is practically feasible and possesses discernible capabilities.

Retraction Note: Huffman quantization approach for optimized EEG signal compression with transformation technique

Retraction Note: Huffman quantization approach for optimized EEG signal compression with transformation technique

Analog Front-End for the Readout of LGAD-Based Particle Detectors

This paper presents an analog front-end channel for the readout of Low Gain Avalanche Diodes (LGADs) based particle detectors for the next generation of space-borne experiments. The circuit has been designed in a 65 nm CMOS technology. The charge generated by the detector is integrated by a Charge Sensitive Amplifier (CSA) with dynamic signal compression feature to achieve improved noise performance, along with enhanced time resolution capabilities over the wide range of input charge to be detected (from 45fC to 16pC). An RC-CR shaper with selectable shaping time, a fast discriminator and a Time-to-Amplitude Converter (TAC) complete the readout circuit. An SNR around 300, together with a time resolution of slightly below 60ps, is achieved in post-layout simulations.

Surface Susceptibility Synthesis of Spatially Dispersive Metasurfaces for Space Compression and Spatial Signal Processing

Surface Susceptibility Synthesis of Spatially Dispersive Metasurfaces for Space Compression and Spatial Signal Processing

Mechanical vibration signal compression based on speech codecs for intelligent manufacturing

ABSTRACT How to effectively compress mechanical signals so that they can support remote and real-time health monitoring is a hot issue in the context of intelligent manufacturing. Due to the characteristics of small repeatability, high noise, and high numerical accuracy of vibration signal, traditional compression algorithms cannot well balance the compression ratio, reconstruction quality, compression time indicators. Therefore, this paper presents a novel mechanical vibration signal compression scheme based on speech codecs, which can realize signal compression in many scenarios such as tool wear detection and bearing fault diagnosis. In this work, mechanical vibration signal compression is first transformed into a multi-objective optimization problem, and the feasibility of solving this problem from the perspective of speech coding is deeply analyzed. Further, this paper presents five possible compression methods with different compression ratios. The presented methods are evaluated with real-time data for tool wear detection in stone processing and public datasets for bearing fault diagnosis. The experimental results show that the proposed methods achieve high compression ratios (4.0 ~ 24.6) while ensuring low time overhead (the average time for compressing a sample point is not more than 0.0194 ms) and high reconstruction quality (the MSE index all less than 0.008), outperforming other state-of-the-art methods.

A novel ECG compression algorithm using Pulse-Width Modulation integrated quantization for low-power real-time monitoring

Cardiac monitoring systems in Internet of Things (IoT) healthcare, reliant on limited battery and computational capacity, need efficient local processing and wireless transmission for comprehensive analysis. Due to the power-intensive wireless transmission in IoT devices, ECG signal compression is essential to minimize data transfer. This paper presents a real-time, low-complexity algorithm for compressing electrocardiogram (ECG) signals. The algorithm uses just nine arithmetic operations per ECG sample point, generating a hybrid Pulse Width Modulation (PWM) signal storable in a compact 4-bit resolution format. Despite its simplicity, it performs comparably to existing methods in terms of Percentage Root-Mean-Square Difference (PRD) and space-saving while significantly reducing complexity and maintaining robustness against signal noise. It achieves an average Bit Compression Ratio (BCR) of 4 and space savings of 90.4% for ECG signals in the MIT-BIH database, with a PRD of 0.33% and a Quality Score (QS) of 12. The reconstructed signal shows no adverse effects on QRS complex detection and heart rate variability, preserving both the signal amplitude and periodicity. This efficient method for transferring ECG data from wearable devices enables real-time cardiac activity monitoring with reduced data storage requirements. Its versatility suggests potential broader applications, extending to compression of various signal types beyond ECG.

Debiasing Welch’s method for spectral density estimation

Abstract Welch’s method provides an estimator of the power spectral density that is statistically consistent. This is achieved by averaging over periodograms calculated from overlapping segments of a time series. For a finite-length time series, while the variance of the estimator decreases as the number of segments increases, the magnitude of the estimator’s bias increases: a bias-variance trade-off ensues when setting the segment number. We address this issue by providing a novel method for debiasing Welch’s method that maintains the computational complexity and asymptotic consistency, and leads to improved finite-sample performance. Theoretical results are given for fourth-order stationary processes with finite fourth-order moments and an absolutely convergent fourth-order cumulant function. The significant bias reduction is demonstrated with numerical simulation and an application to real-world data. Our estimator also permits irregular spacing over frequency and we demonstrate how this may be employed for signal compression and further variance reduction. The code accompanying this work is available in R and python.

Superimposed Semantic Communication for IoT-Based Real-Time ECG Monitoring.

Real-time electrocardiogram (ECG) monitoring and diagnosis through Internet of Things (IoT) are crucial for addressing the severity and timely treatment of cardiovascular diseases, enabling timely intervention and preventing life-threatening complications. However, current ECG monitoring research predominantly focuses on individual aspects such as signal compression, diagnostic analysis, or secure transmission, lacking joint optimization of various modules in IoT scenarios. To address this gap, this work proposes a novel framework based on superimposed semantic communication for real-time ECG monitoring in IoT. The framework comprises three hierarchical levels: the edge level for data collection and processing, the relay level for signal compression and coding, and the cloud level for data analysis and reconstruction. The proposed framework offers several unique advantages. By employing semantic encoding guided by ECG classification tasks, it selectively extracts crucial features within and between signals, improving compression ratio and adaptability to channel noise. The superimposed semantic encoding achieves content encryption without requiring any additional operations. Moreover, the framework utilizes lightweight anomaly detection neural networks, reducing edge device power consumption and conserving communication resources. Simulation and real experimental results demonstrate that the proposed method achieves real-time encoding and transmission of ECG signals with a compression ratio of 0.019 on the MIT-BIH dataset. Furthermore, it attains a heartbeat classification accuracy of 0.988 and a reconstruction error of 0.061.

Diagnosing Incipient Faults for a Faster Adoption of Sustainable Aerospace Technologies

Next-generation aircraft require the development and integration of a deal of innovative green technologies to meet the ambitious sustainability goals set for aviation. Those transformational efforts are associated with a tremendous increase in the complexity of the onboard systems and their multiphysics-coupled behaviors and dynamics. A critical aspect relates to the identification of the coupled faults resulting from the integration of those green technologies, which introduce damage identifiability issues and demand new approaches for the efficient and accurate identification of non-nominal fault conditions. Model-based fault detection and identification (FDI) methodologies have been proven essential to identify onboard the damages affecting the systems from physical signal acquisitions, but existing methods are typically computationally expensive and fail to capture incipient coupled faults, which prevents their adoption and scaling with the increasing complexity of novel multiphysics systems. This work introduces a multifidelity framework to accelerate the identification of fault modes affecting complex systems. An original two-stage compression computes an optimally informative and highly reduced representation of the monitoring signals for the minimum demand of onboard resources. A multifidelity scheme for Bayesian inversion is developed to infer multidomain fault parameters from the compressed signals: variable cost and fidelity models are optimally queried for a major reduction of the overall computational expense. The framework is demonstrated and validated for aerospace electromechanical actuators affected by incipient multimodal faults. Remarkable accelerations of the FDI procedure are observed, and the exact identification of the incipient fault condition was achieved one order of magnitude faster than with standard algorithms.

Non-intrusive method for audio quality assessment of lossy-compressed music recordings using convolutional neural networks

Most of the existing algorithms for the objective audio quality assessment are intrusive, as they require access both to an unimpaired reference recording and an evaluated signal. This feature excludes them from many practical applications. In this paper, we introduce a non-intrusive audio quality assessment method. The proposed method is intended to account for audio artefacts arising from the lossy compression of music signals. During its development, 250 high-quality uncompressed music recordings were collated. They were subsequently processed using the selection of five popular audio codecs, resulting in the repository of 13,000 audio excerpts representing various levels of audio quality. The proposed non-intrusive method was trained with the data obtained employing a well-established intrusive model (ViSQOL v3). Next, the performance of the trained model was evaluated utilizing the quality scores obtained in the subjective listening tests undertaken remotely over the Internet. The listening tests were carried out in compliance with the MUSHRA recommendation (ITU-R BS.1534-3). In this study, the following three convolutional neural networks were compared: (1) a model employing 1D convolutional filters, (2) an Inception-based model, and (3) a VGG-based model. The last-mentioned model outperformed the model employing 1D convolutional filters in terms of predicting the scores from the listening tests, reaching a correlation value of 0.893. The performance of the Inceptionbased model was similar to that of the VGG-based model. Moreover, the VGG-based model outperformed the method employing a stacked gated-recurrent-unit-based deep learning framework, recently introduced by Mumtaz et al. (2022).

Double Cyclization Tandem Mass for Identification and Quantification of Phosphatidylcholines Using Isobaric Six-Plex Capillary nLC-MS/MS.

Multiplexing of phosphatidylcholine analysis is hindered by a lack of appropriate derivatization. Presented here is a tagging scheme that uses a quaternary amine tag and targets the hydroxy group of the phosphate, which switches the net charge from neutral to +2. Quantitative yields were achieved from >99% reaction completion derived by dimethoxymethyl morpholinium (DMTMM) activation. Fragmentation of phosphatidylcholines (PCs) and lysophosphatidylcholines (LPCs) releases two trimethylamines and the acyl chains through neutral loss and generates a unique double cyclization constant mass reporter. Selective incorporation of isotopes onto the tag produces a six-plex set of isobaric reagents. For equivalent six-plex-labeled samples, <14% RSD was achieved, followed by a dynamic range of 1:10 without signal compression. Quantification of PCs/LPCs in human hepatic cancer cells was conducted as six-plex using data-dependent analysis tandem MS. We report a six-plex qualitative and quantitative isobaric tagging strategy expanding the limits of analyzing PCs/LPCs.

SHM data compression and reconstruction based on IGWO-OMP algorithm

The long-term operating structural health monitoring (SHM) system generates massive monitoring data, whose transmission and storage are challenging tasks. To address this issue, this study proposed an improved grey wolf optimizer-orthogonal matching pursuit (IGWO-OMP) algorithm for SHM data compression and reconstruction. The effectiveness of the proposed algorithm was validated using four SHM datasets including ultrasonic guided wave (UGW), acceleration, and audio signals. Firstly, the optimization results of IGWO were compared with those of other classical algorithms to verify its superiority in reconstruction accuracy. Secondly, the reconstruction accuracy under various compression factors was investigated and the optimal compression factor was determined. Finally, the relationship between reconstruction accuracy and signal sparsity as well as the performance of IGWO-OMP in dealing with noise-containing signals were discussed. The results demonstrate the generalizability and excellent noise robustness of IGWO-OMP in SHM data compression and reconstruction. Compared with other optimization algorithms, IGWO exhibits the best global optimal solution searching ability and obtains the reconstructed signal closest to the original one. Comprehensively considering the compressed signal length, reconstruction accuracy and computational efficiency, the optimal compression factor is suggested to be 0.2. In IGWO-OMP, the signal reconstruction accuracy is related to its sparsity, the variation of the CCD index for the reconstructed signal with relative sparsity follows an exponential growth relationship.

Enhanced Sampling in Molecular Dynamics Simulations: How Many MD Snapshots can be Needed to Reproduce the Biological Behavior?

Since its early days in the 19th century, medicinal chemistry has concentrated its efforts on the treatment of diseases, using tools from areas such as chemistry, pharmacology, and molecular biology. The understanding of biological mechanisms and signaling pathways is crucial information for the development of potential agents for the treatment of diseases mainly because they are such complex processes. Given the limitations that the experimental approach presents, computational chemistry is a valuable alternative for the study of these systems and their behavior. Thus, classical molecular dynamics, based on Newton's laws, is considered a technique of great accuracy, when appropriated force fields are used, and provides satisfactory contributions to the scientific community. However, as many configurations are generated in a large MD simulation, methods such as Statistical Inefficiency and Optimal Wavelet Signal Compression Algorithm are great tools that can reduce the number of subsequent QM calculations. Accordingly, this review aims to briefly discuss the importance and relevance of medicinal chemistry allied to computational chemistry as well as to present a case study where, through a molecular dynamics simulation of AMPK protein (50 ns) and explicit solvent (TIP3P model), a minimum number of snapshots necessary to describe the oscillation profile of the protein behavior was proposed. For this purpose, the RMSD calculation, together with the sophisticated OWSCA method was used to propose the minimum number of snapshots.

Efficient in Vivo Neural Signal Compression Using an Autoencoder-Based Neural Network.

Conventional in vivo neural signal processing involves extracting spiking activity within the recorded signals from an ensemble of neurons and transmitting only spike counts over an adequate interval. However, for brain-computer interface (BCI) applications utilizing continuous local field potentials (LFPs) for cognitive decoding, the volume of neural data to be transmitted to a computer imposes relatively high data rate requirements. This is particularly true for BCIs employing high-density intracortical recordings with hundreds or thousands of electrodes. This article introduces the first autoencoder-based compression digital circuit for the efficient transmission of LFP neural signals. Various algorithmic and architectural-level optimizations are implemented to significantly reduce the computational complexity and memory requirements of the designed in vivo compression circuit. This circuit employs an autoencoder-based neural network, providing a robust signal reconstruction. The application-specific integrated circuit (ASIC) of the in vivo compression logic occupies the smallest silicon area and consumes the lowest power among the reported state-of-the-art compression ASICs. Additionally, it offers a higher compression rate and a superior signal-to-noise and distortion ratio.

The radial variation of the LMC-induced reflex motion of the Milky Way disc observed in the stellar halo

ABSTRACT We measure the kinematic signature arising from the Milky Way (MW) disc moving with respect to the outer stellar halo, which is observed as a dipole signal in the kinematics of stellar halo tracers. We quantify how the reflex motion varies as a function of Galactocentric distance, finding that (i) the amplitude of the dipole signal increases as a function of radius, and (ii) the direction moves across the sky. We compare the reflex motion signal against a compilation of published models that follow the MW–LMC interaction. These models show a similar trend of increasing amplitude of the reflex motion as a function of distance, but they do not reproduce the direction of the disc motion with respect to the stellar halo well. We also report mean motions for the stellar halo as a function of distance, finding radial compression in the outer halo and non-zero prograde rotation at all radii. The observed compression signal is also present in MW–LMC models, but the rotation is not, which suggests that the latter is not induced by the LMC. We extensively validate our technique to measure reflex motion against idealized tests. We discuss prospects for directly constraining the mass and orbital history of the LMC through the impact on the motion of the MW stellar disc, and how the modelling of the reflex motion can be improved as more and better data become available.