Compressive Sensing Domain Research Articles

A novel DVAPSO-LSTSVM classifier in compressed sensing domain for intelligent pipeline leakage diagnosis

Leakage diagnosis is of great significance for maintaining the normal operation of pipelines. However, there is a large amount of redundant data in leakage data collection, resulting in increased detection time. In addition, the leakage detection model lacks the necessary optimization, which limits diagnostic accuracy. To overcome these two drawbacks, this paper proposes a novel leakage diagnosis model in compressed sensing (CS) domain. Firstly, the leakage signal is converted into the CS domain to reduce redundant data, and extract features in the CS domain to form a feature dataset. Then, a particle swarm optimization algorithm with decreased and variable amplitude strategy (DVAPSO) is proposed, which enhances the ability of particle optimization and jumping out of local optimum. The diagnosis model LSTSVM is optimized using DVAPSO algorithm to decrease the risk of trapping into local optimum in the training process. Finally, the leakage feature dataset of CS domain is sent to the DVAPSO-LSTSVM diagnosis model to complete the leakage detection. The experimental results show that pipeline data in CS domain improves detection velocity by 71.7% when the compression ratio is 50%. Moreover, the DVAPSO-LSTSVM has higher leakage identification accuracy compared to other diagnosis models, with a detection accuracy of 95.2%.

Self-adaptive reconstruction for compressed sensing based ECG acquisition in wireless body area network

The compressed sensing (CS) has been demonstrated as a promising solution for low-cost signal acquisition in wireless body area network. In this paper, a novel signal reconstruction scheme based on adaptive dictionary and matched filtering in CS domain is proposed for the ECG acquisition. The proposed method selects adaptive overcomplete dictionary based on the QRS estimation of the compressed measurements in each frame. If a QRS complex is estimated in this frame, an adaptive overcomplete dictionary matching the QRS characteristics of this frame is selected for reconstruction, otherwise, a dictionary trained by segments without QRS complex is selected. The ECG frames whose estimated QRS complexes locate in several consecutive locations, the so-called region width, are considered as one category, and will be reconstructed by one overcomplete dictionary which is trained by similar ECG waves. Extensive experiments have been conducted, and the results well demonstrate the effectiveness for signal reconstruction as well as its advantages over some state-of-the-art algorithms.

Data Traffic Reduction with Compressed Sensing in an AIoT System

To provide Artificial Intelligence (AI) services such as object detection, Internet of Things (IoT) sensor devices should be able to send a large amount of data such as images and videos. However, this inevitably causes IoT networks to be severely overloaded. In this paper, therefore, we propose a novel oneM2M-compliant Artificial Intelligence of Things (AIoT) system for reducing overall data traffic and offering object detection. It consists of some IoT sensor devices with random sampling functions controlled by a compressed sensing (CS) rate, an IoT edge gateway with CS recovery and domain transform functions related to compressed sensing, and a YOLOv5 deep learning function for object detection, and an IoT server. By analyzing the effects of compressed sensing on data traffic reduction in terms of data rate per IoT sensor device, we showed that the proposed AIoT system can reduce the overall data traffic by changing compressed sensing rates of random sampling functions in IoT sensor devices. In addition, we analyzed the effects of the compressed sensing on YOLOv5 object detection in terms of performance metrics such as recall, precision, mAP50, and mAP, and found that recall slightly decreases but precision remains almost constant even though the compressed sensing rate decreases and that mAP50 and mAP are gradually degraded according to the decreased compressed sensing rate. Consequently, if proper compressed sensing rates are chosen, the proposed AIoT system will reduce the overall data traffic without significant performance degradation of YOLOv5.

Short Word-Length Entering Compressive Sensing Domain: Improved Energy Efficiency in Wireless Sensor Networks

This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of the distance between anchors and the target thereby achieving range-based localization. The short word-length techniques considered are delta modulation and sigma-delta modulation. The energy efficiency is due to the reduction of the data volume transmitted from anchors to the fusion center by employing any of the two delta modulation variants with compressive sensing techniques. Delta modulation allows the transmission of one bit per TOA sample. The communication energy efficiency is increased by RⱮ, R ≥ 1, where R is the sample reduction ratio of compressive sensing, and Ɱ is the number of bits originally present in a TOA-sample word. It is found that the localization system involving sigma-delta modulation has a superior performance to that using delta-modulation or pure compressive sampling alone, in terms of both energy efficiency and localization error in the presence of TOA measurement noise and transmission noise, owing to the noise shaping property of sigma-delta modulation.

A signal quality analysis method for electrocardiosignals in the CS domain

Compressed sensing (CS) is an important cutting-edge low-power technology for wearable health monitoring systems. To solve the problem of how the quality of compressive sampled electrocardiosignal (ECG) affects the subsequent arrhythmia recognition and signal reconstruction, a novel ECG quality evaluation method in the CS domain is proposed. In order to achieve the evaluation effect in the CS domain, a modified bandpass filter method is designed to obtain the approximate frequency band energy and approximate wavelet entropy, which are used as the evaluation index for the support vector machine classifier to realize the CS ECG signal quality evaluation. Based on the simulation results from the MIT-BIH ECG database, this method can reach an accuracy of more than 92% under different compression ratios (2, 4, 8 and 16). Simulation experiments based on data from the PhysioNet/CinC Challenge 2011 database show that the accuracy of this method can reach above 83% under different compression ratios. In evaluation experiments using actual ECG signals, we show that this method can achieve good accuracy when the compression ratio is less than eight. The accuracy of this method needs to be further improved when the compression ratio is 16. When the compression ratio is less than 8 and a sparse matrix is not used as the measurement matrix, the method proposed in this paper can directly evaluate the quality of ECG signals after compression sampling without reconstruction. Compared with the signal quality evaluation algorithm in the uncompressed domain, the experimental results of the proposed method are highly competitive and, under a certain compression ratio, the amount of transmitted data can be reduced while maintaning accuracy. Meanwhile, the proposed method also provides a new idea for signal quality evaluation in the compressed domain.

RETRACTED ARTICLE: Visual sensor intelligent module based image transmission in industrial manufacturing for monitoring and manipulation problems

Due to technology advancement, smart visual sensing required in terms of data transfer capacity, energy-efficiency, security, and computational-efficiency. The high-quality image transmission in visual sensor networks (VSNs) consumes more space, energy, transmission delay which may experience the various security threats. Image compression is a key phase of visual sensing systems that needs to be effective. This motivates us to propose a fast and efficient intelligent image transmission module to achieve the energy-efficiency, minimum delay, and bandwidth utilization. Compressive sensing (CS) introduced to speedily compressed the image to reduces the consumption of energy, time minimization, and efficient bandwidth utilization. However, CS cannot achieve security against the different kinds of threats. Several methods introduced since the last decade to address the security challenges in the CS domain, but efficiency is a key requirement considering the intelligent manufacturing of VSNs. Furthermore, the random variables selected for the CS having the problem of recovering the image quality due to the accumulation of noise. Thus concerning the above challenges, this paper introduced a novel one-way image transmission module in multiple input multiple output that provides secure and energy-efficient with the CS model. The secured transmission in the CS domain proposed using the security matrix which is called a compressed secured matrix and perfect reconstruction with the random matrix measurement in the CS. Experimental results outwards that the intelligent module provides energy-efficient, secured transmission with low computational time as well as a reduced bit error rate.

Secure medical health care content protection system (SMCPS) with watermark detection for multi cloud computing environment

The aim of this work is to introduce a new secure system for Medical Healthcare records. The proposed design influences cloud infrastructures to give cost competence, fast exploitation, scalability, and flexibility to have room for unstable workloads. The research system could be utilized to safeguard diverse medical health care records. The proposed Secure Medical Healthcare Content Protection System (SMCPS) system contains of two major steps: (i) Hybrid Key Generation (HKG) and (ii) storage management. In the initial step of the work, keys values are created for medical healthcare records by using two encryption techniques such as Advanced Encoding Standard (AES) cryptography and Message Digest (MD5) algorithm for safe multiparty computation. This proposed HKG algorithm is different from usual key generation algorithm, since the proposed HKG algorithm adds the key values of AES and MD5. New key value is created from these two algorithms, thus increases the security level of the system. The HKG algorithm creates dynamic and representative keys of medical healthcare records, it is computationally resourceful to calculate and contrast in addition it needs small storage. In the second step of the work, Compressive Sensing (CS) based framework for storage management of medical healthcare records. The distributed matching engine achieves greater adaptability and it is created to among different healthcare records gathered from UCI machine learning vault for 100 patients. The applicability is further examined for the applicability of sparse coding-based frameworks including the various numbers of medical healthcare records which influences the ability of Multi Cloud Computing (MCC) environment. The experimentation work are performed by using Miscellaneous database for watermark pattern and any medical healthcare records are taken as input in MCC environment results outcomes prove that safe watermark recognition in the CS domain is possible for medical healthcare records are implemented in JAVA environment. The proposed work provides a CS based watermark recognition which influences safe MCC environment. The results are measured as in terms of Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE) and the Structural Similarity Index (SSIM), security and communication cost.

Flying Small Target Detection for Anti-UAV Based on a Gaussian Mixture Model in a Compressive Sensing Domain.

Addressing the problems of visual surveillance for anti-UAV, a new flying small target detection method is proposed based on Gaussian mixture background modeling in a compressive sensing domain and low-rank and sparse matrix decomposition of local image. First of all, images captured by stationary visual sensors are broken into patches and the candidate patches which perhaps contain targets are identified by using a Gaussian mixture background model in a compressive sensing domain. Subsequently, the candidate patches within a finite time period are separated into background images and target images by low-rank and sparse matrix decomposition. Finally, flying small target detection is achieved over separated target images by threshold segmentation. The experiment results using visible and infrared image sequences of flying UAV demonstrate that the proposed methods have effective detection performance and outperform the baseline methods in precision and recall evaluation.

Compressive sensing based secret signals recovery for effective image Steganalysis in secure communications

Conventional image steganalysis mainly focus on presence detection rather than the recovery of the original secret messages that were embedded in the host image. To address this issue, we propose an image steganalysis method featured in the compressive sensing (CS) domain, where block CS measurement matrix senses the transform coefficients of stego-image to reflect the statistical differences between the cover and stego- images. With multi-hypothesis prediction in the CS domain, the reconstruction of hidden signals is achieved efficiently. Extensive experiments have been carried out on five diverse image databases and benchmarked with four typical stegographic algorithms. The comprehensive results have demonstrated the efficacy of the proposed approach as a universal scheme for effective detection of stegography in secure communications whilst it has greatly reduced the numbers of features requested for secret signal reconstruction.

Meaningful Image Encryption Based on Reversible Data Hiding in Compressive Sensing Domain

A novel method of meaningful image encryption is proposed in this paper. A secret image is encrypted into another meaningful image using the algorithm of reversible data hiding (RDH). High covertness can be ensured during the communication, and the possibility of being attacked of the secret image would be reduced to a very low level. The key innovation of the proposed method is that RDH is applied to compressive sensing (CS) domain, which brings a variety of benefits in terms of image sampling, communication and security. The secret image after preliminary encryption is embedded into the sparse representation coefficients of the host image with the help of the dictionary. The embedding rate could reach 2 bpp, which is significantly higher than those of other state-of-art schemes. In addition, the computational complexity of receiver is reduced. Simulations verify our proposal.

A Compressive Sensing based privacy preserving outsourcing of image storage and identity authentication service in cloud

The theory of Compressive Sensing (CS) enables the compact storage of image datasets which are exponentially generated today. In this application, the high computational complexity CS reconstruction process is considered to be outsourced to the cloud for its abundant computing and storage resources. Although it is promising, how to protect data privacy and simultaneously maintain management of the image remains challenging. To address the challenge, we propose a novel outsourced image reconstruction and identity authentication service in cloud, which integrates the techniques of signal processing in the CS domain and computation outsourcing. In our system, the image CS samples are outsourced to cloud for reduced storage. For privacy, the scheme ensures the cloud to securely reconstruct image without revealing the underlying content. For management, whether the cloud determines to supply the reconstruction service is depending on the identity authentication result. Theoretical analysis and empirical evaluations show a satisfactory security performance and low computational complexity of the proposed system. Besides, experimental results also confirm the feasibility of identity authentication in the CS domain.

A New Sparse Signal-Matched Measurement Matrix for Compressive Sensing in UWB Communication

Ultra wideband (UWB) technology is suitable for high data rate short range wireless communication, localization, and imaging techniques. However, UWB systems require high sampling rate and precise synchronization. In order to reduce the sampling rate, have precise synchronization, and for low power requirement, UWB systems are implemented using compressive or sub-Nyquist rate measured samples by exploiting the sparsity of the UWB signal. Compressive sensing (CS)-based UWB systems are being designed in two ways: 1) signal demodulation or detection is performed in the CS domain without full signal recovery at the front-end. Thus, demodulation or detection works on compressive measurements. However, system performance deteriorates in the CS domain as compared with full Nyquist rate sampling and 2) after, Nyquist rate signal is recovered using efficient algorithms at the front-end, the signal demodulation or detection is performed using the conventional receiver. Thus, one requires an efficient CS/sampling of signal measurement at the front-end for better system performance for both the cases stated earlier. In this paper, we propose a deterministic (partial) UWB waveform-matched measurement matrix. The proposed measurement matrix has a circulant structure and is sparse in nature. The proposed matrix is easy to implement in hardware and is operationally time efficient as needed in a practical system. The bit error rate performance of the corresponding UWB system and the operational time complexity with the proposed measurement matrix are better as compared with the existing measurement matrices in the CS domain for both the above receiver designs. The efficacy of the proposed measurement matrix is verified through extensive simulations in both the additive white Gaussian noise and multipath communication environments. In addition, we have also compared other desirable properties of the proposed measurement matrix with the existing measurement matrices.

Cognitive Computation of Compressed Sensing for Watermark Signal Measurement

As an important tool for protecting multimedia contents, scrambling and randomizing of original messages is used in generating digital watermark for satisfying security requirements. Based on the neural perception of high-dimensional data, compressed sensing (CS) is proposed as a new technique in watermarking for improved security and reduced computational complexity. In our proposed methodology, watermark signal is extracted from the CS of the Hadamard measurement matrix. Through construction of the scrambled block Hadamard matrix utilizing a cryptographic key, encrypting the watermark signal in CS domain is achieved without any additional computation required. The extensive experiments have shown that the neural inspired CS mechanism can generate watermark signal of higher security, yet it still maintains a better trade-off between transparency and robustness.

Compressed Sensing-Based Distributed Image Compression

In this paper, a new distributed block-based image compression method based on the principles of compressed sensing (CS) is introduced. The coding and decoding processes are performed entirely in the CS measurement domain. Image blocks are classified into key and non-key blocks and encoded at different rates. The encoder makes use of a new adaptive block classification scheme that is based on the mean square error of the CS measurements between blocks. At the decoder, a simple, but effective, side information generation method is used for the decoding of the non-key blocks. Experimental results show that our coding scheme achieves better results than existing CS-based image coding methods.

A compressive sensing based secure watermark detection and privacy preserving storage framework.

Privacy is a critical issue when the data owners outsource data storage or processing to a third party computing service, such as the cloud. In this paper, we identify a cloud computing application scenario that requires simultaneously performing secure watermark detection and privacy preserving multimedia data storage. We then propose a compressive sensing (CS)-based framework using secure multiparty computation (MPC) protocols to address such a requirement. In our framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a CS domain to protect the privacy. During CS transformation, the privacy of the CS matrix and the watermark pattern is protected by the MPC protocols under the semi-honest security model. We derive the expected watermark detection performance in the CS domain, given the target image, watermark pattern, and the size of the CS matrix (but without the CS matrix itself). The correctness of the derived performance has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the CS domain is feasible. Our framework can also be extended to other collaborative secure signal processing and data-mining applications in the cloud.

Improved total variation minimization method for compressive sensing by intra-prediction

Total variation (TV) minimization algorithms are often used to recover sparse signals or images in the compressive sensing (CS). But the use of TV solvers often suffers from undesirable staircase effect. To reduce this effect, this paper presents an improved TV minimization method for block-based CS by intra-prediction. The new method conducts intra-prediction block by block in the CS reconstruction process and generates a residual for the image block being decoded in the CS measurement domain. The gradient of the residual is sparser than that of the image itself, which can lead to better reconstruction quality in CS by TV regularization. The staircase effect can also be eliminated due to effective reconstruction of the residual. Furthermore, to suppress blocking artifacts caused by intra-prediction, an efficient adaptive in-loop deblocking filter was designed for post-processing during the CS reconstruction process. Experiments show competitive performances of the proposed hybrid method in comparison with state-of-the-art TV models for CS with respect to peak signal-to-noise ratio and the subjective visual quality.