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Ground vibration test is an important part of aircraft model development and strength verification. In aircraft ground vibration test，an appropriate excitation scheme can significantly shorten the test's period and improve the accuracy of mode identification. In this paper，QR decomposition based on the finite element model is used for global planning of exciter placement，and mode participation is introduced to evaluate the plan's exciting efficiency. The multivariate mode indicator function is used to optimize the exciting force vector for closely spaced modes. Finally，these methods are verified in a small general aircraft's ground vibration test，which significantly improves the test's efficiency and reduces the difficulty of closely spaced modes identification. The excitation scheme optimization method adopted in this article has high engineering application value.

Abstract To solve the problems of the low efficiency of parameter allocation and time-consuming computation of output weights in the hidden layers of stochastic configuration networks (SCNs), an optimization method is proposed to improve the SCNs construction efficiency. Firstly, with the increase in the number of hidden layer nodes, the key parameters that determine the strictness of the supervision mechanism are reconstructed to speed up the configuration efficiency of hidden layer input weights and biases. Then, the incremental mechanism of the SCNs are combined with the QR decomposition method, and the output weights are calculated by iteratively updating the transformation matrix, thus reducing the computational complexity of training the SCNs. Finally, the proposed method is validated on four standard datasets and historical data of a municipal solid waste incineration process. The experimental results show that the proposed method improves the efficiency of SCN construction while guaranteeing the prediction accuracy of SCNs model.

Open Radio Access Networks (Open RANs), realized fully in software, require excessive computing resources to support time-sensitive signal-processing algorithms in the physical layer. Among them, multiple-input-multiple-output (MIMO) processing is a key functionality used to drive higher connectivity in the uplink, but it is computationally intensive, triggering the need for hardware acceleration to overcome the processing inefficiency of software-based solutions. Additionally, energy efficiency is becoming a key focus in Open RAN to enable sustainable deployments that utilize available resources efficiently. Because channel-inversion complexity increases polynomially with the number of users in linear detectors, such as zero-forcing (ZF) and minimum-mean-square-error (MMSE), acceleration based on channel-inverse approximations has gained significant attention. However, they unnecessarily multiply the number of base station (BS) antennas to ensure accurate detection, leading to a drastic increase in power consumption owing to the additional radio frequency (RF) chains employed. In contrast, linear detectors achieve a sufficiently good performance with only twice the number of BS antennas as users. This work introduces an exact-MMSE and soft-output hardware accelerator that includes an inversion-free, highly-parallel QR decomposition (QRD) architecture and a low-complexity detector stage with per-cycle soft-output generation, significantly improving the processing latency and throughput. The proposed architecture is fully scalable to support diverse MIMO configurations. Implementation evaluations on a Xilinx Virtex Ultrascale+ field-programmable gate array (FPGA) demonstrate that the proposed exact solution can achieve more than $2\times $ improvement in hardware throughput over existing approximate designs. Moreover, the peak throughput can be increased around 10-fold in slowly fading channels.

Medical video watermarking is one of the beneficial and efficient tools to prohibit important patients' data from illicit enrollment and redistribution. In this paper, a new blind watermarking scheme has been proposed to improve the confidentiality, integrity, authenticity, and perceptual quality of a medical video with minimum distortion. The proposed scheme is based on 2D-DWT and dual Hessenberg-QR decomposition, where the input medical video is initially processed into frames. Then, the processed frames are transformed into sub-bands using 2D-DWT, followed by applying Hessenberg-QR decomposition on the selected wavelet HL2 sub-band. The watermark is scrambled via Arnold cat map to raise confidentiality and then concealed in the modified selected features. The watermark is extracted in a fully blind mode without referencing the original video, which reduces the extraction time. The proposed scheme maintained a fundamental tradeoff between robustness and visual imperceptibility compared to existing methods against many commonly encountered attacks. The visual imperceptibility has been evaluated using well-known metrics PSNR, SSIM, Q-index, and histogram analysis. The proposed scheme achieves a high PSNR value of (70.6899 dB) with minimal distortion and a high robustness level with an average NC value of (0.9998) and BER value of (0.0023) while conserving a large payload capacity. The obtained results show superior performance over similar video watermarking methods. The limitation of this scheme is the elapsed time during the embedding process since we utilized dual Hessenberg-QR decomposition. One possible solution to reduce time consumption is simple decompositions like bound-constrained SVM or similar decompositions.

In recent years,with the rapid development of cloud computing and artificial intelligence,digital images are more and more widely used in multimedia,medical and military fields.Among them,remote sensing technology can detect and process remote targets through the theory of electromagnetic wave,which makes the transmission security of remote sensing images increasingly significant.In order to solve the problems of digital image information security and privacy protection,this paper proposes a reversible remote sensing image hiding algorithm based on novel Hachimoji deoxyribonucleic acid (DNA) and QR decomposition.Firstly,the system parameters and initial values of the coupled map lattice(CML) are updated by using the information entropy of remote sensing image and host image,and the generated chaotic sequences are used as the one-time key stream of the encryption process,which increases resistance to known/selected plain-text attacks.Then,Hachimoji DNA technology is used to encode the remote sensing image with 8-bit base,and key streams are used to perform XOR operation and cyclic shift operation on the image matrix.Finally,the host image is decomposed into blocks,and the encrypted remote sensing image is embedded into the host block in the form of DNA bases.In particular,the host image still visually meaningful after embedding remote sensing information,and the embedded image information can also be extracted from it without loss.Simulation results show that the proposed algorithm has good embedding effect,high security and strong robustness.

In hidden Markov chain (HMC) models, widely used for target tracking, the process noise and measurement noise are in general assumed to be independent and Gaussian for mathematical simplicity. However, the independence and Gaussian assumptions do not always hold in practice. For instance, in a typical radar tracking application, the measurement noise is correlated over time as the sampling frequency of a radar is generally much higher than the bandwidth of the measurement noise. In addition, target maneuvers and measurement outliers imply that the process noise and measurement noise are non-Gaussian. To solve this problem, we resort to triplet Markov chain (TMC) models to describe stochastic systems with correlated noise and derive a new filter under the maximum correntropy criterion to deal with non-Gaussian noise. By stacking the state vector, measurement vector, and auxiliary vector into a triplet state vector, the TMC model can capture the complete dynamics of stochastic systems, which may be subjected to potential parameter uncertainty, non-stationarity, or error sources. Correntropy is used to measure the similarity of two random variables; unlike the commonly used minimum mean square error criterion, which uses only second-order statistics, correntropy uses second-order and higher-order information, and is more suitable for systems in the presence of non-Gaussian noise, particularly some heavy-tailed noise disturbances. Furthermore, to reduce the influence of round-off errors, a square-root implementation of the new filter is provided using QR decomposition. Instead of the full covariance matrices, corresponding Cholesky factors are recursively calculated in the square-root filtering algorithm. This is more numerically stable for ill-conditioned problems compared to the conventional filter. Finally, the effectiveness of the proposed algorithms is illustrated via three numerical examples.

Compressor characterization, either by running experiments in a turbocharger test rig or by detailed CFD modelling, can be expensive and time-consuming. In this work, a novel method is proposed which can be used to build a complete compressor map from a reduced number of measured operating points combined with a previously collected database. The methodology is based on the application of the Singular Value Decomposition (SVD) method to acquire the orthonormal bases of a matrix which contains the information of previous compressor observations. These bases are used along with pivoted QR decomposition to obtain the minimum number of measurement points which are required to implement this technique as well as its optimal placement within the map. The reconstruction of two different compressor maps was made to validate the method. The results show a substantially better trade-off between number of testing points and accuracy compared to standard equidistributed sampling.

In compressed sensing theory, the most critical issue is the construction of the observation matrix. The factors that affect the image reconstruction quality include the independence between the observation matrix column vectors and the cross-correlation between the observation matrix and the sparse basis. Based on this, an optimization algorithm is proposed. The algorithm uses QR decomposition to increase the independence of the observation matrix columns, and at the same time optimizes the Gram matrix contracted using an equiangular tight frame(ETF). By updating the direction of each gradient descent, the convergence rate is accelerated to reduce The cross-correlation between the small observation matrix and the sparse basis. Simulation experiment results show that the method of optimizing the observation matrix in this paper has certain advantages in improving the quality and stability of image reconstruction under the same signal sparsity or observation times.

A linear transformation from vector space to another vector space can be represented as a matrix. This close relationship between the matrix and the linear transformation is helpful for the study of matrices. In this paper, the tensor is regarded as a generalization of the matrix from the viewpoint of the linear transformation instead of the quadratic form in matrix theory; we discuss some operations and present some definitions and theorems related to tensors. For example, we provide the definitions of the triangular form and the eigenvalue of a tensor, and the theorems of the tensor QR decomposition and the tensor singular value decomposition. Furthermore, we explain the significance of our definitions and their differences from existing definitions.

The paper presents a rigorous perturbation analysis of the QR decomposition A=QR of an n×m matrix A using the method of splitting operators. New asymptotic componentwise perturbation bounds are derived for the elements of Q and R and the subspaces spanned by the first p≤m columns of A. The new bounds are less conservative than the known bounds and are significantly better than the normwise bounds. An iterative scheme is proposed to determine global componentwise bounds in the case of perturbations for which such bounds are valid. Several numerical results are given that illustrate the analysis and the quality of the bounds obtained.

Response functions completely define the constitutive equations for a hyperelastic material. A strain measure providing an orthogonal stress response, grants response functions directly from experimental curves. One of these strain measures is the Laplace stretch based on QR-decomposition of the deformation gradient. Such a recovery of response functions from experimental data fits the paradigm of data-driven modeling. The set of independent conjugate stress–strain base pairs were proposed as a simple alternative for constitutive modeling and thus might be efficient for data-driven modeling. In the present paper we explore applicability of the conjugate pairs approach for data-driven modeling. The analysis is based on representation of the conjugate pairs in terms of the response functions due to the Laplace stretch. Our analysis shows that one can not guarantee independence of these pairs except in the case of infinitesimal strain.

A synthesis method for orthogonal beam-forming networks (BFNs) with arbitrary N inputs and N outputs is presented. Compared to those formerly developed, the new method allows the design of a BFN in order to not only generate arbitrary N orthogonal beams and N inputs but also to make the 180° hybrids less. This skill is obtained by means of a new approach to decompose the matrices $Q_{1}$ and $Q_{2}$ which are mentioned by Sodin. The solution of such a design problem can be carried out by applying $QR$ decomposition based on Givens transformations. Such a design method also takes into account the computer programming realization. Numerical results are obtained through the commercial simulator to prove the correctness of the method. The ease, accuracy, and efficiency of this synthesis method for the design of BFN make it very useful in modern applications of multi-beam antenna arrays.

Partial least squares (PLS) and linear regression methods have been widely utilized for quality-related fault detection in industrial processes recently. Since these traditional approaches assume that process variables follow Gaussian distribution approximately, their effectiveness will be challenged when facing non-Gaussian processes. To deal with this difficulty, a new quality relevant process monitoring approach based on improved independent component regression (IICR) is presented in this article. Taking high-order statistical information into account, ICA is performed onto process data to produce independent components (ICs). In order to remove irrelevant variation orthogonal to quality variable and keep as much quality-related fault information as possible, a new quality-related independent components selection method is applied to these ICs. Then the regression relationship between filtered ICs and the product quality is built. QR decomposition for regression coefficient matrix is able to give out quality-related and quality-unrelated projectors. After the measured variable matrix is divided into quality relevant and quality irrelevant parts, novel monitoring indices are designed for fault detection. finally, applications to two simulation cases testify the effectiveness of our proposed quality-related fault detection method for non-Gaussian processes.

This paper deals with the optimization of the CORDIC-based modified Gram-Schmidt (MGS) algorithm for QR decomposition (QRD) and presents a scalable algorithm with maximum throughput, the least possible latency, and hardware resources. The optimized algorithm is implemented on Xilinx Virtex 6 FPGA using ISE software as a fixed point with selected accuracy based on the results of MATLAB simulation. Using the loop unrolling technique with different coefficients, an attempt is made to reduce the latency and increase the throughput. In contrast, increasing the unrolling factor leads to a decrease in the frequency of the CORDIC unit as well as a decrease in the number of resources. As a result, there is a trade-off between the unrolling factor and the frequency of the CORDIC unit. By investigating the different unrolling factors, it is shown that the loop unrolling technique with a factor of 4 has the highest throughput with the value of 5.777 MQRD/s and the lowest latency with the value of 173 ns. Moreover, it is shown that throughput and latency are improved by 42.52% and 73.74% respectively compared to the not optimized case. The proposed method is also scalable for different sizes of m×m complex channel matrices, where log2 m ∈ N.

Multiple Input Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) are considered to be major methods for the ensuing high performance in next generation mobile communications. The undesirable effects on the transmitted signals need to be addressed and eliminated to improve the capacity of the systems. These effects depend on the physical properties of the channel. Hence, there is a need to provide perfect estimate of the channel to counteract these effects and thereby delivering precise base-band processes at the receiving end of the system such as signal demodulation and decoding. In this paper, the channel between multiple antenna elements are investigated and analysed for optimum technique with less complexity and less power requirement to estimate the characteristics of the channel. The bit error rate (BER) and normalised mean square error (NMSE) of the channels in MIMO-OFDM systems are examined for different channel tracking techniques. The simulation results are measured to investigate the working of the system model with different algorithms over Worldwide Interoperability for Microwave Access channel. An efficient QRD method is suggested in this paper based on the available system resources and specifications.

This paper revisits the Bézout, Sylvester, and power-basis matrix representations of the greatest common divisor (GCD) of sets of several polynomials. Furthermore, the present work introduces the application of the QR decomposition with column pivoting to a Bézout matrix achieving the computation of the degree and the coeffcients of the GCD through the range of the Bézout matrix. A comparison in terms of computational complexity and numerical effciency of the Bézout-QR, Sylvester-QR, and subspace-SVD methods for the computation of theGCDof sets of several polynomials with real coeffcients is provided.Useful remarks about the performance of the methods based on computational simulations of sets of several polynomials are also presented.

Aiming at the problem of image tampering and forensic localization,a fragile watermarking algorithm based on chaos sequence and image QR decomposition was proposed to determine the image tampering.The algorithm chooses QR decomposition to decompose the image,embeds the watermark after the image is decomposed,and encrypts the watermark by chaotic system to enhance the security of the watermark.Experimental results show that the algorithm not only improves the security of the location watermarking algorithm,but also can estimate the tampering strength of the tampered area.

Aiming at the problem of image tampering and forensic localization,a fragile watermarking algorithm based on chaos sequence and image QR decomposition was proposed to determine the image tampering.The algorithm chooses QR decomposition to decompose the image,embeds the watermark after the image is decomposed,and encrypts the watermark by chaotic system to enhance the security of the watermark.Experimental results show that the algorithm not only improves the security of the location watermarking algorithm,but also can estimate the tampering strength of the tampered area.

To reduce the computation burden of inverse synthetic aperture radar on rotating targets imaging, a fast distributed simultaneous multiple orthogonal matching pursuit method is proposed in this study. In the proposed method, the distributed matching sparse representation model of the rotating targets is firstly established. Also then the total number of iterations is reduced by using multi-atom recognition strategy. Simultaneously, the computational complexity of each iteration is also reduced through QR decomposition. High-resolution image of the rotating targets can thus be obtained efficiently. Theoretical analysis and simulation results verify the effectiveness of the proposed method.

INTRODUCTION: A rich patient may reside in their home and may prefer to take treatment with all the medical appliances inside the premise. A patient may take treatment in the less specialized hospital, which may be taking a suggestion from a specialist working in a specialized center. In such a scenario, bio-medical images need to be transmitted over the public network. Bio-medical images sent in plain form suffer from confidentiality and integrity problem.OBJECTIVES: To overcome the above problem, medical records are always hidden in the cover image so that others do not know what is being sent.METHODS: In our proposed scheme, the chosen cover image gets divided into the following three planes, namely: R plane, G plane, and B plane. The co-occurrence matrix is computed for each plane by dividing it into 16 x 16 pixels block and then the embedding map is generated from it. The biomedical image is also divided into 8 x 8 pixels block andis hidden into the chosen cover image block using the embedding map. Both cover image blocks and secret image blocks are transformed by RIWT. R matrix of QR decomposition of the cover image and secret image blocks are used in embedding At the end of the embedding phase. These three planes are merged into an RGB image to produce a stego image.RESULTS: To assess the performance of our scheme, parameters like imperceptibility, robustness, and security are considered. With respect to imperceptibility, PSNR values of the stego images are over 50. With respect to robustness, average NCC values between the original secret and the attacked secret is 0.94. With respect to security, stego image cannot be detected easily if it has any secret.CONCLUSION: From the experimental results, our scheme is proved to be better with respect to these three selected parameters.

In this paper, a QR-decomposition-based scheduled belief propagation (BP) detector with interference cancellation (IC) and candidate constraints is proposed for multiple-input multiple-output (MIMO) systems. Based on a bipartite graph generated from an upper triangular channel matrix following linear transformation using QR decomposition, the proposed detector performs a sequential message updating procedure between bit nodes. During this updating procedure, candidate constraints are imposed to restrict the number of possible candidate vectors for the calculation of observation-to-bit messages. In addition, after obtaining the soft message corresponding to the bit sequence in each transmit symbol, a hard-decision IC operation is performed to reduce the size of the bipartite graph and indirectly update the messages for the remaining symbols. Therefore, the proposed scheme provides a huge complexity reduction compared to conventional BP detectors that perform message updating by using all related messages directly. Simulation results confirm that the proposed detector can achieve suboptimum error performance with significantly improved convergence speed and reduced computational complexity compared to conventional BP detectors in MIMO systems.

Internet has affected our everyday life drastically. Expansive volumes of information are exchanged over the Internet consistently which causes numerous security concerns. Issues like content identification, document and image security, audience measurement, ownership, copyrights and others can be settled by using digital watermarking. In this work, robust and imperceptible non-blind color image watermarking algorithm is proposed, which benefit from the fact that watermark can be hidden in different color channel which results into further robustness of the proposed technique to attacks. Given method uses some algorithms such as entropy, discrete wavelet transform, Chirp z-transform, orthogonal-triangular decomposition and Singular value decomposition in order to embed the watermark in a color image. Many experiments are performed using well-known signal processing attacks such as histogram equalization, adding noise and compression. Experimental results show that proposed scheme is imperceptible and robust against common signal processing attacks.

Digital watermarking has been utilized effectively for copyright protection of multimedia contents. This paper suggests a blind symmetric watermarking algorithm using fan beam transform (FBT) and QR decomposition (QRD) for color images. At first, the original image is transferred from RGB to L*a*b* color model and FBT is applied to b* component. Then the b*component of the original image is split into m × m non-overlapping blocks and QRD is conducted to each block. Watermark data is placed into the selected coefficient of the upper triangular matrix using a new embedding function. Simulation results suggest that the presented algorithm is extremely robust against numerous attacks, and also yields watermarked images with high quality. Furthermore, it represents more excellent performance compared with the recent state-of-the-art algorithms for robustness and imperceptibility. The normalized correlation (NC) of the proposed algorithm varies from 0.8252 to 1, the peak signal-to-noise ratio (PSNR) varies from 54.1854 to 54.1892, and structural similarity (SSIM) varies from 0.9285 to 0.9696, respectively. In contrast, the NC of the recent state-of-the-art algorithms varies from 0.5193 to 1, PSNR varies from 38.5471 to 52.64, and SSIM varies from 0.9311 to 0.9663, respectively.

Compressive sensing is a very attractive technique to detect weak signals in a noisy background, and to overcome limitations from traditional Nyquist sampling. A very important part of this approach is the measurement matrix and how it relates to hardware implementation. However, reconstruction accuracy, resistance to noise and construction time are still open challenges. To address these problems, we propose a measurement matrix based on a cyclic direct product and QR decomposition (the product of an orthogonal matrix Q and an upper triangular matrix R). Using the definition and properties of a direct product, a set of high-dimensional orthogonal column vectors is first established by a finite number of cyclic direct product operations on low-dimension orthogonal “seed„ vectors, followed by QR decomposition to yield the orthogonal matrix, whose corresponding rows are selected to form the measurement matrix. We demonstrate this approach with simulations and field measurements of a scaled submarine in a freshwater lake, at frequencies of 40 kHz⁻80 kHz. The results clearly show the advantage of this method in terms of reconstruction accuracy, signal-to-noise ratio (SNR) enhancement, and construction time, by comparison with Gaussian matrix, Bernoulli matrix, partial Hadamard matrix and Toeplitz matrix. In particular, for weak signals with an SNR less than 0 dB, this method still achieves an SNR increase using less data.

The last ten years have seen performance and power requirements pushing computer architectures using only a single core towards so-called manycore systems with hundreds of cores on a single chip. To further increase performance and energy efficiency, we are now seeing the development of heterogeneous architectures with specialized and accelerated cores. However, designing these heterogeneous systems is a challenging task due to their inherent complexity. We proposed an approach for designing domain-specific heterogeneous architectures based on instruction augmentation through the integration of hardware accelerators into simple cores. These hardware accelerators were determined based on their common use among applications within a certain domain.The objective was to generate heterogeneous architectures by integrating many of these accelerated cores and connecting them with a network-on-chip. The proposed approach aimed to ease the design of heterogeneous manycore architectures—and, consequently, exploration of the design space—by automating the design steps. To evaluate our approach, we enhanced our software tool chain with a tool that can generate accelerated cores from dataflow programs. This new tool chain was evaluated with the aid of two use cases: radar signal processing and mobile baseband processing. We could achieve an approximately 4 × improvement in performance, while executing complete applications on the augmented cores with a small impact (2.5–13%) on area usage. The generated accelerators are competitive, achieving more than 90% of the performance of hand-written implementations.

A novel antenna subset selection in V-BLAST systems with ordered successive interference cancellation （OSIC） detection was presented. With an eye to the impacts of ordering and cancellation, by use of permutation and QR decom-position, a terse antenna subset selection mathematic expression with reasonable complexity for V-BLAST was put for-ward. Simulation results show that the proposed selection criterion achieves near-optimal outage capacity and outper-forms several existing ones in performance.

An iterative QR-based soft feedback segment interference cancellation (QRSFSIC) detection and decoder algorithm for a Reed–Muller (RM) space-time turbo system is proposed in this paper. It forms the sufficient statistic for the minimum-mean-square error (MMSE) estimate according to QR decomposition-based soft feedback successive interference cancellation, stemmed from the a priori log-likelihood ratio (LLR) of encoded bits. Then, the signal originating from the symbols of the reliable segment, the symbol reliability metric, in terms of an a posteriori LLR of encoded bits which is larger than a certain threshold, is iteratively cancelled with the QRSFSIC in order to further obtain the residual signal for evaluating the symbols in the unreliable segment. This is done until the unreliable segment is empty, resulting in the extrinsic information for a RM turbo-coded bit with the greatest likelihood. Bridged by de-multiplexing and multiplexing, an iterative QRSFSIC detection is concatenated with an iterative trellis-based maximum a posteriori probability RM turbo decoder as if a principal Turbo detection and decoder is embedded with an iterative subordinate QRSFSIC detection and a RM turbo decoder, exchanging each other’s detection and decoding soft-decision information iteratively. These three stages let the proposed algorithm approach the upper bound of the diversity. The simulation results also show that the proposed scheme outperforms the other suboptimum detectors considered in this paper.

Abstract Multiple-input multiple-output (MIMO) systems have an advantage of spectral efficiency compared to single-input single-output systems, which means that the MIMO systems have significantly higher data throughput. The V-BLAST (Vertical Bell Laboratories Layered Space Time) scheme is a popular transceiver structure which has relatively good performance. In the V-BLAST scheme, ordered successive interference cancellation (OSIC) technique was proposed as a possible efficient detection method in terms of performance and complexity. However, MIMO systems suffer from high complexity and implementation cost. As a practical solution, a technique called antenna selection has been introduced. Since the existing literature considered only the capacity-based selection, we develop an optimal selection method for V-BLAST scheme using OSIC detection with respect to error rate performance in this article. Its complexity is shown to be proportional to the fourth power of the number of transmit antennas. To reduce the complexity without significant performance degradation compared to the optimal selection method, a near-optimal selection method is also proposed. Simulation results show that the proposed selection method is very close to the performance of optimal selection.