Search Results (657)

The emergence of 5G technology promises remarkable advancements in wireless communication, particularly in the realm of mmWave (millimeter-wave) massive multiple input multiple output (m-MIMO) systems. However, the realization of its full potential is hindered by the challenge of pilot overhead, which compromises system efficiency. The efficient usage of pilot signals is crucial for precise channel estimation and interference reduction to maintain data integrity. Nevertheless, this requirement brings up the challenge of pilot overhead, which utilizes precious spectrum space, thus reducing spectral efficiency (SE). To address this obstacle, researchers have progressively turned to artificial intelligence (AI) and machine learning (ML) methods to design hybrid beam-forming systems that enhance SE while reducing changes to the bit error rate (BER). This study addresses the challenge of pilot overhead in hybrid beamforming for 5G mmWave m-MIMO systems by leveraging advanced artificial intelligence (AI) techniques. We propose a framework integrating k-clustering, linear regression, random forest regression, and neural networks with singular value decomposition (NN-SVD) to optimize pilot placement and hybrid beamforming strategies. The results demonstrate an 82% reduction in pilot overhead, a 250% improvement in spectral efficiency, and a tenfold enhancement in bit error rate at low SNR conditions, surpassing state-of-the-art methods. These findings validate the efficacy of the proposed system in advancing next-generation wireless networks. Full article

Machine learning (ML) technologies are currently widely used in many domains of science and technology, to discover models that transform input data into output data. The main advantages of such a procedure are the generality and simplicity of the learning process, while their weaknesses remain the required amount of data needed to perform the training and the recurrent difficulties to explain the involved rationale. At present, a panoply of ML techniques exist, and the selection of a method or another depends, in general, on the type and amount of data being considered. This paper proposes a procedure which provides not a field or an image as an output, but its singular value decomposition (SVD), or an SVD-like decomposition, while injecting as input data scalars or the SVD decomposition of an input field. The result is a tensor-to-tensor decomposition, without the need for the full fields, or an input to an output SVD-like decomposition. The proposed method works for the non-hyper-parallepipedic domain, and for any space dimensionality. The results show the ability of the proposed architecture to link the input filed and output field, without requiring access to full space reconstruction. Full article

Neural networks have become a cornerstone of computer vision applications, with tasks ranging from image classification to object detection. However, challenges such as hyperparameter optimization (HPO) and model compression remain critical for improving performance and deploying models on resource-constrained devices. In this work, we address these challenges using Tensor Network-based methods. For HPO, we propose and evaluate the TetraOpt algorithm against various optimization algorithms. These evaluations were conducted on subsets of the NATS-Bench dataset, including CIFAR-10, CIFAR-100, and ImageNet subsets. TetraOpt consistently demonstrated superior performance, effectively exploring the global optimization space and identifying configurations with higher accuracies. For model compression, we introduce a novel iterative method that combines CP, SVD, and Tucker tensor decompositions. Applied to ResNet-18 and ResNet-152, we evaluated our method on the CIFAR-10 and Tiny ImageNet datasets. Our method achieved compression ratios of up to $14.5\times$ for ResNet-18 and $2.5\times$ for ResNet-152. Additionally, the inference time for processing an image on a CPU remained largely unaffected, demonstrating the practicality of the method. Full article

Partial discharge (PD) detection plays an important role in online condition monitoring of electrical equipment and power cables. However, the noise of PD measurement will significantly reduce the performance of the detection algorithm. In this paper, we focus on the study of a PD noise reduction algorithm based on improved singular value decomposition (SVD) and multivariate variational mode decomposition (MVMD) for white Gaussian noise (WGN) and periodic narrowband interference signal noise. The specific noise reduction algorithm is divided into three noise reduction processes: The first noise reduction completes the suppression of narrowband interference in the noisy PD signal by the SVD algorithm with the guidance signal. The guidance signal is composed of a sinusoidal signal of the accurately estimated narrowband interference frequency component, and the amplitude is twice the maximum amplitude of the noisy PD signal. The second noise reduction decomposes the noisy PD signal after filtering the narrowband interference signal into k optimal intrinsic mode function by the MVMD after parameter optimization. By calculating the kurtosis value of each intrinsic mode function, it is determined whether it is the PD dominant component or the noise dominant component, and the noise dominant component is subjected to 3σ filtering to obtain the reconstructed PD signal. The third noise reduction uses a new wavelet threshold algorithm to denoise the reconstructed PD signal to obtain the denoised PD signal. The overall noise reduction algorithm proposed in this paper is compared with some existing methods. The results show that this method has a good effect on reducing the noise of PD signals measured in simulation and field. Full article

The cluster number can directly affect the clustering effect and its application in real-world scenarios. Its determination is one of the key issues in cluster analysis. According to singular value decomposition (SVD), the characteristic directions of larger singular values likely represent the primary data patterns, trends, or structures corresponding to the main information. In clustering analysis, the main information and structure are likely related to the cluster structure itself. The number of larger singular values may correspond to the number of clusters, and their main information may correspond to different clusters. Based on this, a singular-value-based cluster number detection method is proposed. First, the transferred K-nearest neighbors (TKNN) density formula is proposed to address the limitation of the DPC algorithm in failing to identify centroids in sparse clusters of unbalanced datasets. Second, core data are selected by the DPC algorithm with a modified density formula to better capture the data distribution. Third, based on the selected core data, a sparse similarity matrix is constructed to further highlight the relationships between data and enhance the distribution of data features. Finally, SVD is performed on the sparse similarity matrix to obtain singular values, the cumulative contribution rate is introduced to determine the number of relatively large singular values (i.e., the cluster number). Experimental results show that our method is superior in determining the cluster number for datasets with complex shapes. Full article

In this paper, we propose automation of estimating scattering media information in peplography using singular value decomposition (SVD) and discrete cosine transform (DCT). Conventional scattering media-removal methods reduce light scattering in images utilizing a variety of image-processing techniques and machine learning algorithms. However, under conditions of heavy scattering media, they may not clearly visualize the object information. Peplography has been proposed as a solution to this problem. Peplography is capable of visualizing the object information by estimating the scattering media information and detecting the ballistic photons from heavy scattering media. Following that, 3D information can be obtained by integral imaging. However, it is difficult to apply this method to real-world situations since the process of scattering media estimation in peplography is not automated. To overcome this problem, we use automatic scattering media-estimation methods using SVD and DCT. They can estimate the scattering media information automatically by truncating the singular value matrix and Gaussian low-pass filter in the frequency domain. To evaluate our proposed method, we implement the experiment with two different conditions and compare the result image with the conventional method using metrics such as structural similarity (SSIM), feature similarity (FSIMc), gradient magnitude similarity deviation (GMSD), and learned perceptual image path similarity (LPIPS). Full article

Software-defined radio (SDR) has high flexibility and low cost. It conforms to the miniaturization, lightweight, and digitization trends in through-wall radar systems. Stepped frequency continuous waveform (SFCW) is commonly used in through-wall radar, which has high resolution and strong anti-interference ability. This article develops an SFCW through-wall radar system based on a dual-channel SDR platform. Without changing hardware structure and complicated accessories, a phase compensation method of solving the phase incoherence problem in a low-cost dual-channel SDR platform is proposed. In addition, this article proposes a wall clutter mitigation approach by means of singular value decomposition (SVD) and principal component analysis (PCA) framework for through-wall applications. This approach can process the wall clutter and noise efficiently, and then extract the target subspace to obtain location information. The experimental results indicate that the proposed windowing-based SVD-PCA approach is effective for the developed radar system, which can ensure the accuracy of through-wall detection. It is also superior to the traditional methods in terms of the image quality of range profiles or signal-to-noise ratio (SNR). Full article

Background: The mechanisms underlying extrapyramidal disorders and their anatomical substrate have been extensively investigated. Recently, the role of the claustrum in Parkinson’s disease and other neurodegenerative conditions has been better detailed. The main aim of this review was to summarize the supporting evidence for the role of the claustrum in degenerative and vascular parkinsonism. Methods: The anatomy, biology, vascular supply, and connections of the claustrum in humans were identified and described, providing the substrate for the vascular involvement of the claustrum in large- and small-vessel disease. The vascular supply of the claustrum includes up to three different sources from a single artery, the middle cerebral artery, and it is known as territory with an intermediate hemodynamic risk. The connections of the claustrum make it a sensory integrator and a relevant point in several networks, from consciousness to movement planning. Conclusions: The claustrum is still an incompletely explained structure. However, recent description of its multiple connections indicate that it is involved in several diseases, including Parkinson’s disease. The evidence underlying its potential role in vascular parkinsonism is still scarce, but it might be a field warranting future investigations. Full article

This study investigates the classification of damage modes in adhesively bonded carbon fiber-reinforced plastic (CFRP) composites, a critical factor in advancing lightweight automotive design. Adhesive bonding, replacing traditional riveting, improves structural integrity while reducing weight and CO₂ emissions. Mechanical testing on CFRP composites was performed, and acoustic emission (AE) signals were collected to evaluate damage mechanisms. A deep autoencoder (DAE) framework was developed to automate damage characterization by reducing AE signal dimensionality through singular value decomposition (SVD) and classifying features using the k-means algorithm. This approach effectively identified three primary damage modes: matrix cracking, interfacial debonding, and fiber breakage. Traditional AE features, such as entropy and amplitude were also classified and validated using spectral analysis. The DAE-based strategy demonstrated superior capability in real-time damage mode differentiation. Fractographic analysis confirmed crack growth in the adhesive layer, leading to interfacial debonding, fiber-matrix separation, and eventual fiber rupture. These findings highlight the DAE framework’s effectiveness in enhancing damage mode characterization, offering valuable insights for optimizing the structural performance of bonded CFRP composites in automotive applications. Full article

Solar-induced chlorophyll fluorescence (SIF) is essential for monitoring vegetation photosynthesis. The water vapor absorption band, the broadest absorption window, has a deeper absorption line than the O₂-B band, providing significant potential for SIF retrieval; however, substantial variation in atmospheric water vapor column concentrations limits research on SIF retrieval using this band. This study evaluates seven common SIF retrieval algorithms, including sFLD, 3FLD, iFLD, pFLD, SFM, SVD, and DOAS, using simulated datasets under varying atmospheric water vapor concentrations, spectral resolution (SR), and signal-to-noise ratios (SNRs). Additionally, the SIF retrieval results from the H₂O, O₂-B, and O₂-A absorption bands are compared and analyzed to explore the fluorescence retrieval potential of the water vapor band. Furthermore, the potential of commonly used spectrometers, including Ocean Optics QE Pro and ASD FieldSpec 3, for SIF retrieval using the water vapor absorption band was evaluated. The results were further validated using ground-based tower observations. The results show that sFLD consistently overestimates SIF in the water vapor band, limiting its reliability, while SFM performs best across varying conditions. In comparison, 3FLD and pFLD, along with SVD, are accurate at high resolutions but less effective at lower ones. iFLD performs relatively poorly overall, whereas DOAS excels in low SR retrievals. At the same time, our study also shows that the water vapor band offers higher accuracy in ground-based SIF retrieval compared to the O₂-B band, demonstrating strong application potential and providing valuable references for selecting SIF retrieval algorithms. Full article

The estimation of complex natural frequencies in linear systems through their transient response analysis is a common practice in engineering and applied physics. In this context, the conventional Generalized Pencil of Function (GPOF) method that employs a matrix pencil of degree one, utilizing singular value decomposition (SVD) filtering, has emerged as a prominent strategy to carry out a complex natural frequency estimation. However, some modern engineering applications increasingly demand higher accuracy estimation. In this context, some intrinsic properties of Hankel matrices and exponential functions are utilized in this paper in order to develop a modified GPOF method which employs a matrix pencil of degree greater than one. Under conditions of low noise in the transient response, our method significantly enhances accuracy compared to the conventional GPOF approach. This improvement is especially valuable for applications involving closely spaced complex natural frequencies, where a precise estimation is crucial. Full article

The wear condition of a piston pin is a main factor in determining the operational continuity and life cycle of a diesel engine; identifying its vibration feature is of paramount importance in carrying out necessary maintenance in the early wear stage. As the dynamic vibration features are susceptible to environmental disturbance during operation, an effective signal processing method is necessary to improve the accuracy and fineness of the extracted features, which is essential to build a reliable and precise binary classifier model to identify piston pin wear based on the features. Aiming at the feature extraction requirements of anti-noise, accuracy and effectiveness, this paper proposes a piston pin wear feature extraction algorithm based on dynamic principal component analysis (DPCA) combined with variational mode decomposition (VMD) and singular value decomposition (SVD). An orthogonal sensor layout is applied to collect the vibration signal under normal and worn piston pin conditions, which proved effective in reducing environmental vibration disturbance. DPCA is utilized to extract dynamical vibration features by introducing time lag. Then, the dynamic principal component matrix is further decomposed by VMD to obtain intrinsic mode functions (IMFs) as finer features and is finally decomposed by SVD to compress the features, thus improving the classification efficiency based on the features. To validate the significance of the features extracted by the proposed method, a support vector machine (SVM) is employed to model binary classifiers to evaluate the classification performance trained by different features. A modeling dataset containing 80 samples (40 normal samples and 40 worn samples) is employed, and five-round cross-validation is adopted. For each round, two binary classifier models are trained by features extracted by the proposed method and the empirical mode decomposition (EMD)–auto regressive (AR) spectrum method, fast Fourier transform (FFT) and continuous wavelet transform (CWT), respectively; the classification precision, recall ratio, accuracy and F1 ratio are obtained on the testing set by contrasting the overall performances of the five-round cross-validation, and the proposed method is proved to be more effective in noise reduction and significant feature extraction, which is able to improve the accuracy and efficiency of binary classification for piston pin wear identification. Full article

In this paper, a state estimation method of distributed electric drive articulated vehicle dynamics parameters based on the forgetting factor unscented Kalman filter with singular value decomposition (SVD-UKF) is proposed. The 7DOF nonlinear dynamics model of a distributed electric drive articulated vehicle is established. The unscented Kalman filter algorithm is the foundation, with singular value decomposition replacing the Cholesky decomposition. A forgetting factor is introduced to dynamically adapt the observation noise covariance matrix in real time, resulting in a centralized parameter state estimator for the articulated vehicle. The proposed parameter state estimation method based on the forgetting factor SVD-UKF is simulated and compared with the unscented Kalman filter (UKF) estimation method. Key dynamic parameters are estimated, such as the lateral and longitudinal velocities and accelerations, angular velocity, articulated angle, wheel speeds, and longitudinal and lateral tire forces of both the front and rear vehicle bodies. The results show that the proposed forgetting factor SVD-UKF method outperforms the traditional UKF method. Furthermore, a prototype vehicle test is conducted to compare the estimated values of various dynamic parameters with the actual values, demonstrating minimal errors. This verifies the effectiveness of the proposed dynamic parameter estimation method for articulated vehicles. Full article

Aiming to address the difficulty in extracting the early weak fault features of bearings under complex operating conditions, a fault diagnosis method is proposed based on the adaptive fusion of time-varying filtering empirical mode decomposition (TVF-EMD) modal components and singular value decomposition (SVD) noise reduction. First, the snake optimization (SO) technique is used to optimize the TVF-EMD algorithm in order to determine the optimal parameters that match the input signal. Then, the bearing signal is divided into a number of intrinsic mode functions (IMFs) using TVF-EMD in order to reduce the nonlinearity and non-stationary characteristics of the fault signal. An index for the envelope fault information energy ratio (EFIER) is created to overcome the drawback of there being too many IMF components after TVF-EMD decomposition. The IMF components are ranked in descending order according to the EFIER, and they are fused according to the maximum principle of the energy ratio of envelope fault information until the optimal fusion component is determined. Finally, the fault feature is extracted when the optimal fusion component is denoised using SVD. Two measured bearing fault signals and simulation signals are used to validate the performance of the proposed method. The experimental findings demonstrate that the approach has good sensitive feature screening, fusion, and noise reduction capabilities. The proposed method can more precisely extract the early fault features of bearings and accurately identify fault types. Full article

A novel method for the direction of arrival (DOA) estimation of coherent signals under a space–time–coding metasurface (STCM) is proposed in this paper. Noticeably, the STCM can replace multi-channel arrays with a single channel, which can be utilized to modulate incident electromagnetic waves and generate harmonics. However, coherent signals are overlapping in the frequency spectrum and cannot achieve DOA estimation through subspace methods. Therefore, the proposed method transforms the angle information in the time domain into amplitude and phase information at harmonics in the frequency domain by modulating incident coherent signals using the STCM and performing a fast Fourier transform (FFT) on these signals. Based on the harmonics in the frequency spectrum of the coherent signals, appropriate harmonics are selected. Finally, the ℓ₁ norm singular value decomposition (ℓ₁-SVD) algorithm is utilized for achieving high-precision DOA estimation. Simulation experiments are conducted to show the performance of the proposed method under the condition of different incident angles, harmonic numbers, signal-to-noise ratios (SNRs), etc. Compared to the traditional algorithms, the performance of the proposed algorithm can achieve more accurate DOA estimation under a low SNR. Full article