Journal articles: 'Multi-dimensional signals'

1

Sommer, Gerald, and Di Zang. "Parity symmetry in multi-dimensional signals." Communications on Pure & Applied Analysis 6, no. 3 (2007): 829–52. http://dx.doi.org/10.3934/cpaa.2007.6.829.

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Aiazzi, Bruno, Stefano Baronti, Leonardo Santurri, Massimo Selva, and Luciano Alparone. "Information-theoretic assessment of multi-dimensional signals." Signal Processing 85, no. 5 (May 2005): 903–16. http://dx.doi.org/10.1016/j.sigpro.2004.11.025.

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Heumann, Tibor. "An ascending auction with multi-dimensional signals." Journal of Economic Theory 184 (November 2019): 104938. http://dx.doi.org/10.1016/j.jet.2019.104938.

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KIDA, TAKURO. "THEORY OF GENERALIZED INTERPOLATORY APPROXIMATION OF MULTI-DIMENSIONAL SIGNALS." Journal of Circuits, Systems and Computers 03, no. 03 (September 1993): 673–99. http://dx.doi.org/10.1142/s0218126693000411.

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In this paper, we present a systematic theory of the optimum subband interpolation of a family of n-dimensional signals which are not necessarily band-limited. We assume that the Fourier spectrums of these signals have weighted L2 norms smaller than a given positive number. The proposed method minimizes the measure of error which is equal to the envelope of the approximation errors with respect to the signals. In the following discussion, we assume initially that the infinite number of interpolation functions with different functional forms are used in the corresponding approximation formula. However, the resultant optimum interpolation functions are expressed as the parallel shifts of the finite number of the n-dimensional functions. It should be noted that the optimum interpolation functions presented in this paper satisfy the generalized discrete orthogonality and, as a result, minimize the wide variety of measures of error at the same time. In the literature,6 a similar discussion is presented. However, it is assumed that the signal is band-limited and the interpolation functions are compulsorily time-limited. Hence, these interpolation functions cannot minimize other measures of error except the proposed one. Interesting reciprocal relation in the approximation, is also discussed. An equivalent expression of the approximation formula in the frequency domain is derived.

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Elvander, F., J. Swärd, and A. Jakobsson. "Multi-dimensional grid-less estimation of saturated signals." Signal Processing 145 (April 2018): 37–47. http://dx.doi.org/10.1016/j.sigpro.2017.11.008.

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Heumann, Tibor. "Efficiency in trading markets with multi-dimensional signals." Journal of Economic Theory 191 (January 2021): 105156. http://dx.doi.org/10.1016/j.jet.2020.105156.

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BUCHHOLZ, SVEN, and NICOLAS LE BIHAN. "POLARIZED SIGNAL CLASSIFICATION BY COMPLEX AND QUATERNIONIC MULTI-LAYER PERCEPTRONS." International Journal of Neural Systems 18, no. 02 (April 2008): 75–85. http://dx.doi.org/10.1142/s0129065708001403.

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For polarized signals, which arise in many application fields, a statistical framework in terms of quaternionic random processes is proposed. Based on it, the ability of real-, complex- and quaternionic-valued multi-layer perceptrons (MLPs) of performing classification tasks for such signals is evaluated. For the multi-dimensional neural networks the relevance of class label representations is discussed. For signal to noise separation it is shown that the quaternionic MLP yields an optimal solution. Results on the classification of two different polarized signals are also reported.

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Demuro, Angelo, and Ian Parker. "Multi-dimensional resolution of elementary Ca2+ signals by simultaneous multi-focal imaging." Cell Calcium 43, no. 4 (April 2008): 367–74. http://dx.doi.org/10.1016/j.ceca.2007.07.002.

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Wang, Guotai, Xingguang Geng, Lin Huang, Xiaoxiao Kang, Jun Zhang, Yitao Zhang, and Haiying Zhang. "Multi-Morphological Pulse Signal Feature Point Recognition Based on One-Dimensional Deep Convolutional Neural Network." Information 14, no. 2 (January 26, 2023): 70. http://dx.doi.org/10.3390/info14020070.

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Radial pulse signals are produced by the periodic ejection of blood from the heart, and physiological and pathological information of the human body can be analyzed by extracting the time-domain characteristics of pulse waves. However, since pulse signals are weak physiological signals on the body surface and complex, the acquisition of pulse characteristics using the traditional curvature method will produce a large error, which cannot meet the needs of pulse wave analysis in current clinical practice. To solve this problem, a multi-morphological pulse signal feature recognition algorithm based on the one-dimensional deep convolutional neural network (1D-DCNN) model is proposed. We used the multi-channel pulse diagnosis instrument independently developed by the team to collect radial pulse signals under continuous pressure of the test subjects and collected 115 subjects and extracted a total of 1300 single-cycle pulse signals and then divided these pulse signals into 6 different forms. Five types of pulse signal time-domain feature points were labeled, and five independent feature point datasets were labeled and formed five customized neural network models that were generated to train and identify the pulse feature point datasets independently. The results show that the correction coefficient () of the multi-class pulse signal processing algorithm proposed in this paper for each type of feature point recognition reaches more than 0.92. The performance is significantly better than that of the traditional curvature method, which shows the accuracy and superiority of the proposed method. Therefore, the multi-class pulse signal characteristic parameter recognition model based on the 1D-DCNN model proposed in this paper can efficiently and accurately identify pulse time-domain characteristic parameters, which can be applied to discriminate time-domain pulse information in clinical practice and assist doctors in diagnosis.

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Xie, Shengkun. "Wavelet Power Spectral Domain Functional Principal Component Analysis for Feature Extraction of Epileptic EEGs." Computation 9, no. 7 (July 7, 2021): 78. http://dx.doi.org/10.3390/computation9070078.

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Feature extraction plays an important role in machine learning for signal processing, particularly for low-dimensional data visualization and predictive analytics. Data from real-world complex systems are often high-dimensional, multi-scale, and non-stationary. Extracting key features of this type of data is challenging. This work proposes a novel approach to analyze Epileptic EEG signals using both wavelet power spectra and functional principal component analysis. We focus on how the feature extraction method can help improve the separation of signals in a low-dimensional feature subspace. By transforming EEG signals into wavelet power spectra, the functionality of signals is significantly enhanced. Furthermore, the power spectra transformation makes functional principal component analysis suitable for extracting key signal features. Therefore, we refer to this approach as a double feature extraction method since both wavelet transform and functional PCA are feature extractors. To demonstrate the applicability of the proposed method, we have tested it using a set of publicly available epileptic EEGs and patient-specific, multi-channel EEG signals, for both ictal signals and pre-ictal signals. The obtained results demonstrate that combining wavelet power spectra and functional principal component analysis is promising for feature extraction of epileptic EEGs. Therefore, they can be useful in computer-based medical systems for epilepsy diagnosis and epileptic seizure detection problems.

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Kuzmin, M. S., V. V. Davydov, and S. A. Rogov. "On the use of a multi-raster input of one-dimensional signals in two-dimensional optical correlators." Computer Optics 43, no. 3 (June 2019): 391–96. http://dx.doi.org/10.18287/2412-6179-2019-43-3-391-396.

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A mathematical description of a coherent optical correlator for the multi-raster input of long signals is given. It is shown that such an input makes it possible to reduce the value of false correlation maxima that are generally found at the output of a correlator with a single-raster input. It is shown that false maxima do not appear when processing signals with a thumbtack ambiguity function, allowing one to do without a multi-raster input. The results of the theoretical analysis are confirmed by experiments with chirp signals and M-sequence type signals.

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Ghani, Hadhrami Ab, Mohamad Razwan Abdul Malek, Muhammad Fadzli Kamarul Azmi, Muhammad Jefri Muril, and Azizul Azizan. "A review on sparse fast fourier transform applications in image processing." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (April 1, 2020): 1346. http://dx.doi.org/10.11591/ijece.v10i2.pp1346-1351.

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Fast Fourier Transform has long been established as an essential tool in signal processing. To address the computational issues while helping the analysis work for multi-dimensional signals in image processing, sparse Fast Fourier Transform model is reviewed here when applied in different applications such as lithography optimization, cancer detection, evolutionary arts and wasterwater treatment. As the demand for higher dimensional signals in various applications especially multimedia appplications, the need for sparse Fast Fourier Transform grows higher.

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Chen, Hanxin, Shaoyi Li, and Menglong Li. "Multi-Channel High-Dimensional Data Analysis with PARAFAC-GA-BP for Nonstationary Mechanical Fault Diagnosis." International Journal of Turbomachinery, Propulsion and Power 7, no. 3 (June 28, 2022): 19. http://dx.doi.org/10.3390/ijtpp7030019.

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Conventional signal processing methods such as Principle Component Analysis (PCA) focus on the decomposition of signals in the 2D time–frequency domain. Parallel factor analysis (PARAFAC) is a novel method used to decompose multi-dimensional arrays, which focuses on analyzing the relevant feature information by deleting the duplicated information among the multiple measurement points. In the paper, a novel hybrid intelligent algorithm for the fault diagnosis of a mechanical system was proposed to analyze the multiple vibration signals of the centrifugal pump system and multi-dimensional complex signals created by pressure and flow information. The continuous wavelet transform was applied to analyze the high-dimensional multi-channel signals to construct the 3D tensor, which makes use of the advantages of the parallel factor decomposition to extract feature information of the complex system. The method was validated by diagnosing the nonstationary failure modes under the faulty conditions with impeller blade damage, impeller perforation damage and impeller edge damage. The correspondence between different fault characteristics of a centrifugal pump in a time and frequency information matrix was established. The characteristic frequency ranges of the fault modes are effectively presented. The optimization method for a PARAFAC-BP neural network is proposed using a genetic algorithm (GA) to significantly improve the accuracy of the centrifugal pump fault diagnosis.

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Du, Jinzhi, Weijia Cui, Bin Ba, Chunxiao Jian, and Liye Zhang. "Joint Estimation for Time Delay and Direction of Arrival in Reconfigurable Intelligent Surface with OFDM." Sensors 22, no. 18 (September 19, 2022): 7083. http://dx.doi.org/10.3390/s22187083.

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Recently, the joint estimation for time delay (TD) and direction of arrival (DOA) has suffered from the high complexity of processing multi-dimensional signal models and the ineffectiveness of correlated/coherent signals. In order to improve this situation, a joint estimation method using orthogonal frequency division multiplexing (OFDM) and a uniform planar array composed of reconfigurable intelligent surface (RIS) is proposed. First, the time-domain coding function of the RIS is combined with the multi-carrier characteristic of the OFDM signal to construct the coded channel frequency response in tensor form. Then, the coded channel frequency response covariance matrix is decomposed by CANDECOMP/PARAFAC (CPD) to separate the signal subspaces of TD and DOA. Finally, we perform a one-dimensional (1D) spectral search for TD values and a two-dimensional (2D) spectral search for DOA values. Compared to previous efforts, this algorithm not only enhances the adaptability of coherent signals, but also greatly decreases the complexity. Simulation results indicate the robustness and effectiveness for the proposed algorithm in independent, coherent, and mixed multipath environments and low signal-to-noise ratio (SNR) conditions.

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Djamal, Esmeralda Contessa, and Dimas Andhika Sury. "Multi-channel of electroencephalogram signal in multivariable brain-computer interface." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (June 1, 2023): 618. http://dx.doi.org/10.11591/ijai.v12.i2.pp618-626.

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Brain-computer interface (BCI) usually uses Electroencephalogram (EEG) signals as an intermediate device to drive external devices directly from the brain. The development of BCI capabilities is carried out by involving multivariable EEG signals as movement commands. EEG signals are recorded using multi-channel, enriching information if it uses the suitable method and architecture. This research proposed a two-dimensional convolutional neural networks (CNN) method to recognize multi-channel EEG signals. The vertical dimension is the channel, while the horizontal is the signal sequence. Hence, the signal is connected with the information time series of the same channel and between channels simultaneously. BCI was arranged with multivariable signals, specifically motor imagery and emotion. Both variables have different characteristics, and the information is from different channels. Therefore, it needs multiple CNNs to recognize the two variables in the EEG signal. The experiment showed that the accuracy of multiple 2D-CNN increased to 94.62% compared to 85.44% of single 2D CNN. Multiple 2D-CNN gave accuracy from 82.04% to 94.62% more than multiple 1D-CNN. 2D-CNN makes the channel extraction perfect into vectors to maintain the signal sequence. Signal extraction is essential, so the used Wavelet filter upgraded accuracy from 73.75% to 94.62%.

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Gottlieb, Jacqueline. "Multi-dimensional parietal signals for coordinating attention and decision making." Journal of Vision 16, no. 12 (September 1, 2016): 1300. http://dx.doi.org/10.1167/16.12.1300.

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Yankelevsky, Yael, and Michael Elad. "Finding GEMS: Multi-Scale Dictionaries For High-Dimensional Graph Signals." IEEE Transactions on Signal Processing 67, no. 7 (April 1, 2019): 1889–901. http://dx.doi.org/10.1109/tsp.2019.2899822.

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Shi, Huiping, Hong Xie, and Mengran Wu. "Emotion recognition based on multi-channel EEG signals." Journal of Physics: Conference Series 2078, no. 1 (November 1, 2021): 012028. http://dx.doi.org/10.1088/1742-6596/2078/1/012028.

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Abstract Emotion recognition is a key technology of human-computer emotional interaction, which plays an important role in various fields and has attracted the attention of many researchers. However, the issue of interactivity and correlation between multi-channel EEG signals has not attracted much attention. For this reason, an EEG signal emotion recognition method based on 2DCNN-BiGRU and attention mechanism is tentatively proposed. This method firstly forms a two-dimensional matrix according to the electrode position, and then takes the pre-processed two-dimensional feature matrix as input, in the two-dimensional convolutional neural network (2DCNN) and the bidirectional gated recurrent unit (BiGRU) with the attention mechanism layer Extract spatial features and time domain features in, and finally classify by softmax function. The experimental results show that the average classification accuracy of this model are 93.66% and 94.32% in the valence and arousal, respectively.

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Ji, Yi, Shanlin Sun, and Hong-Bo Xie. "Stationary Wavelet-based Two-directional Two-dimensional Principal Component Analysis for EMG Signal Classification." Measurement Science Review 17, no. 3 (June 1, 2017): 117–24. http://dx.doi.org/10.1515/msr-2017-0015.

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AbstractDiscrete wavelet transform (WT) followed by principal component analysis (PCA) has been a powerful approach for the analysis of biomedical signals. Wavelet coefficients at various scales and channels were usually transformed into a one-dimensional array, causing issues such as the curse of dimensionality dilemma and small sample size problem. In addition, lack of time-shift invariance of WT coefficients can be modeled as noise and degrades the classifier performance. In this study, we present a stationary wavelet-based two-directional two-dimensional principal component analysis (SW2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify eight hand motions using 4-channel electromyographic (EMG) signals recorded in healthy subjects and amputees, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis.

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Qi, Weiqiang, Yuan Gui, Dapeng Duan, Songlin Zhou, and Jianpeng Dong. "Combined diagnosis of partial discharge based on the multi-dimensional characteristic parameters." Filomat 32, no. 5 (2018): 1535–46. http://dx.doi.org/10.2298/fil1805535q.

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This paper presents a comprehensive multi-parameter diagnosis method based on multiple partial discharge signals include high-frequency current, ultrasound, ultrahigh frequency (UHF) etc. First, acquire the high-frequency current, ultrasound, UHF partial discharge data under various types of defects, and extract the characteristic values, including nine basic characteristic parameters, eight phase characteristic parameters and the like. Diagnose signals respectively, with the method based on information fusion and semi-supervised learning for high-frequency current PD data, the method based on adaptive mutation parameters of particle entropy for ultrasonic signals, the method based on IIA-ART2A neural network for UHF signals. Then integrate the diagnostic results, which is the probability of fault of various defects and matrix, of different PD diagnosis signals, and analysis with the multiple classifier based on multi-parameter fuzzy integral to get the final diagnosis.

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Jia, Jinwei, Zhuangzhi Han, Limin Liu, Hui Xie, and Meng Lv. "Design Principle of RF Stealth Anti-Sorting Signal Based on Multi-Dimensional Compound Modulation with Pseudo-Center Width Agility." Electronics 11, no. 23 (December 5, 2022): 4027. http://dx.doi.org/10.3390/electronics11234027.

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Anti-sorting signal design is an important direction of radio frequency (RF) stealth signal design. The RF stealth signal design is based on the anti-sorting signal design principle, which is essentially the failure principle of the radar signal sorting algorithm. Cluster pre-sorting, the key to radar signal sorting, has the advantages of fast sorting, simultaneous sorting of multiple sources, and greatly reduced computational pressure of the main sorting. However, a unified and widely applicable cluster-sorting failure principle guiding the anti-sorting signal design has not been formally reported in RF stealth anti-sorting signal design. In this paper, the principles of the data field-based K-means clustering algorithm and the fuzzy C-means clustering algorithm are first studied. Aiming at the key step of data similarity measurement in the clustering algorithm, the failure principle of cluster sorting based on pseudo-center wide-agile multi-dimensional compound modulation is proposed. This principle can correctly guide the design of the anti-clustering sorting signal, so it is also called the design principle of the RF stealth anti-sorting signal based on pseudo-center wide-agile multi-dimensional compound modulation. The correctness of the principle is proved by formula derivation, signal simulation, and a sorting experiment. Through a signal comparison simulation with random interference pulse anti-sorting signals, it is strongly proved that the anti-sorting performance of signals designed under the guidance of the anti-clustering signal design principle proposed in this paper is stronger than that of random interference pulse signals. This study provides theoretical support for designing RF stealth anti-sorting signals. Using the signal design principle proposed in this paper, the anti-sorting performance of the RF stealth signal is improved by 10%. The principle of signal design helps to improve design efficiency.

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Hou, Xukun, Pengjie Hu, Wenliao Du, Xiaoyun Gong, Hongchao Wang, and Fannian Meng. "Fault diagnosis of rolling bearing based on multi-scale one-dimensional convolutional neural network." IOP Conference Series: Materials Science and Engineering 1207, no. 1 (November 1, 2021): 012003. http://dx.doi.org/10.1088/1757-899x/1207/1/012003.

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Abstract Aiming at the typical non-stationary and nonlinear characteristics of rolling bearing vibration signals, a multi-scale convolutional neural network method for bearing fault diagnosis based on wavelet transform and one-dimensional convolutional neural network is proposed. First, the signal is decomposed into multi scale components with wavelet transform, and then each scale component is reconstructed. The reconstructed signal is subjected to the Fourier transform to obtain the frequency spectrum representation, which is used as the input of the one-dimensional convolutional neural network. Finally, one-dimensional convolution neural network is used to learn the features of the input data and recognize the bearing fault. The performance of the model is verified by using data sets of rolling bearing. The results show that this method can intelligent feature extraction and obtain 99.94% diagnostic accuracy.

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Tilsen, Sam, Seung-Eun Kim, and Claire Wang. "Localizing category-related information in speech with multi-scale analyses." PLOS ONE 16, no. 10 (October 1, 2021): e0258178. http://dx.doi.org/10.1371/journal.pone.0258178.

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Measurements of the physical outputs of speech—vocal tract geometry and acoustic energy—are high-dimensional, but linguistic theories posit a low-dimensional set of categories such as phonemes and phrase types. How can it be determined when and where in high-dimensional articulatory and acoustic signals there is information related to theoretical categories? For a variety of reasons, it is problematic to directly quantify mutual information between hypothesized categories and signals. To address this issue, a multi-scale analysis method is proposed for localizing category-related information in an ensemble of speech signals using machine learning algorithms. By analyzing how classification accuracy on unseen data varies as the temporal extent of training input is systematically restricted, inferences can be drawn regarding the temporal distribution of category-related information. The method can also be used to investigate redundancy between subsets of signal dimensions. Two types of theoretical categories are examined in this paper: phonemic/gestural categories and syntactic relative clause categories. Moreover, two different machine learning algorithms were examined: linear discriminant analysis and neural networks with long short-term memory units. Both algorithms detected category-related information earlier and later in signals than would be expected given standard theoretical assumptions about when linguistic categories should influence speech. The neural network algorithm was able to identify category-related information to a greater extent than the discriminant analyses.

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Zhang, Lishan, Lei Han, Yuzhen Meng, and Wenkui Zhao. "Multi-input Convolutional Neural Network Fault Diagnosis Algorithm Based on the Hydraulic Pump." Journal of Physics: Conference Series 2095, no. 1 (November 1, 2021): 012069. http://dx.doi.org/10.1088/1742-6596/2095/1/012069.

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Abstract Convolutional neural network used in fault diagnosis can effectively extract fault features in vibration signals. However, in the feature extraction of mechanical fault diagnosis, usually more than two feature signals including at least axial and radial vibration signals can be extracted. This paper proposes two multi-input convolutional neural network models based on the fault data of the aircraft hydraulic pump including axial and radial vibration. The first is the Independent Input Multi-input Convolutional Neural Network model. The two inputs are respectively used for convolution pooling operation with CNN, and are combined through the concatenate function before the fully connected layer, and then all frames are integrated and flattened by the flatten function. A one-dimensional array, finally enters the fully connected layer and outputs the result through the softmax function. The second is the Combined Input Multiinput Convolutional Neural Network, that is, combine two one-dimensional signals into a twodimensional signal in the input layer of the convolutional neural network and then perform convolution pooling, and finally output the result through the softmax function. The results show that the two models have good accuracy and stability, and the second one has a higher convergence and fitting efficiency than the first one.

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Vaillant de Guélis, Thibault, Mark A. Vaughan, David M. Winker, and Zhaoyan Liu. "Two-dimensional and multi-channel feature detection algorithm for the CALIPSO lidar measurements." Atmospheric Measurement Techniques 14, no. 2 (February 26, 2021): 1593–613. http://dx.doi.org/10.5194/amt-14-1593-2021.

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Abstract. In this paper, we describe a new two-dimensional and multi-channel feature detection algorithm (2D-McDA) and demonstrate its application to lidar backscatter measurements from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. Unlike previous layer detection schemes, this context-sensitive feature finder algorithm is applied to a 2-D lidar “scene”, i.e., to the image formed by many successive lidar profiles. Features are identified when an extended and contiguous 2-D region of enhanced backscatter signal rises significantly above the expected “clear air” value. Using an iterated 2-D feature detection algorithm dramatically improves the fine details of feature shapes and can accurately identify previously undetected layers (e.g., subvisible cirrus) that are very thin vertically but horizontally persistent. Because the algorithm looks for contiguous 2-D patterns using successively lower detection thresholds, it reports strongly scattering features separately from weakly scattering features, thus potentially offering improved discrimination of juxtaposed cloud and aerosol layers. Moreover, the 2-D detection algorithm uses the backscatter signals from all available channels: 532 nm parallel, 532 nm perpendicular and 1064 nm total. Since the backscatter from some aerosol or cloud particle types can be more pronounced in one channel than another, simultaneously assessing the signals from all channels greatly improves the layer detection. For example, ice particles in subvisible cirrus strongly depolarize the lidar signal and, consequently, are easier to detect in the 532 nm perpendicular channel. Use of the 1064 nm channel greatly improves the detection of dense smoke layers, because smoke extinction at 532 nm is much larger than at 1064 nm, and hence the range-dependent reduction in lidar signals due to attenuation occurs much faster at 532 nm than at 1064 nm. Moreover, the photomultiplier tubes used at 532 nm are known to generate artifacts in an extended area below highly reflective liquid clouds, introducing false detections that artificially lower the apparent cloud base altitude, i.e., the cloud base when the cloud is transparent or the level of complete attenuation of the lidar signal when it is opaque. By adding the information available in the 1064 nm channel, this new algorithm can better identify the true apparent cloud base altitudes of such clouds.

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Ye, Fan, Feng He, Yong-sheng Zhang, and Zhen Dong. "Two-dimensional Fusion of Multi-radar Signals Based on GTD Model." Journal of Electronics & Information Technology 33, no. 1 (March 1, 2011): 55–59. http://dx.doi.org/10.3724/sp.j.1146.2010.00278.

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Yi, Anlin, Lianshan Yan, Yan Pan, Lin Jiang, Zhiyu Chen, Wei Pan, and Bin Luo. "Transmission of multi-dimensional signals for next generation optical communication systems." Optics Communications 408 (February 2018): 42–52. http://dx.doi.org/10.1016/j.optcom.2017.07.046.

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Onishchenko, A. M. "Selection of informative signals in multi-dimensional production quality control devices." Measurement Techniques 34, no. 2 (February 1991): 120–24. http://dx.doi.org/10.1007/bf00990812.

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Kim, Tae, and Dong Kim. "Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks." Energies 11, no. 10 (October 22, 2018): 2855. http://dx.doi.org/10.3390/en11102855.

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In this paper, we propose a multi-dimensional sparse-coded ambient backscatter communication (MSC-AmBC) system for long-range and high-rate massive Internet of things (IoT) networks. We utilize the characteristics of the ambient sources employing orthogonal frequency division multiplexing (OFDM) modulation to mitigate strong direct-link interference and improve signal detection of AmBC at the reader. Also, utilization of the sparsity originated from the duty-cycling operation of batteryless RF tags is proposed to increase the dimension of signal space of backscatter signals to achieve either diversity or multiplexing gains in AmBC. We propose optimal constellation mapping and reflection coefficient projection and expansion methods to effectively construct multi-dimensional constellation for high-order backscatter modulation while guaranteeing sufficient energy harvesting opportunities at these tags. Simulation results confirm the feasibility of the long-range and high-rate AmBC in massive IoT networks where a huge number of active ambient sources and passive RF tags coexist.

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Yang, Chuanlei, Hechun Wang, Zhanbin Gao, and Xinjie Cui. "Improving rolling bearing online fault diagnostic performance based on multi-dimensional characteristics." Royal Society Open Science 5, no. 5 (May 2018): 180066. http://dx.doi.org/10.1098/rsos.180066.

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As the main cause of failure and damage to rotating machinery, rolling bearing failure can result in huge economic losses. As the rolling bearing vibration signal is nonlinear and has non-stationary characteristics, the health status information distributed in the rolling bearing vibration signal is complex. Using common time-domain or frequency-domain approaches cannot easily enable an accurate assessment of rolling bearing health. In this paper, a novel rolling bearing fault diagnostic method based on multi-dimensional characteristics was developed to meet the requirements for accurate diagnosis of different fault types and severities with real-time computational performance. First, a multi-dimensional feature extraction algorithm based on entropy characteristics, Holder coefficient characteristics and improved generalized fractal box-counting dimension characteristics was performed to extract the health status feature vectors from the bearing vibration signals. Second, a grey relation algorithm was employed to achieve bearing fault pattern recognition intelligently using the extracted multi-dimensional feature vector. This experimental study has illustrated that the proposed method can effectively recognize different fault types and severities after integration of the improved fractal box-counting dimension into the multi-dimensional characteristics, in comparison with existing pattern recognition methods.

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Kim, Yeongdae, Sorawit Stapornchaisit, Hiroyuki Kambara, Natsue Yoshimura, and Yasuharu Koike. "Muscle Synergy and Musculoskeletal Model-Based Continuous Multi-Dimensional Estimation of Wrist and Hand Motions." Journal of Healthcare Engineering 2020 (January 28, 2020): 1–13. http://dx.doi.org/10.1155/2020/5451219.

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In this study, seven-channel electromyography signal-based two-dimensional wrist joint movement estimation with and without handgrip motions was carried out. Electromyography signals were analyzed using the synergy-based linear regression model and musculoskeletal model; they were subsequently compared with respect to single and combined wrist joint movements and handgrip. Using each one of wrist motion and grip trial as a training set, the synergy-based linear regression model exhibited a statistically significant performance with 0.7891 ± 0.0844 Pearson correlation coefficient (r) value in two-dimensional wrist motion estimation compared with 0.7608 ± 0.1037 r value of the musculoskeletal model. Estimates on the grip force produced 0.8463 ± 0.0503 r value with 0.2559 ± 0.1397 normalized root-mean-square error of the wrist motion range. This continuous wrist and handgrip estimation can be considered when electromyography-based multi-dimensional input signals in the prosthesis, virtual interface, and rehabilitation are needed.

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Otter, Martin. "Signal Tables: An Extensible Exchange Format for Simulation Data." Electronics 11, no. 18 (September 6, 2022): 2811. http://dx.doi.org/10.3390/electronics11182811.

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This article introduces Signal Tables as a format to exchange data associated with simulations based on dictionaries and multi-dimensional arrays. Typically, simulation results, as well as model parameters, reference signals, table-based input signals, measurement data, look-up tables, etc., can be represented by a Signal Table. Applications can extend the format to add additional data and metadata/attributes, for example, as needed for a credible simulation process. The format follows a logical view based on a few data structures that can be directly mapped to data structures available in programming languages such as Julia, Python, and Matlab. These data structures can be conveniently used for pre- and post-processing in these languages. A Signal Table can be stored on file by mapping the logical view to available textual or binary persistent file formats, for example, JSON, HDF5, BSON, and MessagePack. A subset of a Signal Table can be imported in traditional tables, for example, in Excel, CSV, pandas, or DataFrames.jl, by flattening multi-dimensional arrays and not storing parameters. The format has been developed and evaluated with the Open Source Julia packages SignalTables.jl and Modia.jl.

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KIDA, TAKURO. "AN EXTENDED FORM OF SUB-BAND INTERPOLATION OF MULTI-DIMENSIONAL DISCRETE SIGNALS AND ITS APPLICATIONS." Journal of Circuits, Systems and Computers 01, no. 03 (September 1991): 273–302. http://dx.doi.org/10.1142/s0218126691000094.

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In this paper, we establish an extended form of the optimum sub-band interpolation for a family of n-dimensional discrete signals. We assume that the Fourier spectrums of these discrete signals have weighted L2 norms smaller than a given positive number. It is assumed that the sample points of these discrete signals are identical with the whole vertices of an n-dimensional rectangular lattice in Rn. Among these sample points, certain subsets are used for the interpolation. Selecting appropriate subsets of the sample points, we can realize a wide variety of periodic arrangements of sample points for interpolation such as hexagonal and octagonal lattices or a set of sample points used in interlaces scanning of digital television. The proposed method minimizes the measure of error which is equal to the envelope of the approximation errors with respect to the discrete signals. In the following discussion, we assume initially that the corresponding approximation formula uses an infinite number of interpolation functions having limited supports and functional forms different from each other. However, it should be noted that the resultant optimum interpolation functions are expressed as the parallel shifts of the impulse responses of the finite number of n-dimensional FIR filters. Equivalent analog approximation formula corresponding to the proposed discrete approximation, is derived and interesting reciprocal relation in the approximation, is also discussed. A necessary and sufficient condition for the convergence of the corresponding analog approximation formula to the original band limited signal, is presented. An equivalent expression of the analog approximation formula in the frequency domain, is derived in relation to the convergence condition.

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Ge, Mao, Yong Lv, Yi Zhang, Cancan Yi, and Yubo Ma. "An Effective Bearing Fault Diagnosis Technique via Local Robust Principal Component Analysis and Multi-Scale Permutation Entropy." Entropy 21, no. 10 (September 30, 2019): 959. http://dx.doi.org/10.3390/e21100959.

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The acquired bearing fault signal usually reveals nonlinear and non-stationary nature. Moreover, in the actual environment, some other interference components and strong background noise are unavoidable, which lead to the fault feature signal being weak. Considering the above issues, an effective bearing fault diagnosis technique via local robust principal component analysis (LRPCA) and multi-scale permutation entropy (MSPE) was introduced in this paper. Robust principal component analysis (RPCA) has proven to be a powerful de-noising method, which can extract a low-dimensional submanifold structure representing signal feature from the signal trajectory matrix. However, RPCA can only handle single-component signal. Therefore, in order to suppress background noise, an improved RPCA method named LRPCA is proposed to decompose the signal into several single-components. Since MSPE can efficiently evaluate the dynamic complexity and randomness of the signals under different scales, the fault-related single-components can be identified according the MPSE characteristic of the signals. Thereafter, these identified components are combined into a one-dimensional signal to represent the fault feature component for further diagnosis. The numerical simulation experimentation and the analysis of bearing outer race fault data both verified the effectiveness of the proposed technique.

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Mosallam, Ahmed, Kamal Medjaher, and Noureddine Zerhouni. "Time series trending for condition assessment and prognostics." Journal of Manufacturing Technology Management 25, no. 4 (April 29, 2014): 550–67. http://dx.doi.org/10.1108/jmtm-04-2013-0037.

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Purpose – The developments of complex systems have increased the demand for condition monitoring techniques so as to maximize operational availability and safety while decreasing the costs. Signal analysis is one of the methods used to develop condition monitoring in order to extract important information contained in the sensory signals, which can be used for health assessment. However, extraction of such information from collected data in a practical working environment is always a great challenge as sensory signals are usually multi-dimensional and obscured by noise. The paper aims to discuss this issue. Design/methodology/approach – This paper presents a method for trends extraction from multi-dimensional sensory data, which are then used for machinery health monitoring and maintenance needs. The proposed method is based on extracting successive features from machinery sensory signals. Then, unsupervised feature selection on the features domain is applied without making any assumptions concerning the source of the signals and the number of the extracted features. Finally, empirical mode decomposition (EMD) algorithm is applied on the projected features with the purpose of following the evolution of data in a compact representation over time. Findings – The method is demonstrated on accelerated degradation data set of bearings acquired from PRONOSTIA experimental platform and a second data set acquired form NASA repository. Originality/value – The method showed that it is able to extract interesting signal trends which can be used for health monitoring and remaining useful life prediction.

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Eytan, Danny, Andrew Goodwin, Peter Laussen, and Anne-Marie Guerguerian. "Insights From Multi-Dimensional Physiological Signals to Predict and Prevent Cardiac Arrests*." Pediatric Critical Care Medicine 17, no. 1 (January 2016): 81–82. http://dx.doi.org/10.1097/pcc.0000000000000627.

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Liu, Bin, and Yan Ren. "A design of laser array harp based on multi-dimensional wavelet transform and audio signal reconstruction." Journal of Physics: Conference Series 2113, no. 1 (November 1, 2021): 012059. http://dx.doi.org/10.1088/1742-6596/2113/1/012059.

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Abstract This paper introduces a design scheme of laser array harp based on multi-dimensional wavelet transform and audio signal reconstruction. The green light beams from multiple high-power lasers simulate harp strings, use photoresistors as the signal receiving end, and use a signal conditioning system composed of analog circuits and LM393 comparators to collect and adjust the resistance signal of the laser sensor[1], and finally it is adjusted to a level signal that can be recognized by the CPU. After receiving the signal, the CPU core board analyzes the string signal, and sends control commands to the audio processing system through the industrial bus according to the analyzed digital signal. After receiving the control command, the audio processing system uses the audio signal reconstruction technology composed of multi-dimensional wavelet packets, deep learning and other algorithms to simulate the audio signals of various string music, so as to achieve the purposes of using the lasers as virtual strings and imitating musical instruments for musical performance.[2]

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Xu, Zengbing, Xinyu Tang, and Zhigang Wang. "A Multi-Information Fusion ViT Model and Its Application to the Fault Diagnosis of Bearing with Small Data Samples." Machines 11, no. 2 (February 12, 2023): 277. http://dx.doi.org/10.3390/machines11020277.

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To solve the fault diagnosis difficulty of bearings with small data samples, a novel multi-information fusion vision transformer (ViT) model based on time–frequency representation (TFR) maps is proposed in this paper. The original vibration signal is decomposed into different scale sub-signals by the discrete wavelet transforms (DWTs), and the continuous wavelet transforms (CWTs) are used to transform these different scale sub-signals into time–frequency representation (TFR) maps, which are concatenated to input to the ViT model to diagnose the bearing fault. Through the multifaceted experiment analysis on the fault diagnosis of bearings with small data samples, the diagnosis results demonstrate that the proposed multi-information fusion ViT model can diagnose the fault of bearings with small data samples, with strong generalization and robustness; its average diagnosis accuracy achieved 99.85%, and it was superior to the other fault diagnosis methods, such as the multi-information fusion CNN, ViT model based on one-dimensional vibration signal, and ViT model based on the TFR of the original vibration signal.

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Stergiopoulos, Stergios, and Amar C. Dhanantwari. "High resolution 3D ultrasound imaging system deploying a multi-dimensional array of sensors and method for multi-dimensional beamforming sensor signals." Journal of the Acoustical Society of America 116, no. 3 (2004): 1337. http://dx.doi.org/10.1121/1.1809969.

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Hao, Yukun, Xiaojun Wu, Huiyuan Wang, Xinyi He, Chengpeng Hao, Zirui Wang, and Qiao Hu. "Underwater Reverberation Suppression via Attention and Cepstrum Analysis-Guided Network." Journal of Marine Science and Engineering 11, no. 2 (February 1, 2023): 313. http://dx.doi.org/10.3390/jmse11020313.

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Active sonar systems are one of the most commonly used acoustic devices for underwater equipment. They use observed signals, which mainly include target echo signals and reverberation, to detect, track, and locate underwater targets. Reverberation is the primary background interference for active sonar systems, especially in shallow sea environments. It is coupled with the target echo signal in both the time and frequency domain, which significantly complicates the extraction and analysis of the target echo signal. To combat the effect of reverberation, an attention and cepstrum analysis-guided network (ACANet) is proposed. The baseline system of the ACANet consists of a one-dimensional (1D) convolutional module and a reconstruction module. These are used to perform nonlinear mapping and to reconstruct clean spectrograms, respectively. Then, since most underwater targets contain multiple highlights, a cepstrum analysis module and a multi-head self-attention module are deployed before the baseline system to improve the reverberation suppression performance for multi-highlight targets. The systematic evaluation demonstrates that the proposed algorithm effectively suppresses the reverberation in observed signals and greatly preserves the highlight structure. Compared with NMF methods, the proposed ACANet no longer requires the target echo signal to be low-rank. Thus, it can better suppress the reverberation in multi-highlight observed signals. Furthermore, it demonstrates superior performance over NMF methods in the task of reverberation suppression for single-highlight observed signals. It creates favorable conditions for underwater platforms, such as unmanned underwater vehicles (UUVs), to carry out underwater target detection and tracking tasks.

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Wang, R. J., K. Xu, and X. L. Liu. "A MULTI-STEP DE-NOISING METHOD FOR INTERFEROGRAM IN PS-INSAR." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-3/W1-2022 (October 27, 2022): 53–58. http://dx.doi.org/10.5194/isprs-archives-xlviii-3-w1-2022-53-2022.

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Abstract. In order to reduce the influences of the noises in Synthetic Aperture Radar (SAR) image on accurate extraction of permanent scattered (PS) points in PS-InSAR technology, a multi-step de-noising approach considering the characteristics of the noises is proposed in this study. The method can deal with the noises from one-dimensional signals perspective. First, the two-dimensional interferogram is transformed into one-dimensional phase signals by extracting the phase values of each row in S-shaped. Next, the phase signals are decomposed by the extreme-point symmetric mode decomposition (ESMD) method. Then, for the purpose of reserving more useful phase information, the Lee filtering method is applied to the high-frequency noisy part identified based on the Spearman's correlation coefficients in ESMD decomposition. Finally, the de-noised interferogram is recovered from the phase signals by ways in S-shaped in reverse. The experimental results show that the proposed method is effective in dealing with the noises and can achieve a higher accuracy of interferogram for PS-InSAR.

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Álvarez-Murga, M., P. Bleuet, and J. L. Hodeau. "Diffraction/scattering computed tomography for three-dimensional characterization of multi-phase crystalline and amorphous materials." Journal of Applied Crystallography 45, no. 6 (November 15, 2012): 1109–24. http://dx.doi.org/10.1107/s0021889812041039.

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The three-dimensional characterization method described herein is based on diffraction and scattering techniques combined with tomography and uses the variation of these signals to reconstruct a two-dimensional/three-dimensional structural image. To emphasize the capability of the method in discriminating between different poorly ordered phases, it is named diffraction/scattering computed tomography (DSCT). This combination not only allows structural imaging but also yields an enhancement of the weak signals coming from minor phases, thereby increasing the sensitivity of structural probes. This article reports the suitability of the method for discrimination of polycrystalline and amorphous phases and for extraction of their selective local patterns with a contrast sensitivity of about 0.1% in weight of minor phases relative to the matrix. The required background in tomography is given and then the selectivity of scattering signal, the efficiency of the method, reconstruction artefacts and limitations are addressed. The approach is illustrated through different examples covering a large range of applications based on recent literature, showing the potential of DSCT in crystallography and materials science, particularly when functional and/or precious samples with sub-micrometre features have to be investigated in a nondestructive way.

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Wang, Junlang, Huoyao Xu, Xiangyu Peng, Jie Liu, and Chaoming He. "Pathological voice classification based on multi-domain features and deep hierarchical extreme learning machine." Journal of the Acoustical Society of America 153, no. 1 (January 2023): 423–35. http://dx.doi.org/10.1121/10.0016869.

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The intelligent data-driven screening of pathological voice signals is a non-invasive and real-time tool for computer-aided diagnosis that has attracted increasing attention from researchers and clinicians. In this paper, the authors propose multi-domain features and the hierarchical extreme learning machine (H-ELM) for the automatic identification of voice disorders. A sufficient number of sensitive features are first extracted from the original voice signal through multi-domain feature extraction (i.e., features of the time domain and the sample entropy based on ensemble empirical mode decomposition and gammatone frequency cepstral coefficients). To eliminate redundancy in high-dimensional features, neighborhood component analysis is then applied to filter out sensitive features from the high-dimensional feature vectors to improve the efficiency of network training and reduce overfitting. The sensitive features thus obtained are then used to train the H-ELM for pathological voice classification. The results of the experiments showed that the sensitivity, specificity, F1 score, and accuracy of the H-ELM were 99.37%, 98.61%, 99.37%, and 98.99%, respectively. Therefore, the proposed method is feasible for the initial classification of pathological voice signals.

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Wang, Changdong, Hongchun Sun, Rong Zhao, and Xu Cao. "Research on Bearing Fault Diagnosis Method Based on an Adaptive Anti-Noise Network under Long Time Series." Sensors 20, no. 24 (December 8, 2020): 7031. http://dx.doi.org/10.3390/s20247031.

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In the era of big data, longer time series fault signals will not only be easy to copy and store, but also reduce the labor cost of manual labeling, which can better meet the needs of industrial big data. Aiming to effectively extract the key classification information from a longer time series of bearing vibration signals and achieve high diagnostic accuracy under noise and different load conditions. The one-dimensional adaptive long sequence convolutional network (ALSCN) is proposed. ALSCN can better extract features directly from high-dimensional original signals without manually extracting features and relying on expert knowledge. By adding two improved multi-scale modules, ALSCN can not only extract important features efficiently from noise signals, but also alleviate the problem of losing key information due to continuous down-sampling. Moreover, a Bayesian optimization algorithm is constructed to automatically find the best combination of hyperparameters in ALSCN. Based on two bearing data sets, the model is compared with traditional model such as SVM and deep learning models such as convolutional neural networks (CNN) et al. The results prove that ALSCN has a higher diagnostic accuracy rate on 5120-dimensional sequences under −5 signal to noise ratio (SNR) with better generalization.

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Zou, Hongjie, Yitao Zhang, Jun Zhang, Chuanglu Chen, Xingguang Geng, Shaolong Zhang, and Haiying Zhang. "A Novel Multi-Dimensional Composition Method Based on Time Series Similarity for Array Pulse Wave Signals Detecting." Algorithms 13, no. 11 (November 14, 2020): 297. http://dx.doi.org/10.3390/a13110297.

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Pulse wave signal sensed over the radial artery on the wrist is a crucial physiological indicator in disease diagnosis. The sensor array composed of multiple sensors has the ability to collect abundant pulse wave information. As a result, it has gradually attracted the attention of practitioners. However, few practical methods are used to obtain a one-dimensional pulse wave from the sensor array’s spatial multi-dimensional signals. The current algorithm using pulse wave with the highest amplitude value as the significant data suffers from low consistency because the signal acquired each time differs significantly due to the sensor’s relative position shift to the test area. This paper proposes a processing method based on time series similarity, which can take full advantage of sensor arrays’ spatial multi-dimensional characteristics and effectively avoid the above factors’ influence. A pulse wave acquisition system (PWAS) containing a micro-electro-mechanical system (MEMS) sensor array is continuously extruded using a stable dynamic pressure input source to simulate the pulse wave acquisition process. Experiments are conducted at multiple test locations with multiple data acquisitions to evaluate the performance of the algorithm. The experimental results show that the newly proposed processing method using time series similarity as the criterion has better consistency and stability.

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Wei, Juhui, Zhangming He, Jiongqi Wang, Dayi Wang, and Xuanying Zhou. "Fault Detection Based on Multi-Dimensional KDE and Jensen–Shannon Divergence." Entropy 23, no. 3 (February 24, 2021): 266. http://dx.doi.org/10.3390/e23030266.

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Weak fault signals, high coupling data, and unknown faults commonly exist in fault diagnosis systems, causing low detection and identification performance of fault diagnosis methods based on T2 statistics or cross entropy. This paper proposes a new fault diagnosis method based on optimal bandwidth kernel density estimation (KDE) and Jensen–Shannon (JS) divergence distribution for improved fault detection performance. KDE addresses weak signal and coupling fault detection, and JS divergence addresses unknown fault detection. Firstly, the formula and algorithm of the optimal bandwidth of multidimensional KDE are presented, and the convergence of the algorithm is proved. Secondly, the difference in JS divergence between the data is obtained based on the optimal KDE and used for fault detection. Finally, the fault diagnosis experiment based on the bearing data from Case Western Reserve University Bearing Data Center is conducted. The results show that for known faults, the proposed method has 10% and 2% higher detection rate than T2 statistics and the cross entropy method, respectively. For unknown faults, T2 statistics cannot effectively detect faults, and the proposed method has approximately 15% higher detection rate than the cross entropy method. Thus, the proposed method can effectively improve the fault detection rate.

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Wei, Minghua, and Feng Lin. "A novel multi-dimensional features fusion algorithm for the EEG signal recognition of brain's sensorimotor region activated tasks." International Journal of Intelligent Computing and Cybernetics 13, no. 2 (June 8, 2020): 239–60. http://dx.doi.org/10.1108/ijicc-02-2020-0019.

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PurposeAiming at the shortcomings of EEG signals generated by brain's sensorimotor region activated tasks, such as poor performance, low efficiency and weak robustness, this paper proposes an EEG signals classification method based on multi-dimensional fusion features.Design/methodology/approachFirst, the improved Morlet wavelet is used to extract the spectrum feature maps from EEG signals. Then, the spatial-frequency features are extracted from the PSD maps by using the three-dimensional convolutional neural networks (3DCNNs) model. Finally, the spatial-frequency features are incorporated to the bidirectional gated recurrent units (Bi-GRUs) models to extract the spatial-frequency-sequential multi-dimensional fusion features for recognition of brain's sensorimotor region activated task.FindingsIn the comparative experiments, the data sets of motor imagery (MI)/action observation (AO)/action execution (AE) tasks are selected to test the classification performance and robustness of the proposed algorithm. In addition, the impact of extracted features on the sensorimotor region and the impact on the classification processing are also analyzed by visualization during experiments.Originality/valueThe experimental results show that the proposed algorithm extracts the corresponding brain activation features for different action related tasks, so as to achieve more stable classification performance in dealing with AO/MI/AE tasks, and has the best robustness on EEG signals of different subjects.

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Shalchian, Homayoon, Kais Bouslah, and Bouchra M’Zali. "A Multi-Dimensional Analysis of Corporate Social Responsibility: Different Signals in Different Industries." Journal of Financial Risk Management 04, no. 02 (2015): 92–109. http://dx.doi.org/10.4236/jfrm.2015.42009.

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Sim, Hyoun-Jin, Hae-Jin Lee, You-Yub Lee, Jung-Youn Lee, and Jae-Eung Oh. "Source Identification of Non-Stationary Sound.Vibration Signals Using Multi-Dimensional Spectral Analysis Method." Transactions of the Korean Society of Mechanical Engineers A 30, no. 9 (September 1, 2006): 1154–59. http://dx.doi.org/10.3795/ksme-a.2006.30.9.1154.

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TAKEUCHI, Yoshiki, and Takahiro ISHIKAWA. "The Optimal Transmission of Multi-Dimensional Gaussian Signals for Channels with Noisy Feedback." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 1995 (May 5, 1995): 33–38. http://dx.doi.org/10.5687/sss.1995.33.

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Journal articles on the topic 'Multi-dimensional signals'

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