Journal articles: 'Signal fusion'

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Tejedor, Javier, Constantino A. García, David G. Márquez, Rafael Raya, and Abraham Otero. "Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review." Sensors 19, no. 21 (October 29, 2019): 4708. http://dx.doi.org/10.3390/s19214708.

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This paper presents a review of the techniques found in the literature that aim to achieve a robust heartbeat detection from fusing multi-modal physiological signals (e.g., electrocardiogram (ECG), blood pressure (BP), artificial blood pressure (ABP), stroke volume (SV), photoplethysmogram (PPG), electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG), among others). Techniques typically employ ECG, BP, and ABP, of which usage has been shown to obtain the best performance under challenging conditions. SV, PPG, EMG, EEG, and EOG signals can help increase performance when included within the fusion. Filtering, signal normalization, and resampling are common preprocessing steps. Delay correction between the heartbeats obtained over some of the physiological signals must also be considered, and signal-quality assessment to retain the best signal/s must be considered as well. Fusion is usually accomplished by exploiting regularities in the RR intervals; by selecting the most promising signal for the detection at every moment; by a voting process; or by performing simultaneous detection and fusion using Bayesian techniques, hidden Markov models, or neural networks. Based on the results of the review, guidelines to facilitate future comparison of the performance of the different proposals are given and promising future lines of research are pointed out.

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Devaux, Philippe F. "Signal transduction during biomembrane fusion." Trends in Biochemical Sciences 19, no. 8 (August 1994): 346–47. http://dx.doi.org/10.1016/0968-0004(94)90077-9.

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Gomperts, B. D. "Signal transduction during biomembrane fusion." Trends in Cell Biology 4, no. 1 (January 1994): 31–32. http://dx.doi.org/10.1016/0962-8924(94)90039-6.

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Li, Lian Tian. "The Research on Abnormal Signal Retrieval Methods for Differences Equipments under the Framework of Large-Scale Internet of Things." Advanced Materials Research 846-847 (November 2013): 1060–63. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1060.

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This paper proposes an abnormal signal retrieval method for differences equipments in Internet of Things (IOT) based on Signal Fusion Technology. It elaborates on the fusion rules for communication signal of underlying devices, according to which describes the conditions need to be met when abnormal signal of the underlying devices in IOT is fused. Laplace energy and weighting algorithm are adopted to fuse abnormal signals of the underlying multi-device in IOT in order to achieve fusion detection for abnormal signal. Experimental results show that the algorithm can greatly improve the efficiency of detecting abnormal signal.

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Li, Wang, Shang Guan Wei, Bai Gen Cai, and Jian Wang. "Signal Simulation of the Train Integrated Positioning System." Advanced Materials Research 740 (August 2013): 86–91. http://dx.doi.org/10.4028/www.scientific.net/amr.740.86.

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In order to simulate the multi-sensor signals of the train integrated positioning system, and make signal fusion and verification, the train integrated positioning signal simulation system is introduced in this paper, which is based on GNSS and multi-sensor integrated positioning thoughts, the mathematical models and failure characteristics of the sensors are analyzed, the pulse speed sensor and gyro error models are established, the data simulation approach and data acquisition modules are adopted to simulate pulse speed sensor and gyroscope signals, the GNSS simulator is used to simulate GNSS signal under different scenes and conditions, signal fusion algorithms are used to conduct fusion and filtering of the integrated positioning signal. The results show the availability of the simulated signals. The train integrated positioning signal simulation system provides test environments with the parameters controllable, and lays a good foundation for the researches of the Beidou based integrated positioning system and the multi-mode satellite positioning technology.

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Liu, Wen Qiang, Gui Li Tao, Ze Yuan Gu, and Song Li. "Self-Tuning Weighted Measurement Fusion Kalman Signal Filter." Applied Mechanics and Materials 274 (January 2013): 579–82. http://dx.doi.org/10.4028/www.scientific.net/amm.274.579.

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For the single channel autoregressive moving average (ARMA) signals with multisensor and a colored measurement noise, when the model parameters and noise variances are partially unknown, based on identification method and Gevers-Wouters algorithm with a dead band, a self-tuning weighted measurement fusion Kalman signal filter is presented. A simulation example applied to signal processing shows its effectiveness.

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Jiang, Feng, Jun Yu, and Jian Jun Chen. "Design of Low Voltage Cell Fusion Instrument." Applied Mechanics and Materials 268-270 (December 2012): 1337–40. http://dx.doi.org/10.4028/www.scientific.net/amm.268-270.1337.

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Aimed at the deficiency in the existing cell-elctrofusion devices such as high fusion voltage and so on, a low voltage cell fusion system based on micro system technology is developed. This system is composed by cell fusion chip and low voltage signal generator, and locating cells by precisely designed microstructure and signal. In this chip, small distance between one pair of microelectrodes reduced required voltage. It has advantages of low energy consuming, small bulk and operation safety and easily compared with the existence apparatus. The experiment shows the system can generate cell fusion signal to meet the need for fusing. It can be popularized.

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Meng, Zong, Zuozhou Pan, Zijun Chen, and Ying Shi. "Adaptive signal fusion based on relative fluctuations of variable signals." Measurement 148 (December 2019): 106909. http://dx.doi.org/10.1016/j.measurement.2019.106909.

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Song, Qiang. "Data Fusion for MFL Signal Characterization." Applied Mechanics and Materials 44-47 (December 2010): 3519–23. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3519.

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The objective of data fusion is to be able to draw inferences that may not be feasible with data from a single sensor alone. In this paper, data from three sets of sensors are fused to estimate the defect profile from magnetic flux leakage (MFL) inspection data. The three sensors measure the axial, radial and tangential components of the MFL field. Data is fused at the feature level. Examples of signal features are amplitude, width, etc. A radial basis function network (RBFN) is then employed to map the fused features appropriately to obtain the geometric profile of the defect. The feasibility of the approach is evaluated using the data obtained from the MFL inspection of oil pipes. The results obtained by fusing the axial, radial and tangential components appear to be better than those obtained using the axial and radial component alone.

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Su, Liyun, Meini Li, Shengli Zhao, and Ting Xie. "Distributed Sensor Local Linear Fusion Detection of Weak Pulse Signal in Chaotic Background." Journal of Sensors 2021 (February 10, 2021): 1–11. http://dx.doi.org/10.1155/2021/6661142.

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This paper combines the distributed sensor fusion system with the signal detection under chaotic noise to realize the distributed sensor fusion detection from chaotic background. First, based on the short-term predictability of the chaotic signal and its sensitivity to small interference, the phase space reconstruction of the observation signal of each sensor is carried out. Second, the distributed sensor local linear autoregressive (DS-LLAR) model is constructed to obtain the one-step prediction error of each sensor. Then, we construct a Bayesian risk model and also obtain the corresponding conditional probability density function under each sensor’s hypothesis test which firstly needs to fit the distribution of prediction errors according to the parameter estimation. Finally, the fusion optimization algorithm is designed based on the Bayesian fusion criterion, and the optimal decision rule of each sensor and the optimal fusion rule of the fusion center are jointly solved to effectively detect the weak pulse signal in the observation signal. Simulation experiments show that the proposed method which used a distributed sensor combined with a local linear model can effectively detect weak pulse signals from chaotic background.

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Janknecht, R. "SIGNAL TRANSDUCTION:Nuclear Fusion of Signaling Pathways." Science 284, no. 5413 (April 16, 1999): 443–44. http://dx.doi.org/10.1126/science.284.5413.443.

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Yan Sun, P. Willett, and R. Lynch. "Waveform Fusion in Sonar Signal Processing." IEEE Transactions on Aerospace and Electronic Systems 40, no. 2 (April 2004): 462–77. http://dx.doi.org/10.1109/taes.2004.1309997.

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Li, Hao, Xin Min Dong, Wang Sheng Hao, Ai Guo Liu, Xian De Yin, and Ai Jun Wang. "Applying Full Vector Spectrum for Electric Hoist Gearbox Fault Diagnosis." Applied Mechanics and Materials 365-366 (August 2013): 725–28. http://dx.doi.org/10.4028/www.scientific.net/amm.365-366.725.

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To resolve the noise and incomplete information of rotor vibration signals for electric hoist gearbox, the full vector spectrum technology is applied for information fusion and wavelet analysis. The full vector wavelet is used to de-noising process for the vibration signals, which extract the more complete characterization information of the noise signal. First, the dual-channel vibration signal wavelet decomposition and reconstruction, and then take advantage of the full vector spectrum technology fusion for the reconstructed signal. In this way, which ensure the integrity of the vibration signal feature information, more reliably reflect the vibration characteristics of the rotor, and improve the accuracy of fault diagnosis.

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Gao, M., and D. M. Knipe. "Distal protein sequences can affect the function of a nuclear localization signal." Molecular and Cellular Biology 12, no. 3 (March 1992): 1330–39. http://dx.doi.org/10.1128/mcb.12.3.1330.

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The major DNA-binding protein, or infected-cell protein 8 (ICP8), encoded by herpes simplex virus can localize to the cell nucleus independently of other viral proteins. To define the nuclear localization signals within ICP8, we performed several forms of mutagenesis on the cloned ICP8 gene. Deletion analysis of the ICP8 gene showed that several portions of ICP8 are involved in its nuclear localization. To determine whether these regions were independent localization signals, we introduced various portions of the ICP8 gene into a series of cassette plasmids which allowed expression of fusion proteins containing pyruvate kinase, normally a cytoplasmic protein, fused to various portions of ICP8. These results showed that the carboxyl-terminal 28 residues are the only portion of ICP8 capable of targeting protein kinase into the nucleus. However, inclusion of certain additional regions of ICP8 into the fusion protein led to an inhibition of nuclear localization. Therefore, the carboxyl-terminal 28 residues of ICP8 can act independently as a nuclear localization signal, but certain conformational constraints or folding or assembly requirements in the remainder of the protein can affect the nuclear localization of the protein. Our results demonstrate that sequences distant from a nuclear localization signal can affect its ability to function. A set of fusion vectors has been isolated which should be of general use for making 5' or 3' fusions in any reading frame to rapidly map localization signals.

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Gao, M., and D. M. Knipe. "Distal protein sequences can affect the function of a nuclear localization signal." Molecular and Cellular Biology 12, no. 3 (March 1992): 1330–39. http://dx.doi.org/10.1128/mcb.12.3.1330-1339.1992.

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The major DNA-binding protein, or infected-cell protein 8 (ICP8), encoded by herpes simplex virus can localize to the cell nucleus independently of other viral proteins. To define the nuclear localization signals within ICP8, we performed several forms of mutagenesis on the cloned ICP8 gene. Deletion analysis of the ICP8 gene showed that several portions of ICP8 are involved in its nuclear localization. To determine whether these regions were independent localization signals, we introduced various portions of the ICP8 gene into a series of cassette plasmids which allowed expression of fusion proteins containing pyruvate kinase, normally a cytoplasmic protein, fused to various portions of ICP8. These results showed that the carboxyl-terminal 28 residues are the only portion of ICP8 capable of targeting protein kinase into the nucleus. However, inclusion of certain additional regions of ICP8 into the fusion protein led to an inhibition of nuclear localization. Therefore, the carboxyl-terminal 28 residues of ICP8 can act independently as a nuclear localization signal, but certain conformational constraints or folding or assembly requirements in the remainder of the protein can affect the nuclear localization of the protein. Our results demonstrate that sequences distant from a nuclear localization signal can affect its ability to function. A set of fusion vectors has been isolated which should be of general use for making 5' or 3' fusions in any reading frame to rapidly map localization signals.

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Wang, Dandan, Xuedong Ding, and Philip N. Rather. "Indole Can Act as an Extracellular Signal inEscherichia coli." Journal of Bacteriology 183, no. 14 (July 15, 2001): 4210–16. http://dx.doi.org/10.1128/jb.183.14.4210-4216.2001.

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ABSTRACT Previous work has shown that lacZ fusions to thecysK, astD, tnaB, and gabT genes inEscherichia coli are activated by self-produced extracellular signals. Using a combination of ethyl acetate extraction, reversed-phase C18 chromatography, and thin-layer chromatography, we have purified an extracellular activating signal from E. coli supernatants. Mass spectrometry revealed a molecule with an m/z peak of 117, consistent with indole. Nuclear magnetic resonance analysis of the purified E. colifactor and synthetic indole revealed identical profiles. Using synthetic indole, a dose-dependent activation was observed withlacZ fusions to the gabT, astD, andtnaB genes. However,cysK::lacZ and several control fusions were not significantly activated by indole. Conditioned medium prepared from a tnaA (tryptophanase) mutant, deficient in indole production, supported 26 to 41% lower activation of thegabT and astD fusions. The residual level of activation may be due to a second activating signal. Activation of thetnaB::lacZ fusion was reduced by greater than 70% in conditioned medium from a tnaAmutant.

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Zhang, Jie, Licai Yang, Zhonghua Su, Xueqin Mao, Kan Luo, and Chengyu Liu. "Photoplethysmogram Signal Quality Assessment Using Support Vector Machine and Multi-Feature Fusion." Journal of Medical Imaging and Health Informatics 8, no. 9 (December 1, 2018): 1757–62. http://dx.doi.org/10.1166/jmihi.2018.2530.

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Background: Noise is unavoidable in the physiological signal measurement system. Poor quality signals can affect the results of analysis and disable the following clinical diagnosis. Thus, it is necessary to perform signal quality assessment before we interpreting the signal. Objective: In this work, we describe a method combing support vector machine (SVM) and multi-feature fusion for assessing the signal quality of pulsatile waveforms, concentrating on the photoplethysmogram (PPG). Methods: PPG signals from 53 healthy volunteers were recorded. Each had a 5 min length. Signal quality in each heart beat was manual annotated by clinical expert, and then the signal quality in 5 s episode was automatically calculated according to the results from each beat segments, resulting in a total of 13,294 5-s PPG segments. Then a SVM was trained to classify clean/noisy PPG recordings by inputting a set of twelve signal quality features. Further experiments were carried out to verify the proposed SVM based signal quality classifier method. Results: An average accuracy of 87.90%, a sensitivity of 88.10% and a specificity of 87.66% were found on the 10-fold cross validation. Conclusions: The signal quality of PPGs can be accurately classified by using the proposed method.

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Gashnikov, M. V. "Parameterized interpolation for fusion of multidimensional signals of various resolutions." Computer Optics 44, no. 3 (June 2020): 436–40. http://dx.doi.org/10.18287/2412-6179-co-696.

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Parameterized interpolation algorithms are adapted to fusion of multidimensional signals of various resolutions. Interpolating functions, switching rules for them and local features are specified, based on which the interpolating function is selected at each point of the signal. Parameterized interpolation algorithms are optimized based on minimizing the interpolation error. The recurrent interpolator optimization scheme is considered for the situation of inaccessibility of interpolated samples at the stage of setting up the interpolation procedure. Computational experiments are carried out to study the proposed interpolators for fusion of real multidimensional signals of various types. It is experimentally confirmed that the use of parameterized interpolators allows one to increase the accuracy of signal fusion.

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Abbas, Jabbar, Amin Al-Habaibeh, and Dai Zhong Su. "Sensor Fusion for Condition Monitoring System of End Milling Operations." Key Engineering Materials 450 (November 2010): 267–70. http://dx.doi.org/10.4028/www.scientific.net/kem.450.267.

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This paper describes the utilisation of multi sensor fusion model using force, vibration, acoustic emission, strain and sound sensors for monitoring tool wear in end milling operations. The paper applies the ASPS approach (Automated Sensor and Signal Processing Selection) method for signal processing and sensor selection [1]. The sensory signals were processed using different signal processing methods to create a wide range of Sensory Characteristic Features (SCFs). The sensitivity of these SCFs to tool wear is investigated. The results indicate that the sensor fusion system is capable of detecting machining faults in comparison to a single sensor using the suggested approach.

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Smol, Thomas, and Agnès Daudignon. "Comparison of IGH Profile Signals Using t(4;14) and IGH Break-Apart Probes by FISH in Multiple Myeloma." Cytogenetic and Genome Research 153, no. 1 (2017): 18–21. http://dx.doi.org/10.1159/000481523.

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We compared immunoglobulin heavy chain gene (IGH) signal patterns in multiple myeloma (MM) using the FGFR3-IGH and the IGH break-apart probes to facilitate their understanding and analysis. Forty-nine patients with MM were studied. FISH was performed on samples sorted with an FGFR3-IGH dual-color, dual-fusion translocation probe and an IGH dual-color break-apart rearrangement probe. The IGH deletions were found in 7 MM analyzed with the FGFR3-IGH probe and all confirmed by the IGH break-apart probe. The additional IGH signals were associated with different patterns using the IGH break-apart probe: a normal pattern in 9 cases, trisomy 14 in 3 cases, and splits of IGH in 7 cases. Fusion patterns with the FGFR3-IGH probe were observed in 13 cases. Atypical patterns were identified in 6 cases with multiple presentations of IGH: a deletion of the IGH variable segment in der(4) or in chromosome 14, loss of the IGH locus in chromosome 14, and additional copies of FGFR3-IGH fusion probes. We identified a majority of atypical IGH patterns with the t(4;14) probe, without false-negative results when FGFR3-IGH signal fusions were found. However, the extrapolation of FGFR3-IGH probe signals requires the IGH break-apart probe to obtain unequivocal interpretations.

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Xia, Yu, and Song Li. "Nonuniform recovery of fusion frame structured sparse signals." Analysis and Applications 15, no. 03 (April 18, 2017): 333–52. http://dx.doi.org/10.1142/s0219530516500032.

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This paper considers the nonuniform sparse recovery of block signals in a fusion frame, which is a collection of subspaces that provides redundant representation of signal spaces. Combined with specific fusion frame, the sensing mechanism selects block-vector-valued measurements independently at random from a probability distribution [Formula: see text]. If the probability distribution [Formula: see text] obeys a simple incoherence property and an isotropy property, we can faithfully recover approximately block sparse signals via mixed [Formula: see text]-minimization in ways similar to Compressed Sensing. The number of measurements is significantly reduced by a priori knowledge of a certain incoherence parameter [Formula: see text] associated with the angles between the fusion frame subspaces. As an example, the paper shows that an [Formula: see text]-sparse block signal can be exactly recovered from about [Formula: see text] Fourier coefficients combined with fusion frame [Formula: see text], where [Formula: see text].

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Hoang, Duy Tang, Xuan Toa Tran, Mien Van, and Hee Jun Kang. "A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis." Sensors 21, no. 1 (January 1, 2021): 244. http://dx.doi.org/10.3390/s21010244.

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This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most important finding of this paper is that a deep neural network with wide structure can extract automatically and efficiently discriminant features from multiple sensor signals simultaneously. The feature fusion process is integrated into the deep neural network as a layer of that network. Compared to single sensor cases and other fusion techniques, the proposed method achieves superior performance in experiments with actual bearing data.

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Diao, Chen, Bin Wang, and Ning Cai. "Data Fusion of Multivariate Time Series: Application to Noisy 12-Lead ECG Signals." Applied Sciences 9, no. 1 (December 29, 2018): 105. http://dx.doi.org/10.3390/app9010105.

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Twelve-lead Electrocardiograph (ECG) signals fusion is crucial for further ECG signal processing. In this paper, based on the idea of the local weighted linear prediction algorithm, a novel fusion data algorithm is proposed, which was applied in data fusion of the 12-lead ECG signals. In order to analyze the signal quality comprehensively, the quality characteristics should be adequately retained in the final fused result. In our algorithm, the values for the weighted coefficient of state points were closely related to the final fused result. Thus, two fuzzy inference systems were designed to calculate the weighted coefficients. For the sake of assessing the performance of our method, synthetic ECG signals and realistic ECG signals were applied in the experiments. Experimental results indicate that our method can fuse the 12-lead ECG signals effectively with inherit the quality characteristics of original ECG signals inherited properly.

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Shi, Yaochen, Hongyan Liu, Xuechen Zhang, Qinghua Li, and Xiaocheng Guo. "Wear Identification of Vibration Drilling Bit Based on Improved LMD and BP Neural Network." Mathematical Problems in Engineering 2020 (August 14, 2020): 1–9. http://dx.doi.org/10.1155/2020/2386721.

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In view of the low accuracy of single signal monitoring for the wear state of vibration drilling bit, a multisignal acquisition system for the wear state of ultrasonic axial vibration drilling bit is built to collect the drilling force, vibration, and acoustic emission signals under three different wear states. The drilling force, vibration and acoustic emission signals of the bit in the drilling process are processed by using wavelet decomposition technology, and the signals are extracted from the wear state of the bit, The wavelet energy coefficient with high state correlation is used as the feature parameter to identify the bit wear state. The feature parameter is trained by the combination of noise assisted LMD method and BP neural network. The experiment of single signal and multisignal fusion monitoring bit wear state is carried out, and the neural network structure is optimized according to the error. The results show that the accuracy of monitoring bit wear with a single signal of drilling force is 83.3%, the accuracy of monitoring bit wear with a single signal of vibration is 91.6%, the accuracy of monitoring bit wear with a single signal of acoustic emission is 91.6%, and the accuracy of monitoring bit wear with multisignal fusion is 95.8%; when the number of network layer is 4, the vibration is monitored with the fusion of force signal, acoustic emission signal, and vibration signal The accuracy of the state of drilling tool is up to 100%. The structure model of neural network is optimized reasonably to improve the recognition rate of bit wear in vibration drilling.

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Green, N., and P. Walter. "C-terminal sequences can inhibit the insertion of membrane proteins into the endoplasmic reticulum of Saccharomyces cerevisiae." Molecular and Cellular Biology 12, no. 1 (January 1992): 276–82. http://dx.doi.org/10.1128/mcb.12.1.276.

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We have constructed three gene fusions that encode portions of a membrane protein, arginine permease, fused to a reporter domain, the cytoplasmic enzyme histidinol dehydrogenase (HD), located at the C-terminal end. These fusion proteins contain at least one of the internal signal sequences of arginine permease. When the fusion proteins were expressed in Saccharomyces cerevisiae and inserted into the endoplasmic reticulum (ER), two of the fusion proteins placed HD on the luminal side of the ER membrane, but only when a piece of DNA encoding a spacer protein segment was inserted into the fusion joint. The third fusion protein, with or without the spacer included, placed HD on the cytoplasmic side of the membrane. These results suggest that (i) sequences C-terminal to the internal signal sequence can inhibit membrane insertion and (ii) HD requires a preceding spacer segment to be translocated across the ER membrane.

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Green, N., and P. Walter. "C-terminal sequences can inhibit the insertion of membrane proteins into the endoplasmic reticulum of Saccharomyces cerevisiae." Molecular and Cellular Biology 12, no. 1 (January 1992): 276–82. http://dx.doi.org/10.1128/mcb.12.1.276-282.1992.

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We have constructed three gene fusions that encode portions of a membrane protein, arginine permease, fused to a reporter domain, the cytoplasmic enzyme histidinol dehydrogenase (HD), located at the C-terminal end. These fusion proteins contain at least one of the internal signal sequences of arginine permease. When the fusion proteins were expressed in Saccharomyces cerevisiae and inserted into the endoplasmic reticulum (ER), two of the fusion proteins placed HD on the luminal side of the ER membrane, but only when a piece of DNA encoding a spacer protein segment was inserted into the fusion joint. The third fusion protein, with or without the spacer included, placed HD on the cytoplasmic side of the membrane. These results suggest that (i) sequences C-terminal to the internal signal sequence can inhibit membrane insertion and (ii) HD requires a preceding spacer segment to be translocated across the ER membrane.

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Bandala, Manuel, Tomás Salgado, and Ramón Chávez. "Multi-rate sensor fusion for underwater heading estimation." Industrial Robot: An International Journal 41, no. 4 (June 10, 2014): 347–50. http://dx.doi.org/10.1108/ir-04-2014-0321.

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Purpose – This paper presents the results of a heading estimation method for a remotely operated vehicle (ROV). The output rate of commercially available underwater compasses is typically in the order of a few Hz. Heading frequencies of at least 1 KHz are desirable for navigation and control purposes. Design/methodology/approach – The estimation was performed by fusioning the signals of three inertial sensors: the ROV’s own underwater compass (which operates roughly at 10 Hz or less), the ROV’s embedded gyro and an additional angular rate sensor that provides readings from 1 to 3 KHz. The output signal of the additional angular rate sensor is not part of the proposed Kalman filter. Nonetheless a five-point Newton-Cotes closed integration of such signal is fed into the Kalman filter implementation that performs the required heading estimation at 1 KHz or more. Findings – The proposed Kalman filter implementation is a suitable approach to estimate heading position even though the original compass signal rate is significantly slower than the signal required for both assisted and autonomous control. Research limitations/implications – The estimated heading yield good results in both simulation and experimental environments. Originality/value – The method was embedded in a dedicated 16-bit DSP that handles both the acquisition of the three signals and the heading estimation, hence resulting in a very low-cost solution. The embedded solution was tested in the developed submarine and the obtained high-rate heading parameter is now used by the control system of the ROV.

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Fisher, J. W., and T. Darrell. "Speaker Association With Signal-Level Audiovisual Fusion." IEEE Transactions on Multimedia 6, no. 3 (June 2004): 406–13. http://dx.doi.org/10.1109/tmm.2004.827503.

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Tao, Gui Li, and Zi Li Deng. "The Self-Tuning Distributed Information Fusion Kalman Filter for ARMA Signals." Applied Mechanics and Materials 48-49 (February 2011): 1305–9. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.1305.

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For the multisensor Autoregressive Moving Average (ARMA) signals with unknown model parameters and noise variances, using the Recursive Instrumental Variable (RIV) algorithm, the correlation method and the Gevers-Wouters algorithm with dead band, the fused estimators of unknown model parameters and noise variances can be obtained. Then substituting them into optimal fusion signal filter weighted by scalars, a self-tuning distributed fusion Kalman filter is presented. Using the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning fused Kalman signal filter converges to the optimal fused Kalman signal filter, so that it has asymptotic optimality. A simulation example shows its effectiveness.

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S, BhargaviB G., DeviNallammai RM, and PalaniThanaraj K. "ESTIMATION OF HEART RATE USING SIGNAL FUSION OF ECG AND BP SIGNALS." International Journal of Advanced Research 5, no. 3 (March 31, 2017): 1263–71. http://dx.doi.org/10.21474/ijar01/3631.

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Moran, P., and I. W. Caras. "Proteins containing an uncleaved signal for glycophosphatidylinositol membrane anchor attachment are retained in a post-ER compartment." Journal of Cell Biology 119, no. 4 (November 15, 1992): 763–72. http://dx.doi.org/10.1083/jcb.119.4.763.

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Glycophosphatidylinositol (GPI)-anchored membrane proteins are initially synthesized with a cleavable COOH-terminal extension that signals anchor attachment. Overexpression in COS cells of hGH-DAF fusion proteins containing the GPI signal of decay accelerating factor (DAF) fused to the COOH-terminus of human growth hormone (hGH), produces both GPI-anchored hGH-DAF and uncleaved precursors that retain the GPI signal. Using hGH-DAF fusion proteins containing a mutated, noncleavable GPI signal, we show that uncleaved polypeptides are retained inside the cell and accumulate in a brefeldin A-sensitive, Golgi-like juxtanuclear structure. Retention requires the presence of either a functional or a noncleavable GPI signal; hGH-DAF fusion proteins containing only the COOH-terminal hydrophobic domain (a component of the GPI signal) are secreted. Immunofluorescence analysis shows colocalization of the retained, uncleaved fusion proteins with both a Golgi marker and with p53, a marker of the ER-Golgi intermediate compartment. Since N-linked glycosylation is postulated to facilitate the transport of proteins to the cell surface, we engineered a glycosylation site into hGH-DAF. Glycosylation failed to completely override the transport block, but allowed some uncleaved hGH-DAF to pass through the secretory pathway and acquire endoglycosidase H resistance. The retained molecules remained endoglycosidase H sensitive. We suggest that the uncleaved fusion protein is retained in a sorting compartment between the ER and the medial Golgi complex. We speculate that a mechanism exists to retain proteins containing an uncleaved GPI signal as part of a system for quality control.

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Saimurugan, M., and R. Ramprasad. "A dual sensor signal fusion approach for detection of faults in rotating machines." Journal of Vibration and Control 24, no. 12 (February 1, 2017): 2621–30. http://dx.doi.org/10.1177/1077546316689644.

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The growing industrial sector utilizes machinery that needs to be monitored continuously by proficient experts and robust software to ensure a proper maintenance strategy. Condition monitoring using vibration signal analysis is one of the chief methods used in predictive maintenance strategy for rotating machinery. Generally, sound signal analysis is considered as secondary as it involves noise. In this paper, the signals for various rotating machinery faults are studied by simulating them in a machine fault simulator at various speeds and the best features are fused to obtain more efficiency in the fault diagnosis of rotating machinery. The vibration signal data obtained from accelerometers and sound signal data from a microphone is extracted for features using wavelet transform. The best features from vibration and sound signals are selected using the decision tree algorithm and are fused. Further, the features are classified using an artificial neural network and the corresponding efficiency at various motor speeds is found. The results of this paper imply that the signal fusion of vibration and sound by the decision tree algorithm is effective in machine fault diagnosis methodologies.

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Quan, Hong Wei, and Dong Liang Peng. "Research on Communication Reconnaissance Information Processing and Fusion." Applied Mechanics and Materials 552 (June 2014): 359–62. http://dx.doi.org/10.4028/www.scientific.net/amm.552.359.

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In complex electromagnetic signal environment, the reconnaissance equipments in tactical communication system can uninterruptedly reconnoiter a variety of enemy’s communication signals as well as access a number of characteristic parameters of time, frequency and space domain by searching analysis, feature extraction, direction finding and comprehensive identification. After a series of signal processing, data mining and information fusion, we can get the characteristic parameters of the electromagnetic spectrum of the enemy’s reconnaissance equipments, which provide the basis for analysis and estimation of electromagnetic situation in battlefield. In this paper a multi-hierarchical blackboard model is proposed for multi-sources communication reconnaissance information mining and fusion and the effectiveness of the method is validated in simulation environment.

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MacConaill, Laura E., Gerald F. Fitzgerald, and Douwe van Sinderen. "Investigation of Protein Export in Bifidobacterium breve UCC2003." Applied and Environmental Microbiology 69, no. 12 (December 2003): 6994–7001. http://dx.doi.org/10.1128/aem.69.12.6994-7001.2003.

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ABSTRACT The molecular interactions between the bifidobacterial cell and its natural environment, namely, the gastrointestinal tract of its host, are particularly important in understanding the presumed positive effects of Bifidobacterium on the health status of the host. In this study an export-specific reporter system, designed for use in gram-positive organisms and based on the use of the staphylococcal nuclease (Nuc) as a reporter, was employed to identify exported proteins in Bifidobacterium breve UCC2003. A B. breve genomic library of translational fusions to the Nuc-encoding gene devoid of its own export signal was established in the shuttle vector pFUN (I. Poquet, S. D. Ehrlich, and A. Gruss, J. Bacteriol. 180:1904-1912, 1998) and screened for bifidobacterial export signals. Sequence analysis of the fusion proteins obtained that displayed a nuclease-producing phenotype in both Lactococcus lactis and B. breve predicted the presence of a classical signal peptide and/or single or multiple transmembrane domains, thus indicating that some of the export signals in B. breve are comparable to those used in L. lactis. Cell fractionation studies, zymograms, nuclease assays, and Western blotting were employed to confirm the function of the predicted signals and to determine the location and activity of the exported fusion proteins in B. breve and/or L. lactis.

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Barani, R., and M. Sumathi. "A New Adaptive-Weighted Fusion Rule for Wavelet based PET/CT Fusion." International Journal of Signal Processing, Image Processing and Pattern Recognition 9, no. 11 (November 30, 2016): 271–82. http://dx.doi.org/10.14257/ijsip.2016.9.11.25.

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Wei, Wei, Qingxuan Jia, Yongli Feng, and Gang Chen. "Emotion Recognition Based on Weighted Fusion Strategy of Multichannel Physiological Signals." Computational Intelligence and Neuroscience 2018 (July 5, 2018): 1–9. http://dx.doi.org/10.1155/2018/5296523.

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Emotion recognition is an important pattern recognition problem that has inspired researchers for several areas. Various data from humans for emotion recognition have been developed, including visual, audio, and physiological signals data. This paper proposes a decision-level weight fusion strategy for emotion recognition in multichannel physiological signals. Firstly, we selected four kinds of physiological signals, including Electroencephalography (EEG), Electrocardiogram (ECG), Respiration Amplitude (RA), and Galvanic Skin Response (GSR). And various analysis domains have been used in physiological emotion features extraction. Secondly, we adopt feedback strategy for weight definition, according to recognition rate of each emotion of each physiological signal based on Support Vector Machine (SVM) classifier independently. Finally, we introduce weight in decision level by linear fusing weight matrix with classification result of each SVM classifier. The experiments on the MAHNOB-HCI database show the highest accuracy. The results also provide evidence and suggest a way for further developing a more specialized emotion recognition system based on multichannel data using weight fusion strategy.

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García Plaza, E., P. Núñez López, and E. Beamud González. "Multi-Sensor Data Fusion for Real-Time Surface Quality Control in Automated Machining Systems." Sensors 18, no. 12 (December 11, 2018): 4381. http://dx.doi.org/10.3390/s18124381.

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Multi-sensor data fusion systems entail the optimization of a wide range of parameters related to the selection of sensors, signal feature extraction methods, and predictive modeling techniques. The monitoring of automated machining systems enables the intelligent supervision of the production process by detecting malfunctions, and providing real-time information for continuous process optimization, and production line decision-making. Monitoring technologies are essential for the reduction of production times and costs, and an improvement in product quality, discarding the need for post-process quality controls. In this paper, a multi-sensor data fusion system for the real-time surface quality control based on cutting force, vibration, and acoustic emission signals was assessed. A total of four signal processing methods were analyzed: time direct analysis (TDA), power spectral density (PSD), singular spectrum analysis (SSA), and wavelet packet transform (WPT). Owing to the nonlinear and stochastic nature of the process, two predictive modeling techniques, multiple regression and artificial neural networks, were evaluated to correlate signal parametric characterization with surface quality. The results showed a high correlation of surface finish with cutting force and vibration signals. The signal processing methods based on signal decomposition in a combined time and frequency domain (SSA and WPT) exhibited better signal feature extraction, detecting excitation frequency ranges correlated to surface finish. The artificial neural network model obtained the highest predictive power, with better behavior for the whole data range. The proposed on-line multi-sensor data fusion provided significant improvements for in-process quality control, with excellent predictive power, reliability, and response times.

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Moran, P., and I. W. Caras. "Requirements for glycosylphosphatidylinositol attachment are similar but not identical in mammalian cells and parasitic protozoa." Journal of Cell Biology 125, no. 2 (April 15, 1994): 333–43. http://dx.doi.org/10.1083/jcb.125.2.333.

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The general features of the glycosylphosphatidylinositol (GPI) signal have been conserved in evolution. To test whether the requirements for GPI attachment are indeed the same in mammalian cells and parasitic protozoa, we expressed the prototype GPI-linked protein of Trypanosoma brucei, the variant surface glycoprotein (VSG), in COS cells. Although large amounts of VSG were produced, only a small fraction became GPI linked. This impaired processing is not caused by the VSG ectodomain, since replacement of the VSG GPI signal with that of decay accelerating factor (DAF) produced GPI-linked VSG. Furthermore, whereas fusion of the DAF GPI signal to the COOH terminus of human growth hormone (hGH) produces GPI-linked hGH, an analogous hGH fusion using the VSG GPI signal does not, indicating that the VSG GPI signal functions poorly in mammalian cells. By constructing chimeric VSG-DAF GPI signals and fusing them to the COOH terminus of hGH, we show that of the two critical elements that comprise the GPI-signal--the cleavage/attachment site and the COOH terminal hydrophobic domain--the former is responsible for the impaired activity of the VSG GPI signal in COS cells. To confirm this, we show that the VSG GPI signal can be converted to a viable signal for mammalian cells by altering the amino acid configuration at the cleavage/attachment site. We also show that when fused to the COOH terminus of hGH, the putative GPI signal from the malaria circumsporozoite (CS) protein produces low levels of GPI-anchored hGH, suggesting that the CS protein is indeed GPI linked, but that the CS protein GPI signal, like the VSG-signal, functions poorly in COS cells. The finding that the requirements for GPI attachment are similar but not identical in parasitic protozoa and mammalian cells may allow for the development of selective inhibitors of GPI-anchoring that might prove useful as antiparasite therapeutics.

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Goshvarpour, Atefeh, Ataollah Abbasi, Ateke Goshvarpour, and Sabalan Daneshvar. "A NOVEL SIGNAL-BASED FUSION APPROACH FOR ACCURATE MUSIC EMOTION RECOGNITION." Biomedical Engineering: Applications, Basis and Communications 28, no. 06 (December 2016): 1650040. http://dx.doi.org/10.4015/s101623721650040x.

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The objective of this study is to propose an accurate emotion recognition methodology. To this end, a novel fusion framework based on wavelet transform (WT), and matching pursuit (MP) algorithm was offered. Electrocardiogram (ECG) and galvanic skin response (GSR) of 11 healthy students were collected while subjects listened to emotional music clips. In both fusion techniques, Coiflet wavelet (Coif5 at level 14) was chosen as a wavelet family and MP dictionary, respectively. After employing the proposed fusion framework, some statistical measures were extracted. To describe emotions, three schemes were adopted: two-dimensional model (five classes), valence-(three classes), and arousal-(three classes) based emotion categories. Subsequently, the probabilistic neural network (PNN) was applied to classify affective states. The experiments indicate that the MP-based fusion approach outperform the wavelet-based fusion technique or methods using only ECG or GSR indices. Considering the proposed fusion techniques, the maximum classification rate of 99.64% and 92.31% was reached for the fusion methodology based on the MP algorithm (five classes of emotion) and wavelet-based fusion technique (three classes of valence), respectively.

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Wang, Li Yong, Le Li, and Yang Long Li. "Architecture and Function Modeling Research on Fusion Diagnosis of Reciprocating Engineering Fault." Applied Mechanics and Materials 380-384 (August 2013): 934–37. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.934.

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On the basis of complicated structure, various movement forms and high difficulty of fault detection for reciprocating engine, we have implemented the research on fault diagnostic approach by multi-sensor information, carried out the multi-source information fusion based on crankshaft phase signal, vibration signal, ultrasonic signal, pressure signal, ignition voltage signal and oil analysis information, created distributed structure model with multi-source information fusion, explained the parallel, distributed, serial and tree topology structure models, and established total model for multi-source information fusion of reciprocating engine fault diagnosis.

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Acharya, Sayandeep, Arjun Rajasekar, Barry S. Shender, Leonid Hrebien, and Moshe Kam. "Pulse Oximeter Signal Fusion for Robust Hypoxia Detection." Aerospace Medicine and Human Performance 86, no. 5 (May 1, 2015): 495–96. http://dx.doi.org/10.3357/amhp.4307.2015.

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Xydeas, Costas. "Objective evaluation of signal-level image fusion performance." Optical Engineering 44, no. 8 (August 1, 2005): 087003. http://dx.doi.org/10.1117/1.2009764.

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Friedrich, Bernhard, Irina Matschke, Essam Almasri, and Jürgen Mück. "Data Fusion Techniques for Adaptive Traffic Signal Control." IFAC Proceedings Volumes 36, no. 14 (August 2003): 61–66. http://dx.doi.org/10.1016/s1474-6670(17)32396-0.

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Nunes, F. D., and J. M. N. Leitao. "Signal processing aspects of fusion plasma broadband reflectometry." IEEE Transactions on Signal Processing 47, no. 2 (1999): 378–88. http://dx.doi.org/10.1109/78.740123.

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Guanglong, Ma, Wang Xiangqing, and Yu Junsheng. "ECG Signal Classification Algorithm Based on Fusion Features." Journal of Physics: Conference Series 1207 (April 2019): 012003. http://dx.doi.org/10.1088/1742-6596/1207/1/012003.

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Bell, J. D., J. H. Harris, J. L. Dunlap, N. A. Crocker, and V. K. Paré. "Signal analysis of fluctuations in toroidal fusion plasmas." Review of Scientific Instruments 64, no. 9 (September 1993): 2428–33. http://dx.doi.org/10.1063/1.1143900.

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Ningzhou, Cui, Xie Weixin, and Yu Xiongnan. "Distributed CFAR signal detection based on area fusion." Journal of Electronics (China) 14, no. 1 (January 1997): 7–11. http://dx.doi.org/10.1007/s11767-996-1016-2.

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Cai, Suxian, Shanshan Yang, Fang Zheng, Meng Lu, Yunfeng Wu, and Sridhar Krishnan. "Knee Joint Vibration Signal Analysis with Matching Pursuit Decomposition and Dynamic Weighted Classifier Fusion." Computational and Mathematical Methods in Medicine 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/904267.

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Analysis of knee joint vibration (VAG) signals can provide quantitative indices for detection of knee joint pathology at an early stage. In addition to the statistical features developed in the related previous studies, we extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and the number of significant signal turns detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF) method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM) and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis.

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Wang, Huadong, and Shi Dong. "Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/854085.

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In order to improve the reliability of measurement data, the multisensor data fusion technology has progressed greatly in improving the accuracy of measurement data. This paper utilizes the real-time, recursive, and optimal estimation characteristics of unscented Kalman filter (UKF), as well as the unique advantages of multiscale wavelet transform decomposition in data analysis to effectively integrate observational data from multiple sensors. A new multiscale UKF-based multisensor data fusion algorithm is proposed by combining the UKF with multiscale signal analysis. Firstly, model-based UKF is introduced into the multiple sensors, and then the model is decomposed at multiple scales onto the coarse scale with wavelets. Next, signals decomposed from fine to coarse scales are adjusted using the denoised observational data from corresponding sensors and reconstructed with wavelets to obtain the fused signals. Finally, the processed data are fused using adaptive weighted fusion algorithm. Comparison of simulation and experimental results shows that the proposed method can effectively improve the antijamming capability of the measurement system and ensure the reliability and accuracy of sensor measurement system compared to the use of data fusion algorithm alone.

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Qu, Ling-Zhi, Hui Liu, Ke-Ju Huang, and Jun-An Yang. "Specific Emitter Identification Based on Multi-Domain Feature Fusion and Integrated Learning." Symmetry 13, no. 8 (August 12, 2021): 1481. http://dx.doi.org/10.3390/sym13081481.

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Specific Emitter Identification (SEI) is a key research problem in the field of information countermeasures. It is one of the key technologies required to be solved urgently in the target reconnaissance system. It has the ability to distinguish between different individual radiation sources according to the varying individual characteristics of the emitter hardware within the transmitted signals. In response to the lack of scarcity among labeled samples in specific emitter identification, this paper proposes a method combining multi-domain feature fusion and integrated learning (MDFFIL). First, the received signal is preprocessed to obtain segmented time domain signal samples. Then, the signal is converted to time–frequency distribution using wavelet transform. Afterwards, an integrated learning two-stage recognition classification method is designed to extract data features of 1D time domain signals and 2D time–frequency distribution signals using the symmetry network structures of CVResNet and ResNet. Finally, fused features are fed into the complex-valued residual network classifier to obtain the final classification results. We demonstrate through the analysis results of the measured data that the proposed method has a higher accuracy as compared with the classical feature extraction method, and that this can improve the identification of communication radiation sources with fewer labeled samples.

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Journal articles on the topic 'Signal fusion'

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