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1
Jondral, Friedrich K. "White Gaussian Noise – Models for Engineers." Frequenz 72, no. 5-6 (April 25, 2018): 293–99. http://dx.doi.org/10.1515/freq-2017-0064.
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AbstractThis paper assembles some information about white Gaussian noise (WGN) and its applications. It starts from a description of thermal noise, i. e. the irregular motion of free charge carriers in electronic devices. In a second step, mathematical models of WGN processes and their most important parameters, especially autocorrelation functions and power spectrum densities, are introduced. In order to proceed from mathematical models to simulations, we discuss the generation of normally distributed random numbers. The signal-to-noise ratio as the most important quality measure used in communications, control or measurement technology is accurately introduced. As a practical application of WGN, the transmission of quadrature amplitude modulated (QAM) signals over additive WGN channels together with the optimum maximum likelihood (ML) detector is considered in a demonstrative and intuitive way.
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Fedorko, Gabriel, David Heinz, Vieroslav Molnár, and Tomáš Brenner. "Use of mathematical models and computer software for analysis of traffic noise." Open Engineering 10, no. 1 (March 10, 2020): 129–39. http://dx.doi.org/10.1515/eng-2020-0021.
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AbstractNoise measurement and evaluation of the existing noise situation is carried out in the vicinity of selected roads to demonstrate the need for the design of anti-noise measures or to assess the effectiveness of the measures. The selection and number of measuring points, time and intervals, the road noise measurement procedure and the measuring instruments used shall be used in accordance with the provisions of the STN ISO 1996-1 and STN ISO 1996-2 standards. During the measurement, it is also necessary to determine the microclimatic conditions of measurement, such as temperature and relative humidity, wind direction and speed, barometric pressure, duration and intensity of precipitation at the measuring point. The determinant for the definition of road traffic noise is the equivalent sound level A or the equivalent sound level in the third octave bands over a given time interval. In specific cases, it is possible to determine the equivalent sound pressure level from individual vehicle transit.
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Riabukha, V. P., A. V. Semeniaka, and Ye A. Katiushyn. "Mathematical Models of Cross-Correlated and Uncorrelated Gaussian Noise Jamming from External Sources." Radioelectronics and Communications Systems 64, no. 3 (March 2021): 147–54. http://dx.doi.org/10.3103/s0735272721030043.
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Szupiluk, Ryszard, and Tomasz Ząbkowski. "EGLD system for noise identification in ensemble predictors." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 33, no. 6 (October 28, 2014): 2006–15. http://dx.doi.org/10.1108/compel-11-2013-0354.
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Purpose – The purpose of this paper is to propose a noise identification method for data without temporal structure, in which application of typical mathematical white or colored noise models is very limited due to observation order requirements. The method is used to identify the destructive elements and to eliminate them what finally brings prediction improvement. Design/methodology/approach – The paper concerns noise detection problem presented in the framework of ensemble methods via blind signals separation. The authors utilize the Extended Generalized Lambda Distribution (EGLD) model to compare the signals with the target. Findings – The authors proposed novel signals similarity measure which is based on the EGLD system. The authors showed that it can be applied for data with or without time structure, as well as for data which are mutually uncorrelated. It turned out that method is effective for noise identification and can be an alternative, in many cases, to correlation approach, particularly for noise identification problems. Originality/value – In this method the improvement of prediction results is associated with elimination of the real physical factors rather than mathematical averaging in terms of arbitrary assumed distributions. In this approach, it does not matter what is the structure of aggregated models, what significantly distinct this approach from such techniques as boosting or bagging, in which the aggregation process applies to the models of similar structure. For this reason the methodology is focussed on physical noises elimination from predictions and it is complementary to the other ensemble approaches.
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Lau, D. L., R. Ulichney, and G. R. Arce. "Blue- and green-noise halftoning models." IEEE Signal Processing Magazine 20, no. 4 (July 2003): 28–38. http://dx.doi.org/10.1109/msp.2003.1215229.
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Xue, Xiao Min, Qing Sun, and Xiao Hong Wu. "Anti-Noise Simulation of the Hysteresis Mathematic Model Using Computationally-Efficient GA." Applied Mechanics and Materials 226-228 (November 2012): 725–28. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.725.
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Hysteresis is a particular feature of a wide range of physical systems and devices such as electromagnetic fields, mechanical stress–strain elements and electronic relay circuits. The extended Bouc-Wen model is one of the most widely used hysteresis models in mechanics. It has the capability to emulate the behavior of a wide class of hysteretic systems. However multi parameters have plagued its further application because the capability of computer and algorithm available currently can not meet the need completely. Thus to exploit an effective parallel algorithm is very essential. This paper is committed to propose a novel Genetic Algorithm (GA) so as to identify the parameters of the Bouc-Wen model with noise disturbance efficiently and accurately. Finally a large amount of noise-involved experimental data obtained from a real MR damper is employed to verify the proposed approach has the capability to estimate the satisfactory parameters of the Bouc-Wen model. Also suggested are the implications of the present study on other nonlinear hysteretic models or other complex mathematical models.
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Piazza, Alfredo J., and Can E. Korman. "Computation of the Spectral Density of Current Fluctuations in Bulk Silicon Based on the Solution of the Boltzmann Transport Equation." VLSI Design 6, no. 1-4 (January 1, 1998): 185–89. http://dx.doi.org/10.1155/1998/75094.
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Numerical simulation results for the spectral density of noise due to current fluctuations are presented. The mathematical framework is based on the interpretation of the equations describing electron transport in the semiclassical transport model as stochastic differential equations (SDE). Within this framework, it was previously shown that the autocovariance function of current fluctuations can be obtained from the transient solution of the Boltzmann transport equation (BTE) with special initial conditions. The key aspect which differentiates this approach from other noise models is that this approach directly connects noise characteristics with the physics of scattering in the semiclassical transport model and makes no additional assumptions regarding the nature of noise. The solution of the BTE is based on the Legendre polynomial method. A numerical algorithm is presented for the solution of the transient BTE. Numerical results are in good agreement with Monte Carlo noise simulations for the spectral density of current fluctuations in bulk silicon.
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CAVIN, RALPH K., VICTOR V. ZHIRNOV, JAMES A. HUTCHBY, and GEORGE I. BOURIANOFF. "ENERGY BARRIERS, DEMONS, AND MINIMUM ENERGY OPERATION OF ELECTRONIC DEVICES." Fluctuation and Noise Letters 05, no. 04 (December 2005): C29—C38. http://dx.doi.org/10.1142/s0219477505002951.
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The presence of thermal noise dictates that an energy barrier is needed to preserve a binary state. Therefore, all electronic devices contain at least one energy barrier to control electron flow. The barrier properties determine the operating characteristics of electronic devices. Furthermore, changes in the barrier shape require changes in charge density. Operation of all charge transport devices includes charging/discharging capacitances to change barrier height. We analyze energy dissipation for several schemes of charging capacitors. A basic assumption of Reversible Computing is that the computing system is completely isolated from the thermal bath. An isolated system is a mathematical abstraction never perfectly realized in practice. Errors due to thermal excitations are equivalent to information erasure, and thus computation dissipates energy. Another source of energy dissipation is due to the need of measurement and control. To analyze this side of the problem, the Maxwell's Demon is a useful abstraction. We hold that apparent "energy savings" in models of adiabatic circuits result from neglecting the total energy needed by other parts of the system to implement the circuit.
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Kumar, K., D. Yadav, and B. V. Srinivas. "Adaptive noise models for extended Kalman filter." Journal of Guidance, Control, and Dynamics 14, no. 2 (March 1991): 475–77. http://dx.doi.org/10.2514/3.20665.
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Shi-Xiong Zhang, Anton Ragni, and Mark John Francis Gales. "Structured Log Linear Models for Noise Robust Speech Recognition." IEEE Signal Processing Letters 17, no. 11 (November 2010): 945–48. http://dx.doi.org/10.1109/lsp.2010.2077626.
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Noulis, T., S. Siskos, and G. Sarrabayrouse. "Analysis and selection criteria of BSIM4 flicker noise simulation models." International Journal of Circuit Theory and Applications 36, no. 7 (October 2008): 813–23. http://dx.doi.org/10.1002/cta.461.
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Chow, Winston C., and Paul W. Schumacher. "Using Fractional Gaussian Noise Models in Orbit Determination." Journal of Guidance, Control, and Dynamics 27, no. 2 (March 2004): 319. http://dx.doi.org/10.2514/1.5989.
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Chow, Winston C., and Paul W. Schumacher. "Using Fractional Gaussian Noise Models in Orbit Determination." Journal of Guidance, Control, and Dynamics 26, no. 4 (July 2003): 593–607. http://dx.doi.org/10.2514/2.5087.
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Dong, Junyu, Jun Liu, Kang Yao, Mike Chantler, Lin Qi, Hui Yu, and Muwei Jian. "Survey of Procedural Methods for Two-Dimensional Texture Generation." Sensors 20, no. 4 (February 19, 2020): 1135. http://dx.doi.org/10.3390/s20041135.
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Textures are the most important element for simulating real-world scenes and providing realistic and immersive sensations in many applications. Procedural textures can simulate a broad variety of surface textures, which is helpful for the design and development of new sensors. Procedural texture generation is the process of creating textures using mathematical models. The input to these models can be a set of parameters, random values generated by noise functions, or existing texture images, which may be further processed or combined to generate new textures. Many methods for procedural texture generation have been proposed, but there has been no comprehensive survey or comparison of them yet. In this paper, we present a review of different procedural texture generation methods, according to the characteristics of the generated textures. We divide the different generation methods into two categories: structured texture and unstructured texture generation methods. Example textures are generated using these methods with varying parameter values. Furthermore, we survey post-processing methods based on the filtering and combination of different generation models. We also present a taxonomy of different models, according to the mathematical functions and texture samples they can produce. Finally, a psychophysical experiment is designed to identify the perceptual features of the example textures. Finally, an analysis of the results illustrates the strengths and weaknesses of these methods.
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Naranjo-Hernández, David, Javier Reina-Tosina, and Mart Min. "Fundamentals, Recent Advances, and Future Challenges in Bioimpedance Devices for Healthcare Applications." Journal of Sensors 2019 (July 15, 2019): 1–42. http://dx.doi.org/10.1155/2019/9210258.
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This work develops a thorough review of bioimpedance systems for healthcare applications. The basis and fundamentals of bioimpedance measurements are described covering issues ranging from the hardware diagrams to the configurations and designs of the electrodes and from the mathematical models that describe the frequency behavior of the bioimpedance to the sources of noise and artifacts. Bioimpedance applications such as body composition assessment, impedance cardiography (ICG), transthoracic impedance pneumography, electrical impedance tomography (EIT), and skin conductance are described and analyzed. A breakdown of recent advances and future challenges of bioimpedance is also performed, addressing topics such as transducers for biosensors and Lab-on-Chip technology, measurements in implantable systems, characterization of new parameters and substances, and novel bioimpedance applications.
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Carbone, A., and F. Palma. "Discontinuity correction in piecewise-linear models of oscillators for phase noise characterization." International Journal of Circuit Theory and Applications 35, no. 1 (January 2007): 93–104. http://dx.doi.org/10.1002/cta.383.
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Kozel, V. M., D. A. Podvornaya, and K. A. Kovalev. "Peal factor of signals of 5G mobile service systems." Doklady BGUIR 18, no. 6 (October 1, 2020): 5–10. http://dx.doi.org/10.35596/1729-7648-2020-18-6-5-10.
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This article discusses the possible formats of signals of 5G mobile communication networks (NR, IMT-2020): configurations of the number of resource blocks and frequency shifts of subcarriers. modulation schemes, organizations of the physical level, as well as the broadband signals of these networks and, as a result, the need for introducing a margin on the linearity of the transceiver’s paths to eliminate distortion or the manifestation of blocking effects when the signal interferes with third-party means. We analyze the characteristics of the dynamic range of the signals from IMT-2020 ground mobile systems and make a conclusion about the noise-like signals. To find the ratio of the maximum amplitude to its average value and a given ratio not exceeded with a given probability, the Matlab mathematical models were used. We infer that the law of probability distribution of the module of the instantaneous amplitude of the 5G signals corresponds to the distribution characteristic of narrow-band radio noise. Based on the study, the peak factor of the signal of the IMT-2020 ground mobile systems for various variations is obtained and a sufficient level of power reserve is provided to ensure, with high probability, the transmission of the 5G signals through radio paths, eliminating signal distortion. The results of this study were applied in the examination of the electromagnetic compatibility of radio electronic devices of cellular mobile communications with existing and promising electronic means of civil and special purposes and in the study of the protection of ground satellite stations from the effects of radio electronic means of IMT-2020 cellular mobile telecommunication networks located at border territories.
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Yuan, Jiangbin, Wenhe Yan, Shifeng Li, and Yu Hua. "Demodulation Method for Loran-C at Low SNR Based on Envelope Correlation–Phase Detection." Sensors 20, no. 16 (August 13, 2020): 4535. http://dx.doi.org/10.3390/s20164535.
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Loran-C is the most important backup and supplement system for the global navigation satellite system (GNSS). However, existing Loran-C demodulation methods are easily affected by noise and skywave interference (SWI). Therefore, this article proposes a demodulation method based on Loran-C pulse envelope correlation–phase detection (EC–PD), in which EC has two implementation schemes, namely moving average-cross correlation and matched correlation, to reduce the effects of noise and SWI. The mathematical models of the EC, calculation of the signal-to-noise ratio (SNR) gain, and selection of the EC schemes are given. The simulation results show that compared with an existing method, the proposed method has clear advantages: (1) The demodulation SNR threshold under Gaussian channel is only −2 dB, a reduction of 12.5 dB; (2) The probability of the demodulated SNR threshold, being less than zero under the SWI environment, can reach 0.78, a 26-fold increase. The test results show that the average data availability of the proposed method is 3.3 times higher than that of the existing method. Thus, our demodulation method has higher engineering application value. This will improve the performance of the modern Loran-C system, making it a more reliable backup for the GNSS.
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Lazarov. "Pulsar Emissions, Signal Modeling and Passive ISAR Imaging." Sensors 19, no. 15 (July 30, 2019): 3344. http://dx.doi.org/10.3390/s19153344.
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The present work addresses pulsar Crab Nebula emissions from point of view of their modeling and applications for asteroid detection and imaging by applying inverse synthetic aperture radar (ISAR) principles. A huge value of the plasma’s effective temperature is a reason for pulsar emission coherency, a property of great practical meaning for a space objects navigation, localization and imaging. Based on measurement data obtained by Goldstone-Apple Valley and Arecibo radio telescopes, an original time frequency grid mathematical model of pulsar emissions is created. Passive ISAR scenario, a space object’s geometry and a model of pulsar signals reflected from the space object’s surface are also described and graphically illustrated. A new range compression approach for ISAR imaging is suggested and demonstrated. In order to reduce the level of additive white Gaussian noise in signals and enlarge the signal to noise ratio in the final image, coherent summation of multiple complex images is applied. To prove the correctness of the geometry, signal models and theoretical analysis, results of numerical experiments are provided.
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Palacios Játiva, Pablo, Milton Román Cañizares, Cesar A. Azurdia-Meza, David Zabala-Blanco, Ali Dehghan Firoozabadi, Fabian Seguel, Samuel Montejo-Sánchez, and Ismael Soto. "Interference Mitigation for Visible Light Communications in Underground Mines Using Angle Diversity Receivers." Sensors 20, no. 2 (January 9, 2020): 367. http://dx.doi.org/10.3390/s20020367.
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This paper proposes two solutions based on angle diversity receivers (ADRs) to mitigate inter-cell interference (ICI) in underground mining visible light communication (VLC) systems, one of them is a novel approach. A realistic VLC system based on two underground mining scenarios, termed as mining roadway and mine working face, is developed and modeled. A channel model based on the direct component in line-of-sight (LoS) and reflections of non-line-of-sight (NLoS) links is considered, as well as thermal and shot noises. The design and mathematical models of a pyramid distribution and a new hemi-dodecahedral distribution are addressed in detail. The performances of these approaches, accompanied by signal combining schemes, are evaluated with the baseline of a single photo-diode in reception. Results show that the minimum lighting standards established in both scenarios are met. As expected, the root-mean-square delay spread decreases as the distance between the transmitters and receivers increases. Furthermore, the hemi-dodecahedron ADR in conjunction with the maximum ratio combining (MRC) scheme, presents the best performance in the evaluated VLC system, with a maximum user data rate of 250 Mbps in mining roadway and 120 Mbps in mine working face, received energy per bit/noise power of 32 dB and 23 dB, respectively, when the bit error rate corresponds to 10 − 4 , and finally, values of 120 dB in mining roadway and 118 dB in mine working face for signal-to-interference-plus-noise ratio are observed in a cumulative distribution function.
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Valada, Abhinav, and Wolfram Burgard. "Deep spatiotemporal models for robust proprioceptive terrain classification." International Journal of Robotics Research 36, no. 13-14 (August 31, 2017): 1521–39. http://dx.doi.org/10.1177/0278364917727062.
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Terrain classification is a critical component of any autonomous mobile robot system operating in unknown real-world environments. Over the years, several proprioceptive terrain classification techniques have been introduced to increase robustness or act as a fallback for traditional vision based approaches. However, they lack widespread adaptation due to various factors that include inadequate accuracy, robustness and slow run-times. In this paper, we use vehicle-terrain interaction sounds as a proprioceptive modality and propose a deep long-short term memory based recurrent model that captures both the spatial and temporal dynamics of such a problem, thereby overcoming these past limitations. Our model consists of a new convolution neural network architecture that learns deep spatial features, complemented with long-short term memory units that learn complex temporal dynamics. Experiments on two extensive datasets collected with different microphones on various indoor and outdoor terrains demonstrate state-of-the-art performance compared to existing techniques. We additionally evaluate the performance in adverse acoustic conditions with high-ambient noise and propose a noise-aware training scheme that enables learning of more generalizable models that are essential for robust real-world deployments.
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Li, Ji, Huiqiang Zhang, Jianping Ou, and Wei Wang. "A Multipulse Radar Signal Recognition Approach via HRF-Net Deep Learning Models." Computational Intelligence and Neuroscience 2021 (June 2, 2021): 1–9. http://dx.doi.org/10.1155/2021/9955130.
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In the field of electronic countermeasure, the recognition of radar signals is extremely important. This paper uses GNU Radio and Universal Software Radio Peripherals to generate 10 classes of close-to-real multipulse radar signals, namely, Barker, Chaotic, EQFM, Frank, FSK, LFM, LOFM, OFDM, P1, and P2. In order to obtain the time-frequency image (TFI) of the multipulse radar signal, the signal is Choi–Williams distribution (CWD) transformed. Aiming at the features of the multipulse radar signal TFI, we designed a distinguishing feature fusion extraction module (DFFE) and proposed a new HRF-Net deep learning model based on this module. The model has relatively few parameters and calculations. The experiments were carried out at the signal-to-noise ratio (SNR) of −14 ∼ 4 dB. In the case of −6 dB, the recognition result of HRF-Net reached 99.583% and the recognition result of the network still reached 97.500% under −14 dB. Compared with other methods, HRF-Nets have relatively better generalization and robustness.
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ITANO, Yuka, Shotaro MORIMOTO, Sadayuki YOSHITOMI, and Nobuyuki ITOH. "High-Q MOS Varactor Models for Quasi-Millimeter-Wave Low-Noise LC-VCOs." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E97.A, no. 3 (2014): 759–67. http://dx.doi.org/10.1587/transfun.e97.a.759.
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Deng, J., M. Bouchard, and T. H. Yeap. "Linear Dynamic Models With Mixture of Experts Architecture for Recognition of Speech Under Additive Noise Conditions." IEEE Signal Processing Letters 13, no. 9 (September 2006): 573–76. http://dx.doi.org/10.1109/lsp.2006.874462.
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Li, Ji, Huiqiang Zhang, Jianping Ou, and Wei Wang. "A Radar Signal Recognition Approach via IIF-Net Deep Learning Models." Computational Intelligence and Neuroscience 2020 (August 28, 2020): 1–8. http://dx.doi.org/10.1155/2020/8858588.
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In the increasingly complex electromagnetic environment of modern battlefields, how to quickly and accurately identify radar signals is a hotspot in the field of electronic countermeasures. In this paper, USRP N210, USRP-LW N210, and other general software radio peripherals are used to simulate the transmitting and receiving process of radar signals, and a total of 8 radar signals, namely, Barker, Frank, chaotic, P1, P2, P3, P4, and OFDM, are produced. The signal obtains time-frequency images (TFIs) through the Choi–Williams distribution function (CWD). According to the characteristics of the radar signal TFI, a global feature balance extraction module (GFBE) is designed. Then, a new IIF-Net convolutional neural network with fewer network parameters and less computation cost has been proposed. The signal-to-noise ratio (SNR) range is −10 to 6 dB in the experiments. The experiments show that when the SNR is higher than −2 dB, the signal recognition rate of IIF-Net is as high as 99.74%, and the signal recognition accuracy is still 92.36% when the SNR is −10 dB. Compared with other methods, IIF-Net has higher recognition rate and better robustness under low SNR.
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Yi, Sang-ri, and Junho Song. "Particle Filter Based Monitoring and Prediction of Spatiotemporal Corrosion Using Successive Measurements of Structural Responses." Sensors 18, no. 11 (November 13, 2018): 3909. http://dx.doi.org/10.3390/s18113909.
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Prediction of structural deterioration is a challenging task due to various uncertainties and temporal changes in the environmental conditions, measurement noises as well as errors of mathematical models used for predicting the deterioration progress. Monitoring of deterioration progress is also challenging even with successive measurements, especially when only indirect measurements such as structural responses are available. Recent developments of Bayesian filters and Bayesian inversion methods make it possible to address these challenges through probabilistic assimilation of successive measurement data and deterioration progress models. To this end, this paper proposes a new framework to monitor and predict the spatiotemporal progress of structural deterioration using successive, indirect and noisy measurements. The framework adopts particle filter for the purpose of real-time monitoring and prediction of corrosion states and probabilistic inference of uncertain and/or time-varying parameters in the corrosion progress model. In order to infer deterioration states from sparse indirect inspection data, for example structural responses at sensor locations, a Bayesian inversion method is integrated with the particle filter. The dimension of a continuous domain is reduced by the use of basis functions of truncated Karhunen-Loève expansion. The proposed framework is demonstrated and successfully tested by numerical experiments of reinforcement bar and steel plates subject to corrosion.
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Kolarova, Edita, and Lubomir Brancik. "Confidence intervals for RLCG cell influenced by coloured noise." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 36, no. 4 (July 3, 2017): 838–49. http://dx.doi.org/10.1108/compel-07-2016-0321.
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Purpose The purpose of this paper is to determine confidence intervals for the stochastic solutions in RLCG cells with a potential source influenced by coloured noise. Design/methodology/approach The deterministic model of the basic RLCG cell leads to an ordinary differential equation. In this paper, a stochastic model is formulated and the corresponding stochastic differential equation is analysed using the Itô stochastic calculus. Findings Equations for the first and the second moment of the stochastic solution of the coloured noise-affected RLCG cell are obtained, and the corresponding confidence intervals are determined. The moment equations lead to ordinary differential equations, which are solved numerically by an implicit Euler scheme, which turns out to be very effective. For comparison, the confidence intervals are computed statistically by an implementation of the Euler scheme using stochastic differential equations. Practical implications/implications The theoretical results are illustrated by examples. Numerical simulations in the examples are carried out using Matlab. A possible generalization for transmission line models is indicated. Originality/value The Itô-type stochastic differential equation describing the coloured noise RLCG cell is formulated, and equations for the respective moments are derived. Owing to this original approach, the confidence intervals can be found more effectively by solving a system of ordinary differential equations rather than by using statistical methods.
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Le Besnerais, Jean, Vincent Lanfranchi, Michel Hecquet, Pascal Brochet, and Guy Friedrich. "Acoustic noise of electromagnetic origin in a fractional‐slot induction machine." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 27, no. 5 (September 12, 2008): 1033–52. http://dx.doi.org/10.1108/03321640810890753.
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PurposeThe purpose of this paper is to apply a fast analytical model of the acoustic behaviour of pulse‐width modulation (PWM) controlled induction machines to a fractional‐slot winding machine, and to analytically clarify the interaction between space harmonics and time harmonics in audible electromagnetic noise spectrum.Design/methodology/approachA multilayer single‐phase equivalent circuit calculates the stator and rotor currents. Air‐gap radial flux density, which is supposed to be the only source of acoustic noise, is then computed with winding functions formalism. Mechanical and acoustic models are based on a 2D ring stator model. A method to analytically derive the orders and frequencies of most important vibration lines is detailed. The results are totally independent of the supply strategy and winding type of the machine. Some variable‐speed simulations and tests are run on a 700 W fractional‐slot induction machine in sinusoidal case as a first validation of theoretical results.FindingsThe influence of both winding space harmonics and PWM time harmonics on noise spectrum is exposed. Most dangerous orders and frequencies expressions are demonstrated in sinusoidal and PWM cases. For traditional integral windings, it is shown that vibration orders are necessarily even. When the stator slot number is not even, which is the case for fractional windings, some odd order deflections appear: the radial electromagnetic power can therefore dissipate as vibrations through all stator deformation modes, leading to a potentially lower noise level at resonance.Research limitations/implicationsThe analytical research does not consider saturation and eccentricity harmonics which can play a significant role in noise radiation.Practical implicationsThe analytical model and theoretical results presented help in designing low‐noise induction machines, and diagnosing noise or vibration problems.Originality/valueThe paper details a fully analytical acoustic and electromagnetic model of a PWM fed induction machine, and demonstrate the theoretical expression of main noise spectrum lines combining both time and space harmonics. For the first time, a direct comparison between simulated and experimental vibration spectra is made.
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Krishnan, Sanjay, Animesh Garg, Sachin Patil, Colin Lea, Gregory Hager, Pieter Abbeel, and Ken Goldberg. "Transition state clustering: Unsupervised surgical trajectory segmentation for robot learning." International Journal of Robotics Research 36, no. 13-14 (November 24, 2017): 1595–618. http://dx.doi.org/10.1177/0278364917743319.
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Demonstration trajectories collected from a supervisor in teleoperation are widely used for robot learning, and temporally segmenting the trajectories into shorter, less-variable segments can improve the efficiency and reliability of learning algorithms. Trajectory segmentation algorithms can be sensitive to noise, spurious motions, and temporal variation. We present a new unsupervised segmentation algorithm, transition state clustering (TSC), which leverages repeated demonstrations of a task by clustering segment endpoints across demonstrations. TSC complements any motion-based segmentation algorithm by identifying candidate transitions, clustering them by kinematic similarity, and then correlating the kinematic clusters with available sensory and temporal features. TSC uses a hierarchical Dirichlet process Gaussian mixture model to avoid selecting the number of segments a priori. We present simulated results to suggest that TSC significantly reduces the number of false-positive segments in dynamical systems observed with noise as compared with seven probabilistic and non-probabilistic segmentation algorithms. We additionally compare algorithms that use piecewise linear segment models, and find that TSC recovers segments of a generated piecewise linear trajectory with greater accuracy in the presence of process and observation noise. At the maximum noise level, TSC recovers the ground truth 49% more accurately than alternatives. Furthermore, TSC runs 100× faster than the next most accurate alternative autoregressive models, which require expensive Markov chain Monte Carlo (MCMC)-based inference. We also evaluated TSC on 67 recordings of surgical needle passing and suturing. We supplemented the kinematic recordings with manually annotated visual features that denote grasp and penetration conditions. On this dataset, TSC finds 83% of needle passing transitions and 73% of the suturing transitions annotated by human experts.
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Freundlich, Charles, Yan Zhang, Alex Zihao Zhu, Philippos Mordohai, and Michael M. Zavlanos. "Controlling a robotic stereo camera under image quantization noise." International Journal of Robotics Research 36, no. 12 (October 2017): 1268–85. http://dx.doi.org/10.1177/0278364917735163.
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In this paper, we address the problem of controlling a mobile stereo camera under image quantization noise. Assuming that a pair of images of a set of targets is available, the camera moves through a sequence of Next-Best-Views (NBVs), i.e. a sequence of views that minimizes the trace of the targets’ cumulative state covariance, constructed using a realistic model of the stereo rig that captures image quantization noise and a Kalman Filter (KF) that fuses the observation history with new information. The proposed algorithm decomposes control into two stages: first the NBV is computed in the camera relative coordinates, and then the camera moves to realize this view in the fixed global coordinate frame. This decomposition allows the camera to drive to a new pose that effectively realizes the NBV in camera coordinates while satisfying Field-of-View constraints in global coordinates, a task that is particularly challenging using complex sensing models. We provide simulations and real experiments that illustrate the ability of the proposed mobile camera system to accurately localize sets of targets. We also propose a novel data-driven technique to characterize unmodeled uncertainty, such as calibration errors, at the pixel level and show that this method ensures stability of the KF.
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Choudhary, Siddharth, Luca Carlone, Carlos Nieto, John Rogers, Henrik I. Christensen, and Frank Dellaert. "Distributed mapping with privacy and communication constraints: Lightweight algorithms and object-based models." International Journal of Robotics Research 36, no. 12 (October 2017): 1286–311. http://dx.doi.org/10.1177/0278364917732640.
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We consider the following problem: a team of robots is deployed in an unknown environment and it has to collaboratively build a map of the area without a reliable infrastructure for communication. The backbone for modern mapping techniques is pose graph optimization, which estimates the trajectory of the robots, from which the map can be easily built. The first contribution of this paper is a set of distributed algorithms for pose graph optimization: rather than sending all sensor data to a remote sensor fusion server, the robots exchange very partial and noisy information to reach an agreement on the pose graph configuration. Our approach can be considered as a distributed implementation of a two-stage approach that already exists, where we use the Successive Over-Relaxation and the Jacobi Over-Relaxation as workhorses to split the computation among the robots. We also provide conditions under which the proposed distributed protocols converge to the solution of the centralized two-stage approach. As a second contribution, we extend the proposed distributed algorithms to work with the object-based map models. The use of object-based models avoids the exchange of raw sensor measurements (e.g. point clouds or RGB-D data) further reducing the communication burden. Our third contribution is an extensive experimental evaluation of the proposed techniques, including tests in realistic Gazebo simulations and field experiments in a military test facility. Abundant experimental evidence suggests that one of the proposed algorithms (the Distributed Gauss–Seidel method) has excellent performance. The Distributed Gauss–Seidel method requires minimal information exchange, has an anytime flavor, scales well to large teams (we demonstrate mapping with a team of 50 robots), is robust to noise, and is easy to implement. Our field tests show that the combined use of our distributed algorithms and object-based models reduces the communication requirements by several orders of magnitude and enables distributed mapping with large teams of robots in real-world problems. The source code is available for download at https://cognitiverobotics.github.io/distributed-mapper/
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SHAHEEN, KAMEL M., and SOMESHWAR C. GUPTA. "PERFORMANCE RELIABILITY OF DS-CDMA MOBILE COMMUNICATION SYSTEM." International Journal of Reliability, Quality and Safety Engineering 03, no. 04 (December 1996): 305–24. http://dx.doi.org/10.1142/s021853939600020x.
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Applications of DS-CDMA in mobile communication systems, in forward link and reverse link, imply certain precautions against near–far effect (NFE), multi-access (co-channel) interference (MAI/CCI), and multipath fading. In this paper, two multi-user detection schemes are proposed to enhance the reliability of DS-CDMA system against NFE and MAI for both reverse link and forward link applications. The first scheme, reverse link multistage CCI cancellation (RLCCI) scheme, is derived for a single path channel model and is based on minimization of estimation error between the received signal and its projections on spreading sequences subspace. The second scheme, forward link CCI (FLCCI) cancellation scheme is based on keeping a simple yet robust decoder at the mobile station (MS), reducing the negative impact of CCI, and enhancing CCI positive contributions. FLCCI minimizes the negative impact of CCI by adjusting the magnitudes of vulnerable spreading codes to compensate in advance for CCI losses. Block diagrams, mathematical models, performance analysis and reliability of both cancellation schemes against NFE, MAI, and external noise are discussed in details. The improvements in BER and overall performance are evaluated using Monte Carlo simulation for both schemes.
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Kalaimani, G., K. Manojkumar, and Sathish S. Kumar. "Median Filtering for Removal of Maximum Impulse Noise from Images with a Decision Based Model." Journal of Computational and Theoretical Nanoscience 16, no. 2 (February 1, 2019): 562–67. http://dx.doi.org/10.1166/jctn.2019.7769.
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Filtering the unwanted parameters will guarantee the quality of images for further operations. Sensors and other devices used to capture the images may be subjected to situations where it might not function as programmed. The medium in which the images are transformed may also face the same difficulties. In such cases, salt and pepper noise should be removed before the images are taken into considerations. Otherwise, processes like edge detection, image segmentation and object recognition will be facing hurdles and may not reveal the desired output. This proposal intends to solve such an issue by using an algorithm for median filtering for using filters with medians on highly affected images. The previous techniques used for filtering out noise are Standard Median Filtering (SMF), Adaptive Median Filtering (AMF) and they have shown relatively lesser performance than the proposed approach. Implementation includes a FPGA set up and strives to remove impulse noise to a great extent consuming lesser computation time. The quality of output's visual and quantitative metrics has outperformed the discussed previous models.
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Signoretti, Gabriel, Marianne Silva, Pedro Andrade, Ivanovitch Silva, Emiliano Sisinni, and Paolo Ferrari. "An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity." Sensors 21, no. 12 (June 17, 2021): 4153. http://dx.doi.org/10.3390/s21124153.
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Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases.
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Onwude, Daniel I., Guangnan Chen, Nnanna Eke-emezie, Abraham Kabutey, Alfadhl Yahya Khaled, and Barbara Sturm. "Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce." Processes 8, no. 11 (November 9, 2020): 1431. http://dx.doi.org/10.3390/pr8111431.
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Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (≈50%) still occur during the packaging, pre-cooling, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins for multiple shipments in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables.
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Shahriari, Sayyed Ali Akbar. "Modelling and dynamic state estimation of a doubly fed induction generator wind turbine." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 39, no. 6 (September 11, 2020): 1393–409. http://dx.doi.org/10.1108/compel-07-2019-0277.
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Purpose This paper aims to propose an 18th-order nonlinear model for doubly fed induction generator (DFIG) wind turbines. Based on the proposed model, which is more complete than the models previously developed, an extended Kalman filter (EKF) is used to estimate the DFIG state variables. Design/methodology/approach State estimation is a popular approach in power system control and monitoring because of minimizing measurement noise level and obtaining non-measured state variables. To estimate all state variables of DFIG wind turbine, it is necessary to develop a model that considers all state variables. So, an 18th-order nonlinear model is proposed for DFIG wind turbines. EKF is used to estimate the DFIG state variables based on the proposed model. Findings An 18th-order nonlinear model is proposed for DFIG wind turbines. Furthermore, based on the proposed model, its state variables are estimated. Simulation studies are done in four cases to verify the ability of the proposed model in the estimation of state variables under noisy, wind speed variation and fault condition. The results demonstrate priority of the proposed model in the estimation of DFIG state variables. Originality/value Evaluating DFIG model to estimate its state variables precisely.
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Pupyshev, A. A. "Spectral interferences and their correction in atomic emission spectral analysis." Industrial laboratory. Diagnostics of materials 85, no. 1II) (February 15, 2019): 15–32. http://dx.doi.org/10.26896/1028-6861-2019-85-1-ii-15-32.
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The main sources of spectral interferences in atomic emission spectral analysis (AESA) are considered, including both wide-range (bremsstrahlung and recombination continuum, radiation of hot condensed particles and electrode ends, scattered light in the spectrometer, overlapping of the analytical line by the wings of the neighbor strong spectral lines of interfering elements, imposition of the components of molecular bands with the very close lines) and narrow-band (partial or complete overlapping of the analytical line with atomic or ionic lines of the sample elements, electrodes and discharge atmosphere; superposition of spectra from higher orders of reflection in conventional diffraction spectrometers and from neighboring orders in two-dimensional echelle spectrometers). The features of their manifestation in various sources of spectrum excitation (flames, DC arc, spark discharges, arc plasma discharges, inductively coupled plasma, microwave plasma, low-pressure electric discharges, laser spark) are considered. The possibilities of reducing the level of spectral interferences or elimination of the spectral noise at the stage of design and manufacturing of AESA devices, as well as upon selecting and adjusting of operation conditions of the analysis are shown. Much attention is paid to the most easily implemented in practice off-peak correction of wide-range spectral interferences. The modern methods of background correction under the spectral peak (under-peak) using a software for atomic emission spectrometers and providing creation of various mathematical models of the background signal in the vicinity of the analytical line at the stage of developing a specific AESA technique are considered. The issues of the choice of spectral lines for analytical measurements, tables and atlases of spectral lines, electronic databases used for this purpose are considered in detail. Specific features of application of the method of inter-element correction with direct spectral overlapping of the lines are given. The operating sequence for taking into account spectral interferences when developing the analysis techniques is proposed.
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Mendoza, Juan Pablo, Reid Simmons, and Manuela Veloso. "Detection and correction of subtle context-dependent robot model inaccuracies using parametric regions." International Journal of Robotics Research 38, no. 8 (May 20, 2019): 887–909. http://dx.doi.org/10.1177/0278364919845047.
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Autonomous robots frequently rely on models of their sensing and actions for intelligent decision making. Unfortunately, in complex environments, robots are bound to encounter situations in which their models do not accurately represent the world. Furthermore, these context-dependent model inaccuracies may be subtle, such that multiple observations may be necessary to distinguish them from noise. This paper formalizes the problem of detection and correction of such subtle contextual model inaccuracies in autonomous robots, and presents an algorithm to address this problem. The solution relies on reasoning about these contextual inaccuracies as parametric regions of inaccurate modeling (RIMs) in the robot’s planning space. Empirical results from various real robot domains demonstrate that, by explicitly searching for RIMs, robots are capable of efficiently detecting subtle contextual model inaccuracies, which in turn can lead to task performance improvement.
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Alhashimi, Anas, Martin Magnusson, Steffi Knorn, and Damiano Varagnolo. "Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions." Sensors 21, no. 1 (December 29, 2020): 155. http://dx.doi.org/10.3390/s21010155.
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We consider the problem of calibrating range measurements of a Light Detection and Ranging (lidar) sensor that is dealing with the sensor nonlinearity and heteroskedastic, range-dependent, measurement error. We solved the calibration problem without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground. Then, its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. Moreover, we consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that “fixed features shall have fixed relative distances and angles”. The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploy special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyze their statistical performance both in simulation and with field tests. We report the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observe that in field tests the approach can lead to a tenfold improvement in the accuracy of the raw measurements.
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Eichardt, Roland, Daniel Strohmeier, Alexander Hunold, René Machts, Jens Haueisen, Gregor Oelsner, Christian B. Schmidt, Volkmar Schultze, Ronny Stolz, and Uwe Graichen. "Sensitivity studies and optimization of arrangements of optically pumped magnetometers in simulated magnetoencephalography." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 3 (May 7, 2019): 953–64. http://dx.doi.org/10.1108/compel-09-2018-0372.
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Purpose The purpose of this paper is to present a simulation study using a model of a new optically pumped magnetometer sensor for application in the field of magnetoencephalography. The effects of sensor distance and orientation on the measurement information and the sensitivity to neuronal sources are investigated. Further, this paper uses a combinatorial optimization approach for sensor placement to measure spontaneous activity in the region of the occipital cortex. Design/methodology/approach This paper studies the effects of sensor distance and orientation on sensitivity to cortical sources and measurement information. A three-compartment model of the head, using the boundary element method, is applied. For sensor setup optimization, a combinatorial optimization scheme is developed. Findings The sensor distance to sources considerably affects the sensitivity and the retrieved information. A specific arrangement of four sensors for measuring spontaneous activity over the occipital part of the head is optimized by effectively avoiding position conflicts. Research limitations/implications Individual head models, as well as more detailed noise and signal models, will increase the significance for specific-use cases in future studies. Originality/value Effects of sensor distance and orientation are specifically evaluated for a new optically pumped magnetometer. A discrete optimization scheme for sensor optimization is introduced. The presented methodology is applicable for other sensor characterization and optimization problems. The findings contribute significantly to the development of new sensors.
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Didkovskyi, Vitalii, Vitaly Zaets, and Svetlana Kotenko. "Revealing the effect of rounded noise protection screens with finite sound insulation on an acoustic field around linear sound sources." Eastern-European Journal of Enterprise Technologies 1, no. 5 (109) (February 26, 2021): 16–22. http://dx.doi.org/10.15587/1729-4061.2021.224327.
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This paper reports studying the reduction of traffic noise by rounded noise protection screens with finite sound insulation, that is, those that can pass sound. Almost all models of acoustic screens, which are examined by analytical methods, are either direct or such that disregard the passage of sound through the screen, that is, it is assumed that the screen sound insulation is non-finite. This approach made it possible to solve the problem for a simplified model analytically but made it impossible to analyze the required sound insulation of noise protection screens. In the current paper, the problem of investigating an acoustic field around the screen whose sound insulation is finite has been stated, that is, it was taken into consideration that a sound wave propagates through the body of the screen. In addition, a given problem considers a rounded screen, rather than vertical, which is also used in different countries. Such a problem was solved by the method of partial domains. This method has made it possible to strictly analytically build a solution to the problem by simplifying it to solving an infinite system of algebraic equations, which was solved by the method of reduction. The screen model was set by the values of the density and speed of sound in the screen material. This approach has made it possible to change the acoustic impedance of the screen material and thereby change the sound insulation of the screen. That has made it possible to quantify the effect of screen sound insulation on its effectiveness. It has been shown that the efficiency of noise protection screens with finite sound insulation is approaching the efficiency of acoustically rigid screens, provided that the screen's natural sound insulation is 13–15 dB greater than the estimated efficiency of the rigid screen. The study results could make it possible to more accurately assess the effectiveness of noise protection screens. Determining the screen acoustic efficiency would make it possible to set requirements for its sound insulation characteristics. That could make it possible to select the designs of noise protection screens with minimal physical parameters, such as thickness, weight, etc.
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Prochniewicz, Dominik, Kinga Wezka, and Joanna Kozuchowska. "Empirical Stochastic Model of Multi-GNSS Measurements." Sensors 21, no. 13 (July 3, 2021): 4566. http://dx.doi.org/10.3390/s21134566.
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The stochastic model, together with the functional model, form the mathematical model of observation that enables the estimation of the unknown parameters. In Global Navigation Satellite Systems (GNSS), the stochastic model is an especially important element as it affects not only the accuracy of the positioning model solution, but also the reliability of the carrier-phase ambiguity resolution (AR). In this paper, we study in detail the stochastic modeling problem for Multi-GNSS positioning models, for which the standard approach used so far was to adopt stochastic parameters from the Global Positioning System (GPS). The aim of this work is to develop an individual, empirical stochastic model for each signal and each satellite block for GPS, GLONASS, Galileo and BeiDou systems. The realistic stochastic model is created in the form of a fully populated variance-covariance (VC) matrix that takes into account, in addition to the Carrier-to-Noise density Ratio (C/N0)-dependent variance function, also the cross- and time-correlations between the observations. The weekly measurements from a zero-length and very short baseline are utilized to derive stochastic parameters. The impact on the AR and solution accuracy is analyzed for different positioning scenarios using the modified Kalman Filter. Comparing the positioning results obtained for the created model with respect to the results for the standard elevation-dependent model allows to conclude that the individual empirical stochastic model increases the accuracy of positioning solution and the efficiency of AR. The optimal solution is achieved for four-system Multi-GNSS solution using fully populated empirical model individual for satellite blocks, which provides a 2% increase in the effectiveness of the AR (up to 100%), an increase in the number of solutions with errors below 5 mm by 37% and a reduction in the maximum error by 6 mm compared to the Multi-GNSS solution using the elevation-dependent model with neglected measurements correlations.
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Gillespie, Daniel T. "A mathematical comparison of simple models of Johnson noise and shot noise." Journal of Physics: Condensed Matter 12, no. 18 (May 8, 2000): 4195–205. http://dx.doi.org/10.1088/0953-8984/12/18/305.
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Moghekar, Rajeshwar, and Sachin Ahuja. "Deep Learning Model for Face Recognition in Unconstrained Environment." Journal of Computational and Theoretical Nanoscience 16, no. 10 (October 1, 2019): 4309–12. http://dx.doi.org/10.1166/jctn.2019.8518.
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Face recognition from videos is challenging problem as the face image captured has variations in terms of pose, Occlusion, blur and resolution. It has many applications including security monitoring and authentication. A subset of Indian Movies Face database (IMFDB) which has collection of face images retrieved from movie/video of actors which vary in terms of blur, pose, noise and illumination is used in our work. Our work focuses on the use of pre-trained deep learning models and applies transfer learning to the features extracted from the CNN layers. Later we compare it Fine tuned model. The results show that the accuracy is 99.89 using CNN as feature extractor and 96.3 when we fine tune the VGG-Face. The Fine tuned network of VGG-Face learnt more generic features when compared with its counterpart transfer learning. When applied on VGG16 transfer learning achieved 93.9.
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Bálint, Bálint L. "Embracing Noise and Error." Hungarian Studies Yearbook 1, no. 1 (December 1, 2019): 134–46. http://dx.doi.org/10.2478/hsy-2019-0010.
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Abstract In his article “Embracing Noise and Error”, Bálint L. Bálint argues that human society is going through a profound change as mathematical models are used to predict human behavior both on a personal level and on the level of the entire society. An inherent component of mathematical models is the concept of error or noise, which describes the level of unpredictability of a system by the specific mathematical model. The author reveals the educational origin of the abstract world that can be described by pure mathematics and can be considered an ideal world without errors. While the human perception of the world is different from the abstractions we were taught, the mathematical models need to integrate the error factor to deal with the unpredictability of reality. While scientific thinking developed the statistic-probabilistic model to define the limits of predictability, here we present that in a flow of time driven by entropy, stochastic variability is an in-built characteristic of the material world and represents ultimately the singularity of each individual moment in time and the chance for our freedom of choice.
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Beigman Klebanov, Beata, and Eyal Beigman. "From Annotator Agreement to Noise Models." Computational Linguistics 35, no. 4 (December 2009): 495–503. http://dx.doi.org/10.1162/coli.2009.35.4.35402.
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This article discusses the transition from annotated data to a gold standard, that is, a subset that is sufficiently noise-free with high confidence. Unless appropriately reinterpreted, agreement coefficients do not indicate the quality of the data set as a benchmarking resource: High overall agreement is neither sufficient nor necessary to distill some amount of highly reliable data from the annotated material. A mathematical framework is developed that allows estimation of the noise level of the agreed subset of annotated data, which helps promote cautious benchmarking.
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Liu, Xinhua, Yao Zou, Hailan Kuang, and Xiaolin Ma. "Face Image Age Estimation Based on Data Augmentation and Lightweight Convolutional Neural Network." Symmetry 12, no. 1 (January 10, 2020): 146. http://dx.doi.org/10.3390/sym12010146.
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Face images contain many important biological characteristics. The research directions of face images mainly include face age estimation, gender judgment, and facial expression recognition. Taking face age estimation as an example, the estimation of face age images through algorithms can be widely used in the fields of biometrics, intelligent monitoring, human-computer interaction, and personalized services. With the rapid development of computer technology, the processing speed of electronic devices has greatly increased, and the storage capacity has been greatly increased, allowing deep learning to dominate the field of artificial intelligence. Traditional age estimation methods first design features manually, then extract features, and perform age estimation. Convolutional neural networks (CNN) in deep learning have incomparable advantages in processing image features. Practice has proven that the accuracy of using convolutional neural networks to estimate the age of face images is far superior to traditional methods. However, as neural networks are designed to be deeper, and networks are becoming larger and more complex, this makes it difficult to deploy models on mobile terminals. Based on a lightweight convolutional neural network, an improved ShuffleNetV2 network based on the mixed attention mechanism (MA-SFV2: Mixed Attention-ShuffleNetV2) is proposed in this paper by transforming the output layer, merging classification and regression age estimation methods, and highlighting important features by preprocessing images and data augmentation methods. The influence of noise vectors such as the environmental information unrelated to faces in the image is reduced, so that the final age estimation accuracy can be comparable to the state-of-the-art.
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Elmehdi, Hussein. "Assessing traffic noise in teh City of Sharjah using prediction models." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 2 (August 1, 2021): 4520–25. http://dx.doi.org/10.3397/in-2021-2725.
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Noise originated from traffic on inner-city roads has been recognized as a major issue that has negative effects that go beyond annoyance and adverse health effects on people living near such roads. In this paper, we report the results of employing mathematical models for assessing traffic noise levels near roads in the City of Sharjah, UAE. Our field measurements indicated high noise levels near inter-city roads including roads in residential areas. To further investigate this, measured noise levels arising from principle traffic noise parameters were re-examined using published mathematical models with the objective of validating the acoustic noise levels generated by traffic noise of mixed composition, traffic flow rate and distance from the source. The main sound levels, namely the statistical equivalent sound levels (Leq): L10, L50 and L90 were used in the mathematical predictive models, to calculate the day time sound levels and correlated it with in situ measurements. We have examined 10 linear regression models, reported in the literature, five of which were found to provide strong correlation and were validated for predicting noise arising from traffic. The models are recommended for calculating mixed traffic noise levels and its effects on people living near these inter-city roads.
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Andronov, I. L. "Multiple Time Scales in Cataclysmic Variables: Observations Vs. Mathematical Models." Highlights of Astronomy 11, no. 1 (1998): 375. http://dx.doi.org/10.1017/s1539299600021250.
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Theoretical models and observational evidence for various processes in magnetic cataclysmic variables are briefly reviewed. Among them: modulation of the accretion rate by the magnetic field of the white dwarf; excitation of the orientation change of the magnetic axis of the white dwarf with respect to the secondary; structure of the accretion column and its instability; mass and angular momentum transfer; magnetic activity of the secondary; high/low luminosity state transitions; QPO’s, ”shot noise” and ”red noise” in polars, intermediate polars and nova-like objects.
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Shin, K. "Analysis of Friction Induced Disc Brake Noise Using Simple Mathematical Models." Noise & Vibration Worldwide 35, no. 2 (February 2004): 22–27. http://dx.doi.org/10.1260/0957456041217306.
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