Academic literature on the topic 'Multiple Noisy Sources'
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Journal articles on the topic "Multiple Noisy Sources"
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Silva, Amila, Ling Luo, Shanika Karunasekera, and Christopher Leckie. "Noise-Robust Learning from Multiple Unsupervised Sources of Inferred Labels." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8315–23. http://dx.doi.org/10.1609/aaai.v36i8.20806.
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Deep Neural Networks (DNNs) generally require large-scale datasets for training. Since manually obtaining clean labels for large datasets is extremely expensive, unsupervised models based on domain-specific heuristics can be used to efficiently infer the labels for such datasets. However, the labels from such inferred sources are typically noisy, which could easily mislead and lessen the generalizability of DNNs. Most approaches proposed in the literature to address this problem assume the label noise depends only on the true class of an instance (i.e., class-conditional noise). However, this assumption is not realistic for the inferred labels as they are typically inferred based on the features of the instances. The few recent attempts to model such instance-dependent (i.e., feature-dependent) noise require auxiliary information about the label noise (e.g., noise rates or clean samples). This work proposes a theoretically motivated framework to correct label noise in the presence of multiple labels inferred from unsupervised models. The framework consists of two modules: (1) MULTI-IDNC, a novel approach to correct label noise that is instance-dependent yet not class-conditional; (2) MULTI-CCNC, which extends an existing class-conditional noise-robust approach to yield improved class-conditional noise correction using multiple noisy label sources. We conduct experiments using nine real-world datasets for three different classification tasks (images, text and graph nodes). Our results show that our approach achieves notable improvements (e.g., 6.4% in accuracy) against state-of-the-art baselines while dealing with both instance-dependent and class-conditional noise in inferred label sources.
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Han, Te Sun. "Multicasting Multiple Correlated Sources to Multiple Sinks Over a Noisy Channel Network." IEEE Transactions on Information Theory 57, no. 1 (2011): 4–13. http://dx.doi.org/10.1109/tit.2010.2090223.
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Padmanabhan, Divya, Satyanath Bhat, Shirish Shevade, and Y. Narahari. "Multi-Label Classification from Multiple Noisy Sources Using Topic Models." Information 8, no. 2 (2017): 52. http://dx.doi.org/10.3390/info8020052.
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Woodruff, John, and DeLiang Wang. "Binaural Localization of Multiple Sources in Reverberant and Noisy Environments." IEEE Transactions on Audio, Speech, and Language Processing 20, no. 5 (2012): 1503–12. http://dx.doi.org/10.1109/tasl.2012.2183869.
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KOBAYASHI, Kentaro, Takaya YAMAZATO, and Masaaki KATAYAMA. "Decoding of Separately Encoded Multiple Correlated Sources Transmitted over Noisy Channels." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E92-A, no. 10 (2009): 2402–10. http://dx.doi.org/10.1587/transfun.e92.a.2402.
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Salvati, Daniele, and Sergio Canazza. "Incident Signal Power Comparison for Localization of Concurrent Multiple Acoustic Sources." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/582397.
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In this paper, a method to solve the localization of concurrent multiple acoustic sources in large open spaces is presented. The problem of the multisource localization in far-field conditions is to correctly associate the direction of arrival (DOA) estimated by a network array system to the same source. The use of systems implementing a Bayesian filter is a traditional approach to address the problem of localization in multisource acoustic scenario. However, in a real noisy open space the acoustic sources are often discontinuous with numerous short-duration events and thus the filtering methods may have difficulty to track the multiple sources. Incident signal power comparison (ISPC) is proposed to compute DOAs association. ISPC is based on identifying the incident signal power (ISP) of the sources on a microphone array using beamforming methods and comparing the ISP between different arrays using spectral distance (SD) measurement techniques. This method solves the ambiguities, due to the presence of simultaneous sources, by identifying sounds through a minimization of an error criterion on SD measures of DOA combinations. The experimental results were conducted in an outdoor real noisy environment and the ISPC performance is reported using different beamforming techniques and SD functions.
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Gong, Yuan, Chuyun Cui, Mengmeng Cai, Zehao Dong, Zheng Zhao, and Aihua Wang. "Residents’ Preferences to Multiple Sound Sources in Urban Park: Integrating Soundscape Measurements and Semantic Differences." Forests 13, no. 11 (2022): 1754. http://dx.doi.org/10.3390/f13111754.
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Urban parks provide their residents with a space for activities and interactions, and soundscapes play an important role in influencing the residents’ sense of experience of the park environment, with the result that the preferences of residents regarding multiple sound sources can be used as the basis of improving the soundscape quality and optimizing the function of urban parks. Using Shanghai Century Park as the research area, this study focused on the residents’ preference to multiple sound sources based on registered residence differences, and comprehensively used the semantic differential method, importance-performance analysis model, and multinomial logit model to conduct quantitative research on urban park soundscapes and combined the measurement results of the soundscape with the residents’ perception and preference. The results showed that the artificial sounds in Century Park were noisy during the morning and evening peak, while the human sounds were noisy when residents gathered in the park. There was a close relationship between the favorability and subjective loudness of urban park soundscapes, and the residents’ perceptions of urban park soundscapes substantially differed in terms of loudness, tone, and sense of belonging, whereas the differences in the perception of timbre, coverage, and sound source distance were relatively small. Furthermore, it is necessary to enhance the role of natural sounds as well as control the main noise-producing sound sources so that various sounds in the park will not interfere with each other; the optimization of soundscapes should also focus on the residents’ different soundscape preferences due to their individual characteristics.
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Dai, Xiaofeng, Olli Yli-Harja, and Andre S. Ribeiro. "Determining noisy attractors of delayed stochastic gene regulatory networks from multiple data sources." Bioinformatics 25, no. 18 (2009): 2362–68. http://dx.doi.org/10.1093/bioinformatics/btp411.
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Liu, Nehemiah T., Andriy I. Batchinsky, José Salinas, and Leopoldo C. Cancio. "Robust real-time calculation of heart-rate complexity using multiple noisy waveform sources." Journal of Critical Care 28, no. 1 (2013): e15-e16. http://dx.doi.org/10.1016/j.jcrc.2012.10.046.
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Fang, Yuzhuo, and Zhiyong Xu. "Multiple Sound Source Localization and Counting Using One Pair of Microphones in Noisy and Reverberant Environments." Mathematical Problems in Engineering 2020 (September 7, 2020): 1–12. http://dx.doi.org/10.1155/2020/8937829.
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A multiple sound source localization and counting method based on an angular spectrum is proposed in this paper. Local signal-to-noise ratio tracking, onset detection, and a coherence test are introduced to filter the generalized cross-correlation angular spectrum in the time-frequency domain for multiple sound source localization and counting in noisy and reverberant environments. Then, dual-width matching pursuit is introduced to replace peak search as the method of localization and counting. A comprehensive comparison of two statistical indicators, mean precision and mean absolute estimated error, indicates that the proposed localization and counting algorithm using both the filtered angular spectrum and dual-width matching pursuit method is more robust and accurate than the classic counterpart, especially in environments with low signal-to-noise ratio, strong reverberation, and abundant sound sources.
Dissertations / Theses on the topic "Multiple Noisy Sources"
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Röth, Kai [Verfasser], Julijana [Akademischer Betreuer] Gjorgjieva, Kadow Ilona [Gutachter] Grunwald, and Julijana [Gutachter] Gjorgjieva. "Efficient coding in populations of sensory neurons with multiple sources of noise and its relationship to phase transitions / Kai Röth ; Gutachter: Ilona Grunwald Kadow, Julijana Gjorgjieva ; Betreuer: Julijana Gjorgjieva." München : Universitätsbibliothek der TU München, 2021. http://d-nb.info/1234149141/34.
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Divya, Padmanabhan. "New Methods for Learning from Heterogeneous and Strategic Agents." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/3562.
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1 Introduction
In this doctoral thesis, we address several representative problems that arise in the context of learning from multiple heterogeneous agents. These problems are relevant to many modern applications such as crowdsourcing and internet advertising. In scenarios such as crowdsourcing, there is a planner who is interested in learning a task and a set of noisy agents provide the training data for this learning task. Any learning algorithm making use of the data provided by these noisy agents must account for their noise levels. The noise levels of the agents are unknown to the planner, leading to a non-trivial difficulty. Further, the agents are heterogeneous as they differ in terms of their noise levels. A key challenge in such settings is to learn the noise levels of the agents while simultaneously learning the underlying model. Another challenge arises when the agents are strategic. For example, when the agents are required to perform a task, they could be strategic on the efforts they put in. As another example, when required to report their costs incurred towards performing the task, the agents could be strategic and may not report the costs truthfully. In general, the performance of the learning algorithms could be severely affected if the information elicited from the agents is incorrect. We address the above challenges that arise in the following representative learning problems.
Multi-label Classification from Heterogeneous Noisy Agents Multi-label classification is a well-known supervised machine learning problem where each instance is associated with multiple classes. Since several labels can be assigned to a single instance, one of the key challenges in this problem is to learn the correlations between the classes. We first assume labels from a perfect source and propose a novel topic model called Multi-Label Presence-Absence Latent Dirichlet Allocation (ML-PA-LDA). In the current day scenario, a natural source for procuring the training dataset is through mining user-generated content or directly through users in a crowdsourcing platform. In the more practical scenario of crowdsourcing, an additional challenge arises as the labels of the training instances are provided by noisy, heterogeneous crowd-workers with unknown qualities. With this as the motivation, we further adapt our topic model to the scenario where the labels are provided by multiple noisy sources and refer to this model as ML-PA-LDA-MNS (ML-PA-LDA with Multiple Noisy Sources). With experiments on standard datasets, we show that the proposed models achieve superior performance over existing methods.
Active Linear Regression with Heterogeneous, Noisy and Strategic Agents
In this work, we study the problem of training a linear regression model by procuring labels from multiple noisy agents or crowd annotators, under a budget constraint. We propose a Bayesian model for linear regression from multiple noisy sources and use variational inference for parameter estimation. When labels are sought from agents, it is important to minimize the number of labels procured as every call to an agent incurs a cost. Towards this, we adopt an active learning approach. In this specific context, we prove the equivalence of well-studied criteria of active learning such as entropy minimization and expected error reduction. For the purpose of annotator selection in active learning, we observe a useful connection with the multi-armed bandit framework. Due to the nature of the distribution of the rewards on the arms, we resort to the Robust Upper Confidence Bound (UCB) scheme with truncated empirical mean estimator to solve the annotator selection problem. This yields provable guarantees on the regret. We apply our model to the scenario where annotators are strategic and design suitable incentives to induce them to put in their best efforts.
Ranking with Heterogeneous Strategic Agents
We look at the problem where a planner must rank multiple strategic agents, a problem that has many applications including sponsored search auctions (SSA). Stochastic multi-armed bandit (MAB) mechanisms have been used in the literature to solve this problem. Existing stochastic MAB mechanisms with a deterministic payment rule, proposed in the literature, necessarily suffer a regret of (T 2=3), where T is the number of time steps. This happens because these mechanisms address the worst case scenario where the means of the agents’ stochastic rewards are separated by a very small amount that depends on T . We however take a detour and allow the planner to indicate the resolution, , with which the agents must be distinguished. This immediately leads us to introduce the notion of -Regret. We propose a dominant strategy incentive compatible (DSIC) and individually rational (IR), deterministic MAB mechanism, based on ideas from the Upper Confidence Bound (UCB) family of MAB algorithms. The proposed mechanism - UCB achieves a -regret of O(log T ). We first establish the results for single slot SSA and then non-trivially extend the results to the case of multi-slot SSA.
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Divya, Padmanabhan. "New Methods for Learning from Heterogeneous and Strategic Agents." Thesis, 2017. http://etd.iisc.ernet.in/2005/3562.
Full textAbstract:
1 Introduction
In this doctoral thesis, we address several representative problems that arise in the context of learning from multiple heterogeneous agents. These problems are relevant to many modern applications such as crowdsourcing and internet advertising. In scenarios such as crowdsourcing, there is a planner who is interested in learning a task and a set of noisy agents provide the training data for this learning task. Any learning algorithm making use of the data provided by these noisy agents must account for their noise levels. The noise levels of the agents are unknown to the planner, leading to a non-trivial difficulty. Further, the agents are heterogeneous as they differ in terms of their noise levels. A key challenge in such settings is to learn the noise levels of the agents while simultaneously learning the underlying model. Another challenge arises when the agents are strategic. For example, when the agents are required to perform a task, they could be strategic on the efforts they put in. As another example, when required to report their costs incurred towards performing the task, the agents could be strategic and may not report the costs truthfully. In general, the performance of the learning algorithms could be severely affected if the information elicited from the agents is incorrect. We address the above challenges that arise in the following representative learning problems.
Multi-label Classification from Heterogeneous Noisy Agents Multi-label classification is a well-known supervised machine learning problem where each instance is associated with multiple classes. Since several labels can be assigned to a single instance, one of the key challenges in this problem is to learn the correlations between the classes. We first assume labels from a perfect source and propose a novel topic model called Multi-Label Presence-Absence Latent Dirichlet Allocation (ML-PA-LDA). In the current day scenario, a natural source for procuring the training dataset is through mining user-generated content or directly through users in a crowdsourcing platform. In the more practical scenario of crowdsourcing, an additional challenge arises as the labels of the training instances are provided by noisy, heterogeneous crowd-workers with unknown qualities. With this as the motivation, we further adapt our topic model to the scenario where the labels are provided by multiple noisy sources and refer to this model as ML-PA-LDA-MNS (ML-PA-LDA with Multiple Noisy Sources). With experiments on standard datasets, we show that the proposed models achieve superior performance over existing methods.
Active Linear Regression with Heterogeneous, Noisy and Strategic Agents
In this work, we study the problem of training a linear regression model by procuring labels from multiple noisy agents or crowd annotators, under a budget constraint. We propose a Bayesian model for linear regression from multiple noisy sources and use variational inference for parameter estimation. When labels are sought from agents, it is important to minimize the number of labels procured as every call to an agent incurs a cost. Towards this, we adopt an active learning approach. In this specific context, we prove the equivalence of well-studied criteria of active learning such as entropy minimization and expected error reduction. For the purpose of annotator selection in active learning, we observe a useful connection with the multi-armed bandit framework. Due to the nature of the distribution of the rewards on the arms, we resort to the Robust Upper Confidence Bound (UCB) scheme with truncated empirical mean estimator to solve the annotator selection problem. This yields provable guarantees on the regret. We apply our model to the scenario where annotators are strategic and design suitable incentives to induce them to put in their best efforts.
Ranking with Heterogeneous Strategic Agents
We look at the problem where a planner must rank multiple strategic agents, a problem that has many applications including sponsored search auctions (SSA). Stochastic multi-armed bandit (MAB) mechanisms have been used in the literature to solve this problem. Existing stochastic MAB mechanisms with a deterministic payment rule, proposed in the literature, necessarily suffer a regret of (T 2=3), where T is the number of time steps. This happens because these mechanisms address the worst case scenario where the means of the agents’ stochastic rewards are separated by a very small amount that depends on T . We however take a detour and allow the planner to indicate the resolution, , with which the agents must be distinguished. This immediately leads us to introduce the notion of -Regret. We propose a dominant strategy incentive compatible (DSIC) and individually rational (IR), deterministic MAB mechanism, based on ideas from the Upper Confidence Bound (UCB) family of MAB algorithms. The proposed mechanism - UCB achieves a -regret of O(log T ). We first establish the results for single slot SSA and then non-trivially extend the results to the case of multi-slot SSA.
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Wu, Pei-Yu, and 巫姵褕. "Noise exposure assessment of multiple sources in a hand tool manufacturing company." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/4pyecf.
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Ganesan, T. "Source And Channel Coding Techniques for The MIMO Reverse-link Channel." Thesis, 2014. http://etd.iisc.ac.in/handle/2005/2694.
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In wireless communication systems, the use of multiple antennas, also known as Multiple-Input Multiple-Output(MIMO) communications, is now a widely accepted and important technology for improving their reliability and throughput performance. However, in order to achieve the performance gains predicted by the theory, the transmitter and receiver need to have accurate and up-to-date Channel State Information(CSI) to overcome the vagaries of the fading environment. Traditionally, the CSI is obtained at the receiver by sending a known training sequence in the forward-link direction. This CSI has to be conveyed to the transmitter via a low-rate, low latency and noisy feedback channel in the reverse-link direction. This thesis addresses three key challenges in sending the CSI to the transmitter of a MIMO communication system over the reverse-link channel, and provides novel solutions to them.
The first issue is that the available CSI at the receiver has to be quantized to a finite number of bits, sent over a noisy feedback channel, reconstructed at the transmitter, and used by the transmitter for precoding its data symbols. In particular, the CSI quantization technique has to be resilient to errors introduced by the noisy reverse-link channel, and it is of interest to design computationally simple, linear filters to mitigate these errors. The second issue addressed is the design of low latency and low decoding complexity error correction codes to provide protection against fading conditions and noise in the reverse-link channel. The third issue is to improve the resilience of the reverse-link channel to fading.
The solution to the first problem is obtained by proposing two classes of receive filtering techniques, where the output of the source decoder is passed through a filter designed to reduce the overall distortion including the effect of the channel noise. This work combines the high resolution quantization theory and the optimal Minimum Mean Square Error(MMSE) filtering formulation to analyze, and optimize, the total end-to-end distortion. As a result, analytical expressions for the linear receive filters are obtained that minimize the total end-to-end distortion, given the quantization scheme and source(channel state) distribution. The solution to the second problem is obtained by proposing a new family of error correction codes, termed trellis coded block codes, where a trellis code and block code are concatenated in order to provide good coding gain as well as low latency and low complexity decoding. This code construction is made possible due to the existence of a uniform partitioning of linear block codes. The solution to the third problem is obtained by proposing three novel transmit precoding methods that are applicable to time-division-duplex systems, where the channel reciprocity can be exploited in designing the precoding scheme. The proposed precoding methods convert the Rayleigh fading MIMO channel into parallel Additive White Gaussian Noise(AWGN) channels with fixed gain, while satisfying an average transmit power constraint. Moreover, the receiver does not need to have knowledge of the CSI in order to decode the received data. These precoding methods are also extended to Rayleigh fading multi-user MIMO channels.
Finally, all the above methods are applied to the problem of designing a low-rate, low-latency code for the noisy and fading reverse-link channel that is used for sending the CSI. Simulation results are provided to demonstrate the improvement in the forward-link data rate due to the proposed methods. Note that, although the three solutions are presented in the context of CSI feedback in MIMO communications, their development is fairly general in nature, and, consequently, the solutions are potentially applicable in other communication systems also.
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Ganesan, T. "Source And Channel Coding Techniques for The MIMO Reverse-link Channel." Thesis, 2014. http://etd.iisc.ernet.in/handle/2005/2694.
Full textAbstract:
In wireless communication systems, the use of multiple antennas, also known as Multiple-Input Multiple-Output(MIMO) communications, is now a widely accepted and important technology for improving their reliability and throughput performance. However, in order to achieve the performance gains predicted by the theory, the transmitter and receiver need to have accurate and up-to-date Channel State Information(CSI) to overcome the vagaries of the fading environment. Traditionally, the CSI is obtained at the receiver by sending a known training sequence in the forward-link direction. This CSI has to be conveyed to the transmitter via a low-rate, low latency and noisy feedback channel in the reverse-link direction. This thesis addresses three key challenges in sending the CSI to the transmitter of a MIMO communication system over the reverse-link channel, and provides novel solutions to them.
The first issue is that the available CSI at the receiver has to be quantized to a finite number of bits, sent over a noisy feedback channel, reconstructed at the transmitter, and used by the transmitter for precoding its data symbols. In particular, the CSI quantization technique has to be resilient to errors introduced by the noisy reverse-link channel, and it is of interest to design computationally simple, linear filters to mitigate these errors. The second issue addressed is the design of low latency and low decoding complexity error correction codes to provide protection against fading conditions and noise in the reverse-link channel. The third issue is to improve the resilience of the reverse-link channel to fading.
The solution to the first problem is obtained by proposing two classes of receive filtering techniques, where the output of the source decoder is passed through a filter designed to reduce the overall distortion including the effect of the channel noise. This work combines the high resolution quantization theory and the optimal Minimum Mean Square Error(MMSE) filtering formulation to analyze, and optimize, the total end-to-end distortion. As a result, analytical expressions for the linear receive filters are obtained that minimize the total end-to-end distortion, given the quantization scheme and source(channel state) distribution. The solution to the second problem is obtained by proposing a new family of error correction codes, termed trellis coded block codes, where a trellis code and block code are concatenated in order to provide good coding gain as well as low latency and low complexity decoding. This code construction is made possible due to the existence of a uniform partitioning of linear block codes. The solution to the third problem is obtained by proposing three novel transmit precoding methods that are applicable to time-division-duplex systems, where the channel reciprocity can be exploited in designing the precoding scheme. The proposed precoding methods convert the Rayleigh fading MIMO channel into parallel Additive White Gaussian Noise(AWGN) channels with fixed gain, while satisfying an average transmit power constraint. Moreover, the receiver does not need to have knowledge of the CSI in order to decode the received data. These precoding methods are also extended to Rayleigh fading multi-user MIMO channels.
Finally, all the above methods are applied to the problem of designing a low-rate, low-latency code for the noisy and fading reverse-link channel that is used for sending the CSI. Simulation results are provided to demonstrate the improvement in the forward-link data rate due to the proposed methods. Note that, although the three solutions are presented in the context of CSI feedback in MIMO communications, their development is fairly general in nature, and, consequently, the solutions are potentially applicable in other communication systems also.
Books on the topic "Multiple Noisy Sources"
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Schram, Frederick R., and Stefan Koenemann. Evolution and Phylogeny of Pancrustacea. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780195365764.001.0001.
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The understanding of arthropod phylogeny and evolution in the past three decades has undergone major changes. These have arisen from new sources of data applicable across several fields of study. Developments within ontogenetic studies not only in regard to gross patterns of embryology but also regarding a revolution in the application of development genetics continue to generate remarkable insights into crustaceomorph evolution. Phylogeny techniques of analysis and new sources of data derived from molecular sequencing have forced consideration of new hypotheses concerning the interrelationships of all the pancrustaceans, both crustaceomorphs and Hexapoda. Furthermore, it is not uncommon that this multiplicity of sources for new data from opposing research teams can result in different hypotheses for phylogenetic relationships. This situation should not be treated as a defect, or an impediment, but rather as a source for multiple alternative hypotheses—the bases for further data gathering and analyses. Also, one should never view consideration of fossils as a vexing source of noise. Here, too, consideration of multiple hypotheses has proven useful. Often, fossils can produce deeper understanding of the paleodiversity of body plans. Nevertheless, some fossil groups still remain as enigmas, such as Thylacocephala. But even fossils incompletely understood can help fill in gaps in knowledge of paleobiodiversity that can prove useful, for example, in analyzing the the origin and early evolution of Hexapoda. Old ideas about pancrustacean evolution have served the field well, but results derived from all data inputs should be embraced.
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Wright, A. G. Linear performance. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199565092.003.0009.
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This chapter is concerned with a single consideration: the degree of proportionality between a light signal and its resulting electrical output. This is formally referred to as linearity, which depends on the suitability of the chosen PMT and the mode of operation (pulsed or analogue). Applications fall into two groups: analogue operation (DC) and transient applications. Linearity in a pulsed mode of operation concerns both pulse height (charge) and the rate of events. Generally, in the DC mode, only the mean anode current is relevant. Methods for determining both forms of non-linearity are presented, based on actual measurements. Test methods using multiple light sources, bootstrapping, single step (piggyback), and shot noise are investigated. A method for uncovering non-linearity in high-Z scintillators by using coincident gamma emissions (60Co) is demonstrated. An analytical means for correcting results at the 1 % level is provided.
Book chapters on the topic "Multiple Noisy Sources"
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Kusni, Muhammad, Purnomo, Leonardo Gunawan, and Husein Avicenna Akil. "Design of Active Noise Reduction Equipment Using Multipole Secondary Source." In Advances in Lightweight Materials and Structures. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7827-4_2.
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Ito, Nobutaka, Emmanuel Vincent, Nobutaka Ono, Rémi Gribonval, and Shigeki Sagayama. "Crystal-MUSIC: Accurate Localization of Multiple Sources in Diffuse Noise Environments Using Crystal-Shaped Microphone Arrays." In Latent Variable Analysis and Signal Separation. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15995-4_11.
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Li, Zhiyong, Honglin Chen, and Xia Huang. "Airbnb or Hotel?" In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6303-1.ch078.
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Advances in information technology have hugely influenced the tourism industry. Many tourists can generate and share their travel tips through social media, and people consult online reviews before making travel arrangements because they could access these sources of information easily. Either positive or negative reviews could increase consumer awareness of Airbnb. Using the approach of text mining and sentiment analysis, examining whether guests' emotions are positive or negative, this study investigates the attributes that influence Airbnb consumers' experiences compared with their previous hotel experiences by analysing big data of guests' online reviews. Findings reveal that the factors of guests' positive sentiment are the atmosphere, flexibility, special amenities, and humanized service; the factors of guests' negative sentiment are not value for money, have to clean the room before leaving, sharing amenities and space with strangers, disturbed by hosts' noisy recreational activities, and troubled by hosts' requesting good reviews.
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O'Callaghan, Casey. "Processes." In A Multisensory Philosophy of Perception. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198833703.003.0002.
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Crossmodal perceptual illusions such as ventriloquism, the McGurk effect, the rubber hand, and the sound-induced flash demonstrate that one sense can causally impact perceptual processing and experience that is associated with another sense. This chapter argues that such causal interactions between senses are not merely accidental. Interactions between senses are part of typical perceptual functioning. Unlike synesthesia, they reveal principled perceptual strategies for dealing with noisy, fallible sensory stimulation from multiple sources. Recalibrations resolve conflicts between senses and weight in deference to the more reliable modality. Coordination between senses thus improves the coherence and the reliability of human perceptual capacities. Therefore, some perceptual processes of the sort relevant to empirical psychology are multisensory.
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Bhui, Kamaldeep. "The social determinants of mental health." In Oxford Textbook of Public Mental Health, edited by Dinesh Bhugra, Kamaldeep Bhui, Samuel Y. S. Wong, and Stephen E. Gilman. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198792994.003.0019.
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The social determinants include poverty; material deprivation; unemployment; poor housing, including exposure to hazardous or noisy or unsafe homes; gender disadvantage, poor educational opportunities, violence, social and cultural inequalities in opportunities; and exposures to adversity, including discrimination, stigma, and prejudice. Health-risk behaviours have been the traditional targets of intervention. Interventions need to be situated within broader policy action on multiple disadvantages that are especially troublesome, operating at the structural level in society, influencing institutions and patterning interpersonal and inter-group relations. Action on social determinants needs cross-sector agreement and commitment to operate in harmony, flexibly, tenaciously, and retaining the capacity to adapt to new contexts and sources of disadvantage. Applying such interventions early in the life course, in youth, and to parents will prevent the intergenerational transmission of disadvantage. Structural sources of poor health must be tackled, hence the importance of action to militate against the social determinants of poor health.
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Grossberg, Stephen. "From Seeing and Reaching to Hearing and Speaking." In Conscious Mind, Resonant Brain. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190070557.003.0012.
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This far-ranging chapter provides unified explanations of data about audition, speech, and language, and the general cognitive processes that they specialize. The ventral What stream and dorsal Where cortical stream in vision have analogous ventral sound-to-meaning and dorsal sound-to-action streams in audition. Circular reactions for learning to reach using vision are homologous to circular reactions for learning to speak using audition. VITE circuits control arm movement properties of synergy, synchrony, and speed. Volitional basal ganglia GO signals choose which limb to move and how fast it moves. VAM models use a circular reaction to calibrate VITE circuit signals. VITE is joined with the FLETE model to compensate for variable loads, unexpected perturbations, and obstacles. Properties of cells in cortical areas 4 and 5, spinal cord, and cerebellum are quantitatively simulated. Motor equivalent reaching using clamped joints or tools arises from circular reactions that learn representations of space around an actor. Homologous circuits model motor-equivalent speech production, including coarticulation. Stream-shroud resonances play the role for audition that surface-shroud resonances play in vision. They support auditory consciousness and speech production. Strip maps and spectral-pitch resonances cooperate to solve the cocktail party problem whereby humans track voices of speakers in noisy environments with multiple sources. Auditory streaming and speaker normalization use networks with similar designs. Item-Order-Rank working memories and Masking Field networks temporarily store sequences of events while categorizing them into list chunks. Analog numerical representations and place-value number systems emerge from phylogenetically earlier Where and What stream spatial and categorical processes.
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Amerini, Irene, Roberto Caldelli, Vito Cappellini, Francesco Picchioni, and Alessandro Piva. "Estimate of PRNU Noise Based on Different Noise Models for Source Camera Identification." In Crime Prevention Technologies and Applications for Advancing Criminal Investigation. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1758-2.ch002.
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Identification of the source that has generated a digital content is considered one of the main open issues in multimedia forensics community. The extraction of photo-response non-uniformity (PRNU) noise has been so far indicated as a mean to identify sensor fingerprint. Such a fingerprint can be estimated from multiple images taken by the same camera by means of a de-noising filtering operation. In this paper, the authors propose a novel method for estimating the PRNU noise in source camera identification. In particular, a MMSE digital filter in the un-decimated wavelet domain, based on a signal-dependent noise model, is introduced and compared with others commonly adopted for this purpose. A theoretical framework and experimental results are provided and discussed.
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Deka, Surajit, and Kandarpa Kumar Sarma. "Joint Source Channel Coding and Diversity Techniques for 3G/4G/LTE-A." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0239-5.ch008.
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There has been a tremendous growth in the joint source channel coding techniques of recent wireless communication system. The main objective of this technique is to simplify the architecture which eliminates the network errors and instability. This chapter mainly focuses on Joint Source Channel Coding (JSCC) in Multiple Input Multiple Output (MIMO) diversity to minimize the effects of noise, distortion and related phenomena for better reception of the signal with increased efficiency and spectrum utilization with link reliability. JSCC techniques in presence of 3G/4G/LTE-A for high data rate communication with the carrier aggregation is an important element for communication over wireless channel. In this chapter, the authors shall discuss the current state of research, the challenges related to the deployment of JSCC, the likely solutions and the usefulness as part of upcoming communication networks. A major part of the work shall focus on their suitability in LTE-A frameworks so essential for sustaining the current growth in wireless communication.
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Deka, Surajit, and Kandarpa Kumar Sarma. "Joint Source Channel Coding and Diversity Techniques for 3G/4G/LTE-A." In Research Anthology on Recent Trends, Tools, and Implications of Computer Programming. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3016-0.ch001.
Full textAbstract:
There has been a tremendous growth in the joint source channel coding techniques of recent wireless communication system. The main objective of this technique is to simplify the architecture which eliminates the network errors and instability. This chapter mainly focuses on Joint Source Channel Coding (JSCC) in Multiple Input Multiple Output (MIMO) diversity to minimize the effects of noise, distortion and related phenomena for better reception of the signal with increased efficiency and spectrum utilization with link reliability. JSCC techniques in presence of 3G/4G/LTE-A for high data rate communication with the carrier aggregation is an important element for communication over wireless channel. In this chapter, the authors shall discuss the current state of research, the challenges related to the deployment of JSCC, the likely solutions and the usefulness as part of upcoming communication networks. A major part of the work shall focus on their suitability in LTE-A frameworks so essential for sustaining the current growth in wireless communication.
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"Digital Optical Transconductance Amplifier." In Advances in Medical Technologies and Clinical Practice. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-4875-5.ch005.
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An idea for a digital optical transconductance amplifier is presented in this chapter. The amplifier that is being suggested here enables you to rate each noise modification for each channel while reducing overall power consumption and satisfying all application criteria. Power upgrades for current and power sources, multiplied by various supply voltages, will be used in the low-voltage current-reuse re-topology. High efficiency is a goal of the power management circuit, which offers a range of supply voltages. A low-voltage amplifier is utilized in a typical 0.18-m CMOS and is installed by testing following its power cycle. Both noise efficiency and power efficiency are essential for the amplifier core. Each data point corresponds to a voltage range and is called into the digital architecture concerning the rising edge of the 40MHz clock (sampling clock).
Conference papers on the topic "Multiple Noisy Sources"
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Hou, Jie, and Gerhard Kramer. "Short message noisy network coding for multiple sources." In 2012 IEEE International Symposium on Information Theory - ISIT. IEEE, 2012. http://dx.doi.org/10.1109/isit.2012.6283562.
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Kim, Do Yeong, Nam Soo Kim, and Chong Kwan Un. "Model-based approach for robust speech recognition in noisy environements with multiple noise sources." In 5th European Conference on Speech Communication and Technology (Eurospeech 1997). ISCA, 1997. http://dx.doi.org/10.21437/eurospeech.1997-115.
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Portello, Alban, Gabriel Bustamante, Patrick Danes, and Alexis Mifsud. "Localization of multiple sources from a binaural head in a known noisy environment." In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). IEEE, 2014. http://dx.doi.org/10.1109/iros.2014.6943001.
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Chaudhari, Ashish M., Ilias Bilionis, and Jitesh H. Panchal. "How Do Designers Choose Among Multiple Noisy Information Sources in Engineering Design Optimization? An Experimental Study." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85460.
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Designers make process-level decisions to (i) select designs for performance evaluation, (ii) select information source, and (iii) decide whether to stop design exploration. These decisions are influenced by problem-related factors, such as costs and uncertainty in information sources, and budget constraints for design evaluations. The objective of this paper is to analyze individuals’ strategies for making process-level decisions under the availability of noisy information sources of different cost and uncertainty, and limited budget. Our approach involves a) conducting a behavioral experiment with an engineering optimization task to collect data on subjects’ decision strategies, b) eliciting their decision strategies using a survey, and c) performing a descriptive analysis to compare elicited strategies and observations from the data. We observe that subjects use specific criteria such as fixed values of attributes, highest prediction of performance, highest uncertainty in performance, and attribute thresholds when making decisions of interest. When subjects have higher budget, they are less likely to evaluate points having highest prediction of performance, and more likely to evaluate points having highest uncertainty in performance. Further, subjects conduct expensive evaluations even when their decisions have not sufficiently converged to the region of maximum performance in the design space and improvements from additional cheap evaluations are large. The implications of the results in identifying deviations from optimal strategies and structuring decisions for further model development are discussed.
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Yin, Li'ang, Yunfei Liu, Weinan Zhang, and Yong Yu. "Aggregating Crowd Wisdom with Side Information via a Clustering-based Label-aware Autoencoder." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/214.
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Aggregating crowd wisdom infers true labels for objects, from multiple noisy labels provided by various sources. Besides labels from sources, side information such as object features is also introduced to achieve higher inference accuracy. Usually, the learning-from-crowds framework is adopted. However, the framework considers each object in isolation and does not make full use of object features to overcome label noise. In this paper, we propose a clustering-based label-aware autoencoder (CLA) to alleviate label noise. CLA utilizes clusters to gather objects with similar features and exploits clustering to infer true labels, by constructing a novel deep generative process to simultaneously generate object features and source labels from clusters. For model inference, CLA extends the framework of variational autoencoders and utilizes maximizing a posteriori (MAP) estimation, which prevents the model from overfitting and trivial solutions. Experiments on real-world tasks demonstrate the significant improvement of CLA compared with the state-of-the-art aggregation algorithms.
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Ishi, Carlos T., Olivier Chatot, Hiroshi Ishiguro, and Norihiro Hagita. "Evaluation of a MUSIC-based real-time sound localization of multiple sound sources in real noisy environments." In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009). IEEE, 2009. http://dx.doi.org/10.1109/iros.2009.5354309.
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Cousseau, Vinícius M. R., and Luciano Barbosa. "Industrial Paper: Large-scale Record Linkage of Web-based Place Entities." In XXXIV Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbbd.2019.8820.
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Extracting data about entities from the Web has become commonplace in the industry and academia alike. Web-based entities, however, are inherently noisy and, as such, introduce several normalization issues which must be attended to in order to maintain a clean database. Record linkage, which refers to the detection of replicated datum from possibly multiple sources, is one of the most critical of those issues. This paper presents a practical approach for solving the record linkage problem in the places data domain at an industrial scale, displaying both a model which reaches a normalized Gini coefficient of 0.92, and an architecture that supports large-scale processing.
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Simic, Ines, and Rutger van Aalst. "Underwater Sound Filtering." In SNAME 5th World Maritime Technology Conference. SNAME, 2015. http://dx.doi.org/10.5957/wmtc-2015-247.
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The cocktail party algorithm is one of the most widely used algorithms for source separation of sound. The algorithm aims to find an automated solution for a problem that everyone experiences regularly, namely how to make oneself heard in a noisy environment. The cocktail party algorithm picks up the sound from different microphones, and then applies smart filters once the system has determined which sounds originate from the same source. This problem also becomes topical when developing sensors based on passive sonar, for instance for autonomous aquatic drones who have to develop awareness of ships and other possible obstacles on a busy shipping lane. It is possible to deploy multiple hydrophones to localize sound sources under water, but the system will be hindered considerably by the sound that the drone itself makes, such as the sound produced by the propellers. This paper describes a possible solution to the underwater sound filtering problem, using Blind Source Separation. The problem regards splitting sound from a boat engine and the water waves to prove the possibility to extract one sound fragment from the other on the open sea. The illustrations shown further in the report are tests performed in MATLAB to prove the theory.
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Ohadi, A. R., H. Mehdigholi, and E. Esmailzadeh. "Feed Forward Multiple-Input Active Noise Control Systems." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-43000.
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The use of adaptive feedforward controllers has proven to be a very successful strategy for controlling noise and vibration in a variety of applications. One reason is that the feedforward controller is an open loop controller, which can be designed to cancel the undesired noise in one position with any accuracy. However, the feedforward controller requires an input signal, called a reference signal, correlated to the noise source. As a consequence, a single reference controller can only reduce noise radiated from a single noise source. In many applications, there is a need to attenuate noise produced by several noise sources. In this paper, three different structures, single, modulating and individual controllers, for multiple input feedforward controllers have been studied and some design aspects are investigated.
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Karczub, Denis G., Fred W. Catron, Charles DePenning, and Allen C. Fagerlund. "Piping System Noise Issues: Multiple Noise Sources." In ASME 2005 Power Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/pwr2005-50015.
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Noise levels in power plants continue to be exceeded on occasion in spite of recent developments such as industry wide use of the International Electrotechnical Commission (IEC) control valve standard 534-8-3. There are several reasons for this related to the specific piping system under consideration. This paper presents flow test data showing the addition of sound powers and the resultant increase in noise due to multiple control valves discharging into a common manifold pipe.