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A taxonomy encompasses not only a classification methodology, but also an explicative theory of the phenomena that justify such classification. This paper introduces a taxonomy for noise in images of paper documents and details the Physical Noises according to the proposed taxonomy. [ABSTRACT FROM AUTHOR]

Support Vector Regression (SVR) has been very successful in pattern recognition, text categorization and function approximation. In real application systems, data domain often suffers from noise and outliers. When there is noise and/or outliers existing in sampling data, the SVR may try to fit those improper data and obtained systems may have the phenomenon of overfitting. In addition, the memory space for storing the kernel matrix of SVR will be increment with O (N2), where N is the number of training data. In this paper, a robust support vector regression is proposed for nonlinear function approximation problems with noise and outliers. [ABSTRACT FROM AUTHOR]

This paper presents the VLSI implementation of a scalable and programmable Continuous Restricted Boltzmann Machine (CRBM), a probabilistic model proved useful for recognising biomedical data. Each single-chip system contains 10 stochastic neurons and 25 adaptable connections. The scalability allows the network size to be expanded by interconnecting multiple chips, and the programmability allows all parameters to be set and refreshed to optimum values. In addition, current-mode computation is employed to increase dynamic ranges of signals, and a noise generator is included to induce continous-valued stochasticity on chip. The circuit design and corresponding measurement results are described and discussed. [ABSTRACT FROM AUTHOR]

The paper is based on results of perceptual tests focused on recognition of Czech words in noise. Identification of vowels in the syllabic nuclei proves most consistent. Resistance of individual phonemes and regularities in their substitution are examined. The primary focus concerns the position in a word. It shows that in Czech the position in the stressed syllable is not decisive for correct identification of vowels by listeners. Also the relations to duration and formant characteristics (F2/F1) bring negative results. Identification rates distinctly vary even among particular occurrences of individual phonemes. These differences do not have a verifiable relation to particular acoustic or structural feature. The assumption is vindicated that in the recognition of the acoustic word in adverse conditions a complex of features is in operation where these can act separately, in cooperation or in competition. [ABSTRACT FROM AUTHOR]

The previous regulations about acoustics in buildings ruled only isolation, forgetting other important subjects. The new code in Spain, specifically the DB-HR document, also include the regulations of excessive noise reverberating that produces and causes discomfort in many cases non-speech intelligibility, this circumstance is crucial in certain areas. The passive control restrictions in these limits of frequencies are well known. The insulating materials, soundwalls, acoustic filters, Helmholtz resonator, expansion chamber, the encapsulation of the noise source, mean large dimensions and/or weight to 500 Hz or less. The active noise control (ANC Active Noise Control) can be used to cancel the noise to high-frequency and the passive techniques can be used to low-frequency. This paper presents algorithms for active noise control and the typical industrial applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel computational multimodal model designed for pattern recognition of aircrafts΄ noise in real environments; with an 88.5% of effectiveness, it considers 13 different categories of aircrafts. This method includes measurements of signals of the noise produced during the takeoff at 25,000 samples per second and with a resolution of 24 bits, an spectral analysis made by means of an autoregressive model, an octave analysis, a normalization method created specifically for this work and two feed-forward neural networks. All the signals used for the design and evaluation of the results were obtained by means of field measurements. [ABSTRACT FROM AUTHOR]

Nowadays, successful pause detection plays an important role not only in the process of speech recognition and speech coding but also in the biometrical field for detecting stress in the speaker's emotional state due to uncomfortable situations or in interactive dialog systems for making more natural the human-machine interaction more natural. Most of the recordings exploited in practical applications are made under adverse conditions and few algorithms have been proposed to handle noisy conditions. This paper proposes two methods for non-speech activity pause detection in spontaneous speech recordings made in noisy environments. The input signal is transformed into log spectral energy and is divided into specific frequency bands. Each band is smoothed and tracked by dynamically adjusted thresholds based on noise energy estimation. Thresholds are adapted taking into account the dynamic changes of the speech signal under environmental noise. The proposed methods run in real time and do not require a priori knowledge of the SNR and a priori threshold values. Experimental results show that their performance is comparable with standard VADs. [ABSTRACT FROM AUTHOR]

The strength of Micro Systems Technology (MST) is the ability to fabricate a large number of small devices economically. However such devices tend to have errors caused by the variations of fabrication and inherent noise signals such as Brownian motion or Johnson noise. This paper advances the understanding of Micro Switch Group Sensors (MSGS). In this paper, a MSGS comprising of 20 switches has been built using electronic circuits and tested to verify the performance. The output signal is compared to the number of switches turned on and analysed. The fluctuations of the output signals and the measured performance of the electrical circuit was shown to be in good agreement with the theoretical prediction. The output signal is compared to the input signal and shows that MSGS has been successfully applied as a measurement tool. [ABSTRACT FROM AUTHOR]

This chapter provides a comprehensive overview of current gaps of and challenges for LCA structured into inventory, impact assessment, generic and evolving aspects. A total of 34 gaps and challenges were identified. These include challenges like `allocation', `uncertainty' or `biodiversity', as well as issues like `littering', `animal well-being' or `positive impacts' which are not covered as often in the existing LCA literature. Each of these gaps is described by a high-level overview of the topic and its relevance to LCA, and the state of the art in terms of literature and potential solutions, if any, is presented. The motivation for such an overview is two-fold: First, robust, sustainable and credible use of LCA should avoid the over-interpretation of LCA results without proper consideration of its gaps and limitations. Second, these gaps and challenges represent research needs for the scientific LCA community and hopefully inspire further progress in method development. [ABSTRACT FROM AUTHOR]

The object of this paper is the study of the noise produced by inverter-fed three-phase induction motors with squirrel-cage rotor. A wireless sensor network based measurement system is proposed, which gives the possibility of measuring the sound pressure virtually simultaneously in multiple points around the motor. In the case of inverter fed motors, the phenomena that lead to the production of the magnetic noise become more complex and the motor becomes noisier because of the increased possibility of matching the exciting frequencies with stator natural frequencies. In order to evaluate the influence of the switching frequency of the PWM inverter on the overall motor noise, the noise-frequency level diagrams (spectrograms) have been traced for a two speed motor of 1.5/2.4 kW, 750/1500 rpm, with 36 stator slots and 46 rotor slots. [ABSTRACT FROM AUTHOR]

Segmentation of noisy images is one of the most challenging problems in image analysis and any improvement of segmentation methods can highly influence the performance of many image processing applications. In automated image segmentation, the fuzzy c-means (FCM) clustering has been widely used because of its ability to model uncertainty within the data, applicability to multi-modal data and fairly robust behaviour. However, the standard FCM algorithm does not consider any information about the spatial image context and is highly sensitive to noise and other imaging artefacts. Considering above mentioned problems, we developed a new FCM-based approach for the noise-robust fuzzy clustering and we present it in this paper. In this new iterative algorithm we incorporated both spatial and feature space information into the similarity measure and the membership function. We considered that spatial information depends on the relative location and features of the neighbouring pixels. The performance of the proposed algorithm is tested on synthetic image with different noise levels and real images. Experimental quantitative and qualitative segmentation results show that our method efficiently preserves the homogeneity of the regions and is more robust to noise than other FCM-based methods. [ABSTRACT FROM AUTHOR]

Many modifications have been proposed to improve back-propagation΄s convergence time and generalisation capabilities. Typical techniques involve pruning of hidden neurons, adding noise to hidden neurons which do not learn, and reducing dataset size. In this paper, we wanted to compare these modifications΄ performance in many situations, perhaps for which they were not designed. Seven famous UCI datasets were used. These datasets are different in dimension, size and number of outliers. After experiments, we find some modifications have excellent effect of decreasing network΄s convergence time and improving generalisation capability while some modifications perform much the same as unmodified back-propagation. We also seek to find a combine of modifications which outperforms any single selected modification. [ABSTRACT FROM AUTHOR]

Inter-radar interference can cause the important impact to the radar detection performance because radar operates with high transmitter power, sensitive receiver and wideband. Because short-range surveillance radar is deployed rather close to each other, inter-radar interference is more critical. In this paper, the international criteria for radar interference protection is reviewed based on the ITU-R and NTIA documents, and the radar analysis is presented by taking into account the S-band short-range surveillance radar operating environments. Finally, S-band short-range surveillance radar optimum deployment is presented with the interference impact analysis. [ABSTRACT FROM AUTHOR]

In this paper, a kind of morphological filter with the combination of structure elements is designed to eliminate noise effectively and make weld image edge detection better. Experimental methods are conducted to compare the processing results of the morphological filtering and traditional filtering. And the result shows that this algorithm of the morphological filtering is simple and effective and has high anti-noise performance. The algorithm gives attention to both aspects of smoothing noises and protecting edges; the edge localization accuracy is higher and has practical values for engineering practice. [ABSTRACT FROM AUTHOR]

This paper presents a study of the role of materials in the generation of noise made by plastic gears. Sound levels were recorded for gears running in various running combinations. Frequency analyses were conducted on all the noise measurements and these showed that the harmonics for plastic gears mainly occur at multiples of the tooth meshing frequency. Materials such as Polyoxymethylene (POM), when run against itself were very noisy, but when run against a dissimilar material or steel they became quiet. Gears made from a polymer composite were the quietest. Measurements of surface roughness showed that the nosiest gears were those that developed high surface roughness when run-in. [ABSTRACT FROM AUTHOR]

This paper evaluates the clock generation quality of different digital circuits associated with clock generation and distribution. Circuit΄s noise response, jitter, and uncertainty are evaluated for different noise sources and loading conditions. We present performance simulations for inverters and inverter chains implemented in different technologies from AMS and UMC foundries. We show that the device size-scaling trend is increasing the uncertainty associated with this circuits, decreasing their precision. The correlation between circuit΄s parameters and selected performance metrics is also highlighted. [ABSTRACT FROM AUTHOR]

Although a vast majority of inductive learning algorithms has been developed for handling of the concept drifting data streams, especially the ones in virtue of ensemble classification models, few of them could adapt to the detection on the different types of concept drifts from noisy streaming data in a light demand on overheads of time and space. Motivated by this, a new classification algorithm for Concept drifting Detection based on an ensembling model of Random Decision Trees (called CDRDT) is proposed in this paper. Extensive studies with synthetic and real streaming data demonstrate that in comparison to several representative classification algorithms for concept drifting data streams, CDRDT not only could effectively and efficiently detect the potential concept changes in the noisy data streams, but also performs much better on the abilities of runtime and space with an improvement in predictive accuracy. Thus, our proposed algorithm provides a significant reference to the classification for concept drifting data streams with noise in a light weight way. [ABSTRACT FROM AUTHOR]

In this paper, the technique of image noise cancellation is presented by employing cellular neural networks (CNN) and linear matrix inequality (LMI). The main objective is to obtain the templates of CNN by using a corrupted image and a corresponding desired image. A criterion for the uniqueness and global asymptotic stability of the equilibrium point of CNN is presented based on the Lyapunov stability theorem (i.e., the feedback template "A" of CNN is solved at this step), and the input template "B" of CNN is designed to achieve desirable output by using the property of saturation nonlinearity of CNN. It is shown that the problem of image noise cancellation can be characterized in terms of LMIs. The simulation results indicate that the proposed method is useful for practical application. [ABSTRACT FROM AUTHOR]

Impact acoustic is an effective non-destructive evaluation (NDE) method for many applications especially for inspecting the bonding quality of mosaic tile-walls. However, the audio noise can affect the power spectrum density (PSD) distribution of an acquired signal seriously. So, the traditional method of using PSD as the main identification tool is not sufficient. This paper proposes an evaluation method based on wavelet packet decomposition (WPD). Using WPD, the PSD of the signal is allocated into certain component fields. Investigation on the component PSD indicates it can reveal the bonding quality even in a noisy environment. An artificial neural network (ANN) is chosen as a classifier to simplify the evaluation system and makes it more effective and efficient. The performance of the proposed approach is evaluated experimentally. It is verified that this WPD approach can be applied to impact acoustic method to enhance its evaluation capability in a noisy environment. [ABSTRACT FROM AUTHOR]

Passive radars use illuminators of opportunity to detect target and measure its parameters. The usual processing routine consists of direct path interference and clutter removal and computation of cross ambiguity function (CAF) to estimate amplitude-range-Doppler surface. Main parameters affecting this procedure are direct path interference and clutter, receiver thermal noise and near large targets. This paper presents an investigation on effect of each parameter and tries to estimate processing gain of each Block. A comparison between different adaptive interference removal methods also presented. [ABSTRACT FROM AUTHOR]

Thinking about the “Symbol Grounding Problem” and the brain structure of living things, the author believes that it is the best solution for generating communication in robot-like systems to use a neural network that is trained based on reinforcement learning. As the first step of the research of symbol emergence using neural network, it was examined that parallel analog communication signals are binarized in some degree by noise addition in reinforcement learning-based communication acquisition. In this paper, it is shown that two consecutive analog communication signals are binarized by noise addition using recurrent neural networks. Furthermore, when the noise ratio becomes larger, the degree of the binarization becomes larger. [ABSTRACT FROM AUTHOR]

A low noise amplifier (LNA), intended for use in a single-chip FM Tuner, has been designed with a commercial 0.6 um BiCMOS process. A new common-base structure is adopted because of its simple input matching and high linearity [1]. The spectreRF simulation results show that the proposed low noise amplifier exhibits a forward gain (S21) of 24dB with a noise figure of only 2.5dB in the frequency range. The Input third-order Intercept-Point (IIP3) is -15.25dB. In this paper, we present a detailed analysis of the LNA Architecture. [ABSTRACT FROM AUTHOR]

This paper presents a 5.2GHz CMOS LNA for IEEE 802.11a WLAN applications. The amplifier is designed in a 0.18µm CMOS process for high gain, low power, low voltage and low noise operation. Based on the designed cascode with feedback structure, the fully integrated LNA exhibits 14.25 dB gain and 2.96 dB noise figure, while consuming only 3.6 mW power from a 0.8 supply voltage. [ABSTRACT FROM AUTHOR]

The idea of noise is now widespread in many fields of study. However to a large extent the use of this term is unexamined. It has become part of the practice of science without entering to a significant extent as part of its explicit theory. Here I try to produce a clearer and more coherent account of the term. I start with a picture of noise from electrical engineering. I then generalise this to the widest conception: that of noise as what is unwanted. A closely related conception is noise as what is unexplained. A particular case of this later usage is where a source of randomness can be used to stand-in for this residual. I argue that noise and randomness are not the same. I explore the possible relation between noise and context, and propose a new conception of noise: namely that noise is what can result from an extra-contextual signal. I finish with an application of the analysis of noise to the relation of determinism and randomness. [ABSTRACT FROM AUTHOR]

We present the system identification and the online optimization of feedback controllers applied to combustion systems using evolutionary algorithms. The algorithmis applied to gas turbine combustors that are susceptible to thermoacoustic instabilities resulting in imperfect combustion and decreased lifetime. In order to mitigate these pressure oscillations, feedback controllers sense the pressure and command secondary fuel injectors. The controllers are optimized online with an extension of the CMA evolution strategy capable of handling noise associated with the uncertainties in the pressure measurements. The presented method is independent of the specific noise distribution and prevents premature convergence of the evolution strategy. The proposed algorithm needs only two additional function evaluations per generation and is therefore particularly suitable for online optimization. The algorithm is experimentally verified on a gas turbine combustor test rig. The results show that the algorithm can improve the performance of controllers online and is able to cope with a variety of time dependent operating conditions. [ABSTRACT FROM AUTHOR]

A new BSS method based on independent sub-band function components (ISBF) and wavelet to separate single-channel mixture signal in noise was studied. Through obtaining sub-band functions with independent component characteristic in the time domain, 6-20 sub-band function by ICA were employed as the preparation knowledge for the blind source separation (BSS). By combining the independent sub-band function components (ISBF) into the single-channel mixture signal, a separation modeling of single-channel mixture signal was built based on ISBF. And the separation mathematics model of the single-channel signal in noise is investigated. The wavelet transform was used to eliminate the noise as well. Two simulation samples performed to verify the availability of the proposed methods. The results show that the methods played good role in one sensor source BSS, and had a capability to extract the sound signal feature. [ABSTRACT FROM AUTHOR]

The article talks about the essay "On Denoting," by the English mathematician and philosopher Bertrand Russell. It is proposed in this essay that the most obvious purposes of language are to refer to things and then to describe them. It is stated that by associating certain noises and marks on paper with objects or pictures of objects, referring, or denoting, is how man acquires language in the first place. Man might spontaneously concur that referring is an evident fact about how words are used, and maybe even how words get their meaning.

We discuss several methods for producing plots of uni- and bivariate distributions of confidential numeric microdata so that no single value is disclosed even in the presence of detailed additional knowledge, using the jackknife method of confidentiality protection. For histograms (as for frequency tables) this is similar to adding white noise of constant amplitude to all frequencies. Decreasing the bin size and smoothing, leading to kernel density estimation in the limit, gives more informative plots which need less noise for protection. Detail can be increased by choosing the bandwidth locally. Smoothing also the noise (i.e. using correlated noise) gives more visual improvement. Additional protection comes from robustifying the kernel density estimator or plotting only classified densities as in contour plots. [ABSTRACT FROM AUTHOR]

To study the problem of learning from noisy data, the common approach is to use a statistical model of noise. The influence of the noise is then considered according to pragmatic or statistical criteria, by using a paradigm taking into account a distribution of the data. In this article, we study the noise as a nonstatistical phenomenon, by defining the concept of systematic noise. We establish various ways of learning (in the limit) from noisy data. The first is based on a technique of reduction between problems and consists in learning from the data which one knows noisy, then in denoising the learned function. The second consists in denoising on the fly the training examples, thus to identify in the limit good examples, and then to learn from noncorrupted data. We give in both cases sufficient conditions so that learning is possible and we show through various examples (coming in particular from the field of the grammatical inference) that our techniques are complementary. [ABSTRACT FROM AUTHOR]

Chapter 9 deals with man-made noise and interference. It covers: The characterisation of pulses and impulsive noise; Measurement parameters for impulsive noise including Amplitude Probability Distribution (APD) and Noise Amplitude Distribution (NAD); Specifications for noise-measuring equipment; Practical equipment for measuring APD and NAD; Impulsive noise measurements in urban and suburban environments; Assessment of receiver performance for analogue and digital signals using APD and NAD; Interference in radio systems, protection ratio and grade of service; Single and Multiple interferers. [ABSTRACT FROM PUBLISHER]

Several recent measurement studies have concluded that classical Poisson-like traffic models do not account for time dependencies observed at multiple time scales in a wide range of networking applications. As the resulting temporal correlations are expected to have a significant impact on buffer engineering practices, this "failure of Poisson modeling" has generated an increased interest in a number of alternative traffic models that capture observed (long-range) dependencies. Proposed models include fractional Brownian motion and its discrete-time analog, fractional Gaussian noise. Already both have exposed clearly the limitations of traditional traffic models in predicting storage requirements and devising congestion controls. [ABSTRACT FROM PUBLISHER]