Second Stage Of Processing Research Articles

Two-stage algorithm of spectral analysis for automatic speech recognition systems

Within the framework of a dynamically developing direction of research in the field of acoustic measurements, the task of spectral analysis of speech signals in automatic speech recognition systems is considered. The low efficiency of the systems in unfavorable speech production conditions (noise, insufficient intelligibility of speech sounds) compared to human perception of oral speech is noted. To improve the efficiency of automatic speech recognition systems, a two-stage algorithm for spectral analysis of speech signals is proposed. The first stage of speech signal processing consists of its parametric spectral analysis using an autoregressive model of the vocal tract of a conditional speaker. The second stage of processing is the transformation (modification) of the obtained spectral estimate according to the principle of frequency-selective amplification of the amplitude of the main formants of the intra-periodic power spectrum. The software implementation of the proposed algorithm based on the high-speed computational procedure of the fast Fourier transform is described. Using the author’s software, a full-scale experiment was carried out: an additive mixture of vowel sounds of the control speaker’s speech with white Gaussian noise was studied. Based on the results of the experiment, it was concluded that the amplitude of the main speech signal formants were amplified by 10–20 dB and, accordingly, a significant improvement in the speech sounds intelligibility. The scope of possible application of the developed algorithm covers automatic speech recognition systems based on speech signal processing in the frequency domain, including the use of artificial neural networks.

The effect of varying levels of potassium hydroxide-treated false Yam seed meal on growth performance, haematology and serum biochemistry of male broiler chickens

This study was carried out to evaluate sequential treatment of false yam seeds as substitute for maize in broiler chicken diets on their growth, carcass, haematology and serum biochemistry. Ripped fruits of false yam plants growing in the wild were harvested by hand picking. The fruits were partially sun-dried to reduce their moisture content and facilitate cracking to obtain the false yam seeds (FYS). The fresh FYS were crushed with a stone to reduce size, increase surface area and facilitate processing. The freshly crushed false yam seeds were subjected to multiple-stage processing where seeds were first soaked in ordinary water (i.e., addition of fresh seeds in ordinary water at a ratio of 1:2, wt/vol) for 12 days with water being changed every 3 days. After the 12 days of soaking, the seed sample was washed with clean ordinary water. In the second stage of processing, soaked FYS was soaked in a solution of 1M concentration of potassium hydroxide at a ratio of 1:2 (wt/vol) for 24 hours, after which the sample was washed thoroughly with clean ordinary water. The last stage of processing involved blanching of potassium hydroxide-treated seeds, firstly by immersing the sample in hot water (900C) for 20 minutes and then transferring it into cold water (40C) for 40 minutes. The treated seed sample was then washed with clean water, sun-dried to approximately 12% moisture on a cement floor and ground into gritty flour using a hammer mill. The treated false yam seed meal was labeled as KOH_T FYSM. At 21 d of age, 128 birds were individually weighed and then randomly assigned to one of four dietary treatments in quadruplicate lots. Each replicate had 8 male broilers. The mean live-weight of birds in each replicate was 883 g (±0.05). The four treatments included the control without KOH_T FYSM; treatments 2, 3 and 4 contained 100, 300 and 500g/kg KOH_T FYSM respectively replacing maize (wt. /wt. basis) in a completely randomized design. The birds were fed various diets for 35 d. The growth response of male broiler chickens fed diets supplemented with varying levels of KOH_T FYSM revealed no significant (P<0.05) difference in all growth parameters measured. Carcass dress weight and carcass dressing showed no significant (P>0.05) difference at 500g/kg. However, the relative weight of organs revealed a significant (P<0.041) increase in the heart weight and a reduction in the weights of the liver and the spleen. Haematological parameters evaluated revealed a significant (P<0.05) decrease in the haemoglobin, mean corpuscular volume, mean corpuscular haemoglobin and mean corpuscular haemoglobin concentration as KOH_T FYSM was increased in the diets. The economics of replacing maize with KOH_T FYSM in the diets of male broilers revealed that price per kg of feed reduces as the test material was increased in the diets. Inclusion of KOH_T FYSM up to 500g/kg in the diets of male broilers had no adverse effect on their growth performance. However, its inclusion influenced internal organ weights such as the liver, spleen and heart. Economics of feeding KOH_T to broilers did not increase cost of feeding, suggesting that, There is an economic value for using this product for broiler chickens as an alternative to maize during periods of scarcity and can be recommended for use by farmers where this plant is available.

Mechanism of Interaction of Backfill Mixtures with Natural Rock Fractures within the Zone of Their Intense Manifestation while Developing Steep Ore Deposits

Mining systems for ore deposit extraction with the backfilling of the goaf solve the problem of preserving the surface and the complete extraction of rich ores. This paper considers the filling of mined-out stopes with a viscous fluidal solution for the formation of an artificial strong massif, which results in a conglomerate formed on contact with the ore deposit. It was established that exogenous fracturing at the Pivdenno-Belozirske deposit significantly affects the stability of the sides and ceilings in the chamber. This phenomenon can be observed at the first stage of processing. At chambers (the second stage of processing), the artificial rock mass is exposed. It has been established that the chamber mining systems do not ensure the operational stability of the vertical outcrop in the zones of exogenous intensive fracture of the rock mass, especially in the places where they intersect. The zonal location of intense fracture was established along the strike and dip of the steep ore deposit, as was its importance in the formation of rock fallouts. An analytical solution algorithm has been developed to determine the penetration of the backfilling mixture in the plane of the intersection of zones of intense cracking, with opposite azimuths of incidence at steep angles of macrocracking. The features of penetration into microcracks of the backfilling mixture used at the mine, which are affected by their granulometric and physicochemical compositions, have been determined. The influence of the height of the layer and the procedure of backfilling the chamber space in the liquid phase on the formation of the necessary pressure for the opening of a microcrack was studied. The priority of backfilling the exogenous macrocracks with significant gaps and those between tectonic blocks with mixtures has been analytically substantiated and confirmed by experimental methods of research in the mine.

Fabrication and characterization of aluminum-based powders multi-reinforced with nano- and microparticles

Multi-reinforced powders were obtained by high-energy ball milling in a planetary mill. The process of obtaining heterogeneous powders consisted of two stages. At the first stage, a nanocomposite powder of AlMg6 + 0.3 wt.% C60 was obtained. In the second stage, 10, 30, 50, and 70 wt.% Al2O3 were added to the obtained nanocomposite powder and processing continued. Methods such as scanning electron microscopy, X-ray diffraction, and particle size analysis were used to characterize the obtained powders. It is shown that after the first stage of processing, the particles of the composite powder are characterized by an irregular shape. C60 reinforcing particles in the form of nanosized agglomerates were fixed on the surface of aluminum powder particles. After the second stage of processing, the particle size of the powder mixture decreased from 17.8 to 12.3 μm, while the proportion of Al2O3 particles increased from 10% to 70% by weight. It is shown that the synthesized heterogeneous powders were a mechanical mixture consisting of complex composition agglomerates and micro-sized ceramic particles. Complex composition agglomerates were formed from nanocrystalline matrix material particles and C60, and also with Al2O3 microparticles embedded in them, as well as located on the surface. The concentration of Al2O3 particles on the surface and inside the agglomerates increased with increasing weight fraction of ceramic particles in the mixture. It has been found that the introduction of 10-70 wt. % Al2O3 into the powder mixture increases the microhardness of the powder particles by approximately 16-23%. The resulting multi-reinforced powder mixtures can be utilized for coating deposition using the cold gas dynamic spraying method.

Neural Network Method for Constructing Three-Dimensional Models of Rigid Objects from Satellite Images

A method has been developed for constructing three-dimensional models of rigid objects on the earth’s surface using one satellite image using the example of railway infrastructure. The method consists in step-by-step processing of satellite images with sequential application of two convolutional neural networks. In the first processing step, a satellite image is segmented by a neural network to select a plurality of objects of predetermined classes. At the second stage of processing with the help of neural network local analysis of image areas detected by results of the first stage of processing is performed. The results of the second processing step are used to estimate the parameters of the 3D model of the object. The possibilities of the method are shown by the example of processing a satellite image of the railway infrastructure. The following classes of informative areas are considered: building, wall edge, roof edge, building shadow, railway infrastructure, car, highway; rails, poles and shadows from poles (taken as reference objects for estimating scaling coefficients in certain directions). An example is given of using the developed method of highlighting typical railway infrastructure objects and for subsequent evaluation of the parameters of a three-dimensional building model partially obscured by trees.

Detection of applicative distortions in digital images based on a combination of a neural network and a statistically optimal algorithm

At present, when receiving and processing digital images, they often encounter cases of various noise and distortions appearing on the raster. In practice, we most often have to deal with applicative (impulsive) and additive Gaussian noise. At the same time, noise with a brightness distribution close to a truncated Gaussian, the mode of which falls on the upper or lower limits of the image brightness quantization, can be attributed to a separate class of applicative distortions, which are most often encountered in practice. At present, almost all known algorithms for correcting applicative distortions in images are spatially selective, when at the first stage of processing, the detection of distorted image elements is performed, and at the second stage, the restoration of lost (due to distortions) image elements by some method. To date, a sufficient number of algorithms for detecting (as a rule, statistical) such applicative distortions have been proposed; however, a priori unknown of the laws and parameters of the distribution of random signals and noise, as well as a priori probabilities of the presence and absence of distortions in the image, does not allow achieving their potential quality. One of the ways out of this situation can be the use of a neural network approach and its combination with statistically optimal algorithms. In the problem of detecting distortions, obtaining the necessary information about such parameters of distortions as the prior probability of their presence in the image and the standard deviation is possible through the use of artificial neural networks with high approximation properties acquired by them in the course of training on a training set of examples. This information can then be used by a statistically optimal algorithm. In accordance with this, the aim of the work is to develop a detector of applicative distortions in digital images based on a combination of a neural and statistically optimal algorithm. The essence of the proposed algorithm is to use at the first stage of processing a trained neural network capable of functioning under conditions of complete a priori uncertainty and allowing to obtain approximate estimates of a priori information about distortions in the image, and at the second stage of processing – rules based on the criterion of the minimum average risk (Bayesian criterion) using neural network estimates. As shown by the results of numerical studies, the proposed approach combines the advantage of neural networks, which consists in their high approximating properties, acquired in the course of training on precedents, as well as the optimality of a statistical detector. The presented results of numerical studies of the efficiency of the proposed detector indicate its advantage over the known detector in almost the entire possible range of applicative interference intensity.

Stochastic analysis of impact mixing of bulk materials in a rotary apparatus

The purpose of this work is to analyze the distribution of bulk materials according to the characteristic parameters of the mixing process at the second stage of processing in a rotary apparatus. The design of the rotary device has a wide range, including for the needs of agriculture, for example, when forming mixtures of mineral fertilizers and multifunctional pyrethroid drugs. At this mixing stage, impact interaction with the bump surface of rarefied flows of bulk components occurs, obtained by scattering the rotating drum by elastic blades at the previous stage of the technological chain. The obtained dependencies between the characteristic angle of dispersion of bulk materials by the specified mixing drum (at the first stage) and the angle of reflection of the corresponding flows from the baffle surface (at the second stage) are used in the stochastic analysis of impact mixing. Modeling of the differential functions of the distribution of the number of particles of mixed bulk components by the angle of reflection from the baffle surface is carried out taking into account the physical and mechanical characteristics of the working materials, design parameters, and operating parameters of the rotary apparatus. The calculation results made it possible to reveal the effective ranges of variation of the characteristic parameters for the shock mixing of bulk materials in a rotary apparatus.

GENERATING BOUGUER ANOMALY MAP FROM AIRBORNE GRAVITY DATA (A CASE STUDY IN SOUTH EAST SULAWESI)

Terrestrial measurements can provide accurate gravity data, but it is costly and time-consuming for large and remote area. Airborne gravity measurements have actually been carried out in Indonesia since 2008 by Technical University of Denmark (DTU) in collaboration with the Geospatial Information Agency (BIG). Purpose of the project was to develop a geoid model used for converting elevations from GPS/GNSS measurements that refers to ellipsoid to orthometric elevations that refer to sea level. The data can actually be explored so that it can be used for geophysical and other geoscience purposes, but the data must be carefully treated and extracted into observational gravity data. This study aims to improve the accuracy of gravity airborne data to produce an accurate complete Bouguer anomaly map. The data used in this study were airborne gravity data over Province of Southeast Sulawesi collected on September 29, 2008 to October 1, 2008. Variation in flight height at the time of consecutive data introduced new horizontal acceleration vector. It must be treated as a noise in the measurement of gravity data. The first stage of processing was to eliminate noise due to aircraft acceleration. Gravity data measured in aircraft conditions accelerating more than 5 m.s-2 were eliminated. In this stage, the gravity data were reduced from 64481 observation points to 4900 observation points. The second stage of processing was low pass filtering to eliminate the remaining surges in gravity data. Airborne gravity data that have been snooped and filtered were then applied to calculate the complete Bouguer anomaly. Visually, a complete Bouguer anomaly map through the enhancement process produced a finer map compared to maps from airborne gravity data without enhancement. Comparison of airborne Bouguer anomaly map and terrestrial Bouguer anomaly maps of Kendari sheet showed a correlation of more than 83%. The conclusion of this study was that the enhancement of the airborne data significantly increases the accuracy and reliability of the airborne gravity data for generating a complete bouguer anomaly map. The results of this study also indicated that the airborne archive data has the potential to be used for geophysical and geosciences purposes in Southeast Sulawesi and Indonesia.

A Combined Machine Learning and Residual Analysis Approach for Improved Retrieval of Shallow Bathymetry from Hyperspectral Imagery and Sparse Ground Truth Data

Mapping shallow bathymetry by means of optical remote sensing has been a challenging task of growing interest in recent years. Particularly, many studies exploit earlier empirical models together with the latest multispectral satellite imagery (e.g., Sentinel 2, Landsat 8). However, in these studies, the accuracy of resulting bathymetry is (a) limited for deeper waters (>15 m) and/or (b) is being influenced by seafloor type albedo. This study explores further the capabilities of hyperspectral satellite imagery (Hyperion), which provides several spectral bands in the visible spectrum, along with existing reference bathymetry. Bathymetry predictors are created by applying the semi-empirical approach of band ratios on hyperspectral imagery. Then, these predictors are fed to machine learning regression algorithms for predicting bathymetry. Algorithm performance is being further compared to bathymetry predictions from multiple linear regression analysis. Following the initial predictions, the residual bathymetry values are interpolated by applying the Ordinary Kriging method. Then, the predicted bathymetry from all three algorithms along with their associated residual grids is used as predictors at a second processing stage. Validation results show that by using a second stage of processing, the root-mean-square error values of predicted bathymetry is being improved by ≈1 m even for deeper water (up to 25 m). It is suggested that this approach is suitable for (a) contributing wide-scale, high-resolution shallow bathymetry toward the goals of the Seabed 2030 program and (b) as a coarse resolution alternative to effort-consuming single-beam sonar or costly airborne bathymetric laser surveying.

Performance of multi-soil-layering (MSL) urban domestic wastewater treatment system

This research has the objective of examining the efficiency optimization of a multisoil-layering (MSL) system in three stages through the selection of the most efficient material for permeable layers. The utilized charcoal variations were coconut shell charcoal, rice husk charcoal, and corncob charcoal. The utilized incoming discharge for Q1 and Q2 were 0.0063 L/second and 0.0126 L/second. In the first stage of processing, the pumice and zeolite in Q1 had not been able to reduce the TSS below the quality standard, while silica sand in both discharges were still in accordance with the quality standard. In the second stage of processing, Q1 MSL A-s had the best elimination capability, with the efficiencies of TDS, TSS, pH, and DO respectively being 18.13%, 79.68%, 2.60%, and 126.67%, while for Q2, they were 29.99%, 77.76%, 1.62%, and 95.80%. In the third stage, it was shown that MSL B-m was the most optimal reactor compared to all reactors that had their water qualities measured. For Q1 for MSL B-m, the parameters of TDS, TSS, pH, and DO were respectively 33.16%, 84.32%, 1.29%, and 126.67%, and for Q2 they were 30.80%, 80.54%, 1.50%, and 112.30%. In the third stage of processing, MSL A-m, MSL B-m, and MSL C-m that included the addition of soil mixtures and modifications of soil mixture blocks could increase the efficiency of each parameter and had a more stable quality of water outflow compared to standard MSL; this is because the incoming water flow was slower, which caused water contact with the processing media to be more optimal.

Dermoscopy diagnosis of cancerous lesions utilizing dual deep learning algorithms via visual and audio (sonification) outputs: Laboratory and prospective observational studies.

BackgroundEarly diagnosis of skin cancer lesions by dermoscopy, the gold standard in dermatological imaging, calls for a diagnostic upscale. The aim of the study was to improve the accuracy of dermoscopic skin cancer diagnosis through use of novel deep learning (DL) algorithms. An additional sonification-derived diagnostic layer was added to the visual classification to increase sensitivity.MethodsTwo parallel studies were conducted: a laboratory retrospective study (LABS, n = 482 biopsies) and a non-interventional prospective observational study (OBS, n = 63 biopsies). A training data set of biopsy-verified reports, normal and cancerous skin lesions (n = 3954), were used to develop a DL classifier exploring visual features (System A). The outputs of the classifier were sonified, i.e. data conversion into sound (System B). Derived sound files were analyzed by a second machine learning classifier, either as raw audio (LABS, OBS) or following conversion into spectrograms (LABS) and by image analysis and human heuristics (OBS). The OBS criteria outcomes were System A specificity and System B sensitivity as raw sounds, spectrogram areas or heuristics.FindingsLABS employed dermoscopies, half benign half malignant, and compared the accuracy of Systems A and B. System A algorithm resulted in a ROC AUC of 0.976 (95% CI, 0.965–0.987). Secondary machine learning analysis of raw sound, FFT and Spectrogram ROC curves resulted in AUC's of 0.931 (95% CI 0.881–0.981), 0.90 (95% CI 0.838–0.963) and 0.988 (CI 95% 0.973–1.001), respectively. OBS analysis of raw sound dermoscopies by the secondary machine learning resulted in a ROC AUC of 0.819 (95% CI, 0.7956 to 0.8406). OBS image analysis of AUC for spectrograms displayed a ROC AUC of 0.808 (CI 95% 0.6945 To 0.9208). By applying a heuristic analysis of Systems A and B a sensitivity of 86% and specificity of 91% were derived in the clinical study.InterpretationAdding a second stage of processing, which includes a deep learning algorithm of sonification and heuristic inspection with machine learning, significantly improves diagnostic accuracy. A combined two-stage system is expected to assist clinical decisions and de-escalate the current trend of over-diagnosis of skin cancer lesions as pathological.FundBostel Technologies.Trial Registration clinicaltrials.gov Identifier: NCT03362138

Enhancing Weak Defect Features Using Undecimated and Adaptive Wavelet Transform for Estimation of Roller Defect Size in a Bearing

ABSTRACTDefects in bearings affect the vibration level, resulting in and increase in temperature and decomposition of lubricant. Estimation of roller defect size is a complex task because it revolves as well as rotates during the motion. Signals from a defective roller of a bearing are superimposed by the signal from races, cage, and background noise. In this communication, a signal processing scheme is proposed that makes the signal suitable for estimating the size of the defect in the rolling element of a tapered roller bearing. To achieve this, in the first stage of processing, shift-invariant soft thresholding is applied to denoise the signal. It suppresses the noise without affecting defect-related features. Further, in the second stage of processing, continuous wavelet transform (CWT) using adaptive wavelet is applied. The adaptive wavelet is designed from the impulse extracted from the signal using the least squares fitting method. It results in higher coefficients in the region of impulse produced due to the defect. Finally, time marginal integration (TMI) of CWT coefficients is carried out for estimation of defect width. A study was performed for six different cases in which the size of the defect and orientation varies. Results of measurements of roller defect widths estimated using the proposed scheme were compared with defect widths calculated using image examination. For the nonoverlapping signature of defects (such as defects at 0° and 90° orientations), the maximum deviation in the width measurement using the proposed scheme is 6.52%. The error may increase when signature of two defects are overlapped.

Parallel content-based sub-image retrieval using hierarchical searching

The capacity to systematically search through large image collections and ensembles and detect regions exhibiting similar morphological characteristics is central to pathology diagnosis. Unfortunately, the primary methods used to search digitized, whole-slide histopathology specimens are slow and prone to inter- and intra-observer variability. The central objective of this research was to design, develop, and evaluate a content-based image retrieval system to assist doctors for quick and reliable content-based comparative search of similar prostate image patches. Given a representative image patch (sub-image), the algorithm will return a ranked ensemble of image patches throughout the entire whole-slide histology section which exhibits the most similar morphologic characteristics. This is accomplished by first performing hierarchical searching based on a newly developed hierarchical annular histogram (HAH). The set of candidates is then further refined in the second stage of processing by computing a color histogram from eight equally divided segments within each square annular bin defined in the original HAH. A demand-driven master-worker parallelization approach is employed to speed up the searching procedure. Using this strategy, the query patch is broadcasted to all worker processes. Each worker process is dynamically assigned an image by the master process to search for and return a ranked list of similar patches in the image. The algorithm was tested using digitized hematoxylin and eosin (H&E) stained prostate cancer specimens. We have achieved an excellent image retrieval performance. The recall rate within the first 40 rank retrieved image patches is ∼90%. Both the testing data and source code can be downloaded from http://pleiad.umdnj.edu/CBII/Bioinformatics/.

Economic performance differentiation of the processing stage within the commodity chain

The article is focused on the evaluation of economic differentiation of subjects on particular processing stages within the wheat commodity chain in the Czech Republic with regard to agro-food market development in the last years. The analysis is based on the statement that better economic results of businesses can be achieved not only by successful business strategy or quality management and employees but also by business ability to advance its own interests and to influence market conditions, such as setting higher prices, pressure on lower input prices, better position for negotiations with business partners etc. The Spider Analysis was used for evaluation. The results of analysis have proved worsening of economic position of the second stage of processing (bakeries) in comparison with the first stage of processing (mills) within the commodity chain in the last time, especially as a result of increasing market power of retail. It was also confirmed that decisive subjects in milling industry become bigger mills with joining to raw commodity supplier and subsequent processing stages.

Exploring the “Global Workspace” of Consciousness

As an explanatory principle in biology, vitalism has long been in decline, as one discovery after another revealed that mechanisms provide convincing explanations—hearts are pumps, genes are code—for all manner of life's phenomena. But even through the 20th century, the mind has been vitalism's last redoubt, because there has been no simple, satisfactory, mechanistic explanation of the most puzzling aspect of the mind: the nature of conscious awareness. For many years, even asking questions about the inner workings of this mental black box was taboo among some groups of scientists. But that has all changed. A flood of new discoveries in every area of neuroscience has led to competing models of consciousness, and most importantly, testable hypotheses. A new study by Raphael Gaillard, Lionel Naccache, and colleagues provides support for one such model by showing that conscious, but not nonconscious, visual information is rapidly and widely distributed across the brain, provoking the synchronized brain activity that is the hallmark of conscious processing. The model, called the “global workspace” model, posits that incoming information becomes conscious only when three conditions are met. First, the information must be represented by networks of sensory neurons, such as those in the primary visual cortex at the rear of the brain, that process incoming visual signals. Second, this representation must last long enough to gain access to (“come to the attention of”) a second stage of processing, distributed across the brain's cortex, and especially involving the prefrontal cortex, which is believed to be a major center for associating multiple kinds of information. Third and finally, this combination of bottom-up information propagation and top-down amplification through attention must “ignite” to create a state of reverberating, coherent activity among many different brain centers. That, according to the model, is what we experience as consciousness. A hallmark of conscious visual perception is the sudden increase of communication between distant brain areas. Less than half a second after a word was flashed on a screen, its conscious perception was associated with a burst of synchrony within a dedicated ... A difficulty of consciousness studies is that humans are the best subjects, but probing inside their brains purely for research purposes is unethical. So the authors turned to patients with medically intractable epilepsy, who, in preparation for surgery, had required multiple shallow recording electrodes to be implanted within their cerebral cortexes to locate seizure activity. The authors showed the participants a computer screen, upon which they projected first a set of hatch marks (acting as a meaningless “mask”), then a word, and then either a blank screen or a set of ampersands (another mask). The entire sequence took only half a second, and the word was flashed so briefly that in neither case could the participant name the word. But in both cases the word was registered at the earliest stages of visual processing nonetheless, as shown by electrical activity in the primary visual cortex, thus meeting the first condition of the global workspace model. The words themselves were either of a threatening (“kill,” “danger”) or nonthreatening (“cousin,” “see”) nature. When participants were exposed to words followed by the second mask, they could guess the nature of the words they saw with no better than chance frequency. The second stage was not reached; the fire was doused before it could ignite. But when the second mask was absent, the words were consciously reportable and readable, so the authors could compare masked (nonconscious) perception and unmasked (conscious) perception of briefly flashed words. The electrodes told the same story. There were sustained voltage changes throughout the brain, especially in the prefrontal cortex. The voltage changes were accompanied by an increase in power within specific brain wave frequencies associated with cognitive processing, as well as synchronization of activity among many different brain regions—in short, the brain gave every appearance of thinking about the word. In the language of the global workspace model, the stimulus endured long enough, and gained enough attention, to be promoted to the workspace, at which point information about it was broadcast throughout the brain to be processed in multiple ways—including the determination of its emotional character. The authors point out that consciousness is always “about” something; there may be no “pure” state of consciousness that is independent of the content of our thought, and so an important question is whether these results, concerning a single word the participant could not even name, are generalizable to understanding the ongoing flow of our conscious experience. Further experiments with other kinds of stimuli may reveal which late-stage, widespread brain events are common to all conscious processing, and which are specific to the experiment at hand.

First class classification [mailroom automation

Companies are often under the illusion that deploying a mailroom scanning solution will improve their business efficiency. But that's not the case. Simply digitising documents is only pushing the paper images around, and doesn't actually remove the real obstruction. If the right technology and controls are not in place, the chances of losing documents can actually be increased. It's what happens after the scanning phase that counts. This critical second stage of processing - where recognition and classification takes place before any optical character recognition (OCR) is attempted - is the one that has the largest beneficial effect on mailroom efficiency. This article explains how to deploy this key step in mailroom automation and how can the relevant information be extracted from the paper documents as efficiently as possible to unblock the corporate workflow.

Anger management interventions.

Two anger management interventions for aggressive children, Anger Coping and Coping Power, are described in this review article, including conceptual underpinnings, session format and content, and outcome research findings. Important issues and considerations in the implementation of such interventions are also presented. Overall, Anger Coping and Coping Power have emerged as effective interventions for angry, aggressive children and represent useful resources for clinicians' work with this population. CONTEXTUAL SOCIAL-COGNITIVE MODEL OF ANGRY AGGRESSION The social-cognitive model serving as the conceptual framework for the Anger Coping Program and the Coping Power Program began as a model of anger arousal (Lochman, Nelson, & Sims, 1981). In this conceptualization of anger arousal, which stressed sequential cognitive processing, the child responded to problems such as interpersonal conflicts or frustrations with environmental obstacles (e.g., difficult schoolwork). However, it was not the stimulus event itself that provoked the child's anger and response, but rather the child's cognitive processing of and about that event. This first stage of cognitive processing (appraisal) consisted of labeling, attributions, and perceptions of the problem event, and of the child's subsequent anger. The second stage of processing (problem solution) consisted of the child's cognitive plan for his or her response to the perceived threat or provocation. This early anger arousal model indicated that the child's cognitive and emotional processing of the problem event and of his or her planned response led to the child's actual behavioral response and to the positive or negative consequences that the child experienced as a result. Our current Contextual Social-Cognitive model (Lochman & Wells, 2002a) includes a more comprehensive understanding of social-cognitive processes, maintains an emphasis on anger arousal, and includes recognition of the contextual factors which contribute to children's aggression. Social cognition. The current social-cognitive model of children's aggression (Lochman, Whidby, & Fitzgerald, 2000) underlying the child component of the Coping Power program evolved in large part because of research on aggressive children's social information-processing (Crick & Dodge, 1994). At the appraisal stage of processing, aggressive children have been found to recall fewer relevant cues about events (Lochman & Dodge, 1994), and to selectively attend to hostile rather than neutral cues (Gouze, 1987; Milich & Dodge, 1984). Aggressive children have been shown to have a hostile attributional bias, as they tend to excessively infer that others are acting toward them in a provocative and hostile manner (Katsurada & Sugawara, 1998; Lochman & Dodge, 1994, 1998). At the problem solution stage of social-cognitive processing, aggressive children offer fewer competent verbal problem solutions (Dunn, Lochman, & Colder, 1997), including verbal assertion and compromise solutions (Joffe, Dobson, Fine, Marriage, & Haley, 1990; Lochman & Dodge, 1994; Lochman & Lampron, 1986), and more aggressive and direct action solutions (Lochman & Lampron, 1986; Pepler, Craig, & Roberts, 1998; Waas & French, 1989) to hypothetical vignettes describing interpersonal conflicts. Aggressive children cognitively generate more aggressive strategies in part because they expect that aggressive behavior will lead to desired outcomes (Lochman & Dodge, 1994; Zelli, Dodge, Lochman, Laird, & The Conduct Problems Prevention Research Group, 1999). Anger arousal. Anger is defined as an emotional response to situations that are perceived as threatening or offensive to oneself or others close to them (Lazarus, 1991). Anger can prove adaptive in that it is a motivator for action and tends to focus one's resources toward the threatening or offensive event (Goleman, 1995). …

Shading and texture: separate information channels with a common adaptation mechanism?

We outline a scheme for the way in which early vision may handle information about shading (luminance modulation, LM) and texture (contrast modulation, CM). Previous work on the detection of gratings has found no sub-threshold summation, and no cross-adaptation, between LM and CM patterns. This strongly implied separate channels for the detection of LM and CM structure. However, we now report experiments in which adapting to LM (or CM) gratings creates tilt aftereffects of similar magnitude on both LM and CM test gratings, and reduces the perceived strength (modulation depth) of LM and CM gratings to a similar extent. This transfer of aftereffects between LM and CM might suggest a second stage of processing at which LM and CM information is integrated. The nature of this integration, however, is unclear and several simple predictions are not fulfilled. Firstly, one might expect the integration stage to lose identity information about whether the pattern was LM or CM. We show instead that the identity of barely detectable LM and CM patterns is not lost. Secondly, when LM and CM gratings are combined in-phase or out-of-phase we find no evidence for cancellation, nor for 'phase-blindness'. These results suggest that information about LM and CM is not pooled or merged--shading is not confused with texture variation. We suggest that LM and CM signals are carried by separate channels, but they share a common adaptation mechanism that accounts for the almost complete transfer of perceptual aftereffects.

Hierarchical Model-Based Clustering for Large Datasets

In recent years, hierarchical model-based clustering has provided promising results in a variety of applications. However, its use with large datasets has been hindered by a time and memory complexity that are at least quadratic in the number of observations. To overcome this difficulty, this article proposes to start the hierarchical agglomeration from an efficient classification of the data in many classes rather than from the usual set of singleton clusters. This initial partition is derived from a subgraph of the minimum spanning tree associated with the data. To this end, we develop graphical tools that assess the presence of clusters in the data and uncover observations difficult to classify. We use this approach to analyze two large, real datasets: a multiband MRI image of the human brain and data on global precipitation climatology. We use the real datasets to discuss ways of integrating the spatial information in the clustering analysis. We focus on two-stage methods, in which a second stage of processing using established methods is applied to the output from the algorithm presented in this article, viewed as a first stage.

Automatic syllabic boundary extraction from connected speech

A computational algorithm is presented which locates syllabic boundaries in connected speech. Filtered, digitized speech is processed in two stages. The first stage is an auditory front end which filters the digitized speech into critical bandwidths. The critical bands are amplitude-scaled according to a standard frequency/amplitude function, then low-frequency amplitude modulations in the 2–30-Hz range are emphasized according to a perceptual modulation sensitivity function. Next, the critical bands are low-pass filtered at 100 Hz and decimated at a rate of 25:1. Then they are low-pass filtered at 100 Hz, again creating acoustic envelopes of the processed speech, one for each critical band. During the second stage of processing, an autocorrelation-based algorithm is run on the envelopes. The local minima of this algorithm are pooled across the critical-band envelopes, yielding syllabic boundaries. The utterances used to develop and test this algorithm were taken from the Harvard Phonetically Balanced Sentences. Currently, the algorithm places boundaries within a few tens of milliseconds of where a phonemic syllabification would place them. Work continues on fine-tuning the algorithm. Another goal is to compare the algorithm’s performance against human listeners’. [The author wishes to acknowledge the support of Lucent Technologies in conducting this project.]