Bottom-up spatiotemporal visual attention model for video analysis

The human visual system (HVS) has the ability to fixate quickly on the most informative (salient) regions of a scene and therefore reducing the inherent visual uncertainty. Computational visual attention (VA) schemes have been proposed to account for this important characteristic of the HVS. A video analysis framework based on a spatiotemporal VA model is presented. A novel scheme has been proposed for generating saliency in video sequences by taking into account both the spatial extent and dynamic evolution of regions. To achieve this goal, a common, image-oriented computational model of saliency-based visual attention is extended to handle spatiotemporal analysis of video in a volumetric framework. The main claim is that attention acts as an efficient preprocessing step to obtain a compact representation of the visual content in the form of salient events/objects. The model has been implemented, and qualitative as well as quantitative examples illustrating its performance are shown.     

Remote monitoring of electromagnetic signals and seismic events using smart mobile devices

This study presents the design and development of a novel mobile wireless system to be used for monitoring seismic events and related electromagnetic signals, employing smart mobile devices like personal digital assistants (PDAs) and wireless communication technologies such as wireless local area networks (WLANs), general packet radio service (GPRS) and universal mobile telecommunications system (UMTS). The proposed system enables scientists to access critical data while being geographically independent of the sites of data sources, rendering it as a useful tool for preliminary scientific analysis.     

Automatic real-time event detection for seismic networks

A low-power microprocessor-based seismic signal detection system has been developed for monitoring and recording earthquakes detected from multichannel seismic network data. The system operates on-line to the network and may be battery powered for field applications or as a precaution against power failures. The algorithm is designed to cope with varying noise conditions at the network outstations and detects local, regional, and teleseismic events. The sampling rate for each channel is 100 samples/sec. with 10-bit accuracy. The system is operating with the LOWNET network of BGS and is suitable for large or small aperture networks because 20 sec of data for each channel are stored in memory. The system is designed to detect seismic events for recording on either analogue or digital recorders depending on the requirements and preferences of the network management.     

Continuous sonification enhances adequacy of interactions in peripheral process monitoring

As many users who are charged with process monitoring need to focus mainly on other work while performing monitoring as a secondary task, monitoring systems that purely rely on visual means are often not well suited for this purpose. Sonification, the presentation of data as (non-speech) sound, has proven in several studies that it can help in guiding the user's attention, especially in scenarios where process monitoring is performed in parallel with a different, main task. However, there are several aspects that have not been investigated in this area so far, for example if a continuous soundscape can guide the user's attention better than one that is based on auditory cues. We have developed a system that allows reproducible research to answer such questions. In this system, the participants’ performance both for the main task (simulated by simple arithmetic problems) and for the secondary task (a simulation of a production process) can be measured in a more fine-grained manner than has been the case for existing research in this field. In a within-subject study (n=18), we compared three monitoring conditions – visual only, visual + auditory alerts and a condition combining the visual mode with continuous sonification of process events based on a forest soundscape. Participants showed significantly higher process monitoring performances in the continuous sonification condition, compared to the other two modes. The performance in the main task was at the same time not significantly affected by the continuous sonification.     

Synchronised multimedia for engineering and scientific analysis

Engineering tests and scientific experiments use a variety of techniques to simultaneously record phenomena that frequently are not well correlated to the humans visual and cognitive timeframe. Therefore, the captured data are subsequently analysed in a different timeframe using, for example, slow motion or time lapse, and in some cases the playback rate is variable in a more complex manner. We term this condition time elasticity. We present methods to achieve synchronism amongst the range of media streams captured whilst consuming a low overhead of computing resource. These schemes allow for variable start times and extents for individual media streams as well as providing for variable time increments both within one medium and across a range of media elements. The techniques have been demonstrated within a system for the analysis of vehicle crash tests.Jimmy Robinson: Correspondence to     

Interval Relations in Lexical Semantics of Verbs

Numerous temporal relations of verbal actions have been analysed in terms of various grammatical means of expressing verbal temporalisation such as tense, aspect, duration and iteration. Here the temporal relations within verb semantics, particularly ordered pairs of verb entailment, are studied using Allen's interval-based temporal formalism. Their application to the compositional visual definitions in our intelligent storytelling system, CONFUCIUS, is presented, including the representation of procedural events, achievement events and lexical causatives. In applying these methods we consider both language modalities and visual modalities since CONFUCIUS is a multimodal system.     

Artificial neural networks for earthquake prediction using time series magnitude data or Seismic Electric Signals

The aim of this study is to evaluate the performance of artificial neural networks in predicting earthquakes occurring in the region of Greece with the use of different types of input data. More specifically, two different case studies are considered: the first concerns the prediction of the earthquake magnitude (M) of the following day and the second the prediction of the magnitude of the impending seismic event following the occurrence of pre-seismic signals, the so-called Seismic Electric Signals (SES), which are believed to occur prior to an earthquake, as well as the time lag between the SES and the seismic event itself. The neural network developed for the first case study used only time series magnitude data as input with the output being the magnitude of the following day. The resulting accuracy rate was 80.55% for all seismic events, but only 58.02% for the major seismic events (M ? 5.2 on the Richter scale). Our second case study for earthquake prediction uses SES as input data to the neural networks developed. This case study is separated in two parts with the differentiating element being the way of constructing the missing SES. In the first part, where the missing SES were constructed randomly for all the seismic events, the resulting accuracy rates for the magnitude of upcoming seismic events were just over 60%. In the second part, where the missing SES were constructed for the major seismic events (M ? 5.0 on the Richter scale) only by the use of neural networks reversely, the resulting accuracy rate by predicting only the magnitude was 84.01%, and by predicting both the magnitude and time lag was 83.56% for the magnitude and 92.96% for the time lag. Based on the results we conclude that, when the neural networks are trained by using the appropriate data they are able to generalise and predict unknown seismic events relatively accurately.     

Visualizing the ground motions of the 1906 San Francisco earthquake

With advances in computational capabilities and refinement of seismic wave-propagation models in the past decade large three-dimensional simulations of earthquake ground motion have become possible. The resulting datasets from these simulations are multivariate, temporal and multi-terabyte in size. Past visual representations of results from seismic studies have been largely confined to static two-dimensional maps. New visual representations provide scientists with alternate ways of viewing and interacting with these results potentially leading to new and significant insight into the physical phenomena. Visualizations can also be used for pedagogic and general dissemination purposes. We present a workflow for visual representation of the data from a ground motion simulation of the great 1906 San Francisco earthquake. We have employed state of the art animation tools for visualization of the ground motions with a high degree of accuracy and visual realism.     

Visual signatures in video visualization

Video visualization is a computation process that extracts meaningful information from original video data sets and conveys the extracted information to users in appropriate visual representations. This paper presents a broad treatment of the subject, following a typical research pipeline involving concept formulation, system development, a path-finding user study, and a field trial with real application data. In particular, we have conducted a fundamental study on the visualization of motion events in videos. We have, for the first time, deployed flow visualization techniques in video visualization. We have compared the effectiveness of different abstract visual representations of videos. We have conducted a user study to examine whether users are able to learn to recognize visual signatures of motions, and to assist in the evaluation of different visualization techniques. We have applied our understanding and the developed techniques to a set of application video clips. Our study has demonstrated that video visualization is both technically feasible and cost-effective. It has provided the first set of evidence confirming that ordinary users can be accustomed to the visual features depicted in video visualizations, and can learn to recognize visual signatures of a variety of motion events.     

Study of tectonics in relation to the seismic activity of the Dalvat area,Nasik District,Maharashtra, India using remote sensing and GIS techniques

Well‐documented geological data (from both field and satellite) in the Deccan Volcanic Provinces (DVP) in and around the Dalvat region, Nasik District, India has been analysed by Geographic Information System (GIS) techniques and reported in this paper so as to relate the geology and structures with recent seismicity. It has been the belief among earth scientists that the Deccan Traps in Maharashtra, India is tectonically stable as the region attained solidity long ago. However, recent activity in the study area altered this concept and it is now accepted that seismic activity is still continuing on a mild scale. As such, the need has arisen to take into consideration historical as well as recent geological data to study in detail the tectonic setup in the Deccan Traps.

Using the well‐known relationship between the shear zone, lineaments, and geomorphology, and incorporating these with tectonic events, an attempt has been made to explore the geology and structures in and around the Dalvat region. Field observations and signatures on remote sensing data show that there is evidence of fault traces in the form of shear zones and slickensides in the Deccan Traps near the Kosurde, Dhanoli, Chikhli, and Manchandar villages of the Nasik District. The study has further been incorporated with seismic density data. Magnitudes of 3.9 were recorded as the maximum micro‐epicentres, and they fell on the shear fractures detected in the area of study.

In order to identify seismically vulnerable areas, seismic hazard zonation (SHZ) mapping has been carried out. Different data layers, including structural, lithological, geomorphological, drainage, and soil have been visually interpreted, scanned, and rectified. A rose diagram of the lineaments shows trends in the NNE–SSW to NS falling on major seismic zones of the study area, showing weaker zones beneath the surface. Raster images were digitized for conversion to a vector coverage using ERDAS 8.6. and ArcGIS 8.3. The ordinal scale (qualitative) relative weighting rating technique was used to give a seismic hazard index (SHI) value to delineate various seismic hazard zones; namely very low, low, moderate, high, and very high.     

A graphical model framework for decoding in the visual ERP-based BCI speller

We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating brain signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between brain signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the brain signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.     

ITEA—interactive trajectories and events analysis: exploring sequences of spatio-temporal events in movement data

Widespread use of GPS and similar technologies makes it possible to collect extensive amounts of trajectory data. These data sets are essential for reasonable decision making in various application domains. Additional information, such as events taking place along a trajectory, makes data analysis challenging, due to data size and complexity. We present an integrated solution for interactive visual analysis and exploration of events along trajectories data. Our approach supports analysis of event sequences at three different levels of abstraction, namely spatial, temporal, and events themselves. Customized views as well as standard views are combined to form a coordinated multiple views system. In addition to trajectories and events, we include on-the-fly derived data in the analysis. We evaluate our integrated solution using the IEEE VAST 2015 Challenge data set. A successful detection and characterization of malicious activity indicate the usefulness and efficiency of the presented approach.     

Competition in the development of nerve connections: a review of models

The establishment and refinement of neural circuits involve both the formation of new connections and the elimination of already existing connections. Elimination of connections occurs, for example, in the development of mononeural innervation of muscle fibres and in the formation of ocular dominance columns in the visual cortex. The process that leads to the elimination of connections is often referred to as axonal or synaptic competition. Although the notion of competition is commonly used, the process is not well understood-with respect to, for example, the type of competition, what axons and synapses are competing for, and the role of electrical activity. This article reviews the types of competition that have been distinguished and the models of competition that have been proposed. Models of both the neuromuscular system and the visual system are described. For each of these models, the assumptions on which it is based, its mathematical structure, and the extent to which it is supported by the experimental data are evaluated. Special attention is given to the different modelling approaches and the role of electrical activity in competition.     

一种地震资料解释系统

地震资料解释系统是石油勘探研究的重要工具。通过分析解释业务需求与解释技术发展趋势,针对常规地震资料解释系统的缺陷,提出与实现了一种基于分布集中融合协同工作模式下的大型地震资料解释系统部署方案。该方案已经在陆地及海洋地震资料解释项目上得到广泛应用,能够很好地满足日益发展的石油勘探解释业务需求。     

地震灾害潜在损失评价模型分析与研究——以唐山地震为例

地震灾害潜在损失评价模型的研究是综合应用遥感和地理信息系统技术,估算潜在地震灾区可能的最大损失量,以及潜在震区直接和间接的最大经济损失,为震区灾后的重建提供辅助决策支持信息。     

油田探井生产运行管理协作系统

通过对油田预探井、区探井、评价井三类探井生产运行管理工作的流程梳理和优化,固化了各个工作节点管理规范和信息标准。系统不仅涉及井位设计研究、探井钻井、地质录井、地球物理测井、地震VSP测井、油气层测试、油气层改造、油气井试采等专业领域,还涉及到科学研究、专业工程实施前、后的诸多单项设计的逐级审批与成果的最终评定验收等管理环节。它将探井生产各个专业集成在一个平台上工作,将探井生产运行的管理流、工作流、数据流进行结合,实现了探井运行管理、探井数据库建设、数据应用的三个统一,实现数据产生、集成应用和共享,为探井生产运行和管理提供了一种＂可视化＂的网上协作方法。     

Depicting dynamics using principles of visual art and narrations

In visual art, a single static image frequently represents much more than projected 3D scenery. Artists include subtle visual elements outlining movements, indicating past or future events, sketching ongoing activities, or guiding the observer's attention. Artists have found ways to visualize physical as well as nonphysical dynamics of scenes using graphics techniques. In a sense, we can consider these smart depictions - a form of expressive, visual content adopting the styles of visual art and abstraction techniques. These depictions can serve, for example, as pictograms and signs that advise and assist people. They're also present in comic books and storyboards that effectively present dynamics and narrate sequential processes. Taking smart depictions a step further, we created a system that automatically generates smart, compelling images of 3D scenes that depict dynamics following traditional design principles of visual art, visual narrations, and graphic design, such as those found in comic books and storyboards. These media offer a rich vocabulary of visual art deployed as techniques to facilitate visual communication of a wealth of activities and events in static images. In particular, we can symbolize in a single, static image past, ongoing, and future activities as well as events taking place in 3D scenes. Additionally, we can take into account nonvisual information.     

Clustering analysis of the seismic catalog of Iran

Identification and classification of different seismotectonic provinces with similar characteristics in a region of interest is one of the most important subjects in seismic hazard studies. This task is usually done through subjective interpretations based on geological and seismotectonic information. Seismic data is one of the most important sources of information where visual inspection of this data is a traditional way of identification of seismotectonic provinces. Pattern recognition of historical and instrumental seismic data in a non-subjective way provides more robust results and is a more suitable tool for extracting useful knowledge from a huge amount of data. In this study, applicability and usefulness of an unsupervised fuzzy clustering algorithm in identification of hidden patterns among historical and instrumental seismic catalog of Iran is examined through a comparison between the results of such an analysis and the proposed models for seismotectonic provinces of Iran. The clustering method used in this study is based on fuzzy modification of the maximum likelihood estimation and has the capability to detect elliptical clusters with variable size. Moreover, fuzzy hyper-volume and partition density indexes are used as performance indexes for selection the best number of clusters. The comparison between the results of clustering analyses and the seismotectonic models of Iran reveals that it is possible to partition the spatially distributed epicenters of earthquake events into distinct. These partition units, or clusters, are generally in good agreement with the proposed seismotectonic provinces of Iran and show major seismotectonic features of the Iranian Plateau in addition to some hidden information. Such kind of analysis provides a mathematical basis for seismological interpretations of seismic activities. Moreover, the comparisons of the results of clustering analysis among historical data, combination of historical and instrumental data and major earthquakes with magnitude greater than 5.0 shows that the best results will be achieved by the clustering of major events (i.e. Mw>5.0).     

Identification of nonlinear systems using random amplitude Poissondistributed input functions

Nonlinear system identification using a doubly random input function which is a Poisson train of events with random amplitudes as a system input is investigated. These doubly random input functions are useful for identifying systems that naturally require amplitude modulated point process inputs as stimuli such as the hippocampal formation in the central nervous system. This is an extension of earlier work in which a Poisson train of events with only constant amplitude was used as the input for system identification. Analogous to the Wiener theory, we have developed both continuous and discrete functionals up to second-order for this doubly random input function. Closed form solutions for the diagonal terms of the second-order kernels in both cases have been obtained and convergence properties are demonstrated. Two hypothetical discrete second-order nonlinear systems are illustrated and one of them was simulated to test the theory presented. Discrete kernels computed from the simulated data agree with the theoretical prediction