Kinetostatic-model-based Stiffness Analysis of Exechon PKM

As a comparative newly-invented PKM with over-constraints in kinematic chains, the Exechon has attracted extensive attention from the research society. Different from the well-recognized kinematics analysis, the research on the stiffness characteristics of the Exechon still remains as a challenge due to the structural complexity. In order to achieve a thorough understanding of the stiffness characteristics of the Exechon PKM, this paper proposed an analytical kinetostatic model by using the substructure synthesis technique. The whole PKM system is decomposed into a moving platform subsystem, three limb subsystems and a fixed base subsystem, which are connected to each other sequentially through corresponding joints. Each limb body is modeled as a spatial beam with a uniform cross-section constrained by two sets of lumped springs. The equilibrium equation of each individual limb assemblage is derived through finite element formulation and combined with that of the moving platform derived with Newtonian method to construct the governing kinetostatic equations of the system after introducing the deformation compatibility conditions between the moving platform and the limbs. By extracting the 6 x 6 block matrix from the inversion of the governing compliance matrix, the stiffness of the moving platform is formulated. The computation for the stiffness of the Exechon PKM at a typical configuration as well as throughout the workspace is carried out in a quick manner with a piece-by-piece partition algorithm. The numerical simulations reveal a strong position-dependency of the PKM's stiffness in that it is symmetric relative to a work plane due to structural features. At the last stage, the effects of some design variables such as structural, dimensional and stiffness parameters on system rigidity are investigated with the purpose of providing useful information for the structural optimization and performance enhancement of the Exechon PKM. It is worthy mentioning that the proposed methodology of stiffness modeling in this paper can also be applied to other overconstrained PKMs and can evaluate the global rigidity over workplace efficiently with minor revisions.

Improving signal reliability for indoor off–body communications using spatial diversity at the base station

In this paper, we investigate the potential improvement in signal reliability for indoor off-body communications when using spatial diversity at the base station. In particular, we utilize two hypothetical indoor base stations operating at 5.8 GHz each featuring four antennas which are spaced at either half- or one-wavelength apart. Three on-body locations are considered along with four types of user movement. The cross-correlation between the received signal envelopes observed at each base station antenna element was calculated and found to be always less than 0.5. Selection, maximal ratio, and equal gain combining of the received signal has shown that the greatest improvement is obtained when the user is mobile, with a maximum diversity gain of 11.34 dB achievable when using a four branch receiver. To model the fading envelope obtained at the output of the virtual combiners, we use diversity specific, theoretical probability density functions for multi-branch receivers operating in Nakagami-m fading channels. It is shown that these equations provide an excellent fit to the measured channel data.

Spatial allocation of material flow analysis in residential developments: a case study of Kildare County, Ireland

Studies of urban metabolism provide important insights for environmental management of cities, but are not widely used in planning practice due to a mismatch of data scale and coverage. This paper introduces the Spatial Allocation of Material Flow Analysis (SAMFA) model as a potential decision support tool aimed as a contribution to overcome some of these difficulties and describes its pilot use at the county level in the Republic of Ireland. The results suggest that SAMFA is capable of identifying hotspots of higher material and energy use to support targeted planning initiatives, while its ability to visualise different policy scenarios supports more effective multi-stakeholder engagement. The paper evaluates this pilot use and sets out how this model can act as an analytical platform for the industrial ecology–spatial planning nexus.

A necessarily complex model to explain the biogeography of the amphibians and reptiles of Madagascar

Pattern and process are inextricably linked in biogeographic analyses, though we can observe pattern, we must infer process. Inferences of process are often based on ad hoc comparisons using a single spatial predictor. Here, we present an alternative approach that uses mixed-spatial models to measure the predictive potential of combinations of hypotheses. Biodiversity patterns are estimated from 8,362 occurrence records from 745 species of Malagasy amphibians and reptiles. By incorporating 18 spatially explicit predictions of 12 major biogeographic hypotheses, we show that mixed models greatly improve our ability to explain the observed biodiversity patterns. We conclude that patterns are influenced by a combination of diversification processes rather than by a single predominant mechanism. A ‘one-size-fits-all’ model does not exist. By developing a novel method for examining and synthesizing spatial parameters such as species richness, endemism and community similarity, we demonstrate the potential of these analyses for understanding the diversification history of Madagascar’s biota.

Plant community assembly at small scales: spatial versus environmental factors in a central European grassland

Dispersal limitation and environmental conditions are crucial drivers of plant species distribution and establishment. As these factors operate at different spatial scales, we asked: Do the environmental factors known to determine community assembly at broad scales operate at fine scales (few meters)? How much do these factors account for community variation at fine scales? In which way do biotic and abiotic interactions drive changes in species composition? We surveyed the plant community within a dry grassland along a very steep gradient of soil characteristics like pH and nutrients. We used a spatially explicit sampling design, based on three replicated macroplots of 15x15, 12x12 and 12x12 meters in extent. Soil samples were taken to quantify several soil properties (carbon, nitrogen, plant available phosphorus, pH, water content and dehydrogenase activity as a proxy for overall microbial activity). We performed variance partitioning to assess the effect of these variables on plant composition and statistically controlled for spatial autocorrelation via eigenvector mapping. We also applied null model analysis to test for non-random patterns in species co-occurrence using randomization schemes that account for patterns expected under species interactions. At a fine spatial scale, environmental factors explained 18% of variation when controlling for spatial autocorrelation in the distribution of plant species, whereas purely spatial processes accounted for 14% variation. Null model analysis showed that species spatially segregated in a non-random way and these spatial patterns could be due to a combination of environmental filtering and biotic interactions. Our grassland study suggests that environmental factors found to be directly relevant in broad scale studies are present also at small scales, but are supplemented by spatial processes and more direct interactions like competition.

Spatial turnover in the global avifauna

Despite its wide implications for many ecological issues, the global pattern of spatial turnover in the occurrence of species has been little studied, unlike the global pattern of species richness. Here, using a database on the breeding distributions of birds, we present the first global maps of variation in spatial turnover for an entire taxonomic class, a pattern that has to date remained largely a matter of conjecture, based on theoretical expectations and extrapolation of inconsistent patterns from different biogeographic realms. We use these maps to test four predictions from niche theory as to the form that this variation should take, namely that turnover should increase with species richness, towards lower latitudes, and with the steepness of environmental gradients and that variation in turnover is determined principally by rare (restricted) species. Contrary to prediction, we show that turnover is high both in areas of extremely low and high species richness, does not increase strongly towards the tropics, and is related both to average environmental conditions and spatial variation in those conditions. These results are closely associated with a further important and novel finding, namely that global patterns of spatial turnover are driven principally by widespread species rather than the restricted ones. This complements recent demonstrations that spatial patterns of species richness are also driven principally by widespread species, and thus provides an important contribution towards a unified model of how terrestrial biodiversity varies both within and between the Earth's major land masses.

A metapopulation model of species boundaries

The majority, if not all, species have a limited geographic range bounded by a distribution edge. Violent ecotones such as sea coasts clearly produce edges for many species; however such ecotones, while sufficient for the formation of an edge, are not always necessary. We demonstrate this by simulation in discrete time of a spatially structured finite size metapopulation subjected to a spatial gradient in per-unit-time population extinction probability together with spatially structured dispersal and recolonisation. We find that relatively sharp edges separating a homeland or main geographical range from an outland or zone of relatively sparse and ephemeral colonisation can form in gradual environmental gradients. The form and placing of the edge is an emergent property of the metapopulation dynamics. The sharpness of the edge declines with increasing dispersal distance, and is dependent on the relative scales of dispersal distance and gradient length. The space over which the edge develops is short relative to the potential species range. The edge is robust against changes in both the shape of the environmental gradient and to a lesser extent to alterations in the kind of dispersal operating. Persistence times in the absence of environmental gradients are virtually independent of the shape of the dispersal function describing migration. The common finding of bell shaped population density distributions across geographic ranges may occur without the strict necessity of a niche mediated response to a spatially autocorrelated environment.

PREDICTING THE SPATIAL-DISTRIBUTION OF CLIMATE - TEMPERATURE IN GREAT-BRITAIN

1. The prediction and mapping of climate in areas between climate stations is of increasing importance in ecology.

2. Four categories of model, simple interpolation, thin plate splines, multiple linear regression and mixed spline-regression, were tested for their ability to predict the spatial distribution of temperature on the British mainland. The models were tested by external cross-verification.

3. The British distribution of mean daily temperature was predicted with the greatest accuracy by using a mixed model: a thin plate spline fitted to the surface of the country, after correction of the data by a selection from 16 independent topographical variables (such as altitude, distance from the sea, slope and topographic roughness), chosen by multiple regression from a digital terrain model (DTM) of the country.

4. The next most accurate method was a pure multiple regression model using the DTM. Both regression and thin plate spline models based on a few variables (latitude, longitude and altitude) only were comparatively unsatisfactory, but some rather simple methods of surface interpolation (such as bilinear interpolation after correction to sea level) gave moderately satisfactory results. Differences between the methods seemed to be dependent largely on their ability to model the effect of the sea on land temperatures.

5. Prediction of temperature by the best methods was greater than 95% accurate in all months of the year, as shown by the correlation between the predicted and actual values. The predicted temperatures were calculated at real altitudes, not subject to sea-level correction.

6. A minimum of just over 30 temperature recording stations would generate a satisfactory surface, provided the stations were well spaced.

7. Maps of mean daily temperature, using the best overall methods are provided; further important variables, such as continentality and length of growing season, were also mapped. Many of these are believed to be the first detailed representations at real altitude.

8. The interpolated monthly temperature surfaces are available on disk.

Footprints in the sand: a persistent spatial impression of fishing in a mobile groundfish assemblage

Fishing is well known to curtail the size distribution of fish populations. This paper reports the discovery of small-scale spatial patterns in length appearing in several exploited species of Celtic Sea demersal 'groundfish'. These patterns match well with spatial distributions of fishing activity, estimated from vessel monitoring records taken over a period of 6 years, suggesting that this 'mobile' fish community retains a persistent impression of local-scale fishing pressure. An individual random-walk model of fish movement best matched these exploitation 'footprints' with individual movement rates set to <35 km per year. We propose that Celtic Sea groundfish may have surprisingly low movement rates for much of the year, such that fishing impact is spatially heterogeneous and related to local fishing intensity.

Spatio-Temporal Rich Model For Motion Vector Steganalysis

We propose a spatio-temporal rich model of motion vector planes as a part of a full steganalytic system against motion vector based steganography. Superior detection accuracy of the rich model over the previous methods has been lately demonstrated for digital images in both spatial and DCT domain. It has not been heretofore used for detection of motion vector steganography. We also introduced a transformation so as to extend the feature set with temporal residuals. We carried out the tests along with most recent motion vector steganalysis and steganography methods. Test results show that the proposed model delivers an outstanding performance compared to the previous methods.

Sound Art and Spatial Practices: Situating Sound Installation Art Since 1958

This dissertation examines the emergence and development of sound installation art, an under-recognized tradition that has developed between music, architecture, and media art practices since the late 1950s. Unlike many musical works, which are concerned with organizing sounds in time, sound installations organize sounds in space; they thus necessitate new theoretical and analytical models that take into consideration the spatial situated-ness of sound. Existing discourses on “spatial sound” privilege technical descriptions of sound localization. By contrast, this dissertation examines the ways in which concepts of space are socially, culturally, and politically construed, and how spatially-organized sound works reflect and resist these different constructions. Using an interdisciplinary methodology of critical spatial analysis and critical studies in music, this dissertation explores such topics as: conceptions of acoustic space in postwar Western art music, architecture, and media theory; the development of sound installation art in relation to philosophies of everyday life and social space; the historical links between musical performance, conceptual art, and sound sculpture; the body as a site for sound installations; and sonicspatial strategies that confront politics of race and gender. Through these different investigations, this dissertation proposes an “ontopological” model for considering sound: a critical model of analysis and reception that privileges an understanding of sound in relation to ontologies of space and place.

Spatio-temporal Rich Model Based Video Steganalysis on Cross Sections of Motion Vector Planes

A rich model based motion vector steganalysis benefiting from both temporal and spatial correlations of motion vectors is proposed in this work. The proposed steganalysis method has a substantially superior detection accuracy than the previous methods, even the targeted ones. The improvement in detection accuracy lies in several novel approaches introduced in this work. Firstly, it is shown that there is a strong correlation, not only spatially but also temporally, among neighbouring motion vectors for longer distances. Therefore, temporal motion vector dependency along side the spatial dependency is utilized for rigorous motion vector steganalysis. Secondly, unlike the filters previously used, which were heuristically designed against a specific motion vector steganography, a diverse set of many filters which can capture aberrations introduced by various motion vector steganography methods is used. The variety and also the number of the filter kernels are substantially more than that of used in previous ones. Besides that, filters up to fifth order are employed whereas the previous methods use at most second order filters. As a result of these, the proposed system captures various decorrelations in a wide spatio-temporal range and provides a better cover model. The proposed method is tested against the most prominent motion vector steganalysis and steganography methods. To the best knowledge of the authors, the experiments section has the most comprehensive tests in motion vector steganalysis field including five stego and seven steganalysis methods. Test results show that the proposed method yields around 20% detection accuracy increase in low payloads and 5% in higher payloads.

A biologically inspired appearance model for robust visual tracking

In this work, we propose a biologically inspired appearance model for robust visual tracking. Motivated in part by the success of the hierarchical organization of the primary visual cortex (area V1), we establish an architecture consisting of five layers: whitening, rectification, normalization, coding and polling. The first three layers stem from the models developed for object recognition. In this paper, our attention focuses on the coding and pooling layers. In particular, we use a discriminative sparse coding method in the coding layer along with spatial pyramid representation in the pooling layer, which makes it easier to distinguish the target to be tracked from its background in the presence of appearance variations. An extensive experimental study shows that the proposed method has higher tracking accuracy than several state-of-the-art trackers.

Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.

Spin and spatial dynamics in electron-impact scattering off S-wave He using R-matrix with Time-Dependence theory

R-matrix with time-dependence theory is applied to electron-impact ionisation processes for He in the S-wave model. Cross sections for electron-impact excitation, ionisation and ionisation with excitation for impact energies between 25 and 225 eV are in excellent agreement with benchmark cross sections. Ultra-fast dynamics induced by a scattering event is observed through time-dependent signatures associated with autoionisation from doubly excited states. Further insight into dynamics can be obtained through examination of the spin components of the time-dependent wavefunction.