Automated object identification using optical video cameras on construction sites

Visual recording devices such as video cameras, CCTVs, or webcams have been broadly used to facilitate work progress or safety monitoring on construction sites. Without human intervention, however, both real-time reasoning about captured scenes and interpretation of recorded images are challenging tasks. This article presents an exploratory method for automated object identification using standard video cameras on construction sites. The proposed method supports real-time detection and classification of mobile heavy equipment and workers. The background subtraction algorithm extracts motion pixels from an image sequence, the pixels are then grouped into regions to represent moving objects, and finally the regions are identified as a certain object using classifiers. For evaluating the method, the formulated computer-aided process was implemented on actual construction sites, and promising results were obtained. This article is expected to contribute to future applications of automated monitoring systems of work zone safety or productivity.

Analysis of Object-Specific Authorization Protocol (OSAP) using coloured petri nets - Version 1.0

Analyzing security protocols is an ongoing research in the last years. Different types of tools are developed to make the analysis process more precise, fast and easy. These tools consider security protocols as black boxes that can not easily be composed. It is difficult or impossible to do a low-level analysis or combine different tools with each other using these tools. This research uses Coloured Petri Nets (CPN) to analyze OSAP trusted computing protocol. The OSAP protocol is modeled in different levels and it is analyzed using state space method. The produced model can be combined with other trusted computing protocols in future works.

Spectral–texture feature extraction using statistical moments with application to object-based vegetation species classification

The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral–texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.

Removal of toxic arsenic and antimony from groundwater Spiro Tunnel Bulkhead in Park City Utah using colloidal iron hydroxide : comparison with reverse ssmosis

Verification testing of two model technologies in pilot scale to remove arsenic and antimony based on reverse osmosis and chemical coagulation/filtration systems was conducted in Spiro Tunnel Water Filtration Plant located in Park City, Utah, US. The source water was groundwater in abandoned silver mine, naturally contaminated by 60-80 ppb of arsenic and antimony below 10 ppb. This water represents one of the sources of drinking water for Park City and constitutes about 44% of the water supply. The failure to remove antimony efficiently by coagulation/filtration (only 4.4% removal rate) under design conditions is discussed in terms of the chemistry differences between Sb (III, V) and As (III, V). Removal of Sb(V) at pH > 7, using coagulation/filtration technology, requires much higher (50 to 80 times) concentration of iron (III) than As. The stronger adsorption of arsenate over a wider pH range can be explained by the fact that arsenic acid is tri-protic, whereas antimonic acid is monoprotic. This difference in properties of As(V) and Sb(V) makes antimony (V) more difficult to be efficiently removed in low concentrations of iron hydroxide and alkaline pH waters, especially in concentration of Sb < 10 ppb.

Machine recognition of hand-drawn circuit diagrams

An application of image processing techniques to recognition of hand-drawn circuit diagrams is presented. The scanned image of a diagram is pre-processed to remove noise and converted to bilevel. Morphological operations are applied to obtain a clean, connected representation using thinned lines. The diagram comprises of nodes, connections and components. Nodes and components are segmented using appropriate thresholds on a spatially varying object pixel density. Connection paths are traced using a pixel-stack. Nodes are classified using syntactic analysis. Components are classified using a combination of invariant moments, scalar pixel-distribution features, and vector relationships between straight lines in polygonal representations. A node recognition accuracy of 82% and a component recognition accuracy of 86% was achieved on a database comprising 107 nodes and 449 components. This recogniser can be used for layout “beautification” or to generate input code for circuit analysis and simulation packages

Learning object dynamics for smooth tracking of moving lip contours

A new technique is proposed for learning the dynamic characteristics of a deformable object, applied in particular to the problem of lip-tracking. Experimental results are given which demonstrate that the use of dynamic models allows the system to track more robustly under adverse conditions and to correct spurious, poorly tracked frames

Emergent task-specific object semantics through distributed experience networks

Autonomous development of sensorimotor coordination enables a robot to adapt and change its action choices to interact with the world throughout its lifetime. The Experience Network is a structure that rapidly learns coordination between visual and haptic inputs and motor action. This paper presents methods which handle the high dimensionality of the network state-space which occurs due to the simultaneous detection of multiple sensory features. The methods provide no significant increase in the complexity of the underlying representations and also allow emergent, task-specific, semantic information to inform action selection. Experimental results show rapid learning in a real robot, beginning with no sensorimotor mappings, to a mobile robot capable of wall avoidance and target acquisition.

The structure of the mineral leogangite Cu10(OH)6(SO4)(AsO4)4•8H2O–implications for arsenic accumulation and removal

The objective of this research is to determine the molecular structure of the mineral leogangite. The formation of the types of arsenosulphate minerals offers a mechanism for arsenate removal from soils and mine dumps. Raman and infrared spectroscopy have been used to characterise the mineral. Observed bands are assigned to the stretching and bending vibrations of (SO4)2- and (AsO4)3- units, stretching and bending vibrations of hydrogen bonded (OH)- ions and Cu2+-(O,OH) units. The approximate range of O-H...O hydrogen bond lengths is inferred from the Raman spectra. Raman spectra of leogangite from different origins differ in that some spectra are more complex, where bands are sharp and the degenerate bands of (SO4)2- and (AsO4)3- are split and more intense. Lower wavenumbers of  H2O bending vibration in the spectrum may indicate the presence of weaker hydrogen bonds compared with those in a different leogangite samples. The formation of leogangite offers a mechanism for the removal of arsenic from the environment.

The molecular structure of the mineral sarmientite Fe2(AsO4,SO4)2(OH)6•5H2O : implications for arsenic accumulation and removal

Sarmientite is an environmental mineral; its formation in soils enables the entrapment and immobilisation of arsenic. The mineral sarmientite is often amorphous making the application of X-ray diffraction difficult. Vibrational spectroscopy has been applied to the study of sarmientite. Bands are attributed to the vibrational units of arsenate, sulphate, hydroxyl and water. Raman bands at 794, 814 and 831 cm−1 are assigned to the ν3 (AsO4)3− antisymmetric stretching modes and the ν1 symmetric stretching mode is observed at 891 cm−1. Raman bands at 1003 and 1106 cm−1 are attributed to vibrations. The Raman band at 484 cm−1 is assigned to the triply degenerate (AsO4)3− bending vibration. The high intensity Raman band observed at 355 cm−1 (both lower and upper) is considered to be due to the (AsO4)3−ν2 bending vibration. Bands attributed to water and OH stretching vibrations are observed.

Vibrational spectroscopy of the multi anion mineral zykaite Fe4(AsO4)(SO4)(OH)•15H2O – implications for arsenate removal

Some minerals are colloidal and are poorly diffracting . Vibrational spectroscopy offers one of the few methods for the assessment of the structure of these types of minerals. Among this group of minerals is zykaite with formula Fe4(AsO4)(SO4)(OH)•15H2O. The objective of this research is to determine the molecular structure of the mineral zykaite using vibrational spectroscopy. Raman and infrared bands are attributed to the AsO43-, SO42- and water stretching vibrations. The sharp band at 3515 cm-1 is assigned to the stretching vibration of the OH units. This mineral offers a mechanism for the formation of more crystalline minerals such as scorodite and bukovskyite. Arsenate ions can be removed from aqueous systems through the addition of ferric compounds such as ferric chloride. This results in the formation of minerals such as zykaite and pitticite (Fe3+,AsO4,SO4,H2O).