Genetic Composition and Complexity of Virus Populations at Tungro-Endemic and Outbreak Rice Sites

We have recently demonstrated the geographic isolation of rice tungro bacilliform virus (RTBV) populations in the tungro-endemic provinces of Isabela and North Cotabato, Philippines. In this study, we examined the genetic structure of the virus populations at the tungro-outbreak sites of Lanao del Norte, a province adjacent to North Cotabato. We also analyzed the virus populations at the tungro-endemic sites of Subang, Indonesia, and Dien Khanh, Vietnam. Total DNA extracts from 274 isolates were digested with EcoRV restriction enzyme and hybridized with a full-length probe of RTBV. In the total population, 22 EcoRV-restricted genome profiles (genotypes) were identified. Although overlapping genotypes could be observed, the outbreak sites of Lanao del Norte had a genotype combination distinct from that of Subang or Dien Khanh but a genotype combination similar to that identified earlier from North Cotabato, the adjacent endemic province. Sequence analysis of the intergenic region and part of the ORF1 RTBV genome from randomly selected genotypes confirms the geographic clustering of RTBV genotypes and, combined with restriction analysis, the results suggest a fragmented spatial distribution of RTBV local populations in the three countries. Because RTBV depends on rice tungro spherical virus (RTSV) for transmission, the population dynamics of both tungro viruses were then examined at the endemic and outbreak sites within the Philippines. The RTBV genotypes and the coat protein RTSV genotypes were used as indicators for virus diversity. A shift in population structure of both viruses was observed at the outbreak sites with a reduced RTBV but increased RTSV gene diversity

A crash avoidance framework for heavy equipment control systems using 3D imaging sensors

This paper presents a preliminary crash avoidance framework for heavy equipment control systems. Safe equipment operation is a major concern on construction sites since fatal on-site injuries are an industry-wide problem. The proposed framework has potential for effecting active safety for equipment operation. The framework contains algorithms for spatial modeling, object tracking, and path planning. Beyond generating spatial models in fractions of seconds, these algorithms can successfully track objects in an environment and produce a collision-free 3D motion trajectory for equipment.

A methodology for object identification and tracking in construction based on spatial modeling and image matching techniques

Understanding the motion characteristics of on-site objects is desirable for the analysis of construction work zones, especially in problems related to safety and productivity studies. This article presents a methodology for rapid object identification and tracking. The proposed methodology contains algorithms for spatial modeling and image matching. A high-frame-rate range sensor was utilized for spatial data acquisition. The experimental results indicated that an occupancy grid spatial modeling algorithm could quickly build a suitable work zone model from the acquired data. The results also showed that an image matching algorithm is able to find the most similar object from a model database and from spatial models obtained from previous scans. It is then possible to use the matched information to successfully identify and track objects.

Object identification based on 3D spatial models of construction sites

On obstacle-cluttered construction sites, understanding the motion characteristics of objects is important for anticipating collisions and preventing accidents. This study investigates algorithms for object identification applications that can be used by heavy equipment operators to effectively monitor congested local environment. The proposed framework contains algorithms for three-dimensional spatial modeling and image matching that are based on 3D images scanned by a high-frame rate range sensor. The preliminary results show that an occupancy grid spatial modeling algorithm can successfully build the most pertinent spatial information, and that an image matching algorithm is best able to identify which objects are in the scanned scene.

Development of an automated safety assessment framework for construction activities

This paper presents an ongoing research project concerning the development of an automated safety assessment framework for earthmoving and surface mining activities. This research seeks to determine data needs for safety assessment and investigates how to utilize collected data to promote more informed and efficient safety decision-making. The research first examined accidents and fatalities involved with earthmoving and surface mining activities—more specifically, those involving loading, hauling, and dumping operations,—investigated risk factors involved with the accidents, and finally identified data needs for safety assessment based on safety regulations and practices. An automated safety assessment method was then developed using the data needs that had been identified. This research is expected to contribute to the introduction of a fundamental framework for automated safety assessment and the systematic collection of safety-related data from construction activities. Implementation of the entire safety assessment process on actual construction sites remains a task for future research.

Modeling object identification and tracking errors on image-based safety assessment of earthmoving operations

Object identification and tracking have become critical for automated on-site construction safety assessment. The primary objective of this paper is to present the development of a testbed to analyze the impact of object identification and tracking errors caused by data collection devices and algorithms used for safety assessment. The testbed models workspaces for earthmoving operations and simulates safety-related violations, including speed limit violations, access violations to dangerous areas, and close proximity violations between heavy machinery. Three different cases were analyzed based on actual earthmoving operations conducted at a limestone quarry. Using the testbed, the impacts of device and algorithm errors were investigated for safety planning purposes.

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.

Image-based safety assessment : automated spatial safety risk identification of earthmoving and surface mining activities

This paper presents an automated image‐based safety assessment method for earthmoving and surface mining activities. The literature review revealed the possible causes of accidents on earthmoving operations, investigated the spatial risk factors of these types of accident, and identified spatial data needs for automated safety assessment based on current safety regulations. Image‐based data collection devices and algorithms for safety assessment were then evaluated. Analysis methods and rules for monitoring safety violations were also discussed. The experimental results showed that the safety assessment method collected spatial data using stereo vision cameras, applied object identification and tracking algorithms, and finally utilized identified and tracked object information for safety decision making.

Design of a preliminary error impact analysis model for spatial safety assessment of earthmoving operations

Regardless of technology benefits, safety planners still face difficulties explaining errors related to the use of different technologies and evaluating how the errors impact the performance of safety decision making. This paper presents a preliminary error impact analysis testbed to model object identification and tracking errors caused by image-based devices and algorithms and to analyze the impact of the errors for spatial safety assessment of earthmoving and surface mining activities. More specifically, this research designed a testbed to model workspaces for earthmoving operations, to simulate safety-related violations, and to apply different object identification and tracking errors on the data collected and processed for spatial safety assessment. Three different cases were analyzed based on actual earthmoving operations conducted at a limestone quarry. Using the testbed, the impacts of the errors were investigated for the safety planning purpose.

Intelligent computing and sensing for active safety on construction sites

On obstacle-cluttered construction sites where heavy equipment is in use, safety issues are of major concern. The main objective of this paper is to develop a framework with algorithms for obstacle avoidance and path planning based on real-time three-dimensional job site models to improve safety during equipment operation. These algorithms have the potential to prevent collisions between heavy equipment vehicles and other on-site objects. In this study, algorithms were developed for image data acquisition, real-time 3D spatial modeling, obstacle avoidance, and shortest path finding and were all integrated to construct a comprehensive collision-free path. Preliminary research results show that the proposed approach is feasible and has the potential to be used as an active safety feature for heavy equipment.

Iris based identity verification robust to sample presentation security attacks

Iris based identity verification is highly reliable but it can also be subject to attacks. Pupil dilation or constriction stimulated by the application of drugs are examples of sample presentation security attacks which can lead to higher false rejection rates. Suspects on a watch list can potentially circumvent the iris based system using such methods. This paper investigates a new approach using multiple parts of the iris (instances) and multiple iris samples in a sequential decision fusion framework that can yield robust performance. Results are presented and compared with the standard full iris based approach for a number of iris degradations. An advantage of the proposed fusion scheme is that the trade-off between detection errors can be controlled by setting parameters such as the number of instances and the number of samples used in the system. The system can then be operated to match security threat levels. It is shown that for optimal values of these parameters, the fused system also has a lower total error rate.

Decision fusion from parts and samples for robust iris recognition

Fusion techniques can be used in biometrics to achieve higher accuracy. When biometric systems are in operation and the threat level changes, controlling the trade-off between detection error rates can reduce the impact of an attack. In a fused system, varying a single threshold does not allow this to be achieved, but systematic adjustment of a set of parameters does. In this paper, fused decisions from a multi-part, multi-sample sequential architecture are investigated for that purpose in an iris recognition system. A specific implementation of the multi-part architecture is proposed and the effect of the number of parts and samples in the resultant detection error rate is analysed. The effectiveness of the proposed architecture is then evaluated under two specific cases of obfuscation attack: miosis and mydriasis. Results show that robustness to such obfuscation attacks is achieved, since lower error rates than in the case of the non-fused base system are obtained.

Cell image classification using histograms, higher order statistics and adaboost

A cell classification algorithm that uses first, second and third order statistics of pixel intensity distributions over pre-defined regions is implemented and evaluated. A cell image is segmented into 6 regions extending from a boundary layer to an inner circle. First, second and third order statistical features are extracted from histograms of pixel intensities in these regions. Third order statistical features used are one-dimensional bispectral invariants. 108 features were considered as candidates for Adaboost based fusion. The best 10 stage fused classifier was selected for each class and a decision tree constructed for the 6-class problem. The classifier is robust, accurate and fast by design.