40 CFR 86.085-20 - Incomplete vehicles, classification.

Code of Federal Regulations, 2010 CFR

2010-07-01

..., classification. (a) An incomplete truck less than 8,500 pounds gross vehicle weight rating shall be classified by... 40 Protection of Environment 18 2010-07-01 2010-07-01 false Incomplete vehicles, classification... PROGRAMS (CONTINUED) CONTROL OF EMISSIONS FROM NEW AND IN-USE HIGHWAY VEHICLES AND ENGINES General...

Validating the performance of vehicle classification stations.

DOT National Transportation Integrated Search

2012-05-01

Vehicle classification is used in many transportation applications, e.g., infrastructure management and planning. Typical of most : developed countries, every state in the US maintains a network of vehicle classification stations to explicitly sort v...

Sparse representation based SAR vehicle recognition along with aspect angle.

PubMed

Xing, Xiangwei; Ji, Kefeng; Zou, Huanxin; Sun, Jixiang

2014-01-01

As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC) has attracted much attention in synthetic aperture radar (SAR) automatic target recognition (ATR) recently. In this paper, we develop a novel SAR vehicle recognition method based on sparse representation classification along with aspect information (SRCA), in which the correlation between the vehicle's aspect angle and the sparse representation vector is exploited. The detailed procedure presented in this paper can be summarized as follows. Initially, the sparse representation vector of a test sample is solved by sparse representation algorithm with a principle component analysis (PCA) feature-based dictionary. Then, the coefficient vector is projected onto a sparser one within a certain range of the vehicle's aspect angle. Finally, the vehicle is classified into a certain category that minimizes the reconstruction error with the novel sparse representation vector. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset and the results demonstrate that the proposed method performs robustly under the variations of depression angle and target configurations, as well as incomplete observation.

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation.

PubMed

Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

2016-08-16

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods.

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation

PubMed Central

Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

2016-01-01

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods. PMID:27537888

Length based vehicle classification on freeways from single loop detectors.

DOT National Transportation Integrated Search

2009-10-15

Roadway usage, particularly by large vehicles, is one of the fundamental factors determining the lifespan : of highway infrastructure, e.g., as evidenced by the federally mandated Highway Performance : Monitoring System (HPMS). But the complexity of ...

Research on the transfer learning of the vehicle logo recognition

NASA Astrophysics Data System (ADS)

Zhao, Wei

2017-08-01

The Convolutional Neural Network of Deep Learning has been a huge success in the field of image intelligent transportation system can effectively solve the traffic safety, congestion, vehicle management and other problems of traffic in the city. Vehicle identification is a vital part of intelligent transportation, and the effective information in vehicles is of great significance to vehicle identification. With the traffic system on the vehicle identification technology requirements are getting higher and higher, the vehicle as an important type of vehicle information, because it should not be removed, difficult to change and other features for vehicle identification provides an important method. The current vehicle identification recognition (VLR) is mostly used to extract the characteristics of the method of classification, which for complex classification of its generalization ability to be some constraints, if the use of depth learning technology, you need a lot of training samples. In this paper, the method of convolution neural network based on transfer learning can solve this problem effectively, and it has important practical application value in the task of vehicle mark recognition.

Exploring image-based classification to detect vehicle make and model.

DOT National Transportation Integrated Search

2013-11-01

The goal of this work is to improve the understanding of the impact of carbon emissions caused by vehicular traffic on : highway systems. In order to achieve this goal, this work obtains a novel pipeline for vehicle segmentation, tracking : and class...

49 CFR 523.6 - Heavy-duty vehicle.

Code of Federal Regulations, 2011 CFR

2011-10-01

... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.6 Heavy-duty vehicle. (a) A heavy-duty vehicle is any commercial medium- and heavy-duty on highway vehicle or a work truck, as defined in 49 U.S...; and (3) Truck tractors with a GVWR above 26,000 pounds. (b) The heavy-duty vehicle classification does...

Statistical Methods for Passive Vehicle Classification in Urban Traffic Surveillance and Control

DOT National Transportation Integrated Search

1980-01-01

A statistical approach to passive vehicle classification using the phase-shift signature from electromagnetic presence-type vehicle detectors is developed with digitized samples of the analog phase-shift signature, the problem of classifying vehicle ...

Object-based detection of vehicles using combined optical and elevation data

NASA Astrophysics Data System (ADS)

Schilling, Hendrik; Bulatov, Dimitri; Middelmann, Wolfgang

2018-02-01

The detection of vehicles is an important and challenging topic that is relevant for many applications. In this work, we present a workflow that utilizes optical and elevation data to detect vehicles in remotely sensed urban data. This workflow consists of three consecutive stages: candidate identification, classification, and single vehicle extraction. Unlike in most previous approaches, fusion of both data sources is strongly pursued at all stages. While the first stage utilizes the fact that most man-made objects are rectangular in shape, the second and third stages employ machine learning techniques combined with specific features. The stages are designed to handle multiple sensor input, which results in a significant improvement. A detailed evaluation shows the benefits of our workflow, which includes hand-tailored features; even in comparison with classification approaches based on Convolutional Neural Networks, which are state of the art in computer vision, we could obtain a comparable or superior performance (F1 score of 0.96-0.94).

Single-Frame Terrain Mapping Software for Robotic Vehicles

NASA Technical Reports Server (NTRS)

Rankin, Arturo L.

2011-01-01

This software is a component in an unmanned ground vehicle (UGV) perception system that builds compact, single-frame terrain maps for distribution to other systems, such as a world model or an operator control unit, over a local area network (LAN). Each cell in the map encodes an elevation value, terrain classification, object classification, terrain traversability, terrain roughness, and a confidence value into four bytes of memory. The input to this software component is a range image (from a lidar or stereo vision system), and optionally a terrain classification image and an object classification image, both registered to the range image. The single-frame terrain map generates estimates of the support surface elevation, ground cover elevation, and minimum canopy elevation; generates terrain traversability cost; detects low overhangs and high-density obstacles; and can perform geometry-based terrain classification (ground, ground cover, unknown). A new origin is automatically selected for each single-frame terrain map in global coordinates such that it coincides with the corner of a world map cell. That way, single-frame terrain maps correctly line up with the world map, facilitating the merging of map data into the world map. Instead of using 32 bits to store the floating-point elevation for a map cell, the vehicle elevation is assigned to the map origin elevation and reports the change in elevation (from the origin elevation) in terms of the number of discrete steps. The single-frame terrain map elevation resolution is 2 cm. At that resolution, terrain elevation from 20.5 to 20.5 m (with respect to the vehicle's elevation) is encoded into 11 bits. For each four-byte map cell, bits are assigned to encode elevation, terrain roughness, terrain classification, object classification, terrain traversability cost, and a confidence value. The vehicle s current position and orientation, the map origin, and the map cell resolution are all included in a header for each map. The map is compressed into a vector prior to delivery to another system.

Vehicle classification using mobile sensors.

DOT National Transportation Integrated Search

2013-04-01

In this research, the feasibility of using mobile traffic sensors for binary vehicle classification on arterial roads is investigated. Features (e.g. : speed related, acceleration/deceleration related, etc.) are extracted from vehicle traces (passeng...

Machine learning to classify and predict objective and subjective assessments of vehicle dynamics: the case of steering feel

NASA Astrophysics Data System (ADS)

Gil Gómez, Gaspar L.; Nybacka, Mikael; Drugge, Lars; Bakker, Egbert

2018-01-01

Objective measurements and computer-aided engineering simulations cannot be exploited to their full potential because of the high importance of driver feel in vehicle development. Furthermore, despite many studies, it is not easy to identify the relationship between objective metrics (OM) and subjective assessments (SA), a task further complicated by the fact that SA change between drivers and geographical locations or with time. This paper presents a method which uses two artificial neural networks built on top of each other that helps to close this gap. The first network, based solely on OM, generates a map that groups together similar vehicles, thus allowing a classification of measured vehicles to be visualised. This map objectively demonstrates that there exist brand and vehicle class identities. It also foresees the subjective characteristics of a new vehicle, based on its requirements, simulations and measurements. These characteristics are described by the neighbourhood of the new vehicle in the map, which is made up of known vehicles that are accompanied by word-clouds that enhance this description. This forecast is also extended to perform a sensitivity analysis of the tolerances in the requirements, as well as to validate previously published preferred range of steering feel metrics. The results suggest a few new modifications. Finally, the qualitative information given by this measurement-based classification is complemented with a second superimposed network. This network describes a regression surface that enables quantitative predictions, for example the SA of the steering feel of a new vehicle from its OM.

40 CFR 86.085-20 - Incomplete vehicles, classification.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 19 2013-07-01 2013-07-01 false Incomplete vehicles, classification... PROGRAMS (CONTINUED) CONTROL OF EMISSIONS FROM NEW AND IN-USE HIGHWAY VEHICLES AND ENGINES General Provisions for Emission Regulations for 1977 and Later Model Year New Light-Duty Vehicles, Light-Duty Trucks...

40 CFR 86.085-20 - Incomplete vehicles, classification.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 19 2014-07-01 2014-07-01 false Incomplete vehicles, classification... PROGRAMS (CONTINUED) CONTROL OF EMISSIONS FROM NEW AND IN-USE HIGHWAY VEHICLES AND ENGINES General Provisions for Emission Regulations for 1977 and Later Model Year New Light-Duty Vehicles, Light-Duty Trucks...

Vehicle Maneuver Detection with Accelerometer-Based Classification.

PubMed

Cervantes-Villanueva, Javier; Carrillo-Zapata, Daniel; Terroso-Saenz, Fernando; Valdes-Vela, Mercedes; Skarmeta, Antonio F

2016-09-29

In the mobile computing era, smartphones have become instrumental tools to develop innovative mobile context-aware systems. In that sense, their usage in the vehicular domain eases the development of novel and personal transportation solutions. In this frame, the present work introduces an innovative mechanism to perceive the current kinematic state of a vehicle on the basis of the accelerometer data from a smartphone mounted in the vehicle. Unlike previous proposals, the introduced architecture targets the computational limitations of such devices to carry out the detection process following an incremental approach. For its realization, we have evaluated different classification algorithms to act as agents within the architecture. Finally, our approach has been tested with a real-world dataset collected by means of the ad hoc mobile application developed.

The study of vehicle classification equipment with solutions to improve accuracy in Oklahoma.

DOT National Transportation Integrated Search

2014-12-01

The accuracy of vehicle counting and classification data is vital for appropriate future highway and road : design, including determining pavement characteristics, eliminating traffic jams, and improving safety. : Organizations relying on vehicle cla...

Image acquisition system for traffic monitoring applications

NASA Astrophysics Data System (ADS)

Auty, Glen; Corke, Peter I.; Dunn, Paul; Jensen, Murray; Macintyre, Ian B.; Mills, Dennis C.; Nguyen, Hao; Simons, Ben

1995-03-01

An imaging system for monitoring traffic on multilane highways is discussed. The system, named Safe-T-Cam, is capable of operating 24 hours per day in all but extreme weather conditions and can capture still images of vehicles traveling up to 160 km/hr. Systems operating at different remote locations are networked to allow transmission of images and data to a control center. A remote site facility comprises a vehicle detection and classification module (VCDM), an image acquisition module (IAM) and a license plate recognition module (LPRM). The remote site is connected to the central site by an ISDN communications network. The remote site system is discussed in this paper. The VCDM consists of a video camera, a specialized exposure control unit to maintain consistent image characteristics, and a 'real-time' image processing system that processes 50 images per second. The VCDM can detect and classify vehicles (e.g. cars from trucks). The vehicle class is used to determine what data should be recorded. The VCDM uses a vehicle tracking technique to allow optimum triggering of the high resolution camera of the IAM. The IAM camera combines the features necessary to operate consistently in the harsh environment encountered when imaging a vehicle 'head-on' in both day and night conditions. The image clarity obtained is ideally suited for automatic location and recognition of the vehicle license plate. This paper discusses the camera geometry, sensor characteristics and the image processing methods which permit consistent vehicle segmentation from a cluttered background allowing object oriented pattern recognition to be used for vehicle classification. The image capture of high resolution images and the image characteristics required for the LPRMs automatic reading of vehicle license plates, is also discussed. The results of field tests presented demonstrate that the vision based Safe-T-Cam system, currently installed on open highways, is capable of producing automatic classification of vehicle class and recording of vehicle numberplates with a success rate around 90 percent in a period of 24 hours.

An Automatic Vehicle Classification System.

DTIC Science & Technology

1981-07-01

addi- tion, various portions of the system design can be used by other vehicle study projects, e.g. for projects concerned with vehicle speed or for...traffic study projects that require an axle counter or vehicle height indicator. A *4 UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGE(W1en Data Enrerod...optoelectronic components as the basis for detection. Factors of vehicle length, height, and number of axles are used as identification characteristics. In

Terrain Perception for DEMO III

NASA Technical Reports Server (NTRS)

Manduchi, R.; Bellutta, P.; Matthies, L.; Owens, K.; Rankin, A.

2000-01-01

The Demo III program has as its primary focus the development of autonomous mobility for a small rugged cross country vehicle. In this paper we report recent progress on both stereo-based obstacle detection and terrain cover color-based classification.

Vehicle classification in WAMI imagery using deep network

NASA Astrophysics Data System (ADS)

Yi, Meng; Yang, Fan; Blasch, Erik; Sheaff, Carolyn; Liu, Kui; Chen, Genshe; Ling, Haibin

2016-05-01

Humans have always had a keen interest in understanding activities and the surrounding environment for mobility, communication, and survival. Thanks to recent progress in photography and breakthroughs in aviation, we are now able to capture tens of megapixels of ground imagery, namely Wide Area Motion Imagery (WAMI), at multiple frames per second from unmanned aerial vehicles (UAVs). WAMI serves as a great source for many applications, including security, urban planning and route planning. These applications require fast and accurate image understanding which is time consuming for humans, due to the large data volume and city-scale area coverage. Therefore, automatic processing and understanding of WAMI imagery has been gaining attention in both industry and the research community. This paper focuses on an essential step in WAMI imagery analysis, namely vehicle classification. That is, deciding whether a certain image patch contains a vehicle or not. We collect a set of positive and negative sample image patches, for training and testing the detector. Positive samples are 64 × 64 image patches centered on annotated vehicles. We generate two sets of negative images. The first set is generated from positive images with some location shift. The second set of negative patches is generated from randomly sampled patches. We also discard those patches if a vehicle accidentally locates at the center. Both positive and negative samples are randomly divided into 9000 training images and 3000 testing images. We propose to train a deep convolution network for classifying these patches. The classifier is based on a pre-trained AlexNet Model in the Caffe library, with an adapted loss function for vehicle classification. The performance of our classifier is compared to several traditional image classifier methods using Support Vector Machine (SVM) and Histogram of Oriented Gradient (HOG) features. While the SVM+HOG method achieves an accuracy of 91.2%, the accuracy of our deep network-based classifier reaches 97.9%.

Acoustic⁻Seismic Mixed Feature Extraction Based on Wavelet Transform for Vehicle Classification in Wireless Sensor Networks.

PubMed

Zhang, Heng; Pan, Zhongming; Zhang, Wenna

2018-06-07

An acoustic⁻seismic mixed feature extraction method based on the wavelet coefficient energy ratio (WCER) of the target signal is proposed in this study for classifying vehicle targets in wireless sensor networks. The signal was decomposed into a set of wavelet coefficients using the à trous algorithm, which is a concise method used to implement the wavelet transform of a discrete signal sequence. After the wavelet coefficients of the target acoustic and seismic signals were obtained, the energy ratio of each layer coefficient was calculated as the feature vector of the target signals. Subsequently, the acoustic and seismic features were merged into an acoustic⁻seismic mixed feature to improve the target classification accuracy after the acoustic and seismic WCER features of the target signal were simplified using the hierarchical clustering method. We selected the support vector machine method for classification and utilized the data acquired from a real-world experiment to validate the proposed method. The calculated results show that the WCER feature extraction method can effectively extract the target features from target signals. Feature simplification can reduce the time consumption of feature extraction and classification, with no effect on the target classification accuracy. The use of acoustic⁻seismic mixed features effectively improved target classification accuracy by approximately 12% compared with either acoustic signal or seismic signal alone.

Object-oriented recognition of high-resolution remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Yongyan; Li, Haitao; Chen, Hong; Xu, Yuannan

2016-01-01

With the development of remote sensing imaging technology and the improvement of multi-source image's resolution in satellite visible light, multi-spectral and hyper spectral , the high resolution remote sensing image has been widely used in various fields, for example military field, surveying and mapping, geophysical prospecting, environment and so forth. In remote sensing image, the segmentation of ground targets, feature extraction and the technology of automatic recognition are the hotspot and difficulty in the research of modern information technology. This paper also presents an object-oriented remote sensing image scene classification method. The method is consist of vehicles typical objects classification generation, nonparametric density estimation theory, mean shift segmentation theory, multi-scale corner detection algorithm, local shape matching algorithm based on template. Remote sensing vehicles image classification software system is designed and implemented to meet the requirements .

Confidence level estimation in multi-target classification problems

NASA Astrophysics Data System (ADS)

Chang, Shi; Isaacs, Jason; Fu, Bo; Shin, Jaejeong; Zhu, Pingping; Ferrari, Silvia

2018-04-01

This paper presents an approach for estimating the confidence level in automatic multi-target classification performed by an imaging sensor on an unmanned vehicle. An automatic target recognition algorithm comprised of a deep convolutional neural network in series with a support vector machine classifier detects and classifies targets based on the image matrix. The joint posterior probability mass function of target class, features, and classification estimates is learned from labeled data, and recursively updated as additional images become available. Based on the learned joint probability mass function, the approach presented in this paper predicts the expected confidence level of future target classifications, prior to obtaining new images. The proposed approach is tested with a set of simulated sonar image data. The numerical results show that the estimated confidence level provides a close approximation to the actual confidence level value determined a posteriori, i.e. after the new image is obtained by the on-board sensor. Therefore, the expected confidence level function presented in this paper can be used to adaptively plan the path of the unmanned vehicle so as to optimize the expected confidence levels and ensure that all targets are classified with satisfactory confidence after the path is executed.

A Discussion of Aerodynamic Control Effectors (ACEs) for Unmanned Air Vehicles (UAVs)

NASA Technical Reports Server (NTRS)

Wood, Richard M.

2002-01-01

A Reynolds number based, unmanned air vehicle classification structure has been developed which identifies four classes of unmanned air vehicle concepts. The four unmanned air vehicle (UAV) classes are; Micro UAV, Meso UAV, Macro UAV, and Mega UAV. In a similar fashion a labeling scheme for aerodynamic control effectors (ACE) was developed and eleven types of ACE concepts were identified. These eleven types of ACEs were laid out in a five (5) layer scheme. The final section of the paper correlated the various ACE concepts to the four UAV classes and ACE recommendations are offered for future design activities.

Validating the performance of vehicle classification stations : executive summary report.

DOT National Transportation Integrated Search

2012-05-01

Vehicle classification data are used in many transportation applications, including: pavement design, : environmental impact studies, traffic control, and traffic safety. Typical of most developed countries, every : state in the US maintains a networ...

Mining vehicle classifications from the Columbus Metropolitan Freeway Management System.

DOT National Transportation Integrated Search

2015-01-01

Vehicle classification data are used in many transportation applications, including: pavement design, : environmental impact studies, traffic control, and traffic safety. Ohio has over 200 permanent count stations, : supplemented by many more short-t...

Vehicle classification system : FHWA perspective

DOT National Transportation Integrated Search

2000-08-01

Issues related to vehicle classification in light of the Federal Highway Administration (FHWA) Traffic Monitoring Guide (TMG) revision will be discussed. The discussion will look at the expressed needs of the data users and the associated impacts on ...

Multi-stage classification method oriented to aerial image based on low-rank recovery and multi-feature fusion sparse representation.

PubMed

Ma, Xu; Cheng, Yongmei; Hao, Shuai

2016-12-10

Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary. Then we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and construct a multi-stage terrain classifier. Experimental results on an aerial map database that we prepared verify the classification accuracy and robustness of the proposed method.

Multifunctional Structural-energy Storage Nanocomposites for Ultra Lightweight Micro Autonomous Vehicles

DTIC Science & Technology

2013-02-01

supplement the main power supply. Here we report on the use of flexible carbon nanotube (CNT)-based composites for multifunctional structural energy storage...TERMS Micro vehicle, Supercapacitor, Carbon Nanotubes , CNTs, Energy Storage, Multifunctional Materials 16. SECURITY CLASSIFICATION OF: 17...consists of a current collector, a porous electrode layer ( carbon nanotubes [CNTs], in this case) infiltrated with an electrolyte (i.e., a liquid

Mining vehicle classifications from the Columbus Metropolitan Freeway Management System : [summary].

DOT National Transportation Integrated Search

2015-01-01

Vehicle classification data are used in many transportation applications, including: pavement design, : environmental impact studies, traffic control, and traffic safety. Ohio has over 200 permanent count : stations, supplemented by many more short-t...

Detection, recognition, identification, and tracking of military vehicles using biomimetic intelligence

NASA Astrophysics Data System (ADS)

Pace, Paul W.; Sutherland, John

2001-10-01

This project is aimed at analyzing EO/IR images to provide automatic target detection/recognition/identification (ATR/D/I) of militarily relevant land targets. An increase in performance was accomplished using a biomimetic intelligence system functioning on low-cost, commercially available processing chips. Biomimetic intelligence has demonstrated advanced capabilities in the areas of hand- printed character recognition, real-time detection/identification of multiple faces in full 3D perspectives in cluttered environments, advanced capabilities in classification of ground-based military vehicles from SAR, and real-time ATR/D/I of ground-based military vehicles from EO/IR/HRR data in cluttered environments. The investigation applied these tools to real data sets and examined the parameters such as the minimum resolution for target recognition, the effect of target size, rotation, line-of-sight changes, contrast, partial obscuring, background clutter etc. The results demonstrated a real-time ATR/D/I capability against a subset of militarily relevant land targets operating in a realistic scenario. Typical results on the initial EO/IR data indicate probabilities of correct classification of resolved targets to be greater than 95 percent.

New York State Thruway Authority automatic vehicle classification (AVC) : research report.

DOT National Transportation Integrated Search

2008-03-31

In December 2007, the N.Y.S. Thruway Authority (Thruway) concluded a Federal : funded research effort to study technology and develop a design for retrofitting : devices required in implementing a fully automated vehicle classification system i...

Continuous vehicle classification data : how good is it?

DOT National Transportation Integrated Search

2000-08-01

Florida has a lengthy history of trying to obtain continuous vehicle classification data. They installed their first piezoelectric axle sensors at a continuous count site in October of 1988. At that time, they had 86 continuous count sites operating ...

Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery

NASA Astrophysics Data System (ADS)

Ma, Lei; Cheng, Liang; Li, Manchun; Liu, Yongxue; Ma, Xiaoxue

2015-04-01

Unmanned Aerial Vehicle (UAV) has been used increasingly for natural resource applications in recent years due to their greater availability and the miniaturization of sensors. In addition, Geographic Object-Based Image Analysis (GEOBIA) has received more attention as a novel paradigm for remote sensing earth observation data. However, GEOBIA generates some new problems compared with pixel-based methods. In this study, we developed a strategy for the semi-automatic optimization of object-based classification, which involves an area-based accuracy assessment that analyzes the relationship between scale and the training set size. We found that the Overall Accuracy (OA) increased as the training set ratio (proportion of the segmented objects used for training) increased when the Segmentation Scale Parameter (SSP) was fixed. The OA increased more slowly as the training set ratio became larger and a similar rule was obtained according to the pixel-based image analysis. The OA decreased as the SSP increased when the training set ratio was fixed. Consequently, the SSP should not be too large during classification using a small training set ratio. By contrast, a large training set ratio is required if classification is performed using a high SSP. In addition, we suggest that the optimal SSP for each class has a high positive correlation with the mean area obtained by manual interpretation, which can be summarized by a linear correlation equation. We expect that these results will be applicable to UAV imagery classification to determine the optimal SSP for each class.

Human-inspired sound environment recognition system for assistive vehicles

NASA Astrophysics Data System (ADS)

González Vidal, Eduardo; Fredes Zarricueta, Ernesto; Auat Cheein, Fernando

2015-02-01

Objective. The human auditory system acquires environmental information under sound stimuli faster than visual or touch systems, which in turn, allows for faster human responses to such stimuli. It also complements senses such as sight, where direct line-of-view is necessary to identify objects, in the environment recognition process. This work focuses on implementing human reaction to sound stimuli and environment recognition on assistive robotic devices, such as robotic wheelchairs or robotized cars. These vehicles need environment information to ensure safe navigation. Approach. In the field of environment recognition, range sensors (such as LiDAR and ultrasonic systems) and artificial vision devices are widely used; however, these sensors depend on environment constraints (such as lighting variability or color of objects), and sound can provide important information for the characterization of an environment. In this work, we propose a sound-based approach to enhance the environment recognition process, mainly for cases that compromise human integrity, according to the International Classification of Functioning (ICF). Our proposal is based on a neural network implementation that is able to classify up to 15 different environments, each selected according to the ICF considerations on environment factors in the community-based physical activities of people with disabilities. Main results. The accuracy rates in environment classification ranges from 84% to 93%. This classification is later used to constrain assistive vehicle navigation in order to protect the user during daily activities. This work also includes real-time outdoor experimentation (performed on an assistive vehicle) by seven volunteers with different disabilities (but without cognitive impairment and experienced in the use of wheelchairs), statistical validation, comparison with previously published work, and a discussion section where the pros and cons of our system are evaluated. Significance. The proposed sound-based system is very efficient at providing general descriptions of the environment. Such descriptions are focused on vulnerable situations described by the ICF. The volunteers answered a questionnaire regarding the importance of constraining the vehicle velocities in risky environments, showing that all the volunteers felt comfortable with the system and its performance.

Human-inspired sound environment recognition system for assistive vehicles.

PubMed

Vidal, Eduardo González; Zarricueta, Ernesto Fredes; Cheein, Fernando Auat

2015-02-01

The human auditory system acquires environmental information under sound stimuli faster than visual or touch systems, which in turn, allows for faster human responses to such stimuli. It also complements senses such as sight, where direct line-of-view is necessary to identify objects, in the environment recognition process. This work focuses on implementing human reaction to sound stimuli and environment recognition on assistive robotic devices, such as robotic wheelchairs or robotized cars. These vehicles need environment information to ensure safe navigation. In the field of environment recognition, range sensors (such as LiDAR and ultrasonic systems) and artificial vision devices are widely used; however, these sensors depend on environment constraints (such as lighting variability or color of objects), and sound can provide important information for the characterization of an environment. In this work, we propose a sound-based approach to enhance the environment recognition process, mainly for cases that compromise human integrity, according to the International Classification of Functioning (ICF). Our proposal is based on a neural network implementation that is able to classify up to 15 different environments, each selected according to the ICF considerations on environment factors in the community-based physical activities of people with disabilities. The accuracy rates in environment classification ranges from 84% to 93%. This classification is later used to constrain assistive vehicle navigation in order to protect the user during daily activities. This work also includes real-time outdoor experimentation (performed on an assistive vehicle) by seven volunteers with different disabilities (but without cognitive impairment and experienced in the use of wheelchairs), statistical validation, comparison with previously published work, and a discussion section where the pros and cons of our system are evaluated. The proposed sound-based system is very efficient at providing general descriptions of the environment. Such descriptions are focused on vulnerable situations described by the ICF. The volunteers answered a questionnaire regarding the importance of constraining the vehicle velocities in risky environments, showing that all the volunteers felt comfortable with the system and its performance.

Engine classification using vibrations measured by Laser Doppler Vibrometer on different surfaces

NASA Astrophysics Data System (ADS)

Wei, J.; Liu, Chi-Him; Zhu, Zhigang; Vongsy, Karmon; Mendoza-Schrock, Olga

2015-05-01

In our previous studies, vehicle surfaces' vibrations caused by operating engines measured by Laser Doppler Vibrometer (LDV) have been effectively exploited in order to classify vehicles of different types, e.g., vans, 2-door sedans, 4-door sedans, trucks, and buses, as well as different types of engines, such as Inline-four engines, V-6 engines, 1-axle diesel engines, and 2-axle diesel engines. The results are achieved by employing methods based on an array of machine learning classifiers such as AdaBoost, random forests, neural network, and support vector machines. To achieve effective classification performance, we seek to find a more reliable approach to pick authentic vibrations of vehicle engines from a trustworthy surface. Compared with vibrations directly taken from the uncooperative vehicle surfaces that are rigidly connected to the engines, these vibrations are much weaker in magnitudes. In this work we conducted a systematic study on different types of objects. We tested different types of engines ranging from electric shavers, electric fans, and coffee machines among different surfaces such as a white board, cement wall, and steel case to investigate the characteristics of the LDV signals of these surfaces, in both the time and spectral domains. Preliminary results in engine classification using several machine learning algorithms point to the right direction on the choice of type of object surfaces to be planted for LDV measurements.

Semantic Labelling of Ultra Dense Mls Point Clouds in Urban Road Corridors Based on Fusing Crf with Shape Priors

NASA Astrophysics Data System (ADS)

Yao, W.; Polewski, P.; Krzystek, P.

2017-09-01

In this paper, a labelling method for the semantic analysis of ultra-high point density MLS data (up to 4000 points/m2) in urban road corridors is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds with shape priors. The CRF uses a Random Forest (RF) for generating the unary potentials of nodes and a variant of the contrastsensitive Potts model for the pair-wise potentials of node edges. The foundations of the classification are various geometric features derived by means of co-variance matrices and local accumulation map of spatial coordinates based on local neighbourhoods. Meanwhile, in order to cope with the ultra-high point density, a plane-based region growing method combined with a rule-based classifier is applied to first fix semantic labels for man-made objects. Once such kind of points that usually account for majority of entire data amount are pre-labeled; the CRF classifier can be solved by optimizing the discriminative probability for nodes within a subgraph structure excluded from pre-labeled nodes. The process can be viewed as an evidence fusion step inferring a degree of belief for point labelling from different sources. The MLS data used for this study were acquired by vehicle-borne Z+F phase-based laser scanner measurement, which permits the generation of a point cloud with an ultra-high sampling rate and accuracy. The test sites are parts of Munich City which is assumed to consist of seven object classes including impervious surfaces, tree, building roof/facade, low vegetation, vehicle and pole. The competitive classification performance can be explained by the diverse factors: e.g. the above ground height highlights the vertical dimension of houses, trees even cars, but also attributed to decision-level fusion of graph-based contextual classification approach with shape priors. The use of context-based classification methods mainly contributed to smoothing of labelling by removing outliers and the improvement in underrepresented object classes. In addition, the routine operation of a context-based classification for such high density MLS data becomes much more efficient being comparable to non-contextual classification schemes.

Verification, refinement, and applicability of long-term pavement performance vehicle classification rules.

DOT National Transportation Integrated Search

2014-11-01

The Long-Term Pavement Performance (LTPP) project has developed and deployed a set of rules for converting axle spacing and weight data into estimates of a vehicles classification. These rules are being used at Transportation Pooled Fund Study (TP...

The School-Based Multidisciplinary Team and Nondiscriminatory Assessment.

ERIC Educational Resources Information Center

Pfeiffer, Steven I.

The potential of multidisciplinary teams to control for possible errors in diagnosis, classification, and placement and to provide a vehicle for ensuring effective outcomes of diagnostic practices is illustrated. The present functions of the school-based multidisciplinary team (also called, for example, assessment team, child study team, placement…

Classification of Informal Settlements Through the Integration of 2d and 3d Features Extracted from Uav Data

NASA Astrophysics Data System (ADS)

Gevaert, C. M.; Persello, C.; Sliuzas, R.; Vosselman, G.

2016-06-01

Unmanned Aerial Vehicles (UAVs) are capable of providing very high resolution and up-to-date information to support informal settlement upgrading projects. In order to provide accurate basemaps, urban scene understanding through the identification and classification of buildings and terrain is imperative. However, common characteristics of informal settlements such as small, irregular buildings with heterogeneous roof material and large presence of clutter challenge state-of-the-art algorithms. Especially the dense buildings and steeply sloped terrain cause difficulties in identifying elevated objects. This work investigates how 2D radiometric and textural features, 2.5D topographic features, and 3D geometric features obtained from UAV imagery can be integrated to obtain a high classification accuracy in challenging classification problems for the analysis of informal settlements. It compares the utility of pixel-based and segment-based features obtained from an orthomosaic and DSM with point-based and segment-based features extracted from the point cloud to classify an unplanned settlement in Kigali, Rwanda. Findings show that the integration of 2D and 3D features leads to higher classification accuracies.

a Two-Step Classification Approach to Distinguishing Similar Objects in Mobile LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

He, H.; Khoshelham, K.; Fraser, C.

2017-09-01

Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

49 CFR 523.8 - Heavy-duty vocational vehicle.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 6 2013-10-01 2013-10-01 false Heavy-duty vocational vehicle. 523.8 Section 523.8... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.8 Heavy-duty vocational vehicle. Heavy-duty vocational vehicles are vehicles with a gross vehicle weight rating (GVWR) above 8,500 pounds...

49 CFR 523.8 - Heavy-duty vocational vehicle.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 6 2014-10-01 2014-10-01 false Heavy-duty vocational vehicle. 523.8 Section 523.8... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.8 Heavy-duty vocational vehicle. Heavy-duty vocational vehicles are vehicles with a gross vehicle weight rating (GVWR) above 8,500 pounds...

Staged collision and damage data. Volume 2: Report for accident reconstruction of thirty (30) test vehicles

NASA Astrophysics Data System (ADS)

1981-01-01

Test vehicles were instrumented with accelerometers to measure vehicle accelerator resultants. The vehicles were also identified for residual crush and collision deformation classification (CDC) measurements.

49 CFR 523.8 - Heavy-duty vocational vehicle.

Code of Federal Regulations, 2011 CFR

2011-10-01

... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.8 Heavy-duty vocational vehicle. Heavy... excluding: (a) Heavy-duty pickup trucks and vans defined in § 523.7; (b) Medium duty passenger vehicles; and...

An evaluation of object-oriented image analysis techniques to identify motorized vehicle effects in semi-arid to arid ecosystems of the American West

USGS Publications Warehouse

Mladinich, C.

2010-01-01

Human disturbance is a leading ecosystem stressor. Human-induced modifications include transportation networks, areal disturbances due to resource extraction, and recreation activities. High-resolution imagery and object-oriented classification rather than pixel-based techniques have successfully identified roads, buildings, and other anthropogenic features. Three commercial, automated feature-extraction software packages (Visual Learning Systems' Feature Analyst, ENVI Feature Extraction, and Definiens Developer) were evaluated by comparing their ability to effectively detect the disturbed surface patterns from motorized vehicle traffic. Each package achieved overall accuracies in the 70% range, demonstrating the potential to map the surface patterns. The Definiens classification was more consistent and statistically valid. Copyright ?? 2010 by Bellwether Publishing, Ltd. All rights reserved.

Vehicle Engine Classification Using Spectral Tone-Pitch Vibration Indexing and Neural Network*

PubMed Central

Wei, Jie; Vongsy, Karmon; Mendoza-Schrock, Olga; Liu, Chi-Him

2015-01-01

As a non-invasive and remote sensor, the Laser Doppler Vibrometer (LDV) has found a broad spectrum of applications in various areas such as civil engineering, biomedical engineering, and even security and restoration within art museums. LDV is an ideal sensor to detect threats earlier and provide better protection to society, which is of utmost importance to military and law enforcement institutions. However, the use of LDV in situational surveillance, in particular vehicle classification, is still in its infancy due to the lack of systematic investigations on its behavioral properties. In this work, as a result of the pilot project initiated by Air Force Research Laboratory, the innate features of LDV data from many vehicles are examined, beginning with an investigation of feature differences compared to human speech signals. A spectral tone-pitch vibration indexing scheme is developed to capture the engine’s periodic vibrations and the associated fundamental frequencies over the vehicles’ surface. A two-layer feed-forward neural network with 20 intermediate neurons is employed to classify vehicles’ engines based on their spectral tone-pitch indices. The classification results using the proposed approach over the complete LDV dataset collected by the project are exceedingly encouraging; consistently higher than 96% accuracies are attained for all four types of engines collected from this project. PMID:26788417

Recommendations for Safe Separation Distances from the Kennedy Space Center (KSC) Vehicle Assembly Building (VAB) Using a Heat-Flux-Based Analytical Approach (Abridged)

NASA Technical Reports Server (NTRS)

Cragg, Clinton H.; Bowman, Howard; Wilson, John E.

2011-01-01

The NASA Engineering and Safety Center (NESC) was requested to provide computational modeling to support the establishment of a safe separation distance surrounding the Kennedy Space Center (KSC) Vehicle Assembly Building (VAB). The two major objectives of the study were 1) establish a methodology based on thermal flux to determine safe separation distances from the Kennedy Space Center's (KSC's) Vehicle Assembly Building (VAB) with large numbers of solid propellant boosters containing hazard division 1.3 classification propellants, in case of inadvertent ignition; and 2) apply this methodology to the consideration of housing eight 5-segment solid propellant boosters in the VAB. The results of the study are contained in this report.

A conceptual framework for dynamic extension of the red clearance interval as a countermeasure for red-light-running.

PubMed

Gates, Timothy J; Noyce, David A

2016-11-01

This manuscript describes the development and evaluation of a conceptual framework for real-time operation of dynamic on-demand extension of the red clearance interval as a countermeasure for red-light-running. The framework includes a decision process for determining, based on the real-time status of vehicles arriving at the intersection, when extension of the red clearance interval should occur and the duration of each extension. A zonal classification scheme was devised to assess whether an approaching vehicle requires additional time to safely clear the intersection based on the remaining phase time, type of vehicle, current speed, and current distance from the intersection. Expected performance of the conceptual framework was evaluated through modeling of replicated field operations using vehicular event data collected as part of this research. The results showed highly accurate classification of red-light-running vehicles needing additional clearance time and relatively few false extension calls from stopping vehicles, thereby minimizing the expected impacts to signal and traffic operations. Based on the recommended parameters, extension calls were predicted to occur once every 26.5cycles. Assuming a 90scycle, 1.5 extensions per hour were expected per approach, with an estimated extension time of 2.30s/h. Although field implementation was not performed, it is anticipated that long-term reductions in targeted red-light-running conflicts and crashes will likely occur if red clearance interval extension systems are implemented at locations where start-up delay on the conflicting approach is generally minimal, such as intersections with lag left-turn phasing. Copyright © 2015 Elsevier Ltd. All rights reserved.

Analyses of rear-end crashes based on classification tree models.

PubMed

Yan, Xuedong; Radwan, Essam

2006-09-01

Signalized intersections are accident-prone areas especially for rear-end crashes due to the fact that the diversity of the braking behaviors of drivers increases during the signal change. The objective of this article is to improve knowledge of the relationship between rear-end crashes occurring at signalized intersections and a series of potential traffic risk factors classified by driver characteristics, environments, and vehicle types. Based on the 2001 Florida crash database, the classification tree method and Quasi-induced exposure concept were used to perform the statistical analysis. Two binary classification tree models were developed in this study. One was used for the crash comparison between rear-end and non-rear-end to identify those specific trends of the rear-end crashes. The other was constructed for the comparison between striking vehicles/drivers (at-fault) and struck vehicles/drivers (not-at-fault) to find more complex crash pattern associated with the traffic attributes of driver, vehicle, and environment. The modeling results showed that the rear-end crashes are over-presented in the higher speed limits (45-55 mph); the rear-end crash propensity for daytime is apparently larger than nighttime; and the reduction of braking capacity due to wet and slippery road surface conditions would definitely contribute to rear-end crashes, especially at intersections with higher speed limits. The tree model segmented drivers into four homogeneous age groups: < 21 years, 21-31 years, 32-75 years, and > 75 years. The youngest driver group shows the largest crash propensity; in the 21-31 age group, the male drivers are over-involved in rear-end crashes under adverse weather conditions and the 32-75 years drivers driving large size vehicles have a larger crash propensity compared to those driving passenger vehicles. Combined with the quasi-induced exposure concept, the classification tree method is a proper statistical tool for traffic-safety analysis to investigate crash propensity. Compared to the logistic regression models, tree models have advantages for handling continuous independent variables and easily explaining the complex interaction effect with more than two independent variables. This research recommended that at signalized intersections with higher speed limits, reducing the speed limit to 40 mph efficiently contribute to a lower accident rate. Drivers involved in alcohol use may increase not only rear-end crash risk but also the driver injury severity. Education and enforcement countermeasures should focus on the driver group younger than 21 years. Further studies are suggested to compare crash risk distributions of the driver age for other main crash types to seek corresponding traffic countermeasures.

Track classification within wireless sensor network

NASA Astrophysics Data System (ADS)

Doumerc, Robin; Pannetier, Benjamin; Moras, Julien; Dezert, Jean; Canevet, Loic

2017-05-01

In this paper, we present our study on track classification by taking into account environmental information and target estimated states. The tracker uses several motion model adapted to different target dynamics (pedestrian, ground vehicle and SUAV, i.e. small unmanned aerial vehicle) and works in centralized architecture. The main idea is to explore both: classification given by heterogeneous sensors and classification obtained with our fusion module. The fusion module, presented in his paper, provides a class on each track according to track location, velocity and associated uncertainty. To model the likelihood on each class, a fuzzy approach is used considering constraints on target capability to move in the environment. Then the evidential reasoning approach based on Dempster-Shafer Theory (DST) is used to perform a time integration of this classifier output. The fusion rules are tested and compared on real data obtained with our wireless sensor network.In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of this system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

Optimal loop placement and models for length - based vehicle classification and stop - and - go traffic.

DOT National Transportation Integrated Search

2011-01-01

Inductive loops are widely used nationwide for traffic monitoring as a data source for a variety of : needs in generating traffic information for operation and planning analysis, validations of travel : demand models, freight studies, pavement design...

Piezo-electric automatic vehicle classification system : Oregon Department of Transportation with Castle Rock Consultants for a SHRP Long Term Pavement Performance Site : final report.

DOT National Transportation Integrated Search

1991-07-01

Oregon has twelve pavement test sites that are part of the Strategic Highway Research Program (SHRP), Long Term Pavement Performance (LTPP) studies. Part of the data gathering on these sites involves vehicle weight and classification. This pilot proj...

Piezo-electric automatic vehicle classification system : Oregon Department of Transportation with Castle Rock Consultants for a SHRP Long Term Pavement Performance Site.

DOT National Transportation Integrated Search

1990-05-01

Oregon has twelve sites that are part of the Strategic Highway Research Program (SHRP), Long Term Pavement Performance (LTPP) studies. Part of the data gathering on these sites involves vehicle weight and classification. This pilot project was to hel...

Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

PubMed

Barua, Shaibal; Begum, Shahina; Ahmed, Mobyen Uddin

2015-01-01

Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data are difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.

Draper Laboratory small autonomous aerial vehicle

NASA Astrophysics Data System (ADS)

DeBitetto, Paul A.; Johnson, Eric N.; Bosse, Michael C.; Trott, Christian A.

1997-06-01

The Charles Stark Draper Laboratory, Inc. and students from Massachusetts Institute of Technology and Boston University have cooperated to develop an autonomous aerial vehicle that won the 1996 International Aerial Robotics Competition. This paper describes the approach, system architecture and subsystem designs for the entry. This entry represents a combination of many technology areas: navigation, guidance, control, vision processing, human factors, packaging, power, real-time software, and others. The aerial vehicle, an autonomous helicopter, performs navigation and control functions using multiple sensors: differential GPS, inertial measurement unit, sonar altimeter, and a flux compass. The aerial transmits video imagery to the ground. A ground based vision processor converts the image data into target position and classification estimates. The system was designed, built, and flown in less than one year and has provided many lessons about autonomous vehicle systems, several of which are discussed. In an appendix, our current research in augmenting the navigation system with vision- based estimates is presented.

49 CFR 523.6 - Heavy-duty vehicle.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 6 2013-10-01 2013-10-01 false Heavy-duty vehicle. 523.6 Section 523.6... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.6 Heavy-duty vehicle. (a) A heavy-duty vehicle is any commercial medium- and heavy-duty on highway vehicle or a work truck, as defined in 49 U.S...

49 CFR 523.6 - Heavy-duty vehicle.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 6 2014-10-01 2014-10-01 false Heavy-duty vehicle. 523.6 Section 523.6... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.6 Heavy-duty vehicle. (a) A heavy-duty vehicle is any commercial medium- and heavy-duty on highway vehicle or a work truck, as defined in 49 U.S...

COEFUV: A Computer Implementation of a Generalized Unmanned Vehicle Cost Model.

DTIC Science & Technology

1978-10-01

78 T N OMBER . C A FEUCNTER CLASSIF lED DAS-TRRNL mh~hhhh~hhE DAS-TR-78-4 DAS-TR-78-4 coI COEFUV: A COMPUTER IMPLEMENTATION OF A IM GENERALIZED ...34 and the time to generate them are important. Many DAS participants supported this effort. The authors wish to acknow- ledge Richard H. Anderson for...conflict and the on-going COMBAT ANGEL program at Davis-Monthan Air Force Base, there is not a generally accepted costing methodology for unmanned vehicles

Dollar Summary of Federal Supply Classification and Service Category by Company, FY83, Part 1 (1005-5811).

DTIC Science & Technology

1983-01-01

E STATE TOTAL 590 LEAR S b1 . . . . • I . i I I I . DEPARTMENT OF DEFENSE PAGE 381 PRIME CONTRACTS OVER $25. 000 BY FEDERAL STOCK CLASSIFICATION...MOTOR VEHICLE MAINTENANCE EQUIPMENT 94 RONAN & KUNZL INC MICHIGAN ARMY MOTOR VEHICLE MAINTENANCE EQUIPMENT C9 RUGBY INDUSTRIES INC MINNESOTA ARMY MOTOR

Predicting vehicle fuel consumption patterns using floating vehicle data.

PubMed

Du, Yiman; Wu, Jianping; Yang, Senyan; Zhou, Liutong

2017-09-01

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers' information and vehicles' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model. Copyright © 2017. Published by Elsevier B.V.

49 CFR 567.5 - Requirements for manufacturers of vehicles manufactured in two or more stages.

Code of Federal Regulations, 2012 CFR

2012-10-01

...) Vehicle Identification Number. (c) Intermediate manufacturers. (1) Except as provided in paragraphs (f... that identified by the incomplete vehicle manufacturer. (v) Vehicle identification number. (d) Final...), and (d)(1), and 49 CFR 568.4(a)(9). (vi) Vehicle identification number. (vii) The type classification...

49 CFR 567.5 - Requirements for manufacturers of vehicles manufactured in two or more stages.

Code of Federal Regulations, 2011 CFR

2011-10-01

...) Vehicle Identification Number. (c) Intermediate manufacturers. (1) Except as provided in paragraphs (f... that identified by the incomplete vehicle manufacturer. (v) Vehicle identification number. (d) Final...), and (d)(1), and 49 CFR 568.4(a)(9). (vi) Vehicle identification number. (vii) The type classification...

49 CFR 567.5 - Requirements for manufacturers of vehicles manufactured in two or more stages.

Code of Federal Regulations, 2013 CFR

2013-10-01

...) Vehicle Identification Number. (c) Intermediate manufacturers. (1) Except as provided in paragraphs (f... that identified by the incomplete vehicle manufacturer. (v) Vehicle identification number. (d) Final...), and (d)(1), and 49 CFR 568.4(a)(9). (vi) Vehicle identification number. (vii) The type classification...

49 CFR 567.5 - Requirements for manufacturers of vehicles manufactured in two or more stages.

Code of Federal Regulations, 2014 CFR

2014-10-01

...) Vehicle Identification Number. (c) Intermediate manufacturers. (1) Except as provided in paragraphs (f... that identified by the incomplete vehicle manufacturer. (v) Vehicle identification number. (d) Final...), and (d)(1), and 49 CFR 568.4(a)(9). (vi) Vehicle identification number. (vii) The type classification...

Real-time people and vehicle detection from UAV imagery

NASA Astrophysics Data System (ADS)

Gaszczak, Anna; Breckon, Toby P.; Han, Jiwan

2011-01-01

A generic and robust approach for the real-time detection of people and vehicles from an Unmanned Aerial Vehicle (UAV) is an important goal within the framework of fully autonomous UAV deployment for aerial reconnaissance and surveillance. Here we present an approach for the automatic detection of vehicles based on using multiple trained cascaded Haar classifiers with secondary confirmation in thermal imagery. Additionally we present a related approach for people detection in thermal imagery based on a similar cascaded classification technique combining additional multivariate Gaussian shape matching. The results presented show the successful detection of vehicle and people under varying conditions in both isolated rural and cluttered urban environments with minimal false positive detection. Performance of the detector is optimized to reduce the overall false positive rate by aiming at the detection of each object of interest (vehicle/person) at least once in the environment (i.e. per search patter flight path) rather than every object in each image frame. Currently the detection rate for people is ~70% and cars ~80% although the overall episodic object detection rate for each flight pattern exceeds 90%.

A classification tree based modeling approach for segment related crashes on multilane highways.

PubMed

Pande, Anurag; Abdel-Aty, Mohamed; Das, Abhishek

2010-10-01

This study presents a classification tree based alternative to crash frequency analysis for analyzing crashes on mid-block segments of multilane arterials. The traditional approach of modeling counts of crashes that occur over a period of time works well for intersection crashes where each intersection itself provides a well-defined unit over which to aggregate the crash data. However, in the case of mid-block segments the crash frequency based approach requires segmentation of the arterial corridor into segments of arbitrary lengths. In this study we have used random samples of time, day of week, and location (i.e., milepost) combinations and compared them with the sample of crashes from the same arterial corridor. For crash and non-crash cases, geometric design/roadside and traffic characteristics were derived based on their milepost locations. The variables used in the analysis are non-event specific and therefore more relevant for roadway safety feature improvement programs. First classification tree model is a model comparing all crashes with the non-crash data and then four groups of crashes (rear-end, lane-change related, pedestrian, and single-vehicle/off-road crashes) are separately compared to the non-crash cases. The classification tree models provide a list of significant variables as well as a measure to classify crash from non-crash cases. ADT along with time of day/day of week are significantly related to all crash types with different groups of crashes being more likely to occur at different times. From the classification performance of different models it was apparent that using non-event specific information may not be suitable for single vehicle/off-road crashes. The study provides the safety analysis community an additional tool to assess safety without having to aggregate the corridor crash data over arbitrary segment lengths. Copyright © 2010. Published by Elsevier Ltd.

Multiple-Primitives Hierarchical Classification of Airborne Laser Scanning Data in Urban Areas

NASA Astrophysics Data System (ADS)

Ni, H.; Lin, X. G.; Zhang, J. X.

2017-09-01

A hierarchical classification method for Airborne Laser Scanning (ALS) data of urban areas is proposed in this paper. This method is composed of three stages among which three types of primitives are utilized, i.e., smooth surface, rough surface, and individual point. In the first stage, the input ALS data is divided into smooth surfaces and rough surfaces by employing a step-wise point cloud segmentation method. In the second stage, classification based on smooth surfaces and rough surfaces is performed. Points in the smooth surfaces are first classified into ground and buildings based on semantic rules. Next, features of rough surfaces are extracted. Then, points in rough surfaces are classified into vegetation and vehicles based on the derived features and Random Forests (RF). In the third stage, point-based features are extracted for the ground points, and then, an individual point classification procedure is performed to classify the ground points into bare land, artificial ground and greenbelt. Moreover, the shortages of the existing studies are analyzed, and experiments show that the proposed method overcomes these shortages and handles more types of objects.

Accurate vehicle classification including motorcycles using piezoelectric sensors.

DOT National Transportation Integrated Search

2013-03-01

State and federal departments of transportation are charged with classifying vehicles and monitoring mileage traveled. Accurate data reporting enables suitable roadway design for safety and capacity. Vehicle classifiers currently employ inductive loo...

Design of a Multi-Sensor Cooperation Travel Environment Perception System for Autonomous Vehicle

PubMed Central

Chen, Long; Li, Qingquan; Li, Ming; Zhang, Liang; Mao, Qingzhou

2012-01-01

This paper describes the environment perception system designed for intelligent vehicle SmartV-II, which won the 2010 Future Challenge. This system utilizes the cooperation of multiple lasers and cameras to realize several necessary functions of autonomous navigation: road curb detection, lane detection and traffic sign recognition. Multiple single scan lasers are integrated to detect the road curb based on Z-variance method. Vision based lane detection is realized by two scans method combining with image model. Haar-like feature based method is applied for traffic sign detection and SURF matching method is used for sign classification. The results of experiments validate the effectiveness of the proposed algorithms and the whole system.

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation.

PubMed

Zhang, Zutao; Luo, Dianyuan; Rasim, Yagubov; Li, Yanjun; Meng, Guanjun; Xu, Jian; Wang, Chunbai

2016-02-19

In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver's EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver's vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation

PubMed Central

Zhang, Zutao; Luo, Dianyuan; Rasim, Yagubov; Li, Yanjun; Meng, Guanjun; Xu, Jian; Wang, Chunbai

2016-01-01

In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver’s EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver’s vigilance level . Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model. PMID:26907278

40 CFR 88.311-93 - Emissions standards for Inherently Low-Emission Vehicles.

Code of Federal Regulations, 2014 CFR

2014-07-01

... section depending on the vehicle's weight classification. (ii) The vehicle shall be certified as having... class shall have exhaust emissions which do not exceed the exhaust emission standards in grams per brake...

40 CFR 88.311-93 - Emissions standards for Inherently Low-Emission Vehicles.

Code of Federal Regulations, 2012 CFR

2012-07-01

... section depending on the vehicle's weight classification. (ii) The vehicle shall be certified as having... class shall have exhaust emissions which do not exceed the exhaust emission standards in grams per brake...

40 CFR 88.311-93 - Emissions standards for Inherently Low-Emission Vehicles.

Code of Federal Regulations, 2013 CFR

2013-07-01

... section depending on the vehicle's weight classification. (ii) The vehicle shall be certified as having... class shall have exhaust emissions which do not exceed the exhaust emission standards in grams per brake...

Automated Detection and Classification in High-Resolution Sonar Imagery for Autonomous Underwater Vehicle Operations

DTIC Science & Technology

2008-12-01

n. , ’>, ,. Australian Government Department of Defence Defence Science and Technology Organisation Automated Detection and Classification in... Organisation DSTO-GD-0537 ABSTRACT Autonomous Underwater Vehicles (AUVs) are increasingly being used by military forces to acquire high-resolution sonar...release Published by Maritime Operations Division DsTO Defrnce sdence and Technology Organisation PO Box 1500 Edinburgh South Australia 5111 Australia

Fault classification method for the driving safety of electrified vehicles

NASA Astrophysics Data System (ADS)

Wanner, Daniel; Drugge, Lars; Stensson Trigell, Annika

2014-05-01

A fault classification method is proposed which has been applied to an electric vehicle. Potential faults in the different subsystems that can affect the vehicle directional stability were collected in a failure mode and effect analysis. Similar driveline faults were grouped together if they resembled each other with respect to their influence on the vehicle dynamic behaviour. The faults were physically modelled in a simulation environment before they were induced in a detailed vehicle model under normal driving conditions. A special focus was placed on faults in the driveline of electric vehicles employing in-wheel motors of the permanent magnet type. Several failures caused by mechanical and other faults were analysed as well. The fault classification method consists of a controllability ranking developed according to the functional safety standard ISO 26262. The controllability of a fault was determined with three parameters covering the influence of the longitudinal, lateral and yaw motion of the vehicle. The simulation results were analysed and the faults were classified according to their controllability using the proposed method. It was shown that the controllability decreased specifically with increasing lateral acceleration and increasing speed. The results for the electric driveline faults show that this trend cannot be generalised for all the faults, as the controllability deteriorated for some faults during manoeuvres with low lateral acceleration and low speed. The proposed method is generic and can be applied to various other types of road vehicles and faults.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems.

PubMed

Oh, Sang-Il; Kang, Hang-Bong

2017-01-22

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems

PubMed Central

Oh, Sang-Il; Kang, Hang-Bong

2017-01-01

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226×370 image, whereas the original selective search method extracted approximately 106×n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset. PMID:28117742

A neural net approach to space vehicle guidance

NASA Technical Reports Server (NTRS)

Caglayan, Alper K.; Allen, Scott M.

1990-01-01

The space vehicle guidance problem is formulated using a neural network approach, and the appropriate neural net architecture for modeling optimum guidance trajectories is investigated. In particular, an investigation is made of the incorporation of prior knowledge about the characteristics of the optimal guidance solution into the neural network architecture. The online classification performance of the developed network is demonstrated using a synthesized network trained with a database of optimum guidance trajectories. Such a neural-network-based guidance approach can readily adapt to environment uncertainties such as those encountered by an AOTV during atmospheric maneuvers.

A Robust Real Time Direction-of-Arrival Estimation Method for Sequential Movement Events of Vehicles.

PubMed

Liu, Huawei; Li, Baoqing; Yuan, Xiaobing; Zhou, Qianwei; Huang, Jingchang

2018-03-27

Parameters estimation of sequential movement events of vehicles is facing the challenges of noise interferences and the demands of portable implementation. In this paper, we propose a robust direction-of-arrival (DOA) estimation method for the sequential movement events of vehicles based on a small Micro-Electro-Mechanical System (MEMS) microphone array system. Inspired by the incoherent signal-subspace method (ISM), the method that is proposed in this work employs multiple sub-bands, which are selected from the wideband signals with high magnitude-squared coherence to track moving vehicles in the presence of wind noise. The field test results demonstrate that the proposed method has a better performance in emulating the DOA of a moving vehicle even in the case of severe wind interference than the narrowband multiple signal classification (MUSIC) method, the sub-band DOA estimation method, and the classical two-sided correlation transformation (TCT) method.

Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing

PubMed Central

Guijarro, María; Pajares, Gonzalo; Herrera, P. Javier

2009-01-01

The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the Deterministic Simulated Annealing (DSA) framework. The simple classifiers used are the well tested supervised parametric Bayesian estimator and the Fuzzy Clustering. The DSA is an optimization approach, which minimizes an energy function. The main contribution of DSA is its ability to avoid local minima during the optimization process thanks to the annealing scheme. It outperforms simple classifiers used for the combination and some combined strategies, including a scheme based on the fuzzy cognitive maps and an optimization approach based on the Hopfield neural network paradigm. PMID:22399989

Automatic vehicle counting system for traffic monitoring

NASA Astrophysics Data System (ADS)

Crouzil, Alain; Khoudour, Louahdi; Valiere, Paul; Truong Cong, Dung Nghy

2016-09-01

The article is dedicated to the presentation of a vision-based system for road vehicle counting and classification. The system is able to achieve counting with a very good accuracy even in difficult scenarios linked to occlusions and/or presence of shadows. The principle of the system is to use already installed cameras in road networks without any additional calibration procedure. We propose a robust segmentation algorithm that detects foreground pixels corresponding to moving vehicles. First, the approach models each pixel of the background with an adaptive Gaussian distribution. This model is coupled with a motion detection procedure, which allows correctly location of moving vehicles in space and time. The nature of trials carried out, including peak periods and various vehicle types, leads to an increase of occlusions between cars and between cars and trucks. A specific method for severe occlusion detection, based on the notion of solidity, has been carried out and tested. Furthermore, the method developed in this work is capable of managing shadows with high resolution. The related algorithm has been tested and compared to a classical method. Experimental results based on four large datasets show that our method can count and classify vehicles in real time with a high level of performance (>98%) under different environmental situations, thus performing better than the conventional inductive loop detectors.

The Crescent Project : an evaluation of an element of the HELP Program : executive summary

DOT National Transportation Integrated Search

1994-02-01

The HELP/Crescent Project on the West Coast evaluated the applicability of four technologies for screening transponder-equipped vehicles. The technologies included automatic vehicle identification, weigh-in-motion, automatic vehicle classification, a...

A LiDAR and IMU Integrated Indoor Navigation System for UAVs and Its Application in Real-Time Pipeline Classification

PubMed Central

Kumar, G. Ajay; Patil, Ashok Kumar; Patil, Rekha; Park, Seong Sill; Chai, Young Ho

2017-01-01

Mapping the environment of a vehicle and localizing a vehicle within that unknown environment are complex issues. Although many approaches based on various types of sensory inputs and computational concepts have been successfully utilized for ground robot localization, there is difficulty in localizing an unmanned aerial vehicle (UAV) due to variation in altitude and motion dynamics. This paper proposes a robust and efficient indoor mapping and localization solution for a UAV integrated with low-cost Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) sensors. Considering the advantage of the typical geometric structure of indoor environments, the planar position of UAVs can be efficiently calculated from a point-to-point scan matching algorithm using measurements from a horizontally scanning primary LiDAR. The altitude of the UAV with respect to the floor can be estimated accurately using a vertically scanning secondary LiDAR scanner, which is mounted orthogonally to the primary LiDAR. Furthermore, a Kalman filter is used to derive the 3D position by fusing primary and secondary LiDAR data. Additionally, this work presents a novel method for its application in the real-time classification of a pipeline in an indoor map by integrating the proposed navigation approach. Classification of the pipeline is based on the pipe radius estimation considering the region of interest (ROI) and the typical angle. The ROI is selected by finding the nearest neighbors of the selected seed point in the pipeline point cloud, and the typical angle is estimated with the directional histogram. Experimental results are provided to determine the feasibility of the proposed navigation system and its integration with real-time application in industrial plant engineering. PMID:28574474

Fusing Laser Reflectance and Image Data for Terrain Classification for Small Autonomous Robots

DTIC Science & Technology

2014-12-01

limit us to low power, lightweight sensors , and a maximum range of approximately 5 meters. Contrast these robot characteristics to typical terrain...classifi- cation work which uses large autonomous ground vehicles with sensors mounted high above the ground. Terrain classification for small autonomous...into predefined classes [10], [11]. However, wheeled vehicles offer the ability to use non-traditional sensors such as vibration sensors [12] and

Estimation and prediction of statewide Vehicle Miles Traveled (VMT) by highway category and vehicle classification.

DOT National Transportation Integrated Search

2016-02-01

Vehicle Miles Traveled (VMT) is a critical performance measure that is used extensively in highway transportation management for financial analysis, resource allocation, impact assessments, and reporting to oversight agencies. As highway revenue from...

Laser vibrometry exploitation for vehicle identification

NASA Astrophysics Data System (ADS)

Nolan, Adam; Lingg, Andrew; Goley, Steve; Sigmund, Kevin; Kangas, Scott

2014-06-01

Vibration signatures sensed from distant vehicles using laser vibrometry systems provide valuable information that may be used to help identify key vehicle features such as engine type, engine speed, and number of cylinders. Through the use of physics models of the vibration phenomenology, features are chosen to support classification algorithms. Various individual exploitation algorithms were developed using these models to classify vibration signatures into engine type (piston vs. turbine), engine configuration (Inline 4 vs. Inline 6 vs. V6 vs. V8 vs. V12) and vehicle type. The results of these algorithms will be presented for an 8 class problem. Finally, the benefits of using a factor graph representation to link these independent algorithms together will be presented which constructs a classification hierarchy for the vibration exploitation problem.

Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation

PubMed Central

Gonzalez, Luis F.; Montes, Glen A.; Puig, Eduard; Johnson, Sandra; Mengersen, Kerrie; Gaston, Kevin J.

2016-01-01

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification. PMID:26784196

Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation.

PubMed

Gonzalez, Luis F; Montes, Glen A; Puig, Eduard; Johnson, Sandra; Mengersen, Kerrie; Gaston, Kevin J

2016-01-14

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

Object Classification Based on Analysis of Spectral Characteristics of Seismic Signal Envelopes

NASA Astrophysics Data System (ADS)

Morozov, Yu. V.; Spektor, A. A.

2017-11-01

A method for classifying moving objects having a seismic effect on the ground surface is proposed which is based on statistical analysis of the envelopes of received signals. The values of the components of the amplitude spectrum of the envelopes obtained applying Hilbert and Fourier transforms are used as classification criteria. Examples illustrating the statistical properties of spectra and the operation of the seismic classifier are given for an ensemble of objects of four classes (person, group of people, large animal, vehicle). It is shown that the computational procedures for processing seismic signals are quite simple and can therefore be used in real-time systems with modest requirements for computational resources.

Segmentation, classification, and pose estimation of military vehicles in low resolution laser radar images

NASA Astrophysics Data System (ADS)

Neulist, Joerg; Armbruster, Walter

2005-05-01

Model-based object recognition in range imagery typically involves matching the image data to the expected model data for each feasible model and pose hypothesis. Since the matching procedure is computationally expensive, the key to efficient object recognition is the reduction of the set of feasible hypotheses. This is particularly important for military vehicles, which may consist of several large moving parts such as the hull, turret, and gun of a tank, and hence require an eight or higher dimensional pose space to be searched. The presented paper outlines techniques for reducing the set of feasible hypotheses based on an estimation of target dimensions and orientation. Furthermore, the presence of a turret and a main gun and their orientations are determined. The vehicle parts dimensions as well as their error estimates restrict the number of model hypotheses whereas the position and orientation estimates and their error bounds reduce the number of pose hypotheses needing to be verified. The techniques are applied to several hundred laser radar images of eight different military vehicles with various part classifications and orientations. On-target resolution in azimuth, elevation and range is about 30 cm. The range images contain up to 20% dropouts due to atmospheric absorption. Additionally some target retro-reflectors produce outliers due to signal crosstalk. The presented algorithms are extremely robust with respect to these and other error sources. The hypothesis space for hull orientation is reduced to about 5 degrees as is the error for turret rotation and gun elevation, provided the main gun is visible.

3-D Vision Techniques for Autonomous Vehicles

DTIC Science & Technology

1988-08-01

TITLE (Include Security Classification) W 3-D Vision Techniques for Autonomous Vehicles 12 PERSONAL AUTHOR(S) Martial Hebert, Takeo Kanade, inso Kweoni... Autonomous Vehicles Martial Hebert, Takeo Kanade, Inso Kweon CMU-RI-TR-88-12 The Robotics Institute Carnegie Mellon University Acession For Pittsburgh

Vision-Based Traffic Data Collection Sensor for Automotive Applications

PubMed Central

Llorca, David F.; Sánchez, Sergio; Ocaña, Manuel; Sotelo, Miguel. A.

2010-01-01

This paper presents a complete vision sensor onboard a moving vehicle which collects the traffic data in its local area in daytime conditions. The sensor comprises a rear looking and a forward looking camera. Thus, a representative description of the traffic conditions in the local area of the host vehicle can be computed. The proposed sensor detects the number of vehicles (traffic load), their relative positions and their relative velocities in a four-stage process: lane detection, candidates selection, vehicles classification and tracking. Absolute velocities (average road speed) and global positioning are obtained after combining the outputs provided by the vision sensor with the data supplied by the CAN Bus and a GPS sensor. The presented experiments are promising in terms of detection performance and accuracy in order to be validated for applications in the context of the automotive industry. PMID:22315572

Vision-based traffic data collection sensor for automotive applications.

PubMed

Llorca, David F; Sánchez, Sergio; Ocaña, Manuel; Sotelo, Miguel A

2010-01-01

This paper presents a complete vision sensor onboard a moving vehicle which collects the traffic data in its local area in daytime conditions. The sensor comprises a rear looking and a forward looking camera. Thus, a representative description of the traffic conditions in the local area of the host vehicle can be computed. The proposed sensor detects the number of vehicles (traffic load), their relative positions and their relative velocities in a four-stage process: lane detection, candidates selection, vehicles classification and tracking. Absolute velocities (average road speed) and global positioning are obtained after combining the outputs provided by the vision sensor with the data supplied by the CAN Bus and a GPS sensor. The presented experiments are promising in terms of detection performance and accuracy in order to be validated for applications in the context of the automotive industry.

Vehicle Surveillance with a Generic, Adaptive, 3D Vehicle Model.

PubMed

Leotta, Matthew J; Mundy, Joseph L

2011-07-01

In automated surveillance, one is often interested in tracking road vehicles, measuring their shape in 3D world space, and determining vehicle classification. To address these tasks simultaneously, an effective approach is the constrained alignment of a prior model of 3D vehicle shape to images. Previous 3D vehicle models are either generic but overly simple or rigid and overly complex. Rigid models represent exactly one vehicle design, so a large collection is needed. A single generic model can deform to a wide variety of shapes, but those shapes have been far too primitive. This paper uses a generic 3D vehicle model that deforms to match a wide variety of passenger vehicles. It is adjustable in complexity between the two extremes. The model is aligned to images by predicting and matching image intensity edges. Novel algorithms are presented for fitting models to multiple still images and simultaneous tracking while estimating shape in video. Experiments compare the proposed model to simple generic models in accuracy and reliability of 3D shape recovery from images and tracking in video. Standard techniques for classification are also used to compare the models. The proposed model outperforms the existing simple models at each task.

Automatic road sign detecion and classification based on support vector machines and HOG descriptos

NASA Astrophysics Data System (ADS)

Adam, A.; Ioannidis, C.

2014-05-01

This paper examines the detection and classification of road signs in color-images acquired by a low cost camera mounted on a moving vehicle. A new method for the detection and classification of road signs is proposed based on color based detection, in order to locate regions of interest. Then, a circular Hough transform is applied to complete detection taking advantage of the shape properties of the road signs. The regions of interest are finally represented using HOG descriptors and are fed into trained Support Vector Machines (SVMs) in order to be recognized. For the training procedure, a database with several training examples depicting Greek road sings has been developed. Many experiments have been conducted and are presented, to measure the efficiency of the proposed methodology especially under adverse weather conditions and poor illumination. For the experiments training datasets consisting of different number of examples were used and the results are presented, along with some possible extensions of this work.

Light-duty automotive technology and fuel economy trends : 1975 through 2008

DOT National Transportation Integrated Search

2009-11-01

This report summarizes key trends in carbon dioxide (CO2) emissions, fuel economy and technology usage related to model year (MY) 1975 through 2009 light-duty vehicles sold in the United States. Light-duty vehicles are those vehicles that EPA classif...

Model-Based Building Detection from Low-Cost Optical Sensors Onboard Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Karantzalos, K.; Koutsourakis, P.; Kalisperakis, I.; Grammatikopoulos, L.

2015-08-01

The automated and cost-effective building detection in ultra high spatial resolution is of major importance for various engineering and smart city applications. To this end, in this paper, a model-based building detection technique has been developed able to extract and reconstruct buildings from UAV aerial imagery and low-cost imaging sensors. In particular, the developed approach through advanced structure from motion, bundle adjustment and dense image matching computes a DSM and a true orthomosaic from the numerous GoPro images which are characterised by important geometric distortions and fish-eye effect. An unsupervised multi-region, graphcut segmentation and a rule-based classification is responsible for delivering the initial multi-class classification map. The DTM is then calculated based on inpaininting and mathematical morphology process. A data fusion process between the detected building from the DSM/DTM and the classification map feeds a grammar-based building reconstruction and scene building are extracted and reconstructed. Preliminary experimental results appear quite promising with the quantitative evaluation indicating detection rates at object level of 88% regarding the correctness and above 75% regarding the detection completeness.

Online energy management strategy of fuel cell hybrid electric vehicles based on data fusion approach

NASA Astrophysics Data System (ADS)

Zhou, Daming; Al-Durra, Ahmed; Gao, Fei; Ravey, Alexandre; Matraji, Imad; Godoy Simões, Marcelo

2017-10-01

Energy management strategy plays a key role for Fuel Cell Hybrid Electric Vehicles (FCHEVs), it directly affects the efficiency and performance of energy storages in FCHEVs. For example, by using a suitable energy distribution controller, the fuel cell system can be maintained in a high efficiency region and thus saving hydrogen consumption. In this paper, an energy management strategy for online driving cycles is proposed based on a combination of the parameters from three offline optimized fuzzy logic controllers using data fusion approach. The fuzzy logic controllers are respectively optimized for three typical driving scenarios: highway, suburban and city in offline. To classify patterns of online driving cycles, a Probabilistic Support Vector Machine (PSVM) is used to provide probabilistic classification results. Based on the classification results of the online driving cycle, the parameters of each offline optimized fuzzy logic controllers are then fused using Dempster-Shafer (DS) evidence theory, in order to calculate the final parameters for the online fuzzy logic controller. Three experimental validations using Hardware-In-the-Loop (HIL) platform with different-sized FCHEVs have been performed. Experimental comparison results show that, the proposed PSVM-DS based online controller can achieve a relatively stable operation and a higher efficiency of fuel cell system in real driving cycles.

A review of supervised object-based land-cover image classification

NASA Astrophysics Data System (ADS)

Ma, Lei; Li, Manchun; Ma, Xiaoxue; Cheng, Liang; Du, Peijun; Liu, Yongxue

2017-08-01

Object-based image classification for land-cover mapping purposes using remote-sensing imagery has attracted significant attention in recent years. Numerous studies conducted over the past decade have investigated a broad array of sensors, feature selection, classifiers, and other factors of interest. However, these research results have not yet been synthesized to provide coherent guidance on the effect of different supervised object-based land-cover classification processes. In this study, we first construct a database with 28 fields using qualitative and quantitative information extracted from 254 experimental cases described in 173 scientific papers. Second, the results of the meta-analysis are reported, including general characteristics of the studies (e.g., the geographic range of relevant institutes, preferred journals) and the relationships between factors of interest (e.g., spatial resolution and study area or optimal segmentation scale, accuracy and number of targeted classes), especially with respect to the classification accuracy of different sensors, segmentation scale, training set size, supervised classifiers, and land-cover types. Third, useful data on supervised object-based image classification are determined from the meta-analysis. For example, we find that supervised object-based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object-based framework. Furthermore, spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest (RF) shows the best performance in object-based classification. The area-based accuracy assessment method can obtain stable classification performance, and indicates a strong correlation between accuracy and training set size, while the accuracy of the point-based method is likely to be unstable due to mixed objects. In addition, the overall accuracy benefits from higher spatial resolution images (e.g., unmanned aerial vehicle) or agricultural sites where it also correlates with the number of targeted classes. More than 95.6% of studies involve an area less than 300 ha, and the spatial resolution of images is predominantly between 0 and 2 m. Furthermore, we identify some methods that may advance supervised object-based image classification. For example, deep learning and type-2 fuzzy techniques may further improve classification accuracy. Lastly, scientists are strongly encouraged to report results of uncertainty studies to further explore the effects of varied factors on supervised object-based image classification.

Continuous Active Sonar for Undersea Vehicles Final Report: Input of Factor Graphs into the Detection, Classification, and Localization Chain and Continuous Active SONAR in Undersea Vehicles

DTIC Science & Technology

2015-12-31

image from NURP annual report. in X The ray -cone code simulates the CAS signal received after being reflected form two different targets, and...Cm where m, m, ... , 1fn are X ’s parents, and nodes C1, C1, ... , C,, are X ’s children. Image based on (Duda, Hart, & Stork, 2001). The first...Sorenson, 1970). Using the reference (Welch & Bishop, 2006), the procedure for estimating the real state x , of a discrete-time controlled process , will

49 CFR 523.3 - Automobile.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 6 2011-10-01 2011-10-01 false Automobile. 523.3 Section 523.3 Transportation..., DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.3 Automobile. (a) An automobile is any 4-wheeled... pounds and less than 10,000 pounds gross vehicle weight are determined to be automobiles: (1) Vehicles...

49 CFR 523.3 - Automobile.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 6 2013-10-01 2013-10-01 false Automobile. 523.3 Section 523.3 Transportation..., DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.3 Automobile. (a) An automobile is any 4-wheeled... pounds and less than 10,000 pounds gross vehicle weight are determined to be automobiles: (1) Vehicles...

49 CFR 523.3 - Automobile.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 6 2012-10-01 2012-10-01 false Automobile. 523.3 Section 523.3 Transportation..., DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.3 Automobile. (a) An automobile is any 4-wheeled... pounds and less than 10,000 pounds gross vehicle weight are determined to be automobiles: (1) Vehicles...

49 CFR 523.3 - Automobile.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 6 2014-10-01 2014-10-01 false Automobile. 523.3 Section 523.3 Transportation..., DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.3 Automobile. (a) An automobile is any 4-wheeled... pounds and less than 10,000 pounds gross vehicle weight are determined to be automobiles: (1) Vehicles...

49 CFR 523.3 - Automobile.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 6 2010-10-01 2010-10-01 false Automobile. 523.3 Section 523.3 Transportation..., DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.3 Automobile. (a) An automobile is any 4-wheeled... pounds and less than 10,000 pounds gross vehicle weight are determined to be automobiles: (1) Vehicles...

Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning.

PubMed

Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

2017-03-15

Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.

Data fusion for target tracking and classification with wireless sensor network

NASA Astrophysics Data System (ADS)

Pannetier, Benjamin; Doumerc, Robin; Moras, Julien; Dezert, Jean; Canevet, Loic

2016-10-01

In this paper, we address the problem of multiple ground target tracking and classification with information obtained from a unattended wireless sensor network. A multiple target tracking (MTT) algorithm, taking into account road and vegetation information, is proposed based on a centralized architecture. One of the key issue is how to adapt classical MTT approach to satisfy embedded processing. Based on track statistics, the classification algorithm uses estimated location, velocity and acceleration to help to classify targets. The algorithms enables tracking human and vehicles driving both on and off road. We integrate road or trail width and vegetation cover, as constraints in target motion models to improve performance of tracking under constraint with classification fusion. Our algorithm also presents different dynamic models, to palliate the maneuvers of targets. The tracking and classification algorithms are integrated into an operational platform (the fusion node). In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

23 CFR 500.202 - TMS definitions.

Code of Federal Regulations, 2012 CFR

2012-04-01

... particular location on a highway. These types of data support the estimation of the number of vehicles... used in this part: Highway traffic data means data used to develop estimates of the amount of person or... portion of such vehicles that may be of a particular type (vehicle classification), the weights of such...

23 CFR 500.202 - TMS definitions.

Code of Federal Regulations, 2011 CFR

2011-04-01

... particular location on a highway. These types of data support the estimation of the number of vehicles... used in this part: Highway traffic data means data used to develop estimates of the amount of person or... portion of such vehicles that may be of a particular type (vehicle classification), the weights of such...

Classification of Wind Farm Turbulence and Its Effects on General Aviation Aircraft and Airports : Technical Summary

DOT National Transportation Integrated Search

2018-01-01

The focus of this project was to estimate the potential impact of a new motor vehicle government mandate for vehicle-to-vehicle (V2V) technology on the demand for aftermarket devices, applications, and infrastructure that leverages the same dedicated...

Final NASA Panel Recommendations for Definition of Acceptable Risk of Injury Due to Spaceflight Dynamic Events

NASA Technical Reports Server (NTRS)

Somers, Jeffrey T.; Newby, Nate; Wells, Jessica

2015-01-01

A panel of experts was convened in 2010 to help define acceptable injury risk levels for space vehicle launches, landings, and abort scenarios. Classifications of spaceflight-relevant injuries were defined using four categories ranging from minor to severe injury. Limits for each injury category were agreed to, dependent on the expected number of crew exposures in a given vehicle and on whether the flight was considered nominal or off-nominal. Somers et al. captured the findings of this summit in a NASA technical memorandum. This panel was recently re-convened (December 1, 2014) to determine whether the previous recommended injury limits were applicable to newly-designed commercial space flight vehicles. In particular, previous limits were based in part on the number of crew exposures per vehicle and also were sensitive to a definition of nominal and off-nominal vehicle performance. Reconsideration of these aspects led to a new consensus on a definition of injury risk.

Two-dimensional statistical linear discriminant analysis for real-time robust vehicle-type recognition

NASA Astrophysics Data System (ADS)

Zafar, I.; Edirisinghe, E. A.; Acar, S.; Bez, H. E.

2007-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic License Plate Recognition (ALPR) systems. Several car MMR systems have been proposed in literature. However these approaches are based on feature detection algorithms that can perform sub-optimally under adverse lighting and/or occlusion conditions. In this paper we propose a real time, appearance based, car MMR approach using Two Dimensional Linear Discriminant Analysis that is capable of addressing this limitation. We provide experimental results to analyse the proposed algorithm's robustness under varying illumination and occlusions conditions. We have shown that the best performance with the proposed 2D-LDA based car MMR approach is obtained when the eigenvectors of lower significance are ignored. For the given database of 200 car images of 25 different make-model classifications, a best accuracy of 91% was obtained with the 2D-LDA approach. We use a direct Principle Component Analysis (PCA) based approach as a benchmark to compare and contrast the performance of the proposed 2D-LDA approach to car MMR. We conclude that in general the 2D-LDA based algorithm supersedes the performance of the PCA based approach.

Design of a steering stabilizer based on CAN bus

NASA Astrophysics Data System (ADS)

Zhan, Zhaomin; Yan, Yibin

2018-04-01

This design realizes a posture correction device of griping steering wheel based on CAN bus, which is embedded in the steering wheel of vehicles. The system aims to detect the drivers' abnormal griping postures and provides drivers with classification alerts, by combining the recorded griping postures data and the vehicle speed data that are obtained via the CAN bus. The warning information are automatically stored and retained in the device for 12 months. To enhance the alerting effect, the count of this warning message for both the latest month and the last 12 months are displayed on the dashboard panel. In addition to prevent itself from being blocked and self-detect any faults in advance, the appliance also provide a self-test function, which will communicate with the integrated instrument system in vehicle and do simulation test right after the vehicle power on. This appliance can help to urge and ensure drivers to operate the steering wheel correctly, effectively, and timely; prevent some typical incorrect behaviors which commonly happen along with the change of griping postures, such as the using cellphone, and ultimately, reduce the incidence of traffic accidents.

Object tracking via background subtraction for monitoring illegal activity in crossroad

NASA Astrophysics Data System (ADS)

Ghimire, Deepak; Jeong, Sunghwan; Park, Sang Hyun; Lee, Joonwhoan

2016-07-01

In the field of intelligent transportation system a great number of vision-based techniques have been proposed to prevent pedestrians from being hit by vehicles. This paper presents a system that can perform pedestrian and vehicle detection and monitoring of illegal activity in zebra crossings. In zebra crossing, according to the traffic light status, to fully avoid a collision, a driver or pedestrian should be warned earlier if they possess any illegal moves. In this research, at first, we detect the traffic light status of pedestrian and monitor the crossroad for vehicle pedestrian moves. The background subtraction based object detection and tracking is performed to detect pedestrian and vehicles in crossroads. Shadow removal, blob segmentation, trajectory analysis etc. are used to improve the object detection and classification performance. We demonstrate the experiment in several video sequences which are recorded in different time and environment such as day time and night time, sunny and raining environment. Our experimental results show that such simple and efficient technique can be used successfully as a traffic surveillance system to prevent accidents in zebra crossings.

Classification Behavior in Children Thirty-Six Months of Age.

ERIC Educational Resources Information Center

Shimada, Shoko; Sano, Ryogoro

The purposes of this study were to examine the development of classification ability in 36 month olds and to clarify the positive relationship between classification ability and general cognitive development. Subjects, 16 Japanese children (8 males, 8 females), were individually tested by the use of 12 colored pictures of animals and vehicles.…

Automated site characterization for robotic sample acquisition systems

NASA Astrophysics Data System (ADS)

Scholl, Marija S.; Eberlein, Susan J.

1993-04-01

A mobile, semiautonomous vehicle with multiple sensors and on-board intelligence is proposed for performing preliminary scientific investigations on extraterrestrial bodies prior to human exploration. Two technologies, a hybrid optical-digital computer system based on optical correlator technology and an image and instrument data analysis system, provide complementary capabilities that might be part of an instrument package for an intelligent robotic vehicle. The hybrid digital-optical vision system could perform real-time image classification tasks using an optical correlator with programmable matched filters under control of a digital microcomputer. The data analysis system would analyze visible and multiband imagery to extract mineral composition and textural information for geologic characterization. Together these technologies would support the site characterization needs of a robotic vehicle for both navigational and scientific purposes.

Autonomous target recognition using remotely sensed surface vibration measurements

NASA Astrophysics Data System (ADS)

Geurts, James; Ruck, Dennis W.; Rogers, Steven K.; Oxley, Mark E.; Barr, Dallas N.

1993-09-01

The remotely measured surface vibration signatures of tactical military ground vehicles are investigated for use in target classification and identification friend or foe (IFF) systems. The use of remote surface vibration sensing by a laser radar reduces the effects of partial occlusion, concealment, and camouflage experienced by automatic target recognition systems using traditional imagery in a tactical battlefield environment. Linear Predictive Coding (LPC) efficiently represents the vibration signatures and nearest neighbor classifiers exploit the LPC feature set using a variety of distortion metrics. Nearest neighbor classifiers achieve an 88 percent classification rate in an eight class problem, representing a classification performance increase of thirty percent from previous efforts. A novel confidence figure of merit is implemented to attain a 100 percent classification rate with less than 60 percent rejection. The high classification rates are achieved on a target set which would pose significant problems to traditional image-based recognition systems. The targets are presented to the sensor in a variety of aspects and engine speeds at a range of 1 kilometer. The classification rates achieved demonstrate the benefits of using remote vibration measurement in a ground IFF system. The signature modeling and classification system can also be used to identify rotary and fixed-wing targets.

Method of center localization for objects containing concentric arcs

NASA Astrophysics Data System (ADS)

Kuznetsova, Elena G.; Shvets, Evgeny A.; Nikolaev, Dmitry P.

2015-02-01

This paper proposes a method for automatic center location of objects containing concentric arcs. The method utilizes structure tensor analysis and voting scheme optimized with Fast Hough Transform. Two applications of the proposed method are considered: (i) wheel tracking in video-based system for automatic vehicle classification and (ii) tree growth rings analysis on a tree cross cut image.

Force Protection via UGV-UAV Collaboration: Development of Control Law for Vision Based Target Tracking on SUAV

DTIC Science & Technology

2007-12-01

Hardware - In - Loop , Piccolo, UAV, Unmanned Aerial Vehicle 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT...Maneuvering Target.......................... 35 C. HARDWARE - IN - LOOP SIMULATION............................................... 37 1. Hardware - In - Loop Setup...law as proposed in equation (23) is capable of tracking a maneuvering target. C. HARDWARE - IN - LOOP SIMULATION The intention of HIL simulation

Utilizing feedback in adaptive SAR ATR systems

NASA Astrophysics Data System (ADS)

Horsfield, Owen; Blacknell, David

2009-05-01

Existing SAR ATR systems are usually trained off-line with samples of target imagery or CAD models, prior to conducting a mission. If the training data is not representative of mission conditions, then poor performance may result. In addition, it is difficult to acquire suitable training data for the many target types of interest. The Adaptive SAR ATR Problem Set (AdaptSAPS) program provides a MATLAB framework and image database for developing systems that adapt to mission conditions, meaning less reliance on accurate training data. A key function of an adaptive system is the ability to utilise truth feedback to improve performance, and it is this feature which AdaptSAPS is intended to exploit. This paper presents a new method for SAR ATR that does not use training data, based on supervised learning. This is achieved by using feature-based classification, and several new shadow features have been developed for this purpose. These features allow discrimination of vehicles from clutter, and classification of vehicles into two classes: targets, comprising military combat types, and non-targets, comprising bulldozers and trucks. The performance of the system is assessed using three baseline missions provided with AdaptSAPS, as well as three additional missions. All performance metrics indicate a distinct learning trend over the course of a mission, with most third and fourth quartile performance levels exceeding 85% correct classification. It has been demonstrated that these performance levels can be maintained even when truth feedback rates are reduced by up to 55% over the course of a mission.

Crescent Evaluation : appendix D : crescent computer system components evaluation report

DOT National Transportation Integrated Search

1994-02-01

In 1990, Lockheed Integrated Systems Company (LISC) was awarded a contract, under the Crescent Demonstration Project, to demonstrate the integration of Weigh In Motion (WIM), Automatic Vehicle Classification (AVC) and Automatic Vehicle Identification...

Efficient sensor network vehicle classification using peak harmonics of acoustic emissions

NASA Astrophysics Data System (ADS)

William, Peter E.; Hoffman, Michael W.

2008-04-01

An application is proposed for detection and classification of battlefield ground vehicles using the emitted acoustic signal captured at individual sensor nodes of an ad hoc Wireless Sensor Network (WSN). We make use of the harmonic characteristics of the acoustic emissions of battlefield vehicles, in reducing both the computations carried on the sensor node and the transmitted data to the fusion center for reliable and effcient classification of targets. Previous approaches focus on the lower frequency band of the acoustic emissions up to 500Hz; however, we show in the proposed application how effcient discrimination between battlefield vehicles is performed using features extracted from higher frequency bands (50 - 1500Hz). The application shows that selective time domain acoustic features surpass equivalent spectral features. Collaborative signal processing is utilized, such that estimation of certain signal model parameters is carried by the sensor node, in order to reduce the communication between the sensor node and the fusion center, while the remaining model parameters are estimated at the fusion center. The transmitted data from the sensor node to the fusion center ranges from 1 ~ 5% of the sampled acoustic signal at the node. A variety of classification schemes were examined, such as maximum likelihood, vector quantization and artificial neural networks. Evaluation of the proposed application, through processing of an acoustic data set with comparison to previous results, shows that the improvement is not only in the number of computations but also in the detection and false alarm rate as well.

Wavelength-adaptive dehazing using histogram merging-based classification for UAV images.

PubMed

Yoon, Inhye; Jeong, Seokhwa; Jeong, Jaeheon; Seo, Doochun; Paik, Joonki

2015-03-19

Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results.

Classification of LIDAR Data for Generating a High-Precision Roadway Map

NASA Astrophysics Data System (ADS)

Jeong, J.; Lee, I.

2016-06-01

Generating of a highly precise map grows up with development of autonomous driving vehicles. The highly precise map includes a precision of centimetres level unlike an existing commercial map with the precision of meters level. It is important to understand road environments and make a decision for autonomous driving since a robust localization is one of the critical challenges for the autonomous driving car. The one of source data is from a Lidar because it provides highly dense point cloud data with three dimensional position, intensities and ranges from the sensor to target. In this paper, we focus on how to segment point cloud data from a Lidar on a vehicle and classify objects on the road for the highly precise map. In particular, we propose the combination with a feature descriptor and a classification algorithm in machine learning. Objects can be distinguish by geometrical features based on a surface normal of each point. To achieve correct classification using limited point cloud data sets, a Support Vector Machine algorithm in machine learning are used. Final step is to evaluate accuracies of obtained results by comparing them to reference data The results show sufficient accuracy and it will be utilized to generate a highly precise road map.

A new license plate extraction framework based on fast mean shift

NASA Astrophysics Data System (ADS)

Pan, Luning; Li, Shuguang

2010-08-01

License plate extraction is considered to be the most crucial step of Automatic license plate recognition (ALPR) system. In this paper, a region-based license plate hybrid detection method is proposed to solve practical problems under complex background in which existing large quantity of disturbing information. In this method, coarse license plate location is carried out firstly to get the head part of a vehicle. Then a new Fast Mean Shift method based on random sampling of Kernel Density Estimate (KDE) is adopted to segment the color vehicle images, in order to get candidate license plate regions. The remarkable speed-up it brings makes Mean Shift segmentation more suitable for this application. Feature extraction and classification is used to accurately separate license plate from other candidate regions. At last, tilted license plate regulation is used for future recognition steps.

Source Data Applicability Impacts on Epistemic Uncertainty for Launch Vehicle Fault Tree Models

NASA Technical Reports Server (NTRS)

Al Hassan, Mohammad; Novack, Steven D.; Ring, Robert W.

2016-01-01

Launch vehicle systems are designed and developed using both heritage and new hardware. Design modifications to the heritage hardware to fit new functional system requirements can impact the applicability of heritage reliability data. Risk estimates for newly designed systems must be developed from generic data sources such as commercially available reliability databases using reliability prediction methodologies, such as those addressed in MIL-HDBK-217F. Failure estimates must be converted from the generic environment to the specific operating environment of the system where it is used. In addition, some qualification of applicability for the data source to the current system should be made. Characterizing data applicability under these circumstances is crucial to developing model estimations that support confident decisions on design changes and trade studies. This paper will demonstrate a data-source applicability classification method for assigning uncertainty to a target vehicle based on the source and operating environment of the originating data. The source applicability is determined using heuristic guidelines while translation of operating environments is accomplished by applying statistical methods to MIL-HDK-217F tables. The paper will provide a case study example by translating Ground Benign (GB) and Ground Mobile (GM) to the Airborne Uninhabited Fighter (AUF) environment for three electronic components often found in space launch vehicle control systems. The classification method will be followed by uncertainty-importance routines to assess the need to for more applicable data to reduce uncertainty.

Intelligence and Electronic Warfare (IEW) System Fact Sheets

DTIC Science & Technology

1994-04-06

unattended ground sensor system that detects, classifies, and determines direction of movement of intruding personnel and vehicles . It uses remotely...fixed and moving target locations, speed and direction of movement, and classification of tracked/wheeled vehicles . The GSM is equipped with standard... Vehicle The Pointer is a Hand-Launched Unmanned Aerial Vehicle (HL-UAV) to be employed by battalion scouts for t"over-the-hillll reconnaissance and

Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning

PubMed Central

Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

2017-01-01

Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood. PMID:28294963

Increasing Road Infrastructure Capacity Through the Use of Autonomous Vehicles

DTIC Science & Technology

2016-12-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS Approved for public release. Distribution is unlimited. INCREASING ROAD ...DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE INCREASING ROAD INFRASTRUCTURE CAPACITY THROUGH THE USE OF AUTONOMOUS VEHICLES 5. FUNDING...driverless vehicles, road infrastructure 15. NUMBER OF PAGES 65 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT Unclassified 18. SECURITY

A method of vehicle license plate recognition based on PCANet and compressive sensing

NASA Astrophysics Data System (ADS)

Ye, Xianyi; Min, Feng

2018-03-01

The manual feature extraction of the traditional method for vehicle license plates has no good robustness to change in diversity. And the high feature dimension that is extracted with Principal Component Analysis Network (PCANet) leads to low classification efficiency. For solving these problems, a method of vehicle license plate recognition based on PCANet and compressive sensing is proposed. First, PCANet is used to extract the feature from the images of characters. And then, the sparse measurement matrix which is a very sparse matrix and consistent with Restricted Isometry Property (RIP) condition of the compressed sensing is used to reduce the dimensions of extracted features. Finally, the Support Vector Machine (SVM) is used to train and recognize the features whose dimension has been reduced. Experimental results demonstrate that the proposed method has better performance than Convolutional Neural Network (CNN) in the recognition and time. Compared with no compression sensing, the proposed method has lower feature dimension for the increase of efficiency.

Overview of hybrid electric vehicle trend

NASA Astrophysics Data System (ADS)

Wang, Haomiao; Yang, Weidong; Chen, Yingshu; Wang, Yun

2018-04-01

With the increase of per capita energy consumption, environmental pollution is worsening. Using new alternative sources of energy, reducing the use of conventional fuel-powered engines is imperative. Due to the short period, pure electric vehicles cannot be mass-produced and there are many problems such as imperfect charging facilities. Therefore, the development of hybrid electric vehicles is particularly important in a certain period. In this paper, the classification of hybrid vehicle, research status of hybrid vehicle and future development trends of hybrid vehicles is introduced. It is conducive to the public understanding of hybrid electric vehicles, which has a certain theoretical significance.

UNMANNED AERIAL VEHICLE (UAV) HYPERSPECTRAL REMOTE SENSING FOR DRYLAND VEGETATION MONITORING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nancy F. Glenn; Jessica J. Mitchell; Matthew O. Anderson

2012-06-01

UAV-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Idaho State University, Boise Center Aerospace Lab, were recently tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification. The test flights successfully acquired usable flightline data capable of supporting classifiable composite images. Unsupervised classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. Overall, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels. Future mapping efforts that leverage ground reference data, ultra-high spatial resolution photos and time series analysis shouldmore » be able to effectively distinguish native grasses such as Sandberg bluegrass (Poa secunda), from invasives such as burr buttercup (Ranunculus testiculatus) and cheatgrass (Bromus tectorum).« less

Evaluation of the statutory classification of three-wheeled, motorized invalid vehicles.

DOT National Transportation Integrated Search

1978-01-01

In response to an objection by interested individuals to the fact that Virginia law classifies three-wheeled, motorized invalid vehicles as motorcycles and subjects them to all registration, safety inspection, and operator requirements applicable to ...

Autonomous and Connected Vehicles: A Law Enforcement Primer

DTIC Science & Technology

2015-12-01

CYBERSECURITY FOR AUTOMOBILES Intelligent Transportation Systems (ITS) that are emerging around the globe achieve that classification based on the convergence...Car Works,” October 18, 2011, IEEE Spectrum, http://spectrum.ieee.org/automaton/robotics/ artificial - intelligence /how-google-self-driving-car-works...whereby artificial intelligence acts on behalf of a human, but carries the same life or death consequences.435 States should encourage and engage in

Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images

PubMed Central

Peña, José Manuel; Torres-Sánchez, Jorge; de Castro, Ana Isabel; Kelly, Maggi; López-Granados, Francisca

2013-01-01

The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r2=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (<5% weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance. PMID:24146963

Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images.

PubMed

Peña, José Manuel; Torres-Sánchez, Jorge; de Castro, Ana Isabel; Kelly, Maggi; López-Granados, Francisca

2013-01-01

The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r(2)=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (<5% weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance.

Sources of error in estimating truck traffic from automatic vehicle classification data

DOT National Transportation Integrated Search

1998-10-01

Truck annual average daily traffic estimation errors resulting from sample classification counts are computed in this paper under two scenarios. One scenario investigates an improper factoring procedure that may be used by highway agencies. The study...

Radar based autonomous sensor module

NASA Astrophysics Data System (ADS)

Styles, Tim

2016-10-01

Most surveillance systems combine camera sensors with other detection sensors that trigger an alert to a human operator when an object is detected. The detection sensors typically require careful installation and configuration for each application and there is a significant burden on the operator to react to each alert by viewing camera video feeds. A demonstration system known as Sensing for Asset Protection with Integrated Electronic Networked Technology (SAPIENT) has been developed to address these issues using Autonomous Sensor Modules (ASM) and a central High Level Decision Making Module (HLDMM) that can fuse the detections from multiple sensors. This paper describes the 24 GHz radar based ASM, which provides an all-weather, low power and license exempt solution to the problem of wide area surveillance. The radar module autonomously configures itself in response to tasks provided by the HLDMM, steering the transmit beam and setting range resolution and power levels for optimum performance. The results show the detection and classification performance for pedestrians and vehicles in an area of interest, which can be modified by the HLDMM without physical adjustment. The module uses range-Doppler processing for reliable detection of moving objects and combines Radar Cross Section and micro-Doppler characteristics for object classification. Objects are classified as pedestrian or vehicle, with vehicle sub classes based on size. Detections are reported only if the object is detected in a task coverage area and it is classified as an object of interest. The system was shown in a perimeter protection scenario using multiple radar ASMs, laser scanners, thermal cameras and visible band cameras. This combination of sensors enabled the HLDMM to generate reliable alerts with improved discrimination of objects and behaviours of interest.

Agent Collaborative Target Localization and Classification in Wireless Sensor Networks

PubMed Central

Wang, Xue; Bi, Dao-wei; Ding, Liang; Wang, Sheng

2007-01-01

Wireless sensor networks (WSNs) are autonomous networks that have been frequently deployed to collaboratively perform target localization and classification tasks. Their autonomous and collaborative features resemble the characteristics of agents. Such similarities inspire the development of heterogeneous agent architecture for WSN in this paper. The proposed agent architecture views WSN as multi-agent systems and mobile agents are employed to reduce in-network communication. According to the architecture, an energy based acoustic localization algorithm is proposed. In localization, estimate of target location is obtained by steepest descent search. The search algorithm adapts to measurement environments by dynamically adjusting its termination condition. With the agent architecture, target classification is accomplished by distributed support vector machine (SVM). Mobile agents are employed for feature extraction and distributed SVM learning to reduce communication load. Desirable learning performance is guaranteed by combining support vectors and convex hull vectors. Fusion algorithms are designed to merge SVM classification decisions made from various modalities. Real world experiments with MICAz sensor nodes are conducted for vehicle localization and classification. Experimental results show the proposed agent architecture remarkably facilitates WSN designs and algorithm implementation. The localization and classification algorithms also prove to be accurate and energy efficient.

Classification of Wind Farm Turbulence and Its Effects on General Aviation Aircraft and Airports

DOT National Transportation Integrated Search

2018-01-01

Over the past 10 years, the U.S. Department of Transportation (USDOT) has researched and developed connected vehicle technology, which allows vehicles to communicate with each other, roadway infrastructure, traffic management centers, and travelers' ...

Increasing Intelligence in Inter-Vehicle Communications to Reduce Traffic Congestions: Experiments in Urban and Highway Environments.

PubMed

Meneguette, Rodolfo I; Filho, Geraldo P R; Guidoni, Daniel L; Pessin, Gustavo; Villas, Leandro A; Ueyama, Jó

2016-01-01

Intelligent Transportation Systems (ITS) rely on Inter-Vehicle Communication (IVC) to streamline the operation of vehicles by managing vehicle traffic, assisting drivers with safety and sharing information, as well as providing appropriate services for passengers. Traffic congestion is an urban mobility problem, which causes stress to drivers and economic losses. In this context, this work proposes a solution for the detection, dissemination and control of congested roads based on inter-vehicle communication, called INCIDEnT. The main goal of the proposed solution is to reduce the average trip time, CO emissions and fuel consumption by allowing motorists to avoid congested roads. The simulation results show that our proposed solution leads to short delays and a low overhead. Moreover, it is efficient with regard to the coverage of the event and the distance to which the information can be propagated. The findings of the investigation show that the proposed solution leads to (i) high hit rate in the classification of the level of congestion, (ii) a reduction in average trip time, (iii) a reduction in fuel consumption, and (iv) reduced CO emissions.

Increasing Intelligence in Inter-Vehicle Communications to Reduce Traffic Congestions: Experiments in Urban and Highway Environments

PubMed Central

Filho, Geraldo P. R.; Guidoni, Daniel L.; Pessin, Gustavo; Villas, Leandro A.; Ueyama, Jó

2016-01-01

Intelligent Transportation Systems (ITS) rely on Inter-Vehicle Communication (IVC) to streamline the operation of vehicles by managing vehicle traffic, assisting drivers with safety and sharing information, as well as providing appropriate services for passengers. Traffic congestion is an urban mobility problem, which causes stress to drivers and economic losses. In this context, this work proposes a solution for the detection, dissemination and control of congested roads based on inter-vehicle communication, called INCIDEnT. The main goal of the proposed solution is to reduce the average trip time, CO emissions and fuel consumption by allowing motorists to avoid congested roads. The simulation results show that our proposed solution leads to short delays and a low overhead. Moreover, it is efficient with regard to the coverage of the event and the distance to which the information can be propagated. The findings of the investigation show that the proposed solution leads to (i) high hit rate in the classification of the level of congestion, (ii) a reduction in average trip time, (iii) a reduction in fuel consumption, and (iv) reduced CO emissions PMID:27526048

Automated detection and recognition of wildlife using thermal cameras.

PubMed

Christiansen, Peter; Steen, Kim Arild; Jørgensen, Rasmus Nyholm; Karstoft, Henrik

2014-07-30

In agricultural mowing operations, thousands of animals are injured or killed each year, due to the increased working widths and speeds of agricultural machinery. Detection and recognition of wildlife within the agricultural fields is important to reduce wildlife mortality and, thereby, promote wildlife-friendly farming. The work presented in this paper contributes to the automated detection and classification of animals in thermal imaging. The methods and results are based on top-view images taken manually from a lift to motivate work towards unmanned aerial vehicle-based detection and recognition. Hot objects are detected based on a threshold dynamically adjusted to each frame. For the classification of animals, we propose a novel thermal feature extraction algorithm. For each detected object, a thermal signature is calculated using morphological operations. The thermal signature describes heat characteristics of objects and is partly invariant to translation, rotation, scale and posture. The discrete cosine transform (DCT) is used to parameterize the thermal signature and, thereby, calculate a feature vector, which is used for subsequent classification. Using a k-nearest-neighbor (kNN) classifier, animals are discriminated from non-animals with a balanced classification accuracy of 84.7% in an altitude range of 3-10 m and an accuracy of 75.2% for an altitude range of 10-20 m. To incorporate temporal information in the classification, a tracking algorithm is proposed. Using temporal information improves the balanced classification accuracy to 93.3% in an altitude range 3-10 of meters and 77.7% in an altitude range of 10-20 m.

Detection and Classification of Motor Vehicle Noise in a Forested Landscape

NASA Astrophysics Data System (ADS)

Brown, Casey L.; Reed, Sarah E.; Dietz, Matthew S.; Fristrup, Kurt M.

2013-11-01

Noise emanating from human activity has become a common addition to natural soundscapes and has the potential to harm wildlife and erode human enjoyment of nature. In particular, motor vehicles traveling along roads and trails produce high levels of both chronic and intermittent noise, eliciting varied responses from a wide range of animal species. Anthropogenic noise is especially conspicuous in natural areas where ambient background sound levels are low. In this article, we present an acoustic method to detect and analyze motor vehicle noise. Our approach uses inexpensive consumer products to record sound, sound analysis software to automatically detect sound events within continuous recordings and measure their acoustic properties, and statistical classification methods to categorize sound events. We describe an application of this approach to detect motor vehicle noise on paved, gravel, and natural-surface roads, and off-road vehicle trails in 36 sites distributed throughout a national forest in the Sierra Nevada, CA, USA. These low-cost, unobtrusive methods can be used by scientists and managers to detect anthropogenic noise events for many potential applications, including ecological research, transportation and recreation planning, and natural resource management.

Remote imagery for unmanned ground vehicles: the future of path planning for ground robotics

NASA Astrophysics Data System (ADS)

Frederick, Philip A.; Theisen, Bernard L.; Ward, Derek

2006-10-01

Remote Imagery for Unmanned Ground Vehicles (RIUGV) uses a combination of high-resolution multi-spectral satellite imagery and advanced commercial off-the-self (COTS) object-oriented image processing software to provide automated terrain feature extraction and classification. This information, along with elevation data, infrared imagery, a vehicle mobility model and various meta-data (local weather reports, Zobler Soil map, etc...), is fed into automated path planning software to provide a stand-alone ability to generate rapidly updateable dynamic mobility maps for Manned or Unmanned Ground Vehicles (MGVs or UGVs). These polygon based mobility maps can reside on an individual platform or a tactical network. When new information is available, change files are generated and ingested into existing mobility maps based on user selected criteria. Bandwidth concerns are mitigated by the use of shape files for the representation of the data (e.g. each object in the scene is represented by a shape file and thus can be transmitted individually). User input (desired level of stealth, required time of arrival, etc...) determines the priority in which objects are tagged for updates. This paper will also discuss the planned July 2006 field experiment.

Personnel and Vehicle Data Collection at Aberdeen Proving Ground (APG) and its Distribution for Research

DTIC Science & Technology

2015-10-01

28 Magnetometer Applied Physics Model 1540-digital 3-axis fluxgate 5 Amplifiers Alligator Technologies USBPGF-S1 programmable instrumentation...Acoustic, Seismic, magnetic, footstep, vehicle, magnetometer , geophone, unattended ground sensor (UGS) 16. SECURITY CLASSIFICATION OF: 17. LIMITATION

Multifunctional Structural-Energy Storage Nanocomposites for Ultra Lightweight Micro Autonomous Systems (First-year Report)

DTIC Science & Technology

2012-02-01

SUBJECT TERMS Carbon nanotubes , CNTs, supercapacitor, multifunctional, energy, structural-Energy 16. SECURITY CLASSIFICATION OF: 17. LIMITATION...Pulickel M. Ajayan of Rice University for providing us with the vertically aligned carbon nanotube (CNT) forests used in this project and for helpful...10–18) and man-portable unmanned vehicles (19). In related research, ARL has also investigated using carbon nanotube (CNT)-based electrodes for

Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions

PubMed Central

Rose, Johann Christian; Kicherer, Anna; Wieland, Markus; Klingbeil, Lasse; Töpfer, Reinhard; Kuhlmann, Heiner

2016-01-01

In viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and non-invasive sensor-based methods to increase data acquisition speed, enhance measurement accuracy and objectivity and to reduce labor costs. While many 2D methods based on image processing have been proposed for field phenotyping, only a few 3D solutions are found in the literature. A track-driven vehicle consisting of a camera system, a real-time-kinematic GPS system for positioning, as well as hardware for vehicle control, image storage and acquisition is used to visually capture a whole vine row canopy with georeferenced RGB images. In the first post-processing step, these images were used within a multi-view-stereo software to reconstruct a textured 3D point cloud of the whole grapevine row. A classification algorithm is then used in the second step to automatically classify the raw point cloud data into the semantic plant components, grape bunches and canopy. In the third step, phenotypic data for the semantic objects is gathered using the classification results obtaining the quantity of grape bunches, berries and the berry diameter. PMID:27983669

A Hessian-based methodology for automatic surface crack detection and classification from pavement images

NASA Astrophysics Data System (ADS)

Ghanta, Sindhu; Shahini Shamsabadi, Salar; Dy, Jennifer; Wang, Ming; Birken, Ralf

2015-04-01

Around 3,000,000 million vehicle miles are annually traveled utilizing the US transportation systems alone. In addition to the road traffic safety, maintaining the road infrastructure in a sound condition promotes a more productive and competitive economy. Due to the significant amounts of financial and human resources required to detect surface cracks by visual inspection, detection of these surface defects are often delayed resulting in deferred maintenance operations. This paper introduces an automatic system for acquisition, detection, classification, and evaluation of pavement surface cracks by unsupervised analysis of images collected from a camera mounted on the rear of a moving vehicle. A Hessian-based multi-scale filter has been utilized to detect ridges in these images at various scales. Post-processing on the extracted features has been implemented to produce statistics of length, width, and area covered by cracks, which are crucial for roadway agencies to assess pavement quality. This process has been realized on three sets of roads with different pavement conditions in the city of Brockton, MA. A ground truth dataset labeled manually is made available to evaluate this algorithm and results rendered more than 90% segmentation accuracy demonstrating the feasibility of employing this approach at a larger scale.

Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions.

PubMed

Rose, Johann Christian; Kicherer, Anna; Wieland, Markus; Klingbeil, Lasse; Töpfer, Reinhard; Kuhlmann, Heiner

2016-12-15

In viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and non-invasive sensor-based methods to increase data acquisition speed, enhance measurement accuracy and objectivity and to reduce labor costs. While many 2D methods based on image processing have been proposed for field phenotyping, only a few 3D solutions are found in the literature. A track-driven vehicle consisting of a camera system, a real-time-kinematic GPS system for positioning, as well as hardware for vehicle control, image storage and acquisition is used to visually capture a whole vine row canopy with georeferenced RGB images. In the first post-processing step, these images were used within a multi-view-stereo software to reconstruct a textured 3D point cloud of the whole grapevine row. A classification algorithm is then used in the second step to automatically classify the raw point cloud data into the semantic plant components, grape bunches and canopy. In the third step, phenotypic data for the semantic objects is gathered using the classification results obtaining the quantity of grape bunches, berries and the berry diameter.

Real-time road detection in infrared imagery

NASA Astrophysics Data System (ADS)

Andre, Haritini E.; McCoy, Keith

1990-09-01

Automatic road detection is an important part in many scene recognition applications. The extraction of roads provides a means of navigation and position update for remotely piloted vehicles or autonomous vehicles. Roads supply strong contextual information which can be used to improve the performance of automatic target recognition (ATh) systems by directing the search for targets and adjusting target classification confidences. This paper will describe algorithmic techniques for labeling roads in high-resolution infrared imagery. In addition, realtime implementation of this structural approach using a processor array based on the Martin Marietta Geometric Arithmetic Parallel Processor (GAPPTh) chip will be addressed. The algorithm described is based on the hypothesis that a road consists of pairs of line segments separated by a distance "d" with opposite gradient directions (antiparallel). The general nature of the algorithm, in addition to its parallel implementation in a single instruction, multiple data (SIMD) machine, are improvements to existing work. The algorithm seeks to identify line segments meeting the road hypothesis in a manner that performs well, even when the side of the road is fragmented due to occlusion or intersections. The use of geometrical relationships between line segments is a powerful yet flexible method of road classification which is independent of orientation. In addition, this approach can be used to nominate other types of objects with minor parametric changes.

Analysis of vehicle classification and truck weight data of the New England states

DOT National Transportation Integrated Search

1998-09-01

This report is about a statistical analysis of 1995-96 classification and weigh in motion (WIM) data from 17 continuous traffic-monitoring sites in New England. It documents work performed by Oak Ridge National Laboratory in fulfillment of 'Analysis ...

Sensor network based vehicle classification and license plate identification system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frigo, Janette Rose; Brennan, Sean M; Rosten, Edward J

Typically, for energy efficiency and scalability purposes, sensor networks have been used in the context of environmental and traffic monitoring applications in which operations at the sensor level are not computationally intensive. But increasingly, sensor network applications require data and compute intensive sensors such video cameras and microphones. In this paper, we describe the design and implementation of two such systems: a vehicle classifier based on acoustic signals and a license plate identification system using a camera. The systems are implemented in an energy-efficient manner to the extent possible using commercially available hardware, the Mica motes and the Stargate platform.more » Our experience in designing these systems leads us to consider an alternate more flexible, modular, low-power mote architecture that uses a combination of FPGAs, specialized embedded processing units and sensor data acquisition systems.« less

Test Operation Procedure (TOP) 01-1-010A Vehicle Test Course Severity (Surface Roughness)

DTIC Science & Technology

2017-12-12

Department of Agriculture (USDA) classifications, respectively. TABLE 10. PARTICLE SIZE CLASSES CLASS SIZE Cobble and Gravel >4.75 mm particle diameter...ABBREVIATIONS. USCS Unified Soil Classification System USDA United States Department of Agriculture UTM Universal Transverse Mercator WNS wave number

Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner.

PubMed

An, Jhonghyun; Choi, Baehoon; Sim, Kwee-Bo; Kim, Euntai

2016-07-20

There are several types of intersections such as merge-roads, diverge-roads, plus-shape intersections and two types of T-shape junctions in urban roads. When an autonomous vehicle encounters new intersections, it is crucial to recognize the types of intersections for safe navigation. In this paper, a novel intersection type recognition method is proposed for an autonomous vehicle using a multi-layer laser scanner. The proposed method consists of two steps: (1) static local coordinate occupancy grid map (SLOGM) building and (2) intersection classification. In the first step, the SLOGM is built relative to the local coordinate using the dynamic binary Bayes filter. In the second step, the SLOGM is used as an attribute for the classification. The proposed method is applied to a real-world environment and its validity is demonstrated through experimentation.

Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner

PubMed Central

An, Jhonghyun; Choi, Baehoon; Sim, Kwee-Bo; Kim, Euntai

2016-01-01

There are several types of intersections such as merge-roads, diverge-roads, plus-shape intersections and two types of T-shape junctions in urban roads. When an autonomous vehicle encounters new intersections, it is crucial to recognize the types of intersections for safe navigation. In this paper, a novel intersection type recognition method is proposed for an autonomous vehicle using a multi-layer laser scanner. The proposed method consists of two steps: (1) static local coordinate occupancy grid map (SLOGM) building and (2) intersection classification. In the first step, the SLOGM is built relative to the local coordinate using the dynamic binary Bayes filter. In the second step, the SLOGM is used as an attribute for the classification. The proposed method is applied to a real-world environment and its validity is demonstrated through experimentation. PMID:27447640

Wavelength-Adaptive Dehazing Using Histogram Merging-Based Classification for UAV Images

PubMed Central

Yoon, Inhye; Jeong, Seokhwa; Jeong, Jaeheon; Seo, Doochun; Paik, Joonki

2015-01-01

Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results. PMID:25808767

Current challenges in autonomous vehicle development

NASA Astrophysics Data System (ADS)

Connelly, J.; Hong, W. S.; Mahoney, R. B., Jr.; Sparrow, D. A.

2006-05-01

The field of autonomous vehicles is a rapidly growing one, with significant interest from both government and industry sectors. Autonomous vehicles represent the intersection of artificial intelligence (AI) and robotics, combining decision-making with real-time control. Autonomous vehicles are desired for use in search and rescue, urban reconnaissance, mine detonation, supply convoys, and more. The general adage is to use robots for anything dull, dirty, dangerous or dumb. While a great deal of research has been done on autonomous systems, there are only a handful of fielded examples incorporating machine autonomy beyond the level of teleoperation, especially in outdoor/complex environments. In an attempt to assess and understand the current state of the art in autonomous vehicle development, a few areas where unsolved problems remain became clear. This paper outlines those areas and provides suggestions for the focus of science and technology research. The first step in evaluating the current state of autonomous vehicle development was to develop a definition of autonomy. A number of autonomy level classification systems were reviewed. The resulting working definitions and classification schemes used by the authors are summarized in the opening sections of the paper. The remainder of the report discusses current approaches and challenges in decision-making and real-time control for autonomous vehicles. Suggested research focus areas for near-, mid-, and long-term development are also presented.

Self-Contained Automated Vehicle Washing System

DTIC Science & Technology

2014-09-26

SECURITY CLASSIFICATION OF: The Self Contained Automated Vehicle Washing System is a prototype that offers a reduction in the quantity of water ...supplied to the front lines by recycling wash water used in the cleaning of vehicles as well as capturing debris and other contaminates. The system also...of the warfighter to contaminates in the washing process. The System offers plug and play option for reclamation of the wash water and integration of

Unintended Relevance: The Role of the Stryker Brigade Combat Team in the Decisive Action Environment

DTIC Science & Technology

2017-05-25

Systems , 30MM 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON Walter C. Gray II...Facilities- Policy FCS Future Combat System FM Field Manual FMTV Family of Medium Tactical Vehicles GCV Ground Combat Vehicle GWOT...Vehicle MGS Mobile Gun System MPF Mobile Protected Firepower NATO North Atlantic Treaty Organization ONS Operational Needs Statement RPG

Analysis of vehicle classification and truck weight data of the New England States : is data sharing a good idea?

DOT National Transportation Integrated Search

1998-01-01

This paper is about a statistical research analysis of 1995-96 classification and weigh in motion : (WIM) data from seventeen continuous traffic-monitoring sites in New England. Data screening is : discussed briefly, and a cusum data quality control ...

Application of the SNoW machine learning paradigm to a set of transportation imaging problems

NASA Astrophysics Data System (ADS)

Paul, Peter; Burry, Aaron M.; Wang, Yuheng; Kozitsky, Vladimir

2012-01-01

Machine learning methods have been successfully applied to image object classification problems where there is clear distinction between classes and where a comprehensive set of training samples and ground truth are readily available. The transportation domain is an area where machine learning methods are particularly applicable, since the classification problems typically have well defined class boundaries and, due to high traffic volumes in most applications, massive roadway data is available. Though these classes tend to be well defined, the particular image noises and variations can be challenging. Another challenge is the extremely high accuracy typically required in most traffic applications. Incorrect assignment of fines or tolls due to imaging mistakes is not acceptable in most applications. For the front seat vehicle occupancy detection problem, classification amounts to determining whether one face (driver only) or two faces (driver + passenger) are detected in the front seat of a vehicle on a roadway. For automatic license plate recognition, the classification problem is a type of optical character recognition problem encompassing multiple class classification. The SNoW machine learning classifier using local SMQT features is shown to be successful in these two transportation imaging applications.

A novel framework to evaluate pedestrian safety at non-signalized locations.

PubMed

Fu, Ting; Miranda-Moreno, Luis; Saunier, Nicolas

2018-02-01

This paper proposes a new framework to evaluate pedestrian safety at non-signalized crosswalk locations. In the proposed framework, the yielding maneuver of a driver in response to a pedestrian is split into the reaction and braking time. Hence, the relationship of the distance required for a yielding maneuver and the approaching vehicle speed depends on the reaction time of the driver and deceleration rate that the vehicle can achieve. The proposed framework is represented in the distance-velocity (DV) diagram and referred as the DV model. The interactions between approaching vehicles and pedestrians showing the intention to cross are divided in three categories: i) situations where the vehicle cannot make a complete stop, ii) situations where the vehicle's ability to stop depends on the driver reaction time, and iii) situations where the vehicle can make a complete stop. Based on these classifications, non-yielding maneuvers are classified as "non-infraction non-yielding" maneuvers, "uncertain non-yielding" maneuvers and "non-yielding" violations, respectively. From the pedestrian perspective, crossing decisions are classified as dangerous crossings, risky crossings and safe crossings accordingly. The yielding compliance and yielding rate, as measures of the yielding behavior, are redefined based on these categories. Time to crossing and deceleration rate required for the vehicle to stop are used to measure the probability of collision. Finally, the framework is demonstrated through a case study in evaluating pedestrian safety at three different types of non-signalized crossings: a painted crosswalk, an unprotected crosswalk, and a crosswalk controlled by stop signs. Results from the case study suggest that the proposed framework works well in describing pedestrian-vehicle interactions which helps in evaluating pedestrian safety at non-signalized crosswalk locations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Can vehicle longitudinal jerk be used to identify aggressive drivers? An examination using naturalistic driving data.

PubMed

Feng, Fred; Bao, Shan; Sayer, James R; Flannagan, Carol; Manser, Michael; Wunderlich, Robert

2017-07-01

This paper investigated the characteristics of vehicle longitudinal jerk (change rate of acceleration with respect to time) by using vehicle sensor data from an existing naturalistic driving study. The main objective was to examine whether vehicle jerk contains useful information that could be potentially used to identify aggressive drivers. Initial investigation showed that there are unique characteristics of vehicle jerk in drivers' gas and brake pedal operations. Thus two jerk-based metrics were examined: (1) driver's frequency of using large positive jerk when pressing the gas pedal, and (2) driver's frequency of using large negative jerk when pressing the brake pedal. To validate the performance of the two metrics, drivers were firstly divided into an aggressive group and a normal group using three classification methods (1) traveling at excessive speed (speeding), (2) following too closely to a front vehicle (tailgating), and (3) their association with crashes or near-crashes in the dataset. The results show that those aggressive drivers defined using any of the three methods above were associated with significantly higher values of the two jerk-based metrics. Between the two metrics the frequency of using large negative jerk seems to have better performance in identifying aggressive drivers. A sensitivity analysis shows the findings were largely consistent with varying parameters in the analysis. The potential applications of this work include developing quantitative surrogate safety measures to identify aggressive drivers and aggressive driving, which could be potentially used to, for example, provide real-time or post-ride performance feedback to the drivers, or warn the surrounding drivers or vehicles using the connected vehicle technologies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Translations on USSR Military Affairs, Number 1278

DTIC Science & Technology

1977-05-19

plans for conducting them. Here special responsibility rests on the staff, as the command body. Precisely clear planning based upon a pro - found...swimming 100 meters freestyle , cross-country race of 3 kilo- meters or skiing 10 kilometers (for age group I), 1-kilometer cross-country race or...Classification), swimming 100 meters freestyle , 3-kilometer cross-country race, figure driving of vehicle Categories 290 3’UO 3^0 3^0 1+00 270 300

Acquisition and processing of advanced sensor data for ERW and UXO detection and classification

NASA Astrophysics Data System (ADS)

Schultz, Gregory M.; Keranen, Joe; Miller, Jonathan S.; Shubitidze, Fridon

2014-06-01

The remediation of explosive remnants of war (ERW) and associated unexploded ordnance (UXO) has seen improvements through the injection of modern technological advances and streamlined standard operating procedures. However, reliable and cost-effective detection and geophysical mapping of sites contaminated with UXO such as cluster munitions, abandoned ordnance, and improvised explosive devices rely on the ability to discriminate hazardous items from metallic clutter. In addition to anthropogenic clutter, handheld and vehicle-based metal detector systems are plagued by natural geologic and environmental noise in many post conflict areas. We present new and advanced electromagnetic induction (EMI) technologies including man-portable and towed EMI arrays and associated data processing software. While these systems feature vastly different form factors and transmit-receive configurations, they all exhibit several fundamental traits that enable successful classification of EMI anomalies. Specifically, multidirectional sampling of scattered magnetic fields from targets and corresponding high volume of unique data provide rich information for extracting useful classification features for clutter rejection analysis. The quality of classification features depends largely on the extent to which the data resolve unique physics-based parameters. To date, most of the advanced sensors enable high quality inversion by producing data that are extremely rich in spatial content through multi-angle illumination and multi-point reception.

Automated Snow Extent Mapping Based on Orthophoto Images from Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Spallek, Waldemar; Witek-Kasprzak, Matylda

2018-04-01

The paper presents the application of the k-means clustering in the process of automated snow extent mapping using orthophoto images generated using the Structure-from-Motion (SfM) algorithm from oblique aerial photographs taken by unmanned aerial vehicle (UAV). A simple classification approach has been implemented to discriminate between snow-free and snow-covered terrain. The procedure uses the k-means clustering and classifies orthophoto images based on the three-dimensional space of red-green-blue (RGB) or near-infrared-red-green (NIRRG) or near-infrared-green-blue (NIRGB) bands. To test the method, several field experiments have been carried out, both in situations when snow cover was continuous and when it was patchy. The experiments have been conducted using three fixed-wing UAVs (swinglet CAM by senseFly, eBee by senseFly, and Birdie by FlyTech UAV) on 10/04/2015, 23/03/2016, and 16/03/2017 within three test sites in the Izerskie Mountains in southwestern Poland. The resulting snow extent maps, produced automatically using the classification method, have been validated against real snow extents delineated through a visual analysis and interpretation offered by human analysts. For the simplest classification setup, which assumes two classes in the k-means clustering, the extent of snow patches was estimated accurately, with areal underestimation of 4.6% (RGB) and overestimation of 5.5% (NIRGB). For continuous snow cover with sparse discontinuities at places where trees or bushes protruded from snow, the agreement between automatically produced snow extent maps and observations was better, i.e. 1.5% (underestimation with RGB) and 0.7-0.9% (overestimation, either with RGB or with NIRRG). Shadows on snow were found to be mainly responsible for the misclassification.

Mixing geometric and radiometric features for change classification

NASA Astrophysics Data System (ADS)

Fournier, Alexandre; Descombes, Xavier; Zerubia, Josiane

2008-02-01

Most basic change detection algorithms use a pixel-based approach. Whereas such approach is quite well defined for monitoring important area changes (such as urban growth monitoring) in low resolution images, an object based approach seems more relevant when the change detection is specifically aimed toward targets (such as small buildings and vehicles). In this paper, we present an approach that mixes radiometric and geometric features to qualify the changed zones. The goal is to establish bounds (appearance, disappearance, substitution ...) between the detected changes and the underlying objects. We proceed by first clustering the change map (containing each pixel bitemporal radiosity) in different classes using the entropy-kmeans algorithm. Assuming that most man-made objects have a polygonal shape, a polygonal approximation algorithm is then used in order to characterize the resulting zone shapes. Hence allowing us to refine the primary rough classification, by integrating the polygon orientations in the state space. Tests are currently conducted on Quickbird data.

Simulation of vehicle acoustics in support of netted sensor research and development

NASA Astrophysics Data System (ADS)

Christou, Carol T.; Jacyna, Garry M.

2005-05-01

The MITRE Corporation has initiated a three-year internally-funded research program in netted sensors, the first-year effort focusing on vehicle detection for border monitoring. An important component is developing an understanding of the complex acoustic structure of vehicle noise to aid in netted sensor-based detection and classification. This presentation will discuss the design of a high-fidelity vehicle acoustic simulator to model the generation and transmission of acoustic energy from a moving vehicle to a collection of sensor nodes. Realistic spatially-dependent automobile sounds are generated from models of the engine cylinder firing rates, muffler and manifold resonances, and speed-dependent tire whine noise. Tire noise is the dominant noise source for vehicle speeds in excess of 30 miles per hour (MPH). As a result, we have developed detailed models that successfully predict the tire noise spectrum as a function of speed, road surface wave-number spectrum, tire geometry, and tire tread pattern. We have also included realistic descriptions of the spatial directivity patterns for the engine harmonics, muffler, and tire whine noise components. The acoustic waveforms are propagated to each sensor node using a simple phase-dispersive multi-path model. A brief description of the models and their corresponding outputs is provided.

Red-Light-Running Crashes' Classification, Comparison, and Risk Analysis Based on General Estimates System (GES) Crash Database.

PubMed

Zhang, Yuting; Yan, Xuedong; Li, Xiaomeng; Wu, Jiawei; Dixit, Vinayak V

2018-06-19

Red-light running (RLR) has been identified as one of the prominent contributing factors involved in signalized intersection crashes. In order to reduce RLR crashes, primarily, a better understanding of RLR behavior and crashes is needed. In this study, three RLR crash types were extracted from the general estimates system (GES), including go-straight (GS) RLR vehicle colliding with go-straight non-RLR vehicle, go-straight RLR vehicle colliding with left-turn (LT) non-RLR vehicle, and left-turn RLR vehicle colliding with go-straight non-RLR vehicle. Then, crash features within each crash type scenario were compared, and risk analyses of GS RLR and LT RLR were also conducted. The results indicated that for the GS RLR driver, the speed limit displayed the highest effects on the percentages of GS RLR collision scenarios. For the LT RLR driver, the number of lanes displayed the highest effects on the percentages of LT RLR collision scenarios. Additionally, the drivers who were older than 50 years, distracted, and had a limited view were more likely to be involved in LT RLR accidents. Furthermore, the speeding drivers were more likely to be involved in GS RLR accidents. These findings could give a comprehensive understanding of RLR crash features and propensities for each RLR crash type.

Classification of time series patterns from complex dynamic systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schryver, J.C.; Rao, N.

1998-07-01

An increasing availability of high-performance computing and data storage media at decreasing cost is making possible the proliferation of large-scale numerical databases and data warehouses. Numeric warehousing enterprises on the order of hundreds of gigabytes to terabytes are a reality in many fields such as finance, retail sales, process systems monitoring, biomedical monitoring, surveillance and transportation. Large-scale databases are becoming more accessible to larger user communities through the internet, web-based applications and database connectivity. Consequently, most researchers now have access to a variety of massive datasets. This trend will probably only continue to grow over the next several years. Unfortunately,more » the availability of integrated tools to explore, analyze and understand the data warehoused in these archives is lagging far behind the ability to gain access to the same data. In particular, locating and identifying patterns of interest in numerical time series data is an increasingly important problem for which there are few available techniques. Temporal pattern recognition poses many interesting problems in classification, segmentation, prediction, diagnosis and anomaly detection. This research focuses on the problem of classification or characterization of numerical time series data. Highway vehicles and their drivers are examples of complex dynamic systems (CDS) which are being used by transportation agencies for field testing to generate large-scale time series datasets. Tools for effective analysis of numerical time series in databases generated by highway vehicle systems are not yet available, or have not been adapted to the target problem domain. However, analysis tools from similar domains may be adapted to the problem of classification of numerical time series data.« less

Probabilistic Tracking and Trajectory Planning for Autonomous Ground Vehicles in Urban Environments

DTIC Science & Technology

2016-03-05

SECURITY CLASSIFICATION OF: The aim of this research is to develop a unified theory for perception and planning in autonomous ground vehicles, with a...Report Title The aim of this research is to develop a unified theory for perception and planning in autonomous ground vehicles, with a specific focus on...a combination of experimentally collected vision data and Monte- Carlo simulations. Smoothing for improved perception and robustness in planning

Hardware-in-the-loop simulation for undersea vehicle applications

NASA Astrophysics Data System (ADS)

Kelf, Michael A.

2001-08-01

Torpedoes and other Unmanned Undersea Vehicles (UUV) are employed by submarines and surface combatants, as well as aircraft, for undersea warfare. These vehicles are autonomous devices whose guidance systems rival the complexity of the most sophisticated air combat missiles. The tactical environment for undersea warfare is a difficult one in terms of target detection,k classification, and pursuit because of the physics of underwater sounds. Both hardware-in-the-loop and all-digital simulations have become vital tools in developing and evaluating undersea weapon and vehicle guidance performance in the undersea environment.

Sensor Architecture and Task Classification for Agricultural Vehicles and Environments

PubMed Central

Rovira-Más, Francisco

2010-01-01

The long time wish of endowing agricultural vehicles with an increasing degree of autonomy is becoming a reality thanks to two crucial facts: the broad diffusion of global positioning satellite systems and the inexorable progress of computers and electronics. Agricultural vehicles are currently the only self-propelled ground machines commonly integrating commercial automatic navigation systems. Farm equipment manufacturers and satellite-based navigation system providers, in a joint effort, have pushed this technology to unprecedented heights; yet there are many unresolved issues and an unlimited potential still to uncover. The complexity inherent to intelligent vehicles is rooted in the selection and coordination of the optimum sensors, the computer reasoning techniques to process the acquired data, and the resulting control strategies for automatic actuators. The advantageous design of the network of onboard sensors is necessary for the future deployment of advanced agricultural vehicles. This article analyzes a variety of typical environments and situations encountered in agricultural fields, and proposes a sensor architecture especially adapted to cope with them. The strategy proposed groups sensors into four specific subsystems: global localization, feedback control and vehicle pose, non-visual monitoring, and local perception. The designed architecture responds to vital vehicle tasks classified within three layers devoted to safety, operative information, and automatic actuation. The success of this architecture, implemented and tested in various agricultural vehicles over the last decade, rests on its capacity to integrate redundancy and incorporate new technologies in a practical way. PMID:22163522

Sensor architecture and task classification for agricultural vehicles and environments.

PubMed

Rovira-Más, Francisco

2010-01-01

The long time wish of endowing agricultural vehicles with an increasing degree of autonomy is becoming a reality thanks to two crucial facts: the broad diffusion of global positioning satellite systems and the inexorable progress of computers and electronics. Agricultural vehicles are currently the only self-propelled ground machines commonly integrating commercial automatic navigation systems. Farm equipment manufacturers and satellite-based navigation system providers, in a joint effort, have pushed this technology to unprecedented heights; yet there are many unresolved issues and an unlimited potential still to uncover. The complexity inherent to intelligent vehicles is rooted in the selection and coordination of the optimum sensors, the computer reasoning techniques to process the acquired data, and the resulting control strategies for automatic actuators. The advantageous design of the network of onboard sensors is necessary for the future deployment of advanced agricultural vehicles. This article analyzes a variety of typical environments and situations encountered in agricultural fields, and proposes a sensor architecture especially adapted to cope with them. The strategy proposed groups sensors into four specific subsystems: global localization, feedback control and vehicle pose, non-visual monitoring, and local perception. The designed architecture responds to vital vehicle tasks classified within three layers devoted to safety, operative information, and automatic actuation. The success of this architecture, implemented and tested in various agricultural vehicles over the last decade, rests on its capacity to integrate redundancy and incorporate new technologies in a practical way.

Building a robust vehicle detection and classification module

NASA Astrophysics Data System (ADS)

Grigoryev, Anton; Khanipov, Timur; Koptelov, Ivan; Bocharov, Dmitry; Postnikov, Vassily; Nikolaev, Dmitry

2015-12-01

The growing adoption of intelligent transportation systems (ITS) and autonomous driving requires robust real-time solutions for various event and object detection problems. Most of real-world systems still cannot rely on computer vision algorithms and employ a wide range of costly additional hardware like LIDARs. In this paper we explore engineering challenges encountered in building a highly robust visual vehicle detection and classification module that works under broad range of environmental and road conditions. The resulting technology is competitive to traditional non-visual means of traffic monitoring. The main focus of the paper is on software and hardware architecture, algorithm selection and domain-specific heuristics that help the computer vision system avoid implausible answers.

Improving Night Time Driving Safety Using Vision-Based Classification Techniques.

PubMed

Chien, Jong-Chih; Chen, Yong-Sheng; Lee, Jiann-Der

2017-09-24

The risks involved in nighttime driving include drowsy drivers and dangerous vehicles. Prominent among the more dangerous vehicles around at night are the larger vehicles which are usually moving faster at night on a highway. In addition, the risk level of driving around larger vehicles rises significantly when the driver's attention becomes distracted, even for a short period of time. For the purpose of alerting the driver and elevating his or her safety, in this paper we propose two components for any modern vision-based Advanced Drivers Assistance System (ADAS). These two components work separately for the single purpose of alerting the driver in dangerous situations. The purpose of the first component is to ascertain that the driver would be in a sufficiently wakeful state to receive and process warnings; this is the driver drowsiness detection component. The driver drowsiness detection component uses infrared images of the driver to analyze his eyes' movements using a MSR plus a simple heuristic. This component issues alerts to the driver when the driver's eyes show distraction and are closed for a longer than usual duration. Experimental results show that this component can detect closed eyes with an accuracy of 94.26% on average, which is comparable to previous results using more sophisticated methods. The purpose of the second component is to alert the driver when the driver's vehicle is moving around larger vehicles at dusk or night time. The large vehicle detection component accepts images from a regular video driving recorder as input. A bi-level system of classifiers, which included a novel MSR-enhanced KAZE-base Bag-of-Features classifier, is proposed to avoid false negatives. In both components, we propose an improved version of the Multi-Scale Retinex (MSR) algorithm to augment the contrast of the input. Several experiments were performed to test the effects of the MSR and each classifier, and the results are presented in experimental results section of this paper.

Improving Night Time Driving Safety Using Vision-Based Classification Techniques

PubMed Central

Chien, Jong-Chih; Chen, Yong-Sheng; Lee, Jiann-Der

2017-01-01

The risks involved in nighttime driving include drowsy drivers and dangerous vehicles. Prominent among the more dangerous vehicles around at night are the larger vehicles which are usually moving faster at night on a highway. In addition, the risk level of driving around larger vehicles rises significantly when the driver’s attention becomes distracted, even for a short period of time. For the purpose of alerting the driver and elevating his or her safety, in this paper we propose two components for any modern vision-based Advanced Drivers Assistance System (ADAS). These two components work separately for the single purpose of alerting the driver in dangerous situations. The purpose of the first component is to ascertain that the driver would be in a sufficiently wakeful state to receive and process warnings; this is the driver drowsiness detection component. The driver drowsiness detection component uses infrared images of the driver to analyze his eyes’ movements using a MSR plus a simple heuristic. This component issues alerts to the driver when the driver’s eyes show distraction and are closed for a longer than usual duration. Experimental results show that this component can detect closed eyes with an accuracy of 94.26% on average, which is comparable to previous results using more sophisticated methods. The purpose of the second component is to alert the driver when the driver’s vehicle is moving around larger vehicles at dusk or night time. The large vehicle detection component accepts images from a regular video driving recorder as input. A bi-level system of classifiers, which included a novel MSR-enhanced KAZE-base Bag-of-Features classifier, is proposed to avoid false negatives. In both components, we propose an improved version of the Multi-Scale Retinex (MSR) algorithm to augment the contrast of the input. Several experiments were performed to test the effects of the MSR and each classifier, and the results are presented in experimental results section of this paper. PMID:28946643

Aerial surveillance based on hierarchical object classification for ground target detection

NASA Astrophysics Data System (ADS)

Vázquez-Cervantes, Alberto; García-Huerta, Juan-Manuel; Hernández-Díaz, Teresa; Soto-Cajiga, J. A.; Jiménez-Hernández, Hugo

2015-03-01

Unmanned aerial vehicles have turned important in surveillance application due to the flexibility and ability to inspect and displace in different regions of interest. The instrumentation and autonomy of these vehicles have been increased; i.e. the camera sensor is now integrated. Mounted cameras allow flexibility to monitor several regions of interest, displacing and changing the camera view. A well common task performed by this kind of vehicles correspond to object localization and tracking. This work presents a hierarchical novel algorithm to detect and locate objects. The algorithm is based on a detection-by-example approach; this is, the target evidence is provided at the beginning of the vehicle's route. Afterwards, the vehicle inspects the scenario, detecting all similar objects through UTM-GPS coordinate references. Detection process consists on a sampling information process of the target object. Sampling process encode in a hierarchical tree with different sampling's densities. Coding space correspond to a huge binary space dimension. Properties such as independence and associative operators are defined in this space to construct a relation between the target object and a set of selected features. Different densities of sampling are used to discriminate from general to particular features that correspond to the target. The hierarchy is used as a way to adapt the complexity of the algorithm due to optimized battery duty cycle of the aerial device. Finally, this approach is tested in several outdoors scenarios, proving that the hierarchical algorithm works efficiently under several conditions.

Modeling spatial patterns of link-based PM2.5 emissions and subsequent human exposure in a large canadian metropolitan area

NASA Astrophysics Data System (ADS)

Requia, Weeberb J.; Dalumpines, Ron; Adams, Matthew D.; Arain, Altaf; Ferguson, Mark; Koutrakis, Petros

2017-06-01

Understanding the relationship between mobile source emissions and subsequent human exposure is crucial for emissions control. Determining this relationship over space is fundamental to improve the accuracy and precision of public policies. In this study, we evaluated the spatial patterns of link-based PM2.5 emissions and subsequent human exposure in a large Canadian metropolitan area - the Greater Toronto and Hamilton Area (GTHA). This study was performed in three stages. First, we estimated vehicle emissions using transportation models and emission simulators. Then we evaluated human exposure to PM2.5 emissions using the Intake fraction (iF) approach. Finally, we applied geostatistical methods to assess spatial patterns of vehicle emissions and subsequent human exposure based on three prospective goals: i) classification of emissions (Global Moran's I test), ii) level of emission exposure (Getis-Ord General G test), and; iii) location of emissions (Anselin Local Moran's I). Our results showed that passenger vehicles accounted for the highest total amount of PM2.5 emissions, representing 57% emissions from all vehicles. Examining only the emissions from passenger vehicles, on average, each person in the GTHA inhales 2.58 × 10-3 ppm per day. Accounting the emissions from buses and trucks, on average each person inhales 0.12 × 10-3 and 1.91 × 10-3 ppm per day, respectively. For both PM2.5 emissions and human exposure using iF approach, our analysis showed Moran's Index greater than 0 for all vehicle categories, suggesting the presence of significant clusters (p-value <0.01) in the region. Our study indicates that air pollution control policy must be developed for the whole region, because of the spatial distribution of housing and businesses centers and inter-connectivity of transportation networks across the region, where a policy cannot simply be based on a municipal or other boundaries.

Development of Accommodation Models for Soldiers in Vehicles: Squad

DTIC Science & Technology

2014-09-01

existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments...Distribution Statement A. Approved for public release; distribution is unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Data from a previous study...body armor and body borne gear. 15. SUBJECT TERMS Anthropometry , Posture, Vehicle Occupants, Accommodation 16. SECURITY CLASSIFICATION OF

Use of Pattern Classification Algorithms to Interpret Passive and Active Data Streams from a Walking-Speed Robotic Sensor Platform

NASA Astrophysics Data System (ADS)

Dieckman, Eric Allen

In order to perform useful tasks for us, robots must have the ability to notice, recognize, and respond to objects and events in their environment. This requires the acquisition and synthesis of information from a variety of sensors. Here we investigate the performance of a number of sensor modalities in an unstructured outdoor environment, including the Microsoft Kinect, thermal infrared camera, and coffee can radar. Special attention is given to acoustic echolocation measurements of approaching vehicles, where an acoustic parametric array propagates an audible signal to the oncoming target and the Kinect microphone array records the reflected backscattered signal. Although useful information about the target is hidden inside the noisy time domain measurements, the Dynamic Wavelet Fingerprint process (DWFP) is used to create a time-frequency representation of the data. A small-dimensional feature vector is created for each measurement using an intelligent feature selection process for use in statistical pattern classification routines. Using our experimentally measured data from real vehicles at 50 m, this process is able to correctly classify vehicles into one of five classes with 94% accuracy. Fully three-dimensional simulations allow us to study the nonlinear beam propagation and interaction with real-world targets to improve classification results.

Vehicles, Replicators, and Intercellular Movement of Genetic Information: Evolutionary Dissection of a Bacterial Cell

PubMed Central

Jalasvuori, Matti

2012-01-01

Prokaryotic biosphere is vastly diverse in many respects. Any given bacterial cell may harbor in different combinations viruses, plasmids, transposons, and other genetic elements along with their chromosome(s). These agents interact in complex environments in various ways causing multitude of phenotypic effects on their hosting cells. In this discussion I perform a dissection for a bacterial cell in order to simplify the diversity into components that may help approach the ocean of details in evolving microbial worlds. The cell itself is separated from all the genetic replicators that use the cell vehicle for preservation and propagation. I introduce a classification that groups different replicators according to their horizontal movement potential between cells and according to their effects on the fitness of their present host cells. The classification is used to discuss and improve the means by which we approach general evolutionary tendencies in microbial communities. Moreover, the classification is utilized as a tool to help formulating evolutionary hypotheses and to discuss emerging bacterial pathogens as well as to promote understanding on the average phenotypes of different replicators in general. It is also discussed that any given biosphere comprising prokaryotic cell vehicles and genetic replicators may naturally evolve to have horizontally moving replicators of various types. PMID:22567533

Using deep learning in image hyper spectral segmentation, classification, and detection

NASA Astrophysics Data System (ADS)

Zhao, Xiuying; Su, Zhenyu

2018-02-01

Recent years have shown that deep learning neural networks are a valuable tool in the field of computer vision. Deep learning method can be used in applications like remote sensing such as Land cover Classification, Detection of Vehicle in Satellite Images, Hyper spectral Image classification. This paper addresses the use of the deep learning artificial neural network in Satellite image segmentation. Image segmentation plays an important role in image processing. The hue of the remote sensing image often has a large hue difference, which will result in the poor display of the images in the VR environment. Image segmentation is a pre processing technique applied to the original images and splits the image into many parts which have different hue to unify the color. Several computational models based on supervised, unsupervised, parametric, probabilistic region based image segmentation techniques have been proposed. Recently, one of the machine learning technique known as, deep learning with convolution neural network has been widely used for development of efficient and automatic image segmentation models. In this paper, we focus on study of deep neural convolution network and its variants for automatic image segmentation rather than traditional image segmentation strategies.

Unmanned Ground Vehicle Perception Using Thermal Infrared Cameras

NASA Technical Reports Server (NTRS)

Rankin, Arturo; Huertas, Andres; Matthies, Larry; Bajracharya, Max; Assad, Christopher; Brennan, Shane; Bellut, Paolo; Sherwin, Gary

2011-01-01

TIR cameras can be used for day/night Unmanned Ground Vehicle (UGV) autonomous navigation when stealth is required. The quality of uncooled TIR cameras has significantly improved over the last decade, making them a viable option at low speed Limiting factors for stereo ranging with uncooled LWIR cameras are image blur and low texture scenes TIR perception capabilities JPL has explored includes: (1) single and dual band TIR terrain classification (2) obstacle detection (pedestrian, vehicle, tree trunks, ditches, and water) (3) perception thru obscurants

Effects of the combination of P3-based GKT and reality monitoring on deceptive classification

PubMed Central

Jang, Ki-Won; Kim, Deok-Yong; Cho, Sungkun; Lee, Jang-Han

2013-01-01

The study aimed to investigate whether a combination of the P3-based Guilty Knowledge Test (GKT) and reality monitoring (RM) distinguished between individuals who are guilty, witnesses, or informed, and using both tests provided more accurate information than did the use of either measure alone. Participants consisted of 45 males that were randomly and evenly assigned to three groups (i.e., guilty, witness, and informed). The guilty group conducted a mock crime where they intentionally crashed their vehicle into another vehicle in a virtual environment (VE). As those in the witness group drove their own vehicles, they observed the guilty groups' vehicle crash into another vehicle. The informed group read an account and saw screenshots of the accident. All participants were instructed to insist that they were innocent. Subsequently, they performed the P3-based GKT and wrote an account of the accident for the RM analysis. A higher P3 amplitude corresponded to how well the participants recognized the presented stimulus, and a higher RM score corresponded to how well the participants reported vivid sensory information and how much less they reported uncertain information. Findings for the P3-based GKT indicated that the informed group showed lower P3 amplitude when presented with the probe stimulus than did the guilty and witness groups. Regarding the RM analysis, the informed group obtained higher RM scores on visual, temporal, and spatial details and lower scores on cognitive operations than the guilty and witness groups. Finally, discriminant analysis revealed that the combination of the P3-based GKT and RM more accurately distinguished between the three groups than the use of either measure alone. The findings suggest that RM may build upon a weakness of the P3-based GKT's. More specifically, it may build upon its susceptibility to the leakage of information about the crime, therefore helping protect innocent individuals who have information about a crime from being perceived as guilty. PMID:23386821

Track-to-track association for object matching in an inter-vehicle communication system

NASA Astrophysics Data System (ADS)

Yuan, Ting; Roth, Tobias; Chen, Qi; Breu, Jakob; Bogdanovic, Miro; Weiss, Christian A.

2015-09-01

Autonomous driving poses unique challenges for vehicle environment perception due to the complex driving environment the autonomous vehicle finds itself in and differentiates from remote vehicles. Due to inherent uncertainty of the traffic environments and incomplete knowledge due to sensor limitation, an autonomous driving system using only local onboard sensor information is generally not sufficiently enough for conducting a reliable intelligent driving with guaranteed safety. In order to overcome limitations of the local (host) vehicle sensing system and to increase the likelihood of correct detections and classifications, collaborative information from cooperative remote vehicles could substantially facilitate effectiveness of vehicle decision making process. Dedicated Short Range Communication (DSRC) system provides a powerful inter-vehicle wireless communication channel to enhance host vehicle environment perceiving capability with the aid of transmitted information from remote vehicles. However, there is a major challenge before one can fuse the DSRC-transmitted remote information and host vehicle Radar-observed information (in the present case): the remote DRSC data must be correctly associated with the corresponding onboard Radar data; namely, an object matching problem. Direct raw data association (i.e., measurement-to-measurement association - M2MA) is straightforward but error-prone, due to inherent uncertain nature of the observation data. The uncertainties could lead to serious difficulty in matching decision, especially, using non-stationary data. In this study, we present an object matching algorithm based on track-to-track association (T2TA) and evaluate the proposed approach with prototype vehicles in real traffic scenarios. To fully exploit potential of the DSRC system, only GPS position data from remote vehicle are used in fusion center (at host vehicle), i.e., we try to get what we need from the least amount of information; additional feature information can help the data association but are not currently considered. Comparing to M2MA, benefits of the T2TA object matching approach are: i) tracks taking into account important statistical information can provide more reliable inference results; ii) the track-formed smoothed trajectories can be used for an easier shape matching; iii) each local vehicle can design its own tracker and sends only tracks to fusion center to alleviate communication constraints. A real traffic study with different driving environments, based on a statistical hypothesis test, shows promising object matching results of significant practical implications.

Will Aerosol Hygroscopicity Change with Biodiesel, Renewable Diesel Fuels and Emission Control Technologies?

PubMed

Vu, Diep; Short, Daniel; Karavalakis, Georgios; Durbin, Thomas D; Asa-Awuku, Akua

2017-02-07

The use of biodiesel and renewable diesel fuels in compression ignition engines and aftertreatment technologies may affect vehicle exhaust emissions. In this study two 2012 light-duty vehicles equipped with direct injection diesel engines, diesel oxidation catalyst (DOC), diesel particulate filter (DPF), and selective catalytic reduction (SCR) were tested on a chassis dynamometer. One vehicle was tested over the Federal Test Procedure (FTP) cycle on seven biodiesel and renewable diesel fuel blends. Both vehicles were exercised over double Environmental Protection Agency (EPA) Highway fuel economy test (HWFET) cycles on ultralow sulfur diesel (ULSD) and a soy-based biodiesel blend to investigate the aerosol hygroscopicity during the regeneration of the DPF. Overall, the apparent hygroscopicity of emissions during nonregeneration events is consistently low (κ < 0.1) for all fuels over the FTP cycle. Aerosol emitted during filter regeneration is significantly more CCN active and hygroscopic; average κ values range from 0.242 to 0.439 and are as high as 0.843. Regardless of fuel, the current classification of "fresh" tailpipe emissions as nonhygroscopic remains true during nonregeneration operation. However, aftertreatment technologies such as DPF, will produce significantly more hygroscopic particles during regeneration. To our knowledge, this is the first study to show a significant enhancement of hygroscopic materials emitted during DPF regeneration of on-road diesel vehicles. As such, the contribution of regeneration emissions from a growing fleet of diesel vehicles will be important.

Classification deficits in Alzheimer's disease with special reference to living and nonliving things.

PubMed

Montanes, P; Goldblum, M C; Boller, F

1996-08-01

The present study was conducted to assess the hypothesis that visual similarity between exemplars within a semantic category may affect differentially the recognition process of living and nonliving things, according to task demands, in patients with semantic memory disorders. Thirty-nine Alzheimer's patients and 39 normal elderly subjects were presented with a task in which they had to classify pictures and words, depicting either living or nonliving things, at two levels of classification: subordinate (e.g., mammals versus birds or tools versus vehicles) and attribute (e.g., wild versus domestic animals or fast versus slow vehicles). Contrary to previous results (Montañes, Goldblum, & Boller, 1995) in a naming task, but as expected, living things were better classified than nonliving ones by both controls and patients. As expected, classifications at the subordinate level also gave rise to better performance than classifications at the attribute level. Although (and somewhat unexpectedly) no advantage of picture over word classification emerged, some effects consistent with the hypothesis that visual similarity affects picture classification emerged, in particular within a subgroup of patients with predominant verbal deficits and the most severe semantic memory disorders. This subgroup obtained a better score on classification of pictures than of words depicting living items (that share many visual features) when classification is at the subordinate level (for which visual similarity is a reliable clue to classification), but met with major difficulties when classifying those pictures at the attribute level (for which shared visual features are not reliable clues to classification). These results emphasize the fact that some "normal" effects specific to items in living and nonliving categories have to be considered among the factors causing selective category-specific deficits in patients, as well as their relevance in achieving tasks which require either differentiation between competing exemplars in the same semantic category (naming) or detection of resemblance between those exemplars (categorization).

Informal settlement classification using point-cloud and image-based features from UAV data

NASA Astrophysics Data System (ADS)

Gevaert, C. M.; Persello, C.; Sliuzas, R.; Vosselman, G.

2017-03-01

Unmanned Aerial Vehicles (UAVs) are capable of providing very high resolution and up-to-date information to support informal settlement upgrading projects. In order to provide accurate basemaps, urban scene understanding through the identification and classification of buildings and terrain is imperative. However, common characteristics of informal settlements such as small, irregular buildings with heterogeneous roof material and large presence of clutter challenge state-of-the-art algorithms. Furthermore, it is of interest to analyse which fundamental attributes are suitable for describing these objects in different geographic locations. This work investigates how 2D radiometric and textural features, 2.5D topographic features, and 3D geometric features obtained from UAV imagery can be integrated to obtain a high classification accuracy in challenging classification problems for the analysis of informal settlements. UAV datasets from informal settlements in two different countries are compared in order to identify salient features for specific objects in heterogeneous urban environments. Findings show that the integration of 2D and 3D features leads to an overall accuracy of 91.6% and 95.2% respectively for informal settlements in Kigali, Rwanda and Maldonado, Uruguay.

Data Applicability of Heritage and New Hardware for Launch Vehicle System Reliability Models

NASA Technical Reports Server (NTRS)

Al Hassan Mohammad; Novack, Steven

2015-01-01

Many launch vehicle systems are designed and developed using heritage and new hardware. In most cases, the heritage hardware undergoes modifications to fit new functional system requirements, impacting the failure rates and, ultimately, the reliability data. New hardware, which lacks historical data, is often compared to like systems when estimating failure rates. Some qualification of applicability for the data source to the current system should be made. Accurately characterizing the reliability data applicability and quality under these circumstances is crucial to developing model estimations that support confident decisions on design changes and trade studies. This presentation will demonstrate a data-source classification method that ranks reliability data according to applicability and quality criteria to a new launch vehicle. This method accounts for similarities/dissimilarities in source and applicability, as well as operating environments like vibrations, acoustic regime, and shock. This classification approach will be followed by uncertainty-importance routines to assess the need for additional data to reduce uncertainty.

Man-rating of a launch vehicle

NASA Astrophysics Data System (ADS)

Soeffker, D.

Analysis techniques for hazard identification, classification, and control, developed for Spacelab, are presented. Hazards were classified as catastrophic (leading to crew or vehicle loss) critical (could lead to serious injury or damage) and controlled (counteracted by design). All nonmetallic materials were rated for flammability in oxygen enriched atmospheres, toxic offgassing, and odor. Any element with less than 200 mission capability was rated life limited.

High-Mileage Light-Duty Fleet Vehicle Emissions: Their Potentially Overlooked Importance.

PubMed

Bishop, Gary A; Stedman, Donald H; Burgard, Daniel A; Atkinson, Oscar

2016-05-17

State and local agencies in the United States use activity-based computer models to estimate mobile source emissions for inventories. These models generally assume that vehicle activity levels are uniform across all of the vehicle emission level classifications using the same age-adjusted travel fractions. Recent fuel-specific emission measurements from the SeaTac Airport, Los Angeles, and multi-year measurements in the Chicago area suggest that some high-mileage fleets are responsible for a disproportionate share of the fleet's emissions. Hybrid taxis at the airport show large increases in carbon monoxide, hydrocarbon, and oxide of nitrogen emissions in their fourth year when compared to similar vehicles from the general population. Ammonia emissions from the airport shuttle vans indicate that catalyst reduction capability begins to wane after 5-6 years, 3 times faster than is observed in the general population, indicating accelerated aging. In Chicago, the observed, on-road taxi fleet also had significantly higher emissions and an emissions share that was more than double their fleet representation. When compounded by their expected higher than average mileage accumulation, we estimate that these small fleets (<1% of total) may be overlooked as a significant emission source (>2-5% of fleet emissions).

Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle.

PubMed

Diaz-Varela, R A; Zarco-Tejada, P J; Angileri, V; Loudjani, P

2014-02-15

Agricultural terraces are features that provide a number of ecosystem services. As a result, their maintenance is supported by measures established by the European Common Agricultural Policy (CAP). In the framework of CAP implementation and monitoring, there is a current and future need for the development of robust, repeatable and cost-effective methodologies for the automatic identification and monitoring of these features at farm scale. This is a complex task, particularly when terraces are associated to complex vegetation cover patterns, as happens with permanent crops (e.g. olive trees). In this study we present a novel methodology for automatic and cost-efficient identification of terraces using only imagery from commercial off-the-shelf (COTS) cameras on board unmanned aerial vehicles (UAVs). Using state-of-the-art computer vision techniques, we generated orthoimagery and digital surface models (DSMs) at 11 cm spatial resolution with low user intervention. In a second stage, these data were used to identify terraces using a multi-scale object-oriented classification method. Results show the potential of this method even in highly complex agricultural areas, both regarding DSM reconstruction and image classification. The UAV-derived DSM had a root mean square error (RMSE) lower than 0.5 m when the height of the terraces was assessed against field GPS data. The subsequent automated terrace classification yielded an overall accuracy of 90% based exclusively on spectral and elevation data derived from the UAV imagery. Copyright © 2014 Elsevier Ltd. All rights reserved.

Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles.

PubMed

Zhang, Duona; Ding, Wenrui; Zhang, Baochang; Xie, Chunyu; Li, Hongguang; Liu, Chunhui; Han, Jungong

2018-03-20

Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition and remains challenging for traditional methods due to complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include the following: (1) a convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; (2) a large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and (3) experimental results demonstrate that HDMF is very capable of coping with the AMC problem, and achieves much better performance when compared with the independent network.

Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles

PubMed Central

Ding, Wenrui; Zhang, Baochang; Xie, Chunyu; Li, Hongguang; Liu, Chunhui; Han, Jungong

2018-01-01

Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition and remains challenging for traditional methods due to complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include the following: (1) a convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; (2) a large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and (3) experimental results demonstrate that HDMF is very capable of coping with the AMC problem, and achieves much better performance when compared with the independent network. PMID:29558434

Financial Accounting: Classifications and Standard Terminology for Local and State School Systems. State Educational Records and Reports Series: Handbook II, Revised.

ERIC Educational Resources Information Center

Roberts, Charles T., Comp.; Lichtenberger, Allan R., Comp.

This handbook has been prepared as a vehicle or mechanism for program cost accounting and as a guide to standard school accounting terminology for use in all types of local and intermediate education agencies. In addition to classification descriptions, program accounting definitions, and proration of cost procedures, some units of measure and…

Configuration and Specifications of AN Unmanned Aerial Vehicle for Precision Agriculture

NASA Astrophysics Data System (ADS)

Erena, M.; Montesinos, S.; Portillo, D.; Alvarez, J.; Marin, C.; Fernandez, L.; Henarejos, J. M.; Ruiz, L. A.

2016-06-01

Unmanned Aerial Vehicles (UAVs) with multispectral sensors are increasingly attractive in geosciences for data capture and map updating at high spatial and temporal resolutions. These autonomously-flying systems can be equipped with different sensors, such as a six-band multispectral camera (Tetracam mini-MCA-6), GPS Ublox M8N, and MEMS gyroscopes, and miniaturized sensor systems for navigation, positioning, and mapping purposes. These systems can be used for data collection in precision viticulture. In this study, the efficiency of a light UAV system for data collection, processing, and map updating in small areas is evaluated, generating correlations between classification maps derived from remote sensing and production maps. Based on the comparison of the indices derived from UAVs incorporating infrared sensors with those obtained by satellites (Sentinel 2A and Landsat 8), UAVs show promise for the characterization of vineyard plots with high spatial variability, despite the low vegetative coverage of these crops. Consequently, a procedure for zoning map production based on UAV/UV images could provide important information for farmers.

Concept for Classifying Facade Elements Based on Material, Geometry and Thermal Radiation Using Multimodal Uav Remote Sensing

NASA Astrophysics Data System (ADS)

Ilehag, R.; Schenk, A.; Hinz, S.

2017-08-01

This paper presents a concept for classification of facade elements, based on the material and the geometry of the elements in addition to the thermal radiation of the facade with the usage of a multimodal Unmanned Aerial Vehicle (UAV) system. Once the concept is finalized and functional, the workflow can be used for energy demand estimations for buildings by exploiting existing methods for estimation of heat transfer coefficient and the transmitted heat loss. The multimodal system consists of a thermal, a hyperspectral and an optical sensor, which can be operational with a UAV. While dealing with sensors that operate in different spectra and have different technical specifications, such as the radiometric and the geometric resolution, the challenges that are faced are presented. Addressed are the different approaches of data fusion, such as image registration, generation of 3D models by performing image matching and the means for classification based on either the geometry of the object or the pixel values. As a first step towards realizing the concept, the result from a geometric calibration with a designed multimodal calibration pattern is presented.

Melon yield prediction using small unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Zhao, Tiebiao; Wang, Zhongdao; Yang, Qi; Chen, YangQuan

2017-05-01

Thanks to the development of camera technologies, small unmanned aerial systems (sUAS), it is possible to collect aerial images of field with more flexible visit, higher resolution and much lower cost. Furthermore, the performance of objection detection based on deeply trained convolutional neural networks (CNNs) has been improved significantly. In this study, we applied these technologies in the melon production, where high-resolution aerial images were used to count melons in the field and predict the yield. CNN-based object detection framework-Faster R-CNN is applied in the melon classification. Our results showed that sUAS plus CNNs were able to detect melons accurately in the late harvest season.

Detecting lost persons using the k-mean method applied to aerial photographs taken by unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Stec, Magdalena; Wieczorek, Malgorzata; Slopek, Jacek; Jurecka, Miroslawa

2016-04-01

The objective of this work is to discuss the usefulness of the k-mean method in the process of detecting persons on oblique aerial photographs acquired by unmanned aerial vehicles (UAVs). The detection based on the k-mean procedure belongs to one of the modules of a larger Search and Rescue (SAR) system which is being developed at the University of Wroclaw, Poland (research project no. IP2014 032773 financed by the Ministry of Science and Higher Education of Poland). The module automatically processes individual geotagged visual-light UAV-taken photographs or their orthorectified versions. Firstly, we separate red (R), green (G) and blue (B) channels, express raster data as numeric matrices and acquire coordinates of centres of images using the exchangeable image file format (EXIF). Subsequently, we divide the matrices into matrices of smaller dimensions, the latter being associated with the size of spatial window which is suitable for discriminating between human and terrain. Each triplet of the smaller matrices (R, G and B) serves as input spatial data for the k-mean classification. We found that, in several configurations of the k-mean parameters, it is possible to distinguish a separate class which characterizes a person. We compare the skills of this approach by performing two experiments, based on UAV-taken RGB photographs and their orthorectified versions. This allows us to verify the hypothesis that the two exercises lead to similar classifications. In addition, we discuss the performance of the approach for dissimilar spatial windows, hence various dimensions of the above-mentioned matrices, and we do so in order to find the one which offers the most adequate classification. The numerical experiment is carried out using the data acquired during a dedicated observational UAV campaign carried out in the Izerskie Mountains (SW Poland).

Supervised Learning Applied to Air Traffic Trajectory Classification

NASA Technical Reports Server (NTRS)

Bosson, Christabelle S.; Nikoleris, Tasos

2018-01-01

Given the recent increase of interest in introducing new vehicle types and missions into the National Airspace System, a transition towards a more autonomous air traffic control system is required in order to enable and handle increased density and complexity. This paper presents an exploratory effort of the needed autonomous capabilities by exploring supervised learning techniques in the context of aircraft trajectories. In particular, it focuses on the application of machine learning algorithms and neural network models to a runway recognition trajectory-classification study. It investigates the applicability and effectiveness of various classifiers using datasets containing trajectory records for a month of air traffic. A feature importance and sensitivity analysis are conducted to challenge the chosen time-based datasets and the ten selected features. The study demonstrates that classification accuracy levels of 90% and above can be reached in less than 40 seconds of training for most machine learning classifiers when one track data point, described by the ten selected features at a particular time step, per trajectory is used as input. It also shows that neural network models can achieve similar accuracy levels but at higher training time costs.

Joint passive radar tracking and target classification using radar cross section

NASA Astrophysics Data System (ADS)

Herman, Shawn M.

2004-01-01

We present a recursive Bayesian solution for the problem of joint tracking and classification of airborne targets. In our system, we allow for complications due to multiple targets, false alarms, and missed detections. More importantly, though, we utilize the full benefit of a joint approach by implementing our tracker using an aerodynamically valid flight model that requires aircraft-specific coefficients such as wing area and vehicle mass, which are provided by our classifier. A key feature that bridges the gap between tracking and classification is radar cross section (RCS). By modeling the true deterministic relationship that exists between RCS and target aspect, we are able to gain both valuable class information and an estimate of target orientation. However, the lack of a closed-form relationship between RCS and target aspect prevents us from using the Kalman filter or its variants. Instead, we rely upon a sequential Monte Carlo-based approach known as particle filtering. In addition to allowing us to include RCS as a measurement, the particle filter also simplifies the implementation of our nonlinear non-Gaussian flight model.

Joint passive radar tracking and target classification using radar cross section

NASA Astrophysics Data System (ADS)

Herman, Shawn M.

2003-12-01

We present a recursive Bayesian solution for the problem of joint tracking and classification of airborne targets. In our system, we allow for complications due to multiple targets, false alarms, and missed detections. More importantly, though, we utilize the full benefit of a joint approach by implementing our tracker using an aerodynamically valid flight model that requires aircraft-specific coefficients such as wing area and vehicle mass, which are provided by our classifier. A key feature that bridges the gap between tracking and classification is radar cross section (RCS). By modeling the true deterministic relationship that exists between RCS and target aspect, we are able to gain both valuable class information and an estimate of target orientation. However, the lack of a closed-form relationship between RCS and target aspect prevents us from using the Kalman filter or its variants. Instead, we rely upon a sequential Monte Carlo-based approach known as particle filtering. In addition to allowing us to include RCS as a measurement, the particle filter also simplifies the implementation of our nonlinear non-Gaussian flight model.

Automated time activity classification based on global positioning system (GPS) tracking data

PubMed Central

2011-01-01

Background Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. Methods We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. Results Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data. Conclusions Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns. PMID:22082316

Automated time activity classification based on global positioning system (GPS) tracking data.

PubMed

Wu, Jun; Jiang, Chengsheng; Houston, Douglas; Baker, Dean; Delfino, Ralph

2011-11-14

Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data. Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns.

Crosswalk markings and the risk of pedestrian-motor vehicle collisions in older pedestrians.

PubMed

Koepsell, Thomas; McCloskey, Lon; Wolf, Marsha; Moudon, Anne Vernez; Buchner, David; Kraus, Jess; Patterson, Matthew

2002-11-06

Motor vehicles struck and killed 4739 pedestrians in the United States in the year 2000. Older pedestrians are at especially high risk. To determine whether crosswalk markings at urban intersections influence the risk of injury to older pedestrians. Case-control study in which the units of study were crossing locations. Six cities in Washington and California, with case accrual from February 1995 through January 1999. A total of 282 case sites were street-crossing locations at an intersection where a pedestrian aged 65 years or older had been struck by a motor vehicle while crossing the street; 564 control sites were other nearby crossings that were matched to case sites based on street classification. Trained observers recorded environmental characteristics, vehicular traffic flow and speed, and pedestrian use at each site on the same day of the week and time of day as when the case event had occurred. Risk of pedestrian-motor vehicle collision involving an older pedestrian. After adjusting for pedestrian flow, vehicle flow, crossing length, and signalization, risk of a pedestrian-motor vehicle collision was 2.1-fold greater (95% confidence interval, 1.1-4.0) at sites with a marked crosswalk. Almost all of the excess risk was due to 3.6-fold (95% confidence interval, 1.7-7.9) higher risk associated with marked crosswalks at sites with no traffic signal or stop sign. Crosswalk markings appear associated with increased risk of pedestrian-motor vehicle collision to older pedestrians at sites where no signal or stop sign is present to halt traffic.

Shadow detection and removal in RGB VHR images for land use unsupervised classification

NASA Astrophysics Data System (ADS)

Movia, A.; Beinat, A.; Crosilla, F.

2016-09-01

Nowadays, high resolution aerial images are widely available thanks to the diffusion of advanced technologies such as UAVs (Unmanned Aerial Vehicles) and new satellite missions. Although these developments offer new opportunities for accurate land use analysis and change detection, cloud and terrain shadows actually limit benefits and possibilities of modern sensors. Focusing on the problem of shadow detection and removal in VHR color images, the paper proposes new solutions and analyses how they can enhance common unsupervised classification procedures for identifying land use classes related to the CO2 absorption. To this aim, an improved fully automatic procedure has been developed for detecting image shadows using exclusively RGB color information, and avoiding user interaction. Results show a significant accuracy enhancement with respect to similar methods using RGB based indexes. Furthermore, novel solutions derived from Procrustes analysis have been applied to remove shadows and restore brightness in the images. In particular, two methods implementing the so called "anisotropic Procrustes" and the "not-centered oblique Procrustes" algorithms have been developed and compared with the linear correlation correction method based on the Cholesky decomposition. To assess how shadow removal can enhance unsupervised classifications, results obtained with classical methods such as k-means, maximum likelihood, and self-organizing maps, have been compared to each other and with a supervised clustering procedure.

26 CFR 48.4071-2 - Determination of weight.

Code of Federal Regulations, 2010 CFR

2010-04-01

... EXCISE TAXES MANUFACTURERS AND RETAILERS EXCISE TAXES Motor Vehicles, Tires, Tubes, Tread Rubber, and... each type, size, grade, and classification. The average weights must be established in accordance with...

Complexity, Robustness, and Multistability in Network Systems with Switching Topologies: A Hierarchical Hybrid Control Approach

DTIC Science & Technology

2015-05-22

sensor networks for managing power levels of wireless networks ; air and ground transportation systems for air traffic control and payload transport and... network systems, large-scale systems, adaptive control, discontinuous systems 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF...cover a broad spectrum of ap- plications including cooperative control of unmanned air vehicles, autonomous underwater vehicles, distributed sensor

Positioning challenges in reconfigurable semi-autonomous robotic NDE inspection

NASA Astrophysics Data System (ADS)

Pierce, S. Gareth; Dobie, Gordon; Summan, Rahul; Mackenzie, Liam; Hensman, James; Worden, Keith; Hayward, Gordon

2010-03-01

This paper describes work conducted into mobile, wireless, semi-autonomous NDE inspection robots developed at The University of Strathclyde as part of the UK Research Centre for Non Destructive Evaluation (RCNDE). The inspection vehicles can incorporate a number of different NDE payloads including ultrasonic, eddy current, visual and magnetic based payloads, and have been developed to try and improve NDE inspection techniques in challenging inspection areas (for example oil, gas, and nuclear structures). A significant research challenge remains in the accurate positioning and guidance of such vehicles for real inspection tasks. Employing both relative and absolute position measurements, we discuss a number of approaches to position estimation including Kalman and particle filtering. Using probabilistic approaches enables a common mathematical framework to be employed for both positioning and data fusion from different NDE sensors. In this fashion the uncertainties in both position and defect identification and classification can be dealt with using a consistent approach. A number of practical constraints and considerations to different precision positioning techniques are discussed, along with NDE applications and the potential for improved inspection capabilities by utilising the inherent reconfigurable capabilities of the inspection vehicles.

Research on application of LADAR in ground vehicle recognition

NASA Astrophysics Data System (ADS)

Lan, Jinhui; Shen, Zhuoxun

2009-11-01

For the requirement of many practical applications in the field of military, the research of 3D target recognition is active. The representation that captures the salient attributes of a 3D target independent of the viewing angle will be especially useful to the automatic 3D target recognition system. This paper presents a new approach of image generation based on Laser Detection and Ranging (LADAR) data. Range image of target is obtained by transformation of point cloud. In order to extract features of different ground vehicle targets and to recognize targets, zernike moment properties of typical ground vehicle targets are researched in this paper. A technique of support vector machine is applied to the classification and recognition of target. The new method of image generation and feature representation has been applied to the outdoor experiments. Through outdoor experiments, it can be proven that the method of image generation is stability, the moments are effective to be used as features for recognition, and the LADAR can be applied to the field of 3D target recognition.

Carrier rockets

NASA Astrophysics Data System (ADS)

Aleksandrov, V. A.; Vladimirov, V. V.; Dmitriev, R. D.; Osipov, S. O.

This book takes into consideration domestic and foreign developments related to launch vehicles. General information concerning launch vehicle systems is presented, taking into account details of rocket structure, basic design considerations, and a number of specific Soviet and American launch vehicles. The basic theory of reaction propulsion is discussed, giving attention to physical foundations, the various types of forces acting on a rocket in flight, basic parameters characterizing rocket motion, the effectiveness of various approaches to obtain the desired velocity, and rocket propellants. Basic questions concerning the classification of launch vehicles are considered along with construction and design considerations, aspects of vehicle control, reliability, construction technology, and details of structural design. Attention is also given to details of rocket motor design, the basic systems of the carrier rocket, and questions of carrier rocket development.

Traffic monitoring guide

DOT National Transportation Integrated Search

1995-02-01

This guide is designed to provide direction on the monitoring of traffic characteristics. It begins with a discussion of the structure of traffic characteristics monitoring and traffic counting. The next two sections cover vehicle classification and ...

Evaluation of ISO CRS Envelopes Relative to U.S. Vehicles and Child Restraint Systems.

PubMed

Hu, Jingwen; Manary, Miriam A; Klinich, Kathleen D; Reed, Matthew P

2015-01-01

The objectives of this study are to use computer simulation to evaluate the International Organization for Standardization (ISO) 13216-3:2006(E) child restraint system (CRS) envelopes relative to rear seat compartments from vehicles and CRSs in the U.S. market, investigate the potential compatibility issues of U.S. vehicles and CRSs, and demonstrate whether necessary modifications can be made to introduce such a system into compatibility evaluations between U.S. vehicles and CRSs. Three-dimensional geometry models for 26 vehicles and 16 convertible CRS designs developed previously were used. Geometry models of 3 forward-facing and 3 rear-facing CRS envelopes provided by the ISO were built in the current study. The virtual fit process closely followed the physical procedures described in the ISO standards. The results showed that the current ISO rear-facing envelopes can provide reasonable classifications for CRSs and vehicles, but the forward-facing envelopes do not represent products currently in the U.S. market. In particular, all of the selected vehicles could accommodate the largest forward-facing CRS envelope at the second-row seat location behind the driver seat. In contrast, half of the selected CRSs could not fit within any of the forward-facing ISO CRS envelopes, mainly due to protrusion at the rear-top corner of the envelope. The results also indicate that the rear seat compartment in U.S. vehicles often cannot accommodate a large portion of convertible CRSs in the rear-facing position. The increased demand for vehicle fuel economy and the recommendation to keep children rear-facing longer may lead to smaller cars and larger CRSs, which may increase the potential for fit problems. The virtual classifications indicated that contact between the forward-facing CRSs and the head restraints in the rear seats as well as that between the rear-facing CRSs and the back of the front seats is a main concern regarding the compatibility between the vehicles and the CRSs. Therefore, modification of the current ISO forward-facing CRS envelopes will likely to be necessary to ensure that they are useful for the U.S. market.

Hardened Reentry Vehicle Development Program. Erosion-Resistant Nosetip Technology

DTIC Science & Technology

1978-01-01

Best Available Copy .- / L A- UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGE (When Data Enttered) _____________________ REPORT DOCUMENTATION PAGE...OF PAGES t Washington, D. C. 20305 3" TK~ 14 MONITORING AGENCY NAME a ADDRESfrif different troin Controllmng~ Office) IS. SECURITY CLA5--M~e7ry...tests indicate( 1 low probability of survival for DD IJAN 73 1473 EDITION OF I NOV 65 IS OBSOLETE UCASFE 41 -n0 SECURITY CLASSIFICATION OF THIS PAGE

32 CFR 634.20 - Privately owned vehicle operation requirements.

Code of Federal Regulations, 2012 CFR

2012-07-01

... nation or international drivers license (within appropriate classification), supported by DD Form 2, or... Safety Administration (NHTSA) in 49 CFR 570.1 through 570.10. Lights, turn signals, brake lights, horn...

32 CFR 634.20 - Privately owned vehicle operation requirements.

Code of Federal Regulations, 2014 CFR

2014-07-01

... nation or international drivers license (within appropriate classification), supported by DD Form 2, or... Safety Administration (NHTSA) in 49 CFR 570.1 through 570.10. Lights, turn signals, brake lights, horn...

32 CFR 634.20 - Privately owned vehicle operation requirements.

Code of Federal Regulations, 2013 CFR

2013-07-01

... nation or international drivers license (within appropriate classification), supported by DD Form 2, or... Safety Administration (NHTSA) in 49 CFR 570.1 through 570.10. Lights, turn signals, brake lights, horn...

Emission factors of volatile organic compounds (VOCs) based on the detailed vehicle classification in a tunnel study.

PubMed

Zhang, Qijun; Wu, Lin; Fang, Xiaozhen; Liu, Mingyue; Zhang, Jing; Shao, Min; Lu, Sihua; Mao, Hongjun

2018-05-15

In order to obtain VOCs emission characteristics and emission factors from vehicle, a tunnel experiment was conducted in the Fu Gui Mountain Tunnel in Nanjing, China. The tunnel is located in the middle of city, with total length of 480m and speed limit of 50km/h. The studied vehicle fleet was composed of 87% light duty vehicles and 13% heavy duty vehicles (liquefied natural gas bus, LNGB). The emerging radio frequency identification (RFID) technology was used to divide fine vehicles type including China I, China II, China III, China IV, China V and LNGB. Ambient air samples (4-h averages) were collected inside the tunnel using 3.2L stainless-steel canisters. Samples collected in the canisters were analyzed for 97 individual VOCs using high-resolution GC-MS in the laboratory. The average tunnel emission factor for the collective light-duty vehicles was 160.79±65.94mg/(km∗veh), and for the China I, China II, China III, China IV and China V vehicles, it was 632.07±259.44, 450.35±184.85, 205.42±84.32, 118.51±48.65, and 110.61±45.4mg/(km∗veh), respectively. The average emission factor for heavy-duty vehicles was 358.02±124.86mg/(km∗veh). Ethane, isopentane, propane, ethylene, toluene, propylene and 2,3-dimethylbutane were the most common VOC species in vehicle emissions. The total O 3 formation potential was 373.88mg∗O 3 /(km∗veh) in the tunnel. Ethylene, propylene, m/p-xylene, toluene, and isopentane were the largest contributors to O 3 production. Compared with previous studies, fuel quality increased from China II-FQ to China IV-FQ levels, while the BTEX emission levels exhibited a decreasing trend. Copyright © 2017 Elsevier B.V. All rights reserved.

Refining Time-Activity Classification of Human Subjects Using the Global Positioning System.

PubMed

Hu, Maogui; Li, Wei; Li, Lianfa; Houston, Douglas; Wu, Jun

2016-01-01

Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns. Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods. Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions. The random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations.

Understanding traffic variations by vehicle classifications

DOT National Transportation Integrated Search

1998-08-01

To provide a better understanding of how short-duration truck volume counts can be used to accurately estimate the key variables needed for design, planning, and operational analyses, the Long-Term Pavement Performance (LTPP) program recently complet...

Policy implications of emerging vehicle and infrastructure technology.

DOT National Transportation Integrated Search

2014-08-01

This report considers a broad range of emerging transportation technologies that have potential : for enhancing travel on and operations of the Texas transportation system. It provides an : overview of technology classifications and assesses the poli...

50 CFR 218.170 - Specified activity and specified geographical area and effective dates.

Code of Federal Regulations, 2011 CFR

2011-10-01

... site QUTR site Test Vehicle Propulsion Thermal propulsion systemsElectric/Chemical propulsion systems..., classification and localization 05 4520 1510 Non-Navy testing 5 5 5 Acoustic & non-acoustic sensors (magnetic...

49 CFR 523.9 - Truck tractors.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.9 Truck tractors. Truck tractors for the purpose of this part are considered as any truck tractor as defined in 49 CFR part 571 having a GVWR above 26,000...

Applications and use of transportation data

DOT National Transportation Integrated Search

1979-01-01

This research paper is a compilation of seven documents which focus on the application and use of transportation data. The following papers are included: "Field data collection and sampling procedures for measuring regional vehicle classification and...

Analyzing pedestrian crash injury severity under different weather conditions.

PubMed

Li, Duo; Ranjitkar, Prakash; Zhao, Yifei; Yi, Hui; Rashidi, Soroush

2017-05-19

Pedestrians are the most vulnerable road users due to the lack of mass, speed, and protection compared to other types of road users. Adverse weather conditions may reduce road friction and visibility and thus increase crash risk. There is limited evidence and considerable discrepancy with regard to impacts of weather conditions on injury severity in the literature. This article investigated factors affecting pedestrian injury severity level under different weather conditions based on a publicly available accident database in Great Britain. Accident data from Great Britain that are publicly available through the STATS19 database were analyzed. Factors associated with pedestrian, driver, and environment were investigated using a novel approach that combines a classification and regression tree with random forest approach. Significant severity predictors under fine weather conditions from the models included speed limits, pedestrian age, light conditions, and vehicle maneuver. Under adverse weather conditions, the significant predictors were pedestrian age, vehicle maneuver, and speed limit. Elderly pedestrians are associated with higher pedestrian injury severities. Higher speed limits increase pedestrian injury severity. Based on the research findings, recommendations are provided to improve pedestrian safety.

Statistical sensor fusion of ECG data using automotive-grade sensors

NASA Astrophysics Data System (ADS)

Koenig, A.; Rehg, T.; Rasshofer, R.

2015-11-01

Driver states such as fatigue, stress, aggression, distraction or even medical emergencies continue to be yield to severe mistakes in driving and promote accidents. A pathway towards improving driver state assessment can be found in psycho-physiological measures to directly quantify the driver's state from physiological recordings. Although heart rate is a well-established physiological variable that reflects cognitive stress, obtaining heart rate contactless and reliably is a challenging task in an automotive environment. Our aim was to investigate, how sensory fusion of two automotive grade sensors would influence the accuracy of automatic classification of cognitive stress levels. We induced cognitive stress in subjects and estimated levels from their heart rate signals, acquired from automotive ready ECG sensors. Using signal quality indices and Kalman filters, we were able to decrease Root Mean Squared Error (RMSE) of heart rate recordings by 10 beats per minute. We then trained a neural network to classify the cognitive workload state of subjects from heart rate and compared classification performance for ground truth, the individual sensors and the fused heart rate signal. We obtained an increase of 5 % higher correct classification by fusing signals as compared to individual sensors, staying only 4 % below the maximally possible classification accuracy from ground truth. These results are a first step towards real world applications of psycho-physiological measurements in vehicle settings. Future implementations of driver state modeling will be able to draw from a larger pool of data sources, such as additional physiological values or vehicle related data, which can be expected to drive classification to significantly higher values.

Evaluation of diesel fleet emissions and control policies from plume chasing measurements of on-road vehicles

NASA Astrophysics Data System (ADS)

Lau, Chui Fong; Rakowska, Agata; Townsend, Thomas; Brimblecombe, Peter; Chan, Tat Leung; Yam, Yat Shing; Močnik, Griša; Ning, Zhi

2015-12-01

Vehicle emissions are an important source of urban air pollution. Diesel fuelled vehicles, although constituting a relatively small fraction of fleet population in many cities, are significant contributors to the emission inventory due to their often long mileage for goods and public transport. Recent classification of diesel exhaust as carcinogenic by the World Health Organization also raises attention to more stringent control of diesel emissions to protect public health. Although various mandatory and voluntary based emission control measures have been implemented in Hong Kong, there have been few investigations to evaluate if the fleet emission characteristics have met desired emission reduction objectives and if adoption of an Inspection/Maintenance (I/M) programme has been effective in achieving these objectives. The limitations are partially due to the lack of cost-effective approaches for the large scale characterisation of fleet based emissions to assess the effectiveness of control measures and policy. This study has used a plume chasing method to collect a large amount of on-road vehicle emission data of Hong Kong highways and a detailed analysis was carried out to provide a quantitative evaluation of the emission characteristics in terms of the role of high and super-emitters in total emission reduction, impact of after-treatment on the multi-pollutants reduction strategy and the trend of NO2 emissions with newer emission standards. The study revealed that not all the high-emitters are from those vehicles of older Euro emission standards. Meanwhile, there is clear evidence that high-emitters for one pollutant may not be a high-emitter for another pollutant. Multi-pollutant control strategy needs to be considered in the enactment of the emission control policy which requires more comprehensive retrofitting technological solutions and matching I/M programme to ensure the proper maintenance of fleets. The plume chasing approach used in this study also shows to be a useful approach for assessing city wide vehicle emission characteristics.

The Design and Construction of a Shiplaunched VTOL Unmanned Air Vehicle

DTIC Science & Technology

1990-06-01

Heppenheimer , T. A ., "The Light Stuff: Burt Rutan Transforms Aircraft Design," High Technolonv. pp. 29-35, December 1986. 16. Alexander, J., Foam...AD-A238 053III 1111 II IIII II OII~ NAVAL POSTGRADUATE SCHOOL Monterey, California OTIC J UL 1 1 1991 THESIS THE DESIGN AND CONSTRUCTION OF A ...8217 € (Include Security Classification) THE DESIGN AND CONSTRUCTION OF A SHIPLAUNCHED VTOL UNMANNED AIR VEHICLE 12. PERSONAL AUTHOR(S) Blanchette, Bryan M

Detection of flood effects in montane streams based on fusion of 2D and 3D information from UAV imagery

NASA Astrophysics Data System (ADS)

Langhammer, Jakub; Vacková, Tereza

2017-04-01

In the contribution, we are presenting a novel method, enabling objective detection and classification of the alluvial features resulting from flooding, based on the imagery, acquired by the unmanned aerial vehicles (UAVs, drones). We have proposed and tested a workflow, using two key data products of the UAV photogrammetry - the 2D orthoimage and 3D digital elevation model, together with derived information on surface texture for the consequent classification of erosional and depositional features resulting from the flood. The workflow combines the photogrammetric analysis of the UAV imagery, texture analysis of the DEM, and the supervised image classification. Application of the texture analysis and use of DEM data is aimed to enhance 2D information, resulting from the high-resolution orthoimage by adding the newly derived bands, which enhance potential for detection and classification of key types of fluvial features in the stream and the floodplain. The method was tested on the example of a snowmelt-driven flood in a montane stream in Sumava Mts., Czech Republic, Central Europe, that occurred in December 2015. Using the UAV platform DJI Inspire 1 equipped with the RGB camera there was acquired imagery covering a 1 km long stretch of a meandering creek with elevated fluvial dynamics. Agisoft Photoscan Pro was used to derive a point cloud and further the high-resolution seamless orthoimage and DEM, Orfeo toolkit and SAGA GIS tools were used for DEM analysis. From the UAV-based data inputs, a multi-band dataset was derived as a source for the consequent classification of fluvial landforms. The RGB channels of the derived orthoimage were completed by the selected texture feature layers and the information on 3D properties of the riverscape - the normalized DEM and terrain ruggedness. Haralick features, derived from the RGB channels, are used for extracting information on the surface texture, the terrain ruggedness index is used as a measure of local topographical variability. Based on this dataset, the supervised classification was performed to identify the fluvial features, including the fresh and old accumulations of different size, fresh bank erosion, in-stream features and the riparian zone vegetation, verified later by the field survey. The classification based on the fusion of high-resolution 2D and 3D data, derived from UAV imagery, enabled to identify and quantify the extent of recent and old accumulations, to distinguish the coarse and fine sediments or to separate the shallow and deep zones in the submerged zone of the channel. With the high operability of the data acquisition process, the proposed method appears to be a promising tool for rapid mapping and classification of flood effects in streams and floodplains.

An improvement of vehicle detection under shadow regions in satellite imagery

NASA Astrophysics Data System (ADS)

Karim, Shahid; Zhang, Ye; Ali, Saad; Asif, Muhammad Rizwan

2018-04-01

The processing of satellite imagery is dependent upon the quality of imagery. Due to low resolution, it is difficult to extract accurate information according to the requirements of applications. For the purpose of vehicle detection under shadow regions, we have used HOG for feature extraction, SVM is used for classification and HOG is discerned worthwhile tool for complex environments. Shadow images have been scrutinized and found very complex for detection as observed very low detection rates therefore our dedication is towards enhancement of detection rate under shadow regions by implementing appropriate preprocessing. Vehicles are precisely detected under non-shadow regions with high detection rate than shadow regions.

Temporal variation of traffic on highways and the development of accurate temporal allocation factors for air pollution analyses

NASA Astrophysics Data System (ADS)

Batterman, Stuart; Cook, Richard; Justin, Thomas

2015-04-01

Traffic activity encompasses the number, mix, speed and acceleration of vehicles on roadways. The temporal pattern and variation of traffic activity reflects vehicle use, congestion and safety issues, and it represents a major influence on emissions and concentrations of traffic-related air pollutants. Accurate characterization of vehicle flows is critical in analyzing and modeling urban and local-scale pollutants, especially in near-road environments and traffic corridors. This study describes methods to improve the characterization of temporal variation of traffic activity. Annual, monthly, daily and hourly temporal allocation factors (TAFs), which describe the expected temporal variation in traffic activity, were developed using four years of hourly traffic activity data recorded at 14 continuous counting stations across the Detroit, Michigan, U.S. region. Five sites also provided vehicle classification. TAF-based models provide a simple means to apportion annual average estimates of traffic volume to hourly estimates. The analysis shows the need to separate TAFs for total and commercial vehicles, and weekdays, Saturdays, Sundays and observed holidays. Using either site-specific or urban-wide TAFs, nearly all of the variation in historical traffic activity at the street scale could be explained; unexplained variation was attributed to adverse weather, traffic accidents and construction. The methods and results presented in this paper can improve air quality dispersion modeling of mobile sources, and can be used to evaluate and model temporal variation in ambient air quality monitoring data and exposure estimates.

Temporal variation of traffic on highways and the development of accurate temporal allocation factors for air pollution analyses

PubMed Central

Batterman, Stuart; Cook, Richard; Justin, Thomas

2015-01-01

Traffic activity encompasses the number, mix, speed and acceleration of vehicles on roadways. The temporal pattern and variation of traffic activity reflects vehicle use, congestion and safety issues, and it represents a major influence on emissions and concentrations of traffic-related air pollutants. Accurate characterization of vehicle flows is critical in analyzing and modeling urban and local-scale pollutants, especially in near-road environments and traffic corridors. This study describes methods to improve the characterization of temporal variation of traffic activity. Annual, monthly, daily and hourly temporal allocation factors (TAFs), which describe the expected temporal variation in traffic activity, were developed using four years of hourly traffic activity data recorded at 14 continuous counting stations across the Detroit, Michigan, U.S. region. Five sites also provided vehicle classification. TAF-based models provide a simple means to apportion annual average estimates of traffic volume to hourly estimates. The analysis shows the need to separate TAFs for total and commercial vehicles, and weekdays, Saturdays, Sundays and observed holidays. Using either site-specific or urban-wide TAFs, nearly all of the variation in historical traffic activity at the street scale could be explained; unexplained variation was attributed to adverse weather, traffic accidents and construction. The methods and results presented in this paper can improve air quality dispersion modeling of mobile sources, and can be used to evaluate and model temporal variation in ambient air quality monitoring data and exposure estimates. PMID:25844042

Transportation Modes Classification Using Sensors on Smartphones.

PubMed

Fang, Shih-Hau; Liao, Hao-Hsiang; Fei, Yu-Xiang; Chen, Kai-Hsiang; Huang, Jen-Wei; Lu, Yu-Ding; Tsao, Yu

2016-08-19

This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user's transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size. Results show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes.

Transportation Modes Classification Using Sensors on Smartphones

PubMed Central

Fang, Shih-Hau; Liao, Hao-Hsiang; Fei, Yu-Xiang; Chen, Kai-Hsiang; Huang, Jen-Wei; Lu, Yu-Ding; Tsao, Yu

2016-01-01

This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user’s transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size. Results show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes. PMID:27548182

Model-based approach to the detection and classification of mines in sidescan sonar.

PubMed

Reed, Scott; Petillot, Yvan; Bell, Judith

2004-01-10

This paper presents a model-based approach to mine detection and classification by use of sidescan sonar. Advances in autonomous underwater vehicle technology have increased the interest in automatic target recognition systems in an effort to automate a process that is currently carried out by a human operator. Current automated systems generally require training and thus produce poor results when the test data set is different from the training set. This has led to research into unsupervised systems, which are able to cope with the large variability in conditions and terrains seen in sidescan imagery. The system presented in this paper first detects possible minelike objects using a Markov random field model, which operates well on noisy images, such as sidescan, and allows a priori information to be included through the use of priors. The highlight and shadow regions of the object are then extracted with a cooperating statistical snake, which assumes these regions are statistically separate from the background. Finally, a classification decision is made using Dempster-Shafer theory, where the extracted features are compared with synthetic realizations generated with a sidescan sonar simulator model. Results for the entire process are shown on real sidescan sonar data. Similarities between the sidescan sonar and synthetic aperture radar (SAR) imaging processes ensure that the approach outlined here could be made applied to SAR image analysis.

California state of the pavement report, 1999

DOT National Transportation Integrated Search

2001-03-01

This report presents the results of the 1999 Pavement Condition Survey for the State of California. It gives an overview of pavement conditions, vehicle miles traveled on rough pavements, needs classification, and pavement performance. For each of th...

Using Smart Phone Sensors to Detect Transportation Modes

PubMed Central

Xia, Hao; Qiao, Yanyou; Jian, Jun; Chang, Yuanfei

2014-01-01

The proliferation of mobile smart devices has led to a rapid increase of location-based services, many of which are amassing large datasets of user trajectory information. Unfortunately, current trajectory information is not yet sufficiently rich to support classification of user transportation modes. In this paper, we propose a method that employs both the Global Positioning System and accelerometer data from smart devices to classify user outdoor transportation modes. The classified modes include walking, bicycling, and motorized transport, in addition to the motionless (stationary) state, for which we provide new depth analysis. In our classification, stationary mode has two sub-modes: stay (remaining in the same place for a prolonged time period; e.g., in a parked vehicle) and wait (remaining at a location for a short period; e.g., waiting at a red traffic light). These two sub-modes present different semantics for data mining applications. We use support vector machines with parameters that are optimized for pattern recognition. In addition, we employ ant colony optimization to reduce the dimension of features and analyze their relative importance. The resulting classification system achieves an accuracy rate of 96.31% when applied to a dataset obtained from 18 mobile users. PMID:25375756

Using smart phone sensors to detect transportation modes.

PubMed

Xia, Hao; Qiao, Yanyou; Jian, Jun; Chang, Yuanfei

2014-11-04

The proliferation of mobile smart devices has led to a rapid increase of location-based services, many of which are amassing large datasets of user trajectory information. Unfortunately, current trajectory information is not yet sufficiently rich to support classification of user transportation modes. In this paper, we propose a method that employs both the Global Positioning System and accelerometer data from smart devices to classify user outdoor transportation modes. The classified modes include walking, bicycling, and motorized transport, in addition to the motionless (stationary) state, for which we provide new depth analysis. In our classification, stationary mode has two sub-modes: stay (remaining in the same place for a prolonged time period; e.g., in a parked vehicle) and wait (remaining at a location for a short period; e.g., waiting at a red traffic light). These two sub-modes present different semantics for data mining applications. We use support vector machines with parameters that are optimized for pattern recognition. In addition, we employ ant colony optimization to reduce the dimension of features and analyze their relative importance. The resulting classification system achieves an accuracy rate of 96.31% when applied to a dataset obtained from 18 mobile users.

Classification of Urban Feature from Unmanned Aerial Vehicle Images Using Gasvm Integration and Multi-Scale Segmentation

NASA Astrophysics Data System (ADS)

Modiri, M.; Salehabadi, A.; Mohebbi, M.; Hashemi, A. M.; Masumi, M.

2015-12-01

The use of UAV in the application of photogrammetry to obtain cover images and achieve the main objectives of the photogrammetric mapping has been a boom in the region. The images taken from REGGIOLO region in the province of, Italy Reggio -Emilia by UAV with non-metric camera Canon Ixus and with an average height of 139.42 meters were used to classify urban feature. Using the software provided SURE and cover images of the study area, to produce dense point cloud, DSM and Artvqvtv spatial resolution of 10 cm was prepared. DTM area using Adaptive TIN filtering algorithm was developed. NDSM area was prepared with using the difference between DSM and DTM and a separate features in the image stack. In order to extract features, using simultaneous occurrence matrix features mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation for each of the RGB band image was used Orthophoto area. Classes used to classify urban problems, including buildings, trees and tall vegetation, grass and vegetation short, paved road and is impervious surfaces. Class consists of impervious surfaces such as pavement conditions, the cement, the car, the roof is stored. In order to pixel-based classification and selection of optimal features of classification was GASVM pixel basis. In order to achieve the classification results with higher accuracy and spectral composition informations, texture, and shape conceptual image featureOrthophoto area was fencing. The segmentation of multi-scale segmentation method was used.it belonged class. Search results using the proposed classification of urban feature, suggests the suitability of this method of classification complications UAV is a city using images. The overall accuracy and kappa coefficient method proposed in this study, respectively, 47/93% and 84/91% was.

Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones.

PubMed

Lu, Dang-Nhac; Nguyen, Duc-Nhan; Nguyen, Thi-Hau; Nguyen, Ha-Nam

2018-03-29

In this paper, we present a flexible combined system, namely the Vehicle mode-driving Activity Detection System (VADS), that is capable of detecting either the current vehicle mode or the current driving activity of travelers. Our proposed system is designed to be lightweight in computation and very fast in response to the changes of travelers' vehicle modes or driving events. The vehicle mode detection module is responsible for recognizing both motorized vehicles, such as cars, buses, and motorbikes, and non-motorized ones, for instance, walking, and bikes. It relies only on accelerometer data in order to minimize the energy consumption of smartphones. By contrast, the driving activity detection module uses the data collected from the accelerometer, gyroscope, and magnetometer of a smartphone to detect various driving activities, i.e., stopping, going straight, turning left, and turning right. Furthermore, we propose a method to compute the optimized data window size and the optimized overlapping ratio for each vehicle mode and each driving event from the training datasets. The experimental results show that this strategy significantly increases the overall prediction accuracy. Additionally, numerous experiments are carried out to compare the impact of different feature sets (time domain features, frequency domain features, Hjorth features) as well as the impact of various classification algorithms (Random Forest, Naïve Bayes, Decision tree J48, K Nearest Neighbor, Support Vector Machine) contributing to the prediction accuracy. Our system achieves an average accuracy of 98.33% in detecting the vehicle modes and an average accuracy of 98.95% in recognizing the driving events of motorcyclists when using the Random Forest classifier and a feature set containing time domain features, frequency domain features, and Hjorth features. Moreover, on a public dataset of HTC company in New Taipei, Taiwan, our framework obtains the overall accuracy of 97.33% that is considerably higher than that of the state-of the art.

77 FR 47528 - International Trademark Classification Changes

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-09

..., ventilating, water supply and sanitary purposes. 12. Vehicles; apparatus for locomotion by land, air or water.... Treatment of materials. 41. Education; providing of training; entertainment; sporting and cultural... providing food and drink; temporary accommodation. 44. Medical services; veterinary services; hygienic and...

Runflat Testing

DTIC Science & Technology

2015-09-09

guidance and procedures for testing the performance characteristics of runflat tires as equipped on ground vehicles. 15. SUBJECT TERMS runflat... tire assembly tread life combat flat central tire inflation system (CTIS) 16. SECURITY CLASSIFICATION OF: 17. LIMITATION...3 3.4 Tire Assemblies ..................................................................... 3 3.5 Environmental

U.S. speeding-related motor vehicle fatalities by road function classification, 1995-1999

DOT National Transportation Integrated Search

2013-10-01

This document serves as an Operational Concept for the Applications for the Environment: Real-Time Information Synthesis (AERIS) Eco-Signal Operations Transformative Concept. It was developed along with two other Operational Concept documents that de...

Portable bench tester for piezo weigh-in-motion equipment : final report, June 2006.

DOT National Transportation Integrated Search

2006-06-01

The Ohio Department of Transportation's (ODOT) piezo weigh-in-motion (WIM) equipment must be tested for initial working operation and to insure continued correct operation. Currently, the only available method to verify the vehicle classification par...

DIAGNOSTIC TOOL DEVELOPMENT AND APPLICATION THROUGH REGIONAL CASE STUDIES

EPA Science Inventory

Case studies are a useful vehicle for developing and testing conceptual models, classification systems, diagnostic tools and models, and stressor-response relationships. Furthermore, case studies focused on specific places or issues of interest to the Agency provide an excellent ...

Identifying chronic errors at freeway loop detectors- splashover, pulse breakup, and sensitivity settings.

DOT National Transportation Integrated Search

2011-03-01

Traffic Management applications such as ramp metering, incident detection, travel time prediction, and vehicle : classification greatly depend on the accuracy of data collected from inductive loop detectors, but these data are : prone to various erro...

Woodland Mapping at Single-Tree Levels Using Object-Oriented Classification of Unmanned Aerial Vehicle (uav) Images

NASA Astrophysics Data System (ADS)

Chenari, A.; Erfanifard, Y.; Dehghani, M.; Pourghasemi, H. R.

2017-09-01

Remotely sensed datasets offer a reliable means to precisely estimate biophysical characteristics of individual species sparsely distributed in open woodlands. Moreover, object-oriented classification has exhibited significant advantages over different classification methods for delineation of tree crowns and recognition of species in various types of ecosystems. However, it still is unclear if this widely-used classification method can have its advantages on unmanned aerial vehicle (UAV) digital images for mapping vegetation cover at single-tree levels. In this study, UAV orthoimagery was classified using object-oriented classification method for mapping a part of wild pistachio nature reserve in Zagros open woodlands, Fars Province, Iran. This research focused on recognizing two main species of the study area (i.e., wild pistachio and wild almond) and estimating their mean crown area. The orthoimage of study area was consisted of 1,076 images with spatial resolution of 3.47 cm which was georeferenced using 12 ground control points (RMSE=8 cm) gathered by real-time kinematic (RTK) method. The results showed that the UAV orthoimagery classified by object-oriented method efficiently estimated mean crown area of wild pistachios (52.09±24.67 m2) and wild almonds (3.97±1.69 m2) with no significant difference with their observed values (α=0.05). In addition, the results showed that wild pistachios (accuracy of 0.90 and precision of 0.92) and wild almonds (accuracy of 0.90 and precision of 0.89) were well recognized by image segmentation. In general, we concluded that UAV orthoimagery can efficiently produce precise biophysical data of vegetation stands at single-tree levels, which therefore is suitable for assessment and monitoring open woodlands.

Comparison of Random Forest and Support Vector Machine classifiers using UAV remote sensing imagery

NASA Astrophysics Data System (ADS)

Piragnolo, Marco; Masiero, Andrea; Pirotti, Francesco

2017-04-01

Since recent years surveying with unmanned aerial vehicles (UAV) is getting a great amount of attention due to decreasing costs, higher precision and flexibility of usage. UAVs have been applied for geomorphological investigations, forestry, precision agriculture, cultural heritage assessment and for archaeological purposes. It can be used for land use and land cover classification (LULC). In literature, there are two main types of approaches for classification of remote sensing imagery: pixel-based and object-based. On one hand, pixel-based approach mostly uses training areas to define classes and respective spectral signatures. On the other hand, object-based classification considers pixels, scale, spatial information and texture information for creating homogeneous objects. Machine learning methods have been applied successfully for classification, and their use is increasing due to the availability of faster computing capabilities. The methods learn and train the model from previous computation. Two machine learning methods which have given good results in previous investigations are Random Forest (RF) and Support Vector Machine (SVM). The goal of this work is to compare RF and SVM methods for classifying LULC using images collected with a fixed wing UAV. The processing chain regarding classification uses packages in R, an open source scripting language for data analysis, which provides all necessary algorithms. The imagery was acquired and processed in November 2015 with cameras providing information over the red, blue, green and near infrared wavelength reflectivity over a testing area in the campus of Agripolis, in Italy. Images were elaborated and ortho-rectified through Agisoft Photoscan. The ortho-rectified image is the full data set, and the test set is derived from partial sub-setting of the full data set. Different tests have been carried out, using a percentage from 2 % to 20 % of the total. Ten training sets and ten validation sets are obtained from each test set. The control dataset consist of an independent visual classification done by an expert over the whole area. The classes are (i) broadleaf, (ii) building, (iii) grass, (iv) headland access path, (v) road, (vi) sowed land, (vii) vegetable. The RF and SVM are applied to the test set. The performances of the methods are evaluated using the three following accuracy metrics: Kappa index, Classification accuracy and Classification Error. All three are calculated in three different ways: with K-fold cross validation, using the validation test set and using the full test set. The analysis indicates that SVM gets better results in terms of good scores using K-fold cross or validation test set. Using the full test set, RF achieves a better result in comparison to SVM. It also seems that SVM performs better with smaller training sets, whereas RF performs better as training sets get larger.

An Active Patch Model for Real World Texture and Appearance Classification

PubMed Central

Mao, Junhua; Zhu, Jun; Yuille, Alan L.

2014-01-01

This paper addresses the task of natural texture and appearance classification. Our goal is to develop a simple and intuitive method that performs at state of the art on datasets ranging from homogeneous texture (e.g., material texture), to less homogeneous texture (e.g., the fur of animals), and to inhomogeneous texture (the appearance patterns of vehicles). Our method uses a bag-of-words model where the features are based on a dictionary of active patches. Active patches are raw intensity patches which can undergo spatial transformations (e.g., rotation and scaling) and adjust themselves to best match the image regions. The dictionary of active patches is required to be compact and representative, in the sense that we can use it to approximately reconstruct the images that we want to classify. We propose a probabilistic model to quantify the quality of image reconstruction and design a greedy learning algorithm to obtain the dictionary. We classify images using the occurrence frequency of the active patches. Feature extraction is fast (about 100 ms per image) using the GPU. The experimental results show that our method improves the state of the art on a challenging material texture benchmark dataset (KTH-TIPS2). To test our method on less homogeneous or inhomogeneous images, we construct two new datasets consisting of appearance image patches of animals and vehicles cropped from the PASCAL VOC dataset. Our method outperforms competing methods on these datasets. PMID:25531013

Application of foodborne disease outbreak data in the development and maintenance of HACCP systems.

PubMed

Panisello, P J; Rooney, R; Quantick, P C; Stanwell-Smith, R

2000-09-10

Five-hundred and thirty general foodborne outbreaks of food poisoning reported in England and Wales between 1992 and 1996 were reviewed to study their application to the development and maintenance of HACCP systems. Retrospective investigations of foodborne disease outbreaks provided information on aetiological agents, food vehicles and factors that contributed to the outbreaks. Salmonella spp. and foods of animal origin (red meat, poultry and seafood) were most frequently associated with outbreaks during this period. Improper cooking, inadequate storage, cross-contamination and use of raw ingredients in the preparation of food were the most common factors contributing to outbreaks. Classification and cross tabulation of surveillance information relating to aetiological agents, food vehicles and contributory factors facilitates hazard analysis. In forming control measures and their corresponding critical limits, this approach focuses monitoring on those aspects that are critical to the safety of the product. Incorporation of epidemiological data in the documentation of HACCP systems provides assurance that the system is based on the best scientific information available.

Reactive navigation for autonomous guided vehicle using neuro-fuzzy techniques

NASA Astrophysics Data System (ADS)

Cao, Jin; Liao, Xiaoqun; Hall, Ernest L.

1999-08-01

A Neuro-fuzzy control method for navigation of an Autonomous Guided Vehicle robot is described. Robot navigation is defined as the guiding of a mobile robot to a desired destination or along a desired path in an environment characterized by as terrain and a set of distinct objects, such as obstacles and landmarks. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Neural network and fuzzy logic control techniques can improve real-time control performance for mobile robot due to its high robustness and error-tolerance ability. For a mobile robot to navigate automatically and rapidly, an important factor is to identify and classify mobile robots' currently perceptual environment. In this paper, a new approach of the current perceptual environment feature identification and classification, which are based on the analysis of the classifying neural network and the Neuro- fuzzy algorithm, is presented. The significance of this work lies in the development of a new method for mobile robot navigation.

49 CFR 523.4 - Passenger automobile.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 6 2011-10-01 2011-10-01 false Passenger automobile. 523.4 Section 523.4... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.4 Passenger automobile. A passenger automobile is any automobile (other than an automobile capable of off-highway operation) manufactured...

49 CFR 523.4 - Passenger automobile.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 6 2014-10-01 2014-10-01 false Passenger automobile. 523.4 Section 523.4... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.4 Passenger automobile. A passenger automobile is any automobile (other than an automobile capable of off-highway operation) manufactured...

49 CFR 523.4 - Passenger automobile.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 6 2012-10-01 2012-10-01 false Passenger automobile. 523.4 Section 523.4... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.4 Passenger automobile. A passenger automobile is any automobile (other than an automobile capable of off-highway operation) manufactured...

49 CFR 523.4 - Passenger automobile.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 6 2013-10-01 2013-10-01 false Passenger automobile. 523.4 Section 523.4... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.4 Passenger automobile. A passenger automobile is any automobile (other than an automobile capable of off-highway operation) manufactured...

49 CFR 523.4 - Passenger automobile.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 6 2010-10-01 2010-10-01 false Passenger automobile. 523.4 Section 523.4... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.4 Passenger automobile. A passenger automobile is any automobile (other than an automobile capable of off-highway operation) manufactured...

Crack identification for rigid pavements using unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Bahaddin Ersoz, Ahmet; Pekcan, Onur; Teke, Turker

2017-09-01

Pavement condition assessment is an essential piece of modern pavement management systems as rehabilitation strategies are planned based upon its outcomes. For proper evaluation of existing pavements, they must be continuously and effectively monitored using practical means. Conventionally, truck-based pavement monitoring systems have been in-use in assessing the remaining life of in-service pavements. Although such systems produce accurate results, their use can be expensive and data processing can be time consuming, which make them infeasible considering the demand for quick pavement evaluation. To overcome such problems, Unmanned Aerial Vehicles (UAVs) can be used as an alternative as they are relatively cheaper and easier-to-use. In this study, we propose a UAV based pavement crack identification system for monitoring rigid pavements’ existing conditions. The system consists of recently introduced image processing algorithms used together with conventional machine learning techniques, both of which are used to perform detection of cracks on rigid pavements’ surface and their classification. Through image processing, the distinct features of labelled crack bodies are first obtained from the UAV based images and then used for training of a Support Vector Machine (SVM) model. The performance of the developed SVM model was assessed with a field study performed along a rigid pavement exposed to low traffic and serious temperature changes. Available cracks were classified using the UAV based system and obtained results indicate it ensures a good alternative solution for pavement monitoring applications.

Real-time, resource-constrained object classification on a micro-air vehicle

NASA Astrophysics Data System (ADS)

Buck, Louis; Ray, Laura

2013-12-01

A real-time embedded object classification algorithm is developed through the novel combination of binary feature descriptors, a bag-of-visual-words object model and the cortico-striatal loop (CSL) learning algorithm. The BRIEF, ORB and FREAK binary descriptors are tested and compared to SIFT descriptors with regard to their respective classification accuracies, execution times, and memory requirements when used with CSL on a 12.6 g ARM Cortex embedded processor running at 800 MHz. Additionally, the effect of x2 feature mapping and opponent-color representations used with these descriptors is examined. These tests are performed on four data sets of varying sizes and difficulty, and the BRIEF descriptor is found to yield the best combination of speed and classification accuracy. Its use with CSL achieves accuracies between 67% and 95% of those achieved with SIFT descriptors and allows for the embedded classification of a 128x192 pixel image in 0.15 seconds, 60 times faster than classification with SIFT. X2 mapping is found to provide substantial improvements in classification accuracy for all of the descriptors at little cost, while opponent-color descriptors are offer accuracy improvements only on colorful datasets.

Cumulated UDC Supplement, 1965-1975. Volume III: Classes 6/62 (61 Medical Sciences, 62 Engineering and Technology Generally, 621 Mechanical and Electrical Engineering, 622 Mining, 623 Military and Naval Engineering, 624 Civil and Structural Engineering, 625 Railway and Highway Engineering, 626/627 Hydraulic Engineering Works, 628 Public Health Engineering, 629 Transport (Vehicle) Engineering).

ERIC Educational Resources Information Center

International Federation for Documentation, The Hague (Netherlands). Committee on Classification Research.

In continuation of the "Cumulated UDC Supplement - 1964" published by the International Federation for Documentation, this document provides a cumulative supplement to the Universal Decimal Classification for 1965-1975. This third of five volumes lists new classification subdivisions in the following subject areas: (1) medical sciences; (2)…

Study of the microdoppler signature of a bicyclist for different directions of approach

NASA Astrophysics Data System (ADS)

Rodriguez-Hervas, Berta; Maile, Michael; Flores, Benjamin C.

2015-05-01

The successful implementation of autonomous driving in an urban setting depends on the ability of the environment perception system to correctly classify vulnerable road users such as pedestrians and bicyclists in dense, complex scenarios. Self-driving vehicles include sensor systems such as cameras, lidars, and radars to enable decision making. Among these systems, radars are particularly relevant due to their operational robustness under adverse weather and night light conditions. Classification of pedestrian and car in urban settings using automotive radar has been widely investigated, suggesting that micro-Doppler signatures are useful for target discrimination. Our objective is to analyze and study the micro-Doppler signature of bicyclists approaching a vehicle from different directions in order to establish the basis of a classification criterion to distinguish bicycles from other targets including clutter. The micro-Doppler signature is obtained by grouping individual reflecting points using a clustering algorithm and observing the evolution of all the points belonging to an object in the Doppler domain over time. A comparison is then made with simulated data that uses a kinematic model of bicyclists' movement. The suitability of the micro-Doppler bicyclist signature as a classification feature is determined by comparing it to those belonging to cars and pedestrians approaching the automotive radar system.

Hyperparameter Classification of Arctic Sea Ice and Snow Based on Aerial Laser Data, Passive Microwave Data and Field Data

NASA Astrophysics Data System (ADS)

Herzfeld, U. C.; Maslanik, J.; Williams, S.; Sturm, M.; Cavalieri, D.

2006-12-01

In the past year, the Arctic sea-ice cover has been shrinking at an alarming rate. Remote-sensing technologies provide opportunities for observations of the sea ice at unprecedented repetition rates and spatial resolutions. The advance of new observational technologies is not only fascinating, it also brings with it the challenge and necessity to derive adequate new geoinformatical and geomathematical methods as a basis for analysis and geophysical interpretation of new data types. Our research includes validation and analysis of NASA EOS data, development of observational instrumentation and advanced geoinformatics. In this talk we emphasize the close linkage between technological development and geoinformatics along case studies of sea-ice near Point Barrow, Alaska, based on the following data types: AMSR-E and PSR passive microwave data, RADARSAT and ERS SAR data, manually-collected snow-depth data and laser-elevation data from unmanned aerial vehicles. The hyperparameter concept is introduced to facilitate characterization and classification of the same sea-ice properties and spatial structures from these data sets, which differ with respect to spatial resolution, measured parameters and observed geophysical variables. Mathematically, this requires parameter identification in undersampled, oversampled or overprinted situations.

Refining Time-Activity Classification of Human Subjects Using the Global Positioning System

PubMed Central

Hu, Maogui; Li, Wei; Li, Lianfa; Houston, Douglas; Wu, Jun

2016-01-01

Background Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns. Methods Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods. Results Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions. Conclusions The random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations. PMID:26919723

Micro-Doppler analysis of multiple frequency continuous wave radar signatures

NASA Astrophysics Data System (ADS)

Anderson, Michael G.; Rogers, Robert L.

2007-04-01

Micro-Doppler refers to Doppler scattering returns produced by non rigid-body motion. Micro-Doppler gives rise to many detailed radar image features in addition to those associated with bulk target motion. Targets of different classes (for example, humans, animals, and vehicles) produce micro-Doppler images that are often distinguishable even by nonexpert observers. Micro-Doppler features have great potential for use in automatic target classification algorithms. Although the potential benefit of using micro-Doppler in classification algorithms is high, relatively little experimental (non-synthetic) micro-Doppler data exists. Much of the existing experimental data comes from highly cooperative targets (human or vehicle targets directly approaching the radar). This research involved field data collection and analysis of micro-Doppler radar signatures from non-cooperative targets. The data was collected using a low cost Xband multiple frequency continuous wave (MFCW) radar with three transmit frequencies. The collected MFCW radar signatures contain data from humans, vehicles, and animals. The presented data includes micro-Doppler signatures previously unavailable in the literature such as crawling humans and various animal species. The animal micro-Doppler signatures include deer, dog, and goat datasets. This research focuses on the analysis of micro-Doppler from noncooperative targets approaching the radar at various angles, maneuvers, and postures.

Vision-Based Corrosion Detection Assisted by a Micro-Aerial Vehicle in a Vessel Inspection Application

PubMed Central

Ortiz, Alberto; Bonnin-Pascual, Francisco; Garcia-Fidalgo, Emilio; Company-Corcoles, Joan P.

2016-01-01

Vessel maintenance requires periodic visual inspection of the hull in order to detect typical defective situations of steel structures such as, among others, coating breakdown and corrosion. These inspections are typically performed by well-trained surveyors at great cost because of the need for providing access means (e.g., scaffolding and/or cherry pickers) that allow the inspector to be at arm’s reach from the structure under inspection. This paper describes a defect detection approach comprising a micro-aerial vehicle which is used to collect images from the surfaces under inspection, particularly focusing on remote areas where the surveyor has no visual access, and a coating breakdown/corrosion detector based on a three-layer feed-forward artificial neural network. As it is discussed in the paper, the success of the inspection process depends not only on the defect detection software but also on a number of assistance functions provided by the control architecture of the aerial platform, whose aim is to improve picture quality. Both aspects of the work are described along the different sections of the paper, as well as the classification performance attained. PMID:27983627

Vision-Based Corrosion Detection Assisted by a Micro-Aerial Vehicle in a Vessel Inspection Application.

PubMed

Ortiz, Alberto; Bonnin-Pascual, Francisco; Garcia-Fidalgo, Emilio; Company-Corcoles, Joan P

2016-12-14

Vessel maintenance requires periodic visual inspection of the hull in order to detect typical defective situations of steel structures such as, among others, coating breakdown and corrosion. These inspections are typically performed by well-trained surveyors at great cost because of the need for providing access means (e.g., scaffolding and/or cherry pickers) that allow the inspector to be at arm's reach from the structure under inspection. This paper describes a defect detection approach comprising a micro-aerial vehicle which is used to collect images from the surfaces under inspection, particularly focusing on remote areas where the surveyor has no visual access, and a coating breakdown/corrosion detector based on a three-layer feed-forward artificial neural network. As it is discussed in the paper, the success of the inspection process depends not only on the defect detection software but also on a number of assistance functions provided by the control architecture of the aerial platform, whose aim is to improve picture quality. Both aspects of the work are described along the different sections of the paper, as well as the classification performance attained.

Assessment of data collection for ESAL determinations for the Kentucky Transportation Cabinet, Division of Planning.

DOT National Transportation Integrated Search

2004-06-01

The Kentucky Transportation Cabinet Cabinets Division of Planning collects weight data, traffic volume data, and vehicle classification data. These data are used as inputs to determine ESALs (equivalent single axle loads). ESALs are used in the pa...

Factor complexity of crash occurrence: An empirical demonstration using boosted regression trees.

PubMed

Chung, Yi-Shih

2013-12-01

Factor complexity is a characteristic of traffic crashes. This paper proposes a novel method, namely boosted regression trees (BRT), to investigate the complex and nonlinear relationships in high-variance traffic crash data. The Taiwanese 2004-2005 single-vehicle motorcycle crash data are used to demonstrate the utility of BRT. Traditional logistic regression and classification and regression tree (CART) models are also used to compare their estimation results and external validities. Both the in-sample cross-validation and out-of-sample validation results show that an increase in tree complexity provides improved, although declining, classification performance, indicating a limited factor complexity of single-vehicle motorcycle crashes. The effects of crucial variables including geographical, time, and sociodemographic factors explain some fatal crashes. Relatively unique fatal crashes are better approximated by interactive terms, especially combinations of behavioral factors. BRT models generally provide improved transferability than conventional logistic regression and CART models. This study also discusses the implications of the results for devising safety policies. Copyright © 2012 Elsevier Ltd. All rights reserved.

High area rate reconnaissance (HARR) and mine reconnaissance/hunter (MR/H) exploratory development programs

NASA Astrophysics Data System (ADS)

Lathrop, John D.

1995-06-01

This paper describes the sea mine countermeasures developmental context, technology goals, and progress to date of the two principal Office of Naval Research exploratory development programs addressing sea mine reconnaissance and minehunting technology development. The first of these programs, High Area Rate Reconnaissance, is developing toroidal volume search sonar technology, sidelooking sonar technology, and associated signal processing technologies (motion compensation, beamforming, and computer-aided detection and classification) for reconnaissance and hunting against volume mines and proud bottom mines from 21-inch diameter vehicles operating in deeper waters. The second of these programs, Amphibious Operation Area Mine Reconnaissance/Hunter, is developing a suite of sensor technologies (synthetic aperture sonar, ahead-looking sonar, superconducting magnetic field gradiometer, and electro-optic sensor) and associated signal processing technologies for reconnaissance and hunting against all mine types (including buried mines) in shallow water and very shallow water from 21-inch diameter vehicles. The technologies under development by these two programs must provide excellent capabilities for mine detection, mine classification, and discrimination against false targets.

Clinical research data warehouse governance for distributed research networks in the USA: a systematic review of the literature

PubMed Central

Holmes, John H; Elliott, Thomas E; Brown, Jeffrey S; Raebel, Marsha A; Davidson, Arthur; Nelson, Andrew F; Chung, Annie; La Chance, Pierre; Steiner, John F

2014-01-01

Objective To review the published, peer-reviewed literature on clinical research data warehouse governance in distributed research networks (DRNs). Materials and methods Medline, PubMed, EMBASE, CINAHL, and INSPEC were searched for relevant documents published through July 31, 2013 using a systematic approach. Only documents relating to DRNs in the USA were included. Documents were analyzed using a classification framework consisting of 10 facets to identify themes. Results 6641 documents were retrieved. After screening for duplicates and relevance, 38 were included in the final review. A peer-reviewed literature on data warehouse governance is emerging, but is still sparse. Peer-reviewed publications on UK research network governance were more prevalent, although not reviewed for this analysis. All 10 classification facets were used, with some documents falling into two or more classifications. No document addressed costs associated with governance. Discussion Even though DRNs are emerging as vehicles for research and public health surveillance, understanding of DRN data governance policies and procedures is limited. This is expected to change as more DRN projects disseminate their governance approaches as publicly available toolkits and peer-reviewed publications. Conclusions While peer-reviewed, US-based DRN data warehouse governance publications have increased, DRN developers and administrators are encouraged to publish information about these programs. PMID:24682495

Design of Small MEMS Microphone Array Systems for Direction Finding of Outdoors Moving Vehicles

PubMed Central

Zhang, Xin; Huang, Jingchang; Song, Enliang; Liu, Huawei; Li, Baoqing; Yuan, Xiaobing

2014-01-01

In this paper, a MEMS microphone array system scheme is proposed which implements real-time direction of arrival (DOA) estimation for moving vehicles. Wind noise is the primary source of unwanted noise on microphones outdoors. A multiple signal classification (MUSIC) algorithm is used in this paper for direction finding associated with spatial coherence to discriminate between the wind noise and the acoustic signals of a vehicle. The method is implemented in a SHARC DSP processor and the real-time estimated DOA is uploaded through Bluetooth or a UART module. Experimental results in different places show the validity of the system and the deviation is no bigger than 6° in the presence of wind noise. PMID:24603636

Design of small MEMS microphone array systems for direction finding of outdoors moving vehicles.

PubMed

Zhang, Xin; Huang, Jingchang; Song, Enliang; Liu, Huawei; Li, Baoqing; Yuan, Xiaobing

2014-03-05

In this paper, a MEMS microphone array system scheme is proposed which implements real-time direction of arrival (DOA) estimation for moving vehicles. Wind noise is the primary source of unwanted noise on microphones outdoors. A multiple signal classification (MUSIC) algorithm is used in this paper for direction finding associated with spatial coherence to discriminate between the wind noise and the acoustic signals of a vehicle. The method is implemented in a SHARC DSP processor and the real-time estimated DOA is uploaded through Bluetooth or a UART module. Experimental results in different places show the validity of the system and the deviation is no bigger than 6° in the presence of wind noise.

Vehicles instability criteria for flood risk assessment of a street network

NASA Astrophysics Data System (ADS)

Arrighi, Chiara; Huybrechts, Nicolas; Ouahsine, Abdellatif; Chassé, Patrick; Oumeraci, Hocine; Castelli, Fabio

2016-05-01

The mutual interaction between floods and human activity is a process, which has been evolving over history and has shaped flood risk pathways. In developed countries, many events have illustrated that the majority of the fatalities during a flood occurs in a vehicle, which is considered as a safe shelter but it may turn into a trap for several combinations of water depth and velocity. Thus, driving a car in floodwaters is recognized as the most crucial aggravating factor for people safety. On the other hand, the entrainment of vehicles may locally cause obstructions to the flow and induce the collapse of infrastructures. Flood risk to vehicles can be defined as the combination of the probability of a vehicle of being swept away (i.e. the hazard) and the actual traffic/parking density, i.e. the vulnerability. Hazard for vehicles can be assessed through the spatial identification and mapping of the critical conditions for vehicles incipient motion. This analysis requires a flood map with information on water depth and velocity and consistent instability criteria accounting for flood and vehicles characteristics. Vulnerability is evaluated thanks to the road network and traffic data. Therefore, vehicles flood risk mapping can support people's education and management practices in order to reduce the casualties. In this work, a flood hazard classification for vehicles is introduced and an application to a real case study is presented and discussed.

Intelligent Sensing and Classification in DSR-Based Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Dempsey, Tae; Sahin, Gokhan; Morton, Yu T. (Jade

Wireless ad hoc networks have fundamentally altered today's battlefield, with applications ranging from unmanned air vehicles to randomly deployed sensor networks. Security and vulnerabilities in wireless ad hoc networks have been considered at different layers, and many attack strategies have been proposed, including denial of service (DoS) through the intelligent jamming of the most critical packet types of flows in a network. This paper investigates the effectiveness of intelligent jamming in wireless ad hoc networks using the Dynamic Source Routing (DSR) and TCP protocols and introduces an intelligent classifier to facilitate the jamming of such networks. Assuming encrypted packet headers and contents, our classifier is based solely on the observable characteristics of size, inter-arrival timing, and direction and classifies packets with up to 99.4% accuracy in our experiments.

Multi-source feature extraction and target recognition in wireless sensor networks based on adaptive distributed wavelet compression algorithms

NASA Astrophysics Data System (ADS)

Hortos, William S.

2008-04-01

Proposed distributed wavelet-based algorithms are a means to compress sensor data received at the nodes forming a wireless sensor network (WSN) by exchanging information between neighboring sensor nodes. Local collaboration among nodes compacts the measurements, yielding a reduced fused set with equivalent information at far fewer nodes. Nodes may be equipped with multiple sensor types, each capable of sensing distinct phenomena: thermal, humidity, chemical, voltage, or image signals with low or no frequency content as well as audio, seismic or video signals within defined frequency ranges. Compression of the multi-source data through wavelet-based methods, distributed at active nodes, reduces downstream processing and storage requirements along the paths to sink nodes; it also enables noise suppression and more energy-efficient query routing within the WSN. Targets are first detected by the multiple sensors; then wavelet compression and data fusion are applied to the target returns, followed by feature extraction from the reduced data; feature data are input to target recognition/classification routines; targets are tracked during their sojourns through the area monitored by the WSN. Algorithms to perform these tasks are implemented in a distributed manner, based on a partition of the WSN into clusters of nodes. In this work, a scheme of collaborative processing is applied for hierarchical data aggregation and decorrelation, based on the sensor data itself and any redundant information, enabled by a distributed, in-cluster wavelet transform with lifting that allows multiple levels of resolution. The wavelet-based compression algorithm significantly decreases RF bandwidth and other resource use in target processing tasks. Following wavelet compression, features are extracted. The objective of feature extraction is to maximize the probabilities of correct target classification based on multi-source sensor measurements, while minimizing the resource expenditures at participating nodes. Therefore, the feature-extraction method based on the Haar DWT is presented that employs a maximum-entropy measure to determine significant wavelet coefficients. Features are formed by calculating the energy of coefficients grouped around the competing clusters. A DWT-based feature extraction algorithm used for vehicle classification in WSNs can be enhanced by an added rule for selecting the optimal number of resolution levels to improve the correct classification rate and reduce energy consumption expended in local algorithm computations. Published field trial data for vehicular ground targets, measured with multiple sensor types, are used to evaluate the wavelet-assisted algorithms. Extracted features are used in established target recognition routines, e.g., the Bayesian minimum-error-rate classifier, to compare the effects on the classification performance of the wavelet compression. Simulations of feature sets and recognition routines at different resolution levels in target scenarios indicate the impact on classification rates, while formulas are provided to estimate reduction in resource use due to distributed compression.

40 CFR 205.151 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... classification unit of a manufacturer's product line and is comprised of all vehicle designs, models or series... surface. (3) Competition motorcycle means any motorcycle designed and marketed solely for use in closed.... (7) Acoustical Assurance Period (AAP) means a specified period of time or miles driven after sale to...

40 CFR 205.151 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... classification unit of a manufacturer's product line and is comprised of all vehicle designs, models or series... surface. (3) Competition motorcycle means any motorcycle designed and marketed solely for use in closed.... (7) Acoustical Assurance Period (AAP) means a specified period of time or miles driven after sale to...

40 CFR 205.151 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... classification unit of a manufacturer's product line and is comprised of all vehicle designs, models or series... surface. (3) Competition motorcycle means any motorcycle designed and marketed solely for use in closed.... (7) Acoustical Assurance Period (AAP) means a specified period of time or miles driven after sale to...

49 CFR 523.2 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 6 2010-10-01 2010-10-01 false Definitions. 523.2 Section 523.2 Transportation..., DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.2 Definitions. Approach angle means the smallest... to the nearest tenth of a square foot. For purposes of this definition, track width is the lateral...

Thermal signature characteristics of vehicle/terrain interaction disturbances: implications for battlefield vehicle classification.

PubMed

Eastes, John W; Mason, George L; Kusinger, Alan E

2004-05-01

Thermal emissivity spectra (8-14 microm) of track impressions/background were determined in conjunction with operation of six military vehicle types, T-72 and M1 Tanks, an M2 Bradley Fighting Vehicle, a 5-ton truck, a D7 tractor, and a High Mobility Multipurpose Wheeled Vehicle (HMMWV), over diverse soil surfaces to determine if vehicle type could be related to track thermal signatures. Results suggest soil compaction and fragmentation/pulverization are primary parameters affecting track signatures and that soil and vehicle/terrain-contact type determine which parameter dominates. Steel-tracked vehicles exert relatively low ground-contact pressure but tend to fragment/pulverize soil more so than do rubber-tired vehicles, which tend mainly to compact. In quartz-rich, lean clay soil tracked vehicles produced impressions with spectral contrast of the quartz reststrahlen features decreased from that of the background. At the same time, 5-ton truck tracks exhibited increased contrast on the same surface, suggesting that steel tracks fragmented soil while rubber tires mainly produced compaction. The structure of materials such as sand and moist clay-rich river sediment makes them less subject to further fragmentation/pulverization; thus, compaction was the main factor affecting signatures in these media, and both tracked and wheeled vehicles created impressions with increased spectral contrast on these surfaces. These results suggest that remotely sensed thermal signatures could differentiate tracked and wheeled vehicles on terrain in many areas of the world of strategic interest. Significant applications include distinguishing visually/spectrally identical lightweight decoys from actual threat vehicles.

Rocket propulsion hazard summary: Safety classification, handling experience and application to space shuttle payload

NASA Technical Reports Server (NTRS)

Pennington, D. F.; Man, T.; Persons, B.

1977-01-01

The DOT classification for transportation, the military classification for quantity distance, and hazard compatibility grouping used to regulate the transportation and storage of explosives are presented along with a discussion of tests used in determining sensitivity of propellants to an impact/shock environment in the absence of a large explosive donor. The safety procedures and requirements of a Scout launch vehicle, Western and Eastern Test Range, and the Minuteman, Delta, and Poseidon programs are reviewed and summarized. Requirements of the space transportation system safety program include safety reviews from the subsystem level to the completed payload. The Scout safety procedures will satisfy a portion of these requirements but additional procedures need to be implemented to comply with the safety requirements for Shuttle operation from the Eastern Test Range.

Public private partnerships : evaluating ESALs and weigh-in-motion data for US 550 in northern New Mexico, roadLife, Altris vehicle classification system.

DOT National Transportation Integrated Search

2007-08-01

Public-private partnerships as an alternative means of delivering goods and services are receiving increased attention as state departments of transportation consider ways to maximize limited resources. In 1998 the New Mexico Department of Transporta...

Classification of energy-conserving engine oil for passenger cars, vans, sport utility vehicles, and light-duty trucks (revised May 97). (SAE standard)

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1997-05-01

This SAE Standard was developed cooperatively by SAE, ASTM, and API to define and identify energy conserving engine oils for passenger cars, vans, and light-duty (3856 kg (8500 lb) GVW or less) trucks.

Commercial Vehicle Fleet Management And Information Systems, Technical Memorandum 1, Classification Of Fleet Operations, And Selection Of Candidate Case-Study Fleets

DOT National Transportation Integrated Search

1994-11-04

THE ECONOMIC WELL-BEING AND COMPETITIVENESS OF THE U.S. ECONOMY DEPEND HEAVILY ON RELIABLE AND EFFICIENT FREIGHT MOVEMENTS. TRUCKING ACCOUNTS FOR ABOUT 75 PERCENT ($270 BILLION) OF THE $350 BILLION SPENT ANNUALLY ON FREIGHT TRANSPORTATION. THE APPLIC...

49 CFR 523.5 - Non-passenger automobile.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 6 2011-10-01 2011-10-01 false Non-passenger automobile. 523.5 Section 523.5... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.5 Non-passenger automobile. A non-passenger automobile means an automobile that is not a passenger automobile or a work truck and includes...

49 CFR 523.5 - Non-passenger automobile.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 6 2010-10-01 2010-10-01 false Non-passenger automobile. 523.5 Section 523.5... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.5 Non-passenger automobile. A non-passenger automobile means an automobile that is not a passenger automobile or a work truck and includes...

49 CFR 523.5 - Non-passenger automobile.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 6 2014-10-01 2014-10-01 false Non-passenger automobile. 523.5 Section 523.5... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.5 Non-passenger automobile. A non-passenger automobile means an automobile that is not a passenger automobile or a work truck and includes...

49 CFR 523.5 - Non-passenger automobile.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 6 2013-10-01 2013-10-01 false Non-passenger automobile. 523.5 Section 523.5... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.5 Non-passenger automobile. A non-passenger automobile means an automobile that is not a passenger automobile or a work truck and includes...

49 CFR 523.5 - Non-passenger automobile.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 6 2012-10-01 2012-10-01 false Non-passenger automobile. 523.5 Section 523.5... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION VEHICLE CLASSIFICATION § 523.5 Non-passenger automobile. A non-passenger automobile means an automobile that is not a passenger automobile or a work truck and includes...

Analysis of vehicle classification data, including monthly and seasonal ADT factors, hourly distribution factors, and lane distribution factors

DOT National Transportation Integrated Search

1998-11-01

This report documents the development of monthly and seasonal average daily traffic (ADT) factors for performing estimating AADTs. It appears that seasonal factors can estimate AADT as well as monthly factors, and it is recommended that seasonal fact...

Determining the Effectiveness of Incorporating Geographic Information Into Vehicle Performance Algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sera White

2012-04-01

This thesis presents a research study using one year of driving data obtained from plug-in hybrid electric vehicles (PHEV) located in Sacramento and San Francisco, California to determine the effectiveness of incorporating geographic information into vehicle performance algorithms. Sacramento and San Francisco were chosen because of the availability of high resolution (1/9 arc second) digital elevation data. First, I present a method for obtaining instantaneous road slope, given a latitude and longitude, and introduce its use into common driving intensity algorithms. I show that for trips characterized by >40m of net elevation change (from key on to key off), themore » use of instantaneous road slope significantly changes the results of driving intensity calculations. For trips exhibiting elevation loss, algorithms ignoring road slope overestimated driving intensity by as much as 211 Wh/mile, while for trips exhibiting elevation gain these algorithms underestimated driving intensity by as much as 333 Wh/mile. Second, I describe and test an algorithm that incorporates vehicle route type into computations of city and highway fuel economy. Route type was determined by intersecting trip GPS points with ESRI StreetMap road types and assigning each trip as either city or highway route type according to whichever road type comprised the largest distance traveled. The fuel economy results produced by the geographic classification were compared to the fuel economy results produced by algorithms that assign route type based on average speed or driving style. Most results were within 1 mile per gallon ({approx}3%) of one another; the largest difference was 1.4 miles per gallon for charge depleting highway trips. The methods for acquiring and using geographic data introduced in this thesis will enable other vehicle technology researchers to incorporate geographic data into their research problems.« less

Kardashev's Classification at 50+: A Fine Vehicle With Room for Improvement

NASA Astrophysics Data System (ADS)

Ćirković, M. M.

2015-12-01

We review the history and status of the famous classification of extraterrestrial civilizations given by the great Russian astrophysicist Nikolai Semenovich Kardashev, roughly half a century after it has been proposed. While Kardashev's classification (or Kardashev's scale) has often been seen as oversimplified, and multiple improvements, refinements, and alternatives to it have been suggested, it is still one of the major tools for serious theoretical investigation of SETI issues. During these 50+ years, several attempts at modifying or reforming the classification have been made; we review some of them here, together with presenting some of the scenarios which present difficulties to the standard version. Recent results in both theoretical and observational SETI studies, especially the {Ĝ infrared survey (2014-2015), have persuasively shown that the emphasis on detectability inherent in Kardashev's classification obtains new significance and freshness. Several new movements and conceptual frameworks, such as the Dysonian SETI, tally extremely well with these developments. So, the apparent simplicity of the classification is highly deceptive: Kardashev's work offers a wealth of still insufficiently studied methodological and epistemological ramifications and it remains, in both letter and spirit, perhaps the worthiest legacy of the SETI "founding fathers".

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

PubMed Central

Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

2015-01-01

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions. PMID:26089862

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.

PubMed

Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

2015-01-01

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

Supervisory Power Management Control Algorithms for Hybrid Electric Vehicles. A Survey

DOE PAGES

Malikopoulos, Andreas

2014-03-31

The growing necessity for environmentally benign hybrid propulsion systems has led to the development of advanced power management control algorithms to maximize fuel economy and minimize pollutant emissions. This paper surveys the control algorithms for hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs) that have been reported in the literature to date. The exposition ranges from parallel, series, and power split HEVs and PHEVs and includes a classification of the algorithms in terms of their implementation and the chronological order of their appearance. Remaining challenges and potential future research directions are also discussed.

Low-Power Analog Processing for Sensing Applications: Low-Frequency Harmonic Signal Classification

PubMed Central

White, Daniel J.; William, Peter E.; Hoffman, Michael W.; Balkir, Sina

2013-01-01

A low-power analog sensor front-end is described that reduces the energy required to extract environmental sensing spectral features without using Fast Fouriér Transform (FFT) or wavelet transforms. An Analog Harmonic Transform (AHT) allows selection of only the features needed by the back-end, in contrast to the FFT, where all coefficients must be calculated simultaneously. We also show that the FFT coefficients can be easily calculated from the AHT results by a simple back-substitution. The scheme is tailored for low-power, parallel analog implementation in an integrated circuit (IC). Two different applications are tested with an ideal front-end model and compared to existing studies with the same data sets. Results from the military vehicle classification and identification of machine-bearing fault applications shows that the front-end suits a wide range of harmonic signal sources. Analog-related errors are modeled to evaluate the feasibility of and to set design parameters for an IC implementation to maintain good system-level performance. Design of a preliminary transistor-level integrator circuit in a 0.13 μm complementary metal-oxide-silicon (CMOS) integrated circuit process showed the ability to use online self-calibration to reduce fabrication errors to a sufficiently low level. Estimated power dissipation is about three orders of magnitude less than similar vehicle classification systems that use commercially available FFT spectral extraction. PMID:23892765

Remote operation of the Black Knight unmanned ground combat vehicle

NASA Astrophysics Data System (ADS)

Valois, Jean-Sebastien; Herman, Herman; Bares, John; Rice, David P.

2008-04-01

The Black Knight is a 12-ton, C-130 deployable Unmanned Ground Combat Vehicle (UGCV). It was developed to demonstrate how unmanned vehicles can be integrated into a mechanized military force to increase combat capability while protecting Soldiers in a full spectrum of battlefield scenarios. The Black Knight is used in military operational tests that allow Soldiers to develop the necessary techniques, tactics, and procedures to operate a large unmanned vehicle within a mechanized military force. It can be safely controlled by Soldiers from inside a manned fighting vehicle, such as the Bradley Fighting Vehicle. Black Knight control modes include path tracking, guarded teleoperation, and fully autonomous movement. Its state-of-the-art Autonomous Navigation Module (ANM) includes terrain-mapping sensors for route planning, terrain classification, and obstacle avoidance. In guarded teleoperation mode, the ANM data, together with automotive dials and gages, are used to generate video overlays that assist the operator for both day and night driving performance. Remote operation of various sensors also allows Soldiers to perform effective target location and tracking. This document covers Black Knight's system architecture and includes implementation overviews of the various operation modes. We conclude with lessons learned and development goals for the Black Knight UGCV.

Measurements from an Aerial Vehicle: A New Tool for Planetary Exploration

NASA Technical Reports Server (NTRS)

Wright, Henry S.; Levine, Joel S.; Croom, Mark A.; Edwards, William C.; Qualls, Garry D.; Gasbarre, Joseph F.

2004-01-01

Aerial vehicles fill a unique planetary science measurement gap, that of regional-scale, near-surface observation, while providing a fresh perspective for potential discovery. Aerial vehicles used in planetary exploration bridge the scale and resolution measurement gaps between orbiters (global perspective with limited spatial resolution) and landers (local perspective with high spatial resolution) thus complementing and extending orbital and landed measurements. Planetary aerial vehicles can also survey scientifically interesting terrain that is inaccessible or hazardous to landed missions. The use of aerial assets for performing observations on Mars, Titan, or Venus will enable direct measurements and direct follow-ons to recent discoveries. Aerial vehicles can be used for remote sensing of the interior, surface and atmosphere of Mars, Venus and Titan. Types of aerial vehicles considered are airplane "heavier than air" and airships and balloons "lighter than air". Interdependencies between the science measurements, science goals and objectives, and platform implementation illustrate how the proper balance of science, engineering, and cost, can be achieved to allow for a successful mission. Classification of measurement types along with how those measurements resolve science questions and how these instruments are accommodated within the mission context are discussed.

Fine-grained visual marine vessel classification for coastal surveillance and defense applications

NASA Astrophysics Data System (ADS)

Solmaz, Berkan; Gundogdu, Erhan; Karaman, Kaan; Yücesoy, Veysel; Koç, Aykut

2017-10-01

The need for capabilities of automated visual content analysis has substantially increased due to presence of large number of images captured by surveillance cameras. With a focus on development of practical methods for extracting effective visual data representations, deep neural network based representations have received great attention due to their success in visual categorization of generic images. For fine-grained image categorization, a closely related yet a more challenging research problem compared to generic image categorization due to high visual similarities within subgroups, diverse applications were developed such as classifying images of vehicles, birds, food and plants. Here, we propose the use of deep neural network based representations for categorizing and identifying marine vessels for defense and security applications. First, we gather a large number of marine vessel images via online sources grouping them into four coarse categories; naval, civil, commercial and service vessels. Next, we subgroup naval vessels into fine categories such as corvettes, frigates and submarines. For distinguishing images, we extract state-of-the-art deep visual representations and train support-vector-machines. Furthermore, we fine tune deep representations for marine vessel images. Experiments address two scenarios, classification and verification of naval marine vessels. Classification experiment aims coarse categorization, as well as learning models of fine categories. Verification experiment embroils identification of specific naval vessels by revealing if a pair of images belongs to identical marine vessels by the help of learnt deep representations. Obtaining promising performance, we believe these presented capabilities would be essential components of future coastal and on-board surveillance systems.

Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture.

PubMed

Yamamoto, Kyosuke; Togami, Takashi; Yamaguchi, Norio

2017-11-06

Unmanned aerial vehicles (UAVs or drones) are a very promising branch of technology, and they have been utilized in agriculture-in cooperation with image processing technologies-for phenotyping and vigor diagnosis. One of the problems in the utilization of UAVs for agricultural purposes is the limitation in flight time. It is necessary to fly at a high altitude to capture the maximum number of plants in the limited time available, but this reduces the spatial resolution of the captured images. In this study, we applied a super-resolution method to the low-resolution images of tomato diseases to recover detailed appearances, such as lesions on plant organs. We also conducted disease classification using high-resolution, low-resolution, and super-resolution images to evaluate the effectiveness of super-resolution methods in disease classification. Our results indicated that the super-resolution method outperformed conventional image scaling methods in spatial resolution enhancement of tomato disease images. The results of disease classification showed that the accuracy attained was also better by a large margin with super-resolution images than with low-resolution images. These results indicated that our approach not only recovered the information lost in low-resolution images, but also exerted a beneficial influence on further image analysis. The proposed approach will accelerate image-based phenotyping and vigor diagnosis in the field, because it not only saves time to capture images of a crop in a cultivation field but also secures the accuracy of these images for further analysis.

Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture

PubMed Central

Togami, Takashi; Yamaguchi, Norio

2017-01-01

Unmanned aerial vehicles (UAVs or drones) are a very promising branch of technology, and they have been utilized in agriculture—in cooperation with image processing technologies—for phenotyping and vigor diagnosis. One of the problems in the utilization of UAVs for agricultural purposes is the limitation in flight time. It is necessary to fly at a high altitude to capture the maximum number of plants in the limited time available, but this reduces the spatial resolution of the captured images. In this study, we applied a super-resolution method to the low-resolution images of tomato diseases to recover detailed appearances, such as lesions on plant organs. We also conducted disease classification using high-resolution, low-resolution, and super-resolution images to evaluate the effectiveness of super-resolution methods in disease classification. Our results indicated that the super-resolution method outperformed conventional image scaling methods in spatial resolution enhancement of tomato disease images. The results of disease classification showed that the accuracy attained was also better by a large margin with super-resolution images than with low-resolution images. These results indicated that our approach not only recovered the information lost in low-resolution images, but also exerted a beneficial influence on further image analysis. The proposed approach will accelerate image-based phenotyping and vigor diagnosis in the field, because it not only saves time to capture images of a crop in a cultivation field but also secures the accuracy of these images for further analysis. PMID:29113104

Australian sea-floor survey data, with images and expert annotations.

PubMed

Bewley, Michael; Friedman, Ariell; Ferrari, Renata; Hill, Nicole; Hovey, Renae; Barrett, Neville; Marzinelli, Ezequiel M; Pizarro, Oscar; Figueira, Will; Meyer, Lisa; Babcock, Russ; Bellchambers, Lynda; Byrne, Maria; Williams, Stefan B

2015-01-01

This Australian benthic data set (BENTHOZ-2015) consists of an expert-annotated set of georeferenced benthic images and associated sensor data, captured by an autonomous underwater vehicle (AUV) around Australia. This type of data is of interest to marine scientists studying benthic habitats and organisms. AUVs collect georeferenced images over an area with consistent illumination and altitude, and make it possible to generate broad scale, photo-realistic 3D maps. Marine scientists then typically spend several minutes on each of thousands of images, labeling substratum type and biota at a subset of points. Labels from four Australian research groups were combined using the CATAMI classification scheme, a hierarchical classification scheme based on taxonomy and morphology for scoring marine imagery. This data set consists of 407,968 expert labeled points from around the Australian coast, with associated images, geolocation and other sensor data. The robotic surveys that collected this data form part of Australia's Integrated Marine Observing System (IMOS) ongoing benthic monitoring program. There is reuse potential in marine science, robotics, and computer vision research.

Australian sea-floor survey data, with images and expert annotations

PubMed Central

Bewley, Michael; Friedman, Ariell; Ferrari, Renata; Hill, Nicole; Hovey, Renae; Barrett, Neville; Pizarro, Oscar; Figueira, Will; Meyer, Lisa; Babcock, Russ; Bellchambers, Lynda; Byrne, Maria; Williams, Stefan B.

2015-01-01

This Australian benthic data set (BENTHOZ-2015) consists of an expert-annotated set of georeferenced benthic images and associated sensor data, captured by an autonomous underwater vehicle (AUV) around Australia. This type of data is of interest to marine scientists studying benthic habitats and organisms. AUVs collect georeferenced images over an area with consistent illumination and altitude, and make it possible to generate broad scale, photo-realistic 3D maps. Marine scientists then typically spend several minutes on each of thousands of images, labeling substratum type and biota at a subset of points. Labels from four Australian research groups were combined using the CATAMI classification scheme, a hierarchical classification scheme based on taxonomy and morphology for scoring marine imagery. This data set consists of 407,968 expert labeled points from around the Australian coast, with associated images, geolocation and other sensor data. The robotic surveys that collected this data form part of Australia's Integrated Marine Observing System (IMOS) ongoing benthic monitoring program. There is reuse potential in marine science, robotics, and computer vision research. PMID:26528396

Generalized implementation of software safety policies

NASA Technical Reports Server (NTRS)

Knight, John C.; Wika, Kevin G.

1994-01-01

As part of a research program in the engineering of software for safety-critical systems, we are performing two case studies. The first case study, which is well underway, is a safety-critical medical application. The second, which is just starting, is a digital control system for a nuclear research reactor. Our goal is to use these case studies to permit us to obtain a better understanding of the issues facing developers of safety-critical systems, and to provide a vehicle for the assessment of research ideas. The case studies are not based on the analysis of existing software development by others. Instead, we are attempting to create software for new and novel systems in a process that ultimately will involve all phases of the software lifecycle. In this abstract, we summarize our results to date in a small part of this project, namely the determination and classification of policies related to software safety that must be enforced to ensure safe operation. We hypothesize that this classification will permit a general approach to the implementation of a policy enforcement mechanism.

Australian sea-floor survey data, with images and expert annotations

NASA Astrophysics Data System (ADS)

Bewley, Michael; Friedman, Ariell; Ferrari, Renata; Hill, Nicole; Hovey, Renae; Barrett, Neville; Pizarro, Oscar; Figueira, Will; Meyer, Lisa; Babcock, Russ; Bellchambers, Lynda; Byrne, Maria; Williams, Stefan B.

2015-10-01

This Australian benthic data set (BENTHOZ-2015) consists of an expert-annotated set of georeferenced benthic images and associated sensor data, captured by an autonomous underwater vehicle (AUV) around Australia. This type of data is of interest to marine scientists studying benthic habitats and organisms. AUVs collect georeferenced images over an area with consistent illumination and altitude, and make it possible to generate broad scale, photo-realistic 3D maps. Marine scientists then typically spend several minutes on each of thousands of images, labeling substratum type and biota at a subset of points. Labels from four Australian research groups were combined using the CATAMI classification scheme, a hierarchical classification scheme based on taxonomy and morphology for scoring marine imagery. This data set consists of 407,968 expert labeled points from around the Australian coast, with associated images, geolocation and other sensor data. The robotic surveys that collected this data form part of Australia's Integrated Marine Observing System (IMOS) ongoing benthic monitoring program. There is reuse potential in marine science, robotics, and computer vision research.

An approach for combining airborne LiDAR and high-resolution aerial color imagery using Gaussian processes

NASA Astrophysics Data System (ADS)

Liu, Yansong; Monteiro, Sildomar T.; Saber, Eli

2015-10-01

Changes in vegetation cover, building construction, road network and traffic conditions caused by urban expansion affect the human habitat as well as the natural environment in rapidly developing cities. It is crucial to assess these changes and respond accordingly by identifying man-made and natural structures with accurate classification algorithms. With the increase in use of multi-sensor remote sensing systems, researchers are able to obtain a more complete description of the scene of interest. By utilizing multi-sensor data, the accuracy of classification algorithms can be improved. In this paper, we propose a method for combining 3D LiDAR point clouds and high-resolution color images to classify urban areas using Gaussian processes (GP). GP classification is a powerful non-parametric classification method that yields probabilistic classification results. It makes predictions in a way that addresses the uncertainty of real world. In this paper, we attempt to identify man-made and natural objects in urban areas including buildings, roads, trees, grass, water and vehicles. LiDAR features are derived from the 3D point clouds and the spatial and color features are extracted from RGB images. For classification, we use the Laplacian approximation for GP binary classification on the new combined feature space. The multiclass classification has been implemented by using one-vs-all binary classification strategy. The result of applying support vector machines (SVMs) and logistic regression (LR) classifier is also provided for comparison. Our experiments show a clear improvement of classification results by using the two sensors combined instead of each sensor separately. Also we found the advantage of applying GP approach to handle the uncertainty in classification result without compromising accuracy compared to SVM, which is considered as the state-of-the-art classification method.

Localization and recognition of traffic signs for automated vehicle control systems

NASA Astrophysics Data System (ADS)

Zadeh, Mahmoud M.; Kasvand, T.; Suen, Ching Y.

1998-01-01

We present a computer vision system for detection and recognition of traffic signs. Such systems are required to assist drivers and for guidance and control of autonomous vehicles on roads and city streets. For experiments we use sequences of digitized photographs and off-line analysis. The system contains four stages. First, region segmentation based on color pixel classification called SRSM. SRSM limits the search to regions of interest in the scene. Second, we use edge tracing to find parts of outer edges of signs which are circular or straight, corresponding to the geometrical shapes of traffic signs. The third step is geometrical analysis of the outer edge and preliminary recognition of each candidate region, which may be a potential traffic sign. The final step in recognition uses color combinations within each region and model matching. This system maybe used for recognition of other types of objects, provided that the geometrical shape and color content remain reasonably constant. The method is reliable, easy to implement, and fast, This differs form the road signs recognition method in the PROMETEUS. The overall structure of the approach is sketched.

Eddy-Current Sensors with Asymmetrical Point Spread Function

PubMed Central

Gajda, Janusz; Stencel, Marek

2016-01-01

This paper concerns a special type of eddy-current sensor in the form of inductive loops. Such sensors are applied in the measuring systems classifying road vehicles. They usually have a rectangular shape with dimensions of 1 × 2 m, and are installed under the surface of the traffic lane. The wide Point Spread Function (PSF) of such sensors causes the information on chassis geometry, contained in the measurement signal, to be strongly averaged. This significantly limits the effectiveness of the vehicle classification. Restoration of the chassis shape, by solving the inverse problem (deconvolution), is also difficult due to the fact that it is ill-conditioned. An original approach to solving this problem is presented in this paper. It is a hardware-based solution and involves the use of inductive loops with an asymmetrical PSF. Laboratory experiments and simulation tests, conducted with models of an inductive loop, confirmed the effectiveness of the proposed solution. In this case, the principle applies that the higher the level of sensor spatial asymmetry, the greater the effectiveness of the deconvolution algorithm. PMID:27782033

Eddy-Current Sensors with Asymmetrical Point Spread Function.

PubMed

Gajda, Janusz; Stencel, Marek

2016-10-04

This paper concerns a special type of eddy-current sensor in the form of inductive loops. Such sensors are applied in the measuring systems classifying road vehicles. They usually have a rectangular shape with dimensions of 1 × 2 m, and are installed under the surface of the traffic lane. The wide Point Spread Function (PSF) of such sensors causes the information on chassis geometry, contained in the measurement signal, to be strongly averaged. This significantly limits the effectiveness of the vehicle classification. Restoration of the chassis shape, by solving the inverse problem (deconvolution), is also difficult due to the fact that it is ill-conditioned. An original approach to solving this problem is presented in this paper. It is a hardware-based solution and involves the use of inductive loops with an asymmetrical PSF. Laboratory experiments and simulation tests, conducted with models of an inductive loop, confirmed the effectiveness of the proposed solution. In this case, the principle applies that the higher the level of sensor spatial asymmetry, the greater the effectiveness of the deconvolution algorithm.

29 CFR 779.360 - Classification of liquefied-petroleum-gas sales.

Code of Federal Regulations, 2014 CFR

2014-07-01

... bus fuel and the repair and servicing of trucks and buses used in over-the-road commercial transportation (including parts and accessories for such vehicles). (b) Sales or repairs of tanks. Sales or repairs of tanks for the storage of liquefied-petroleum-gas are recognized as retail in the industry...

23 CFR 971.210 - Federal lands bridge management system (BMS).

Code of Federal Regulations, 2014 CFR

2014-04-01

... vehicle classification (as appropriate); and (v) A history of conditions and actions taken on each bridge... 23 Highways 1 2014-04-01 2014-04-01 false Federal lands bridge management system (BMS). 971.210... lands bridge management system (BMS). In addition to the requirements provided in § 971.204, the BMS...

23 CFR 971.210 - Federal lands bridge management system (BMS).

Code of Federal Regulations, 2013 CFR

2013-04-01

... vehicle classification (as appropriate); and (v) A history of conditions and actions taken on each bridge... 23 Highways 1 2013-04-01 2013-04-01 false Federal lands bridge management system (BMS). 971.210... lands bridge management system (BMS). In addition to the requirements provided in § 971.204, the BMS...

Stress analysis on passenger deck due to modification from passenger ship to vehicle-carrying ship

NASA Astrophysics Data System (ADS)

Zubaydi, A.; Sujiatanti, S. H.; Hariyanto, T. R.

2018-03-01

Stress is a basic concept in learning about material mechanism. The main focus that needs to be brought to attention in analyzing stress is strength, which is the structural capacity to carry or distribute loads. The structural capacity not only measured by comparing the maximum stress with the material’s yield strength but also with the permissible stress required by the Indonesian Classification Bureau (BKI), which certainly makes it much safer. This final project analyzes stress in passenger deck that experiences modification due to load changes, from passenger load to vehicle one, carrying: 6-wheels truck with maximum weight of 14 tons, a passenger car with maximum weight of 3.5 tons, and a motorcycle with maximum weight of 0.4 tons. The deck structure is modelled using finite element software. The boundary conditions given to the structural model are fix and simple constraint. The load that works on this deck is the deck load which comes from the vehicles on deck with three vehicles’ arrangement plans. After that, software modelling is conducted for analysis purpose. Analysis result shows a variation of maximum stress that occurs i.e. 135 N/mm2, 133 N/mm2, and 152 N/mm2. Those maximum stresses will not affect the structure of passenger deck’s because the maximum stress that occurs indicates smaller value compared to the Indonesian Classification Bureau’s permissible stress (175 N/mm2) as well as the material’s yield strength (235 N/mm2). Thus, the structural strength of passenger deck is shown to be capable of carrying the weight of vehicles in accordance with the three vehicles’ arrangement plans.

Automated Terrestrial EMI Emitter Detection, Classification, and Localization

NASA Astrophysics Data System (ADS)

Stottler, R.; Bowman, C.; Bhopale, A.

2016-09-01

Clear operating spectrum at ground station antenna locations is critically important for communicating with, commanding, controlling, and maintaining the health of satellites. Electro Magnetic Interference (EMI) can interfere with these communications so tracking down the source of EMI is extremely important to prevent it from occurring in the future. The Terrestrial RFI-locating Automation with CasE based Reasoning (TRACER) system is designed to automate terrestrial EMI emitter localization and identification, providing improved space situational awareness, realizing significant manpower savings, dramatically shortening EMI response time, providing capabilities for the system to evolve without programmer involvement, and offering increased support for adversarial scenarios (e.g. jamming). TRACER has been prototyped and tested with real data (amplitudes versus frequency over time) for both satellite communication antennas and sweeping Direction Finding (DF) antennas located near them. TRACER monitors the satellite communication and DF antenna signals to detect and classify EMI using neural network technology trained on past cases of both normal communications and EMI events. Based on details of the signal (its classification, its direction and strength, etc.) one or more cases of EMI investigation methodologies are retrieved, represented as graphical behavior transition networks (BTNs), which very naturally represent the flowchart-like process often followed by experts in time pressured situations, are intuitive to SMEs, and easily edited by them. The appropriate actions, as determined by the BTN are executed and the resulting data processed by Bayesian Networks to update the probabilities of the various possible platforms and source types of the EMI. Bearing sweep of the EMI is used to determine if the EMI's platform is aerial, a ground vehicle or ship, or stationary. If moving, the Friis transmission equation is used to plot the emitter's location and compare it to current flights or moving vehicles. This paper describes the TRACER technologies and results of prototype testing.

Pedestrian recognition using automotive radar sensors

NASA Astrophysics Data System (ADS)

Bartsch, A.; Fitzek, F.; Rasshofer, R. H.

2012-09-01

The application of modern series production automotive radar sensors to pedestrian recognition is an important topic in research on future driver assistance systems. The aim of this paper is to understand the potential and limits of such sensors in pedestrian recognition. This knowledge could be used to develop next generation radar sensors with improved pedestrian recognition capabilities. A new raw radar data signal processing algorithm is proposed that allows deep insights into the object classification process. The impact of raw radar data properties can be directly observed in every layer of the classification system by avoiding machine learning and tracking. This gives information on the limiting factors of raw radar data in terms of classification decision making. To accomplish the very challenging distinction between pedestrians and static objects, five significant and stable object features from the spatial distribution and Doppler information are found. Experimental results with data from a 77 GHz automotive radar sensor show that over 95% of pedestrians can be classified correctly under optimal conditions, which is compareable to modern machine learning systems. The impact of the pedestrian's direction of movement, occlusion, antenna beam elevation angle, linear vehicle movement, and other factors are investigated and discussed. The results show that under real life conditions, radar only based pedestrian recognition is limited due to insufficient Doppler frequency and spatial resolution as well as antenna side lobe effects.

Comparison of injury mortality risk in motor vehicle crash versus other etiologies.

PubMed

Kilgo, Patrick D; Weaver, Ashley A; Barnard, Ryan T; Love, Timothy P; Stitzel, Joel D

2014-06-01

The mortality risk ratio (MRR), a measure of the proportion of people who died that sustained a given injury, is reported to be among the most powerful discriminators of mortality following trauma. The primary aim was to determine whether mechanistic differences exist and are quantifiable when comparing MRR-based injury severity across two broadly defined etiologies (motor vehicle crash (MVC) versus non-MVC) for the clarification of important injury types that have some room for improvement by emergency treatment and vehicle design. All International Classification of Diseases, 9th revision (ICD-9) coded injuries in the National Trauma Data Bank (NTDB) database were stratified into MVC and non-MVC groups and the MRR for each injury was computed within each group. Injuries were classified as 11 different types for MRR comparison between etiologies. Overall, MRRs for specific injuries were 10-18% lower for MVC compared to non-MVC etiologies. MVCs however produced much higher mean MRRs for crushing injuries (0.184 versus 0.072) and internal injuries to the thorax, abdomen, and pelvis (0.200 versus 0.169). Non-MVCs produced much higher MRRs for intracranial injuries (0.199 versus 0.250). Analysis of the top 95% most frequent MVC injuries revealed higher MVC MRR values for 78% of the injuries with MRR ratios indicating an average 50% increase in a given injury's MRR when MVC was the etiology. Addressing the large differences in MRR in between etiologies for identical injuries could provide a reduction in fatalities and may be important to patient triage and vehicle safety design. Copyright © 2014 Elsevier Ltd. All rights reserved.

Source Data Impacts on Epistemic Uncertainty for Launch Vehicle Fault Tree Models

NASA Technical Reports Server (NTRS)

Al Hassan, Mohammad; Novack, Steven; Ring, Robert

2016-01-01

Launch vehicle systems are designed and developed using both heritage and new hardware. Design modifications to the heritage hardware to fit new functional system requirements can impact the applicability of heritage reliability data. Risk estimates for newly designed systems must be developed from generic data sources such as commercially available reliability databases using reliability prediction methodologies, such as those addressed in MIL-HDBK-217F. Failure estimates must be converted from the generic environment to the specific operating environment of the system in which it is used. In addition, some qualification of applicability for the data source to the current system should be made. Characterizing data applicability under these circumstances is crucial to developing model estimations that support confident decisions on design changes and trade studies. This paper will demonstrate a data-source applicability classification method for suggesting epistemic component uncertainty to a target vehicle based on the source and operating environment of the originating data. The source applicability is determined using heuristic guidelines while translation of operating environments is accomplished by applying statistical methods to MIL-HDK-217F tables. The paper will provide one example for assigning environmental factors uncertainty when translating between operating environments for the microelectronic part-type components. The heuristic guidelines will be followed by uncertainty-importance routines to assess the need for more applicable data to reduce model uncertainty.

Acoustic target detection and classification using neural networks

NASA Technical Reports Server (NTRS)

Robertson, James A.; Conlon, Mark

1993-01-01

A neural network approach to the classification of acoustic emissions of ground vehicles and helicopters is demonstrated. Data collected during the Joint Acoustic Propagation Experiment conducted in July of l991 at White Sands Missile Range, New Mexico was used to train a classifier to distinguish between the spectrums of a UH-1, M60, M1 and M114. An output node was also included that would recognize background (i.e. no target) data. Analysis revealed specific hidden nodes responding to the features input into the classifier. Initial results using the neural network were encouraging with high correct identification rates accompanied by high levels of confidence.

Real-time adaptive off-road vehicle navigation and terrain classification

NASA Astrophysics Data System (ADS)

Muller, Urs A.; Jackel, Lawrence D.; LeCun, Yann; Flepp, Beat

2013-05-01

We are developing a complete, self-contained autonomous navigation system for mobile robots that learns quickly, uses commodity components, and has the added benefit of emitting no radiation signature. It builds on the au­tonomous navigation technology developed by Net-Scale and New York University during the Defense Advanced Research Projects Agency (DARPA) Learning Applied to Ground Robots (LAGR) program and takes advantage of recent scientific advancements achieved during the DARPA Deep Learning program. In this paper we will present our approach and algorithms, show results from our vision system, discuss lessons learned from the past, and present our plans for further advancing vehicle autonomy.

Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland

NASA Astrophysics Data System (ADS)

Lu, Bing; He, Yuhong

2017-06-01

Investigating spatio-temporal variations of species composition in grassland is an essential step in evaluating grassland health conditions, understanding the evolutionary processes of the local ecosystem, and developing grassland management strategies. Space-borne remote sensing images (e.g., MODIS, Landsat, and Quickbird) with spatial resolutions varying from less than 1 m to 500 m have been widely applied for vegetation species classification at spatial scales from community to regional levels. However, the spatial resolutions of these images are not fine enough to investigate grassland species composition, since grass species are generally small in size and highly mixed, and vegetation cover is greatly heterogeneous. Unmanned Aerial Vehicle (UAV) as an emerging remote sensing platform offers a unique ability to acquire imagery at very high spatial resolution (centimetres). Compared to satellites or airplanes, UAVs can be deployed quickly and repeatedly, and are less limited by weather conditions, facilitating advantageous temporal studies. In this study, we utilize an octocopter, on which we mounted a modified digital camera (with near-infrared (NIR), green, and blue bands), to investigate species composition in a tall grassland in Ontario, Canada. Seven flight missions were conducted during the growing season (April to December) in 2015 to detect seasonal variations, and four of them were selected in this study to investigate the spatio-temporal variations of species composition. To quantitatively compare images acquired at different times, we establish a processing flow of UAV-acquired imagery, focusing on imagery quality evaluation and radiometric correction. The corrected imagery is then applied to an object-based species classification. Maps of species distribution are subsequently used for a spatio-temporal change analysis. Results indicate that UAV-acquired imagery is an incomparable data source for studying fine-scale grassland species composition, owing to its high spatial resolution. The overall accuracy is around 85% for images acquired at different times. Species composition is spatially attributed by topographical features and soil moisture conditions. Spatio-temporal variation of species composition implies the growing process and succession of different species, which is critical for understanding the evolutionary features of grassland ecosystems. Strengths and challenges of applying UAV-acquired imagery for vegetation studies are summarized at the end.

Semantic Information Extraction of Lanes Based on Onboard Camera Videos

NASA Astrophysics Data System (ADS)

Tang, L.; Deng, T.; Ren, C.

2018-04-01

In the field of autonomous driving, semantic information of lanes is very important. This paper proposes a method of automatic detection of lanes and extraction of semantic information from onboard camera videos. The proposed method firstly detects the edges of lanes by the grayscale gradient direction, and improves the Probabilistic Hough transform to fit them; then, it uses the vanishing point principle to calculate the lane geometrical position, and uses lane characteristics to extract lane semantic information by the classification of decision trees. In the experiment, 216 road video images captured by a camera mounted onboard a moving vehicle were used to detect lanes and extract lane semantic information. The results show that the proposed method can accurately identify lane semantics from video images.

Mortality from violent causes in the Americas.

PubMed

Yunes, J

1993-01-01

This article provides an assessment of 1986 mortality from violent causes in the Americas. Directed at assisting with development of preventive public health measures, it employs data available in the PAHO data base to focus on the under-25 year age group, compare mortality from violent causes with mortality from infectious and parasitic diseases, and evaluate the relative role of motor vehicle traffic accidents, other accidents, suicide, homicide, and deaths from unknown causes in mortality from violent causes. The study uses the classification of causes presented in the International Classification of Diseases, Ninth Revision. The results show that 517,465 deaths from violent causes were registered in 28 countries and political units of the Americas in 1986, mortality from these causes ranging from 19.3 deaths per 100,000 inhabitants in Jamaica to 125 in El Salvador. Examination of available 1980-1986 data from five countries points to steady increases in mortality from violent causes in Brazil and Cuba that began respectively in 1983 and 1984. Assessment of male and female 1986 mortality from these causes in nine countries showed male mortality to be substantially higher, the lowest male:female ratio (in Cuba) being 1.9:1. Among infants, infectious and parasitic disease mortality was greater than mortality from violent causes in most countries. However, from age 1 to the study's 25-year cutoff, mortality from violent causes was found to exceed infectious and parasitic disease mortality in most countries and to play an especially large role in deaths among those 19-24 years old. Data from eight countries suggested that accidents other than motor vehicle traffic accidents were accounting for much of the mortality from violent causes among infants and the 1-4 year age group in 1986, while motor vehicle traffic accidents rivaled other accidents in importance among the older (5-9, 10-14, 15-19, and 19-24) age groups. It appears that the information presented could prove of considerable use in developing policies designed to reduce morbidity and mortality from violent causes (1).

Report to the Higher Education Policy Commission. West Virginia Higher Education Facilities Information System Statewide Institution Report.

ERIC Educational Resources Information Center

West Virginia Higher Education Policy Commission, 2004

2004-01-01

The West Virginia Higher Education Facilities Information System was formed as a method for instituting statewide standardization of space use and classification; to serve as a vehicle for statewide data acquisition; and to provide statistical data that contributes to detailed institutional planning analysis. The result thus far is the production…

Lunar Flight Study Series: Volume 4. Preliminary Investigation of the Astronautics of Earth - Moon Transits

NASA Technical Reports Server (NTRS)

Braud, Nolan J.

1963-01-01

Preliminary information on flight profiles, velocity budgets and launch windows for Apollo and Support Vehicle flights is presented in this report. A newly conceived method of establishing a flight mechanical classification of the earth-moon transits is discussed. The results are empirical and are designed to contribute to the mission mode selection.

75 FR 80430 - Passenger Car and Light Truck Average Fuel Economy Standards Request for Product Plan Information...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-22

.... Battery Type--classification such as NiMH = Nickel Metal Hydride; Li-ion = Lithium Ion; Li-Air = Lithium...; defined per 49 CFR 523.2. 20. Curb Weight--total weight of vehicle including batteries, lubricants, and... other fuels (or chemical battery energy). 39. Electrical System Voltage--measured in volts, e.g., 12...

Effect of air bags and restraining devices on the pattern of facial fractures in motor vehicle crashes.

PubMed

Simoni, Payman; Ostendorf, Robert; Cox, Artemus J

2003-01-01

To examine the relationship between the use of restraining devices and the incidence of specific facial fractures in motor vehicle crashes. Retrospective analysis of patients with facial fractures following a motor vehicle crash. University of Alabama at Birmingham Hospital level I trauma center from 1996 to 2000. Of 3731 patients involved in motor vehicle crashes, a total of 497 patients were found to have facial fractures as determined by International Classification of Diseases, Ninth Revision (ICD-9) codes. Facial fractures were categorized as mandibular, orbital, zygomaticomaxillary complex (ZMC), and nasal. Use of seat belts alone was more effective in decreasing the chance of facial fractures in this population (from 17% to 8%) compared with the use of air bags alone (17% to 11%). The use of seat belts and air bags together decreased the incidence of facial fractures from 17% to 5%. Use of restraining devices in vehicles significantly reduces the chance of incurring facial fractures in a severe motor vehicle crash. However, use of air bags and seat belts does not change the pattern of facial fractures greatly except for ZMC fractures. Air bags are least effective in preventing ZMC fractures. Improving the mechanics of restraining devices might be needed to minimize facial fractures.

Classification of Traffic Related Short Texts to Analyse Road Problems in Urban Areas

NASA Astrophysics Data System (ADS)

Saldana-Perez, A. M. M.; Moreno-Ibarra, M.; Tores-Ruiz, M.

2017-09-01

The Volunteer Geographic Information (VGI) can be used to understand the urban dynamics. In the classification of traffic related short texts to analyze road problems in urban areas, a VGI data analysis is done over a social media's publications, in order to classify traffic events at big cities that modify the movement of vehicles and people through the roads, such as car accidents, traffic and closures. The classification of traffic events described in short texts is done by applying a supervised machine learning algorithm. In the approach users are considered as sensors which describe their surroundings and provide their geographic position at the social network. The posts are treated by a text mining process and classified into five groups. Finally, the classified events are grouped in a data corpus and geo-visualized in the study area, to detect the places with more vehicular problems.

Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS)

NASA Technical Reports Server (NTRS)

Alexander, Tiffaney Miller

2017-01-01

Research results have shown that more than half of aviation, aerospace and aeronautics mishaps incidents are attributed to human error. As a part of Safety within space exploration ground processing operations, the identification and/or classification of underlying contributors and causes of human error must be identified, in order to manage human error. This research provides a framework and methodology using the Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS), as an analysis tool to identify contributing factors, their impact on human error events, and predict the Human Error probabilities (HEPs) of future occurrences. This research methodology was applied (retrospectively) to six (6) NASA ground processing operations scenarios and thirty (30) years of Launch Vehicle related mishap data. This modifiable framework can be used and followed by other space and similar complex operations.

Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS)

NASA Technical Reports Server (NTRS)

Alexander, Tiffaney Miller

2017-01-01

Research results have shown that more than half of aviation, aerospace and aeronautics mishaps/incidents are attributed to human error. As a part of Safety within space exploration ground processing operations, the identification and/or classification of underlying contributors and causes of human error must be identified, in order to manage human error. This research provides a framework and methodology using the Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS), as an analysis tool to identify contributing factors, their impact on human error events, and predict the Human Error probabilities (HEPs) of future occurrences. This research methodology was applied (retrospectively) to six (6) NASA ground processing operations scenarios and thirty (30) years of Launch Vehicle related mishap data. This modifiable framework can be used and followed by other space and similar complex operations.

Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS)

NASA Technical Reports Server (NTRS)

Alexander, Tiffaney Miller

2017-01-01

Research results have shown that more than half of aviation, aerospace and aeronautics mishaps incidents are attributed to human error. As a part of Quality within space exploration ground processing operations, the identification and or classification of underlying contributors and causes of human error must be identified, in order to manage human error.This presentation will provide a framework and methodology using the Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS), as an analysis tool to identify contributing factors, their impact on human error events, and predict the Human Error probabilities (HEPs) of future occurrences. This research methodology was applied (retrospectively) to six (6) NASA ground processing operations scenarios and thirty (30) years of Launch Vehicle related mishap data. This modifiable framework can be used and followed by other space and similar complex operations.

Effect of traffic and driving characteristics on morphology of atmospheric soot particles at freeway on-ramps.

PubMed

China, Swarup; Salvadori, Neila; Mazzoleni, Claudio

2014-03-18

Vehicles represent a major source of soot in urban environments. Knowledge of the morphology and mixing of soot particles is fundamental to understand their potential health and climatic impacts. We investigate 5738 single particles collected at six different cloverleaf freeway on-ramps in Southern Michigan, using 2D images from scanning electron microscopy. Of those, 3364 particles are soot. We present an analysis of the morphological and mixing properties of those soot particles. The relative abundance of soot particles shows a positive association with traffic density (number of vehicles per minute). A classification of the mixing state of freshly emitted soot particles shows that most of them are bare (or thinly coated) (72%) and some are partly coated (22%). We find that the fractal dimension of soot particles (one of the most relevant morphological descriptors) varies from site to site, and increases with increasing vehicle specific power that represents the driving/engine load conditions, and with increasing percentage of vehicles older than 15 years. Our results suggest that driving conditions, and vehicle age and type have significant influence on the morphology of soot particles.

Automatic Centerline Extraction of Coverd Roads by Surrounding Objects from High Resolution Satellite Images

NASA Astrophysics Data System (ADS)

Kamangir, H.; Momeni, M.; Satari, M.

2017-09-01

This paper presents an automatic method to extract road centerline networks from high and very high resolution satellite images. The present paper addresses the automated extraction roads covered with multiple natural and artificial objects such as trees, vehicles and either shadows of buildings or trees. In order to have a precise road extraction, this method implements three stages including: classification of images based on maximum likelihood algorithm to categorize images into interested classes, modification process on classified images by connected component and morphological operators to extract pixels of desired objects by removing undesirable pixels of each class, and finally line extraction based on RANSAC algorithm. In order to evaluate performance of the proposed method, the generated results are compared with ground truth road map as a reference. The evaluation performance of the proposed method using representative test images show completeness values ranging between 77% and 93%.

Flow diagram analysis of electrical fatalities in construction industry.

PubMed

Chi, Chia-Fen; Lin, Yuan-Yuan; Ikhwan, Mohamad

2012-01-01

The current study reanalyzed 250 electrical fatalities in the construction industry from 1996 to 2002 into seven patterns based on source of electricity (power line, energized equipment, improperly installed or damaged equipment), direct contact or indirect contact through some source of injury (boom vehicle, metal bar or pipe, and other conductive material). Each fatality was coded in terms of age, company size, experience, performing tasks, source of injury, accident cause and hazard pattern. The Chi-square Automatic Interaction Detector (CHAID) was applied to the coded data of the fatal electrocution to find a subset of predictors that might derive meaningful classifications or accidents scenarios. A series of Flow Diagrams was constructed based on CHAID result to illustrate the flow of electricity travelling from electrical source to human body. Each of the flow diagrams can be directly linked with feasible prevention strategies by cutting the flow of electricity.

Modeling and performance of HF/OTH (High-Frequency/Over-the-Horizon) radar target identification systems

NASA Astrophysics Data System (ADS)

Strausberger, Donald J.

Several Radar Target Identification (RTI) techniques have been developed at The Ohio State University in recent years. Using the ElectroScience Laboratory compact range a large database of coherent RCS measurement has been constructed for several types of targets (aircraft, ships, and ground vehicles) at a variety of polarizations, aspect angles, and frequency bands. This extensive database has been used to analyze the performance of several different classification algorithms through the use of computer simulations. In order to optimize classification performance, it was concluded that the radar frequency range should lie in the Rayleigh-resonance frequency range, where the wavelength is on the order of or larger than the target size. For aircraft and ships with general dimensions on the order of 10 meters to 100 meters it is apparent that the High Frequency (HF) band provides optimal classification performance. Since existing HF radars are currently being used for detection and tracking or aircraft and ships of these dimensions, it is natural to further investigate the possibility of using these existing radars as the measurement devices in a radar target classification system.

Flexible mobile robot system for smart optical pipe inspection

NASA Astrophysics Data System (ADS)

Kampfer, Wolfram; Bartzke, Ralf; Ziehl, Wolfgang

1998-03-01

Damages of pipes can be inspected and graded by TV technology available on the market. Remotely controlled vehicles carry a TV-camera through pipes. Thus, depending on the experience and the capability of the operator, diagnosis failures can not be avoided. The classification of damages requires the knowledge of the exact geometrical dimensions of the damages such as width and depth of cracks, fractures and defect connections. Within the framework of a joint R&D project a sensor based pipe inspection system named RODIAS has been developed with two partners from industry and research institute. It consists of a remotely controlled mobile robot which carries intelligent sensors for on-line sewerage inspection purpose. The sensor is based on a 3D-optical sensor and a laser distance sensor. The laser distance sensor is integrated in the optical system of the camera and can measure the distance between camera and object. The angle of view can be determined from the position of the pan and tilt unit. With coordinate transformations it is possible to calculate the spatial coordinates for every point of the video image. So the geometry of an object can be described exactly. The company Optimess has developed TriScan32, a special software for pipe condition classification. The user can start complex measurements of profiles, pipe displacements or crack widths simply by pressing a push-button. The measuring results are stored together with other data like verbal damage descriptions and digitized images in a data base.

Rotating optical geometry sensor for inner pipe-surface reconstruction

NASA Astrophysics Data System (ADS)

Ritter, Moritz; Frey, Christan W.

2010-01-01

The inspection of sewer or fresh water pipes is usually carried out by a remotely controlled inspection vehicle equipped with a high resolution camera and a lightning system. This operator-oriented approach based on offline analysis of the recorded images is highly subjective and prone to errors. Beside the subjective classification of pipe defects through the operator standard closed circuit television (CCTV) technology is not suitable for detecting geometrical deformations resulting from e.g. structural mechanical weakness of the pipe, corrosion of e.g. cast-iron material or sedimentations. At Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB) in Karlsruhe, Germany, a new Rotating Optical Geometry Sensor (ROGS) for pipe inspection has been developed which is capable of measuring the inner pipe geometry very precisely over the whole pipe length. This paper describes the developed ROGS system and the online adaption strategy for choosing the optimal system parameters. These parameters are the rotation and traveling speed dependent from the pipe diameter. Furthermore, a practicable calibration methodology is presented which guarantees an identification of the several internal sensor parameters. ROGS has been integrated in two different systems: A rod based system for small fresh water pipes and a standard inspection vehicle based system for large sewer Pipes. These systems have been successfully applied to different pipe systems. With this measurement method the geometric information can be used efficiently for an objective repeatable quality evaluation. Results and experiences in the area of fresh water pipe inspection will be presented.

Small Imaging Depth LIDAR and DCNN-Based Localization for Automated Guided Vehicle †

PubMed Central

Ito, Seigo; Hiratsuka, Shigeyoshi; Ohta, Mitsuhiko; Matsubara, Hiroyuki; Ogawa, Masaru

2018-01-01

We present our third prototype sensor and a localization method for Automated Guided Vehicles (AGVs), for which small imaging LIght Detection and Ranging (LIDAR) and fusion-based localization are fundamentally important. Our small imaging LIDAR, named the Single-Photon Avalanche Diode (SPAD) LIDAR, uses a time-of-flight method and SPAD arrays. A SPAD is a highly sensitive photodetector capable of detecting at the single-photon level, and the SPAD LIDAR has two SPAD arrays on the same chip for detection of laser light and environmental light. Therefore, the SPAD LIDAR simultaneously outputs range image data and monocular image data with the same coordinate system and does not require external calibration among outputs. As AGVs travel both indoors and outdoors with vibration, this calibration-less structure is particularly useful for AGV applications. We also introduce a fusion-based localization method, named SPAD DCNN, which uses the SPAD LIDAR and employs a Deep Convolutional Neural Network (DCNN). SPAD DCNN can fuse the outputs of the SPAD LIDAR: range image data, monocular image data and peak intensity image data. The SPAD DCNN has two outputs: the regression result of the position of the SPAD LIDAR and the classification result of the existence of a target to be approached. Our third prototype sensor and the localization method are evaluated in an indoor environment by assuming various AGV trajectories. The results show that the sensor and localization method improve the localization accuracy. PMID:29320434

Small Imaging Depth LIDAR and DCNN-Based Localization for Automated Guided Vehicle.

PubMed

Ito, Seigo; Hiratsuka, Shigeyoshi; Ohta, Mitsuhiko; Matsubara, Hiroyuki; Ogawa, Masaru

2018-01-10

We present our third prototype sensor and a localization method for Automated Guided Vehicles (AGVs), for which small imaging LIght Detection and Ranging (LIDAR) and fusion-based localization are fundamentally important. Our small imaging LIDAR, named the Single-Photon Avalanche Diode (SPAD) LIDAR, uses a time-of-flight method and SPAD arrays. A SPAD is a highly sensitive photodetector capable of detecting at the single-photon level, and the SPAD LIDAR has two SPAD arrays on the same chip for detection of laser light and environmental light. Therefore, the SPAD LIDAR simultaneously outputs range image data and monocular image data with the same coordinate system and does not require external calibration among outputs. As AGVs travel both indoors and outdoors with vibration, this calibration-less structure is particularly useful for AGV applications. We also introduce a fusion-based localization method, named SPAD DCNN, which uses the SPAD LIDAR and employs a Deep Convolutional Neural Network (DCNN). SPAD DCNN can fuse the outputs of the SPAD LIDAR: range image data, monocular image data and peak intensity image data. The SPAD DCNN has two outputs: the regression result of the position of the SPAD LIDAR and the classification result of the existence of a target to be approached. Our third prototype sensor and the localization method are evaluated in an indoor environment by assuming various AGV trajectories. The results show that the sensor and localization method improve the localization accuracy.

Poster presentations at the fifth engineering foundation conference on automated cytology, Pensacola, Florida, December 12--17, 1976

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sullivan, E.M.

1977-02-01

Poster sessions were used as a vehicle of information exchange. Of the 101 posters presented, abstracts were received for 71. The 71 abstracts presented are concerned with cell-cycle analysis by flow cytometry, flow microfluorometric DNA measurements, application of microfluorometry to cancer chemotherapy, automated classification of neutrophils, and other aspects of automated cytology. (HLW)

FET. Control and equipment building (TAN630). Sections. Earth cover. Shielded ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

FET. Control and equipment building (TAN-630). Sections. Earth cover. Shielded access entries for personnel and vehicles. Ralph M. Parsons 1229-2 ANP/GE-5-630-A-3. Date: March 1957. Approved by INEEL Classification Office for public release. INEEL index code no. 036-0630-00-693-107082 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

Gunshot injuries.

PubMed

Hinkle, J; Betz, S

1995-05-01

If current trends for this nation continue, by the year 2003 the number of people killed by firearms will exceed the number of people killed in motor vehicle accidents. Critical care practitioners must understand the mechanism of injury associated with firearm injuries to provide optimal care. This article reviews internal, exterior, and terminal ballistics, bullet design, wound classification, and initial assessment and treatment of firearm injuries.

Environmental Influences On Diel Calling Behavior In Baleen Whales

DTIC Science & Technology

2015-09-30

and calm seas were infrequent and short (Figure 1b), making traditional shipboard marine mammal observations difficult. The real time detection...first use of real-time detection and reporting of marine mammal calls from autonomous underwater vehicles to adaptively plan research activities. 3...conferences: • 6th International Workshop on Detection, Classification, Localization, and Density Estimation (DCLDE) of Marine Mammals using

Error analysis in a stereo vision-based pedestrian detection sensor for collision avoidance applications.

PubMed

Llorca, David F; Sotelo, Miguel A; Parra, Ignacio; Ocaña, Manuel; Bergasa, Luis M

2010-01-01

This paper presents an analytical study of the depth estimation error of a stereo vision-based pedestrian detection sensor for automotive applications such as pedestrian collision avoidance and/or mitigation. The sensor comprises two synchronized and calibrated low-cost cameras. Pedestrians are detected by combining a 3D clustering method with Support Vector Machine-based (SVM) classification. The influence of the sensor parameters in the stereo quantization errors is analyzed in detail providing a point of reference for choosing the sensor setup according to the application requirements. The sensor is then validated in real experiments. Collision avoidance maneuvers by steering are carried out by manual driving. A real time kinematic differential global positioning system (RTK-DGPS) is used to provide ground truth data corresponding to both the pedestrian and the host vehicle locations. The performed field test provided encouraging results and proved the validity of the proposed sensor for being used in the automotive sector towards applications such as autonomous pedestrian collision avoidance.

Error Analysis in a Stereo Vision-Based Pedestrian Detection Sensor for Collision Avoidance Applications

PubMed Central

Llorca, David F.; Sotelo, Miguel A.; Parra, Ignacio; Ocaña, Manuel; Bergasa, Luis M.

2010-01-01

This paper presents an analytical study of the depth estimation error of a stereo vision-based pedestrian detection sensor for automotive applications such as pedestrian collision avoidance and/or mitigation. The sensor comprises two synchronized and calibrated low-cost cameras. Pedestrians are detected by combining a 3D clustering method with Support Vector Machine-based (SVM) classification. The influence of the sensor parameters in the stereo quantization errors is analyzed in detail providing a point of reference for choosing the sensor setup according to the application requirements. The sensor is then validated in real experiments. Collision avoidance maneuvers by steering are carried out by manual driving. A real time kinematic differential global positioning system (RTK-DGPS) is used to provide ground truth data corresponding to both the pedestrian and the host vehicle locations. The performed field test provided encouraging results and proved the validity of the proposed sensor for being used in the automotive sector towards applications such as autonomous pedestrian collision avoidance. PMID:22319323

Small unmanned aerial vehicles (micro-UAVs, drones) in plant ecology.

PubMed

Cruzan, Mitchell B; Weinstein, Ben G; Grasty, Monica R; Kohrn, Brendan F; Hendrickson, Elizabeth C; Arredondo, Tina M; Thompson, Pamela G

2016-09-01

Low-elevation surveys with small aerial drones (micro-unmanned aerial vehicles [UAVs]) may be used for a wide variety of applications in plant ecology, including mapping vegetation over small- to medium-sized regions. We provide an overview of methods and procedures for conducting surveys and illustrate some of these applications. Aerial images were obtained by flying a small drone along transects over the area of interest. Images were used to create a composite image (orthomosaic) and a digital surface model (DSM). Vegetation classification was conducted manually and using an automated routine. Coverage of an individual species was estimated from aerial images. We created a vegetation map for the entire region from the orthomosaic and DSM, and mapped the density of one species. Comparison of our manual and automated habitat classification confirmed that our mapping methods were accurate. A species with high contrast to the background matrix allowed adequate estimate of its coverage. The example surveys demonstrate that small aerial drones are capable of gathering large amounts of information on the distribution of vegetation and individual species with minimal impact to sensitive habitats. Low-elevation aerial surveys have potential for a wide range of applications in plant ecology.

Superconducting magnetic sensors for mine detection and classification

NASA Astrophysics Data System (ADS)

Clem, Ted R.; Koch, Roger H.; Keefe, George A.

1995-06-01

Sensors incorporating Superconducting Quantum Interference Devices (SQUIDs) provide the greatest sensitivity for magnetic anomaly detection available with current technology. During the 1980's, the Naval Surface Warfare Center Coastal Systems Station (CSS) developed a superconducting magnetic sensor capable of operation outside of the laboratory environment. This sensor demonstrated rugged, reliable performance even onboard undersea towed platforms. With this sensor, the CSS was able to demonstrate buried mine detection for the US Navy. Subsequently the sensor was incorporated into a multisensor suite onboard an underwater towed vehicle to provide a robust mine hunting capability for the Magnetic and Acoustic Detection of Mines (MADOM) project. This sensor technology utilized niobium superconducting componentry cooled by liquid helium to temperatures on the order of 4 degrees Kelvin (K). In the late 1980's a new class of superconductors was discovered with critical temperatures above the boiling point of liquid nitrogen (77K). This advance has opened up new opportunities, especially for mine reconnaissance and hunting from small unmanned underwater vehicles (UUVs). This paper describes the magnetic sensor detection and classification concept developed for MADOM. In addition, opportunities for UUV operations made possible with high Tc technology and the Navy's current efforts in this area will be addressed.

Statistical classification of road pavements using near field vehicle rolling noise measurements.

PubMed

Paulo, Joel Preto; Coelho, J L Bento; Figueiredo, Mário A T

2010-10-01

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.

Method of radiometric quality assessment of NIR images acquired with a custom sensor mounted on an unmanned aerial vehicle

NASA Astrophysics Data System (ADS)

Wierzbicki, Damian; Fryskowska, Anna; Kedzierski, Michal; Wojtkowska, Michalina; Delis, Paulina

2018-01-01

Unmanned aerial vehicles are suited to various photogrammetry and remote sensing missions. Such platforms are equipped with various optoelectronic sensors imaging in the visible and infrared spectral ranges and also thermal sensors. Nowadays, near-infrared (NIR) images acquired from low altitudes are often used for producing orthophoto maps for precision agriculture among other things. One major problem results from the application of low-cost custom and compact NIR cameras with wide-angle lenses introducing vignetting. In numerous cases, such cameras acquire low radiometric quality images depending on the lighting conditions. The paper presents a method of radiometric quality assessment of low-altitude NIR imagery data from a custom sensor. The method utilizes statistical analysis of NIR images. The data used for the analyses were acquired from various altitudes in various weather and lighting conditions. An objective NIR imagery quality index was determined as a result of the research. The results obtained using this index enabled the classification of images into three categories: good, medium, and low radiometric quality. The classification makes it possible to determine the a priori error of the acquired images and assess whether a rerun of the photogrammetric flight is necessary.

Objective measures, sensors and computational techniques for stress recognition and classification: a survey.

PubMed

Sharma, Nandita; Gedeon, Tom

2012-12-01

Stress is a major growing concern in our day and age adversely impacting both individuals and society. Stress research has a wide range of benefits from improving personal operations, learning, and increasing work productivity to benefiting society - making it an interesting and socially beneficial area of research. This survey reviews sensors that have been used to measure stress and investigates techniques for modelling stress. It discusses non-invasive and unobtrusive sensors for measuring computed stress, a term we coin in the paper. Sensors that do not impede everyday activities that could be used by those who would like to monitor stress levels on a regular basis (e.g. vehicle drivers, patients with illnesses linked to stress) is the focus of the discussion. Computational techniques have the capacity to determine optimal sensor fusion and automate data analysis for stress recognition and classification. Several computational techniques have been developed to model stress based on techniques such as Bayesian networks, artificial neural networks, and support vector machines, which this survey investigates. The survey concludes with a summary and provides possible directions for further computational stress research. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Detection and Classification of Pole-Like Objects from Mobile Mapping Data

NASA Astrophysics Data System (ADS)

Fukano, K.; Masuda, H.

2015-08-01

Laser scanners on a vehicle-based mobile mapping system can capture 3D point-clouds of roads and roadside objects. Since roadside objects have to be maintained periodically, their 3D models are useful for planning maintenance tasks. In our previous work, we proposed a method for detecting cylindrical poles and planar plates in a point-cloud. However, it is often required to further classify pole-like objects into utility poles, streetlights, traffic signals and signs, which are managed by different organizations. In addition, our previous method may fail to extract low pole-like objects, which are often observed in urban residential areas. In this paper, we propose new methods for extracting and classifying pole-like objects. In our method, we robustly extract a wide variety of poles by converting point-clouds into wireframe models and calculating cross-sections between wireframe models and horizontal cutting planes. For classifying pole-like objects, we subdivide a pole-like object into five subsets by extracting poles and planes, and calculate feature values of each subset. Then we apply a supervised machine learning method using feature variables of subsets. In our experiments, our method could achieve excellent results for detection and classification of pole-like objects.

Clinical research data warehouse governance for distributed research networks in the USA: a systematic review of the literature.

PubMed

Holmes, John H; Elliott, Thomas E; Brown, Jeffrey S; Raebel, Marsha A; Davidson, Arthur; Nelson, Andrew F; Chung, Annie; La Chance, Pierre; Steiner, John F

2014-01-01

To review the published, peer-reviewed literature on clinical research data warehouse governance in distributed research networks (DRNs). Medline, PubMed, EMBASE, CINAHL, and INSPEC were searched for relevant documents published through July 31, 2013 using a systematic approach. Only documents relating to DRNs in the USA were included. Documents were analyzed using a classification framework consisting of 10 facets to identify themes. 6641 documents were retrieved. After screening for duplicates and relevance, 38 were included in the final review. A peer-reviewed literature on data warehouse governance is emerging, but is still sparse. Peer-reviewed publications on UK research network governance were more prevalent, although not reviewed for this analysis. All 10 classification facets were used, with some documents falling into two or more classifications. No document addressed costs associated with governance. Even though DRNs are emerging as vehicles for research and public health surveillance, understanding of DRN data governance policies and procedures is limited. This is expected to change as more DRN projects disseminate their governance approaches as publicly available toolkits and peer-reviewed publications. While peer-reviewed, US-based DRN data warehouse governance publications have increased, DRN developers and administrators are encouraged to publish information about these programs. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems.

PubMed

Xu, Chang; Lu, Rongxing; Wang, Huaxiong; Zhu, Liehuang; Huang, Cheng

2017-03-03

Air pollution has become one of the most pressing environmental issues in recent years. According to a World Health Organization (WHO) report, air pollution has led to the deaths of millions of people worldwide. Accordingly, expensive and complex air-monitoring instruments have been exploited to measure air pollution. Comparatively, a vehicle sensing system (VSS), as it can be effectively used for many purposes and can bring huge financial benefits in reducing high maintenance and repair costs, has received considerable attention. However, the privacy issues of VSS including vehicles' location privacy have not been well addressed. Therefore, in this paper, we propose a new privacy-preserving data aggregation scheme, called PAVS, for VSS. Specifically, PAVS combines privacy-preserving classification and privacy-preserving statistics on both the mean E(·) and variance Var(·), which makes VSS more promising, as, with minimal privacy leakage, more vehicles are willing to participate in sensing. Detailed analysis shows that the proposed PAVS can achieve the properties of privacy preservation, data accuracy and scalability. In addition, the performance evaluations via extensive simulations also demonstrate its efficiency.

Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques

PubMed Central

Kim, Il-Hwan; Bong, Jae-Hwan; Park, Jooyoung; Park, Shinsuk

2017-01-01

Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics. PMID:28604582

Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

PubMed Central

Lee, Boon-Giin; Lee, Boon-Leng; Chung, Wan-Young

2014-01-01

Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG) and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz) regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT) method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI) technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM) and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals. PMID:25264954

U.S. regional greenhouse gas emissions analysis comparing highly resolved vehicle miles traveled and CO2 emissions: mitigation implications and their effect on atmospheric measurements

NASA Astrophysics Data System (ADS)

Mendoza, D. L.; Gurney, K. R.

2010-12-01

Carbon dioxide (CO2) is the most abundant anthropogenic greenhouse gas and projections of fossil fuel energy demand show CO2 concentrations increasing indefinitely into the future. After electricity production, the transportation sector is the second largest CO2 emitting economic sector in the United States, accounting for 32.3% of the total U.S. emissions in 2002. Over 80% of the transport sector is composed of onroad emissions, with the remainder shared by the nonroad, aircraft, railroad, and commercial marine vessel transportation. In order to construct effective mitigation policy for the onroad transportation sector and more accurately predict CO2 emissions for use in transport models and atmospheric measurements, analysis must incorporate the three components that determine the CO2 onroad transport emissions: vehicle fleet composition, average speed of travel, and emissions regulation strategies. Studies to date, however, have either focused on one of these three components, have been only completed at the national scale, or have not explicitly represented CO2 emissions instead relying on the use of vehicle miles traveled (VMT) as an emissions proxy. National-level projections of VMT growth is not sufficient to highlight regional differences in CO2 emissions growth due to the heterogeneity of vehicle fleet and each state’s road network which determines the speed of travel of vehicles. We examine how an analysis based on direct CO2 emissions and an analysis based on VMT differ in terms of their emissions and mitigation implications highlighting potential biases introduced by the VMT-based approach. This analysis is performed at the US state level and results are disaggregated by road and vehicle classification. We utilize the results of the Vulcan fossil fuel CO2 emissions inventory which quantified emissions for the year 2002 across all economic sectors in the US at high resolution. We perform this comparison by fuel type,12 road types, and 12 vehicle types for US census regions and individual states. At the national level, rural roads show a 5% higher CO2 relative fraction compared to the VMT relative fraction, mostly due to a 15% higher CO2 fraction on rural interstates as a result of a higher proportion of heavy-duty vehicles such as large trucks. The diesel vehicle fleet has a 62% higher CO2 fraction compared to VMT with the largest contributors being buses and the heaviest truck classes. The differences become larger when analyzed at the state level. For example, Tennessee has 30% higher CO2 fractions compared to VMT on rural interstates and New York has 175% higher CO2 fractions compared to VMT for the bus vehicle class. Using VMT as a proxy for CO2 emissions results in incorrect estimations of CO2 emissions because of the strong space and time variations in fleet composition and road type. At the national scale the differences among the two methods are very small, but the spatial signature of CO2 emitted by onroad traffic is very strong and highly dependent on the region which can be confirmed with atmospheric measurements from aircraft and flux towers.

Detection and Mapping of the Geomorphic Effects of Flooding Using UAV Photogrammetry

NASA Astrophysics Data System (ADS)

Langhammer, Jakub; Vacková, Tereza

2018-04-01

In this paper, we present a novel technique for the objective detection of the geomorphological effects of flooding in riverbeds and floodplains using imagery acquired by unmanned aerial vehicles (UAVs, also known as drones) equipped with an panchromatic camera. The proposed method is based on the fusion of the two key data products of UAV photogrammetry, the digital elevation model (DEM), and the orthoimage, as well as derived qualitative information, which together serve as the basis for object-based segmentation and the supervised classification of fluvial forms. The orthoimage is used to calculate textural features, enabling detection of the structural properties of the image area and supporting the differentiation of features with similar spectral responses but different surface structures. The DEM is used to derive a flood depth model and the terrain ruggedness index, supporting the detection of bank erosion. All the newly derived information layers are merged with the orthoimage to form a multi-band data set, which is used for object-based segmentation and the supervised classification of key fluvial forms resulting from flooding, i.e., fresh and old gravel accumulations, sand accumulations, and bank erosion. The method was tested on the effects of a snowmelt flood that occurred in December 2015 in a montane stream in the Sumava Mountains, Czech Republic, Central Europe. A multi-rotor UAV was used to collect images of a 1-km-long and 200-m-wide stretch of meandering stream with fresh traces of fluvial activity. The performed segmentation and classification proved that the fusion of 2D and 3D data with the derived qualitative layers significantly enhanced the reliability of the fluvial form detection process. The assessment accuracy for all of the detected classes exceeded 90%. The proposed technique proved its potential for application in rapid mapping and detection of the geomorphological effects of flooding.

Validity of PALMS GPS scoring of active and passive travel compared with SenseCam.

PubMed

Carlson, Jordan A; Jankowska, Marta M; Meseck, Kristin; Godbole, Suneeta; Natarajan, Loki; Raab, Fredric; Demchak, Barry; Patrick, Kevin; Kerr, Jacqueline

2015-03-01

The objective of this study is to assess validity of the personal activity location measurement system (PALMS) for deriving time spent walking/running, bicycling, and in vehicle, using SenseCam (Microsoft, Redmond, WA) as the comparison. Forty adult cyclists wore a Qstarz BT-Q1000XT GPS data logger (Qstarz International Co., Taipei, Taiwan) and SenseCam (camera worn around the neck capturing multiple images every minute) for a mean time of 4 d. PALMS used distance and speed between global positioning system (GPS) points to classify whether each minute was part of a trip (yes/no), and if so, the trip mode (walking/running, bicycling, or in vehicle). SenseCam images were annotated to create the same classifications (i.e., trip yes/no and mode). Contingency tables (2 × 2) and confusion matrices were calculated at the minute level for PALMS versus SenseCam classifications. Mixed-effects linear regression models estimated agreement (mean differences and intraclass correlation coefficients) between PALMS and SenseCam with regard to minutes/day in each mode. Minute-level sensitivity, specificity, and negative predictive value were ≥88%, and positive predictive value was ≥75% for non-mode-specific trip detection. Seventy-two percent to 80% of outdoor walking/running minutes, 73% of bicycling minutes, and 74%-76% of in-vehicle minutes were correctly classified by PALMS. For minutes per day, PALMS had a mean bias (i.e., amount of over or under estimation) of 2.4-3.1 min (11%-15%) for walking/running, 2.3-2.9 min (7%-9%) for bicycling, and 4.3-5 min (15%-17%) for vehicle time. Intraclass correlation coefficients were ≥0.80 for all modes. PALMS has validity for processing GPS data to objectively measure time spent walking/running, bicycling, and in vehicle in population studies. Assessing travel patterns is one of many valuable applications of GPS in physical activity research that can improve our understanding of the determinants and health outcomes of active transportation as well as its effect on physical activity.

Exploring Neuro-Physiological Correlates of Drivers' Mental Fatigue Caused by Sleep Deprivation Using Simultaneous EEG, ECG, and fNIRS Data

PubMed Central

Ahn, Sangtae; Nguyen, Thien; Jang, Hyojung; Kim, Jae G.; Jun, Sung C.

2016-01-01

Investigations of the neuro-physiological correlates of mental loads, or states, have attracted significant attention recently, as it is particularly important to evaluate mental fatigue in drivers operating a motor vehicle. In this research, we collected multimodal EEG/ECG/EOG and fNIRS data simultaneously to develop algorithms to explore neuro-physiological correlates of drivers' mental states. Each subject performed simulated driving under two different conditions (well-rested and sleep-deprived) on different days. During the experiment, we used 68 electrodes for EEG/ECG/EOG and 8 channels for fNIRS recordings. We extracted the prominent features of each modality to distinguish between the well-rested and sleep-deprived conditions, and all multimodal features, except EOG, were combined to quantify mental fatigue during driving. Finally, a novel driving condition level (DCL) was proposed that distinguished clearly between the features of well-rested and sleep-deprived conditions. This proposed DCL measure may be applicable to real-time monitoring of the mental states of vehicle drivers. Further, the combination of methods based on each classifier yielded substantial improvements in the classification accuracy between these two conditions. PMID:27242483

Analyzing the threat of unmanned aerial vehicles (UAV) to nuclear facilities

DOE PAGES

Solodov, Alexander; Williams, Adam; Al Hanaei, Sara; ...

2017-04-18

Unmanned aerial vehicles (UAV) are among the major growing technologies that have many beneficial applications, yet they can also pose a significant threat. Recently, several incidents occurred with UAVs violating privacy of the public and security of sensitive facilities, including several nuclear power plants in France. The threat of UAVs to the security of nuclear facilities is of great importance and is the focus of this work. This paper presents an overview of UAV technology and classification, as well as its applications and potential threats. We show several examples of recent security incidents involving UAVs in France, USA, and Unitedmore » Arab Emirates. Further, the potential threats to nuclear facilities and measures to prevent them are evaluated. The importance of measures for detection, delay, and response (neutralization) of UAVs at nuclear facilities are discussed. An overview of existing technologies along with their strength and weaknesses are shown. Finally, the results of a gap analysis in existing approaches and technologies is presented in the form of potential technological and procedural areas for research and development. Furthermore based on this analysis, directions for future work in the field can be devised and prioritized.« less

Large-scale machine learning and evaluation platform for real-time traffic surveillance

NASA Astrophysics Data System (ADS)

Eichel, Justin A.; Mishra, Akshaya; Miller, Nicholas; Jankovic, Nicholas; Thomas, Mohan A.; Abbott, Tyler; Swanson, Douglas; Keller, Joel

2016-09-01

In traffic engineering, vehicle detectors are trained on limited datasets, resulting in poor accuracy when deployed in real-world surveillance applications. Annotating large-scale high-quality datasets is challenging. Typically, these datasets have limited diversity; they do not reflect the real-world operating environment. There is a need for a large-scale, cloud-based positive and negative mining process and a large-scale learning and evaluation system for the application of automatic traffic measurements and classification. The proposed positive and negative mining process addresses the quality of crowd sourced ground truth data through machine learning review and human feedback mechanisms. The proposed learning and evaluation system uses a distributed cloud computing framework to handle data-scaling issues associated with large numbers of samples and a high-dimensional feature space. The system is trained using AdaBoost on 1,000,000 Haar-like features extracted from 70,000 annotated video frames. The trained real-time vehicle detector achieves an accuracy of at least 95% for 1/2 and about 78% for 19/20 of the time when tested on ˜7,500,000 video frames. At the end of 2016, the dataset is expected to have over 1 billion annotated video frames.

Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery

PubMed Central

Moran, Emilio Federico.

2010-01-01

High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land cover classification with Quickbird imagery. Traditional per-pixel spectral-based supervised classification, incorporation of textural images and multispectral images, spectral-spatial classifier, and segmentation-based classification are examined in a relatively new developing urban landscape, Lucas do Rio Verde in Mato Grosso State, Brazil. This research shows that use of spatial information during the image classification procedure, either through the integrated use of textural and spectral images or through the use of segmentation-based classification method, can significantly improve land cover classification performance. PMID:21643433

Neural Network Based Sensory Fusion for Landmark Detection

NASA Technical Reports Server (NTRS)

Kumbla, Kishan -K.; Akbarzadeh, Mohammad R.

1997-01-01

NASA is planning to send numerous unmanned planetary missions to explore the space. This requires autonomous robotic vehicles which can navigate in an unstructured, unknown, and uncertain environment. Landmark based navigation is a new area of research which differs from the traditional goal-oriented navigation, where a mobile robot starts from an initial point and reaches a destination in accordance with a pre-planned path. The landmark based navigation has the advantage of allowing the robot to find its way without communication with the mission control station and without exact knowledge of its coordinates. Current algorithms based on landmark navigation however pose several constraints. First, they require large memories to store the images. Second, the task of comparing the images using traditional methods is computationally intensive and consequently real-time implementation is difficult. The method proposed here consists of three stages, First stage utilizes a heuristic-based algorithm to identify significant objects. The second stage utilizes a neural network (NN) to efficiently classify images of the identified objects. The third stage combines distance information with the classification results of neural networks for efficient and intelligent navigation.

Mortality-based Quantification of Injury Severity for Frequently Occurring Motor Vehicle Crash Injuries

PubMed Central

Weaver, Ashley A.; Barnard, Ryan T.; Kilgo, Patrick D.; Martin, R. Shayn; Stitzel, Joel D.

2013-01-01

The study purpose was to develop mortality-based metrics of injury severity for frequent motor vehicle crash (MVC) injuries. Injury severity was quantified with mortality-based metrics for 240 injuries comprising the top 95% most frequently occurring AIS 2+ injuries in the National Automotive Sampling System – Crashworthiness Data System (NASS-CDS) 2000–2011. Mortality risk ratios (MRRs) were computed by dividing the number of deaths by occurrences for each of the 240 injuries using National Trauma Data Bank Research Data System (NTDB-RDS) MVC cases. MRRMAIS was computed using only patients with a maximum AIS (MAIS) equal to the AIS severity of a given injury. Each injury had an associated MRR and MRRMAIS which ranged from zero (0% mortality representing low severity) to one (100% or universal mortality representing high severity). Injuries with higher MRR and MRRMAIS values are considered more severe because they resulted in a greater proportion of deaths among injured patients. The results illustrated an overall positive trend between AIS severity and the MRR and MRRMAIS values as expected, but showed large variations in MRR and MRRMAIS for some injuries of the same AIS severity. Mortality differences up to 83% (MRR) and 54% (MRRMAIS) were observed for injuries of the same AIS severity. The MRR-based measures of injury severity indicate that some lower AIS severity injuries may result in as many deaths as higher AIS severity injuries. This data-driven determination of injury severity using MRR and MRRMAIS provides a supplement or an alternative to AIS severity classification. PMID:24406961

Mortality-based Quantification of Injury Severity for Frequently Occurring Motor Vehicle Crash Injuries.

PubMed

Weaver, Ashley A; Barnard, Ryan T; Kilgo, Patrick D; Martin, R Shayn; Stitzel, Joel D

The study purpose was to develop mortality-based metrics of injury severity for frequent motor vehicle crash (MVC) injuries. Injury severity was quantified with mortality-based metrics for 240 injuries comprising the top 95% most frequently occurring AIS 2+ injuries in the National Automotive Sampling System - Crashworthiness Data System (NASS-CDS) 2000-2011. Mortality risk ratios (MRRs) were computed by dividing the number of deaths by occurrences for each of the 240 injuries using National Trauma Data Bank Research Data System (NTDB-RDS) MVC cases. MRRMAIS was computed using only patients with a maximum AIS (MAIS) equal to the AIS severity of a given injury. Each injury had an associated MRR and MRRMAIS which ranged from zero (0% mortality representing low severity) to one (100% or universal mortality representing high severity). Injuries with higher MRR and MRRMAIS values are considered more severe because they resulted in a greater proportion of deaths among injured patients. The results illustrated an overall positive trend between AIS severity and the MRR and MRRMAIS values as expected, but showed large variations in MRR and MRRMAIS for some injuries of the same AIS severity. Mortality differences up to 83% (MRR) and 54% (MRRMAIS) were observed for injuries of the same AIS severity. The MRR-based measures of injury severity indicate that some lower AIS severity injuries may result in as many deaths as higher AIS severity injuries. This data-driven determination of injury severity using MRR and MRRMAIS provides a supplement or an alternative to AIS severity classification.

Advances in neutron based bulk explosive detection

NASA Astrophysics Data System (ADS)

Gozani, Tsahi; Strellis, Dan

2007-08-01

Neutron based explosive inspection systems can detect a wide variety of national security threats. The inspection is founded on the detection of characteristic gamma rays emitted as the result of neutron interactions with materials. Generally these are gamma rays resulting from thermal neutron capture and inelastic scattering reactions in most materials and fast and thermal neutron fission in fissile (e.g.235U and 239Pu) and fertile (e.g.238U) materials. Cars or trucks laden with explosives, drugs, chemical agents and hazardous materials can be detected. Cargo material classification via its main elements and nuclear materials detection can also be accomplished with such neutron based platforms, when appropriate neutron sources, gamma ray spectroscopy, neutron detectors and suitable decision algorithms are employed. Neutron based techniques can be used in a variety of scenarios and operational modes. They can be used as stand alones for complete scan of objects such as vehicles, or for spot-checks to clear (or validate) alarms indicated by another inspection system such as X-ray radiography. The technologies developed over the last two decades are now being implemented with good results. Further advances have been made over the last few years that increase the sensitivity, applicability and robustness of these systems. The advances range from the synchronous inspection of two sides of vehicles, increasing throughput and sensitivity and reducing imparted dose to the inspected object and its occupants (if any), to taking advantage of the neutron kinetic behavior of cargo to remove systematic errors, reducing background effects and improving fast neutron signals.

Introducing the MCHF/OVRP/SDMP: Multicapacitated/Heterogeneous Fleet/Open Vehicle Routing Problems with Split Deliveries and Multiproducts

PubMed Central

Yilmaz Eroglu, Duygu; Caglar Gencosman, Burcu; Cavdur, Fatih; Ozmutlu, H. Cenk

2014-01-01

In this paper, we analyze a real-world OVRP problem for a production company. Considering real-world constrains, we classify our problem as multicapacitated/heterogeneous fleet/open vehicle routing problem with split deliveries and multiproduct (MCHF/OVRP/SDMP) which is a novel classification of an OVRP. We have developed a mixed integer programming (MIP) model for the problem and generated test problems in different size (10–90 customers) considering real-world parameters. Although MIP is able to find optimal solutions of small size (10 customers) problems, when the number of customers increases, the problem gets harder to solve, and thus MIP could not find optimal solutions for problems that contain more than 10 customers. Moreover, MIP fails to find any feasible solution of large-scale problems (50–90 customers) within time limits (7200 seconds). Therefore, we have developed a genetic algorithm (GA) based solution approach for large-scale problems. The experimental results show that the GA based approach reaches successful solutions with 9.66% gap in 392.8 s on average instead of 7200 s for the problems that contain 10–50 customers. For large-scale problems (50–90 customers), GA reaches feasible solutions of problems within time limits. In conclusion, for the real-world applications, GA is preferable rather than MIP to reach feasible solutions in short time periods. PMID:25045735

River reach classification for the Greater Mekong Region at high spatial resolution

NASA Astrophysics Data System (ADS)

Ouellet Dallaire, C.; Lehner, B.

2014-12-01

River classifications have been used in river health and ecological assessments as coarse proxies to represent aquatic biodiversity when comprehensive biological and/or species data is unavailable. Currently there are no river classifications or biological data available in a consistent format for the extent of the Greater Mekong Region (GMR; including the Irrawaddy, the Salween, the Chao Praya, the Mekong and the Red River basins). The current project proposes a new river habitat classification for the region, facilitated by the HydroSHEDS (HYDROlogical SHuttle Elevation Derivatives at multiple Scales) database at 500m pixel resolution. The classification project is based on the Global River Classification framework relying on the creation of multiple sub-classifications based on different disciplines. The resulting classes from the sub-classification are later combined into final classes to create a holistic river reach classification. For the GMR, a final habitat classification was created based on three sub-classifications: a hydrological sub-classification based only on discharge indices (river size and flow variability); a physio-climatic sub-classification based on large scale indices of climate and elevation (biomes, ecoregions and elevation); and a geomorphological sub-classification based on local morphology (presence of floodplains, reach gradient and sand transport). Key variables and thresholds were identified in collaboration with local experts to ensure that regional knowledge was included. The final classification is composed 54 unique final classes based on 3 sub-classifications with less than 15 classes each. The resulting classifications are driven by abiotic variables and do not include biological data, but they represent a state-of-the art product based on best available data (mostly global data). The most common river habitat type is the "dry broadleaf, low gradient, very small river". These classifications could be applied in a wide range of hydro-ecological assessments and useful for a variety of stakeholders such as NGO, governments and researchers.

An explanatory analysis of driver injury severity in rear-end crashes using a decision table/Naïve Bayes (DTNB) hybrid classifier.

PubMed

Chen, Cong; Zhang, Guohui; Yang, Jinfu; Milton, John C; Alcántara, Adélamar Dely

2016-05-01

Rear-end crashes are a major type of traffic crashes in the U.S. Of practical necessity is a comprehensive examination of its mechanism that results in injuries and fatalities. Decision table (DT) and Naïve Bayes (NB) methods have both been used widely but separately for solving classification problems in multiple areas except for traffic safety research. Based on a two-year rear-end crash dataset, this paper applies a decision table/Naïve Bayes (DTNB) hybrid classifier to select the deterministic attributes and predict driver injury outcomes in rear-end crashes. The test results show that the hybrid classifier performs reasonably well, which was indicated by several performance evaluation measurements, such as accuracy, F-measure, ROC, and AUC. Fifteen significant attributes were found to be significant in predicting driver injury severities, including weather, lighting conditions, road geometry characteristics, driver behavior information, etc. The extracted decision rules demonstrate that heavy vehicle involvement, a comfortable traffic environment, inferior lighting conditions, two-lane rural roadways, vehicle disabled damage, and two-vehicle crashes would increase the likelihood of drivers sustaining fatal injuries. The research limitations on data size, data structure, and result presentation are also summarized. The applied methodology and estimation results provide insights for developing effective countermeasures to alleviate rear-end crash injury severities and improve traffic system safety performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

Advanced Unsupervised Classification Methods to Detect Anomalies on Earthen Levees Using Polarimetric SAR Imagery

PubMed Central

Marapareddy, Ramakalavathi; Aanstoos, James V.; Younan, Nicolas H.

2016-01-01

Fully polarimetric Synthetic Aperture Radar (polSAR) data analysis has wide applications for terrain and ground cover classification. The dynamics of surface and subsurface water events can lead to slope instability resulting in slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We used L-band Synthetic Aperture Radar (SAR) to screen levees for anomalies. SAR technology, due to its high spatial resolution and soil penetration capability, is a good choice for identifying problematic areas on earthen levees. Using the parameters entropy (H), anisotropy (A), alpha (α), and eigenvalues (λ, λ1, λ2, and λ3), we implemented several unsupervised classification algorithms for the identification of anomalies on the levee. The classification techniques applied are H/α, H/A, A/α, Wishart H/α, Wishart H/A/α, and H/α/λ classification algorithms. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the Southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers. PMID:27322270

Object-Based Random Forest Classification of Land Cover from Remotely Sensed Imagery for Industrial and Mining Reclamation

NASA Astrophysics Data System (ADS)

Chen, Y.; Luo, M.; Xu, L.; Zhou, X.; Ren, J.; Zhou, J.

2018-04-01

The RF method based on grid-search parameter optimization could achieve a classification accuracy of 88.16 % in the classification of images with multiple feature variables. This classification accuracy was higher than that of SVM and ANN under the same feature variables. In terms of efficiency, the RF classification method performs better than SVM and ANN, it is more capable of handling multidimensional feature variables. The RF method combined with object-based analysis approach could highlight the classification accuracy further. The multiresolution segmentation approach on the basis of ESP scale parameter optimization was used for obtaining six scales to execute image segmentation, when the segmentation scale was 49, the classification accuracy reached the highest value of 89.58 %. The classification accuracy of object-based RF classification was 1.42 % higher than that of pixel-based classification (88.16 %), and the classification accuracy was further improved. Therefore, the RF classification method combined with object-based analysis approach could achieve relatively high accuracy in the classification and extraction of land use information for industrial and mining reclamation areas. Moreover, the interpretation of remotely sensed imagery using the proposed method could provide technical support and theoretical reference for remotely sensed monitoring land reclamation.

Detection of Road Surface States from Tire Noise Using Neural Network Analysis

NASA Astrophysics Data System (ADS)

Kongrattanaprasert, Wuttiwat; Nomura, Hideyuki; Kamakura, Tomoo; Ueda, Koji

This report proposes a new processing method for automatically detecting the states of road surfaces from tire noises of passing vehicles. In addition to multiple indicators of the signal features in the frequency domain, we propose a few feature indicators in the time domain to successfully classify the road states into four categories: snowy, slushy, wet, and dry states. The method is based on artificial neural networks. The proposed classification is carried out in multiple neural networks using learning vector quantization. The outcomes of the networks are then integrated by the voting decision-making scheme. Experimental results obtained from recorded signals for ten days in the snowy season demonstrated that an accuracy of approximately 90% can be attained for predicting road surface states using only tire noise data.

Cooperative Mission Concepts Using Biomorphic Explorers

NASA Technical Reports Server (NTRS)

Thakoor, S.; Miralles, C.; Martin, T.; Kahn, R.; Zurek, R.

2000-01-01

Inspired by the immense variety of naturally curious explorers (insects, animals, and birds), their wellintegrated biological sensor-processor suites, efficiently packaged in compact but highly dexterous forms, and their complex, intriguing, cooperative behavior, this paper focuses on "Biomorphic Explorers", their defination/classification, their designs, and presents planetary exploration scenarios based on the designs. Judicious blend of bio-inspired concepts and recent advances in micro-air vehicles, microsensors, microinstruments, MEMS, and microprocessors clearly suggests that the time of small, dedicated, low cost explorers that capture some of the key features of biological systems has arrived. Just as even small insects like ants, termites, honey bees etc working cooperatively in colonies can achieve big tasks, the biomorphic explorers hold the potential for obtaining science in-accessible by current large singular exploration platforms.

Composite Wavelet Filters for Enhanced Automated Target Recognition

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper.

Surge of Bering Glacier and Bagley Ice Field: Parameterization of surge characteristics based on automated analysis of crevasse image data and laser altimeter data

NASA Astrophysics Data System (ADS)

Stachura, M.; Herzfeld, U. C.; McDonald, B.; Weltman, A.; Hale, G.; Trantow, T.

2012-12-01

The dynamical processes that occur during the surge of a large, complex glacier system are far from being understood. The aim of this paper is to derive a parameterization of surge characteristics that captures the principle processes and can serve as the basis for a dynamic surge model. Innovative mathematical methods are introduced that facilitate derivation of such a parameterization from remote-sensing observations. Methods include automated geostatistical characterization and connectionist-geostatistical classification of dynamic provinces and deformation states, using the vehicle of crevasse patterns. These methods are applied to analyze satellite and airborne image and laser altimeter data collected during the current surge of Bering Glacier and Bagley Ice Field, Alaska.

Land Cover Analysis by Using Pixel-Based and Object-Based Image Classification Method in Bogor

NASA Astrophysics Data System (ADS)

Amalisana, Birohmatin; Rokhmatullah; Hernina, Revi

2017-12-01

The advantage of image classification is to provide earth’s surface information like landcover and time-series changes. Nowadays, pixel-based image classification technique is commonly performed with variety of algorithm such as minimum distance, parallelepiped, maximum likelihood, mahalanobis distance. On the other hand, landcover classification can also be acquired by using object-based image classification technique. In addition, object-based classification uses image segmentation from parameter such as scale, form, colour, smoothness and compactness. This research is aimed to compare the result of landcover classification and its change detection between parallelepiped pixel-based and object-based classification method. Location of this research is Bogor with 20 years range of observation from 1996 until 2016. This region is famous as urban areas which continuously change due to its rapid development, so that time-series landcover information of this region will be interesting.

Using Fluid Dynamics and Field Experiments to Improve Vehicle-based Wind Measurements for Environmental Monitoring

NASA Astrophysics Data System (ADS)

Hanlon, T.; Bourlon, E.; Jensen, N.; Risk, D. A.

2017-12-01

Vehicle-based measurements of wind speed and direction are presently used for a range of applications, including gas plume detection. Theoretically, vehicle-based measurements could also be integrated with fixed-site measurements to add spatial richness in weather and atmospheric observing systems, but the quality and accuracy of such measurements is currently not well understood. Our research objective for this field-simulation study was to understand how anemometer placement and the vehicle's external air flow field affect measurement accuracy of vehicle-mounted anemometers. We used a truck-mounted anemometer to investigate wind measurements at different vehicle speeds and anemometer placements. We conducted field tests on a square 3.2 km route in flat, treeless terrain and positioned stationary sonic anemometers at each corner. We drove the route in replicate under varying wind conditions and vehicle speeds, and with multiple sonic anemometer placements on the vehicle. The vehicle-based anemometer measurements were corrected to remove the vehicle speed and course vector. In the lab, Computational Fluid Dynamic (CFD) simulations were generated in Ansys FLUENT to model the external flow fields at the locations of measurement under varying vehicle speed and yaw angle. In field trials we observed that vehicle-based measurements differed from stationary measurements by a different magnitude in each of the upwind, downwind and crosswind directions. The difference from stationary anemometers increased with vehicle speed, suggesting the vehicle's flow field does indeed impact the accuracy of vehicle-based anemometer measurements. We used the CFD simulations to develop a quantitative understanding of fluid flow around the vehicle, and to develop speed-based corrections that were applied to the field data. We were also able to make recommendations for anemometer placement. This study demonstrates the importance of applying aerodynamics-based correction factors to vehicle based wind measurements.

Multiple kernel based feature and decision level fusion of iECO individuals for explosive hazard detection in FLIR imagery

NASA Astrophysics Data System (ADS)

Price, Stanton R.; Murray, Bryce; Hu, Lequn; Anderson, Derek T.; Havens, Timothy C.; Luke, Robert H.; Keller, James M.

2016-05-01

A serious threat to civilians and soldiers is buried and above ground explosive hazards. The automatic detection of such threats is highly desired. Many methods exist for explosive hazard detection, e.g., hand-held based sensors, downward and forward looking vehicle mounted platforms, etc. In addition, multiple sensors are used to tackle this extreme problem, such as radar and infrared (IR) imagery. In this article, we explore the utility of feature and decision level fusion of learned features for forward looking explosive hazard detection in IR imagery. Specifically, we investigate different ways to fuse learned iECO features pre and post multiple kernel (MK) support vector machine (SVM) based classification. Three MK strategies are explored; fixed rule, heuristics and optimization-based. Performance is assessed in the context of receiver operating characteristic (ROC) curves on data from a U.S. Army test site that contains multiple target and clutter types, burial depths and times of day. Specifically, the results reveal two interesting things. First, the different MK strategies appear to indicate that the different iECO individuals are all more-or-less important and there is not a dominant feature. This is reinforcing as our hypothesis was that iECO provides different ways to approach target detection. Last, we observe that while optimization-based MK is mathematically appealing, i.e., it connects the learning of the fusion to the underlying classification problem we are trying to solve, it appears to be highly susceptible to over fitting and simpler, e.g., fixed rule and heuristics approaches help us realize more generalizable iECO solutions.

Classification of Urban Aerial Data Based on Pixel Labelling with Deep Convolutional Neural Networks and Logistic Regression

NASA Astrophysics Data System (ADS)

Yao, W.; Poleswki, P.; Krzystek, P.

2016-06-01

The recent success of deep convolutional neural networks (CNN) on a large number of applications can be attributed to large amounts of available training data and increasing computing power. In this paper, a semantic pixel labelling scheme for urban areas using multi-resolution CNN and hand-crafted spatial-spectral features of airborne remotely sensed data is presented. Both CNN and hand-crafted features are applied to image/DSM patches to produce per-pixel class probabilities with a L1-norm regularized logistical regression classifier. The evidence theory infers a degree of belief for pixel labelling from different sources to smooth regions by handling the conflicts present in the both classifiers while reducing the uncertainty. The aerial data used in this study were provided by ISPRS as benchmark datasets for 2D semantic labelling tasks in urban areas, which consists of two data sources from LiDAR and color infrared camera. The test sites are parts of a city in Germany which is assumed to consist of typical object classes including impervious surfaces, trees, buildings, low vegetation, vehicles and clutter. The evaluation is based on the computation of pixel-based confusion matrices by random sampling. The performance of the strategy with respect to scene characteristics and method combination strategies is analyzed and discussed. The competitive classification accuracy could be not only explained by the nature of input data sources: e.g. the above-ground height of nDSM highlight the vertical dimension of houses, trees even cars and the nearinfrared spectrum indicates vegetation, but also attributed to decision-level fusion of CNN's texture-based approach with multichannel spatial-spectral hand-crafted features based on the evidence combination theory.

Unmanned Aerial Vehicle Non Line of Sight Chemical Detection Final Report

DTIC Science & Technology

2016-12-01

aircraft system that is used to perform point detection of chemical warfare agents and collection of vapor, liquid, and solid samples. A modular payload...Standoff Quadcopter Unmanned aircraft system Modular payload 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF...Manufacturing Division, modular payloads are being developed to perform point detection and CBRNE sampling. The available UAS is a quadcopter capable of

AUTOMOTIVE DIESEL MAINTENANCE 1. UNIT XXVI, I--CATERPILLAR LUBRICATION SYSTEMS AND COMPONENTS, II--LEARNING ABOUT BRAKES (PART I).

ERIC Educational Resources Information Center

Minnesota State Dept. of Education, St. Paul. Div. of Vocational and Technical Education.

THIS MODULE OF A 30-MODULE COURSE IS DESIGNED TO DEVELOP AN UNDERSTANDING OF THE FUNCTIONS OF DIESEL ENGINE LUBRICATION SYSTEMS AND COMPONENTS AND THE PRINCIPLES OF OPERATION OF BRAKE SYSTEMS USED ON DIESEL POWERED VEHICLES. TOPICS ARE (1) THE NEED FOR OIL, (2) SERVICE CLASSIFICATION OF OILS, (3) CATERPILLAR LUBRICATION SYSTEM COMPONENTS (4)…

Current Concepts of Posterolateral Corner Injuries of the Knee

PubMed Central

Shon, Oog-Jin; Park, Jae-Woo; Kim, Beum-Jung

2017-01-01

The number of posterolateral corner (PLC) injury patients has risen owing to the increased motor vehicle accidents and sports activities. Careful examination is required because this injury is easy to overlook and may lead to chronic instability. The purpose of this article is to review the anatomy, biomechanics, diagnosis, classification and, treatment of PLC injuries and summarize the recent literatures regarding the treatment outcomes. PMID:29172386

Effects of gross motor function and manual function levels on performance-based ADL motor skills of children with spastic cerebral palsy.

PubMed

Park, Myoung-Ok

2017-02-01

[Purpose] The purpose of this study was to determine effects of Gross Motor Function Classification System and Manual Ability Classification System levels on performance-based motor skills of children with spastic cerebral palsy. [Subjects and Methods] Twenty-three children with cerebral palsy were included. The Assessment of Motor and Process Skills was used to evaluate performance-based motor skills in daily life. Gross motor function was assessed using Gross Motor Function Classification Systems, and manual function was measured using the Manual Ability Classification System. [Results] Motor skills in daily activities were significantly different on Gross Motor Function Classification System level and Manual Ability Classification System level. According to the results of multiple regression analysis, children categorized as Gross Motor Function Classification System level III scored lower in terms of performance based motor skills than Gross Motor Function Classification System level I children. Also, when analyzed with respect to Manual Ability Classification System level, level II was lower than level I, and level III was lower than level II in terms of performance based motor skills. [Conclusion] The results of this study indicate that performance-based motor skills differ among children categorized based on Gross Motor Function Classification System and Manual Ability Classification System levels of cerebral palsy.

Vision-based vehicle detection and tracking algorithm design

NASA Astrophysics Data System (ADS)

Hwang, Junyeon; Huh, Kunsoo; Lee, Donghwi

2009-12-01

The vision-based vehicle detection in front of an ego-vehicle is regarded as promising for driver assistance as well as for autonomous vehicle guidance. The feasibility of vehicle detection in a passenger car requires accurate and robust sensing performance. A multivehicle detection system based on stereo vision has been developed for better accuracy and robustness. This system utilizes morphological filter, feature detector, template matching, and epipolar constraint techniques in order to detect the corresponding pairs of vehicles. After the initial detection, the system executes the tracking algorithm for the vehicles. The proposed system can detect front vehicles such as the leading vehicle and side-lane vehicles. The position parameters of the vehicles located in front are obtained based on the detection information. The proposed vehicle detection system is implemented on a passenger car, and its performance is verified experimentally.

The generalization ability of online SVM classification based on Markov sampling.

PubMed

Xu, Jie; Yan Tang, Yuan; Zou, Bin; Xu, Zongben; Li, Luoqing; Lu, Yang

2015-03-01

In this paper, we consider online support vector machine (SVM) classification learning algorithms with uniformly ergodic Markov chain (u.e.M.c.) samples. We establish the bound on the misclassification error of an online SVM classification algorithm with u.e.M.c. samples based on reproducing kernel Hilbert spaces and obtain a satisfactory convergence rate. We also introduce a novel online SVM classification algorithm based on Markov sampling, and present the numerical studies on the learning ability of online SVM classification based on Markov sampling for benchmark repository. The numerical studies show that the learning performance of the online SVM classification algorithm based on Markov sampling is better than that of classical online SVM classification based on random sampling as the size of training samples is larger.

Vehicle tracking using fuzzy-based vehicle detection window with adaptive parameters

NASA Astrophysics Data System (ADS)

Chitsobhuk, Orachat; Kasemsiri, Watjanapong; Glomglome, Sorayut; Lapamonpinyo, Pipatphon

2018-04-01

In this paper, fuzzy-based vehicle tracking system is proposed. The proposed system consists of two main processes: vehicle detection and vehicle tracking. In the first process, the Gradient-based Adaptive Threshold Estimation (GATE) algorithm is adopted to provide the suitable threshold value for the sobel edge detection. The estimated threshold can be adapted to the changes of diverse illumination conditions throughout the day. This leads to greater vehicle detection performance compared to a fixed user's defined threshold. In the second process, this paper proposes the novel vehicle tracking algorithms namely Fuzzy-based Vehicle Analysis (FBA) in order to reduce the false estimation of the vehicle tracking caused by uneven edges of the large vehicles and vehicle changing lanes. The proposed FBA algorithm employs the average edge density and the Horizontal Moving Edge Detection (HMED) algorithm to alleviate those problems by adopting fuzzy rule-based algorithms to rectify the vehicle tracking. The experimental results demonstrate that the proposed system provides the high accuracy of vehicle detection about 98.22%. In addition, it also offers the low false detection rates about 3.92%.

[New varieties of lateral metatarsophalangeal dislocations of the great toe].

PubMed

Bousselmame, N; Rachid, K; Lazrak, K; Galuia, F; Taobane, H; Moulay, I

2001-04-01

We report seven cases of traumatic dislocation of the great toe, detailing the anatomy, the mechanism of injury and the radiographic diagnosis. We propose an additional classification based on three hereto unreported cases. Between october 1994 and october 1997, we treated seven patients with traumatic dislocation of the first metatarso-phalangeal joint of the great toe. There were six men and one woman, mean age 35 years (range 24 - 44 years). Dislocation was caused by motor vehicle accidents in four cases and by falls in three. Diagnosis was made on anteroposterior, lateral and medial oblique radiographs. According to Jahss' classification, there was one type I and three type IIB dislocations. There was also one open lateral dislocation and two dorsomedial dislocations. Only these dorsomedial dislocations required open reduction, done via a dorsal approach. Mean follow-up was 17.5 months (range 9 - 24 months) in six cases. One patient was lost to follow-up. The outcome was good in six cases and poor in one (dorsomedial dislocation). Dislocation of the first metatarso-phalangeal joint of the great toe is an uncommon injury. In 1980, Jahss reported two cases and reviewed three others described in the literature. He proposed three types of dislocation based on the feasibility of closed reduction (type I, II and IIB). In 1991, Copeland and Kanat reported a unique case in which there was an association of IIA and IIB lesions. They proposed an addition to the classification (type IIC). In 1994, Garcia Mata et al. reported another case which had not been described by Jahss and proposed another addition. All dislocations reported to date have been sagittal dislocations. Pathological alteration of the collateral ligaments has not been previously reported. In our experience, we have seen one case of open lateral dislocation due, at surgical exploration, to medial ligament rupture and two cases of dorsomedial dislocation due, at surgical exploration, to lateral ligament rupture. We propose another additional classification with pure lateral dislocation (type III) and dorso-lateral dislocation (type IL or IIL+), which are related to the formerly described variants.

Person detection and tracking with a 360° lidar system

NASA Astrophysics Data System (ADS)

Hammer, Marcus; Hebel, Marcus; Arens, Michael

2017-10-01

Today it is easily possible to generate dense point clouds of the sensor environment using 360° LiDAR (Light Detection and Ranging) sensors which are available since a number of years. The interpretation of these data is much more challenging. For the automated data evaluation the detection and classification of objects is a fundamental task. Especially in urban scenarios moving objects like persons or vehicles are of particular interest, for instance in automatic collision avoidance, for mobile sensor platforms or surveillance tasks. In literature there are several approaches for automated person detection in point clouds. While most techniques show acceptable results in object detection, the computation time is often crucial. The runtime can be problematic, especially due to the amount of data in the panoramic 360° point clouds. On the other hand, for most applications an object detection and classification in real time is needed. The paper presents a proposal for a fast, real-time capable algorithm for person detection, classification and tracking in panoramic point clouds.

Object Classification in Semi Structured Enviroment Using Forward-Looking Sonar

PubMed Central

dos Santos, Matheus; Ribeiro, Pedro Otávio; Núñez, Pedro; Botelho, Silvia

2017-01-01

The submarine exploration using robots has been increasing in recent years. The automation of tasks such as monitoring, inspection, and underwater maintenance requires the understanding of the robot’s environment. The object recognition in the scene is becoming a critical issue for these systems. On this work, an underwater object classification pipeline applied in acoustic images acquired by Forward-Looking Sonar (FLS) are studied. The object segmentation combines thresholding, connected pixels searching and peak of intensity analyzing techniques. The object descriptor extract intensity and geometric features of the detected objects. A comparison between the Support Vector Machine, K-Nearest Neighbors, and Random Trees classifiers are presented. An open-source tool was developed to annotate and classify the objects and evaluate their classification performance. The proposed method efficiently segments and classifies the structures in the scene using a real dataset acquired by an underwater vehicle in a harbor area. Experimental results demonstrate the robustness and accuracy of the method described in this paper. PMID:28961163

EXTENDING AQUATIC CLASSIFICATION TO THE LANDSCAPE SCALE HYDROLOGY-BASED STRATEGIES

EPA Science Inventory

Aquatic classification of single water bodies (lakes, wetlands, estuaries) is often based on geologic origin, while stream classification has relied on multiple factors related to landform, geomorphology, and soils. We have developed an approach to aquatic classification based o...

Classifications of Acute Scaphoid Fractures: A Systematic Literature Review.

PubMed

Ten Berg, Paul W; Drijkoningen, Tessa; Strackee, Simon D; Buijze, Geert A

2016-05-01

Background In the lack of consensus, surgeon-based preference determines how acute scaphoid fractures are classified. There is a great variety of classification systems with considerable controversies. Purposes The purpose of this study was to provide an overview of the different classification systems, clarifying their subgroups and analyzing their popularity by comparing citation indexes. The intention was to improve data comparison between studies using heterogeneous fracture descriptions. Methods We performed a systematic review of the literature based on a search of medical literature from 1950 to 2015, and a manual search using the reference lists in relevant book chapters. Only original descriptions of classifications of acute scaphoid fractures in adults were included. Popularity was based on citation index as reported in the databases of Web of Science (WoS) and Google Scholar. Articles that were cited <10 times in WoS were excluded. Results Our literature search resulted in 308 potentially eligible descriptive reports of which 12 reports met the inclusion criteria. We distinguished 13 different (sub) classification systems based on (1) fracture location, (2) fracture plane orientation, and (3) fracture stability/displacement. Based on citations numbers, the Herbert classification was most popular, followed by the Russe and Mayo classifications. All classification systems were based on plain radiography. Conclusions Most classification systems were based on fracture location, displacement, or stability. Based on the controversy and limited reliability of current classification systems, suggested research areas for an updated classification include three-dimensional fracture pattern etiology and fracture fragment mobility assessed by dynamic imaging.

7 CFR 27.36 - Classification and Micronaire determinations based on official standards.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Classification and Micronaire determinations based on... COMMODITY STANDARDS AND STANDARD CONTAINER REGULATIONS COTTON CLASSIFICATION UNDER COTTON FUTURES LEGISLATION Regulations Classification and Micronaire Determinations § 27.36 Classification and Micronaire...

7 CFR 27.36 - Classification and Micronaire determinations based on official standards.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 2 2011-01-01 2011-01-01 false Classification and Micronaire determinations based on... COMMODITY STANDARDS AND STANDARD CONTAINER REGULATIONS COTTON CLASSIFICATION UNDER COTTON FUTURES LEGISLATION Regulations Classification and Micronaire Determinations § 27.36 Classification and Micronaire...

A comprehensive classification method for VOC emission sources to tackle air pollution based on VOC species reactivity and emission amounts.

PubMed

Li, Guohao; Wei, Wei; Shao, Xia; Nie, Lei; Wang, Hailin; Yan, Xiao; Zhang, Rui

2018-05-01

In China, volatile organic compound (VOC) control directives have been continuously released and implemented for important sources and regions to tackle air pollution. The corresponding control requirements were based on VOC emission amounts (EA), but never considered the significant differentiation of VOC species in terms of atmospheric chemical reactivity. This will adversely influence the effect of VOC reduction on air quality improvement. Therefore, this study attempted to develop a comprehensive classification method for typical VOC sources in the Beijing-Tianjin-Hebei region (BTH), by combining the VOC emission amounts with the chemical reactivities of VOC species. Firstly, we obtained the VOC chemical profiles by measuring 5 key sources in the BTH region and referencing another 10 key sources, and estimated the ozone formation potential (OFP) per ton VOC emission for these sources by using the maximum incremental reactivity (MIR) index as the characteristic of source reactivity (SR). Then, we applied the data normalization method to respectively convert EA and SR to normalized EA (NEA) and normalized SR (NSR) for various sources in the BTH region. Finally, the control index (CI) was calculated, and these sources were further classified into four grades based on the normalized CI (NCI). The study results showed that in the BTH region, furniture coating, automobile coating, and road vehicles are characterized by high NCI and need to be given more attention; however, the petro-chemical industry, which was designated as an important control source by air quality managers, has a lower NCI. Copyright © 2017. Published by Elsevier B.V.

Autonomous vehicles: from paradigms to technology

NASA Astrophysics Data System (ADS)

Ionita, Silviu

2017-10-01

Mobility is a basic necessity of contemporary society and it is a key factor in global economic development. The basic requirements for the transport of people and goods are: safety and duration of travel, but also a number of additional criteria are very important: energy saving, pollution, passenger comfort. Due to advances in hardware and software, automation has penetrated massively in transport systems both on infrastructure and on vehicles, but man is still the key element in vehicle driving. However, the classic concept of ‘human-in-the-loop’ in terms of ‘hands on’ in driving the cars is competing aside from the self-driving startups working towards so-called ‘Level 4 autonomy’, which is defined as “a self-driving system that does not requires human intervention in most scenarios”. In this paper, a conceptual synthesis of the autonomous vehicle issue is made in connection with the artificial intelligence paradigm. It presents a classification of the tasks that take place during the driving of the vehicle and its modeling from the perspective of traditional control engineering and artificial intelligence. The issue of autonomous vehicle management is addressed on three levels: navigation, movement in traffic, respectively effective maneuver and vehicle dynamics control. Each level is then described in terms of specific tasks, such as: route selection, planning and reconfiguration, recognition of traffic signs and reaction to signaling and traffic events, as well as control of effective speed, distance and direction. The approach will lead to a better understanding of the way technology is moving when talking about autonomous cars, smart/intelligent cars or intelligent transport systems. Keywords: self-driving vehicle, artificial intelligence, deep learning, intelligent transport systems.

Biological-Warfare Agent Decontamination Efficacy Testing: Large-Scale Chamber mVHP (Trademark) Decontamination System Evaluation for Biological Contamination

DTIC Science & Technology

2007-08-01

Aluminum - +- - - Viton + + + _ . Silicone .... Polyimide (Kapton) + . _ . 81 - Apex .... B1 - Stens .... 21 3.5.5 Enumerated Coupon Results. The first...Vaporous Hydrogen Peroxide mVHP B. anthracis Silicone G. stearothermophilus CARC Metal 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF 18. NUMBER OF 19a...aircraft, vehicles, protective- and sensitive-equipment that encompass a variety of material properties, compositions and porosities. The test

Alcohol-to-Jet (ATJ) Fuel Blending Study

DTIC Science & Technology

2015-09-01

distribution unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT The U.S. Army sought to study the effect of blending highly iso-paraffinic ATJ blending...stock into JP-8 in order to understand the effect ATJ fuel blends will have on ground vehicle engines and support equipment. This subtask under Work... Synthetic Fuel, JP-8, diesel engine, combustion 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF

Real-Time and High-Fidelity Simulation Environment for Autonomous Ground Vehicle Dynamics

DTIC Science & Technology

2013-08-01

ENGINEERING AND TECHNOLOGY SYMPOSIUM (GVSETS), SET FOR AUG. 21-22, 2013 14. ABSTRACT briefing charts 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17...EDL & Aero-Flight DSENDS Airships Planetary & Terrain models SimScape Simulation framework Dshell Flex & Multibody dynamics DARTS 3D...7 DARTS Rigid/Flexible Real-Time Multibody Dynamics Engine Recipient of the NASA Software of the Year Award. Abhinandan Jain, "Robot and

Optimization of damping in the passive automotive suspension system with using two quarter-car models

NASA Astrophysics Data System (ADS)

Lozia, Z.; Zdanowicz, P.

2016-09-01

The paper presents the optimization of damping in the passive suspension system of a motor vehicle moving rectilinearly with a constant speed on a road with rough surface of random irregularities, described according to the ISO classification. Two quarter-car 2DoF models, linear and non-linear, were used; in the latter, nonlinearities of spring characteristics of the suspension system and pneumatic tyres, sliding friction in the suspension system, and wheel lift-off were taken into account. The smoothing properties of vehicle tyres were represented in both models. The calculations were carried out for three roads of different quality, with simulating four vehicle speeds. Statistical measures of vertical vehicle body vibrations and of changes in the vertical tyre/road contact force were used as the criteria of system optimization and model comparison. The design suspension displacement limit was also taken into account. The optimum suspension damping coefficient was determined and the impact of undesirable sliding friction in the suspension system on the calculation results was estimated. The results obtained make it possible to evaluate the impact of the structure and complexity of the model used on the results of the optimization.

Small unmanned aerial vehicles (micro-UAVs, drones) in plant ecology1

PubMed Central

Cruzan, Mitchell B.; Weinstein, Ben G.; Grasty, Monica R.; Kohrn, Brendan F.; Hendrickson, Elizabeth C.; Arredondo, Tina M.; Thompson, Pamela G.

2016-01-01

Premise of the study: Low-elevation surveys with small aerial drones (micro–unmanned aerial vehicles [UAVs]) may be used for a wide variety of applications in plant ecology, including mapping vegetation over small- to medium-sized regions. We provide an overview of methods and procedures for conducting surveys and illustrate some of these applications. Methods: Aerial images were obtained by flying a small drone along transects over the area of interest. Images were used to create a composite image (orthomosaic) and a digital surface model (DSM). Vegetation classification was conducted manually and using an automated routine. Coverage of an individual species was estimated from aerial images. Results: We created a vegetation map for the entire region from the orthomosaic and DSM, and mapped the density of one species. Comparison of our manual and automated habitat classification confirmed that our mapping methods were accurate. A species with high contrast to the background matrix allowed adequate estimate of its coverage. Discussion: The example surveys demonstrate that small aerial drones are capable of gathering large amounts of information on the distribution of vegetation and individual species with minimal impact to sensitive habitats. Low-elevation aerial surveys have potential for a wide range of applications in plant ecology. PMID:27672518

Analysis of Unmanned Aerial Vehicle (UAV) hyperspectral remote sensing monitoring key technology in coastal wetland

NASA Astrophysics Data System (ADS)

Ma, Yi; Zhang, Jie; Zhang, Jingyu

2016-01-01

The coastal wetland, a transitional zone between terrestrial ecosystems and marine ecosystems, is the type of great value to ecosystem services. For the recent 3 decades, area of the coastal wetland is decreasing and the ecological function is gradually degraded with the rapid development of economy, which restricts the sustainable development of economy and society in the coastal areas of China in turn. It is a major demand of the national reality to carry out the monitoring of coastal wetlands, to master the distribution and dynamic change. UAV, namely unmanned aerial vehicle, is a new platform for remote sensing. Compared with the traditional satellite and manned aerial remote sensing, it has the advantage of flexible implementation, no cloud cover, strong initiative and low cost. Image-spectrum merging is one character of high spectral remote sensing. At the same time of imaging, the spectral curve of each pixel is obtained, which is suitable for quantitative remote sensing, fine classification and target detection. Aimed at the frontier and hotspot of remote sensing monitoring technology, and faced the demand of the coastal wetland monitoring, this paper used UAV and the new remote sensor of high spectral imaging instrument to carry out the analysis of the key technologies of monitoring coastal wetlands by UAV on the basis of the current situation in overseas and domestic and the analysis of developing trend. According to the characteristic of airborne hyperspectral data on UAV, that is "three high and one many", the key technology research that should develop are promoted as follows: 1) the atmosphere correction of the UAV hyperspectral in coastal wetlands under the circumstance of complex underlying surface and variable geometry, 2) the best observation scale and scale transformation method of the UAV platform while monitoring the coastal wetland features, 3) the classification and detection method of typical features with high precision from multi scale hyperspectral images based on time sequence. The research results of this paper will help to break the traditional concept of remote sensing monitoring coastal wetlands by satellite and manned aerial vehicle, lead the trend of this monitoring technology, and put forward a new technical proposal for grasping the distribution of the coastal wetland and the changing trend and carrying out the protection and management of the coastal wetland.

Vehicle Related Factors that Influence Injury Outcome in Head-On Collisions

PubMed Central

Blum, Jeremy J.; Scullion, Paul; Morgan, Richard M.; Digges, Kennerly; Kan, Cing-Dao; Park, Shinhee; Bae, Hanil

2008-01-01

This study specifically investigated a range of vehicle-related factors that are associated with a lower risk of serious or fatal injury to a belted driver in a head-on collision. This analysis investigated a range of structural characteristics, quantities that describes the physical features of a passenger vehicle, e.g., stiffness or frontal geometry. The study used a data-mining approach (classification tree algorithm) to find the most significant relationships between injury outcome and the structural variables. The algorithm was applied to 120,000 real-world, head-on collisions, from the National Highway Traffic Safety Administration’s (NHTSA’s) State Crash data files, that were linked to structural attributes derived from frontal crash tests performed as part of the USA New Car Assessment Program. As with previous literature, the analysis found that the heavier vehicles were correlated with lower injury risk to their drivers. This analysis also found a new and significant correlation between the vehicle’s stiffness and injury risk. When an airbag deployed, the vehicle’s stiffness has the most statistically significant correlation with injury risk. These results suggest that in severe collisions, lower intrusion in the occupant cabin associated with higher stiffness is at least as important to occupant protection as vehicle weight for self-protection of the occupant. Consequently, the safety community might better improve self-protection by a renewed focus on increasing vehicle stiffness in order to improve crashworthiness in head-on collisions. PMID:19026230

Racial and demographic differences in household travel and fuel purchase behavior

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gur, Y.; Millar, M.

1987-01-01

Monthly fuel purchase logs from the Residential Energy Consumption Survey's Household Transportation Panel (TP) were analyzed to determine the relationship between various household characteristics and purchase frequency, tank inventories, vehicle-miles traveled, and fuel expenditures. Multiple classification analysis (MCA) was used to relate observed differences in dependent variables to such index-type household characteristics as income and residence location, and sex, race and age of household head. Because it isolates the net effect of each parameter, after accounting for the effects of all other parameters, MCA is particularly appropriate for this type of analysis. Results reveal clear differences in travel and fuelmore » purchase behavior for four distinct groups of vehicle-owning households. Black households tend to own far fewer vehicles with lower fuel economy, to use them more intensively, to purchase fuel more frequently, and to maintain lower fuel inventories than white households. Similarly, poor households own fewer vehicles with lower fuel economy, but they drive them less intensively, purchase fuel more frequently, and maintain lower fuel inventories than nonpoor households. Elderly households also own fewer vehicles with lower fuel economy. But since they drive them much less intensively, their fuel purchases are much less frequent and their fuel inventories are higher than nonelderly households. Female-headed households also own fewer vehicles but with somewhat higher fuel economy. They drive them less intensively, maintain higher fuel inventories, and purchase fuel less frequently than male-headed households. 13 refs., 8 tabs.« less

Testing of a Composite Wavelet Filter to Enhance Automated Target Recognition in SONAR

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.

2011-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low resolution SONAR and camera videos taken from Unmanned Underwater Vehicles (UUVs). These SONAR images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both SONAR and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this report.

Development of search prefilters for infrared library searching of clear coat paint smears.

PubMed

Lavine, Barry K; Fasasi, Ayuba; Mirjankar, Nikhil; Sandercock, Mark

2014-02-01

Search prefilters developed from spectral data collected on two 6700 Thermo-Nicolet FTIR spectrometers were able to identify the respective manufacturing plant and the production line of an automotive vehicle from its clear coat paint smear using IR transmission spectra collected on a Bio-Rad 40A or Bio-Rad 60 FTIR spectrometer. All four spectrometers were equipped with DTGS detectors. An approach based on instrumental line functions was used to transfer the classification model between the Thermo-Nicolet and Bio-Rad instruments. In this study, 209 IR spectra of clear coat paint smears comprising the training set were collected using two Thermo-Nicolet 6700 IR spectrometers, whereas the validation set consisted of 242 IR spectra of clear coats obtained using two Bio-Rad FTIR instruments. © 2013 Published by Elsevier B.V.

Light vehicle crash avoidance needs and countermeasure profiles for safety applications based on vehicle-to-vehicle communications

DOT National Transportation Integrated Search

2013-04-30

This report discusses light-vehicle crash countermeasure profiles and functions for five target pre-crash scenario groups based on vehicle-to-vehicle (V2V) communications. Target pre-crash scenario groups include rear-end, lane change, opposite direc...

Semiotics and agents for integrating and navigating through multimedia representations of concepts

NASA Astrophysics Data System (ADS)

Joyce, Dan W.; Lewis, Paul H.; Tansley, Robert H.; Dobie, Mark R.; Hall, Wendy

1999-12-01

The purpose of this paper is two-fold. We begin by exploring the emerging trend to view multimedia information in terms of low-level and high-level components; the former being feature-based and the latter the 'semantics' intrinsic to what is portrayed by the media object. Traditionally, this has been viewed by employing analogies with generative linguistics. Recently, a new perceptive based on the semiotic tradition has been alluded to in several papers. We believe this to be a more appropriate approach. From this, we propose an approach for tackling this problem which uses an associative data structure expressing authored information together with intelligent agents acting autonomously over this structure. We then show how neural networks can be used to implement such agents. The agents act as 'vehicles' for bridging the gap between multimedia semantics and concrete expressions of high-level knowledge, but we suggest that traditional neural network techniques for classification are not architecturally adequate.

Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China.

PubMed

Yang, Xiaoyan; Chen, Longgao; Li, Yingkui; Xi, Wenjia; Chen, Longqian

2015-07-01

Land use/land cover (LULC) inventory provides an important dataset in regional planning and environmental assessment. To efficiently obtain the LULC inventory, we compared the LULC classifications based on single satellite imagery with a rule-based classification based on multi-seasonal imagery in Lianyungang City, a coastal city in China, using CBERS-02 (the 2nd China-Brazil Environmental Resource Satellites) images. The overall accuracies of the classification based on single imagery are 78.9, 82.8, and 82.0% in winter, early summer, and autumn, respectively. The rule-based classification improves the accuracy to 87.9% (kappa 0.85), suggesting that combining multi-seasonal images can considerably improve the classification accuracy over any single image-based classification. This method could also be used to analyze seasonal changes of LULC types, especially for those associated with tidal changes in coastal areas. The distribution and inventory of LULC types with an overall accuracy of 87.9% and a spatial resolution of 19.5 m can assist regional planning and environmental assessment efficiently in Lianyungang City. This rule-based classification provides a guidance to improve accuracy for coastal areas with distinct LULC temporal spectral features.

An evaluation of open set recognition for FLIR images

NASA Astrophysics Data System (ADS)

Scherreik, Matthew; Rigling, Brian

2015-05-01

Typical supervised classification algorithms label inputs according to what was learned in a training phase. Thus, test inputs that were not seen in training are always given incorrect labels. Open set recognition algorithms address this issue by accounting for inputs that are not present in training and providing the classifier with an option to reject" unknown samples. A number of such techniques have been developed in the literature, many of which are based on support vector machines (SVMs). One approach, the 1-vs-set machine, constructs a slab" in feature space using the SVM hyperplane. Inputs falling on one side of the slab or within the slab belong to a training class, while inputs falling on the far side of the slab are rejected. We note that rejection of unknown inputs can be achieved by thresholding class posterior probabilities. Another recently developed approach, the Probabilistic Open Set SVM (POS-SVM), empirically determines good probability thresholds. We apply the 1-vs-set machine, POS-SVM, and closed set SVMs to FLIR images taken from the Comanche SIG dataset. Vehicles in the dataset are divided into three general classes: wheeled, armored personnel carrier (APC), and tank. For each class, a coarse pose estimate (front, rear, left, right) is taken. In a closed set sense, we analyze these algorithms for prediction of vehicle class and pose. To test open set performance, one or more vehicle classes are held out from training. By considering closed and open set performance separately, we may closely analyze both inter-class discrimination and threshold effectiveness.

DEPENDENCE OF NITRIC OXIDE EMISSIONS ON VEHICLE LOAD: RESULTS FROM THE GTRP INSTRUMENTED VEHICLE PROGRAM

EPA Science Inventory

The presentation discussed the dependence of nitric oxide (NO) emissions on vehicle load, bases on results from an instrumented-vehicle program. The accuracy and feasibility of modal emissions models depend on algorithms to allocate vehicle emissions based on a vehicle operation...

Pavement type and wear condition classification from tire cavity acoustic measurements with artificial neural networks.

PubMed

Masino, Johannes; Foitzik, Michael-Jan; Frey, Michael; Gauterin, Frank

2017-06-01

Tire road noise is the major contributor to traffic noise, which leads to general annoyance, speech interference, and sleep disturbances. Standardized methods to measure tire road noise are expensive, sophisticated to use, and they cannot be applied comprehensively. This paper presents a method to automatically classify different types of pavement and the wear condition to identify noisy road surfaces. The methods are based on spectra of time series data of the tire cavity sound, acquired under normal vehicle operation. The classifier, an artificial neural network, correctly predicts three pavement types, whereas there are few bidirectional mis-classifications for two pavements, which have similar physical characteristics. The performance measures of the classifier to predict a new or worn out condition are over 94.6%. One could create a digital map with the output of the presented method. On the basis of these digital maps, road segments with a strong impact on tire road noise could be automatically identified. Furthermore, the method can estimate the road macro-texture, which has an impact on the tire road friction especially on wet conditions. Overall, this digital map would have a great benefit for civil engineering departments, road infrastructure operators, and for advanced driver assistance systems.

Biologically inspired circuitry that mimics mammalian hearing

NASA Astrophysics Data System (ADS)

Hubbard, Allyn; Cohen, Howard; Karl, Christian; Freedman, David; Mountain, David; Ziph-Schatzberg, Leah; Nourzad Karl, Marianne; Kelsall, Sarah; Gore, Tyler; Pu, Yirong; Yang, Zibing; Xing, Xinyu; Deligeorges, Socrates

2009-05-01

We are developing low-power microcircuitry that implements classification and direction finding systems of very small size and small acoustic aperture. Our approach was inspired by the fact that small mammals are able to localize sounds despite their ears may be separated by as little as a centimeter. Gerbils, in particular are good low-frequency localizers, which is a particularly difficult task, since a wavelength at 500 Hz is on the order of two feet. Given such signals, crosscorrelation- based methods to determine direction fail badly in the presence of a small amount of noise, e.g. wind noise and noise clutter common to almost any realistic environment. Circuits are being developed using both analog and digital techniques, each of which process signals in fundamentally the same way the peripheral auditory system of mammals processes sound. A filter bank represents filtering done by the cochlea. The auditory nerve is implemented using a combination of an envelope detector, an automatic gain stage, and a unique one-bit A/D, which creates what amounts to a neural impulse. These impulses are used to extract pitch characteristics, which we use to classify sounds such as vehicles, small and large weaponry from AK-47s to 155mm cannon, including mortar launches and impacts. In addition to the pitchograms, we also use neural nets for classification.

Improving Driver Alertness through Music Selection Using a Mobile EEG to Detect Brainwaves

PubMed Central

Liu, Ning-Han; Chiang, Cheng-Yu; Hsu, Hsiang-Ming

2013-01-01

Driving safety has become a global topic of discussion with the recent development of the Smart Car concept. Many of the current car safety monitoring systems are based on image discrimination techniques, such as sensing the vehicle drifting from the main road, or changes in the driver's facial expressions. However, these techniques are either too simplistic or have a low success rate as image processing is easily affected by external factors, such as weather and illumination. We developed a drowsiness detection mechanism based on an electroencephalogram (EEG) reading collected from the driver with an off-the-shelf mobile sensor. This sensor employs wireless transmission technology and is suitable for wear by the driver of a vehicle. The following classification techniques were incorporated: Artificial Neural Networks, Support Vector Machine, and k Nearest Neighbor. These classifiers were integrated with integration functions after a genetic algorithm was first used to adjust the weighting for each classifier in the integration function. In addition, since past studies have shown effects of music on a person's state-of-mind, we propose a personalized music recommendation mechanism as a part of our system. Through the in-car stereo system, this music recommendation mechanism can help prevent a driver from becoming drowsy due to monotonous road conditions. Experimental results demonstrate the effectiveness of our proposed drowsiness detection method to determine a driver's state of mind, and the music recommendation system is therefore able to reduce drowsiness. PMID:23803789

Mapping coral and sponge habitats on a shelf-depth environment using multibeam sonar and ROV video observations: Learmonth Bank, northern British Columbia, Canada

NASA Astrophysics Data System (ADS)

Neves, Bárbara M.; Du Preez, Cherisse; Edinger, Evan

2014-01-01

Efforts to locate and map deep-water coral and sponge habitats are essential for the effective management and conservation of these vulnerable marine ecosystems. Here we test the applicability of a simple multibeam sonar classification method developed for fjord environments to map the distribution of shelf-depth substrates and gorgonian coral- and sponge-dominated biotopes. The studied area is a shelf-depth feature Learmonth Bank, northern British Columbia, Canada and the method was applied aiming to map primarily non-reef forming coral and sponge biotopes. Aside from producing high-resolution maps (5 m2 raster grid), biotope-substrate associations were also investigated. A multibeam sonar survey yielded bathymetry, acoustic backscatter strength and slope. From benthic video transects recorded by remotely operated vehicles (ROVs) six primary substrate types and twelve biotope categories were identified, defined by the primary sediment and dominant biological structure, respectively. Substrate and biotope maps were produced using a supervised classification mostly based on the inter-quartile range of the acoustic variables for each substrate type and biotope. Twenty-five percent of the video observations were randomly reserved for testing the classification accuracy. The dominant biotope-defining corals were red tree coral Primnoa pacifica and small styasterids, of which Stylaster parageus was common. Demosponges and hexactinellid sponges were frequently observed but no sponge reefs were observed. The substrate classification readily distinguished fine sediment, Sand and Bedrock from the other substrate types, but had greater difficulty distinguishing Bedrock from Boulders and Cobble. The biotope classification accurately identified Gardens (dense aggregations of sponges and corals) and Primnoa-dominated biotopes (67% accuracy), but most other biotopes had lower accuracies. There was a significant correspondence between Learmonth's biotopes and substrate types, with many biotopes strongly associated with only a single substrate type. This strong correspondence allowed substrate types to function as a surrogate for helping to map biotope distributions. Our results add new information on the distribution of corals and sponges at Learmonth Bank, which can be used to guide management at this location.

Assessment of skin sensitization under REACH: A case report on vehicle choice in the LLNA and its crucial role preventing false positive results.

PubMed

Watzek, Nico; Berger, Franz; Kolle, Susanne Noreen; Kaufmann, Tanja; Becker, Matthias; van Ravenzwaay, Bennard

2017-04-01

In the EU, chemicals with a production or import volume in quantities of one metric ton per year or more have to be tested for skin sensitizing properties under the REACH regulation. The murine Local Lymph Node Assay (LLNA) and its modifications are widely used to fulfil the data requirement, as it is currently considered the first-choice method for in vivo testing to cover this endpoint. This manuscript describes a case study highlighting the importance of understanding the chemistry of the test material during testing for 'skin sensitization' of MCDA (mixture of 2,4- and 2,6-diamino-methylcyclohexane) with particular focus on the vehicle used. While the BrdU-ELISA modification of the LLNA using acetone/olive oil (AOO) as vehicle revealed expectable positive results. However, the concentration control analysis unexpectedly revealed an instability of MCDA in the vehicle AOO. Further studies on the reactivity showed MCDA to rapidly react with AOO under formation of various imine structures, which might have caused the positive LLNA result. The repetition of the LLNA using propylene glycol (PG) as vehicle did not confirm the positive results of the LLNA using AOO. Finally, a classification of MCDA as skin sensitizer according to the Globally Harmonized System (GHS) was not justified. Copyright © 2017 Elsevier Inc. All rights reserved.

Classification of rollovers according to crash severity.

PubMed

Digges, K; Eigen, A

2006-01-01

NASS/CDS 1995-2004 was used to classify rollovers according to severity. The rollovers were partitioned into two classes - rollover as the first event and rollover preceded by an impact with a fixed or non-fixed object. The populations of belted and unbelted were examined separately and combined. The average injury rate for the unbelted was five times that for the belted. Approximately 21% of the severe injuries suffered by belted occupants were in crashes with harmful events prior to the rollover that produced severe damage to the vehicle. This group carried a much higher injury risk than the average. A planar damage measure in addition to the rollover measure was required to adequately capture the crash severity of this population. For rollovers as the first event, approximately 1% of the serious injuries to belted occupants occurred during the first quarter-turn. Rollovers that were arrested during the 1 ( st ) quarter-turn carried a higher injury rate than average. The number of quarter-turns were grouped in various ways including the number of times the vehicle roof faces the ground (number of vehicle inversions). The number of vehicle inversions was found to be a statistically significant injury predictor for 78% of the belted and unbelted populations with MAIS 3+F injuries in rollovers. The remaining 22% required crash severity metrics in addition to the number of vehicle inversions.

Classification of Rollovers According to Crash Severity

PubMed Central

Digges, K.; Eigen, A.

2006-01-01

NASS/CDS 1995–2004 was used to classify rollovers according to severity. The rollovers were partitioned into two classes – rollover as the first event and rollover preceded by an impact with a fixed or non-fixed object. The populations of belted and unbelted were examined separately and combined. The average injury rate for the unbelted was five times that for the belted. Approximately 21% of the severe injuries suffered by belted occupants were in crashes with harmful events prior to the rollover that produced severe damage to the vehicle. This group carried a much higher injury risk than the average. A planar damage measure in addition to the rollover measure was required to adequately capture the crash severity of this population. For rollovers as the first event, approximately 1% of the serious injuries to belted occupants occurred during the first quarter-turn. Rollovers that were arrested during the 1st quarter-turn carried a higher injury rate than average. The number of quarter-turns were grouped in various ways including the number of times the vehicle roof faces the ground (number of vehicle inversions). The number of vehicle inversions was found to be a statistically significant injury predictor for 78% of the belted and unbelted populations with MAIS 3+F injuries in rollovers. The remaining 22% required crash severity metrics in addition to the number of vehicle inversions. PMID:16968634

Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping.

PubMed

Borra-Serrano, Irene; Peña, José Manuel; Torres-Sánchez, Jorge; Mesas-Carrascosa, Francisco Javier; López-Granados, Francisca

2015-08-12

Unmanned aerial vehicles (UAVs) combined with different spectral range sensors are an emerging technology for providing early weed maps for optimizing herbicide applications. Considering that weeds, at very early phenological stages, are similar spectrally and in appearance, three major components are relevant: spatial resolution, type of sensor and classification algorithm. Resampling is a technique to create a new version of an image with a different width and/or height in pixels, and it has been used in satellite imagery with different spatial and temporal resolutions. In this paper, the efficiency of resampled-images (RS-images) created from real UAV-images (UAV-images; the UAVs were equipped with two types of sensors, i.e., visible and visible plus near-infrared spectra) captured at different altitudes is examined to test the quality of the RS-image output. The performance of the object-based-image-analysis (OBIA) implemented for the early weed mapping using different weed thresholds was also evaluated. Our results showed that resampling accurately extracted the spectral values from high spatial resolution UAV-images at an altitude of 30 m and the RS-image data at altitudes of 60 and 100 m, was able to provide accurate weed cover and herbicide application maps compared with UAV-images from real flights.

Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping

PubMed Central

Borra-Serrano, Irene; Peña, José Manuel; Torres-Sánchez, Jorge; Mesas-Carrascosa, Francisco Javier; López-Granados, Francisca

2015-01-01

Unmanned aerial vehicles (UAVs) combined with different spectral range sensors are an emerging technology for providing early weed maps for optimizing herbicide applications. Considering that weeds, at very early phenological stages, are similar spectrally and in appearance, three major components are relevant: spatial resolution, type of sensor and classification algorithm. Resampling is a technique to create a new version of an image with a different width and/or height in pixels, and it has been used in satellite imagery with different spatial and temporal resolutions. In this paper, the efficiency of resampled-images (RS-images) created from real UAV-images (UAV-images; the UAVs were equipped with two types of sensors, i.e., visible and visible plus near-infrared spectra) captured at different altitudes is examined to test the quality of the RS-image output. The performance of the object-based-image-analysis (OBIA) implemented for the early weed mapping using different weed thresholds was also evaluated. Our results showed that resampling accurately extracted the spectral values from high spatial resolution UAV-images at an altitude of 30 m and the RS-image data at altitudes of 60 and 100 m, was able to provide accurate weed cover and herbicide application maps compared with UAV-images from real flights. PMID:26274960

Classification scheme and prevention measures for caught-in-between occupational fatalities.

PubMed

Chi, Chia-Fen; Lin, Syuan-Zih

2018-04-01

The current study analyzed 312 caught-in-between fatalities caused by machinery and vehicles. A comprehensive and mutually exclusive coding scheme was developed to analyze and code each caught-in-between fatality in terms of age, gender, experience of the victim, type of industry, source of injury, and causes for these accidents. Boolean algebra analysis was applied on these 312 caught-in-between fatalities to derive minimal cut set (MCS) causes associated with each source of injury. Eventually, contributing factors and common accident patterns associated with (1) special process machinery including textile, printing, packaging machinery, (2) metal, woodworking, and special material machinery, (3) conveyor, (4) vehicle, (5) crane, (6) construction machinery, and (7) elevator can be divided into three major groups through Boolean algebra and MCS analysis. The MCS causes associated with conveyor share the same primary causes as those of the special process machinery including textile, printing, packaging and metal, woodworking, and special material machinery. These fatalities can be eliminated by focusing on the prevention measures associated with lack of safeguards, working on a running machine or process, unintentional activation, unsafe posture or position, unsafe clothing, and defective safeguards. Other precise and effective intervention can be developed based on the identified groups of accident causes associated with each source of injury. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparative of signal processing techniques for micro-Doppler signature extraction with automotive radar systems

NASA Astrophysics Data System (ADS)

Rodriguez-Hervas, Berta; Maile, Michael; Flores, Benjamin C.

2014-05-01

In recent years, the automotive industry has experienced an evolution toward more powerful driver assistance systems that provide enhanced vehicle safety. These systems typically operate in the optical and microwave regions of the electromagnetic spectrum and have demonstrated high efficiency in collision and risk avoidance. Microwave radar systems are particularly relevant due to their operational robustness under adverse weather or illumination conditions. Our objective is to study different signal processing techniques suitable for extraction of accurate micro-Doppler signatures of slow moving objects in dense urban environments. Selection of the appropriate signal processing technique is crucial for the extraction of accurate micro-Doppler signatures that will lead to better results in a radar classifier system. For this purpose, we perform simulations of typical radar detection responses in common driving situations and conduct the analysis with several signal processing algorithms, including short time Fourier Transform, continuous wavelet or Kernel based analysis methods. We take into account factors such as the relative movement between the host vehicle and the target, and the non-stationary nature of the target's movement. A comparison of results reveals that short time Fourier Transform would be the best approach for detection and tracking purposes, while the continuous wavelet would be the best suited for classification purposes.

Object-based land cover classification based on fusion of multifrequency SAR data and THAICHOTE optical imagery

NASA Astrophysics Data System (ADS)

Sukawattanavijit, Chanika; Srestasathiern, Panu

2017-10-01

Land Use and Land Cover (LULC) information are significant to observe and evaluate environmental change. LULC classification applying remotely sensed data is a technique popularly employed on a global and local dimension particularly, in urban areas which have diverse land cover types. These are essential components of the urban terrain and ecosystem. In the present, object-based image analysis (OBIA) is becoming widely popular for land cover classification using the high-resolution image. COSMO-SkyMed SAR data was fused with THAICHOTE (namely, THEOS: Thailand Earth Observation Satellite) optical data for land cover classification using object-based. This paper indicates a comparison between object-based and pixel-based approaches in image fusion. The per-pixel method, support vector machines (SVM) was implemented to the fused image based on Principal Component Analysis (PCA). For the objectbased classification was applied to the fused images to separate land cover classes by using nearest neighbor (NN) classifier. Finally, the accuracy assessment was employed by comparing with the classification of land cover mapping generated from fused image dataset and THAICHOTE image. The object-based data fused COSMO-SkyMed with THAICHOTE images demonstrated the best classification accuracies, well over 85%. As the results, an object-based data fusion provides higher land cover classification accuracy than per-pixel data fusion.

Object based image analysis for the classification of the growth stages of Avocado crop, in Michoacán State, Mexico

NASA Astrophysics Data System (ADS)

Gao, Yan; Marpu, Prashanth; Morales Manila, Luis M.

2014-11-01

This paper assesses the suitability of 8-band Worldview-2 (WV2) satellite data and object-based random forest algorithm for the classification of avocado growth stages in Mexico. We tested both pixel-based with minimum distance (MD) and maximum likelihood (MLC) and object-based with Random Forest (RF) algorithm for this task. Training samples and verification data were selected by visual interpreting the WV2 images for seven thematic classes: fully grown, middle stage, and early stage of avocado crops, bare land, two types of natural forests, and water body. To examine the contribution of the four new spectral bands of WV2 sensor, all the tested classifications were carried out with and without the four new spectral bands. Classification accuracy assessment results show that object-based classification with RF algorithm obtained higher overall higher accuracy (93.06%) than pixel-based MD (69.37%) and MLC (64.03%) method. For both pixel-based and object-based methods, the classifications with the four new spectral bands (overall accuracy obtained higher accuracy than those without: overall accuracy of object-based RF classification with vs without: 93.06% vs 83.59%, pixel-based MD: 69.37% vs 67.2%, pixel-based MLC: 64.03% vs 36.05%, suggesting that the four new spectral bands in WV2 sensor contributed to the increase of the classification accuracy.

Mapping lava flow textures using three-dimensional measures of surface roughness

NASA Astrophysics Data System (ADS)

Mallonee, H. C.; Kobs-Nawotniak, S. E.; McGregor, M.; Hughes, S. S.; Neish, C.; Downs, M.; Delparte, D.; Lim, D. S. S.; Heldmann, J. L.

2016-12-01

Lava flow emplacement conditions are reflected in the surface textures of a lava flow; unravelling these conditions is crucial to understanding the eruptive history and characteristics of basaltic volcanoes. Mapping lava flow textures using visual imagery alone is an inherently subjective process, as these images generally lack the resolution needed to make these determinations. Our team has begun mapping lava flow textures using visual spectrum imagery, which is an inherently subjective process involving the challenge of identifying transitional textures such as rubbly and slabby pāhoehoe, as these textures are similar in appearance and defined qualitatively. This is particularly problematic for interpreting planetary lava flow textures, where we have more limited data. We present a tool to objectively classify lava flow textures based on quantitative measures of roughness, including the 2D Hurst exponent, RMS height, and 2D:3D surface area ratio. We collected aerial images at Craters of the Moon National Monument (COTM) using Unmanned Aerial Vehicles (UAVs) in 2015 and 2016 as part of the FINESSE (Field Investigations to Enable Solar System Science and Exploration) and BASALT (Biologic Analog Science Associated with Lava Terrains) research projects. The aerial images were stitched together to create Digital Terrain Models (DTMs) with resolutions on the order of centimeters. The DTMs were evaluated by the classification tool described above, with output compared against field assessment of the texture. Further, the DTMs were downsampled and reevaluated to assess the efficacy of the classification tool at data resolutions similar to current datasets from other planetary bodies. This tool allows objective classification of lava flow texture, which enables more accurate interpretations of flow characteristics. This work also gives context for interpretations of flows with comparatively low data resolutions, such as those on the Moon and Mars. Textural maps based on quantitative measures of roughness are a valuable asset for studies of lava flows on Earth and other planetary bodies.

Acoustic/Seismic Ground Sensors for Detection, Localization and Classification on the Battlefield

DTIC Science & Technology

2006-10-01

controlled so that collisions are avoided. Figure 1 presents BACH system components. 3 BACH Sensor Posts (1 to 8) Command Post BACH MMI PC VHF...2.2.4 Processing scheme Processing inside SP is dedicated to stationary spectral lines extraction and derives from ASW algorithms. Special attention...is similar to that used for helicopters (see figure 4), with adaptations to cope with vehicles signatures (fuzzy unstable spectral lines, abrupt

Computer Description of the Field Artillery Ammunition Supply Vehicle

DTIC Science & Technology

1983-04-01

Combinatorial Geometry (COM-GEOM) GIFT Computer Code Computer Target Description 2& AfTNACT (Cmne M feerve shb N ,neemssalyan ify by block number) A...input to the GIFT computer code to generate target vulnerability data. F.a- 4 ono OF I NOV 5S OLETE UNCLASSIFIED SECUOITY CLASSIFICATION OF THIS PAGE...Combinatorial Geometry (COM-GEOM) desrription. The "Geometric Information for Tarqets" ( GIFT ) computer code accepts the CO!-GEOM description and

Operational Manning Considerations for Spartan Scout and Sea Fox Unmanned Surface Vehicles (USV)

DTIC Science & Technology

2006-09-01

Maintenance Manual for Javelin , TM 9-1425-688-12. Standards: The CLU passes the operational check, all components are clean and free of corrosion...support USV Hellfire and Javelin missile modules. The Navy should establish a GM Navy Enlisted Classification (NEC) to support Hellfire and Javelin ...Aviation Ordnancemen (AO) are potential source ratings to support USV Hellfire and Javelin missile modules. The Navy should establish a GM Navy Enlisted

Simulation of an Air Cushion Vehicle

DTIC Science & Technology

1977-03-01

Massachusetts 02139 ! DDC Niov 219T March 1977 Final Report for Period January 1975 - December 1976 DOD DISTRIBUTION STATEMENT Approved for public...or in ,art is permitted for any purpose of the United States Government. II II JI UNCLASSI FIED SECURITY CLASSIFICATiON OF TIlS PAGE flWhen Dato...overflow Floating point fault Decimal arithmetic fault Watch Dog timer runout 186 NAVTRAEQUIPCEN 75-C-0057- 1 PROGRAM ENi\\TRY Initial Program - LOAD Inhibit

Identifying driver characteristics influencing overtaking crashes.

PubMed

Mohaymany, Afshin Shariat; Kashani, Ali Tavakoli; Ranjbari, Andishe

2010-08-01

To identify the most important driver characteristics influencing crash-causing overtaking maneuvers on 2-lane, 2-way rural roads of Iran. Based on the crash data for rural roads of Iran over 3 years from 2006 to 2008, the classification and regression tree (CART) method combined with the quasi-induced exposure concept was applied for 4 independent variables and one target variable of "driver status" with 2 classes of at fault and not at fault. The independent variables were vehicle type, driver's age, driving license, and driving experience of the driver-the latter 2 driver characteristics are relatively new in traffic safety studies. According to the data set, 16,809 drivers were involved in 2-lane, 2-way rural roads overtaking crashes. The analysis revealed that drivers who are younger than 28 years old, whose driving license is type 2--a common driving license that is for driving with passenger car and light vehicles--and whose driving experience is less than 2 years are most probably responsible for overtaking crashes. It was indicated that vehicle type is the most important factor associated with drivers being responsible for the crashes. The results also revealed that younger drivers (18-28 years) are most likely to be at fault in overtaking crashes. Therefore, enforcement and education should be more concentrated on this age group. Due to the incompliant nature of this group, changing the type and amount of traffic fines is essential for more preventing objectives. The research also found 2 relatively new factors of driving license and driving experience to have considerable effects on drivers being at fault, such that type 2 licensed drivers are more responsible compared to type 1 (a driving license for driving with all motor vehicles, which has some age and experience requirements) licensed drivers or drivers with a special license (a driving license with special vehicle types). Moreover, drivers with less than 2 years' driving experience are more responsible for these kind of crashes; thus prohibiting new drivers from driving on rural roads for new drivers seems substantial.

A web-based land cover classification system based on ontology model of different classification systems

NASA Astrophysics Data System (ADS)

Lin, Y.; Chen, X.

2016-12-01

Land cover classification systems used in remote sensing image data have been developed to meet the needs for depicting land covers in scientific investigations and policy decisions. However, accuracy assessments of a spate of data sets demonstrate that compared with the real physiognomy, each of the thematic map of specific land cover classification system contains some unavoidable flaws and unintended deviation. This work proposes a web-based land cover classification system, an integrated prototype, based on an ontology model of various classification systems, each of which is assigned the same weight in the final determination of land cover type. Ontology, a formal explication of specific concepts and relations, is employed in this prototype to build up the connections among different systems to resolve the naming conflicts. The process is initialized by measuring semantic similarity between terminologies in the systems and the search key to produce certain set of satisfied classifications, and carries on through searching the predefined relations in concepts of all classification systems to generate classification maps with user-specified land cover type highlighted, based on probability calculated by votes from data sets with different classification system adopted. The present system is verified and validated by comparing the classification results with those most common systems. Due to full consideration and meaningful expression of each classification system using ontology and the convenience that the web brings with itself, this system, as a preliminary model, proposes a flexible and extensible architecture for classification system integration and data fusion, thereby providing a strong foundation for the future work.

Preceding Vehicle Detection and Tracking Adaptive to Illumination Variation in Night Traffic Scenes Based on Relevance Analysis

PubMed Central

Guo, Junbin; Wang, Jianqiang; Guo, Xiaosong; Yu, Chuanqiang; Sun, Xiaoyan

2014-01-01

Preceding vehicle detection and tracking at nighttime are challenging problems due to the disturbance of other extraneous illuminant sources coexisting with the vehicle lights. To improve the detection accuracy and robustness of vehicle detection, a novel method for vehicle detection and tracking at nighttime is proposed in this paper. The characteristics of taillights in the gray level are applied to determine the lower boundary of the threshold for taillights segmentation, and the optimal threshold for taillight segmentation is calculated using the OTSU algorithm between the lower boundary and the highest grayscale of the region of interest. The candidate taillight pairs are extracted based on the similarity between left and right taillights, and the non-vehicle taillight pairs are removed based on the relevance analysis of vehicle location between frames. To reduce the false negative rate of vehicle detection, a vehicle tracking method based on taillights estimation is applied. The taillight spot candidate is sought in the region predicted by Kalman filtering, and the disturbed taillight is estimated based on the symmetry and location of the other taillight of the same vehicle. Vehicle tracking is completed after estimating its location according to the two taillight spots. The results of experiments on a vehicle platform indicate that the proposed method could detect vehicles quickly, correctly and robustly in the actual traffic environments with illumination variation. PMID:25195855

Preceding vehicle detection and tracking adaptive to illumination variation in night traffic scenes based on relevance analysis.

PubMed

Guo, Junbin; Wang, Jianqiang; Guo, Xiaosong; Yu, Chuanqiang; Sun, Xiaoyan

2014-08-19

Preceding vehicle detection and tracking at nighttime are challenging problems due to the disturbance of other extraneous illuminant sources coexisting with the vehicle lights. To improve the detection accuracy and robustness of vehicle detection, a novel method for vehicle detection and tracking at nighttime is proposed in this paper. The characteristics of taillights in the gray level are applied to determine the lower boundary of the threshold for taillights segmentation, and the optimal threshold for taillight segmentation is calculated using the OTSU algorithm between the lower boundary and the highest grayscale of the region of interest. The candidate taillight pairs are extracted based on the similarity between left and right taillights, and the non-vehicle taillight pairs are removed based on the relevance analysis of vehicle location between frames. To reduce the false negative rate of vehicle detection, a vehicle tracking method based on taillights estimation is applied. The taillight spot candidate is sought in the region predicted by Kalman filtering, and the disturbed taillight is estimated based on the symmetry and location of the other taillight of the same vehicle. Vehicle tracking is completed after estimating its location according to the two taillight spots. The results of experiments on a vehicle platform indicate that the proposed method could detect vehicles quickly, correctly and robustly in the actual traffic environments with illumination variation.

A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification

PubMed Central

Wang, Guizhou; Liu, Jianbo; He, Guojin

2013-01-01

This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy. PMID:24453808

A Ground-Based Research Vehicle for Base Drag Studies at Subsonic Speeds

NASA Technical Reports Server (NTRS)

Diebler, Corey; Smith, Mark

2002-01-01

A ground research vehicle (GRV) has been developed to study the base drag on large-scale vehicles at subsonic speeds. Existing models suggest that base drag is dependent upon vehicle forebody drag, and for certain configurations, the total drag of a vehicle can be reduced by increasing its forebody drag. Although these models work well for small projectile shapes, studies have shown that they do not provide accurate predictions when applied to large-scale vehicles. Experiments are underway at the NASA Dryden Flight Research Center to collect data at Reynolds numbers to a maximum of 3 x 10(exp 7), and to formulate a new model for predicting the base drag of trucks, buses, motor homes, reentry vehicles, and other large-scale vehicles. Preliminary tests have shown errors as great as 70 percent compared to Hoerner's two-dimensional base drag prediction. This report describes the GRV and its capabilities, details the studies currently underway at NASA Dryden, and presents preliminary results of both the effort to formulate a new base drag model and the investigation into a method of reducing total drag by manipulating forebody drag.

A Mars/phobos Transportation System

NASA Technical Reports Server (NTRS)

1989-01-01

A transportation system will be necessary to support construction and operation of bases on Phobos and Mars beginning in the year 2020 or later. An approach to defining a network of vehicles and the types of vehicles which may be used in the system are presented. The network will provide a convenient, integrated means for transporting robotically constructed bases to Phobos and Mars. All the technology needed for the current plan is expected to be available for use at the projected date of cargo departure from the Earth system. The modular design of the transportation system provides easily implemented contingency plans, so that difficulties with any one vehicle will have a minimal effect on the progress of the total mission. The transportation network proposed consists of orbital vehicles and atmospheric entry vehicles. Initially, only orbital vehicles will participate in the robotic construction phase of the Phobos base. The Interplanetary Transfer Vehicle (ITV) will carry the base and construction equipment to Phobos where the Orbital Maneuvering Vehicles (OMV's) will participate in the initial construction of the base. When the Mars base is ready to be sent, one or more ITV's will be used to transport the atmospheric entry vehicles from Earth. These atmospheric vehicles are the One Way Landers (OWL's) and the Ascent/Descent Vehicles (ADV's). They will be used to carry the base components and/or construction equipment. The OMV's and the Orbital Transfer Vehicles (OTV's) will assist in carrying the atmospheric entry vehicles to low Martian orbit where the OWL's or ADV's will descent to the planet surface. The ADV's were proposed to accommodate expansion of the system. Additionally, a smaller version of the ADV class is capable of transporting personnel between Mars and Phobos.

Classification of weld defect based on information fusion technology for radiographic testing system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Hongquan; Liang, Zeming, E-mail: heavenlzm@126.com; Gao, Jianmin

Improving the efficiency and accuracy of weld defect classification is an important technical problem in developing the radiographic testing system. This paper proposes a novel weld defect classification method based on information fusion technology, Dempster–Shafer evidence theory. First, to characterize weld defects and improve the accuracy of their classification, 11 weld defect features were defined based on the sub-pixel level edges of radiographic images, four of which are presented for the first time in this paper. Second, we applied information fusion technology to combine different features for weld defect classification, including a mass function defined based on the weld defectmore » feature information and the quartile-method-based calculation of standard weld defect class which is to solve a sample problem involving a limited number of training samples. A steam turbine weld defect classification case study is also presented herein to illustrate our technique. The results show that the proposed method can increase the correct classification rate with limited training samples and address the uncertainties associated with weld defect classification.« less

Classification of weld defect based on information fusion technology for radiographic testing system.

PubMed

Jiang, Hongquan; Liang, Zeming; Gao, Jianmin; Dang, Changying

2016-03-01

Improving the efficiency and accuracy of weld defect classification is an important technical problem in developing the radiographic testing system. This paper proposes a novel weld defect classification method based on information fusion technology, Dempster-Shafer evidence theory. First, to characterize weld defects and improve the accuracy of their classification, 11 weld defect features were defined based on the sub-pixel level edges of radiographic images, four of which are presented for the first time in this paper. Second, we applied information fusion technology to combine different features for weld defect classification, including a mass function defined based on the weld defect feature information and the quartile-method-based calculation of standard weld defect class which is to solve a sample problem involving a limited number of training samples. A steam turbine weld defect classification case study is also presented herein to illustrate our technique. The results show that the proposed method can increase the correct classification rate with limited training samples and address the uncertainties associated with weld defect classification.

A Computerized English-Spanish Correlation Index to Five Biomedical Library Classification Schemes Based on MeSH*

PubMed Central

Muench, Eugene V.

1971-01-01

A computerized English/Spanish correlation index to five biomedical library classification schemes and a computerized English/Spanish, Spanish/English listings of MeSH are described. The index was accomplished by supplying appropriate classification numbers of five classification schemes (National Library of Medicine; Library of Congress; Dewey Decimal; Cunningham; Boston Medical) to MeSH and a Spanish translation of MeSH The data were keypunched, merged on magnetic tape, and sorted in a computer alphabetically by English and Spanish subject headings and sequentially by classification number. Some benefits and uses of the index are: a complete index to classification schemes based on MeSH terms; a tool for conversion of classification numbers when reclassifying collections; a Spanish index and a crude Spanish translation of five classification schemes; a data base for future applications, e.g., automatic classification. Other classification schemes, such as the UDC, and translations of MeSH into other languages can be added. PMID:5172471

Heavy truck pre-crash scenarios for safety applications based on vehicle-to-vehicle communications

DOT National Transportation Integrated Search

2014-06-01

This report describes pre-crash scenarios involving at least one heavy truck (gross vehicle weight rating greater than 10,000 pounds), which might be addressed with crash-imminent warning systems based on short range vehicle-to-vehicle (V2V) communic...

Time Series of Images to Improve Tree Species Classification

NASA Astrophysics Data System (ADS)

Miyoshi, G. T.; Imai, N. N.; de Moraes, M. V. A.; Tommaselli, A. M. G.; Näsi, R.

2017-10-01

Tree species classification provides valuable information to forest monitoring and management. The high floristic variation of the tree species appears as a challenging issue in the tree species classification because the vegetation characteristics changes according to the season. To help to monitor this complex environment, the imaging spectroscopy has been largely applied since the development of miniaturized sensors attached to Unmanned Aerial Vehicles (UAV). Considering the seasonal changes in forests and the higher spectral and spatial resolution acquired with sensors attached to UAV, we present the use of time series of images to classify four tree species. The study area is an Atlantic Forest area located in the western part of São Paulo State. Images were acquired in August 2015 and August 2016, generating three data sets of images: only with the image spectra of 2015; only with the image spectra of 2016; with the layer stacking of images from 2015 and 2016. Four tree species were classified using Spectral angle mapper (SAM), Spectral information divergence (SID) and Random Forest (RF). The results showed that SAM and SID caused an overfitting of the data whereas RF showed better results and the use of the layer stacking improved the classification achieving a kappa coefficient of 18.26 %.

[Land cover classification of Four Lakes Region in Hubei Province based on MODIS and ENVISAT data].

PubMed

Xue, Lian; Jin, Wei-Bin; Xiong, Qin-Xue; Liu, Zhang-Yong

2010-03-01

Based on the differences of back scattering coefficient in ENVISAT ASAR data, a classification was made on the towns, waters, and vegetation-covered areas in the Four Lakes Region of Hubei Province. According to the local cropping systems and phenological characteristics in the region, and by using the discrepancies of the MODIS-NDVI index from late April to early May, the vegetation-covered areas were classified into croplands and non-croplands. The classification results based on the above-mentioned procedure was verified by the classification results based on the ETM data with high spatial resolution. Based on the DEM data, the non-croplands were categorized into forest land and bottomland; and based on the discrepancies of mean NDVI index per month, the crops were identified as mid rice, late rice, and cotton, and the croplands were identified as paddy field and upland field. The land cover classification based on the MODIS data with low spatial resolution was basically consistent with that based on the ETM data with high spatial resolution, and the total error rate was about 13.15% when the classification results based on ETM data were taken as the standard. The utilization of the above-mentioned procedures for large scale land cover classification and mapping could make the fast tracking of regional land cover classification.

Multi-label literature classification based on the Gene Ontology graph.

PubMed

Jin, Bo; Muller, Brian; Zhai, Chengxiang; Lu, Xinghua

2008-12-08

The Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of biomedical knowledge in literature, which urges the development of text mining approaches to facilitate the process by automatically extracting the Gene Ontology annotation from literature. The task is usually cast as a text classification problem, and contemporary methods are confronted with unbalanced training data and the difficulties associated with multi-label classification. In this research, we investigated the methods of enhancing automatic multi-label classification of biomedical literature by utilizing the structure of the Gene Ontology graph. We have studied three graph-based multi-label classification algorithms, including a novel stochastic algorithm and two top-down hierarchical classification methods for multi-label literature classification. We systematically evaluated and compared these graph-based classification algorithms to a conventional flat multi-label algorithm. The results indicate that, through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods can significantly improve predictions of the Gene Ontology terms implied by the analyzed text. Furthermore, the graph-based multi-label classifiers are capable of suggesting Gene Ontology annotations (to curators) that are closely related to the true annotations even if they fail to predict the true ones directly. A software package implementing the studied algorithms is available for the research community. Through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods have better potential than the conventional flat multi-label classification approach to facilitate protein annotation based on the literature.

Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

PubMed

Younghak Shin; Balasingham, Ilangko

2017-07-01

Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

14 CFR 1203.412 - Classification guides.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 14 Aeronautics and Space 5 2013-01-01 2013-01-01 false Classification guides. 1203.412 Section... PROGRAM Guides for Original Classification § 1203.412 Classification guides. (a) General. A classification guide, based upon classification determinations made by appropriate program and classification...

14 CFR 1203.412 - Classification guides.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 14 Aeronautics and Space 5 2012-01-01 2012-01-01 false Classification guides. 1203.412 Section... PROGRAM Guides for Original Classification § 1203.412 Classification guides. (a) General. A classification guide, based upon classification determinations made by appropriate program and classification...

14 CFR 1203.412 - Classification guides.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 14 Aeronautics and Space 5 2011-01-01 2010-01-01 true Classification guides. 1203.412 Section 1203... Guides for Original Classification § 1203.412 Classification guides. (a) General. A classification guide, based upon classification determinations made by appropriate program and classification authorities...

Evaluation of different shadow detection and restoration methods and their impact on vegetation indices using UAV high-resolution imageries over vineyards

NASA Astrophysics Data System (ADS)

Aboutalebi, M.; Torres-Rua, A. F.; McKee, M.; Kustas, W. P.; Nieto, H.

2017-12-01

Shadows are an unavoidable component of high-resolution imagery. Although shadows can be a useful source of information about terrestrial features, they are a hindrance for image processing and lead to misclassification errors and increased uncertainty in defining surface reflectance properties. In precision agriculture activities, shadows may affect the performance of vegetation indices at pixel and plant scales. Thus, it becomes necessary to evaluate existing shadow detection and restoration methods, especially for applications that makes direct use of pixel information to estimate vegetation biomass, leaf area index (LAI), plant water use and stress, chlorophyll content, just to name a few. In this study, four high-resolution imageries captured by the Utah State University - AggieAir Unmanned Aerial Vehicle (UAV) system flown in 2014, 2015, and 2016 over a commercial vineyard located in the California for the USDA-Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program are used for shadow detection and restoration. Four different methods for shadow detection are compared: (1) unsupervised classification, (2) supervised classification, (3) index-based method, and (4) physically-based method. Also, two different shadow restoration methods are evaluated: (1) linear correlation correction, and (2) gamma correction. The models' performance is evaluated over two vegetation indices: normalized difference vegetation index (NDVI) and LAI for both sunlit and shadowed pixels. Histogram and analysis of variance (ANOVA) are used as performance indicators. Results indicated that the performance of the supervised classification and the index-based method are better than other methods. In addition, there is a statistical difference between the average of NDVI and LAI on the sunlit and shadowed pixels. Among the shadow restoration methods, gamma correction visually works better than the linear correlation correction. Moreover, the statistical difference between sunlit and shadowed NDVI and LAI decreases after the application of the gamma restoration method. Potential effects of shadows on modeling surface energy balance and evapotranspiration using very high resolution UAV imagery over the GRAPEX vineyard will be discussed.

Method for reducing the drag of blunt-based vehicles by adaptively increasing forebody roughness

NASA Technical Reports Server (NTRS)

Whitmore, Stephen A. (Inventor); Saltzman, Edwin J. (Inventor); Moes, Timothy R. (Inventor); Iliff, Kenneth W. (Inventor)

2005-01-01

A method for reducing drag upon a blunt-based vehicle by adaptively increasing forebody roughness to increase drag at the roughened area of the forebody, which results in a decrease in drag at the base of this vehicle, and in total vehicle drag.

Feature selection and classification of multiparametric medical images using bagging and SVM

NASA Astrophysics Data System (ADS)

Fan, Yong; Resnick, Susan M.; Davatzikos, Christos

2008-03-01

This paper presents a framework for brain classification based on multi-parametric medical images. This method takes advantage of multi-parametric imaging to provide a set of discriminative features for classifier construction by using a regional feature extraction method which takes into account joint correlations among different image parameters; in the experiments herein, MRI and PET images of the brain are used. Support vector machine classifiers are then trained based on the most discriminative features selected from the feature set. To facilitate robust classification and optimal selection of parameters involved in classification, in view of the well-known "curse of dimensionality", base classifiers are constructed in a bagging (bootstrap aggregating) framework for building an ensemble classifier and the classification parameters of these base classifiers are optimized by means of maximizing the area under the ROC (receiver operating characteristic) curve estimated from their prediction performance on left-out samples of bootstrap sampling. This classification system is tested on a sex classification problem, where it yields over 90% classification rates for unseen subjects. The proposed classification method is also compared with other commonly used classification algorithms, with favorable results. These results illustrate that the methods built upon information jointly extracted from multi-parametric images have the potential to perform individual classification with high sensitivity and specificity.

Image-classification-based global dimming algorithm for LED backlights in LCDs

NASA Astrophysics Data System (ADS)

Qibin, Feng; Huijie, He; Dong, Han; Lei, Zhang; Guoqiang, Lv

2015-07-01

Backlight dimming can help LCDs reduce power consumption and improve CR. With fixed parameters, dimming algorithm cannot achieve satisfied effects for all kinds of images. The paper introduces an image-classification-based global dimming algorithm. The proposed classification method especially for backlight dimming is based on luminance and CR of input images. The parameters for backlight dimming level and pixel compensation are adaptive with image classifications. The simulation results show that the classification based dimming algorithm presents 86.13% power reduction improvement compared with dimming without classification, with almost same display quality. The prototype is developed. There are no perceived distortions when playing videos. The practical average power reduction of the prototype TV is 18.72%, compared with common TV without dimming.

Distributed data fusion across multiple hard and soft mobile sensor platforms

NASA Astrophysics Data System (ADS)

Sinsley, Gregory

One of the biggest challenges currently facing the robotics field is sensor data fusion. Unmanned robots carry many sophisticated sensors including visual and infrared cameras, radar, laser range finders, chemical sensors, accelerometers, gyros, and global positioning systems. By effectively fusing the data from these sensors, a robot would be able to form a coherent view of its world that could then be used to facilitate both autonomous and intelligent operation. Another distinct fusion problem is that of fusing data from teammates with data from onboard sensors. If an entire team of vehicles has the same worldview they will be able to cooperate much more effectively. Sharing worldviews is made even more difficult if the teammates have different sensor types. The final fusion challenge the robotics field faces is that of fusing data gathered by robots with data gathered by human teammates (soft sensors). Humans sense the world completely differently from robots, which makes this problem particularly difficult. The advantage of fusing data from humans is that it makes more information available to the entire team, thus helping each agent to make the best possible decisions. This thesis presents a system for fusing data from multiple unmanned aerial vehicles, unmanned ground vehicles, and human observers. The first issue this thesis addresses is that of centralized data fusion. This is a foundational data fusion issue, which has been very well studied. Important issues in centralized fusion include data association, classification, tracking, and robotics problems. Because these problems are so well studied, this thesis does not make any major contributions in this area, but does review it for completeness. The chapter on centralized fusion concludes with an example unmanned aerial vehicle surveillance problem that demonstrates many of the traditional fusion methods. The second problem this thesis addresses is that of distributed data fusion. Distributed data fusion is a younger field than centralized fusion. The main issues in distributed fusion that are addressed are distributed classification and distributed tracking. There are several well established methods for performing distributed fusion that are first reviewed. The chapter on distributed fusion concludes with a multiple unmanned vehicle collaborative test involving an unmanned aerial vehicle and an unmanned ground vehicle. The third issue this thesis addresses is that of soft sensor only data fusion. Soft-only fusion is a newer field than centralized or distributed hard sensor fusion. Because of the novelty of the field, the chapter on soft only fusion contains less background information and instead focuses on some new results in soft sensor data fusion. Specifically, it discusses a novel fuzzy logic based soft sensor data fusion method. This new method is tested using both simulations and field measurements. The biggest issue addressed in this thesis is that of combined hard and soft fusion. Fusion of hard and soft data is the newest area for research in the data fusion community; therefore, some of the largest theoretical contributions in this thesis are in the chapter on combined hard and soft fusion. This chapter presents a novel combined hard and soft data fusion method based on random set theory, which processes random set data using a particle filter. Furthermore, the particle filter is designed to be distributed across multiple robots and portable computers (used by human observers) so that there is no centralized failure point in the system. After laying out a theoretical groundwork for hard and soft sensor data fusion the thesis presents practical applications for hard and soft sensor data fusion in simulation. Through a series of three progressively more difficult simulations, some important hard and soft sensor data fusion capabilities are demonstrated. The first simulation demonstrates fusing data from a single soft sensor and a single hard sensor in order to track a car that could be driving normally or erratically. The second simulation adds the extra complication of classifying the type of target to the simulation. The third simulation uses multiple hard and soft sensors, with a limited field of view, to track a moving target and classify it as a friend, foe, or neutral. The final chapter builds on the work done in previous chapters by performing a field test of the algorithms for hard and soft sensor data fusion. The test utilizes an unmanned aerial vehicle, an unmanned ground vehicle, and a human observer with a laptop. The test is designed to mimic a collaborative human and robot search and rescue problem. This test makes some of the most important practical contributions of the thesis by showing that the algorithms that have been developed for hard and soft sensor data fusion are capable of running in real time on relatively simple hardware.

Pedestrian signalization and the risk of pedestrian-motor vehicle collisions in Lima, Peru

PubMed Central

Quistberg, D. Alex; Koepsell, Thomas D.; Boyle, Linda Ng; Miranda, J. Jaime; Johnston, Brian D.; Ebel, Beth E.

2014-01-01

Safe walking environments are essential for protecting pedestrians and promoting physical activity. In Peru, pedestrians comprise of over three-quarters of road fatality victims. Pedestrian signalization plays an important role managing pedestrian and vehicle traffic and may help improve pedestrian safety. We examined the relationship between pedestrian-motor vehicle collisions and the presence of visible traffic signals, pedestrian signals, and signal timing to determine whether these countermeasures improved pedestrian safety. A matched case-control design was used where the units of study were crossing locations. We randomly sampled 97 control-matched collisions (weighted N=1134) at intersections occurring from October, 2010 to January, 2011 in Lima. Each case-control pair was matched on proximity, street classification, and number of lanes. Sites were visited between February, 2011 and September, 2011. Each analysis accounted for sampling weight and matching and was adjusted for vehicle and pedestrian traffic flow, crossing width, and mean vehicle speed. Collisions were more common where a phased pedestrian signal (green or red-lit signal) was present compared to no signalization (odds ratio [OR] 8.88, 95% Confidence Interval [CI] 1.32–59.6). A longer pedestrian-specific signal duration was associated with collision risk (OR 5.31, 95% CI 1.02–9.60 per 15-second interval). Collisions occurred more commonly in the presence of any signalization visible to pedestrians or pedestrian-specific signalization, though these associations were not statistically significant. Signalization efforts were not associated with lower risk for pedestrians; rather, they were associated with an increased risk of pedestrian-vehicle collisions. PMID:24821630

Pedestrian signalization and the risk of pedestrian-motor vehicle collisions in Lima, Peru.

PubMed

Quistberg, D Alex; Koepsell, Thomas D; Boyle, Linda Ng; Miranda, J Jaime; Johnston, Brian D; Ebel, Beth E

2014-09-01

Safe walking environments are essential for protecting pedestrians and promoting physical activity. In Peru, pedestrians comprise over three-quarters of road fatality victims. Pedestrian signalization plays an important role managing pedestrian and vehicle traffic and may help improve pedestrian safety. We examined the relationship between pedestrian-motor vehicle collisions and the presence of visible traffic signals, pedestrian signals, and signal timing to determine whether these countermeasures improved pedestrian safety. A matched case-control design was used where the units of study were crossing locations. We randomly sampled 97 control-matched collisions (weighted N=1134) at intersections occurring from October, 2010 to January, 2011 in Lima. Each case-control pair was matched on proximity, street classification, and number of lanes. Sites were visited between February, 2011 and September, 2011. Each analysis accounted for sampling weight and matching and was adjusted for vehicle and pedestrian traffic flow, crossing width, and mean vehicle speed. Collisions were more common where a phased pedestrian signal (green or red-light signal) was present compared to no signalization (odds ratio [OR] 8.88, 95% Confidence Interval [CI] 1.32-59.6). A longer pedestrian-specific signal duration was associated with collision risk (OR 5.31, 95% CI 1.02-9.60 per 15-s interval). Collisions occurred more commonly in the presence of any signalization visible to pedestrians or pedestrian-specific signalization, though these associations were not statistically significant. Signalization efforts were not associated with lower risk for pedestrians; rather, they were associated with an increased risk of pedestrian-vehicle collisions. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

NASA Astrophysics Data System (ADS)

Zhao, Jianing; Gao, Wanlin; Liu, Zili; Mou, Guifen; Lu, Lin; Yu, Lina

The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.

Depiction of priority light-vehicle pre-crash scenarios for safety applications based on vehicle-to-vehicle communications

DOT National Transportation Integrated Search

2013-04-30

A template of pre-crash scenarios is presented to depict national crash statistics and kinematic information of time-to-collision for the design of appropriate crash countermeasures based on vehicle-to-vehicle (V2V) communications. This template serv...

From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification.

PubMed

Slabbinck, Bram; Waegeman, Willem; Dawyndt, Peter; De Vos, Paul; De Baets, Bernard

2010-01-30

Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context.

From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

PubMed Central

2010-01-01

Background Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. Results In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. Conclusions FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context. PMID:20113515

The groningen laryngomalacia classification system--based on systematic review and dynamic airway changes.

PubMed

van der Heijden, Martijn; Dikkers, Frederik G; Halmos, Gyorgy B

2015-12-01

Laryngomalacia is the most common cause of dyspnea and stridor in newborn infants. Laryngomalacia is a dynamic change of the upper airway based on abnormally pliable supraglottic structures, which causes upper airway obstruction. In the past, different classification systems have been introduced. Until now no classification system is widely accepted and applied. Our goal is to provide a simple and complete classification system based on systematic literature search and our experiences. Retrospective cohort study with literature review. All patients with laryngomalacia under the age of 5 at time of diagnosis were included. Photo and video documentation was used to confirm diagnosis and characteristics of dynamic airway change. Outcome was compared with available classification systems in literature. Eighty-five patients were included. In contrast to other classification systems, only three typical different dynamic changes have been identified in our series. Two existing classification systems covered 100% of our findings, but there was an unnecessary overlap between different types in most of the systems. Based on our finding, we propose a new a classification system for laryngomalacia, which is purely based on dynamic airway changes. The groningen laryngomalacia classification is a new, simplified classification system with three types, based on purely dynamic laryngeal changes, tested in a tertiary referral center: Type 1: inward collapse of arytenoids cartilages, Type 2: medial displacement of aryepiglottic folds, and Type 3: posterocaudal displacement of epiglottis against the posterior pharyngeal wall. © 2015 Wiley Periodicals, Inc.

Information extraction with object based support vector machines and vegetation indices

NASA Astrophysics Data System (ADS)

Ustuner, Mustafa; Abdikan, Saygin; Balik Sanli, Fusun

2016-07-01

Information extraction through remote sensing data is important for policy and decision makers as extracted information provide base layers for many application of real world. Classification of remotely sensed data is the one of the most common methods of extracting information however it is still a challenging issue because several factors are affecting the accuracy of the classification. Resolution of the imagery, number and homogeneity of land cover classes, purity of training data and characteristic of adopted classifiers are just some of these challenging factors. Object based image classification has some superiority than pixel based classification for high resolution images since it uses geometry and structure information besides spectral information. Vegetation indices are also commonly used for the classification process since it provides additional spectral information for vegetation, forestry and agricultural areas. In this study, the impacts of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) on the classification accuracy of RapidEye imagery were investigated. Object based Support Vector Machines were implemented for the classification of crop types for the study area located in Aegean region of Turkey. Results demonstrated that the incorporation of NDRE increase the classification accuracy from 79,96% to 86,80% as overall accuracy, however NDVI decrease the classification accuracy from 79,96% to 78,90%. Moreover it is proven than object based classification with RapidEye data give promising results for crop type mapping and analysis.

A Vehicle Steering Recognition System Based on Low-Cost Smartphone Sensors.

PubMed

Liu, Xinhua; Mei, Huafeng; Lu, Huachang; Kuang, Hailan; Ma, Xiaolin

2017-03-20

Recognizing how a vehicle is steered and then alerting drivers in real time is of utmost importance to the vehicle and driver's safety, since fatal accidents are often caused by dangerous vehicle maneuvers, such as rapid turns, fast lane-changes, etc. Existing solutions using video or in-vehicle sensors have been employed to identify dangerous vehicle maneuvers, but these methods are subject to the effects of the environmental elements or the hardware is very costly. In the mobile computing era, smartphones have become key tools to develop innovative mobile context-aware systems. In this paper, we present a recognition system for dangerous vehicle steering based on the low-cost sensors found in a smartphone: i.e., the gyroscope and the accelerometer. To identify vehicle steering maneuvers, we focus on the vehicle's angular velocity, which is characterized by gyroscope data from a smartphone mounted in the vehicle. Three steering maneuvers including turns, lane-changes and U-turns are defined, and a vehicle angular velocity matching algorithm based on Fast Dynamic Time Warping (FastDTW) is adopted to recognize the vehicle steering. The results of extensive experiments show that the average accuracy rate of the presented recognition reaches 95%, which implies that the proposed smartphone-based method is suitable for recognizing dangerous vehicle steering maneuvers.

Comparison and validation of injury risk classifiers for advanced automated crash notification systems.

PubMed

Kusano, Kristofer; Gabler, Hampton C

2014-01-01

The odds of death for a seriously injured crash victim are drastically reduced if he or she received care at a trauma center. Advanced automated crash notification (AACN) algorithms are postcrash safety systems that use data measured by the vehicles during the crash to predict the likelihood of occupants being seriously injured. The accuracy of these models are crucial to the success of an AACN. The objective of this study was to compare the predictive performance of competing injury risk models and algorithms: logistic regression, random forest, AdaBoost, naïve Bayes, support vector machine, and classification k-nearest neighbors. This study compared machine learning algorithms to the widely adopted logistic regression modeling approach. Machine learning algorithms have not been commonly studied in the motor vehicle injury literature. Machine learning algorithms may have higher predictive power than logistic regression, despite the drawback of lacking the ability to perform statistical inference. To evaluate the performance of these algorithms, data on 16,398 vehicles involved in non-rollover collisions were extracted from the NASS-CDS. Vehicles with any occupants having an Injury Severity Score (ISS) of 15 or greater were defined as those requiring victims to be treated at a trauma center. The performance of each model was evaluated using cross-validation. Cross-validation assesses how a model will perform in the future given new data not used for model training. The crash ΔV (change in velocity during the crash), damage side (struck side of the vehicle), seat belt use, vehicle body type, number of events, occupant age, and occupant sex were used as predictors in each model. Logistic regression slightly outperformed the machine learning algorithms based on sensitivity and specificity of the models. Previous studies on AACN risk curves used the same data to train and test the power of the models and as a result had higher sensitivity compared to the cross-validated results from this study. Future studies should account for future data; for example, by using cross-validation or risk presenting optimistic predictions of field performance. Past algorithms have been criticized for relying on age and sex, being difficult to measure by vehicle sensors, and inaccuracies in classifying damage side. The models with accurate damage side and including age/sex did outperform models with less accurate damage side and without age/sex, but the differences were small, suggesting that the success of AACN is not reliant on these predictors.

Couple Graph Based Label Propagation Method for Hyperspectral Remote Sensing Data Classification

NASA Astrophysics Data System (ADS)

Wang, X. P.; Hu, Y.; Chen, J.

2018-04-01

Graph based semi-supervised classification method are widely used for hyperspectral image classification. We present a couple graph based label propagation method, which contains both the adjacency graph and the similar graph. We propose to construct the similar graph by using the similar probability, which utilize the label similarity among examples probably. The adjacency graph was utilized by a common manifold learning method, which has effective improve the classification accuracy of hyperspectral data. The experiments indicate that the couple graph Laplacian which unite both the adjacency graph and the similar graph, produce superior classification results than other manifold Learning based graph Laplacian and Sparse representation based graph Laplacian in label propagation framework.

14 CFR § 1203.412 - Classification guides.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 14 Aeronautics and Space 5 2014-01-01 2014-01-01 false Classification guides. § 1203.412 Section... PROGRAM Guides for Original Classification § 1203.412 Classification guides. (a) General. A classification guide, based upon classification determinations made by appropriate program and classification...

Personal factors influencing the visual reaction time of pedestrians to detect turn indicators in the presence of Daytime Running Lamps.

PubMed

Peña-García, Antonio; de Oña, Rocío; García, Pedro Antonio; de Oña, Juan

2016-12-01

Daytime running lamps (DRL) on vehicles have proven to be an effective measure to prevent accidents during the daytime, particularly when pedestrians and cyclists are involved. However, there are negative interactions of DRL with other functions in automotive lighting, such as delays in pedestrians' visual reaction time (VRT) when turn indicators are activated in the presence of DRL. These negative interactions need to be reduced. This work analyses the influence of variables inherent to pedestrians, such as height, gender and visual defects, on the VRT using a classification and regression tree as an exploratory analysis and a generalized linear model to validate the results. Some pedestrian characteristics, such as gender, alone or combined with the DRL colour, and visual defects, were found to have a statistically significant influence on VRT and, hence, on traffic safety. These results and conclusions concerning the interaction between pedestrians and vehicles are presented and discussed. Practitioner Summary: Visual interactions of vehicle daytime running lamps (DRL) with other functions in automotive lighting, such as turn indicators, have an important impact on a vehicle's conspicuity for pedestrians. Depending on several factors inherent to pedestrians, the visual reaction time (VRT) can be remarkably delayed, which has implications in traffic safety.

Method for Reducing the Drag of Increasing Forebody Roughness Blunt-Based Vehicles by Adaptively Increasing Forebody Roughness

NASA Technical Reports Server (NTRS)

Whitmore, Stephen A. (Inventor); Saltzman, Edwin J. (Inventor); Moes, Timothy R. (Inventor); Iliff, Kenneth W. (Inventor)

2005-01-01

A method for reducing drag upon a blunt-based vehicle by adaptively increasing forebody roughness to increase drag at the roughened area of the forebody, which results in a decrease in drag at the base of this vehicle, and in total vehicle drag.

Infrared Thermal Testing Of Mechanical Assemblies At The Military Depot And Field Level: A Progress Report

NASA Astrophysics Data System (ADS)

Kaplan, Herbert

1988-01-01

Based on encouraging results on the Army's programs for infrared mass screening of printed circuit boards at the depot level, the US Army CECOM (Communication-Electronics Command) undertook a one-year investigation of the applicability of similar techniques to screening and diagnostics of mechanical assemblies. These included tanks, helicopters, transport vehicles and their major subassemblies (transmissions, engines, axles, etc.) at field and depot levels. Honeyhill Technical Company was tasked to classify candidate assemblies and perform preliminary measurements using Army-owned general-purpose thermal imaging equipment. The investigations yielded positive results, and it was decided to pursue a comprehensive measurements program using field-mobile equipment specifically procured for the program. This paper summarizes the results of the investigations, outlines the measurements techniques utilized, describes the classification and selection of candidate assemblies, and reports on progress toward the goals of the program.

Robust lane detection and tracking using multiple visual cues under stochastic lane shape conditions

NASA Astrophysics Data System (ADS)

Huang, Zhi; Fan, Baozheng; Song, Xiaolin

2018-03-01

As one of the essential components of environment perception techniques for an intelligent vehicle, lane detection is confronted with challenges including robustness against the complicated disturbance and illumination, also adaptability to stochastic lane shapes. To overcome these issues, we proposed a robust lane detection method named classification-generation-growth-based (CGG) operator to the detected lines, whereby the linear lane markings are identified by synergizing multiple visual cues with the a priori knowledge and spatial-temporal information. According to the quality of linear lane fitting, the linear and linear-parabolic models are dynamically switched to describe the actual lane. The Kalman filter with adaptive noise covariance and the region of interests (ROI) tracking are applied to improve the robustness and efficiency. Experiments were conducted with images covering various challenging scenarios. The experimental results evaluate the effectiveness of the presented method for complicated disturbances, illumination, and stochastic lane shapes.

Audio Tracking in Noisy Environments by Acoustic Map and Spectral Signature.

PubMed

Crocco, Marco; Martelli, Samuele; Trucco, Andrea; Zunino, Andrea; Murino, Vittorio

2018-05-01

A novel method is proposed for generic target tracking by audio measurements from a microphone array. To cope with noisy environments characterized by persistent and high energy interfering sources, a classification map (CM) based on spectral signatures is calculated by means of a machine learning algorithm. Next, the CM is combined with the acoustic map, describing the spatial distribution of sound energy, in order to obtain a cleaned joint map in which contributions from the disturbing sources are removed. A likelihood function is derived from this map and fed to a particle filter yielding the target location estimation on the acoustic image. The method is tested on two real environments, addressing both speaker and vehicle tracking. The comparison with a couple of trackers, relying on the acoustic map only, shows a sharp improvement in performance, paving the way to the application of audio tracking in real challenging environments.

Urban forest topographical mapping using UAV LIDAR

NASA Astrophysics Data System (ADS)

Putut Ash Shidiq, Iqbal; Wibowo, Adi; Kusratmoko, Eko; Indratmoko, Satria; Ardhianto, Ronni; Prasetyo Nugroho, Budi

2017-12-01

Topographical data is highly needed by many parties, such as government institution, mining companies and agricultural sectors. It is not just about the precision, the acquisition time and data processing are also carefully considered. In relation with forest management, a high accuracy topographic map is necessary for planning, close monitoring and evaluating forest changes. One of the solution to quickly and precisely mapped topography is using remote sensing system. In this study, we test high-resolution data using Light Detection and Ranging (LiDAR) collected from unmanned aerial vehicles (UAV) to map topography and differentiate vegetation classes based on height in urban forest area of University of Indonesia (UI). The semi-automatic and manual classifications were applied to divide point clouds into two main classes, namely ground and vegetation. There were 15,806,380 point clouds obtained during the post-process, in which 2.39% of it were detected as ground.

[Exposure to whole body vibrations in workers moving heavy items by mechanical vehicles in the warehouse of a large retail outlet].

PubMed

Siciliano, E; Rossi, A; Nori, L

2007-01-01

Efficient warehouse management and item transportation is of fundamental importance in the commercial outlet in exam. Whole body vibrations have been measured in various types of machines, some of which not widely studied yet, like the electrical pallet truck. In some tasks (fork lifts drivers) vibrations propagate through the driving seat whereas in some other tasks (electrical pallet trucks, stackers), operated in a standing posture, vibrations propagate through the lower limbs. Results have been provided for a homogeneous job tasks. In particular conditions, the action level of the Italian national (and European) regulations on occupational exposure to WBV may be exceeded. The authors propose a simple system of probabilistic classification of the risk of exposure to whole body vibrations, based on the respective areas of the distribution which lay within the three risk classes.

Traffic intensity monitoring using multiple object detection with traffic surveillance cameras

NASA Astrophysics Data System (ADS)

Hamdan, H. G. Muhammad; Khalifah, O. O.

2017-11-01

Object detection and tracking is a field of research that has many applications in the current generation with increasing number of cameras on the streets and lower cost for Internet of Things(IoT). In this paper, a traffic intensity monitoring system is implemented based on the Macroscopic Urban Traffic model is proposed using computer vision as its source. The input of this program is extracted from a traffic surveillance camera which has another program running a neural network classification which can identify and differentiate the vehicle type is implanted. The neural network toolbox is trained with positive and negative input to increase accuracy. The accuracy of the program is compared to other related works done and the trends of the traffic intensity from a road is also calculated. relevant articles in literature searches, great care should be taken in constructing both. Lastly the limitation and the future work is concluded.

Wireless Authentication Protocol Implementation: Descriptions of a Zero-Knowledge Proof (ZKP) Protocol Implementation for Testing on Ground and Airborne Mobile Networks

DTIC Science & Technology

2015-01-01

on AFRL’s small unmanned aerial vehicle (UAV) test bed . 15. SUBJECT TERMS Zero-Knowledge Proof Protocol Testing 16. SECURITY CLASSIFICATION OF...VERIFIER*** edition Version Information: Version 1.1.3 Version Details: Successful ZK authentication between two networked machines. Fixed a bug ...that causes intermittent bignum errors. Fixed a network hang bug and now allows continually authentication at the Verifier. Also now removing

The C2 Workstation and Data Replication over Disadvantaged Tactical Communication Links

DTIC Science & Technology

2007-09-01

person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number...SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 14 19a. NAME OF RESPONSIBLE PERSON a. REPORT unclassified b...PUBLIC RELEASE C2WS Physical Environment Zodiac Network Radio Network Combat Level Node (Vehicle) Radio Local Store Local Store Local Store To Other

Investigation of Terrain Analysis and Classification Methods for Ground Vehicles

DTIC Science & Technology

2012-08-27

exteroceptive terrain classifier takes exteroceptive sensor data (here, color stereo images of the terrain) as its input and returns terrain class...Mishkin & Laubach, 2006), the rover cannot safely travel beyond the distance it can image with its cameras, which has been as little as 15 meters or...field of view roughly 44°×30°, capturing pairs of color images at 640×480 pixels each (Videre Design, 2001). Range data were extracted from the stereo

78 FR 18252 - Prevailing Rate Systems; North American Industry Classification System Based Federal Wage System...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-26

...-AM78 Prevailing Rate Systems; North American Industry Classification System Based Federal Wage System... 2007 North American Industry Classification System (NAICS) codes currently used in Federal Wage System... (OPM) issued a final rule (73 FR 45853) to update the 2002 North American Industry Classification...