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Airborne hyperspectral imaging in the visible-to-mid wave infrared spectral range by fusing three spectral sensors 5

Airborne hyperspectral imaging in the visible-to-mid wave in...

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conference on Earth Resources and Environmental Remote Sensing/GIS applications V

作者： Jakovels, Dainis Filipovs, Jevgenijs Erins, Gatis Taskovs, Juris Inst Environm Solut Lidlauks LV-4101 Priekuli Parish Priekuli Cty Latvia

ISBN: (纸本)9781628413083

Airborne hyperspectral imaging is widely used for remote sensing of environment. The choice of spectral region usually depends on the availability and cost of the sensor. Visible-to-near infrared (400-1100 nm) spectral range corresponds to spectral sensitivity of relatively cheap Si detectors therefore it is the most commonly used. The implementation of shortwave infrared (1100-3000 nm) requires more expensive solutions, but can provide valuable information about the composition of the substance. Mid wave infrared (3000-8000 nm) is rarely used for civilian applications, but it provides information on the thermal emission of materials. The fusion of different sensors allows spectral analysis of a wider spectral range combining and improving already existing algorithms for the analysis of chemical content and classification. Here we introduce our Airborne Surveillance and Environmental Monitoring System (ARSENAL) that was developed by fusing seven sensors. The first test results from the fusion of three hyperspectral imaging sensors in the visible-to-mid wave infrared (365-5000 nm) are demonstrated. Principal component analysis (PCA) is applied to test correlation between principal components (PCs) and common vegetation indices.

关键词： Airborne remote sensing hyperspectral imaging visible-to-mid wave infrared mid wave infrared data fusion

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Video Observations for Cloud Activity-Based Intelligence (VOCABI)

Video Observations for Cloud Activity-Based Intelligence (VO...

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IEEE National Aerospace and Electronics conference (NAECON)

作者： Blasch, Erik DiBona, Phillip Czajkowski, Michael Barry, Kevin Rimey, Ray Freeman, Jeff Newman, Kevin Aved, Alex Hinman, Mike Air Force Res Lab Rome NY 13441 USA LM ATL INL Cherry Hill NJ 08002 USA

ISBN: (纸本)9781479946891

The availability of video imagery through reduction in sensor size, cost, and power has enabled an explosion of collection opportunities. With the increased amount of imagery there is a need to understand the usefulness of video for applications such as Activity-Based Intelligence (ABI), situation understanding, and event-based processing. In this paper, we explore some of the emerging developments in video observations with a focus on cloud technology. Cloud technology supports integration of multiple algorithms, storage of large data sets, indexing over multimedia, and workflow opportunities between humans and machines. We highlight multiple tools such as GeoFlix (TM), Application Knowledge Interface To algorithms (AKITA (TM)), and Intelligence Preparation of the Operational Environment (IPOE) using the Ozone Widget Framework (OWF) for permissive surveillance, data to decisions, and information fusion. These tools enable data analytics, algorithm comparison, and user-defined visualizations. An example is presented for target localization and tracking through planned video observations.

关键词： Video Analytics Cloud Computing automation and autonomy information fusion visualization

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Acoustic Events Semantic Detection, Classification, and Annotation for Persistent Surveillance applications

Acoustic Events Semantic Detection, Classification, and Anno...

引用

conference on Signal Processing, sensor/Information fusion, and Target Recognition XXIII

作者： Alkilani, Amjad Shirkhodaie, Amir Tennessee State Univ Ctr Excellence Battlefield Sensor Fus Dept Mech & Mfg Engn Nashville TN 37209 USA

ISBN: (纸本)9781628410280

Understanding of group activity based on analysis of spatiotemporally correlated acoustic sound events has received a minimum attention in the literature and hence is not well understood. Identification of group sub-activities such as: Human-Vehicle Interactions (HVI), Human-Object Interactions (HOI), and Human-Human Interactions (HHI) can significantly improve Situational Awareness (SA) in Persistent Surveillance Systems (PSS). In this paper, salient sound events associated with group activities are preliminary identified and applied for training a Gaussian Mixture Model (GMM) whose features are employed as feature vectors for training of algorithms for acoustic sound recognition. In this paper, discrimination of salient sounds associated with the HVI, HHI, and HOI events is achieved via a Correlation Based Template Matching (CMTM) classifier. To interlinked salient events representing an ontology-based hypothesis, a Hidden Markov Model (HMM) is employed to recognize spatiotemporally correlated events. Once such a connection is established, then, the system generates an annotation of each perceived sound event. This paper discusses the technical aspects of this approach and presents the experimental results for several outdoor group activities monitored by an array of acoustic sensors.

关键词： Human-Object Interactions (HOI) Human-Human Interactions (HHI) Human-Vehicle Interactions (HVI) Acoustic Sounds Recognition and Classification Spectral Analysis Hidden Markov Model Persistent Surveillance Systems

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Towards a unified architecture for mapping static environments

Towards a unified architecture for mapping static environmen...

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International conference on Information fusion

作者： Tim Kubertschak Mirko Maehlisch Hans-Joachim Wuensche AUDI AG I/EE-31 Ingolstadt Germany

sensor fusion systems are becoming more and more important in automotive applications, especially for future driver assistance systems and autonomous driving. While it was sufficient to use single sensors in early driving assistance systems, future systems will rely on several sensors with different measurement principles. The probably conflicting measurements need to be fused to retrieve a complete picture of the current surroundings. However, an increased number of sensors leads to difficulties while incorporating their measurements, since every sensor provides its data via special interfaces. This inconvenience of current sensor fusion architectures has been solved for moving objects. A fusion architecture based on a generic interface - the object list - has been developed over the last years. It allows an easy use of additional sensors as long as they provide their measurements as object lists. Similar solutions for static environments are rare, but the recent proposal of the Fences-approach [1] is promising. This work shows, how sensors and occupancy grids are represented in the Fences-architecture. Steps and algorithms are presented to transform sensor reading into Fences as well as extracting Fences from common grid-based strategies for mapping static environments. The algorithms are applied to the conversion of ultrasonic sensor readings and LiDAR measurements to Fences. Furthermore, the construction of Fences from Bayesian occupancy grids is presented.

关键词： Uncertainty sensor phenomena and characterization Vehicles sensor fusion Acoustics Robot sensing systems

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Networked filtering and fusion in wireless sensor networks 1

引用

2014年

作者： Magdi S Mahmoud

ISBN: (数字)9781482250961

ISBN: (纸本)1482250969;9781482250961;9781482250978

By exploiting the synergies among available data, information fusion can reduce data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. Networked Filtering and fusion in Wireless sensor Networks introduces the subject of multi-sensor fusion as the method of choice for implementing distributed *** book examines the state of the art in information fusion. It presents the known methods, algorithms, architectures, and models of information fusion and discusses their applicability in the context of wireless sensor networks (WSNs). Paying particular attention to the wide range of topics that have been covered in recent literature, the text presents the results of a number of typical case *** with research supported elements and comprehensive references, this teaching-oriented volume uses standard scientific terminology, conventions, and notations throughout. It applies recently developed convex optimization theory and highly efficient algorithms in estimation fusion to open up discussion and provide researchers with an ideal starting point for further research on distributed estimation and fusion for *** book supplies a cohesive overview of the key results of theory and applications of information-fusion-related problems in networked systems in a unified framework. Providing advanced mathematical treatment of fundamental problems with information fusion, it will help you broaden your understanding of prospective applications and how to address such problems in *** reading the book, you will gain the understanding required to model parts of dynamic systems and use those models to develop distributed fusion control algorithms that are based on feedback control theory.

关键词： Computer Science Engineering & Technology

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Biobotic Insect Swarm based sensor Networks for Search and Rescue

Biobotic Insect Swarm based Sensor Networks for Search and R...

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conference on Signal Processing, sensor/Information fusion, and Target Recognition XXIII

作者： Bozkurt, Alper Lobaton, Edgar Sichitiu, Mihail Hedrick, Tyson Latif, Tahmid Dirafzoon, Alireza Whitmire, Eric Verderber, Alexander Marin, Juan Xiong, Hong North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA Univ N Carolina Dept Biol Chapel Hill NC 27599 USA Middle Creek High Sch Apex NC 27539 USA

ISBN: (纸本)9781628410280

The potential benefits of distributed robotics systems in applications requiring situational awareness, such as search-and-rescue in emergency situations, are indisputable. The efficiency of such systems requires robotic agents capable of coping with uncertain and dynamic environmental conditions. For example, after an earthquake, a tremendous effort is spent for days to reach to surviving victims where robotic swarms or other distributed robotic systems might play a great role in achieving this faster. However, current technology falls short of offering centimeter scale mobile agents that can function effectively under such conditions. Insects, the inspiration of many robotic swarms, exhibit an unmatched ability to navigate through such environments while successfully maintaining control and stability. We have benefitted from recent developments in neural engineering and neuromuscular stimulation research to fuse the locomotory advantages of insects with the latest developments in wireless networking technologies to enable biobotic insect agents to function as search-and-rescue agents. Our research efforts towards this goal include development of biobot electronic backpack technologies, establishment of biobot tracking testbeds to evaluate locomotion control efficiency, investigation of biobotic control strategies with Gromphadorhina portentosa cockroaches and Manduca sexta moths, establishment of a localization and communication infrastructure, modeling and controlling collective motion by learning deterministic and stochastic motion models, topological motion modeling based on these models, and the development of a swarm robotic platform to be used as a testbed for our algorithms.

关键词： Insect biobots Seach and rescue Neural engineering Swarm robotics Topological mapping sensor networks

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Fusing Kinect sensor and inertial sensors with multi-rate Kalman filter

Fusing Kinect sensor and inertial sensors with multi-rate Ka...

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IET conference on Data fusion & Target Tracking 2014: algorithms and applications (DF&TT 2014)

作者： Shimin Feng Roderick Murray-Smith School of Computing Science University of Glasgow Glasgow UK

ISBN: (纸本)9781632666642

This paper presents a sensor fusion approach to fusing Microsoft Kinect sensor and the built-in inertial sensors in a mobile device. A multi-rate Kalman filter is designed and applied for fusing the low-sampling-rate (30Hz) uncertain positions sensed by the Kinect sensor and the high-sampling-rate (90Hz) accelerations measured by the inertial sensors. These sensors have complementary properties. The Kinect can be applied for skeleton tracking, which gives the joints' positions. Meanwhile, the built-in inertial sensors in the mobile device sense the hand motion and the acceleration can be estimated through inertial sensor fusion. Firstly, convert the acceleration estimated with inertial sensors from the body frame into the Kinect coordinate system. Experimental results show that the hand accelerations estimated with the Kinect sensor and the inertial sensors are comparable. Secondly, design and apply a multi-rate Kalman filter for sensor fusion. The sensor fusion helps improve the accuracy of the system state estimation including the position, the velocity and the acceleration. This is of great benefit for combining inertial sensors and the external position sensing device for indoor augmented reality (AR) and other location-aware sensing applications.

关键词： inertia sensor sensor fusion game player acceleration body frame State estimation Kalman filters sensors

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15th International conference on Engineering applications of Neural Networks, EANN 2014

15th International Conference on Engineering Applications of...

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15th International conference on Engineering applications of Neural Networks, EANN 2014

ISBN: (纸本)9783319110707

The proceedings contain 23 papers. The special focus in this conference is on Engineering applications of Neural Networks. The topic include: Remarks on computational facial expression recognition from HOG features using quaternion multi-layer neural network;classification of database by using parallelization of algorithms third generation in a GPU;an iterative feature filter for sensor timeseries in pervasive computing applications;fuzzy-logic decision fusion for nonintrusive early detection of driver fatigue or drowsiness;neural trade-offs among specialist and generalist neurons in pattern recognition;classification of events in switch machines using bayes, fuzzy logic system and neural network;an accurate flood forecasting model using wireless sensor networks and chaos theory;regenerative braking control strategy for hybrid and electric vehicles using artificial neural networks;automatic screening and classification of diabetic retinopathy fundus images;brain neural data analysis using machine learning feature selection and classification methods;application of neural networks solar radiation prediction for hybrid renewable energy systems;a new user similarity computation method for collaborative filtering using artificial neural network;a new user similarity computation method for collaborative filtering using artificial neural network;detecting port scans against mobile devices with neural networks and decision trees;categorization and construction of rule based systems;tiling of satellite images to capture an island object;learning user models in multi-criteria recommender systems;estimation of the electric field across medium voltage surge arresters using artificial neural networks and decoding hand trajectory from primary motor cortex ECoG using time delay neural network.

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TDA2X, A SOC OPTIMIZED FOR ADVANCED DRIVER ASSISTANCE SYSTEMS

TDA2X, A SOC OPTIMIZED FOR ADVANCED DRIVER ASSISTANCE SYSTEM...

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IEEE International conference on Acoustics, Speech and Signal Processing

作者： Jagadeesh Sankaran Nikolic Zoran Texas Instruments Incorporated

ISBN: (纸本)9781479928941

TDA2X is an optimized scalable system on chip (SoC) solution from Texas Instruments that spans various application areas of ADAS such as front-camera, surround-view and the emerging area of sensor fusion. It accomplishes this through a focused set of heterogeneous processors, brought together in a scalable architecture with a rich set of integrated peripherals, providing an optimal mix of performance in a low power footprint for Advanced Driver Assistance Systems (ADAS) vision analytics. Computer vision algorithms across the various ADAS application systems have a rich variation and diversity in processing requirements along with the need to run them concurrently within challenging thermal budgets. A heterogeneous architecture with various programmable elements allows system developers to map various portions of the algorithms to the architectures that are best suited for the underlying task allowing maximizing system performance and reducing development time and effort in developing these complex systems. Scalability of the architecture by varying the number of cores and clock speeds of these heterogeneous architectures, allows for scalability in performance and power across low, mid and high end products with one software investment. A critical focus on functional safety across the cores and various memories is particularly essential given the mission critical nature of ADAS applications.

关键词： advanced driver assistance systems (ADAS) ARCHITECTURE Scalability core Building construction extensible architecture Drivers system-on-chip program area sensor fusion end products development time Timing devices System performance

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Multi-sensor soft-computing system for driver drowsiness detection 17

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17th Online World conference on Soft Computing in Industrial applications, WSC17

作者： Li, Li Werber, Klaudius Calvillo, Carlos F. Dinh, Khac Dong Guarde, Ander König, Andreas TU Kaiserslautern Department of Electrical and Computer Engineering Institute of Integrated Sensor Systems Erwin-Schrödinger-Str. Gebäude 12 Kaiserslautern67663 Germany University of the Basque Country Bilbao Spain

ISBN: (纸本)9783319009292

Advanced sensing systems, sophisticated algorithms and increasing computational resources continuously enhance active safety technology for vehicles. Driver status monitoring belongs to the key components of advanced driver assistance system which is capable of improving car and road safety without compromising driving experience. This paper presents a novel approach to driver status monitoring aimed at drowsiness detection based on depth camera, pulse rate sensor and steering angle sensor. Due to NIR active illumination depth camera can provide reliable head movement information in 3D alongside eye gaze estimation and blink detection in a non-intrusive manner. Multi-sensor data fusion on feature level and multilayer neural network facilitate the classification of driver drowsiness level based on which a warning can be issued to prevent traffic accidents. The presented approach is implemented on an integrated soft-computing system for driving simulation (DeCaDrive) with multi-sensing interfaces. The classification accuracy of 98:9% for up to three drowsiness levels has been achieved based on data sets of five test subjects with 588-min driving sequence. © Springer International Publishing Switzerland 2014.

关键词： Advanced driver assistance systems

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