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23rd IEEE Conference on Signal Processing algorithms, architectures, Arrangements, and applications (SPA)

作者： Mazurek, Pawel Wagner, Jakub Morawski, Roman Z. Warsaw Univ Technol Fac Elect & Informat Technol Inst Radioelect & Multimedia Technol Nowowiejska 15-19 PL-00665 Warsaw Poland

ISBN: (纸本)9788362065363

The research reported in this paper is related to the fusion of measurement data from the impulse-radar sensors and infrared depth sensors applied in a system for unobtrusive monitoring of elderly persons. The performance of monitoring systems, based on both types of sensors - when used separately and when used in combination - has been compared with respect to their potential for decreasing the uncertainty of estimation of several quantities, carrying information important for medical and healthcare services, viz. the state of being in motion, the direction of the movement, the travelled distance and the mean walking speed. The results of the numerical experimentation performed on real-world data - show that the fusion of data enables more robust and accurate estimation of those parameters, and therefore more reliable monitoring of elderly persons.

关键词： measurement data fusion impulse-radar sensor depth sensor healthcare

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Combination of fuzzy sets and Dempster-Shafer theories in forest map updating using multispectral data

Combination of fuzzy sets and Dempster-Shafer theories in fo...

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Conference on sensor fusion - architectures, algorithms, and applications V

作者： Leduc, F Solaiman, B Cavayas, F ENST Bretagne Dept ITI F-29285 Brest France

ISBN: (纸本)0819440809

This paper explains a new approach to change detection and interpretation in a context of forest map updating. In this temporal change analysis we use a data set composed of map at time To and a satellite image at time T-1 and we refer to this as a mixed fusion approach. The analysis of remotely sensed data always necessitates the use of approximate reasoning. For this purpose, we use fuzzy logic to evaluate the objects' membership values to the considered classes and the Dempster-Shafer theory to analyse the confusion between classes and to find the more evident class to which an object belong.

关键词： map updating forestry change detection fuzzy logic Dempster-Shafer theory data fusion

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Estimation of subpixel-resolution motion fields from segmented image sequences

Estimation of subpixel-resolution motion fields from segment...

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Conference on sensor fusion - architectures, algorithms, and applications ii

作者： Schultz, RR Stevenson, RL Univ N Dakota Dept Elect Engn Grand Forks ND 58202 USA

ISBN: (纸本)0819428256

Super-resolution enhancement algorithms are used to estimate a high-resolution video still (HRVS) from several low-resolution frames, provided that objects within the image sequence move with subpixel increments. However, estimating an accurate subpixel-resolution motion field between two low-resolution, noisy video frames has proven to be a formidable challenge. Up-sampling the image sequence frames followed by the application of block matching, optical flow estimation, or Bayesian motion estimation results in relatively poor subpixel-resolution motion fields, and consequently inaccurate regions within the super-resolution enhanced video still. This is particularly true for large interpolation factors (greater than or equal to 4). To improve the quality of the subpixel motion fields and the corresponding HRVS, motion can be estimated for each object within a segmented image sequence. First, a reference video frame is segmented into its constituent objects, and a mask is generated for each object which describes its spatial location. As described previously, subpixel-resolution motion estimation is then conducted by video frame up-sampling followed by the application of a motion estimation algorithm. Finally, the motion vectors are averaged over the region of each mask by applying an a-trimmed mean filter to the horizontal and vertical components separately. Since each object moves as a single entity, averaging eliminates many of the motion estimation errors and results in much more consistent subpixel motion fields. A substantial improvement is also visible within particular regions of the HRVS estimates. Subpixel-resolution motion fields and HRVS estimates are computed for interpolation factors of 2, 4, 8, and 16, to examine the benefits of object segmentation and motion field averaging.

关键词： interpolation motion estimation segmentation sensor fusion super-resolution enhancement

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PASES: Combining radar and video sensing for improved pedestrian safety

PASES: Combining radar and video sensing for improved pedest...

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Conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Chenault, David B. Edmondson, Richard P. Polaris Sensor Technol Inc 200 West Side SqSuite 320 Huntsville AL 35801 USA

ISBN: (纸本)9781510601130

Polaris sensor Technologies reports on the development of Pedestrian Automated System for Enforcement and Safety (PASES), a radar and video based system used to monitor vehicle and pedestrian traffic with the intent of improving pedestrian safety. Data is fused from a system of multiple sensors and multiple sensor modalities to identify vehicular violations of pedestrian right of way. A focus was placed on the selection of low cost COTS sensors to make the system more widely available to state and local DOTs with limited budgets. applications include automated enforcement, adaptive traffic control, and improved intersection and crosswalk design based on high quality data available for traffic engineering. We discuss early results with high fidelity sensors, and the performance trades made in order to make the system affordable. A discussion of the system processing architecture is included which highlights the treatment of each sensor data type, and the means of combining the processed data products into state information related to traffic incidents involving vehicles and pedestrians.

关键词： Pedestrian radar video tracking sensor fusion

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fusion of imaging and non-imaging data for surveillance aircraft

Fusion of imaging and non-imaging data for surveillance airc...

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Conference on sensor fusion - architectures, algorithms, and applications

作者： Shahbazian, E Gagnon, L Duquet, JR Macieszczak, M Valin, P Lockheed Martin Canada (Canada)

ISBN: (纸本)081942482X

This paper describes a phased incremental integration approach for application of image analysis and data fusion technologies to provide automated intelligent target tracking and identification for Airborne Surveillance on board an Aurora Maritime Patrol Aircraft. The sensor suite of the Aurora consists of a radar, an Identification Friend or Foe (IFF) system, an Electronic Support Measures (ESM) system, a Spotlight Synthetic Aperture Radar (SSAR), a Forward Looking Infra-Red (FLIR) sensor and a Link-11 tactical datalink system. Lockheed Martin Canada (LMCan) is developing a testbed, which will be used to analyze and evaluate approaches for combining the data provided by the existing sensors, which were initially not designed to feed a fusion system. Three concurrent research proof-of-concept activities provide techniques, algorithms and methodology into three sequential phases of integration of this testbed. These activities are: (a) analysis of the fusion architecture (track/contact/hybrid) most appropriate for the type of data available, (b) extraction and fusion of simple features from the imaging data into the fusion system performing automatic target identification, and (c) development of a unique software architecture which will permit integration and independent evolution, enhancement and optimization of various decision aid capabilities, such as Multi-sensor Data fusion (MSDF), Situation and Threat Assessment (STA) and Resource Management (RM).

关键词： intelligent decision aids data fusion image analysis automatic target recognition real-time adaptive planning situation and threat assessment resource management

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Knowledge Transfer for Body sensor Networks: Characterization and Resistance of Negative Transfer in Overconstrained Environments

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IEEE INTERNET OF THINGS JOURNAL 2024年第19期11卷 31959-31973页

作者： Shi, Han Liu, Yang Luo, Hao Zhao, Hai Liaoning Univ Coll Informat Shenyang 110036 Peoples R China Liaoning Univ Sch Phys Shenyang 110036 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110169 Peoples R China

Transfer learning (TL) has shown potential for body sensor network (BSN) applications. However, TL performance is influenced by various factors, such as the BSN environment, user-specific physiological signs, or sensor modalities, leading to negative transfer (NT) effects. Limited research has defined NT from perspectives, such as data quality, domain transferability, and transfer components, but the cognition of NT in multimodal BSNs remains insufficient. In this article, we explore the correlation between transfer modes and NT, proposing a knowledge transfer framework composed of common domain adaptation constraints to reveal the NT effects in overconstrained environments. This effect can explain why strongly constrained transfer algorithms are effective but not necessarily optimal in terms of performance. In the three typical BSN scenarios of emotion recognition, fall detection, and daily activity recognition, we demonstrate the NT in overconstrained environments and its potential influencing factors using different constraint combinations and based on different multimodal body sensor fusion architectures. Extensive experiments focus on discussing the impact of category-condition constraints, domain similarity, and fusion architectures on the NT effect in BSNs. This work can provide a new perspective for the design of multimodal BSN knowledge transfer scheme.

关键词： Transfer learning Task analysis Data models Internet of Things Knowledge transfer Accuracy Training Body sensor network (BSN) information fusion negative transfer (NT) transfer learning (TL)

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Formal methods of automated reasoning for situational estimation

Formal methods of automated reasoning for situational estima...

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Conference on sensor fusion - architectures, algorithms, and applications

作者： Llinas, J SUNY/Buffalo (United States)

ISBN: (纸本)081942482X

Most research and prototype development of automated methods for situational estimation in the data fusion community have applied heuristic approaches coupled to techniques for uncertainty management. Reasoning theorists would label these methods as those of the parametric reasoning class. Such methods are reasonable when the so-called ''closed world'' assumption can be confidently applied (ability to full prespecify expected conditions) which might have been reasonable in the ''Soviet Era'' but would appear fragile/brittle for current-day application. Motivated in part by these considerations and by the need to consider much more cost-effective knowledge-based-system development in an era of declining budgets, this paper offers some discussion on the applicability of more formal methods of reasoning for KBS. It is concluded that strictly formal methods for real-world applications require yet further theoretical development but that movement toward formalization is possible.

关键词： automated reasoning data fusion information fusion formal methods truth maintenance default reasoning

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Technical Overview of the Sarnoff Acadia ii Vision Processor

Technical Overview of the Sarnoff Acadia II Vision Processor

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Conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： van der Wal, Gooitzen S. Sarnoff Corp Princeton NJ 08540 USA

ISBN: (纸本)9780819481740

The Sarnoff Acadia (R) ii is a powerful vision processing SoC (System-on-a-Chip) that was specifically developed to support advanced vision applications where system size, weight and/or power are severely constrained. This paper, targeted at vision system developers, presents a detailed technical overview of the Acadia (R) ii, highlighting its architecture, processing capabilities, memory and peripheral interfaces. All major subsystems will be covered, including: video preprocessing, specialized vision processing cores for multi-spectral image fusion, multi-resolution contrast normalization, noise coring, image warping, and motion estimation. Application processing via the MPCore (R), an integrated set of four ARM (R) 11 floating point processors with associated peripheral interfaces is presented in detail. The paper will emphasize the programmability of the Acadia (R) ii, while describing its ability to provide state-of-the-art real-time image processing in a small, power optimized package.*

关键词： Real-time Multiresolution Vision processing Image fusion Stabilization Embedded Processing Multicore multi-spectral

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A Metamaterial-Inspired Combined Inductive-Capacitive sensor

A Metamaterial-Inspired Combined Inductive-Capacitive Sensor

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Conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Long, Jiang Wang, Bingnan Mitsubishi Elect Res Labs Cambridge MA 02139 USA

ISBN: (纸本)9781628410587

This paper reports a metamaterial inspired combined inductive-capacitive sensing method for detecting and distinguishing metallic and non-metallic objects. Metallic and non-metallic objects can be distinguished by measuring both of their inductive and capacitive responses based on the fact that they respond differently to inductive and capacitive sensing. The proposed method is inspired by metamaterial structures. Both inductive and capacitive sensing are simultaneously realized when the sensor is operating at off-resonant frequencies. The proposed method is demonstrated with typical printed circuit board (PCB) technology. The designed sensor can distinguish the metallic and dielectric objects with a sensing range about 10 mm, showing a competitive performance compared with commercially available proximity sensors.

关键词： proximity sensor metamaterials combined sensing

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Recursive algorithm of the optimal estimation fusion

Recursive algorithm of the optimal estimation fusion

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Multisensor. Multisource Information fusion: architectures, algorithms, and applications 2003

作者： Zhou, Jie Zhu, Yunmin College of Mathematics Sichuan University Chengdu Sichuan 610064 China

In a distributed estimation or tracking system, the global estimate are generated by the local estimates. Under the assumption of independence cross sensor noises, Bar-shalom proposed a two sensor tracking fusion formula, which used only the inverses of covariances of single sensor estimation errors. In this paper, we present more general multi-sensor estimation fusion formula, where the assumption of independence cross sensor noises and the direct computation of the generalized inverse of joint covariance of multiple sensor estimation errors are not necessary. Instead, as the number of sensors increases, a recursive algorithm is presented, in which only the inverses or the generalized inverses of the matrices having the same dimension as that of the covariance matrices of single sensor estimate errors are required.

关键词： sensor data fusion

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