The Experts below are selected from a list of 9825 Experts worldwide ranked by ideXlab platform
Pramod K Varshney - One of the best experts on this subject based on the ideXlab platform.
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decision Fusion by people experiments models and sociotechnical system design
IEEE Global Conference on Signal and Information Processing, 2015Co-Authors: Aditya Vempaty, Lav R Varshney, Gregory J Koop, Amy H Criss, Pramod K VarshneyAbstract:People and machines perform tasks differently. Building optimal systems that include people and machines, requires understanding their respective behavioral properties. The task of decision Fusion is considered and the performance of people is compared to the optimal Fusion Rule. Our behavioral experiments demonstrate that people perform decision Fusion in a stochastic manner dependent on various factors, whereas optimal Rule is deterministic. A Bayesian hierarchical model is developed to characterize the observed human behavior. This model captures the differences observed in people at individual level, crowd level, and population level. The implications of such a model on developing large-scale human-machine systems are presented by developing optimal decision Fusion trees with both human and machine agents.
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distributed detection of a nuclear radioactive source using Fusion of correlated decisions
International Conference on Information Fusion, 2007Co-Authors: Ashok Sundaresan, Pramod K Varshney, Nageswara S V RaoAbstract:A distributed detection method is developed for the detection of a nuclear radioactive source using a small number of radiation counters. Local one bit decisions are made at each sensor over a period of time and a Fusion center makes the global decision. A novel test for the Fusion of correlated decisions is derived using the theory of copulas and optimal sensor thresholds are obtained using the Normal copula function. The performance of the derived Fusion Rule is compared with that of the Chair-Varshney Rule. An increase in detection performance is observed. A method to estimate the correlation between the sensor observations using only the vector of sensor decisions is also proposed.
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a combined decision Fusion and channel coding scheme for distributed fault tolerant classification in wireless sensor networks
IEEE Transactions on Wireless Communications, 2006Co-Authors: Tsang-yi Wang, Biao Chen, Yunghsiang S Han, Pramod K VarshneyAbstract:In this paper, we consider the distributed classification problem in wireless sensor networks. Local decisions made by local sensors, possibly in the presence of faults, are transmitted to a Fusion center through fading channels. Classification performance could be degraded due to the errors caused by both sensor faults and fading channels. Integrating channel decoding into the distributed fault-tolerant classification Fusion algorithm, we obtain a new Fusion Rule that combines both soft-decision decoding and local decision Rules without introducing any redundancy. The soft decoding scheme is utilized to combat channel fading, while the distributed classification Fusion structure using error correcting codes provides good sensor fault-tolerance capability. Asymptotic performance of the proposed approach is also investigated. Performance evaluation of the proposed approach with both sensor faults and fading channel impairments is carried out. These results show that the proposed approach outperforms the system employing the MAP Fusion Rule designed without regard to sensor faults and the multiclass equal gain combining Fusion Rule
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Fusion of decisions transmitted over rayleigh fading channels in wireless sensor networks
IEEE Transactions on Signal Processing, 2006Co-Authors: Ruixin Niu, Biao Chen, Pramod K VarshneyAbstract:In this paper, we revisit the problem of fusing decisions transmitted over fading channels in a wireless sensor network. Previous development relies on instantaneous channel state information (CSI). However, acquiring channel information may be too costly for resource constrained sensor networks. In this paper, we propose a new likelihood ratio (LR)-based Fusion Rule which requires only the knowledge of channel statistics instead of instantaneous CSI. Based on the assumption that all the sensors have the same detection performance and the same channel signal-to-noise ratio (SNR), we show that when the channel SNR is low, this Fusion Rule reduces to a statistic in the form of an equal gain combiner (EGC), which explains why EGC is a very good choice with low or medium SNR; at high-channel SNR, it is equivalent to the Chair-Varshney Fusion Rule. Performance evaluation shows that the new Fusion Rule exhibits only slight performance degradation compared with the optimal LR-based Fusion Rule using instantaneous CSI.
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distributed detection and Fusion in a large wireless sensor network of random size
Eurasip Journal on Wireless Communications and Networking, 2005Co-Authors: Ruixin Niu, Pramod K VarshneyAbstract:For a wireless sensor network (WSN) with a random number of sensors, we propose a decision Fusion Rule that uses the total number of detections reported by local sensors as a statistic for hypothesis testing. We assume that the signal power attenuates as a function of the distance from the target, the number of sensors follows a Poisson distribution, and the locations of sensors follow a uniform distribution within the region of interest (ROI). Both analytical and simulation results for system-level detection performance are provided. This Fusion Rule can achieve a very good system-level detection performance even at very low signal-to-noise ratio (SNR), as long as the average number of sensors is sufficiently large. For all the different system parameters we have explored, the proposed Fusion Rule is equivalent to the optimal Fusion Rule, which requires much more prior information. The problem of designing an optimum local sensor-level threshold is investigated. For various system parameters, the optimal thresholds are found numerically by maximizing the deflection coefficient. Guidelines on selecting the optimal local sensor-level threshold are also provided.
Biao Chen - One of the best experts on this subject based on the ideXlab platform.
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a combined decision Fusion and channel coding scheme for distributed fault tolerant classification in wireless sensor networks
IEEE Transactions on Wireless Communications, 2006Co-Authors: Tsang-yi Wang, Biao Chen, Yunghsiang S Han, Pramod K VarshneyAbstract:In this paper, we consider the distributed classification problem in wireless sensor networks. Local decisions made by local sensors, possibly in the presence of faults, are transmitted to a Fusion center through fading channels. Classification performance could be degraded due to the errors caused by both sensor faults and fading channels. Integrating channel decoding into the distributed fault-tolerant classification Fusion algorithm, we obtain a new Fusion Rule that combines both soft-decision decoding and local decision Rules without introducing any redundancy. The soft decoding scheme is utilized to combat channel fading, while the distributed classification Fusion structure using error correcting codes provides good sensor fault-tolerance capability. Asymptotic performance of the proposed approach is also investigated. Performance evaluation of the proposed approach with both sensor faults and fading channel impairments is carried out. These results show that the proposed approach outperforms the system employing the MAP Fusion Rule designed without regard to sensor faults and the multiclass equal gain combining Fusion Rule
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Fusion of decisions transmitted over rayleigh fading channels in wireless sensor networks
IEEE Transactions on Signal Processing, 2006Co-Authors: Ruixin Niu, Biao Chen, Pramod K VarshneyAbstract:In this paper, we revisit the problem of fusing decisions transmitted over fading channels in a wireless sensor network. Previous development relies on instantaneous channel state information (CSI). However, acquiring channel information may be too costly for resource constrained sensor networks. In this paper, we propose a new likelihood ratio (LR)-based Fusion Rule which requires only the knowledge of channel statistics instead of instantaneous CSI. Based on the assumption that all the sensors have the same detection performance and the same channel signal-to-noise ratio (SNR), we show that when the channel SNR is low, this Fusion Rule reduces to a statistic in the form of an equal gain combiner (EGC), which explains why EGC is a very good choice with low or medium SNR; at high-channel SNR, it is equivalent to the Chair-Varshney Fusion Rule. Performance evaluation shows that the new Fusion Rule exhibits only slight performance degradation compared with the optimal LR-based Fusion Rule using instantaneous CSI.
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Fusion of censored decisions in wireless sensor networks
IEEE Transactions on Wireless Communications, 2005Co-Authors: Ruixiang Jiang, Biao ChenAbstract:Sensor censoring has been introduced for reduced communication rate in a decentralized detection system where decisions made at peripheral nodes need to be communicated to a Fusion center. In this letter, the Fusion of decisions from censoring sensors transmitted over wireless fading channels is investigated. The knowledge of fading channels, either in the form of instantaneous channel envelopes or the fading statistics, is integrated in the optimum and suboptimum Fusion Rule design. The sensor censoring and the ensuing Fusion Rule design have two major advantages compared with the previous work. 1) Communication overhead is dramatically reduced. 2) It allows incoherent detection, hence, the phase information of transmission channels is no longer required. As such, it is particularly suitable for wireless sensor network applications with severe resource constraints.
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channel aware decision Fusion in wireless sensor networks
IEEE Transactions on Signal Processing, 2004Co-Authors: Biao Chen, Ruixiang Jiang, Teerasit Kasetkasem, Pramod K VarshneyAbstract:Information Fusion by utilizing multiple distributed sensors is studied in this work. Extending the classical parallel Fusion structure by incorporating the fading channel layer that is omnipresent in wireless sensor networks, we derive the likelihood ratio based Fusion Rule given fixed local decision devices. This optimum Fusion Rule, however, requires perfect knowledge of the local decision performance indices as well as the fading channel. To address this issue, two alternative Fusion schemes, namely, the maximum ratio combining statistic and a two-stage approach using the Chair-Varshney Fusion Rule, are proposed that alleviate these requirements and are shown to be the low and high signal-to-noise ratio (SNR) equivalents of the likelihood-based Fusion Rule. To further robustify the Fusion Rule and motivated by the maximum ratio combining statistics, we also propose a statistic analogous to an equal gain combiner that requires minimum a priori information. Performance evaluation is performed both analytically and through simulation.
Guanqiu Qi - One of the best experts on this subject based on the ideXlab platform.
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a phase congruency and local laplacian energy based multi modality medical image Fusion method in nsct domain
IEEE Access, 2019Co-Authors: Mingyao Zheng, Guanqiu Qi, Di Wang, Yan XiangAbstract:Multi-modality image Fusion provides more comprehensive and sophisticated information in modern medical diagnosis, remote sensing, video surveillance, and so on. This paper presents a novel multi-modality medical image Fusion method based on phase congruency and local Laplacian energy. In the proposed method, the non-subsampled contourlet transform is performed on medical image pairs to decompose the source images into high-pass and low-pass subbands. The high-pass subbands are integrated by a phase congruency-based Fusion Rule that can enhance the detailed features of the fused image for medical diagnosis. A local Laplacian energy-based Fusion Rule is proposed for low-pass subbands. The local Laplacian energy consists of weighted local energy and the weighted sum of Laplacian coefficients that describe the structured information and the detailed features of source image pairs, respectively. Thus, the proposed Fusion Rule can simultaneously integrate two key components for the Fusion of low-pass subbands. The fused high-pass and low-pass subbands are inversely transformed to obtain the fused image. In the comparative experiments, three categories of multi-modality medical image pairs are used to verify the effectiveness of the proposed method. The experiment results show that the proposed method achieves competitive performance in both the image quantity and computational costs.
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a novel multi modality image Fusion method based on image decomposition and sparse representation
Information Sciences, 2017Co-Authors: Yi Chai, Guanqiu Qi, Yanxia LiAbstract:Abstract Multi-modality image Fusion is an effective technique to fuse the complementary information from multi-modality images into an integrated image. The additional information can not only enhance visibility to human eyes, but also mutually complement the limitations of each image. To preserve the structure information and perform the detailed information of source images, a novel image Fusion scheme based on image cartoon-texture decomposition and sparse representation is proposed. In proposed image Fusion method, source multi-modality images are decomposed into cartoon and texture components. For cartoon components a proper spatial-based method is presented for morphological structure preservation. An energy based Fusion Rule is used to preserve structure information of each source image. For texture components, a sparse-representation based method is proposed. A dictionary with strong representation ability is trained for the proposed sparse-representation based Fusion method. Finally, according to the texture enhancement Fusion Rule, the fused cartoon and texture components are integrated. The experimentation results have clearly shown that the proposed method outperforms the state-of-art methods, in terms of visual and quantitative evaluations.
Yi Chai - One of the best experts on this subject based on the ideXlab platform.
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a novel multi modality image Fusion method based on image decomposition and sparse representation
Information Sciences, 2017Co-Authors: Yi Chai, Guanqiu Qi, Yanxia LiAbstract:Abstract Multi-modality image Fusion is an effective technique to fuse the complementary information from multi-modality images into an integrated image. The additional information can not only enhance visibility to human eyes, but also mutually complement the limitations of each image. To preserve the structure information and perform the detailed information of source images, a novel image Fusion scheme based on image cartoon-texture decomposition and sparse representation is proposed. In proposed image Fusion method, source multi-modality images are decomposed into cartoon and texture components. For cartoon components a proper spatial-based method is presented for morphological structure preservation. An energy based Fusion Rule is used to preserve structure information of each source image. For texture components, a sparse-representation based method is proposed. A dictionary with strong representation ability is trained for the proposed sparse-representation based Fusion method. Finally, according to the texture enhancement Fusion Rule, the fused cartoon and texture components are integrated. The experimentation results have clearly shown that the proposed method outperforms the state-of-art methods, in terms of visual and quantitative evaluations.
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A novel approach for multimodal medical image Fusion
Expert Systems With Applications, 2014Co-Authors: Yi Chai, Simon X. YangAbstract:Fusion of multimodal medical images increases robustness and enhances accuracy in biomedical research and clinical diagnosis. It attracts much attention over the past decade. In this paper, an efficient multimodal medical image Fusion approach based on compressive sensing is presented to fuse computed tomography (CT) and magnetic resonance imaging (MRI) images. The significant sparse coefficients of CT and MRI images are acquired via multi-scale discrete wavelet transform. A proposed weighted Fusion Rule is utilized to fuse the high frequency coefficients of the source medical images; while the pulse coupled neural networks (PCNN) Fusion Rule is exploited to fuse the low frequency coefficients. Random Gaussian matrix is used to encode and measure. The fused image is reconstructed via Compressive Sampling Matched Pursuit algorithm (CoSaMP). To show the efficiency of the proposed approach, several comparative experiments are conducted. The results reveal that the proposed approach achieves better fused image quality than the existing state-of-the-art methods. Furthermore, the novel Fusion approach has the superiority of high stability, good flexibility and low time consumption.
Goto Satoshi - One of the best experts on this subject based on the ideXlab platform.
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On the Fusion Algebras of Bimodules Arising from Goodman-de la Harpe-Jones Subfactors
Department of Information and Communication Sciences Sophia University, 2013Co-Authors: Goto SatoshiAbstract:By using Ocneanu’s result on the classification of all irreducible connections on the Dynkin diagrams, we show that the dual principal graphs as well as the Fusion Rules of bimodules arising from any Goodman-de la Harpe-Jones subfactors are obtained by a purely combinatorial method. In particular we obtain the dual principal graph and the Fusion Rule of bimodules arising from the Goodmande la Harpe-Jones subfactor corresponding to the Dynkin diagram E8. As an application, we also show some subequivalence among A-D-E paragroups
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On the Fusion algebras of bimodules arising from Goodman-de la Harpe-Jones subfactors
2012Co-Authors: Goto SatoshiAbstract:By using Ocneanu's result on the classification of all irreducible connections on the Dynkin diagrams, we show that the dual principal graphs as well as the Fusion Rules of bimodules arising from any Goodman-de la Harpe-Jones subfactors are obtained by a purely combinatorial method. In particular we obtain the dual principal graph and the Fusion Rule of bimodules arising from the Goodman-de la Harpe-Jones subfactor corresponding to the Dynkin diagram $E_8$. As an application, we also show some subequivalence among $A$-$D$-$E$ paragroups.Comment: 124 figure
