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Abstract: MIMO radar with properly placed antennas that employs a coherent processing approach can provide superior MSE performance, as indicated by recent work. This paper demonstrates that the magnitude of these gains decreases with an increase in the product of the number of transmit and receive antennas if the antennas for the noncoherent system are also suitably placed, using a placement which is generally different from the one for the coherent processing approach. Initially, we study the systems without constraining the complexity and energy, where each added transmit antenna employs a fixed energy so that the total transmitted energy is allowed to increase as we increase the number of transmit antennas. Later we also look at constrained systems, where adding a transmit antenna splits the total system energy and the total number of antennas employed is restricted. A rigorous theorem is presented for the case of orthogonal signals in temporally and spatially white clutter-plus-noise, but numerical results for nonorthogonal signals and colored clutter-plus-noise follow a similar pattern. [Copyright &y& Elsevier]

Abstract: The security of smart grid systems is under threat as a consequence of sophisticated intrusion and imperceptible faults. To make the smart grid systems more secure, the development of an efficient fault detection approach is significantly important. Considering that the time evolution of a smart grid system can be modeled by a discrete-time linear state space model, we focus on faults that can be described by changes in system matrices of the state space model. Hypothesis testing-based approaches are employed to detect the faults and a few new locally optimum testing procedures are introduced. We present numerical results that verify the superiority of the new approaches in detecting the change of the system and show that this advantage is particularly obvious when the change is small. [Copyright &y& Elsevier]

Abstract: Automated image quality assessment is highly desirable to evaluate the performance of various image fusion algorithms for night vision applications. In this paper we propose a perceptual quality evaluation method for image fusion which is based on human visual system (HVS) models. Our method assesses the image quality of a fused image using the following steps. First, the source and fused images are filtered by a contrast sensitivity function (CSF) after which a local contrast map is computed for each image. Second, a contrast preservation map is generated to describe the relationship between the fused image and each source image. Finally, the preservation maps are weighted by a saliency map to obtain an overall quality map. The mean of the quality map indicates the quality of the fused image. Experimental results compare the predictions made by our algorithm with human perceptual evaluations for several different parameter settings in our algorithm. The most popular existing algorithms are also evaluated. For some specific parameter settings, we find our algorithm provides better predictions, which are more closely matched to human perceptual evaluations, than the existing algorithms. The evaluations focus on the night vision application, but the algorithm we propose is applicable to other applications also. [Copyright &y& Elsevier]

Most image or video fusion quality metrics are designed to evaluate different video fusion methods for spatial-temporal extraction. And there is limited research on the evaluation of spatial-temporal consistency. In this paper, a video fusion quality metric is proposed to evaluate different fusion methods for spatial-temporal consistency, where spatial-temporal phase congruency is employed as a feature to be compared and 3D zero-mean normalized cross-correlation is employed as the similarity measure. Firstly, the spatial-temporal phase congruency maps for input and fused videos are computed using a set of predefined 3D Log-Gabor filters. Then the spatial-temporal phase congruency maps are divided into many non-overlapped spatial-temporal blocks and a local block-based quality metric is defined by performing 3D zero-mean normalized cross-correlation on the relevant spatial-temporal phase congruency maps of the input and fused videos. Finally, the global quality metric is constructed as the weighted average of all the block-based quality metrics. The required local and global weights are defined by the spatial-temporal structure tensor. Several sets of experiments demonstrate the validity and feasibility of the proposed metric. Moreover, the proposed metric shows higher stability and robustness than some other metrics in a noisy environment. [ABSTRACT FROM AUTHOR]

Abstract: A new electrical-domain precoder is proposed to mitigate polarization mode dispersion (PMD) in optical communications by modeling a PMD-dominant fiber optical channel using a single input multiple output (SIMO) channel. Employing a bank of finite impulse response filters and a polarization modulator, and using parameters derived from the Jones matrix representation of PMD, the proposed precoder efficiently adapts to the time-varying nature of PMD and simultaneously pre-equalizes both polarization modes at the transmitter. The transmitter-only structure avoids losing phase and polarization information due to the nonlinearity of the commonly used square law direct-detection receiver. Analysis is performed to evaluate the impact of channel mismatch due to feedback delay, channel estimation errors, and the impact of the finite length of the precoder filters. The analytical results are used to guide selection of the appropriate feedback rate for the adaptive system. Extensive simulation results confirm the efficiency of the proposed Jones precoder, and present it as an effective, low-cost replacement to the complicated, expensive optical-domain counter-parts. [Copyright &y& Elsevier]

• Proposes a day-ahead electricity-gas systems coordinated operation scheme. • Defines a new class FDI attacks on natural gas systems. • Investigates the impact of the defined attacks on the operation of both systems. The increased penetration of natural gas-fired generation units in electricity systems calls for coordination of natural gas and electricity systems. Moreover, concerns over security of coordinated gas-electricity systems are spreading in recent years. Notably, both energy systems are vulnerable to malicious cyber-attacks. Within this context, this article (1) proposes a coordination scheme for the day-ahead schedule of natural gas and electricity systems; (2) defines a new class of false data injection (FDI) cyber-attacks on the gas demand and scheduling information of the natural gas system; and (3) investigates the impacts of such attacks on the operation of both energy systems. The coordination mechanism consists of three nested optimization problems. The natural gas system scheduling step in the mechanism is modeled as a bi-level max–min optimization problem with consideration of attacks. We use a column and constraint generation (C&CG) algorithm to solve this bi-level problem. The coordination mechanism and bi-level attack problem are examined using a case study based on the IEEE 24-node test system and a 9-node gas system, as well as a case study based on the IEEE 118-node test system and a 25-node gas system. [ABSTRACT FROM AUTHOR]

Recently, sparse representation-based (SR) methods have been presented for the fusion of multi-focus images. However, most of them independently consider the local information from each image patch during sparse coding and fusion, giving rise to the spatial artifacts on the fused image. In order to overcome this issue, we present a novel multi-focus image fusion method by jointly considering information from each local image patch as well as its spatial contextual information during the sparse coding and fusion in this paper. Specifically, we employ a robust sparse representation (LR_RSR, for short) model with a Laplacian regularization term on the sparse error matrix in the sparse coding phase, ensuring the local consistency among the spatially-adjacent image patches. In the subsequent fusion process, we define a focus measure to determine the focused and de-focused regions in the multi-focus images by collaboratively employing information from each local image patch as well as those from its 8-connected spatial neighbors. As a result of that, the proposed method is likely to introduce fewer spatial artifacts to the fused image. Moreover, an over-complete dictionary with small atoms that maintains good representation capability, rather than using the input data themselves, is constructed for the LR_RSR model during sparse coding. By doing that, the computational complexity of the proposed fusion method is greatly reduced, while the fusion performance is not degraded and can be even slightly improved. Experimental results demonstrate the validity of the proposed method, and more importantly, it turns out that our LR-RSR algorithm is more computationally efficient than most of the traditional SR-based fusion methods. [ABSTRACT FROM AUTHOR]

Abstract: We proposed a statistical modeling method for the quantitative evaluation of segmentation methods used in image guided radiotherapy. A statistical model parameterized on a Beta distribution was built upon the observations of the volume overlap between the segmented structure and the referenced structure. A statistical performance profile (SPP) was then estimated from the model using the generalized maximum likelihood approach. The SPP defines the probability density function characterizing the distribution of performance values and provides a graphical visualization of the segmentation performance. Different segmentation approaches may be influenced by image quality or observer variability. Our statistical model was able to quantify the impact of these variations and displays the underlying statistical performance of the segmentation algorithm. We demonstrated the efficacy of this statistical model using both simulated data and clinical evaluation studies in head and neck radiotherapy. Furthermore, the resulting SPP facilitates the measurement of the correlation between quantitative metrics and clinical experts’ decision, and ultimately is able to guide the clinicians in selecting segmentation methods for radiotherapy. [Copyright &y& Elsevier]

Abstract: A nonrigid registration method is proposed to automatically align two images by registering two sets of sparse features extracted from the images. Motivated by the paradigm of Robust Point Matching (RPM) algorithms , which were originally proposed for shape registration, we develop Robust Hybrid Image Matching (RHIM) algorithm by alternatively optimizing feature correspondence and spatial transformation for image registration. Our RHIM algorithm is built to be robust to feature extraction errors. A novel dynamic outlier rejection approach is described for removing outliers and a local refinement technique is applied to correct non-exactly matched correspondences arising from image noise and deformations. Experimental results demonstrate the robustness and accuracy of our method. [Copyright &y& Elsevier]