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We present an effective blind image deblurring algorithm based on superpixel segmentation prior (SSP). The motivation of this work is an interesting observation that the more rough the segmentation boundaries are, the clearer the image will be. Intuitively, the blurry images have less image details, which results in more smooth segmentation boundaries. The clear images have more vivid textural details and obtain more rough boundaries. The segmentation roughness could be defined as the length of image segmentation boundaries. However, the segmentation boundary length is not differentiable, making it difficult to integrate into existing joint optimization framework. Therefore, we transform the segmentation boundary length into the segmentation entropy to guide the process of image deblurring. With the image becomes clearer, its boundary becomes more rough, while the segmentation entropy is much smaller. The analysis of relationship between segmentation entropy and segmentation boundaries is detailed. Benefiting from the convexity of segmentation entropy, we propose a novel algorithm by integrating half-quadratic split and gradient descent to alternately minimize energy function. Extensive experiments show that the proposed method achieves best performance with the state-of-the-art blind deblurring methods on natural and face image deblurring. [ABSTRACT FROM AUTHOR]

This paper derives the optimal power allocation for a coordinated orthogonal frequency-division multiplexing (OFDM) transmission system in which $K$ coordinated transmission points (CTPs) coherently transmit and allocate power across $N$ subchannels under both total and individual power constraints. In maximizing the system capacity, previous works showed that, under a total power constraint, the optimal transmission strategy is a maximum-ratio transmission (MRT) for CTPs with a waterfilling type of power allocation solution for the subchannels. For CTPs with both total and individual power constraints, we derive a new optimal co-phasing power allocation with the following property: For any given subchannel, if the optimal power allocation of one CTP is zero, then the power allocation of all the other $K$ − 1 CTPs on that subchannel must also be zero; otherwise, the non-zero power allocation on all CTPs must follow a proportional principle which establishes the relationship between the optimal power allocation for all subchannels and all CTPs. This property highlights that the optimal power allocation for CTPs with individual power constraints is different from waterfilling and MRT, as more power is not necessarily allocated to the subchannels with better channel conditions. Numerical results are presented to verify our theoretical findings. [ABSTRACT FROM AUTHOR]

Existing unsupervised video object segmentation generates object information from the whole video, which ignores analysis of the local clips. However, we observe that local clips and their relationships are also useful for the video object segmentation. For example, the simple background clips can be used to improve the segmentation of complex background clips. In this paper, we propose a novel unsupervised segmentation framework to segment the primary object based on two aspects, i.e., the complexity awareness of video clips and their segmentation propagation. The first one is used to select the simple clips with smooth backgrounds and the second one generates an object prior from the simple clips and propagates the object prior to help and improve the segmentation of the complex clips. A complexity awareness method using the static cues and the dynamic cues are proposed to evaluate the complexity of the video frames. A new object prior learning model based on the local part structure is designed and a local part-based prior propagation is proposed for the complex clip segmentation. To verify our method, we collect a new challenging video segmentation data set, in which each video contains diverse backgrounds. Experimental results demonstrate that our method outperforms several state-of-the-art methods both on a classical data set and our new data set. [ABSTRACT FROM AUTHOR]

The design of a flying odor compass is proposed for the localization of gas source. The compass is built on a quad-rotor helicopter and contains three gas sensors. A data processing method is proposed to estimate the direction which the odor comes from. The method adopts continuous wavelet transform and modulus maxima approaches to uncover the time difference information hidden in the gas sensor signals. Experiments have demonstrated the effectiveness of this design. [ABSTRACT FROM PUBLISHER]

In order to provide reference for the operation and maintenance of porcelain insulators on DC transmission lines, the test method for electrolytic corrosion of iron caps was studied in National Engineering Laboratory for Ultra High Voltage Engineering Technology (Kunming) of China, with XZP2-300 porcelain insulators that had no protection zinc collar. The results indicated that the spray water method could simulate the field corrosion condition when the conductivity and volume flow rate were 12 mS/cm and 6-12 L/h respectively, and the differences in the length, width and depth of corrosion area between the test results and the field condition were less than 8%. Besides, the differences in the test results with spray water method from two experimenters were less than 7%. Our study demonstrated that the spray water method proposed in this paper had good equivalence and repeatability, which could be used to research the electrolytic corrosion problem of iron caps of porcelain insulator on HVDC transmission line. [ABSTRACT FROM AUTHOR]

Due to the discrete nature of the saddle coils for the active feedback control of the plasma instabilities, the emergence of the sideband modes is inevitable. In the Keda Torus eXperiment (KTX), the edge Mirnov coils are unevenly distributed on the inner surface of the vacuum vessel to suppress the sideband modes. These arrays of the Mirnov coils are used for the feedback control system of KTX. They are also used for the magnetohydrodynamics (MHD) mode identification, which is a fundamental and significant method to distinguish and describe the plasma instabilities. A set of suitable MHD mode analysis methods has been utilized to complete the mode detection, including the spatial discrete Fourier transform (SDFT) method, the singular value decomposition (SVD) method and the Lomb periodogram method. These methods can obtain comprehensive mode information of the plasma instabilities and their results can be used as the feedback of the feedback control system. These methods have been successfully applied to detect and characterize an impulsive mode of ($m = 1$ , $n = 0$) and rotating modes of ($m = 2$ , 3, 4, $n = 1$) in the tokamak plasma of KTX, while the matrix decomposition technique is not applicable for KTX. [ABSTRACT FROM AUTHOR]

There are two hypotheses in the leader progression model to illustrate the insulator breakdown process. One is the streamer onset time is relatively small that can be ignored. The other is the leader velocity at both sides of the composite insulator strings are equal. These assumptions are mostly based on the experimental results in rod-rod air gaps. In this paper, experiments were performed to study the breakdown process of a 220-kV composite insulator string with grading rings under positive short tail lightning impulses. The discharge current at the high-voltage side and the high-speed photographs were recorded synchronously. The method of identifying four typical stages of the breakdown process from the current waveform was proposed. The experimental results show that the streamer onset time should not be ignored when calculating the breakdown time, especially for the composite insulator string with grading rings. By fitting the experimental results, the relationship between the streamer onset time andUP/U ${}_{\text{50}}$ has been given. The results also indicate that the velocity of the positive leader is nearly 1.3 times than that of the negative leader during the breakdown process when the positive impulse is applied to the composite insulator string. [ABSTRACT FROM AUTHOR]

The tracking wheel test specified in IEC/TR 62730:2012 provides a method that can be used to evaluate the resistance of composite insulators to tracking and erosion for rejecting inadequate materials or designs. Currently, most of the tests have been conducted on composite line insulators and some other insulator types with relatively small diameters. As the usage of large-diameter composite insulators, such as composite station insulators and composite cross-arms, increases rapidly, it is urgent to investigate if the tracking wheel test method is also suitable for their evaluation. In this study, a tracking wheel device for composite insulators with different diameters, designed and constructed in accordance with the IEC standard, is presented. The relationships between the leakage current and NaCl content as well as the average diameter of the specimen are investigated. It is shown that the test parameters specified in the IEC standard are not suitable for specimens of relatively large diameters. Accordingly, a relevant modification of test parameters has been introduced and verified, showing similar phenomena during the verified test as those observed during previous studies on composite line insulators. It is thus claimed that the modified test procedure is feasible for being applied on large-diameter insulators. [ABSTRACT FROM AUTHOR]

A mutual coupling reduction strategy that employs meta-structures is introduced for wideband, dual-polarized, high-density, planar, and patch antenna arrays. The meta-structures consist of two types of resonators: grounded capacitively loaded loops (GCLLs) and $\pi$ -shaped elements. By incorporating the meta-structures into the array configuration, the isolation levels between adjacent radiating elements in both the E- and H-planes orientations are improved by as much as 7.15 dB. The surface current distribution behaviors of the array with only the GCLLs and with only the $\pi$ -shaped elements are investigated thoroughly to explain the mutual coupling reduction mechanisms. A proof-of-concept array was constructed and tests were performed that validate the reported design principles and simulation results. [ABSTRACT FROM AUTHOR]

For in-service transmission lines, insulator icing condition depends on analysis of images collected by cameras installed on towers, and ice type analysis is mostly completed by human beings, which is infeasible to monitor a large area. Although image processing is successfully applied to segmentation of insulators from aerial images, there are few literatures on automatically determining ice types on insulators via image processing. In this paper, a recognition method of ice types on in-service glass insulators is established based on a texture feature description operator. Uniform Local Binary Patterns (ULBP) and Improved Uniform Local Binary Patterns (IULBP) were adopted for texture feature extraction of typical images of six ice types including glaze ice, heavy rime, medium rime, light rime, partial rime and snow. The six ice types are concluded based on icing insulator images and corresponding meteorological data from China Southern Power Grid Transmission Line (Icing) Online Monitoring System. Ice types are recognized by correlation coefficient calculation of texture histograms. The experiment results show that IULBP obtains better results for recognition of the six ice types as each ice type has distinguished texture features. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient blind image quality assessment (BIQA) algorithm, which is characterized by a new feature fusion scheme and a k -nearest-neighbor (KNN)-based quality prediction model. Our goal is to predict the perceptual quality of an image without any prior information of its reference image and distortion type. Since the reference image is inaccessible in many applications, the BIQA is quite desirable in this context. In our method, a new feature fusion scheme is first introduced by combining an image’s statistical information from multiple domains (i.e., discrete cosine transform, wavelet, and spatial domains) and multiple color channels (i.e., Y, Cb, and Cr). Then, the predicted image quality is generated from a nonparametric model, which is referred to as the label transfer (LT). Based on the assumption that similar images share similar perceptual qualities, we implement the LT with an image retrieval procedure, where a query image’s KNNs are searched for from some annotated images. The weighted average of the KNN labels (e.g., difference mean opinion score or mean opinion score) is used as the predicted quality score. The proposed method is straightforward and computationally appealing. Experimental results on three publicly available databases (i.e., LIVE II, TID2008, and CSIQ) show that the proposed method is highly consistent with human perception and outperforms many representative BIQA metrics. [ABSTRACT FROM AUTHOR]

This paper proposes a new constrained directed graph clustering (DGC) method and segmentation propagation method for the multiple foreground cosegmentation. We solve the multiple object cosegmentation with the perspective of classification and propagation, where the classification is used to obtain the object prior of each class and the propagation is used to propagate the prior to all images. In our method, the DGC method is designed for the classification step, which adds clustering constraints in cosegmentation to prevent the clustering of the noise data. A new clustering criterion such as the strongly connected component search on the graph is introduced. Moreover, a linear time strongly connected component search algorithm is proposed for the fast clustering performance. Then, we extract the object priors from the clusters, and propagate these priors to all the images to obtain the foreground maps, which are used to achieve the final multiple objects extraction. We verify our method on both the cosegmentation and clustering tasks. The experimental results show that the proposed method can achieve larger accuracy compared with both the existing cosegmentation methods and clustering methods. [ABSTRACT FROM AUTHOR]

The problem of unsupervised segmentation of multi-class regions can be significantly boosted when they irregularly recur in multiple images. The existing segmentation methods are either weakly supervised, such as tagging images with object classes, or are limited by the assumption that each image contains all the object instances. In this paper, we propose a new method to cosegment multiclass regions from a group of images without the assumption about object configurations. The key idea is to discover the unknown object-like proposals via a robust ensemble clustering scheme. The proposals are then used to derive unary and pairwise energy potentials across all the images, which can be minimized with the $\alpha$ -expansion. Experimental evaluation on a number of image groups demonstrates the good performance of the proposed method on the multiclass region cosegmentation. [ABSTRACT FROM PUBLISHER]