Traditional synthetic aperture radar (SAR) target detection methods use matched filtered (MF) SAR images as input, and the detection performance is restricted due to the high side lobes and speckle ...noise of these images. Sparse SAR imaging methods developed in recent years provide the advantages of reducing side lobes, noise and clutter. The imaging results obtained with these methods could help improve the SAR target detection performance. In this paper, to improve the target detection performance using sparse SAR images as input, we proposed a convolutional sparse feature enhancement method to meet the needs of Bayesian saliency detection. The proposed Bayesian saliency joint target detection method comprised the following three steps: first, to obtain sparse SAR images with continuous contours and fewer holes in the target area, we proposed a convolutional L1 sparse regularization method. Second, a regularization parameter optimization method was derived to quickly obtain optimal regularization parameters for saliency detection. Finally, target detection results were obtained through a superpixel-based Bayesian saliency joint detector. Extensive experiments verified that the proposed method could improve the SAR target detection accuracy in complex backgrounds.
•Copper nanoclusters (Cu NCs) possess unique chemical properties for sensing.•Progress in the analytical applications of Cu NCs is amazing.•Analytical applications of Cu NCs are reviewed.
Metal ...nanoclusters (MNCs) are composed of several to tens of atoms and have drawn considerable research interest due to their unique electrical, physical and optical properties. However, in comparison to the extensively investigated Au NCs and Ag NCs, analytical applications of the copper nanoclusters (Cu NCs) are relatively limited and still at an early stage. In this review, we focus on recent advances in the analytical applications of Cu NCs based on their optical, electrochemical, and catalytical properties for the detection of various analytes, including metal ions, anions, biomoleculars (proteins, nucleic acids etc.), small molecules and pH. In addition, their applications in biological labeling and bioimaging were summarized.
Background coming from the Formula omittedAr decay chain is considered to be one of the most relevant for the Gerda experiment, which searches for the neutrinoless double beta decay of Formula ...omittedGe. The sensitivity strongly relies on the absence of background around the Q-value of the decay. Background coming from Formula omittedK, a progeny of Formula omittedAr, can contribute to that background via electrons from the continuous spectrum with an endpoint at 3.5 MeV. Research and development on the suppression methods targeting this source of background were performed at the low-background test facility LArGe . It was demonstrated that by reducing Formula omittedK ion collection on the surfaces of the broad energy germanium detectors in combination with pulse shape discrimination techniques and an argon scintillation veto, it is possible to suppress Formula omittedK background by three orders of magnitude. This is sufficient for Phase II of the Gerda experiment.
Cyber intrusions to substations of a power grid are a source of vulnerability since most substations are unmanned and with limited protection of the physical security. In the worst case, simultaneous ...intrusions into multiple substations can lead to severe cascading events, causing catastrophic power outages. In this paper, an integrated Anomaly Detection System (ADS) is proposed which contains host- and network-based anomaly detection systems for the substations, and simultaneous anomaly detection for multiple substations. Potential scenarios of simultaneous intrusions into the substations have been simulated using a substation automation testbed. The host-based anomaly detection considers temporal anomalies in the substation facilities, e.g., user-interfaces, Intelligent Electronic Devices (IEDs) and circuit breakers. The malicious behaviors of substation automation based on multicast messages, e.g., Generic Object Oriented Substation Event (GOOSE) and Sampled Measured Value (SMV), are incorporated in the proposed network-based anomaly detection. The proposed simultaneous intrusion detection method is able to identify the same type of attacks at multiple substations and their locations. The result is a new integrated tool for detection and mitigation of cyber intrusions at a single substation or multiple substations of a power grid.
Saliency Detection via Absorbing Markov Chain Jiang, Bowen; Zhang, Lihe; Lu, Huchuan ...
2013 IEEE International Conference on Computer Vision,
12/2013
Conference Proceeding, Journal Article
Open access
In this paper, we formulate saliency detection via absorbing Markov chain on an image graph model. We jointly consider the appearance divergence and spatial distribution of salient objects and the ...background. The virtual boundary nodes are chosen as the absorbing nodes in a Markov chain and the absorbed time from each transient node to boundary absorbing nodes is computed. The absorbed time of transient node measures its global similarity with all absorbing nodes, and thus salient objects can be consistently separated from the background when the absorbed time is used as a metric. Since the time from transient node to absorbing nodes relies on the weights on the path and their spatial distance, the background region on the center of image may be salient. We further exploit the equilibrium distribution in an ergodic Markov chain to reduce the absorbed time in the long-range smooth background regions. Extensive experiments on four benchmark datasets demonstrate robustness and efficiency of the proposed method against the state-of-the-art methods.
In the last few years, huge amounts of progress have been made regarding remote sensing in the field of computer vision. This success and progress is mostly due to the effectiveness of deep learning ...(DL) algorithms. In addition, the remote sensing community has shifted its attention to DL, and DL algorithms have been used to achieve significant success in many image analysis tasks. However, with regard to remote sensing, a number of challenges caused by difficulties in data acquisition and annotation have not been fully solved yet. This reprint is a collection of novel developments in the field of remote sensing using computer vision, deep learning, and artificial intelligence. The articles published involve fundamental theoretical analyses as well as those demonstrating their application to real-world problems.
In the last few years, huge amounts of progress have been made regarding remote sensing in the field of computer vision. This success and progress is mostly due to the effectiveness of deep learning ...(DL) algorithms. In addition, the remote sensing community has shifted its attention to DL, and DL algorithms have been used to achieve significant success in many image analysis tasks. However, with regard to remote sensing, a number of challenges caused by difficulties in data acquisition and annotation have not been fully solved yet. This reprint is a collection of novel developments in the field of remote sensing using computer vision, deep learning, and artificial intelligence. The articles published involve fundamental theoretical analyses as well as those demonstrating their application to real-world problems.
This paper proposes a multi-scale rotation-invariant haar-like (MSRI-HL) feature integrated convolutional neural network (MSRIHL-CNN)-based ship detection algorithm of the multiple-target environment ...in synthetic aperture radar (SAR) imagery. Usually, ship detection includes preprocessing, prescreening, discrimination, and classification. Among them, prescreening and discrimination are the most two important stages so that they catch great intention. Based on our previous work, we propose a truncated-clutter-statistics-based joint, constant false alarm rate (CFAR) detector (TCS-JCFAR) for ship target prescreening in the multiple-target environment. TCS-JCFAR greatly enhances the prescreening rate in the multiple-target environment while achieving a low observed FAR. In the discrimination stage, conventional CNN extracts the deep features (high-level features); however, it will lose the local texture and edge information (low-level features) which are of great significance for target discrimination. Hence, the MSRI-HL features are used to represent the multi-scale, rotation-invariant texture, and edge information that conventional CNN fails to capture. The extracted low-level MSRI-HL features and the high-level deep features are optimally fused to a multi-layered feature vector. Finally, the multi-layered feature vector is fed into a typical support vector machine (SVM) classifier for ship target discrimination. The proposed MSRIHL-CNN combines the low-level texture and edge features and the high-level deep features; moreover, they are optimally fused to fully represent the ship targets. Undoubtedly, MSRIHL-CNN has better discrimination performance. The superiority of the proposed TCS-JCFAR-based prescreener and MSRIHL-CNN-based discriminator is validated on the Chinese Gaofen-3 SAR imagery.
Synthetic aperture radar (SAR) is an active microwave imaging sensor with the capability of working in all-weather, all-day to provide high-resolution SAR images. Recently, SAR images have been ...widely used in civilian and military fields, such as ship detection. The scales of different ships vary in SAR images, especially for small-scale ships, which only occupy few pixels and have lower contrast. Compared with large-scale ships, the current ship detection methods are insensitive to small-scale ships. Therefore, the ship detection methods are facing difficulties with multi-scale ship detection in SAR images. A novel multi-scale ship detection method based on a dense attention pyramid network (DAPN) in SAR images is proposed in this paper. The DAPN adopts a pyramid structure, which densely connects convolutional block attention module (CBAM) to each concatenated feature map from top to bottom of the pyramid network. In this way, abundant features containing resolution and semantic information are extracted for multi-scale ship detection while refining concatenated feature maps to highlight salient features for specific scales by CBAM. Then, the salient features are integrated with global unblurred features to improve accuracy effectively in SAR images. Finally, the fused feature maps are fed to the detection network to obtain the final detection results. Experiments on the data set of SAR ship detection data set (SSDD) including multi-scale ships in various SAR images show that the proposed method can detect multi-scale ships in different scenes of SAR images with extremely high accuracy and outperforms other ship detection methods implemented on SSDD.
3D printing is fast evolving as an additive manufacturing technique that has been adopted in (bio)analytical science because of the ample variety of materials and technologies currently available for ...highly affordable prototyping. This review focuses on the unique characteristics of 3D printing for manufacturing of optical and electrochemical detection systems, and sampling interfaces for analytical purposes using fused deposition modelling, vat polymerization (stereolithography and digital light processing) and photopolymer inkjet printing. The majority of works surveyed within the time span of mid-2018 to mid-2020 encompassed the fabrication of several components of the detection systems, yet recent reports on totally printed electrochemical detectors are paving the way of 3D printing toward self-dedicated fully printed detectors. From the application viewpoint, the merits and weaknesses of the new sensing platforms as compared with commercially available detectors will be critically analyzed to uncover the actual advantages of using 3D printed materials and devices. Finally, the current state-of-the-art and future perspectives of this emerging technology for fabrication of unique detection systems are highlighted.
Display omitted
•Role of 3D printing in designing optical and electrochemical detection systems.•Critical evaluation of articles published since 2018.•FDM, SLA and DLP are by far the most commonly employed techniques.•Most of the works focused on printing spare parts and detection components.•Trends are directed towards one-step printing of the overall detection system using multimaterial printers.
PDF
