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The purpose of this paper is to propose a biological edge detection approach for aircraft such as unmanned combat air vehicle (UCAV), with the objective of making the UCAV recognize targets, especially in complex noisy environment.

The hybrid model of saliency-based visual attention and artificial bee colony (ABC) algorithm is established for edge detection of UCAV. Visual attention can extract the region of interesting objects, and this approach can narrow the searching region for object segmentation, which can reduce the computational complexity. An improved ABC algorithm is applied in edge detection of the salient region.

This work improved ABC algorithm by modifying the search strategy and adding some limits, so that it can be applied to edge detection problem. A hybrid model of saliency-based visual attention and ABC algorithm is developed. Experimental results demonstrated the feasibility and effectiveness of the proposed method: it can guarantee efficient target localization, with accurate edge detection in complex noisy environment.

The biological edge detection model developed in this paper can be easily applied to practice and can steer the UCAV during target recognition, which will considerably increase the autonomy of the UCAV.

A hybrid model of saliency-based visual attention and ABC algorithm is proposed for biological edge detection. An improved ABC algorithm is applied in edge detection of the salient region.

The purpose of this paper is to present an automatic inspection and control method for a reagent rapid test strip production system, with image processing techniques.

Fluorescence, color arrangement and combination matching with the database were used to identify the responses of biochemicals. The position accuracy and insufflation consistency between the control line and test line on a reagent rapid test strip will be analyzed from the image after series processing.

The system can identify failed products and regulate production conditions to insure that the quality standard is maintained. The idea edges of the control line and test line are the boundary at which a significant change occurs in the surface reflectance and illumination of the viewer. But the change of the real boundary of the test line may be insufficient for identification.

As the illumination of biological reagent images cannot be measured precisely in the production process, and the intensity of the background light source is difficult to control, there are always significant errors in the production process. If the environment at sampling could be precisely controlled, the accuracy of the system could be enhanced.

This study developed software architecture for a biological reagent production and inspection system. Future studies will focus on the implementation of testing, and improvement of the system, so that it can be applied to medical systems for the benefit of all patients.

This paper aims to present the dielectrophoresis (DEP) force (FDEP), defined as microelectrofluidics mechanism capabilities in performing selective detection and rapid manipulation of blood components such as red blood cells (RBC) and platelets. The purpose of this investigation is to understand FDEP correlation to the variation of dynamic dielectric properties of cells under an applied voltage bias.

In this paper, tapered design DEP microelectrodes are used and explained. To perform the characterization and optimization by analysing the DEP polarization factor, the change in dynamic dielectric properties of blood components are observed according to the crossover frequency (f_xo) and adjustment frequency (f_adj) variation for selective detection and rapid manipulation.

Experimental observation of dynamic dielectric properties change shows clear correlation to DEP polarization factor when performing selective detection and rapid manipulation. These tapered DEP microelectrodes demonstrate an in situ DEP patterning efficiency more than 95%.

The capabilities of tapered DEP microelectrode devices are introduced in this paper. However, they are not yet mature in medical research studies for various purposes such as identifying cells and bio-molecules for detection, isolation and manipulation application. This is because of biological property variations that require further DEP characterization and optimization.

The introduction of microelectrofluidics using DEP microelectrodes operate by selective detecting and rapid manipulating via lateral and vertical forces. This can be implemented on precision health-care development for lab-on-a-chip application in microfluidic diagnostic and prognostic devices.

This study introduces a new concept to understand the dynamic dielectric properties change. This is useful for rapid, label free and precise methods to conduct selective detection and rapid manipulation of mixtures of RBC and platelets. Further, potential applications that can be considered are for protein, toxin, cancer cell and bacteria detections and manipulation. Implementation of tapered DEP microelectrodes can be used based on the understanding of dynamic dielectric properties of polarization factor analysis.

Community detection is a significant research field in the study of social networks and analysis because of its tremendous applicability in multiple domains such as recommendation systems, link prediction and information diffusion. The majority of the present community detection methods considers either node information only or edge information only, but not both, which can result in loss of important information regarding network structures. In real-world social networks such as Facebook and Twitter, there are many heterogeneous aspects of the entities that connect them together such as different type of interactions occurring, which are difficult to study with the help of homogeneous network structures. The purpose of this study is to explore multilayer network design to capture these heterogeneous aspects by combining different modalities of interactions in single network.

In this work, multilayer network model is designed while taking into account node information as well as edge information. Existing community detection algorithms are applied on the designed multilayer network to find the densely connected nodes. Community scoring functions and partition comparison are used to further analyze the community structures. In addition to this, analytic hierarchical processing-technique for order preference by similarity to ideal solution (AHP-TOPSIS)-based framework is proposed for selection of an optimal community detection algorithm.

In the absence of reliable ground-truth communities, it becomes hard to perform evaluation of generated network communities. To overcome this problem, in this paper, various community scoring functions are computed and studied for different community detection methods.

In this study, evaluation criteria are considered to be independent. The authors observed that the criteria used are having some interdependencies, which could not be captured by the AHP method. Therefore, in future, analytic network process may be explored to capture these interdependencies among the decision attributes.

Proposed ranking can be used to improve the search strategy of algorithms to decrease the search time of the best fitting one according to the case study. The suggested study ranks existing community detection algorithms to find the most appropriate one.

Community detection is useful in many applications such as recommendation systems, health care, politics, economics, e-commerce, social media and communication network.

Ranking of the community detection algorithms is performed using community scoring functions as well as AHP-TOPSIS methods.

Nanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc.

In this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021.

Among these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

This is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.

Detecting objects in images and videos is a difficult task that has challenged the field of computer vision. Most of the algorithms for object detection are sensitive to background clutter and occlusion, and cannot localize the edge of the object. An object's shape is typically the most discriminative cue for its recognition by humans. The purpose of this paper is to introduce a model‐based object detection method which uses only shape‐fragment features.

The object shape model is learned from a small set of training images and all object models are composed of shape fragments. The model of the object is in multi‐scales.

The major contributions of this paper are the application of learned shape fragments‐based model for object detection in complex environment and a novel two‐stage object detection framework.

The results presented in this paper are competitive with other state‐of‐the‐art object detection methods.

Background subtraction is a particularly popular foreground detection method, whose background model can be updated by using input images. However, foreground object cannot be detected accurately if the background model is broken. In order to improve the performance of foreground detection in human-robot interaction (HRI), the purpose of this paper is to propose a new background subtraction method based on image parameters, which helps to improve the robustness of the existing background subtraction method.

The proposed method evaluates the image and foreground results according to the image parameters representing the change features of the image. It ignores the image that is similar to the first image and the previous image in image sequence, filters the image that may break the background model and detects the abnormal background model. The method also helps to rebuild the background model when the model is broken.

Experimental results of typical interaction scenes validate that the proposed method helps to reduce the broken probability of background model and improve the robustness of background subtraction.

Different threshold values of image parameters may affect the results in different environments. Future researches should focus on the automatic selection of parameters’ threshold values according to the interaction scene.

A useful method for foreground detection in HRI.

This paper proposes a method which employs image parameters to improve the robustness of the background subtraction for foreground detection in HRI.

The purpose of this paper is to present a detection method based on computer vision for automatic flexible printed circuit (FPC) defect detection.

This paper proposes a new method of watershed segmentation based on morphology. A dimensional increment matrix calculation method and an image segmentation method combined with a fuzzy clustering algorithm are provided. The visibility of the segmented image and the segmentation accuracy of a defective image are guaranteed.

Compared with the traditional one, the segmentation result obtained in this study is superior in aspects of noise control and defect segmentation. It completely proves that the segmentation method proposed in this study is better matches the requirements of FPC defect extraction and can more effectively provide the segmentation result. Compared with traditional human operators, this system ensures greater accuracy and more objective detection results.

The extraction of FPC defect characteristics contains some obvious characteristics as well as many implied characteristics. These characteristics can be extracted through specific space conversion and arithmetical operation. Therefore, more images are required for analysis and foresight to establish a more widely used FPC defect detection sorting algorithm.

This paper proposes a new method of watershed segmentation based on morphology. It combines a traditional edge detection algorithm and mathematical morphology. The FPC surface defect detection system can meet the requirements of online detection through constant design and improvement. Therefore, human operators will be replaced by machine vision, which can preferably reduce the production costs and improve the efficiency of FPC production.

Community detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.

This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.

Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.

To enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.