Retraction notice: Hybrid cumulative approach for localization of nodes with adaptive threshold gradient feature on energy minimization using federated learning

Purpose Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed adaptive threshold gradient feature (ATGF) model. A received signal strength indicator (RSSI) model with node estimated features is implicated with localization problem and enhanced with hybrid cumulative approach (HCA) algorithm for node optimizations with distance predicting. Design/methodology/approach Using a theoretical or empirical signal propagation model, the RSSI (known transmitting power) is converted to distance, the received power (measured at the receiving node) is converted to distance and the distance is converted to RSSI (known receiving power). As a result, the approximate distance between the transceiver node and the receiver may be determined by measuring the intensity of the received signal. After acquiring information on the distance between the anchor node and the unknown node, the location of the unknown node may be determined using either the trilateral technique or the maximum probability estimate approach, depending on the circumstances using federated learning. Findings Improvisation of localization for wireless sensor network has become one of the prime design features for estimating the different conditional changes externally and internally. One such feature of improvement is observed in this paper, via HCA where each feature of localization is depicted with machine learning algorithms imparting the energy reduction problem for each newer localized nodes in Section 5. All affected parametric features on energy levels and localization problem for newer and extinct nodes are implicated with hybrid cumulative approach as in Section 4. The proposed algorithm (HCA with AGTF) has implicated with significant change in energy levels of nodes which are generated newly and which are non-active for a stipulated time which are mentioned and tabulated in figures and tables in Section 6. Originality/value Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed ATGF model. An RSSI model with node estimated features is implicated with localization problem and enhanced with HCA algorithm for node optimizations with distance predicting.

In modern warfare, frequency-hopping communication serves as the primary method for battlefield information transmission, with its significance continuously growing. Fighting for the control of electromagnetic power on the battlefield has become an important factor affecting the outcome of war. As communication electronic warfare evolves, jammers employing deep neural networks (DNNs) to decode frequency-hopping communication parameters for smart jamming pose a significant threat to communicators. This paper proposes a method to generate adversarial samples of frequency-hopping communication signals using adaptive whitening and feature gradient smoothing. This method targets the DNN cognitive link of the jammer, aiming to reduce modulation recognition accuracy and counteract smart interference. First, the frequency-hopping signal is adaptively whitened. Subsequently, rich spatiotemporal features are extracted from the hidden layer after inputting the signal into the deep neural network model for gradient calculation. The signal’s average feature gradient replaces the single-point gradient for iteration, enhancing anti-disturbance capabilities. Simulation results show that, compared with the existing gradient symbol attack algorithm, the attack success rate and migration rate of the adversarial samples generated by this method are greatly improved in both white box and black box scenarios.

A new algorithm based on shape from orientation formulation in a regularization framework is proposed for correcting intensity inhomogeneities in MR images. Unlike most previous methods, this algorithm is fully automatic and very efficient. In addition, it can be applied to a wide variety of images since no prior classification knowledge is assumed. In this algorithm, the authors use a finite element basis to represent the bias field function. Orientation constraints are computed at the nodes of the finite element discretization away from the boundary between different regions. The selection of reliable orientation constraints is facilitated by the goodness of fitting a first-order polynomial model to the neighborhood of each nodal location. The selected orientation constraints are integrated in a regularization framework, which leads to the minimization of a convex and quadratic energy function. This energy minimization is achieved by solving a linear system with a large, sparse, symmetric and positive semi-definite stiffness matrix. The authors employ an adaptive preconditioned conjugate gradient algorithm to solve the linear system efficiently. Experimental results on a variety of MR images are given to demonstrate the effectiveness and efficiency of the proposed algorithm.

When the matching cost function in Semiglobal Matching is unstable, the inaccurate matching cost values will be propagated in the cost aggregation process. It will lead to a serious mismatching phenomenon. To address the problem, a binocular images dense matching method considering image adaptive color weights and feature points was proposed. Firstly, The Color Birchfield Tomasi (CBT) matching cost calculation method was proposed to obtain a stable initial cost volume, which combined image adaptive color weights and gradient information. Secondly, the Scale-invariant Feature Transform matching algorithm was used to extract the a priori feature points from binocular images. Then, the feature points were filtrated. The cost volume was optimized by using their coordinate information and disparity information. Finally, an aggregation path segmentation rectification method was adopted to optimize the SGM aggregation paths and reduce the propagation of incorrect paths. Experimental results demonstrate that the proposed method can effectively improve the stability and accuracy of dense matching, reduce the mismatching phenomenon, and finally produce high-quality disparity maps.

Study of image retrieval and classification based on adaptive features using genetic algorithm feature selection

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.

Wireless sensor networks (WSNs) encounter a variety of challenges as a result of their constrained resource and power restrictions for different network topologies. Due to the restricted resource limits and wireless transmission system characteristics, secure communication can be difficult. An extensive amount of study has been done to effectively optimize or minimize Direction of Arrival (DOA). The reduction of communication overhead can be improved through perfect node localization in wireless sensor networks (WSNs). In this survey, minimization of energy in sensor nodes problem has been addressed along with various optimization Meta heuristic algorithms. In this article, we examine the principal coverage optimization procedures and outline current unresolved questions in the field of energy-efficient covering.

Visual object recognition is a key challenge to the deployment of robots in domains characterized by partial observability and unforeseen changes. Sophisticated algorithms developed for modeling and recognizing objects using different visual cues [Mikolajczyk:IJCV04,Porway:PAMI11] are computationally expensive, sensitive to changes in object configurations and environmental factors, and require many training samples and accurate domain knowledge to learn object models, making it difficult for robots to reliably and efficiently model and recognize objects. These challenges are partially offset by the fact that many objects possess unique characteristics (e.g., color and shape) and motion patterns, although these characteristics and patterns are not known in advance and may change over time. Furthermore, only a subset of domain objects are relevant to any given task and a variety of cues can be extracted from images to represent objects. This paper presents an algorithm that enables robots to identify a set of interesting objects, using appearance-based and contextual cues extracted from a small number of images to efficiently learn models of these objects. Robots learn the domain map and consider objects that move to be interesting, using motion cues to identify the corresponding image regions. Object models learned automatically from these regions consist of spatial arrangement of gradient features, graph-based models of neighborhoods of gradient features, parts-based models of image segments, color distributions, and mixture models of local context. The learned models are used for object recognition in novel scenes based on energy minimization and a generative model for information fusion. All algorithms are evaluated on wheeled robots in indoor and outdoor domains.

Halftoning algorithms attempt to match the tone of an input image despite lower color resolution in the output. However, in some artistic media and styles, tone matching is not at all the goal; rather, details are either portrayed sharply or omitted entirely.In this paper, we present an algorithm for abstracting arbitrary input images into black and white images. Our goal is to preserve details while as much as possible producing large regions of solid color in the output. We present two methods based on energy minimization, using loopy belief propagation and graph cuts, but it is difficult to devise a single energy term that both sufficiently promotes coherence and adequately preserves details. We next propose a third algorithm separating these two concerns. Our third algorithm involves composing a base layer, consisting of large flat-colored regions, with a detail layer, containing the small high-contrast details. The base layer is computed with energy minimization, while local adaptive thresholding gives the detail layer. The final labeling is tidied by removing small components, vectorizing, and smoothing the region boundaries. The output images satisfy our goal of high spatial coherence with detail preservation.

Although magnetic resonance imaging has revolutionised the management of intracranial lesions with improved visualisation of anatomical structures, it only produces two-dimensional images, from which the clinician has to extrapolate a three-dimensional interpretation. Several approaches can be used to create 3D images; the discipline of image segmentation has encompassed a number of these techniques. Such techniques allow the clinician to delineate areas of interest. The resulting computer-generated outlines can be reconstructed in a three-dimensional arrangement. Although a plethora of "generic" segmentation techniques exist, we have developed a refined form, dependent on general and particular properties of the anatomical structures under investigation. High-contrast structures such as the ventricles and external surface of the head are found by using a localised adaptive thresholding technique. Less definable structures, with poor or nonexistent signal change across neighbouring structures, such as brain stem or pituitary, are found by applying an "energy minimisation"-based technique. To demonstrate the techniques we used the example of an 8-year-old boy with uncontrolled gelastic seizures due to a hypothalamic hamartoma, who is being considered for surgery. We were able to demonstrate the anatomical relationships between the hypothalamic hamartoma and adjacent structures such as optic chiasm, brain stem and ventricular system. We were subsequently able to create a video, reproducing the stages of craniotomy for excision of this tumour. By creating true 3D objects, we were able at any stage of the simulation to visualise structures situated contralaterally to the approaching surgical dissector. These 3D representations of the structures can be either invisible or opaque, in order to afford 3D localisation as the "virtual" surgical dissection proceeds. The clinical application of such techniques will enable surgeons to improve their understanding of anatomical relations of intracranial lesions and has obvious implications in image-guided surgery.

Retraction notice: An efficient three-dimensional node localization using recurrent neural networks in unmanned aerial vehicle-assisted wireless networks: an optimization perspective

In this paper, an image de-noising model with gradient and adaptive curvature features is proposed for the visual inspection of the appearance defects of high-density flexible integrated circuit package substrates with strict line-width and line distance. Firstly, the model proposed in this paper adaptively adjusts the weight of the level set curvature feature and the gradient feature of the image, and uses more abundant first-order differential and second-order differential information of the image as the detection factor for image de-noising. Secondly, theoretical analysis shows that the diffusion intensity of the model in the flat region is larger than that of the classical model, and the de-noising effect is better. Furthermore, at the corners and peaks of the image, the proposed model can suppress the reduction of the gray value and preserve more detailed information of the image and edges. Finally, the experimental analysis shows that the proposed model has the best de-noising effect compared with other models. The method proposed in this paper can effectively remove the noise of the image of the high-density flexible integrated circuit package substrate, and at the same time retain more original details and edge information of the image which has practical significance.

We segment an image of a porous structure by successively identifying individual grains, using a process that requires no manual initialization. Adaptive thresholding is used to extract an incomplete edge map from the image. Then, seed points are created on a rectangular grid. Rays are cast from each point to identify the local grain. The grain with the best shape is selected by energy minimization, and the grain is used to update the edge map. This is repeated until all the grains have been recognized. Tests on scanning electron microscope images of titanium oxide and aluminium oxide show that their process achieves better results than five other contour detection techniques.

VIsual Background Extractor (VIBe) is a simple and effective background subtraction model, it is important to choose the threshold according to different environments. A new method based on the VIBe is presented which uses the adaptive threshold. We give a new description of the similarity degree without threshold, and then use the energy minimization model to combine the spatial information and show that dynamically choosing threshold in our kernel estimates at each individual pixel can significantly improve results. To compare with the traditional VIBe algorithms, a synthetic test SABS and a real world test I2R are used. Experiment shows that the presented method is on average 10% better than the traditional VIBe in accuracy.

Background subtraction is commonly employed in foreground object detection in urban traffic scenes. Most of the current color or texture feature-based background subtraction models are easily contaminated by sudden and gradual illumination variations in urban traffic scenes. To resolve this deficiency, an adaptive local median texture feature, which extracts the adaptive distance threshold employing the median information in a predefined local region of a pixel and Weber's law, is introduced. In addition, a sample consensus-based model that evolved from portable visual background extractor is proposed using an adaptive local median texture feature. Then, the foreground is labeled by comparing the input video frames feature with the model. Moreover, to adapt the dynamic background, the random update scheme is used to update the model. Extensive experimental results on the public Change Detection data set of 2014 (CDnet2014) and the real-world urban traffic videos demonstrate that our background subtraction method is superior to the other state-of-the-art texture-feature-based methods. The qualitative and quantitative results show the encouraging efficiency of the proposed technique to deal with sudden and gradual illumination variations in real-world urban traffic scenes.