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Steganography is widely recognized as an effective method for protecting information via digital media. This paper presents an innovative image steganography algorithm incorporating image compression, chaotic maps, and the least significant bit. The process begins with the compression of a confidential medical image using Huffman encoding. The compressed image then undergoes shuffling, facilitated by the chaotic logistic map. The bits from the shuffled image are discreetly embedded into randomly selected pixels of the cover image, guided by the chaotic piecewise smooth map. The resulting stego image is generated. Statistical analyses are applied to both the cover and stego images for evaluation. The proposed algorithm is compared against state-of-the-art algorithms, and the results demonstrate its superiority over existing methods. [ABSTRACT FROM AUTHOR]

The template matching method has been widely utilized in the defect detection of wafer surfaces. However, the traditional matching approaches are limited by illumination, noise, and deformation, which cannot meet the requirements of accuracy and robustness. In this paper, a novel multiple targets localization method, named Normalized Cross-correlation Adaptive Variable Step-Size Dynamic Template (NCC-AVSSDT) matching, is proposed to improve the accuracy and efficiency of image localization, which combines the advantages of NCC and AVSSDT. The AVSSDT method is utilized to dynamically adjust the scanning step size based on the NCC matching coefficients. This approach optimizes the scanning process, accelerating convergence toward the optimal matching position. Experimental results verify the accuracy and robustness of the proposed method under different conditions, especially when dealing with rotational variations and variations in noise textures. Therefore, NCC-AVSSDT can be used to perform multiple targets localization of chip image in nearly real-time. Three experiment types were used for comprehensive evaluations, including multiple targets, noise, and rotation angles. Experimental results show that NCC-AVSSDT is much better than the sequential similarity detection algorithm and mean absolute deviation methods in terms of multiple targets (0.667 vs 0.811 s, 0.832 s) and success rate (100% vs 35%, 20%). [ABSTRACT FROM AUTHOR]

Direction-of-arrival (DOA) estimation algorithms are crucial in localizing acoustic sources. Traditional localization methods rely on block-level processing to extract the directional information from multiple measurements processed together. However, these methods assume that DOA remains constant throughout the block, which may not be true in practical scenarios. Also, the performance of localization methods is limited when the true parameters do not lie on the parameter search grid. In this paper, two trajectory models are proposed, namely the polynomial and harmonic trajectory models, to capture the DOA dynamics. To estimate trajectory parameters, two gridless algorithms are adopted: (i) Sliding Frank–Wolfe (SFW), which solves the Beurling LASSO problem, and (ii) Newtonized orthogonal matching pursuit (NOMP), which is improved over orthogonal matching pursuit (OMP) using cyclic refinement. Furthermore, our analysis is extended to include multi-frequency processing. The proposed models and algorithms are validated using both simulated and real-world data. The results indicate that the proposed trajectory localization algorithms exhibit improved performance compared to grid-based methods in terms of resolution, robustness to noise, and computational efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach—an efficient closed-form approximation iterative fitting algorithm based on laser absorption spectra. Through this closed-form approximation iterative fitting, key parameters such as peak value, spectral line width, and normalized signal area serve as indicators for iteration completion, improving the speed without compromising accuracy. Furthermore, it employs the spectral signal of n cycles as a window for further processing, minimizing external interference. The results show that the proposed method averages 9.75 iterations, while the Levenberg–Marquardt fitting method averages 60.17 iterations. The average iteration time for the proposed method is 588.83 ms, a substantial 81.7% reduction compared to the 3210.5 ms required by the Levenberg–Marquardt fitting. These results decisively demonstrate the efficacy of the proposed method in reducing iteration time and enhancing measurement precision. [ABSTRACT FROM AUTHOR]

Eddy current thermography (ECT) is a non-invasive testing method that combines electromagnetic induction and infrared thermography to identify flaws in materials that conduct electricity. However, ECT faces difficulties in accurately locating and classifying defects owing to its low signal-to-noise ratio and complex defect patterns. In this paper, we propose a new method that integrates an improved faster region-convolutional neural network (R-CNN) and an adaptive Canny algorithm to enhance the defect detection performance of ECT. An improved faster R-CNN is a deep neural network that can automatically detect and locate multiple defects in a single ECT image, whereas the adaptive Canny algorithm is an edge detection technique that can identify defect boundaries. The proposed method was tested using a dataset of ECT images with different types of defects. The results demonstrated that our method achieved better accuracy, precision, and speed than existing methods. [ABSTRACT FROM AUTHOR]

After the service provider temporarily selects the required edge nodes based on social and storage capabilities, application execution causes the edge nodes to cache part of the application data. Therefore, offloading part of the application computing tasks to the selected edge nodes can effectively improve application execution performance. However, in cases where the resources of user's IoT devices are insufficient, tasks can be further offloaded to traditional edge servers or even to the cloud to maximize application execution efficiency. In this paper, the entire uninstall utility is modeled as a weighted sum of task completion time and energy consumption. Under the premise of considering users' preferences for completion time and energy consumption, a game-based uninstallation algorithm is proposed. The algorithm performs uninstallation by optimizing the uninstallation decision. Based on user preferences, the total system overhead is relatively small. The subsequent simulation experiments show that the algorithm can reduce system overhead on the basis of satisfying user preferences and has relatively good adaptability. [ABSTRACT FROM AUTHOR]

This document is an erratum for a publication titled "Path planning of water surface garbage cleaning robot based on improved immune particle swarm algorithm" in the journal AIP Advances. The erratum acknowledges several errors in the original publication, including missing names of components in a figure, a mistake in the placement of another figure, and a correction in a table. The authors assure readers that these corrections do not significantly affect the overall findings and conclusions of the paper. [Extracted from the article]