This paper outlines a proposed method of modifying parking trajectory during the autonomous valet parking process. Most autonomous valet parking systems calculate trajectories by first finding the location of a parkin...
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Precise detection of hydrophobicity grades (HGs) of silicone rubber insulator is necessary to prevent premature tripping of transmission lines. Keeping this in mind, in this paper, a novel HG detection framework emplo...
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Image steganography plays a pivotal role in secure data communication and confidentiality protection, particularly in cloud-based environments. In this study, we propose a novel hybrid approach, CNN-DCT Steganography,...
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Image steganography plays a pivotal role in secure data communication and confidentiality protection, particularly in cloud-based environments. In this study, we propose a novel hybrid approach, CNN-DCT Steganography, which combines the power of convolutional neural networks (CNNs) and discrete cosine transform (DCT) for efficient and secure data hiding within images over cloud storage. The proposed method capitalizes on the robust feature extraction capabilities of CNNs and the spatial frequency domain transformation of DCT to achieve imperceptible embedding and enhanced data-hiding capacity. In the proposed CNN-DCT Steganography approach, the cover image undergoes a two-step process. First, feature extraction using a deep CNN enables the selection of appropriate regions for data embedding, ensuring minimal visual distortions. Next, the selected regions are subjected to the DCT-based steganography technique, where secret data is seamlessly embedded into the image, rendering it visually indistinguishable from the original. To evaluate the effectiveness of our approach, extensive experiments are conducted using a diverse dataset comprising 500 high-resolution images. Comparative analysis with existing steganography methods demonstrates the superiority of the proposed CNN-DCT Steganography approach. The results showcase higher data hiding capacity, superior visual quality with an MSE of 112.5, steganalysis resistance with a false positive rate of 2.1%, and accurate data retrieval with a bit error rate of 0.028. Furthermore, the proposed method exhibits robustness against common image transformations, ensuring the integrity of the concealed data even under various modifications. Moreover, the computational efficiency of our approach is demonstrated by a competitive execution time of 2.3 s, making it feasible for real-world cloud-based applications. The combination of deep learning techniques and DCT-based steganography ensures a balance between security and visual qual
Gesture recognition is a computing process that attempts to recognize and interpret human gestures through the use of mathematical algorithms. In this paper, we describe Point Based Gesture Recognition and Point Cloud...
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Wearable technology holds promise for monitoring and managing Social Anxiety Disorder (SAD), yet the absence of clear biomarkers specific to SAD hampers its effectiveness. This article explores this issue by presentin...
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Wearable technology holds promise for monitoring and managing Social Anxiety Disorder (SAD), yet the absence of clear biomarkers specific to SAD hampers its effectiveness. This article explores this issue by presenting a study investigating variances in heart rate, heart rate variability, and skin conductance between socially anxious and non-anxious individuals. One hundred eleven non-clinical student participants participated in groups of three in three anxiety-provoking activities (i.e., speech, group discussion, and interview) in a controlled lab-based study. During the study, electrocardiogram (ECG) and electrodermal activity (EDA) signals were captured via on-body electrodes. During data analysis, participants were divided into four groups based on their self-reported anxiety level (“none,” “mild,” “moderate,” and “severe”). Between-group analysis shows that discriminating ECG features (i.e., heart rate and MeanNN) could identify anxious individuals during anxiety-provoking activities, while EDA could not. Moreover, the discriminating ECG features improved the classification accuracy of anxious and non-anxious individuals in different machine-learning techniques. The findings need to be further scrutinized in real-world settings for the generalizability of the results.
Blockchain-enabled Federated Learning (BFL) enables model updates to be stored in blockchain in a reliable manner. However, one problem is the increase of the training latency due to the mining process. Moreover, mobi...
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A dual-band and high-isolation shared-aperture antenna for unmanned aerial vehicle(UAV)platforms has been *** shared-aperture antenna consists of a rectangular monopole antenna and a 4-element multiple input multiple ...
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A dual-band and high-isolation shared-aperture antenna for unmanned aerial vehicle(UAV)platforms has been *** shared-aperture antenna consists of a rectangular monopole antenna and a 4-element multiple input multiple output(MIMO)*** order to increase the isolation,several double split ring metamaterial(MTM)structures are introduced between antenna *** antenna radiator and the MTM structure are located on the front and back of the same dielectric substrate,respectively,and are perpendicular to a circular metal *** overall size of the antenna substrate is 124 mm×38 mm×1.016 ***,the antenna prototype is constructed and measured,and the simulated and measured results are in good *** measured results show that the-10 dB bandwidth of the monopole antenna is 1.92 GHz to 2.75 GHz,and the common-6.0 dB bandwidth of the MIMO antenna element is 4.75 GHz to 4.91 GHz,covering 2.2 GHz to 2.4 GHz in the S-band and 4.8 GHz to 4.9 GHz in the 5G band,*** the 5G band,the isolation between any element of the MIMO antenna and the S-band monopole antenna is not less than 21 dB,and the isolation between the MIMO antenna elements is better than 23 dB,indicating t-hat the isolation between the antenna elements is *** proposed antenna is suitable for the application on UAV airborne platforms.
This paper studies efficient algorithms for dynamic curing policies and the corresponding network design problems to guarantee fast extinction of epidemic spread in a Markov process-based susceptible-infected-suscepti...
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The optical diffraction effect imposes a radical obstacle preventing conventional optical microscopes from achieving an imaging resolution beyond the Abbe diffraction limit and thereby restricting their usage in a mul...
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The optical diffraction effect imposes a radical obstacle preventing conventional optical microscopes from achieving an imaging resolution beyond the Abbe diffraction limit and thereby restricting their usage in a multitude of nanoscale *** the past decade,the optical microsphere nanoimaging technique has been demonstrated to be a cost-effective solution for overcoming the diffraction limit and has achieved an imaging resolution of up to about k6k8 in a real-time and label-free manner,making it highly competitive among numerous super-resolution imaging *** this review,we summarize the underlying nano-imaging mechanisms of the microsphere nanoscope and key advancements aimed at imaging performance enhancement:first,to change the working environment or modify the peripheral hardware of a single microsphere nanoscope at the system level;second,to compose the microsphere compound lens;and third,to engineer the geometry or ingredients of *** also analyze challenges yet to be overcome in optical microsphere nano-imaging,followed by an outlook of this technique.
As the cost of conventional fuel rises, the need for sustainable and cost-effective energy solutions becomes paramount, particularly in advancing agricultural practices. This paper introduces a Hybridized Solar-Biogas...
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