In the modern age, watermarking techniques are mandatory to secure digital communication over the internet. For an optimal technique, a high signal-to-noise ratio and normalized correctional is required. In this paper, a digital watermarking technique is proposed on the basis of the least significant bit through an image gradient and chaotic map. The image is segmented into noncorrelated blocks, and the gradient of each block is calculated. The gradient of the image expresses the rapid changes in an image. A chaotic substitution box (S-Box) is used to scramble the watermark according to a piecewise linear chaotic map (PWLCM). PWLCM has a positive Lyapunov exponent and better balance property as compared to other chaotic maps. This S-Box technique is capable of producing a disperse sequence with high nonlinearity in the generated sequence. Least significant bit is a simple technique for embedding but it has a high payload capacity and direct pixel manipulation. The embedding payload introduces a tradeoff between robustness and imperceptibility; hence, the image gradient is a technique to identify the best-suited place to embed a watermark and avoid image degradation. By modifying the least significant bits of the original image, the watermark signal is embedded according to the image gradient. In the image gradient, the direction and magnitude decide how much embeding can be done. In comparison with other methods, the experimental results show satisfactory progress in robustness against several image processing and geometrical attacks while maintaining the imperceptibility of the watermark signal.

Technology brings a remarkable advancement in every field of life, whether its industry or agriculture. Our lives are essentially dependent on agricultural ...

The evolving landscape of cloud computing design poses challenges for power consumption and quality of services (QoS). Virtual machine (VM) consolidation is vital in minimizing power consumption and boosting QoS by transferring VMs between hosts. OpenStack Neat is a prominent framework in VM consolidation which uses Modified Best-Fit Decreasing (MBFD) VM placement technique that suffers through energy consumption and QoS related issues. So, we proposed an energy efficient VM consolidation (EEVMC) approach, which uses a novel host selection criterion based on the loss incurred during VM placement to identify the most efficient host. For validation, we conducted simulations using real-time workload traces from Planet-Lab and Materna over ten-days, utilizing the latest CloudSim toolkit for comparison with state-of-the-art techniques. For Planet-Lab’s workload, our approach outperforms with 80.35%, 59.76%, 21.59%, 7.40% in lower energy consumption and 94.51%, 94.85%, 47.17%, 17.78% in fewer system level agreement (SLA) violations when compared to MBFD, PABFD, MFPED, and PEBFD, respectively. Similarly, for Materna, 16.10%, 61.0%, 4.94%, 4.82% in reduced energy consumption and 76.99%, 88.88%, 12.50%, 48.65% in fewer SLA violations against the same benchmarks. Additionally, LA-PED significantly decreases the total number of VM migrations and the SLA time per active host, presenting a substantial improvement in cloud computing efficiency.

Diabetes is a chronic disease with exponential growth and poses significant challenges to global healthcare. Regular blood glucose (BG) monitoring is key for avoiding diabetic complications. Traditional BG measurement techniques are invasive and minimally invasive, causing pain, discomfort, cost, and infection risks. To address these issues, we developed a noninvasive BG monitoring approach on photoplethysmography (PPG) signals using multi-view attention and cascaded BiLSTM hierarchical feature fusion approach. Firstly, we implemented a convolutional multi-view attention block to extract the temporal features through adaptive contextual information aggregation. Secondly, we built a cascaded BiLSTM network to efficiently extract the fine-grained features through bidirectional learning. Finally, we developed a hierarchical feature fusion with bilinear polling through cross-layer interaction to obtain higher-order features for BG monitoring. For validation, we conducted comprehensive experimentation on up to 6 days of PPG and BG data from 21 participants. The proposed approach showed competitive results compared to existing approaches by RMSE of 1.67 mmol/L and MARD of 17.88%. Additionally, the clinical accuracy using Clarke error grid (CEG) analysis showed 98.80% of BG values in Zone A+B. Therefore, the proposed approach offers a favorable solution in diabetes management by noninvasively monitoring the BG levels.

In present-day data communication, the imperative to safeguard information and shield it from a multitude of potential threats and attacks has gained significant prominence. One of the pivotal tools employed in the pursuit of data protection is the design of robust substitution boxes (S-Boxes). These S-Boxes often find their roots in the realm of chaotic maps, leveraging the inherent unpredictability and complexity of these mathematical constructs. An S-Box is an integral and critical component in the design of modern ciphers offering a means to enhance the security of data by introducing non-linearity and confusion into the encryption process. In this research endeavor, we propose the development of a novel chaotic map to create an S-Box that exhibits a notably high nonlinear score making it more formidable for potential adversaries to decrypt the ciphertext. To substantiate the efficacy of the proposed S-Box, a comprehensive analysis is done by comparing its performance using standard criteria including nonlinearity (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), presence of fixed points (FP), linear probability (LP), differential probability (DP), etc. to other state-of-the-art S-Boxes. The comparative analysis reinforces the security fortitude of the proposed S-Box in meeting the rigorous demands of modern data security and presents a vital contribution to the ever-evolving field of cryptographic techniques and data protection in the digital age.

Cuffless blood pressure (BP) estimation is critical for managing growing concerns about hypertension and cardiovascular diseases. Despite recent advancements in multimodal (ECG and PPG) BP estimation methods, which have achieved varying degrees of success, several challenges remain to be addressed. These include capturing the full spectrum of BPrelevant information, redundant feature spaces, and handling the multigrade classification. To address these issues, we propose a Multimodal Fusion BP Network (MuFuBP-Net), featuring a novel dual-feature pipeline architecture designed to extract hierarchical and modality-specific features from both ECG and PPG signals. Additionally, the Cascading Cross-Feature Enhancer (CCFE) module integrates multiple fusion strategies with a squeeze-and-excitation mechanism to apply channel-wise attention to spatial features, enabling dynamic re-weighting. We also employed a Sequence Context Network (SCN) module to capture global sequential features. Subsequently, a Probabilistic Feature Encoder (PFE) encodes the multilevel features from both pipelines into a compact latent space, preserving their discriminative characteristics. Our approach achieved MAE ± SDE of 2.99 ± 4.37 mmHg (SBP) and 2.63 ± 4.19 mmHg (DBP) on MIMIC-II, and 2.27 ± 4.15 mmHg (SBP) and 1.63 ± 2.96 mmHg (DBP) on MIMIC-III dataset, meeting AAMI, BHS, and IEEE grade A standards. The proposed approach demonstrated competitive results compared to existing techniques, highlighting its significance as a reliable solution for cuffless BP monitoring.