Introduction: In recent times, an upsurge in the investigation related to the effects of meditation in reconditioning various cardiovascular and psychological disorders is seen. In majority of these studies, heart rate variability (HRV) signal is used, probably for its ease of acquisition and low cost. Although understanding the dynamical complexity of HRV is not an easy task, the advances in nonlinear analysis has significantly helped in analyzing the impact of meditation of heart regulations. In this review, we intend to present the various nonlinear approaches, scientific findings and their limitations to develop deeper insights to carry out further research on this topic. Results: Literature have shown that research focus on nonlinear domain is mainly concentrated on assessing predictability, fractality, and entropy-based dynamical complexity of HRV signal. Although there were some conflicting results, most of the studies observed a reduced dynamical complexity, reduced fractal dimension, and decimated long-range correlation behavior during meditation. However, techniques, such as multiscale entropy (MSE) and multifractal analysis (MFA) of HRV can be more effective in analyzing non-stationary HRV signal, which were hardly used in the existing research works on meditation. Conclusions: After going through the literature, it is realized that there is a requirement of a more rigorous research to get consistent and new findings about the changes in HRV dynamics due to the practice of meditation. The lack of adequate standard open access database is a concern in drawing statistically reliable results. Albeit, data augmentation technique is an alternative option to deal with this problem, data from adequate number of subjects can be more effective. Multiscale entropy analysis is scantily employed in studying the effect of meditation, which probably need more attention along with multifractal analysis. Methods: Scientific databases, namely PubMed, Google Scholar, Web of Science, Scopus were searched to obtain the literature on “HRV analysis during meditation by nonlinear methods”. Following an exclusion criteria, 26 articles were selected to carry out this scientific analysis.

A millimeter-wave (mmW) classifier system applied to images synthesized from a coded-aperture based computational imaging (CI) radar is presented. A developed physical model of a CI system is used to generate the image dataset for the classification algorithm. A convolutional neural network (CNN) is integrated with the physical model and trained using the dataset comprising of synthesized mmW images obtained directly from the developed CI physical model. A k-fold cross validation technique is applied during the training process to validate the classification model. The coded-aperture CI concept enables image reconstruction from a significantly reduced number of back-scattered measurements by facilitating physical layer compression. This physical layer compression can substantially simplify the data acquisition layer of imaging radars, which is realized using only two channels in this article. The integration of the classification algorithm with the CI numerical model is particularly important in enabling the training step to be carried out using relevant system metrics and without the necessity for experimental data. Leveraging the CI numerical model generated data, training step for the classification algorithm is achieved in real-time while also confirming that the numerically trained CI classifier offers high accuracy with both simulated and experimental data. The classifier integrated physical model also enables performance analysis of the classification algorithm to be carried out as a function of key system metrics such as signal-to-noise (SNR) level, ensuring a complete understanding of the classification accuracy under different operating conditions. The trained CI system is tested with synthesized mmW images from the physical model and a classification accuracy of 89% is achieved. The proposed model is also verified using experimental data validating the fidelity of the developed CI integrated classifier system. A classification latency of 3.8 ms per frame is achieved, paving the way for real-time automated threat detection (ATD) for security-screening applications.

In the field of precision agriculture, accurate crop detection is crucial for crop yield estimation, and health monitoring using photogrammetric measurements. Achieving high precision requires advance object detection models and multiscale feature fusion. This paper addresses key research gaps in litchi crop monitoring, including the lack of a suitable dataset for litchi detection in natural environment and the limitations of conventional deep learning models in handling challenges such as occlusion, overlapping, and background complexities. Firstly, we prepare high-resolution litchi dataset called “UAVLitchi” of 5000 images that include both RGB and multispectral images and next, we propose a selective feature fusion (SFF)-based architecture for litchi detection. By utilizing both RGB and multispectral images, this architecture effectively mitigates the challenges of visual detection arising from the complex cluster growth structure of litchis, offering a robust solution for accurate detection. The integration of SFF within a dual-channel mask-region based convolutional neural network (Mask-RCNN) leading to significant improvements in feature extraction for litchi detection. Experimental results demonstrate impressive performance, achieving an mean average precession (mAP50) of 94.65%, mAP75 of 89.23%, recall of 90.16%, and F1-score of 91.44%.

A priori knowledge of the signal/image support based on its statistical and structural information in the transformed domain improves the quality of compressed sensing (CS) reconstruction. Hidden Markov tree models the wavelet domain support of magnetic resonance images obtained from undersampled k-space data very well. With the support information, parent-child pairs in the wavelet tree are detected accurately, and hence, iterative regularization problems for CS-based magnetic resonance imaging reconstruction are solved with high throughputs. Simulation results with artificial and real MRI datasets show significant improvements over existing methods in terms of just noticeable difference scanning besides other objective and subjective metrics.

Electromagnetic waves at millimetre-wave (mmW) frequencies have found applications in a variety of imaging systems, from security screening to defence and automotive radars, with the research and development of mmW imaging systems gaining interest in recent years. Despite their significant advantages, mmW imaging systems suffer from poor resolution as compared to higher frequency reconstructions, such as optical images. To improve the resolution of mmW images, various super-resolution (SR) techniques have been introduced. One such technique is the use of machine learning algorithms in the signal processing layer of the imaging system without altering any of the system’s parameters. This paper focuses on the use of a convolutional neural network (CNN) architecture to achieve SR when applied to three-dimensional mmW input images. To exploit the phase information content of the input images along with the magnitude, a complex-valued CNN is designed that can accommodate complex-valued data. To simplify the learning process, the resolution difference between the input and output images is divided into smaller parts by using sub-networks in the CNN architecture. The trained model is tested on simulated as well as experimental targets. The average mean-squared-error score and structural similarity index obtained on a test dataset of 460 samples are 0.0127 and 0.9225, respectively. It can be inferred that the model has the capability to improve the resolution of input mmW images to a high degree of fidelity, hence paving the way for an end-to-end SR imaging system.