Epilepsy, a neurological condition affecting approximately 50 million individuals globally, is among the most common nervous system disorders. Electroencephalography (EEG) is vital for evaluating epilepsy, yet its intricate nature often restricts its application to specialized clinical environments. OptiEEG, a remote monitoring system incorporating OpenBCI’s EEG technology, addresses challenges by integrating a communication gateway and a mobile application for user-friendly operation. This study benchmarks OptiEEG’s performance against the clinically validated Natus NicoletOne EEG System through three routine EEG tests: Eye Open Close, Hyperventilation, and Photic Stimulation. Signal quality, component analysis, and reliability were evaluated using error metrics, time-frequency analysis, Bland-Altman plots, repeatability, Pearson Correlation Coefficient (PCC) and also EEG characteristics analysis of individual channels. OptiEEG demonstrated comparable signal quality to Natus, with average standard deviations for signal-to-noise ratio (Natus: 3.27 vs. OptiEEG: 2.95), peak signal-to-noise ratio (Natus: 2.76 vs. OptiEEG: 2.16), and mean squared error (Natus: 0.01 vs. OptiEEG: 0.04). Time-frequency analysis revealed less than 10% differences across alpha, theta, and delta bands. Reliability tests confirmed repeatability, with intra-system differences lower than inter-system differences, and Bland-Altman plots meeting 83% agreement criteria. PCC analysis highlighted moderate signal alignment, confirming similar EEG patterns across systems. Channel-specific analysis showed median differences as low as 0.80%, validating OptiEEG’s ability to capture critical EEG features. The results establish OptiEEG as a reliable alternative to traditional systems, combining clinically comparable performance with a portable design. These findings highlight its potential as a robust remote monitoring tool for epilepsy, enabling broader access to EEG diagnostics and management.

Ambulatory EEGs began emerging in the healthcare industry over the years, setting a new norm for long-term monitoring services. The present devices in the market are neither meant for remote monitoring due to their technical complexity nor for meeting clinical setting needs in epilepsy patient monitoring. In this paper, we propose an ambulatory EEG device, OptiEEG, that has low setup complexity, for the remote EEG monitoring of epilepsy patients. OptiEEG’s signal quality was compared with a gold standard clinical device, Natus. The experiment between OptiEEG and Natus included three different tests: eye open/close (EOC); hyperventilation (HV); and photic stimulation (PS). Statistical and wavelet analysis of retrieved data were presented when evaluating the performance of OptiEEG. The SNR and PSNR of OptiEEG were slightly lower than Natus, but within an acceptable bound. The standard deviations of MSE for both devices were almost in a similar range for the three tests. The frequency band energy analysis is consistent between the two devices. A rhythmic slowdown of theta and delta was observed in HV, whereas photic driving was observed during PS in both devices. The results validated the performance of OptiEEG as an acceptable EEG device for remote monitoring away from clinical environments.

Health monitoring in plants is vital for agricultural sustainability. Currently, the number of techniques able to detect plant stress and disease at an early stage is limited. Prevention of diseases and stress, while the plants are still in an asymptomatic stage could lead to better crop management in agricultural industries. This review focuses on the applications of visible and near-infrared (Vis-NIR) spectroscopy in disease detection and the implications of stress in various species of plants. It is a rapid and non-destructive technique that doesn’t require or requires only minimal sample processing before measurements and data analysis. The visible and near-infrared region can detect almost all functional groups and compounds making it a promising tool for data analysis. A brief overview of the methods used and the absorption bands in the Vis-NIR range related to plant disease and stress will be discussed. The comprehensive review of the application of the visible and near-infrared range regions according to different types of disease and stress including the methods used for the data analysis is being addressed.

Prevention of stress in the asymptomatic stage of the plants could result in improved crop management. In this study, the water and light stress of three oil palm seedlings (Elaeis Guineensis) was examined by assessing the leaves' colour using CIELAB colour space. The oil palm seedlings were subjected to water stress for 33 days and then to water and light stress from 55 to 78 day (for 25 days). The variation of the colour of the leaves due to water stress was discussed in detail. The approach used in this study to identify the drought stress may allow for differentiating mild environmental and severe drought stress in oil palm plants and may be used for remote field-scale estimation of plant stress resistance and health.

In this study, the spectral responses to drought stress of different parts in the leaf of oil palm seedlings named base, middle, and tip were investigated. The ability to detect stress even before symptoms emerge requires knowledge of which part of oil palm leaves responds more quickly to the stress. The analysis of the reflectance spectra in region 650–1050 nm was conducted on respective sections of the leaves of the oil palm seedlings. The analysis revealed that the stress affects the tip of the leaf, followed by the middle and then the base. It was noticed that the greatest loss of water and chlorophyll content was at the tip of the leaf. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used for clustering, while support vector machine (SVM) and linear discriminant analysis (LDA) were applied for categorization purposes. The outcomes of the PCA and HCA showed that the separation between the samples was based on the day and stress levels at respective sections of the leaf. With this, the possession of distinct morphological and physiological features by each part of the leaf can be concluded. From the PCA loadings, it was found that the regions 699–756 nm, 833–877 nm, and 933–958 nm showed noticeable bands and can be used to distinguish between the oil palm seedlings under stress. In addition, LDA and SVM classifiers demonstrated that the prediction accuracy of the stress level in oil palm seedlings was not influenced by the location in the leaf where the spectra were acquired.

A method was developed based on spectral analysis and classification models for early detection of water stress level in the leaves of oil palm seedlings. The healthy (well-watered: D0) and water-stressed (subjected to water stress for five days: D1-D5) leaves of oil palm seedlings were investigated to identify and classify the stress levels. The stress levels were grouped as light, moderate, and severe. The region 900–1000 nm was selected because it is highly correlated with water content, particularly in terms of first and second derivatives. The measured reflectance spectra at 900–1000 nm were pre-processed using smoothing, standard normal variate (SNV), and first and second Savitzky-Golay (SG) derivatives. Principal component analysis (PCA) was performed on several transformed datasets to reduce the reflectance spectral dimension and derive the principal components (PCs). Support vector machine (SVM) and linear discriminant analysis (LDA) classification models were employed to the scores of PCs to achieve six classification levels of water stress. Classification accuracy was assessed using the overall accuracy and confusion matrix of testing datasets. The SVM and PCA-LDA classification models predicted the water stress levels with high average overall classification accuracy of 92 % and 94 % using the smoothed + SNV + first derivative and smoothed + SNV spectral dataset, respectively. The findings confirmed the potential of 900–1000 nm region to distinguish the different levels of water stress in oil palm seedlings.

Path planning in robotics and automation often demands solutions that effectively balance efficiency and path smoothness, particularly in diverse and large-scale environments. To address this, this paper presents the Rapidly Exploring Random Tree-Modified Whale Optimization Algorithm II (RRT-MWOAII), an enhanced version of the previous Rapidly Exploring Random Tree-Modified Whale Optimization Algorithm (RRT-MWOA) approach. The RRT-MWOAII is a hybrid technique that combines the strengths of the RRT path planning method and the MWOAII. It utilizes the RRT path as the initial solution and then applies the MWOAII to improve the smoothness of the RRT path. The key advancement in the RRT-MWOAII algorithm is the incorporation of a targeted global search, where the weakest solution is targeted instead of a random one during the global search phase. This targeted approach helps to efficiently identify and refine the weakest link in the solution, leading to improved overall path optimization. Additionally, the algorithm further refines its local solution by employing a neighborhood search, which allows for fine-tuning of the path. Simulations were conducted and evaluated on seven benchmarks, including RRT, RRT*, Bidirectional RRT (BiRRT), and state-of-the-art optimization methods such as the original Whale Optimization Algorithm (WOA), Improved Whale Optimization Algorithm (IWOA) and Sparrow Search Algorithm (SpSA). Experimental results show that MWOAII consistently produced shorter and smoother paths, outperforming its predecessor with a 31.6% improvement in the initial RRT path smoothness and a 21.05% better average smooth cost across all nine maps. Note to Practitioners—Path planning in robotics and automation often requires solutions that balance efficiency and path smoothness across diverse and sometimes large-scale environments. This work introduces the Rapidly Exploring Random Tree-Modified Whale Optimization Algorithm II (RRT-MWOAII), an advanced path planning techniq...

This research addresses the challenges of using the Rapidly Exploring Random Tree (RRT) algorithm as a mobile robot path planner. While RRT is known for its flexibility and wide applicability, it has limitations, including careful tuning, susceptibility to local minima, and generating jagged paths. The main objective is to improve the smoothness of RRT-generated trajectories and reduce significant path curvature. A novel approach is proposed to achieve these, integrating the RRT path planner with a modified version of the Whale Optimization Algorithm (RRT-WOA). The modified WOA algorithm incorporates parameter variation (𝐶𝐶⃗) specifically designed to optimize trajectory smoothness. Additionally, Piecewise