Solar-powered pumping systems using series pumps are commonly applied in the delivery of water to remote agricultural regions, particularly in hilly tropical terrain. The synchronization of these pumps typically depends on reliable communication; however, dense vegetation, elevation changes, and weather conditions often disrupt signals. To address these limitations, a fully decentralized, communication-free control system is proposed. Each pumping station operates independently while maintaining synchronized operation through emulated neighbor sensing. The system applies a discrete-time control algorithm with virtual sensing that estimates neighboring pump statuses. Each station consists of a solar photovoltaic (PV) array, variable-speed drive, variable inlet valve, reserve tank, and local control unit. The controller adjusts the valve positions and pump power based on real-time water level measurements and virtual neighbor sensing. The simulation results across four scenarios, including clear sky, cloudy conditions, temporary outage, and varied irradiance, demonstrated steady-state operation with no water overflow or shortage and a steady-state error less than 4% for 3 m³ transfer. The error decreased as the average power increased. The proposed method maintained system functionality under simulated power outage and variable irradiance, confirming its suitability for remote agricultural areas where communication infrastructure is limited.

Adopting simple soft-core processors rather than real CPUs has become a recognized educational approach used by many computer design/architecture courses. Over ten years have passed since we developed a low complexity processor (LCP) targeted to development boards available in our programmable devices’ laboratory. In this paper, we present three successive versions of the LCP developed as part of our ongoing efforts to make the LCP a practical and useful educational tool.

The research investigated Electroencephalography (EEG) based biomarkers to distinguish Alzheimer’s disease (AD) from healthy controls (HC). The study applied graph theoretical analysis of directed functional connectivity. A dataset of 65 participants, including 36 with AD and 29 HC, was used. Resting-state, eyes-closed EEG data were obtained from the participants. The preprocessing steps included notch and band-pass filtering, resampling, artifact removal, and re-referencing. The preprocessed data were segmented into overlapping 5-second windows. The segments were filtered into three frequency bands: alpha (8–12 Hz), beta (13–30 Hz), and gamma (30–45 Hz). Dynamic functional connectivity was quantified using the directed phase lag index (dPLI) across 19 channels. Graph theoretical metrics were computed for each subject and frequency band. The metrics included global efficiency, modularity, characteristic path length, clustering coefficient, and rich-club coefficient. Group differences were evaluated with Mann–Whitney U tests and false discovery rate (FDR) correction. The correlation between metrics and cognitive status were evaluated using Spearman correlation with Mini-Mental State Examination (MMSE) scores. Then logistic regression with receiver operating characteristic (ROC) analysis quantified the discriminative performance of significant features. Additional analyses included channel-wise connectivity comparisons, power spectral density (PSD) estimation, and grand average waveform analysis at midline electrodes. The findings indicated significant changes were limited to the alpha band. Participants with AD exhibited higher mean global efficiency, lower temporal variability (CV) and reduced modularity in the alpha band. The results reflect altered network integration with reduced temporal variability (lower CV) and weakened functional segregation in the alpha band. ROC analysis showed moderate discrimination from the alpha band biomarkers (area under curve AUC up to 0.74); using the same biomarkers with alternative classification methods, AUCs reached up to 0.88. Channel-wise analysis revealed selective frontoparietal and occipitofrontal disruptions. The PSD results showed flattened alpha peaks, consistent with EEG slowing in AD. Compared to HC group, AD group exhibited altered amplitudes, latencies, and polarity shifts after grand average waveform analysis. The results of the study also indicates that alpha band network disruptions are reliable EEG markers of AD, specifically in global efficiency and modularity. The results show the frequency specific network dysfunction in AD. The findings support the use of graph theoretical EEG analysis as a noninvasive biomarker method for clinical applications.