Efficient interaction between PHP (Hypertext Preprocessor) applications and MySQL databases is essential for the performance of modern web systems. This study systematically compares the two most widely used PHP APIs for working with MySQL databases—MySQLi (MySQL Improved extension) and PDO (PHP Data Objects)—under identical experimental conditions. The analysis covers execution time, memory consumption, and the stability and variability of results across different types of SQL (Structured Query Language) queries (simple queries, complex JOIN, GROUP BY/HAVING). A specialized benchmarking tool was developed to collect detailed metrics over several hundred repetitions and to enable graphical and statistical evaluation. Across the full benchmark suite, MySQLi exhibits the lowest mean wall-clock execution time on average (≈15% overall). However, under higher query complexity and in certain connection-handling regimes, PDO prepared statement modes provide competitive latency with improved predictability. These results should be interpreted as context-aware rankings for the tested single-host environment and workload design, and as a reusable benchmarking framework intended for replication under alternative deployment models. Statistical analysis (Kruskal–Wallis and Mann–Whitney tests) confirms significant differences between the methods, while Box-plots and histograms visualize deviations and the presence of outliers. Unlike earlier studies, this work provides a controlled and replicable benchmarking environment that tests both MySQLi and PDO across multiple API modes and isolates the impact of native versus emulated prepared statements. It also evaluates performance under complex-query workloads that reflect typical reporting and analytics patterns on the ClassicModels schema. To our knowledge, no previous study has analyzed these factors jointly or provided a reusable tool enabling transparent comparison across PHP–MySQL access layers. The findings provide empirical evidence and practical guidelines for choosing the optimal API depending on the application scenario, as well as a tool that can be applied for further testing in various web environments.

Precise time interval generation is a cornerstone of modern measurement, automation, and distributed control systems, particularly within Internet of Things (IoT) architectures. This paper presents the design, implementation, and evaluation of a low-cost and high-precision time interval generator based on Complementary Metal-Oxide Semiconductor (CMOS) logic counters (Integrated Circuit (IC) IC 7493 and IC 4017) and inverter-based crystal oscillators (IC 74LS04). The proposed system enables frequency division from 1 MHz down to 1 Hz through a cascade of binary and Johnson counters, enhanced with digitally controlled multiplexers for output signal selection. Unlike conventional timing systems relying on expensive Field-Programmable Gate Array (FPGA) or Global Navigation Satellite System (GNSS)-based synchronization, this approach offers a robust, locally controlled reference clock suitable for IoT nodes without network access. The hardware is integrated with Arduino and ESP32 microcontrollers via General-Purpose Input/Output (GPIO) level interfacing, supporting real-time timestamping, deterministic task execution, and microsecond-level synchronization. The system was validated through Python-based simulations incorporating Gaussian jitter models, as well as real-time experimental measurements using Arduino’s micros() function. Results demonstrated stable pulse generation with timing deviations consistently below ±3 µs across various frequency modes. A comparative analysis confirms the advantages of this CMOS-based timing solution over Real-Time Clock (RTC), Network Time Protocol (NTP), and Global Positioning System (GPS)-based methods in terms of local autonomy, cost, and integration simplicity. This work provides a practical and scalable time reference architecture for educational, industrial, and distributed applications, establishing a new bridge between classical digital circuit design and modern Internet of Things (IoT) timing requirements.

This paper presents a deterministic embedded control architecture for an industrial electroplating line. The validated system includes two autonomous trolleys, 18 station-aligned process positions, shared-track motion, and redundant grouped baths. The proposed controller addresses the limitations of rigid sequential automation by combining asynchronous finite-state trolley execution, runtime allocation of equivalent technological stations, dwell-time-preserving retrieval, distributed thermal supervision, and layered fail-safe protection within a single ATmega2560-based implementation. The core contribution is the integration of virtual process groups and temporal FIFO logic into a compact plant-side embedded controller. This enables adaptive bath selection and process-completion-based retrieval without reliance on a real-time operating system or a computationally heavy supervisory runtime. The architecture also incorporates predictive pre-start validation, runtime software arbitration, hardware-wired interlocks, binary-coded trolley positioning, and a distributed 1-Wire thermal measurement network. Validation was performed in a controller-centered hardware-in-the-loop representation of an 18-station zinc electroplating line. Over a 100-batch horizon, the proposed architecture reduced makespan from 1642 min to 1244 min, corresponding to a 24.2% throughput improvement. Average trolley idle time decreased from 18.4 min/batch to 4.1 min/batch. Grouped-bath utilization increased from 64% to 91%, while tracked bottleneck incidents decreased from 18 to 2. These results show that adaptive, resource-aware, and safety-layered electroplating control can be realized effectively on a compact embedded platform in an industry-representative HIL setting, while preserving dwell-time integrity and controller-level safety invariants.

Aperiodic pseudo-random impulse (APPI) trains represent deterministic yet reproducible sequences that mimic the irregularity of natural processes. They allow complete control over inter-spike intervals (ISIs) and pulse widths (PWs). Such signals are increasingly relevant for low-probability-of-intercept (LPI) communications, radar testing, and biomedical applications, where controlled variability mitigates adaptation and enhances stimulation efficiency. This paper presents a modular APPI generator implemented on an Arduino Mega platform, featuring programmable statistical models for ISI (exponential distribution) and PW (uniform distribution), dual-timing mechanisms (baseline loop and Timer/ISR, clear-timer on compare (CTC)), a real-time telemetry and software interface, and a safe output chain with opto-isolation and current limitation. The generator provides both reproducibility and tunable stochastic dynamics. Experimental validation includes jitter analysis, Kolmogorov–Smirnov tests, Q–Q plots, spectral and autocorrelation analysis, and load integration using a constant-current source with compliance margins. The results demonstrate that the Timer/ISR (CTC) implementation achieves significantly reduced jitter compared to the baseline loop, while maintaining the statistical fidelity of ISI and PW distributions, broad spectral characteristics, and fast decorrelation. Experimental verification was extended across a wider parameter space (λ = 0.1–100 Hz, PW = 10 µs–100 ms, 10 repetitions per condition), confirming robustness and repeatability. Experimental validation confirmed accurate Poisson/Uniform ISI generation, sub-millisecond jitter stability in the timer-controlled mode, robustness across λ = 0.1–100 Hz and PW = 10 µs–100 ms, and preliminary compliance with isolation and leakage limits. The accompanying Python GUI provides real-time control, telemetry, and data-logging capabilities. This work establishes a reproducible, low-cost, and open-source framework for APPI generation, with direct applicability in laboratory and field environments.