A cyber–physical multi-robot system integrates robotic agents that share data over communication networks in real time to achieve common objectives by making decisions collectively based on the knowledge of their surroundings. This work introduces a formation control strategy for two groups of mobile robots placed in two separate workspaces connected by a communication network. The control technique generates two similar formations on each workspace using virtual agents that mirror the behavior of the corresponding physical robot in the opposite workspace. Control laws are derived for a single integrator and unicycle-type real and virtual robots that converge to the desired formation, even in the presence of communication delays. The numerical simulations performed show the convergence of the control strategy. A low-cost cyber–physical micro-robot platform was developed to run experiments with real robots. The setup uses a camera as a position and orientation sensor and the MQTT protocol for server communication and data exchange. Results obtained on this platform show the feasibility of the approach in an actual physical setting.

The inverse kinematics (IK) problem of the UR3 collaborative manipulator is addressed through a singularity-aware optimization framework and a statistically grounded benchmarking methodology. The IK task is formulated as a full-pose optimization problem minimizing a physically scaled residual combining Cartesian position and orientation errors. Emphasizing consistency between error formulation and optimization paradigms, a matrix-based pose-error representation is adopted as a numerically stable residual for stochastic search. Simultaneously, a smooth Jacobian-conditioning penalty is incorporated to mitigate instability near ill-conditioned configurations. Five metaheuristic solvers (PSO, GWO, GA, JADE, ALO) are implemented under a unified, reproducible experimental protocol with common maximum search settings. The Levenberg–Marquardt (LM) numerical method is included as a deterministic baseline to compare gradient-based precision against derivative-free global exploration. Performance is evaluated across nominal, industrial, and near-singular poses using 1000 Monte Carlo runs per configuration. Final-solution accuracy, variability, and computational time are analyzed directly from the Monte Carlo outcome distributions, descriptive statistics, and nonparametric rank-based tests. Results indicate that LM achieves superior numerical precision and computational speed. Among the metaheuristics, GA provides the lowest mean objective values and the smallest objective dispersion across the three tested poses, whereas JADE is the fastest solver. GWO provides an intermediate solution profile, with competitive objective values and substantially shorter execution times than GA and ALO. The optimized solutions are first verified in a RoboDK virtual environment. Subsequently, representative GWO-based configurations are experimentally validated on a physical UR3 robot through both isolated static poses and a continuous multi-pose trajectory tracking task, confirming practical kinematic feasibility and sequential stability. The proposed framework establishes a reproducible benchmark for statistically robust evaluation of metaheuristic-based IK optimization in collaborative robotics.

Seizure episodes pose a considerable threat to individuals with neurological disorders, particularly epilepsy. Prompt detection and intervention are essential to mitigate injury risk and improve clinical outcomes. This paper presents the design and implementation of a portable seizure detection system that is intended to be attached to a waist belt. The system integrates a gyroscope-based motion sensor to identify abrupt movements indicative of seizure activity. The hardware architecture is based on the ESP32-S3 microcontroller and the MPU6500 inertial measurement unit (IMU) for real-time motion analysis. A classification algorithm is used to differentiate seizures from normal daily activities. Upon detection, the device transmits a notification via a paired mobile phone using a Telegram bot, enabling immediate alerts to caregivers or emergency services. The proposed system contributes to the development of intelligent wearable medical devices, offering a cost-effective solution for continuous monitoring and rapid emergency response.

Distributed Denial-of-Service (DDoS) attacks constitute a principal threat to networked services by saturating targets with illegitimate HTTP requests. This paper presents an adaptive detection framework based on high-fidelity synthetic traffic generated over seven continuous days at ≤ 10μs timestamp precision. Baseline traffic follows a Poisson process (λn≈ 50reqs −1 ) and is augmented by four attack events—including volumetric bursts peaking at 2×10 4 reqs −1 —yielding ≈ 3.57×10 7 request logs (≈ 4.8GB) on a single 8-core x86 workstation (32 GB RAM, NVMe SSD). A hyper-parameter-tuned Random Forest classifier achieves AUROC = 0.994 (95% CI ±0.003), true-positive rate ≥ 0.941, false-positive rate ≤ 0.004, precision ≈ 0.970, and recall ≈ 0.941; statistical significance is confirmed with McNemar’s test and Wilson confidence intervals. The study delivers a rigorously specified synthetic baseline and lays the groundwork for future validation on live traffic.