This study introduces a novel method that addresses the challenge of visualizing and quantifying detection coverage areas in wireless sensor networks. The method involves projecting a network of range-free sensors and pre-existing transmitters, located within a predefined area of interest, onto a global coordinate system. Detection areas are defined as those covered by the sensing range of at least three sensors. Pre-existing transmitters located within the detection range of the sensors are assumed to degrade the networks’ performance by causing coverage gaps. Interactive satellite maps facilitate the dynamic exploration of coverage via the calculation and visualization of the resulting detection areas. The algorithmic structure of the proposed tool is explained in detail, and four example scenarios demonstrate the tool’s capabilities, as well as its flexibility, adaptability, and effectiveness in identifying the triangulated detection areas. Designed primarily as a geometry calculation and visualization tool that allows for the adjustment of sensor parameters such as locations, ranges, and angular ranges of detection, the proposed tool has the potential to enhance decision-making in sensor network configuration, prior to final sensor placement, across a wide range of applications.

Insects play essential roles in ecosystems, providing services such as pollination and pest regulation. However, global insect populations are in decline due to factors like habitat loss and climate change, raising concerns about ecosystem stability. Traditional insect monitoring methods are limited in scope, but advancements in AI and machine learning enable automated, non-invasive monitoring with camera traps. In this study, we leverage the new Diopsis dataset that contains images from field operations to explore an approach that emphasizes both background extraction from images and the SAHI approach. By creating augmented backgrounds from extracting insects from training images and using these backgrounds as canvases to artificially relocate insects, we can improve detection accuracy, reaching mAP50 72.7% with YOLO10nano, and reduce variability when counting insects on different backgrounds and image sizes, supporting efficient insect monitoring on low-power devices such as Raspberry Pi Zero W 2.

The purpose of this paper is to demonstrate a new discovery regarding the interaction between materials and very low radio frequencies. Specifically, we observed a feedback response on an inertia active sensor when specific frequencies (around 2–4 kHz) are used to irradiate targeted pharmaceutical samples like aspirin or paracetamol drugs. The characteristics of this phenomenon, such as excitation and relaxation time, the relation between deceleration and a material’s quantity, and signal amplitude, are presented and analyzed. Although the underlying physics of this phenomenon is not yet known, we have shown that it has potential applications in remote identification of compounds, detection, and location sensing, as well as identifying substances that exist in plants without the need for any processing. This method is fast, accurate, low-cost, non-destructive, and non-invasive, making it a valuable area for further research that could yield spectacular results in the future.

Wireless sensor networks increasingly support time-critical monitoring applications, where coverage optimization must often be performed under limited computational resources. This work addresses a previously underexplored WSN coverage problem involving range-free, angular-limited sensors with transmitter-induced sensing degradation and discrete sector orientation. We formulate a mixed combinatorial problem that jointly optimizes $K$-out-of-$N$ sensor activation and sector assignment under strict feasibility constraints. A constraint-aware genetic algorithm with repair-based feasibility enforcement is proposed and validated against the global optimum obtained via exhaustive enumeration, enabling direct quantification of optimality. The repair mechanism corrects infeasible offspring after each genetic operation to guarantee that exactly K sensors remain active, eliminating the need for penalty-based constraint handling. A brute-force search is used to establish the global optimum of our small-scale scenario, serving as a ground-truth optimality benchmark for evaluating the proposed method. The purpose of this comparison is not to assess competitiveness against other metaheuristic algorithms, but to quantify how closely the proposed approach approximates the true optimal solution under strict problem constraints. The constraint-aware genetic algorithm is developed using an integer chromosome encoding, two initialization strategies, two crossover pairing schemes, elitism, and per-gene mutation, combined with alternative constraint-handling strategies. Two experimental series evaluate the impact of population size, crossover method, mutation probability, and constraint handling using problem-specific metrics, alongside convergence and fitness statistics. The proposed algorithm reliably reaches near-optimal solutions with significantly reduced computational cost when compared to exhaustive search. By integrating problem-specific constraints directly into the process, the proposed evolutionary optimization method effectively balances solution quality and execution time, making it well suited for scenarios requiring rapid sensor reconfiguration.