Experimental validation of positioning and tracking system using ultra-wideband and low-cost microcontroller units

Indoor positioning systems (IPS) have become increasingly critical in various applications, from asset tracking to smart environments. While global positioning system (GPS) offers precise outdoor localization, its signal is unavailable indoors. Ultra-wideband (UWB) technology emerges as a promising alternative due to its high accuracy, robustness against multipath interference, and ability to operate in dense environments. Aiming to develop an affordable and efficient system, we present a UWB-based IPS using the DW1000 UWB chip, evaluated with two different low-cost microcontroller units (MCUs): the ESP8266 system-on-chip (SoC) and the Arduino Uno R3. The findings suggest that the ESP8266 SoC is a superior choice for building an affordable and efficient UWB IPS, making it a compelling option for widespread adoption in budget-sensitive applications

Optimal active disturbance rejection control with applications in electric vehicles

This work proposes an optimal control strategy based on a modified active disturbance rejection control (ADRC) that considers disturbance weighting for a three-phase induction motor under rotor field-oriented control (FOC) to enhance energy efficiency. Induction motors (IMs) are widely used in electric vehicles (EVs) due to their cost-effectiveness and technological maturity. However, improving energy efficiency remains a key challenge, as it directly impacts vehicle range. The proposed approach employs ADRC, where part of the disturbance rejection task is handled offline by a hybrid optimization algorithm combining particle swarm optimization (PSO), tabu search (TS), and simulated annealing (SA) to tune a state-feedback controller. The controller parameters are optimized using a composite cost function that balances energy consumption and performance. Simulation and experimental results indicate that disturbance weighting has a significant impact on both problem complexity and performance. Optimal weighting improves the overall system response compared to conventional disturbance rejection methods. Energy and performance analyses show that disturbance weighting enhances energy usage compared to the traditional ADRC method, suggesting a novel efficiency control strategy for electric machines

Integrating artificial intelligence into accounting systems: a qualitative study on user experiences and challenges

This research explores the integration of artificial intelligence (AI) in accounting systems, focusing on user experiences and challenges faced by accountants and financial professionals. Using qualitative methods, in-depth interviews with diverse accounting professionals reveal key themes: optimism mixed with skepticism about AI’s potential, concerns over algorithm transparency, and trust issues due to the “black box” nature of AI systems. Participants highlight inadequate training programs, which hinder effective AI use and fuel resistance to adoption. The study also discusses the impact of AI on job roles, emphasizing a shift towards strategic thinking and advisory functions while routine tasks are automated. Implementation challenges include system compatibility, data integration issues, and significant resource investments, compounded by organizational resistance and lack of executive support. The findings stress the need for transparent AI algorithms, comprehensive training programs, and managed job role transitions to maximize AI benefits. This research provides insights into real-world user experiences, offering a roadmap for organizations to support effective AI integration in accounting, leading to improved performance, job satisfaction, and acceptance of AI technologies

A multiband sub-6 THz patch antenna with high gain for IoT and 6G communication

This comprehensive study introduces a meticulously designed and characterized terahertz (THz) multiple-input multiple-output (MIMO) antenna engineered to operate within the 0.4 THz to 1.6 THz frequency range. The antenna’s construction includes a copper patch and ground plane integrated into a polyimide substrate, ensuring exceptional durability and robust performance. Significantly, the antenna reveals four distinct resonance frequencies at 0.46 THz, 0.9 THz, 1.31 THz, and 1.44 THz each accompanied by bandwidths of 0.005 THz, 0.17 THz, and 0.34 THz, respectively. Moreover, the antenna delivers notable gains of 8.52 dB, 11.54 dB, and 13.25 dB at these frequencies, coupled with substantial efficiencies of 88.32%, 92.02%, and 89.89%, respectively. Additionally, the antenna showcases exceptional isolation of 26 dB, a low envelope correlation coefficient (ECC) of 0.003, and a diversity gain (DG) of 9.98. These remarkable attributes underscore the antenna’s aptness for high-performance THz applications, offering substantial advantages in terms of gain, efficiency, and isolation for next-generation wireless communication systems

Realization of Bernstein-Vazirani quantum algorithm in an interactive educational game

Quantum algorithms are celebrated for their computational superiority over classical counterparts, yet they pose significant learning challenges for non-physics audiences. Among these, the Bernstein-Vazirani (BV) algorithm stands out for its quantum speedup by efficiently identifying a secret binary string. However, the accessibility of such algorithms remains constrained by their inherent technical complexity. To address this educational gap, this paper introduces a gamified, web-based tool that innovatively reinterprets the BV algorithm’s complex mathematical settings through an into engaging scenario of identifying broken lamps. Players assume the role of an investigator, utilizing both classical and quantum solvers to identify faulty lamps with minimal queries. By transforming the BV algorithm into an intuitive gameplay experience, the tool helps reducing technical barriers, making quantum concepts much more comprehensible for educators and students than traditional methods that demand rigorous mathematical understanding. Developed using Qiskit, IBM’s Python package for quantum computation, and deployed via Flask, a popular Python microframework for building web applications, the game effectively simplifies complex quantum algorithms while demonstrating the practical applications of quantum speedup. This contribution advances quantum education by merging technical depth with interactive design, fostering a broader understanding of quantum principles and inspiring new innovations in gamified learning

Energy analysis and comparative study of n-wheel graphs in hierarchical wireless sensor network architectures

The energy analysis of the newly introduced n-wheel graph, employs diverse matrix representations such as the adjacency matrix, Laplacian matrix, and maximum degree matrix. This novel graph model resembles a hierarchical wireless sensor network (WSN), with a central hub serving as the communication center. The graph is organized into cycles, reflecting tiers of devices or sensors, with the hub managing wireless communication across these tiers. Through comparative analysis of energy variations, particularly focusing on ordinary energy, Laplacian energy, and maximum degree energy, offers a deeper understanding on the potential benefits of the n-wheel graph model, guiding future research and practical applications in the design of advanced hierarchical network structures

Adaptive diving depth control system for the drifting autonomous underwater vehicle

This article considers the system for controlling the diving depth of a drifting autonomous underwater vehicle (DAUV), which navigates underwater under the influence of sea currents in order to collect scientific information. The paper solves the problem of identifying non-stationary hydrodynamic parameters of the DAUV with the aim of adaptive adjustment of the DAUV control algorithm to increase the accuracy of bringing the DAUV to a given depth and minimizing the consumption of electricity consumed by power actuators. The solution to the problem is based on the use of parametric identification apparatus and adaptive control principles. The high quality of the DAUV diving depth control is achieved through the use of the method of adaptive adjustment of the parameters of the DAUV program model. The use of parametric identification of the hydrodynamic parameters of the DAUV made it possible to quickly adjust the corrective link in the control chain of the executing mechanism of the DAUV. The developed computer models and a set of semi-realistic tests made it possible to choose the most acceptable identification algorithm and configure the software implementation of the DAUV diving depth control law

Prototype of alternate wetting and drying rice cultivation using internet of things for precision agriculture

This study introduces a semi-automatic system for alternating wet and dry rice cultivation using internet of things (IoT) technology to enhance precision agriculture and address critical challenges in water resource management. The prototype consists of node and master devices powered by ESP32 microcontrollers integrated with sensors to monitor air temperature, humidity, and water levels. Communication between the devices is achieved through the low-latency, low-power encrypted secure protocol-network over wireless (ESP-NOW) protocol, enabling real-time monitoring and remote control of water pumps. Data collected by the system is displayed on ThinkSpeak servers and Nextion touch screens, aiding efficient irrigation and environmental management for farmers. Performance testing demonstrates that the system achieves reliable communication up to 115 meters with efficient energy consumption, operating for approximately two hours with a 3,000 mAh battery. By optimizing irrigation practices, the system reduces water waste while ensuring adequate crop hydration, promoting sustainable farming practices. This scalable IoT solution not only enhances productivity and resource efficiency but also contributes to broader efforts in agricultural sustainability by supporting precise environmental control and minimizing dependency on manual labor

EdgeShield: a robust and agile cybersecurity architecture for the internet of medical things

We present EdgeShield, a lightweight pipeline that streamlines internet of medical things (IoMT) traffic analysis by pairing aggressive dimensionality-reduction with federated model aggregation. It employs systematic preprocessing, advanced feature selection, and robust sampling to reduce computational overhead while enhancing performance. Through feature engineering techniques such as principal component analysis (PCA), targeted feature selection, and embedding methods, EdgeShield reduces dataset dimensionality by 96%, enabling near real-time detection and prevention of cyber attacks on resource-constrained edge devices. To harden the IoMT perimeter, EdgeShield trains ten lightweight edge models in just 54s and merges their parameters into a single global clas sifier with negligible extra delay. This method requires no additional training or predictions, thus accelerating deployment. Additionally, by using a compact dataset with five top-performing features and PCA with two components, EdgeShield consistently achieves accuracy levels exceeding 99.2% for individual edge models and the consolidated global model. With a built-in continuous improvement loop, EdgeShield dynamically adapts to emerging data patterns and operational conditions, driving substantial advancements in IoMT ecosystem management. This approach delivers both rapid machine learning model deployment and robust cyber attack detection, illustrating its potential to revolutionize IoMT security and elevate healthcare data integrity

The comparison of underwater source localization between Riemannian MFP and blind channel equalizer

Blind channel equalization (BCE) has been widely used in underwater communications due to its strong robustness against multipath propagation and its suitability for rapidly varying environments. However, there has been little research on the application of BCE for underwater source localization. On the other hand, conventional matched field processing (MFP), and particularly Riemannian MFP (RMFP), have been regarded as highly effective for this problem. In this paper, based on the statistical characterization of the signal-to-noise ratio (SNR) in underwater acoustic channels, we propose a method for estimating the channel transfer function, which is then used to construct a blind channel equalizer. A source localization approach using the proposed BCE is also presented. The localization performance using BCE is comparable to that of RMFP, achieving a depth error of 10 meters and a range error of 100 meters, while requiring significantly lower computational complexity