Rıdvan Yayla, Hakan Üçgün^*, Onur Ali Korkmaz

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4055-4087, 2025, DOI:10.32604/cmes.2025.072703 - 23 December 2025

Abstract Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems. Artificial intelligence enables real-time sensing, decision-making, and control on embedded platforms with improved efficiency. This study presents the design and implementation of an autonomous radio-controlled (RC) vehicle prototype capable of lane line detection, obstacle avoidance, and navigation through dynamic path planning. The system integrates image processing and ultrasonic sensing, utilizing Raspberry Pi for vision-based tasks and Arduino Nano for real-time control. Lane line detection is achieved through conventional image processing techniques, providing the basis for local path generation, while traffic sign classification employs a… More > Graphic Abstract

R. Rajeev^a,b,*, C. M. S. Negi^a

Chalcogenide Letters, Vol.22, No.5, pp. 469-480, 2025, DOI:10.15251/CL.2025.225.469

Abstract Cobalt-doped cadmium sulphide nanoparticles of semiconductors (CDs: Co NPs) were synthesised using various cobalt concentrations utilising a microwave-assisted approach. Debye-Scherer equation revealed the nanoparticles' size range to be between 2 and 4 nm. Diffraction from X-rays revealed a zinc mix structure. According to the structure in the optical bandgap energies indicates that, doping has systematically raised the bandgap energy as the doping concentration raises. The composition of the nanoparticles which was verified by EDAX, validated the effective integration of cobalt into the CdS structure. The detection of different functional and vibrational groups was performed at More >

A. X. Yang^a, L. F. Huang^b,*, Y. Y. Liu^c

Chalcogenide Letters, Vol.22, No.7, pp. 649-663, 2025, DOI:10.15251/CL.2025.227.649

Abstract An innovative flexible electronic device was developed by integrating functionalized carbon nanotubes, bismuth sulfide nanostructures, and a polyvinylidene fluoride matrix to create a highly water‐resistant strain detection platform. The fabricated film exhibited a remarkable static water contact angle of 141°, with only a 3–4° reduction after 48 hours of immersion, confirming its excellent hydrophobic performance. Mechanical testing revealed a tensile strength of 43.2 MPa and maintained over 96% of its original strength following 1000 bending cycles, thereby demonstrating outstanding durability under repetitive deformation. Electrical characterization showed an initial conductivity of 12.3 S/m and a baseline resistance near… More >

A. W. Jabbar^a, N. K. Abbas^b,*

Chalcogenide Letters, Vol.22, No.8, pp. 735-752, 2025, DOI:10.15251/CL.2025.228.735

Abstract Nanostructured CdS and CdS: Cu thin films were synthesized by pulsed laser deposition with a Nd: YAG laser of different energies, 0.1, 0.5, and 1 W. The number of pulses was 300, and the frequency was 20 kHz. The CdS nanoparticles were deposited on a glass substrate. The optical, structural, and morphological properties were investigated utilized X-ray diffraction, UV-Vis spectrophotometry, and field emission scanning electron microscopy. From 2.25 to 2.1 eV, the results demonstrate that the band gap energy reduces as laser energy increases. Morphological investigations reveal that the laser energy has a significant impact More >

X. Y. Chen^a,b, Y. H. Cai^a, Y. S. Chen^c, S. J. Huang^b, M. H. Li^d, Y. H. Li^e, C. H. Lin^c, H. Chen^a,*

Chalcogenide Letters, Vol.22, No.9, pp. 777-785, 2025, DOI:10.15251/CL.2025.229.777

Abstract This study focuses on the development of zinc oxide (ZnO)/zinc sulfide (ZnS) core-shell structures on flexible polyethylene terephthalate (PET) substrates for enhanced light sensing. PET offers high elasticity, optical transparency, and chemical resistance, making it ideal for wearable optoelectronics. By optimizing the vulcanization process, a uniform ZnS shell is formed on the exposed regions of ZnO nanorods (NRs), significantly enhancing ZnO-based sensor’s sensitivity to visible light, especially red light (peak wavelength at 630 nm). Structural and spectral analyses confirm the successful formation of the ZnO/ZnS heterostructure, improved charge separation, and broadened light response. To improve More >

F. T. Ibrahim^1,*, A. A. Qassim², S. M. A. Al-Dujayli¹

Chalcogenide Letters, Vol.22, No.11, pp. 929-937, 2025, DOI:10.15251/CL.2025.2211.929

Abstract This work, pulse laser deposition technique was employee to synthesize Cu2ZnSnS4 (CZTS) thin films with different lasing energy (500, 600, 700, 800, 900 mJ). Through using different characterization technique to study structural, optical and gas sensing properties. the use of X-ray diffraction, the samples have polycrystalline with cubic structure. The EDX examination showed that the sample contains a suitable amount of Zn, Sn, Cu, and S atoms to form CZTS. UV-VIS measurement indicates that the synthesis of thin films employing a lower laser energy result in a drop in deposit sample thickness, which in turn More >

Yu Tao¹, Tian Yin², Yang Jie^1,*

Journal on Artificial Intelligence, Vol.7, pp. 549-570, 2025, DOI:10.32604/jai.2025.072727 - 01 December 2025

Abstract To address the issue of insufficient accuracy in attitude estimation using Inertial Measurement Units (IMU), this paper proposes a multi-sensor fusion attitude estimation method based on an improved Error-State Kalman Filter (ESKF). Several adaptive mechanisms are introduced within the standard ESKF framework: first, the process noise covariance is dynamically adjusted based on gyroscope angular velocity to enhance the algorithm’s adaptability under both static and dynamic conditions; second, the Sage-Husa algorithm is employed to estimate the measurement noise covariance of the accelerometer and magnetometer in real-time, mitigating disturbances caused by external accelerations and magnetic fields. Additionally,… More >

Hongrong Wang^1,2, Xinjian Li^3,4, Xingjing She¹, Wenjian Ma^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2039-2055, 2025, DOI:10.32604/cmes.2025.072572 - 26 November 2025

Abstract In modern complex systems, real-time regression prediction plays a vital role in performance evaluation and risk warning. Nevertheless, existing methods still face challenges in maintaining stability and predictive accuracy under complex conditions. To address these limitations, this study proposes an online prediction approach that integrates event tracking sensitivity analysis with machine learning. Specifically, a real-time event tracking sensitivity analysis method is employed to capture and quantify the impact of key events on system outputs. On this basis, a mutual-information–based self-extraction mechanism is introduced to construct prior weights, which are then incorporated into a LightGBM prediction More >

Farhad Mortezapour Shiri^*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1389-1485, 2025, DOI:10.32604/cmes.2025.071858 - 26 November 2025

Abstract Human Activity Recognition (HAR) represents a rapidly advancing research domain, propelled by continuous developments in sensor technologies and the Internet of Things (IoT). Deep learning has become the dominant paradigm in sensor-based HAR systems, offering significant advantages over traditional machine learning methods by eliminating manual feature extraction, enhancing recognition accuracy for complex activities, and enabling the exploitation of unlabeled data through generative models. This paper provides a comprehensive review of recent advancements and emerging trends in deep learning models developed for sensor-based human activity recognition (HAR) systems. We begin with an overview of fundamental HAR… More > Graphic Abstract

Mukesh Kumar Verma^1,2,*, Manohar Yadav¹

Revue Internationale de Géomatique, Vol.34, pp. 855-879, 2025, DOI:10.32604/rig.2025.069914 - 17 November 2025

Abstract Precision Agriculture (PA) is revolutionizing modern farming by leveraging remote sensing (RS) technologies for continuous, non-destructive crop monitoring. This review comprehensively explores RS systems categorized by platform—terrestrial, airborne, and space-borne—and evaluates the role of multi-sensor fusion in addressing the spatial and temporal complexity of agricultural environments. Emphasis is placed on data from LiDAR, GNSS, cameras, and radar, alongside derived metrics such as plant height, projected leaf area, and biomass. The study also highlights the significance of data processing methods, particularly machine learning (ML) and deep learning (DL), in extracting actionable insights from large datasets. By More >