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2025
Volume: 23 Issue 2
7 Article(s)
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Multi-UAV path planning for multiple emergency payloads delivery in natural disaster scenarios
Zarina Kutpanova, Mustafa Kadhim, Xu Zheng, and Nurkhat Zhakiyev
Unmanned aerial vehicles (UAVs) are widely used in situations with uncertain and risky areas lacking network coverage. In natural disasters, timely delivery of first aid supplies is crucial. Current UAVs face risks such as crashing into birds or unexpected structures. Airdrop systems with parachutes risk dispersing payUnmanned aerial vehicles (UAVs) are widely used in situations with uncertain and risky areas lacking network coverage. In natural disasters, timely delivery of first aid supplies is crucial. Current UAVs face risks such as crashing into birds or unexpected structures. Airdrop systems with parachutes risk dispersing payloads away from target locations. The objective here is to use multiple UAVs to distribute payloads cooperatively to assigned locations. The civil defense department must balance coverage, accurate landing, and flight safety while considering battery power and capability. Deep Q-network (DQN) models are commonly used in multi-UAV path planning to effectively represent the surroundings and action spaces. Earlier strategies focused on advanced DQNs for UAV path planning in different configurations, but rarely addressed non-cooperative scenarios and disaster environments. This paper introduces a new DQN framework to tackle challenges in disaster environments. It considers unforeseen structures and birds that could cause UAV crashes and assumes urgent landing zones and winch-based airdrop systems for precise delivery and return. A new DQN model is developed, which incorporates the battery life, safe flying distance between UAVs, and remaining delivery points to encode surrounding hazards into the state space and Q-networks. Additionally, a unique reward system is created to improve UAV action sequences for better delivery coverage and safe landings. The experimental results demonstrate that multi-UAV first aid delivery in disaster environments can achieve advanced performance..
Journal of Electronic Science and Technology
- Publication Date: Jun. 25, 2025
- Vol. 23, Issue 2, 100303 (2025)
Layer-2 transferable belief model: Manage uncertainty on random permutation sets
Qian-Li Zhou, and Yong Deng
In this paper, the transferable belief model established on power sets is extended to the permutation event space (PES) and is referred to as the layer-2 transferable belief model. Our goal is to provide a comprehensive approach for handling and modeling uncertainty, capable of representing both quantitative and qualitIn this paper, the transferable belief model established on power sets is extended to the permutation event space (PES) and is referred to as the layer-2 transferable belief model. Our goal is to provide a comprehensive approach for handling and modeling uncertainty, capable of representing both quantitative and qualitative information. First, the motivation for proposing the layer-2 transferable belief model and its information processing principles are explored from the perspective of weak propensity. Then, based on these principles, the corresponding information processing methods for the credal and pignistic levels are developed. Finally, the advantages of this model are validated through a classifier that leverages attribute fusion to enhance performance and decision-making accuracy..
Journal of Electronic Science and Technology
- Publication Date: Jun. 25, 2025
- Vol. 23, Issue 2, 100304 (2025)
2D PdSe2: Pioneering innovations in polarized photodetection
Waqas Ahmad, Amine El Moutaouakil, Wen Lei, and Zhi-Ming Wang
Palladium diselenide (PdSe2), a novel two-dimensional (2D) material with a unique pentagonal crystal structure including anisotropic properties, has emerged as a highly promising candidate for developing the next generation photoelectronic devices. In this review, firstly, we have shed light on key figures of merit forPalladium diselenide (PdSe2), a novel two-dimensional (2D) material with a unique pentagonal crystal structure including anisotropic properties, has emerged as a highly promising candidate for developing the next generation photoelectronic devices. In this review, firstly, we have shed light on key figures of merit for polarization detection. After that, this review mainly highlights the structural and electronic properties of PdSe2 focusing on its strong polarization sensitivity, tunable bandgap, and excellent environmental stability, making it ideal for developing the photoelectronic devices such as broadband photodetectors and their further applications in polarization detection-based imaging systems. We also discuss challenges in scalable synthesis, material stability, and integration with other low-dimensional materials, offering future research directions to optimize PdSe2 for commercial applications. Owing to the outstanding optoelectronic properties of PdSe2, it stands at the forefront of optoelectronic materials, poised to enable new innovations in polarization photodetection..
Journal of Electronic Science and Technology
- Publication Date: Jun. 25, 2025
- Vol. 23, Issue 2, 100305 (2025)
Comprehensive performance analysis of CMOS and CNTFET based 8T SRAM cell
Mahamudul Hassan Fuad, Md Faysal Nayan, Sheikh Shahrier Noor, Rahbaar Yeassin, and Russel Reza Mahmud
In recent years, carbon nanotube field effect transistor (CNTFET) has become an attractive alternative to silicon for designing high-performance, highly stable, and low-power static random access memory (SRAM). SRAM serves as a cache memory in computers and many portable devices. Carbon nanotubes (CNTs), because of theIn recent years, carbon nanotube field effect transistor (CNTFET) has become an attractive alternative to silicon for designing high-performance, highly stable, and low-power static random access memory (SRAM). SRAM serves as a cache memory in computers and many portable devices. Carbon nanotubes (CNTs), because of their exceptional transport capabilities, outstanding thermal conductivities, and impressive current handling capacities, have demonstrated great potential as an alternative device to the standard complementary metal-oxide-semiconductor (CMOS). The SRAM cell design using CNTFET is being compared to SRAM cell designs built using traditional CMOS technology. This paper presents the comprehensive analysis of CMOS & CNTFET based 8T SRAM cell design. Because of the nanoscale size, ballistic transport, and higher carrier mobility of the semiconducting nanotubes in CNTFET, it is integrated into the 8T SRAM cell. The approach incorporates several nonidealities, including the presence of quantum confinement consequences in the peripheral and transverse prescriptions, acoustic and transparent photon diffraction in the region surrounding the channel, as well as the screening effects by parallel CNTs in CNTFETs with multiple CNTs. By incorporating Stanford University CNTFET model in CADENCE (virtuoso) 32 nm simulation, we have found that CNTFET SRAM cell is 4 times faster in terms of write/read delay and the write/read power delay product (PDP) value is almost 5 times lower compared to CMOS based SRAM. We have also analyzed the effect of temperature & different tube positions of CNTs on the performance evaluation of the 8T SRAM cell..
Journal of Electronic Science and Technology
- Publication Date: Jun. 25, 2025
- Vol. 23, Issue 2, 100306 (2025)
MWIR narrowband filter based on guided-mode resonance subwavelength structure
He-Zhuang Liu, and Jiang Wu
This work proposes a novel design for a narrowband filter operating in the mid-wave infrared (MWIR) spectrum. The filter is designed with a single layer of slab waveguide decorated with a layer of gold grating arrays. This design demonstrates superior narrowband transmission properties within the MWIR range, which can This work proposes a novel design for a narrowband filter operating in the mid-wave infrared (MWIR) spectrum. The filter is designed with a single layer of slab waveguide decorated with a layer of gold grating arrays. This design demonstrates superior narrowband transmission properties within the MWIR range, which can be explained in the framework of guided-mode resonance (GMR). Since MWIR spectral data is crucial for identifying the chemical fingerprint of man-made objects and natural materials, the GMR filters hold great potential in integration with commercial MWIR photodetectors and focal plane arrays (FPAs) and addressing the market’s demand for ultra-compact spectral detection solutions. Theoretical studies have investigated the influential parameters in the GMR filter design and provided the methods towards optimal filtering performance. The center wavelength of these transmission filters exhibits significant tunability, spanning from 3 μm to 5 μm across the MWIR spectrum, while the full width at half maximum (FWHM) exhibits remarkable variability, ranging from 5.7 nm to 101.0 nm, enabling the attainment of desired filter performance contingent upon judicious waveguide material selection and optimized structural design. This work forges a path toward integrating multifunctional capabilities into ultra-compact MWIR sensors..
Journal of Electronic Science and Technology
- Publication Date: Jun. 25, 2025
- Vol. 23, Issue 2, 100313 (2025)
Robust visual tracking using temporal regularization correlation filter with high-confidence strategy
Xiao-Gang Dong, Ke-Xuan Li, Hong-Xia Mao, Chen Hu, and Tian Pu
Target tracking is an essential task in contemporary computer vision applications. However, its effectiveness is susceptible to model drift, due to the different appearances of targets, which often compromises tracking robustness and precision. In this paper, a universally applicable method based on correlation filtersTarget tracking is an essential task in contemporary computer vision applications. However, its effectiveness is susceptible to model drift, due to the different appearances of targets, which often compromises tracking robustness and precision. In this paper, a universally applicable method based on correlation filters is introduced to mitigate model drift in complex scenarios. It employs temporal-confidence samples as a priori to guide the model update process and ensure its precision and consistency over a long period. An improved update mechanism based on the peak side-lobe to peak correlation energy (PSPCE) criterion is proposed, which selects high-confidence samples along the temporal dimension to update temporal-confidence samples. Extensive experiments on various benchmarks demonstrate that the proposed method achieves a competitive performance compared with the state-of-the-art methods. Especially when the target appearance changes significantly, our method is more robust and can achieve a balance between precision and speed. Specifically, on the object tracking benchmark (OTB-100) dataset, compared to the baseline, the tracking precision of our model improves by 8.8%, 8.8%, 5.1%, 5.6%, and 6.9% for background clutter, deformation, occlusion, rotation, and illumination variation, respectively. The results indicate that this proposed method can significantly enhance the robustness and precision of target tracking in dynamic and challenging environments, offering a reliable solution for applications such as real-time monitoring, autonomous driving, and precision guidance..
Journal of Electronic Science and Technology
- Publication Date: Jun. 25, 2025
- Vol. 23, Issue 2, 100314 (2025)
Efficient feature selection based on Gower distance for breast cancer diagnosis
Salwa Shakir Baawi, Mustafa Noaman Kadhim, and Dhiah Al-Shammary
This study presents an efficient feature selection method based on the Gower distance to enhance the accuracy and efficiency of standard classifiers on high-dimensional medical datasets. High-dimensional data poses significant challenges for traditional classifiers due to feature redundancy or being irrelevant. The proThis study presents an efficient feature selection method based on the Gower distance to enhance the accuracy and efficiency of standard classifiers on high-dimensional medical datasets. High-dimensional data poses significant challenges for traditional classifiers due to feature redundancy or being irrelevant. The proposed method addresses these challenges by partitioning the dataset into blocks, calculating the Gower distance within each block, and selecting features based on their average similarity. Technically, the Gower distance normalizes the absolute difference between numerical features, ensuring that each feature contributes equally to the distance calculation. This normalization prevents features with larger scales from overshadowing those with smaller scales. This process facilitates the identification of features that exhibit high harmony and are the most relevant for classification. The proposed feature selection strategy significantly reduces dimensionality, retains the most relevant features, and improves model performance. Experimental results show that the accuracy for the classifiers including k-nearest neighbors (KNN), naive Bayes (NB), decision tree (DT), random forest (RF), support vector machine (SVM), and logistic regression (LR) was increased by 4.38%–7.02%. Besides, the reduction in the feature set size contributes to a considerable decrease in computational complexity and thus faster diagnosis speed. The execution time was averagely reduced by 77.82% for all samples and 76.45% for one sample. These results demonstrate that the proposed feature selection method shows enhanced performance on both prediction accuracy and diagnostic speed, making it a promising tool for real-time clinical decision-making and improving patient care outcomes..
Journal of Electronic Science and Technology
- Publication Date: Jun. 25, 2025
- Vol. 23, Issue 2, 100315 (2025)
