Search Results (100,912)

Hyperspectral image (HSI) reconstruction is a critical and indispensable step in spectral compressive imaging (CASSI) systems and directly affects our ability to capture high-quality images in dynamic environments. Recent research has increasingly focused on deep unfolding frameworks for HSI reconstruction, showing notable progress. However, these approaches have to break the optimization task into two sub-problems, solving them iteratively over multiple stages, which leads to large models and high computational overheads. This study presents a simple yet effective method that passes the degradation information (sensing mask) through a deep learning network to disentangle the degradation and the latent target’s representations. Specifically, we design a lightweight MLP block to capture non-local similarities and long-range dependencies across both spatial and spectral domains, and investigate an attention-based mask modelling module to achieve the spatial–spectral-adaptive degradation representationthat is fed to the MLP-based network. To enhance the information flow between MLP blocks, we introduce a multi-level fusion module and apply reconstruction heads to different MLP features for deeper supervision. Additionally, we combine the projection loss from compressive measurements with reconstruction loss to create a dual-domain loss, ensuring consistent optical detection during HS reconstruction. Experiments on benchmark HS datasets show that our method outperforms state-of-the-art approaches in terms of both reconstruction accuracy and efficiency, reducing computational and memory costs. Full article

There are many papers presenting quantum computing models. The definitions parallel the classical definitions of finite state automata, push-down automata, context-free grammars, etc. Classical computing model definitions define languages precisely. We can state that a string belongs to a language or does not belong to it with no room for error. Quantum definitions do not possess this certainty. Sacrificing the certainty and adopting a quantum definition of a computing model does not appear to provide any concrete power to the model. Therefore, the path of this paper is to begin from the classical definition and end in a quantum circuit. In this paper, we start from a deterministic push-down automaton (DPDA). We present circuits for state transition and stack operations. The circuits presented can be viewed as independent algorithms. As an example, the approach used to construct the circuit for state transition can be utilized to build the circuit for a function presented as a Boolean matrix. Full article

The short cultivation cycle and high essential oil content of basil plants render them a valuable raw material source for diverse industries. However, large-scale production is hindered by the lack of specific protocols to assess seed vigor; thus, a consistent supply of high-quality seeds that meet consumer demands cannot be ensured. This study investigated the effectiveness of an automated system for seedling analysis as a tool for evaluating basil seed vigor and compared it to traditional tests. For this purpose, seeds from eight commercial lots were evaluated in two separate trials spaced six months apart using the following tests: germination, first germination count, saturated salt accelerated aging, primary root emergence, mean germination time, seedling emergence, seedling emergence speed index, and computerized seedling image analysis. The parameters provided by the system allowed us to clearly and objectively classify the basil seed lots based on vigor, and the results were strongly and significantly correlated with the findings of traditional vigor tests, particularly between the vigor index and seedling length. Digital analysis of four-day-old seedlings proved to be a fast and efficient technique for evaluating basil seed vigor and has the potential for use in automating the data collection and analysis process. Full article

This paper presents a novel driving torque control strategy for the front and rear independently driven electric vehicle (FRIDEV) to reduce energy consumption and enhance vehicle stability. The strategy is built on a comprehensive vehicle model that integrates vertical load transfer, tire slip dynamics, and an electric system model that accounts for losses in induction motors (IMs), permanent magnet synchronous motors (PMSMs), inverters, and batteries. The torque control problem is framed with a nonlinear model predictive control (MPC) method, utilizing state-space equations as representations of vehicle dynamics. The optimization targets adjust in real-time based on road traction conditions, with the slip rate of front and rear wheels determining the torque control strategy. Active slip control is applied when slip rates exceed critical thresholds, while under normal conditions, torque distribution is optimized to minimize energy losses. To enable online real-time implementation, an improved sparrow search algorithm (SSA) is designed. Simulations in MATLAB/Simulink confirm that the proposed online strategy reduces energy consumption by 2.3% under the China light-duty vehicle test cycle-passenger cars (CLTC-P) compared to a rule-based strategy. Under low-adhesion conditions, the proposed online strategy effectively manages slip ratios, ensuring stability and performance. Improved SSA also enhances computational efficiency by approximately 44%–52%, making the online strategy viable for real-time applications. Full article

The rapid evolution of deep learning has led to significant achievements in computer vision, primarily driven by complex convolutional neural networks (CNNs). However, the increasing depth and parameter count of these networks often result in overfitting and elevated computational demands. Knowledge distillation (KD) has emerged as a promising technique to address these issues by transferring knowledge from a large, well-trained teacher model to a more compact student model. This paper introduces a novel knowledge distillation method that simplifies the distillation process and narrows the performance gap between teacher and student models without relying on intricate knowledge representations. Our approach leverages a unique teacher network architecture designed to enhance the efficiency and effectiveness of knowledge transfer. Additionally, we introduce a streamlined teacher network architecture that transfers knowledge effectively through a simplified distillation process, enabling the student model to achieve high accuracy with reduced computational demands. Comprehensive experiments conducted on the CIFAR-10 dataset demonstrate that our proposed model achieves superior performance compared to traditional KD methods and established architectures such as ResNet and VGG networks. The proposed method not only maintains high accuracy but also significantly reduces training and validation losses. Key findings highlight the optimal hyperparameter settings (temperature T = 15.0 and smoothing factor α = 0.7), which yield the highest validation accuracy and lowest loss values. This research contributes to the theoretical and practical advancements in knowledge distillation, providing a robust framework for future applications and research in neural network compression and optimization. The simplicity and efficiency of our approach pave the way for more accessible and scalable solutions in deep learning model deployment. Full article

The current work investigates a recently introduced unidirectional wave model, applicable in science and engineering to understand complex systems and phenomena. This investigation has two primary aims. First, it employs a novel modified Sardar sub-equation method, not yet explored in the literature, to derive new solutions for the governing model. Second, it analyzes the complex dynamical structure of the governing model using bifurcation, chaos, and sensitivity analyses. To provide a more accurate depiction of the underlying dynamics, they use quantum mechanics to explain the intricate behavior of the system. To illustrate the physical behavior of the obtained solutions, 2D and 3D plots, along with a phase plane analysis, are presented using appropriate parameter values. These results validate the effectiveness of the employed method, providing thorough and consistent solutions with significant computational efficiency. The investigated soliton solutions will be valuable in understanding complex physical structures in various scientific fields, including ferromagnetic dynamics, nonlinear optics, soliton wave theory, and fiber optics. This approach proves highly effective in handling the complexities inherent in engineering and mathematical problems, especially those involving fractional-order systems. Full article

The reactor pressure vessel (RPV) in onshore nuclear power plants is typically analysed for fatigue life by considering the temperature, internal pressure, and seismic effects using a simplified time-domain fatigue analysis. In contrast, the frequency-domain fatigue analysis method is commonly employed to assess the fatigue life of ship structures. The RPV of a floating nuclear power plant (FNPP) is subjected to a combination of temperature, internal pressure, and wave loads in the marine environment. Consequently, it is essential to effectively integrate the frequency-domain fatigue analysis method used for hull structures with the time-domain fatigue analysis method for RPVs in FNPPs or, alternatively, to develop a suitable method that effectively accounts for the temperature, internal pressure, and wave loads. In this study, a quasi-time-domain method is proposed for the fatigue analysis of RPVs in FNPPs. In this method, secondary components of marine environmental loads are filtered out using principal component analysis. Subsequently, the stress spectrum induced by waves is transformed into a stress time history. Fatigue stress under the combined influence of temperature, internal pressure, and wave loads is then obtained through a stress component superposition method. Finally, the accuracy of the quasi-time-domain method was validated through three numerical examples. The results indicate that the calculated values obtained by the quasi-time-domain method are slightly higher than those obtained by the traditional time-domain method, with a maximum deviation of no more than 24%. Additionally, the computation time of the quasi-time-domain method is reduced by 98.67% compared to the traditional time-domain method. Full article

In the domain of Battery Management System (BMS) research, the precise acquisition and estimation of internal temperature distribution within lithium-ion cells is a significant challenge. The commercial viability precludes the use of internal temperature sensors, and existing methodologies for online estimation of internal temperatures under various electrical loads are constrained by computational limitations and model accuracy. This study presents a layered electro-thermal equivalent circuit model (LETECM), developed by integrating a layered second-order fractional equivalent circuit model with a layered thermal equivalent circuit model. A lithium-ion battery divided into three layers was employed to illustrate the development of this LETECM. The model’s precision was validated against a 3D Newman Finite Element Model (3DNFEM), constructed using actual battery parameters. Given that the thermal gradient inside the battery is usually more pronounced under high load conditions, a 10C direct current discharge for 60 s followed by a rest period of 240 s was adopted as the test condition in the simulation. The results indicate that at the end of the DC discharge, the temperature difference between the inner layer and the surface of the battery was the largest and the maximum temperature difference predicted by the LETECM was 3.58 °C, while the 3DNFEM exhibited a temperature difference of 3.74 °C. The trends in each layer temperature and battery surface temperature obtained by the two models are highly consistent. The proposed model offers computational efficiency and maintains notable accuracy, suggesting its potential integration into BMS for real-time online applications. This advancement could provide critical internal temperature data for refining battery charging and discharging performance assessments and lifespan predictions, thereby optimizing battery management strategies. Full article

The remarkable increase in published medical imaging datasets for chest X-rays has significantly improved the performance of deep learning techniques to classify lung diseases efficiently. However, large datasets require special arrangements to make them suitable, accessible, and practically usable in remote clinics and emergency rooms. Additionally, it increases the computational time and image-processing complexity. This study investigates the efficiency of converting the 2D chest X-ray into one-dimensional texture representation data using descriptive statistics and local binary patterns, enabling the use of feed-forward neural networks to efficiently classify lung diseases within a short time and with cost effectiveness. This method bridges diagnostic gaps in healthcare services and improves patient outcomes in remote hospitals and emergency rooms. It also could reinforce the crucial role of technology in advancing healthcare. Utilizing the Guangzhou and PA datasets, our one-dimensional texture representation achieved 99% accuracy with a training time of 10.85 s and 0.19 s for testing. In the PA dataset, it achieved 96% accuracy with a training time of 38.14 s and a testing time of 0.17 s, outperforming EfficientNet, EfficientNet-V2-Small, and MobileNet-V3-Small. Therefore, this study suggests that the dimensional texture representation is fast and effective for lung disease classification. Full article

With a view to improve measurements, this paper presents a statistical approach for characterizing the behaviour of roughness parameters based on measurements performed on ground surface topographies (grit #080/#120). A S neox^TM (Sensofar^®, Terrassa, Spain), equipped with three optical instrument modes (Focus Variation (FV), Coherence Scanning Interferometry (CSI), and Confocal Microscopy (CM)), is used according to a specific measurement plan, called Morphomeca Monitoring, including topography representativeness and several time-based measurements. Previously applied to the Sa parameter, the statistical approach based here solely on the Quality Index (QI) has now been extended to a multi-parameter approach. Firstly, the study focuses on detecting and explaining parameter disturbances in raw data by identifying and quantifying outliers of the parameter’s values, as a new first indicator. This allows us to draw parallels between these outliers and the surface topography, providing reflection tracks. Secondly, the statistical approach is applied to highlight disturbed parameters concerning the instrument mode used and the concerned grit level with two other indicators computed from QI, named homogeneity and number of modes. The applied method shows that a cleaning of the data containing the parameters values is necessary to remove outlier values, and a set of roughness parameters could be determined according to the assessment of the indicators. The final aim is to provide a set of parameters which best describe the measurement conditions based on monitoring data, statistical indexes, and surface topographies. It is shown that the parameters Sal, Sz and Sci are the most reliable roughness parameters, unlike Sdq and S5p, which appear as the most unstable parameters. More globally, the volume roughness parameters appear as the most stable, differing from the form parameters. This investigated point of view offers thus a complementary framework for improving measurement processes. In addition, this method aims to provide a global and more generalizable alternative than traditional methods of uncertainty calculation, based on a thorough analysis of multi-parameter and statistical indexes. Full article

Privacy and security is very critical for mobile users and in-depth research into the area highlights a need for more scientific literature on the perception and challenges of end users to better align the design of privacy and security controls with user expectations. In this paper, we have explored the perceptions of the usability of privacy and security settings in mobile applications from mobile users in Saudi Arabia. The findings highlight that gender, age, and education level of users do not have any positive correlation with the privacy and security usability perceptions of mobile users. On the other hand, user concerns about privacy and security and the trustworthiness levels of end users regarding mobile phone privacy and security have a positive impact on end users’ perception of privacy and security usability. Furthermore, privacy usability perception has a positive impact on users’ feelings about their control over the privacy and security of their mobile phones. Based on the results of this empirical study, we propose that user-centric design of privacy and security controls, transparent data handling policies, periodic data management status preview and validation by end users, user education guidelines, strict governmental policies, and automated security settings recommendations can enhance the usability of the privacy and security of mobile phone applications. Our study did not take the geographical location of respondents into account, nor were the respondents balanced based on age and gender. In future work, these weaknesses need to be taken into account, and more qualitative studies can help to extract design guidelines for usable and secure mobile applications. Full article

Holographic displays are an upcoming technology for AR and VR applications, with the ability to show 3D content with accurate depth cues, including accommodation and motion parallax. Recent research reveals that only a fraction of holographic pixels are needed to display images with high fidelity, improving energy efficiency in future holographic displays. However, the existing iterative method for computing sparse amplitude and phase layouts does not run in real time; instead, it takes hundreds of milliseconds to render an image into a sparse hologram. In this paper, we present a non-iterative amplitude and phase computation for sparse Fourier holograms that uses Perlin noise in the image–plane phase. We conduct simulated and optical experiments. Compared to the Gaussian-weighted Gerchberg–Saxton method, our method achieves a run time improvement of over 600 times while producing a nearly equal PSNR and SSIM quality. The real-time performance of our method enables the presentation of dynamic content crucial to AR and VR applications, such as video streaming and interactive visualization, on holographic displays. Full article

Numerical analysis of a U-type solid oxide fuel cell stack was performed using computational fluid dynamics to investigate the effects of stack capacities and fuel/air utilization rates on the internal flow uniformity. The results indicated that increasing the fuel/air utilization rate improved the gas flow uniformity within the stack for the same stack capacity. The uniformity in the anode fluid domain was better than that in the cathode fluid domain. Furthermore, the flow uniformity within the stack was associated with the percentage of pressure drop in the core region of the stack. The larger the percentage of pressure drop in the core region, the more uniform the flow inside the stack. Additionally, under a fuel utilization rate of 75%, the computational results exhibited excessively high fuel utilization rates in the top cell of a 3 kWe stack, indicating a potential risk of fuel depletion during actual stack operation. Full article

Micro/nanorobotics is becoming part of the future of medicine. One of the most efficient approaches to the construction of small medical robots is to base them on unicellular organisms. This approach inherently allows for obtaining complex capabilities, such as motility or environmental resistance. Single-celled organisms usually live in groups and are known to interact in many ways (matter, energy, and information), paving the way for potentially beneficial emergent effects. One such naturally expected effect is an increase in the sustainability of a population as a result of a more even redistribution of energy within the population. Our in silico experiments show that under harsh conditions, such as resource scarcity and a rapidly changing environment, altruistic energy exchange (supplying energy to weaker agents) can indeed markedly increase the sustainability of model bacteriabot groups, potentially increasing the efficiency of treatment. Although our work is limited exclusively to the development and use of a phenomenological computer model, we consider our results to be an important argument in favor of practical efforts aimed at implementing altruistic energy exchange strategies in real swarms of single-cell medical robots. Full article

Precise modeling and state of charge (SoC) estimation of a lithium-ion battery (LIB) are crucial for the safety and longevity of battery systems in electric vehicles. Traditional methods often fail to adapt to the dynamic, nonlinear, and time-varying behavior of LIBs under different operating conditions. In this paper, an advanced joint estimation approach of the model parameters and SoC is proposed utilizing an enhanced Sigma Point Kalman Filter (SPKF). Based on the second-order equivalent circuit model (2RC-ECM), the proposed approach was compared to the two most widely used methods for simultaneously estimating the model parameters and SoC, including a hybrid recursive least square (RLS)-extended Kalman filter (EKF) method, and simple joint SPKF. The proposed adaptive joint SPKF (ASPKF) method addresses the limitations of both the RLS+EKF and simple joint SPKF, especially under dynamic operating conditions. By dynamically adjusting to changes in the battery’s characteristics, the method significantly enhances model accuracy and performance. The results demonstrate the robustness, computational efficiency, and reliability of the proposed ASPKF approach compared to traditional methods, making it an ideal solution for battery management systems (BMS) in modern EVs. Full article