Search Results (136)

Cloud detection is an indispensable step in satellite remote sensing of cloud properties and objects under the influence of cloud occlusion. Nevertheless, interfering targets such as snow and haze pollution are easily misjudged as clouds for most of the current algorithms. Hence, a robust cloud detection algorithm is urgently needed, especially for regions with high latitudes or severe air pollution. This paper demonstrated that the passive satellite detector Advanced Geosynchronous Radiation Imager (AGRI) onboard the FY-4A satellite has a great possibility to misjudge the dense aerosols in haze pollution as clouds during the daytime, and constructed an algorithm based on the spectral information of the AGRI’s 14 bands with a concise and high-speed calculation. This study adjusted the previously proposed cloud mask rectification algorithm of Moderate-Resolution Imaging Spectroradiometer (MODIS), rectified the MODIS cloud detection result, and used it as the accurate cloud mask data. The algorithm was constructed based on adjusted Fisher discrimination analysis (AFDA) and spectral spatial variability (SSV) methods over four different underlying surfaces (land, desert, snow, and water) and two seasons (summer and winter). This algorithm divides the identification into two steps to screen the confident cloud clusters and broken clouds, which are not easy to recognize, respectively. In the first step, channels with obvious differences in cloudy and cloud-free areas were selected, and AFDA was utilized to build a weighted sum formula across the normalized spectral data of the selected bands. This step transforms the traditional dynamic-threshold test on multiple bands into a simple test of the calculated summation value. In the second step, SSV was used to capture the broken clouds by calculating the standard deviation (STD) of spectra in every 3 × 3-pixel window to quantify the spectral homogeneity within a small scale. To assess the algorithm’s spatial and temporal generalizability, two evaluations were conducted: one examining four key regions and another assessing three different moments on a certain day in East China. The results showed that the algorithm has an excellent accuracy across four different underlying surfaces, insusceptible to the main interferences such as haze and snow, and shows a strong detection capability for broken clouds. This algorithm enables widespread application to different regions and times of day, with a low calculation complexity, indicating that a new method satisfying the requirements of fast and robust cloud detection can be achieved. Full article

Grounded in the theory of metacognitive prediction error minimization, this study is the first to propose and empirically validate the mechanism of implicit metacognitive predictive processing by which bodily interaction influences the Aha! experience. Three experimental groups were designed to manipulate the level of temporal synchrony in bodily interaction: Immediate Mirror Group, Delayed Mirror Group, and No-Interaction Control Group. A three-stage experimental paradigm—Prediction, Execution, and Feedback—was constructed to decompose the traditional holistic insight task into three sequential components: solution time prediction (prediction phase), riddle solving (execution phase), and self-evaluation of Aha! experience (feedback phase). Behavioral results indicated that bodily interaction significantly influenced the intensity of the Aha! experience, likely mediated by metacognitive predictive processing. Significant or marginally significant differences emerged across key measures among the three groups. Furthermore, fNIRS results revealed that low-frequency amplitude during the “solution time prediction” task was associated with the Somato-Cognitive Action Network (SCAN), suggesting its involvement in the early predictive stage. Functional connectivity analysis also identified Channel 16 within the reward network as potentially critical to the Aha! experience, warranting further investigation. Additionally, the high similarity in functional connectivity patterns between the Mirror Game and the three insight tasks implies that shared neural mechanisms of metacognitive predictive processing are engaged during both bodily interaction and insight. Brain network analyses further indicated that the Reward Network (RN), Dorsal Attention Network (DAN), and Ventral Attention Network (VAN) are key neural substrates supporting this mechanism, while the SCAN network was not consistently involved during the insight formation stage. In sum, this study makes three key contributions: (1) it proposes a novel theoretical mechanism—implicit metacognitive predictive processing; (2) it establishes a quantifiable, three-stage paradigm for insight research; and (3) it outlines a dynamic neural pathway from bodily interaction to insight experience. Most importantly, the findings offer an integrative model that bridges embodied cognition, enactive cognition, and metacognitive predictive processing, providing a unified account of the Aha! experience. Full article

We consider a compression problem in a multi-source status-updating system through a representative two-source scenario. The status updates are generated by two independent sources following heterogeneous Poisson processes. These updates are then compressed into binary strings and sent to the receiver via a shared, error-free channel with a unit rate. We propose two compression schemes—a multi-quantizer compression scheme, where a dedicated quantizer–encoder pair is assigned to each source for compression, and a single-quantizer compression scheme, employing a unified quantizer–encoder pair shared across both sources. For each scheme, we formulate an optimization problem to jointly design quantizer–encoder pairs, with the objective of minimizing the sum of the average ages subject to a distortion constraint of symbols, respectively. The following three theoretical results are established: (1) The combination of two uniform quantizers with different parameters, along with their corresponding AoI-optimal encoders, provides an asymptotically optimal solution for the multi-quantizer compression scheme. (2) The combination of a piecewise uniform w-quantizer with an AoI-optimal encoder provides an asymptotically optimal solution for the single-quantizer compression scheme. (3) For both schemes, the optimal sum of the average ages is asymptotically linear with respect to the log distortion, with the same slope determined by the sources’ arrival rates. Full article

Accurate loss prediction since the preliminary design steps is crucial to improve the development process and the aerodynamic performance of turbines. Initial design phases typically employ reduced-order models in which the different loss-generating mechanisms are assessed through correlations. These correlations are often based on the hypothesis of loss linearity, which assumes that losses from different sources can be summed to obtain the total losses. However, this assumption could constitute an oversimplification, as losses occur concurrently and can interact with each other, potentially impacting overall performance, all the more in low aspect ratio turbomachinery. The aim of this paper is to investigate the role of interactions between different phenomena in the generation of loss. 3D RANS simulations are run on two simplified representations of a turbine blade channel, a curved duct and a linear cascade, and on a real turbine vane. Several inlet and wall boundary conditions are employed to examine loss-generating phenomena both separately and simultaneously. This approach enables the analysis of where and how interactions occur and quantifies their influence on the overall losses. Losses caused by boundary layer–vortex interactions are found to be highly sensitive to the relative positions of these two phenomena. It was observed that the loss linearity assumption may be acceptable in certain cases, but it is generally inadequate for off-design conditions and twisted annular configurations. Full article

During the last 20 years, there has been increasing concern about inefficient vector control efforts due to insecticide resistance. A common mechanism causing insecticide resistance is mutational changes in the voltage-gated sodium channel, deemed knockdown resistance (kdr), resulting from continued pyrethroid application. Although closely related, there have been documented kdr differences and frequencies between Aedes aegypti and Aedes albopictus. Individual Ae. aegypti and Ae. albopictus from five counties in Texas, USA were tested using four single nucleotide polymorphisms genotyping assays to assess the kdr (F1534C, V1016I, V410L, and S989P) differences between the two species. Each mutation was analyzed independently by calculating frequencies and analyzing the difference using a Wilcox Rank Sum test. Significant differences were observed between Ae. aegypti and Ae. albopictus when comparing F1534C and V410L (p-value < 0.0001). Knockdown resistant mutation V1016I was not different between the two species. Individuals from both species had di-locus mutations, and individuals from Ae. aegypti had tri-locus mutations detected in combinations that have been reported to influence insecticide resistance. Given our findings, one can speculate that populations of both species are resistant to pyrethroids, thus likely limiting the success of control methods. Full article

A novel approach to the quantum version of $\kappa$-entropy that incorporates it into the conceptual, mathematical and operational framework of quantum computation is put forward. Various alternative expressions stemming from its definition emphasizing computational and algorithmic aspects are worked out: First, for the case of canonical Gibbs states, it is shown that $\kappa$-entropy is cast in the form of an expectation value for an observable that is determined. Also, an operational method named “the two-temperatures protocol” is introduced that provides a way to obtain the $\kappa$-entropy in terms of the partition functions of two auxiliary Gibbs states with temperatures $\kappa$-shifted above, the hot-system, and $\kappa$-shifted below, the cold-system, with respect to the original system temperature. That protocol provides physical procedures for evaluating entropy for any $\kappa$. Second, two novel additional ways of expressing the $\kappa$-entropy are further introduced. One determined by a non-negativity definite quantum channel, with Kraus-like operator sum representation and its extension to a unitary dilation via a qubit ancilla. Another given as a simulation of the $\kappa$-entropy via the quantum circuit of a generalized version of the Hadamard test. Third, a simple inter-relation of the von Neumann entropy and the quantum $\kappa$-entropy is worked out and a bound of their difference is evaluated and interpreted. Also the effect on the $\kappa$-entropy of quantum noise, implemented as a random unitary quantum channel acting in the system’s density matrix, is addressed and a bound on the entropy, depending on the spectral properties of the noisy channel and the system’s density matrix, is evaluated. The results obtained amount to a quantum computational tool-box for the $\kappa$-entropy that enhances its applicability in practical problems. Full article

Sintered Neodymium–iron–boron (NdFeB) exhibits high hardness and brittleness, resulting in low electrical discharge machining (EDM) efficiency. The study on the interaction effect of parameters on the material removal rate (MRR) of sintered NdFeB processed by EDM-milling is carried out to improve machining efficiency. The interaction significance of parameters on the MRR is analysed based on the interaction curve of two parameters. Meanwhile, the variation of MRR caused by the change in △_y(x), △_{y x}, and the sum and product of △_y(x) are obtained by calculation. The interaction significance of parameters on MRR is obtained by comparing the sum and product of △_y(x), while the significance of each parameter on MRR is obtained by comparing △_y(x) and △_{y x}. The reasons for the variation of interaction significance are revealed by analysing the differences in crater diameters and discharge waveforms. It is concluded that different levels of interactions exist between the processing parameters. The interaction between pulse on time and pulse off time affects the MRR most significantly, and current affects the MRR most significantly among single factors. The interaction of parameters can affect processes such as dielectric breakdown, discharge channel expansion, discharge energy and its utilisation, dielectric deionisation, significantly affecting the MRR of sintered NdFeB processed by EDM-milling. Full article

Thoracic electrical impedance tomography (EIT) provides real-time, bedside imaging of pulmonary function and has demonstrated significant clinical value in guiding treatment strategies for critically ill patients. However, the practical application of EIT remains challenging due to its susceptibility to measurement disturbances, such as electrode contact problems and patient movement. These disturbances often manifest as spike-like noise that can severely degrade EIT image quality. To address this issue, we propose a robust Principal Component Analysis (RPCA)-based approach that models EIT data as the sum of a low-rank matrix and a sparse matrix. The low-rank matrix captures the underlying physiological signals, while the sparse matrix contains spike-like noise components. In simulation studies considering different spike magnitudes, widths and channels, all the image correlation coefficients between RPCA-processed images and the ground truth exceeded 0.99, and the image error of the original fEIT image with spike-like noise was much larger than that after RPCA processing. In eight patient cases, RPCA significantly improved the image quality (image error: p < 0.001; image correlation coefficient: p < 0.001) and enhanced the clinical EIT-based indexes accuracy (p < 0.001). Therefore, we conclude that RPCA is a promising technique for reducing spike-like noise in clinical EIT data, thereby improving data quality and potentially facilitating broader clinical application of EIT. Full article

Aiming to address the issue of multi-user dynamic spectrum access in an opportunistic mode in cognitive radio networks leading to low sum throughput, we propose a multi-user opportunistic spectrum access method based on multi-head self-attention and multi-agent deep reinforcement learning. First, an optimization model for joint channel selection and power control in multi-user systems is constructed based on centralized training with a decentralized execution framework. In the training phase, the decision-making policy is optimized using global information, while in the execution phase, each agent makes decisions according to its observations. Meanwhile, a multi-constraint dynamic proportional reward function is designed to guide the agent in selecting more rational actions by refining the constraints and dynamically adjusting the reward proportion. Furthermore, a multi-head self-attention mechanism is incorporated into the critic network to dynamically allocate attention weights to different users, thereby enhancing the ability of the network to estimate the joint action value. Finally, the proposed method is evaluated in terms of convergence, throughput, and dynamic performance. Simulation results demonstrate that the proposed method significantly improves the sum throughput of secondary users in opportunistic spectrum access. Full article

In this paper, a new communication protocol is proposed to allow direct communication between internet of things (IoT)-enabled home energy management systems (HEMSs) in a smart microgrid. The direct communication features are an important attribute for decentralised demand management and local energy trading operations in a microgrid equipped with renewable energy resources. The proposed scheme utilises the intermediate HEMSs as relay nodes that forward the sum of the received signals from nearby HEMSs to both ends of the entire network. The scheme can achieve lower latency compared to the cases when HEMSs adopt direct decode−and−forward (DF) or transmit through the central controller. For the proposed protocol, we have analytically obtained expressions for the error probability at different HEMSs, as well as the average bit error rate (BER) to indicate the overall error performance of the microgrid communication. To evaluate the proposed protocol in different channel conditions, numerical simulation is performed. The results demonstrate that the channel conditions between the HEMSs at the middle of the network have a greater impact on the system error performance. Overall, it can be observed that the proposed protocol suffers a lower degradation in error performance in comparison to direct DF when one of the users experiences worse channel conditions. Full article

In deep learning-based speaker verification, the loss function plays a crucial role. Most systems rely on a single loss function, or simply sum multiple losses with manually adjusted weights, increasing experimental complexity and failing to fully leverage the complementary characteristics of different losses. To address this, this paper proposes a speaker verification system based on channel attention and adaptive joint loss optimization. An adaptive joint loss function dynamically adjusts loss weights, allowing the model to better learn the similarities and differences of speakers, narrowing the gap between closed- and open-set testing, and enhancing generalization ability. A channel attention squeeze-and-excitation module is designed to improve the network’s ability to extract channel-specific features. On the AISHELL-1 dataset, the system achieved an equal error rate of 0.84% and a minimum detection cost function of 0.0528. Experimental results demonstrate a significant improvement in speaker verification performance, confirming the effectiveness of the proposed system. Full article

Vascular interventional surgical robots (VISRs) can help doctors to avoid X-ray radiation. This paper proposes a leader–follower isomorphic robot where the structural form and operational logic are completely identical. The doctor’s operation on the leader robot is precisely replicated on the follower robot, enabling delivery and rotation capabilities. It can further achieve collaborative operation. This control system adopts a four-channel scheme based on acceleration and can achieve approximately ideal transparency. The leader–follower delivery error of the catheter/guidewire is less than 1 mm, and the leader–follower rotation error of the guidewire is less than 0.3° in an actual intervention task based on a human vascular model. Subsequently, the cumulative sum (CUSUM) method was used to evaluate the learning curve of the robot system, demonstrating that both operators could master the operation method within 10 trials. We classified operators with different operational experience using machine learning methods. The classification process includes time-frequency domain feature extraction, feature selection based on the Relief method and random forest (RF) method, and a BP neural network (NN) classifier. The results indicate that this method can achieve accuracy of 94%. This paper comprehensively discusses the robot system from the perspectives of the mechanism design, control methods, and evaluation methods, providing valuable insights for the design of related robotic systems. Full article

The advent of millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, coupled with reconfigurable intelligent surfaces (RISs), presents a significant opportunity for advancing wireless communication technologies. This integration enhances data transmission rates and broadens coverage areas, but challenges in channel estimation (CE) remain due to the limitations of the signal processing capabilities of RIS. To address this, we propose an adaptive channel estimation framework comprising two algorithms: log-sum normalized least mean squares (Log-Sum NLMS) and hybrid normalized least mean squares-normalized least mean fourth (Hybrid NLMS-NLMF). These algorithms leverage the sparse nature of mmWave channels to improve estimation accuracy. The Log-Sum NLMS algorithm incorporates a log-sum penalty in its cost function for faster convergence, while the Hybrid NLMS-NLMF employs a mixed error function for better performance across varying signal-to-noise ratio (SNR) conditions. Our analysis also reveals that both algorithms have lower computational complexity compared to existing methods. Extensive simulations validate our findings, with results illustrating the performance of the proposed algorithms under different parameters, demonstrating significant improvements in channel estimation accuracy and convergence speed over established methods, including NLMS, sparse exponential forgetting window least mean square (SEFWLMS), and sparse hybrid adaptive filtering algorithms (SHAFA). Full article

A rapid and simple spectral method for determining the linear birefringence of thin anisotropic films, using the channeled spectra, is proposed in this article. Two channeled spectra must be recorded for a transparent system containing a thick anisotropic layer and a thin stretched polymer film, when the two anisotropic uniaxial layers have parallel and perpendicular optical axes, respectively. The sum and difference of the two channeled spectra indicate (by the positions of the maxima and minima in the resulting channeled spectra) the phase difference introduced by the thin polymer film. One must measure with precision only the thickness of the polymer film in order to compute the linear birefringence of the thin polymer film, using the position of the maxima and minima of the sum and difference. The experimental data obtained for poly (vinyl alcohol)—PVA—and poly (ethylene terephthalate)—PET—films attest to the applicability of the proposed method to the uniaxial transparent polymeric thin films. Full article

Forest degradation is a major issue in ecosystem monitoring, and to take reformative measures, it is important to detect, map, and quantify the losses of forests. Synthetic Aperture Radar (SAR) time-series data have the potential to detect forest loss. However, its sensitivity is influenced by the ecoregion, forest type, and site conditions. In this work, we assessed the accuracy of open-source C-band time-series data from Sentinel-1 SAR for detecting deforestation across forests in Africa, South Asia, and Southeast Asia. The statistical Cumulative Sums of Change (CuSUM) algorithm was applied to determine the point of change in the time-series data. The algorithm’s robustness was assessed for different forest site conditions, SAR polarizations, resolutions, and under varying moisture conditions. We observed that the change detection algorithm was affected by the site- and forest-management activities, and also by the precipitation. The forest type and eco-region affected the detection performance, which varied for the co- and cross-pol backscattering components. The cross-pol channel showed better deforested region delineation with less spurious detection. The results for Kalimantan showed a better accuracy at a 100 m spatial resolution, with a 25.1% increase in the average Kappa coefficient for the VH polarization channel in comparison with a 25 m spatial resolution. To avoid false detection due to the high impact of soil moisture in the case of Haldwani, a seasonal analysis was carried out based on dry and wet seasons. For the seasonal analysis, the cross-pol channel showed good accuracy, with an average Kappa coefficient of 0.85 at the 25 m spatial resolution. This work was carried out in support of the upcoming NISAR mission. The datasets were repackaged to the NISAR-like HDF5 format and processing was carried out with methods similar to NISAR ATBDs. Full article