We propose a novel algorithm based on the split-step non-paraxial model for different intensity diffraction tomography setups to recover the 3D refractive index distribution of multiple-scattering biological samples. ...
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Subwavelength manipulation of light waves with high precision can enable new and exciting applications in spectroscopy,sensing,and medical *** these applications,miniaturized spectrometers are desirable to enable the ...
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Subwavelength manipulation of light waves with high precision can enable new and exciting applications in spectroscopy,sensing,and medical *** these applications,miniaturized spectrometers are desirable to enable the on-chip analysis of spectral *** particular,for imaging-based spectroscopic sensing mechanisms,the key challenge is to determine the spatial-shift information accurately(i.e.,the spatial displacement introduced by wavelength shift or biological or chemical surface binding),which is similar to the challenge presented by super-resolution ***,we report a unique"rainbow"trapping metasurface for on-chip spectrometers and *** with super-resolution image processing,the low-setting 4×optical microscope system resolves a displacement of the resonant position within 35 nm on the plasmonic rainbow trapping metasurface with a tiny area as small as0.002 *** unique feature of the spatial manipulation of efficiently coupled rainbow plasmonic resonances reveals a new platform for miniaturized on-chip spectroscopic analysis with a spectral resolution of 0.032 nm in wavelength *** this low-setting 4×microscope imaging system,we demonstrate a biosensing resolution of 1.92×109exosomes per milliliter for A549-derived exosomes and distinguish between patient samples and healthy controls using exosomal epidermal growth factor receptor(EGFR)expression values,thereby demonstrating a new on-chip sensing system for personalized accurate bio/chemical sensing applications.
Accurate identification of late mechanical activation (LMA) regions is crucial for optimal cardiac resynchronization therapy (CRT) lead implantation. However, existing approaches using cardiac magnetic resonance (CMR)...
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A chronic stroke affects hand mobility limiting the normal functioning of the finger joints. The voluntary tasks with a repetitive motion can identify the limitation in the range of motion (ROM) to enhance the hand fu...
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Intrusion Detection Systems (IDS) are essential for protecting networks from cyber threats and ensuring the security of critical infrastructures. This paper introduces an innovative hybrid model that combines Dee...
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Intrusion Detection Systems (IDS) are essential for protecting networks from cyber threats and ensuring the security of critical infrastructures. This paper introduces an innovative hybrid model that combines Deep Belief Networks (DBNs), Principal Component Analysis (PCA), and Support Vector Machines (SVM) with adversarial learning to enhance the detection of both known and emerging cyber threats. The proposed model achieves a strong balance of accuracy, efficiency, and scalability, making it highly effective for real-world applications. By incorporating adversarial learning, the system can detect zero-day exploits—previously unseen attacks—and adapt to evolving threats. This is achieved by training the model on adversarial samples, which improves its resilience to variations in attack patterns. SVM further enhances the model’s ability to classify known and unknown threats with high precision. The model was rigorously tested on two benchmark datasets, NSL-KDD and CICIDS2017, achieving outstanding results. It demonstrated a high accuracy rate of 99.73% on NSL-KDD and a low false positive rate of 0.55%, while on CICIDS2017, it achieved a false positive rate of 0.73%. The model also outperformed existing approaches in precision, recall, and F1-score, particularly in detecting complex attack types such as R2L (Remote-to-Local), U2R (User-to-Root), and Botnet attacks. Additionally, PCA reduced the feature space by 40%, significantly lowering the model’s inference time to just 9.0 ms per sample. This makes the model highly suitable for real-time intrusion detection in high-traffic network environments, where speed and efficiency are critical. Overall, this hybrid model strengthens cybersecurity defenses and contributes to building resilient infrastructures. By effectively detecting and mitigating cyber threats, it supports the creation of safer, fairer, and more inclusive digital communities. The integration of adversarial learning and dimensionality reduction ensures rob
Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive *** paper attempts to tackle the outlier filtering ...
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Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive *** paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://***/YifanLu2000/TIM.
With the increasing integration of renewable energy sources into electrical grids, energy storage systems have become crucial for stability and regulation. Thus, dedicated two-stage AC-DC converters are essential for ...
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In this paper, we present a vanadium dioxide (VO2) based metamaterial absorber at terahertz (THz) frequencies, achieving near unity absorption from 3.5 THz to 5 THz through full vector numerical simulation which refer...
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