Underwater optical imaging produces images with high resolution and abundant information and hence has outstanding advantages in short-distance underwater target ***,low-light and high-noise scenarios pose great chall...
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Underwater optical imaging produces images with high resolution and abundant information and hence has outstanding advantages in short-distance underwater target ***,low-light and high-noise scenarios pose great challenges in un-derwater image and video *** improve the accuracy and anti-noise performance of underwater target image edge detection,an underwater target edge detection method based on ant colony optimization and reinforcement learning is proposed in this ***,the reinforcement learning concept is integrated into artificial ants’movements,and a variable radius sensing strategy is pro-posed to calculate the transition probability of each *** methods aim to avoid undetection and misdetection of some pixels in image ***,a double-population ant colony strategy is proposed,where the search process takes into account global search and local search *** results show that the algorithm can effectively extract the contour information of underwater targets and keep the image texture well and also has ideal anti-interference performance.
The identification of blood-secretory proteins and the detection of protein biomarkers in the blood have an important clinical application *** methods for predicting blood-secretory proteins are mainly based on tradit...
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The identification of blood-secretory proteins and the detection of protein biomarkers in the blood have an important clinical application *** methods for predicting blood-secretory proteins are mainly based on traditional machine learning algorithms,and heavily rely on annotated protein *** traditional machine learning algorithms,deep learning algorithms can automatically learn better feature representations from raw data,and are expected to be more promising to predict blood-secretory *** present a novel deep learning model(DeepHBSP)combined with transfer learning by integrating a binary classification network and a ranking network to identify blood-secretory proteins from the amino acid sequence information *** loss function of DeepHBSP in the training step is designed to apply descriptive loss and compactness loss to the binary classification network and the ranking network,*** feature extraction subnetwork of DeepHBSP is composed of a multi-lane capsule ***,transfer learning is used to train a highly accurate generalized model with small samples of blood-secretory *** main contributions of this study are as follows:1)a novel deep learning architecture by integrating a binary classification network and a ranking network is proposed,superior to existing traditional machine learning algorithms and other state-of-the-art deep learning architectures for biological sequence analysis;2)the proposed model for blood-secretory protein prediction uses only amino acid sequences,overcoming the heavy dependence of existing methods on annotated protein features;3)the blood-secretory proteins predicted by our model are statistically significant compared with existing blood-based biomarkers of cancer.
In the heavy clutter environment, the information capacity is large,the relationships among information are complicated, and track initiationoften has a high false alarm rate or missing alarm rate. Obviously, it is ad...
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In the heavy clutter environment, the information capacity is large,the relationships among information are complicated, and track initiationoften has a high false alarm rate or missing alarm rate. Obviously, it is adifficult task to get a high-quality track initiation in the limited measurementcycles. This paper studies the multi-target track initiation in heavy *** first, a relaxed logic-based clutter filter algorithm is presented. In thealgorithm, the raw measurement is filtered by using the relaxed logic *** not only design a kind of incremental and adaptive filtering gate, but alsoadd the angle extrapolation based on polynomial extrapolation. The algorithm eliminates most of the clutter and obtains the environment with highdetection rate and less clutter. Then, we propose a fuzzy sequential Houghtransform-based track initiation algorithm. The algorithm establishes a newmeshing rule according to system noise to balance the relationship between thegrid granularity and the track initiation quality. And a flexible superpositionmatrix based on fuzzy clustering is constructed, which avoids the transformation error caused by 0–1 voting method in traditional Hough *** addition, the algorithm allows the superposition matrixes of nonadjacentcycles to be associated to overcome the shortcoming that the track can’t beinitiated in time when the measurements appear in an intermittent way. Anda slope verification method is introduced to detect formation-intensive serialtracks. Last, the sliding window method is employed to feedback the trackinitiation results timely and confirm the track. Simulation results verify thatthe proposed algorithms can initiate the tracks accurately in heavy clutter.
Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the hig...
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The worldwide spread of COVID-19 has made a severe impact on human health and life. It has shown rapid propagation, long in vitro survival, and a long incubation period. More seriously, COVID-19 is more susceptible to...
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Energy consumption data collected by smart meters is increasingly used by various subscribers in the smart grid for load management, energy monitoring, and policy planning. To protect user privacy, edge-assisted priva...
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Gaze input is a popular hands-free input method that allows for intuitive and rapid pointing but lacks a confirmation mechanism. This study introduces GazePuffer, an interaction method that combines puffing cheeks wit...
Gaze input is a popular hands-free input method that allows for intuitive and rapid pointing but lacks a confirmation mechanism. This study introduces GazePuffer, an interaction method that combines puffing cheeks with gaze. We explored the design space of mouth gestures, proposed a set of candidate gestures, filtered them through user subjective evaluation, and selected five basic gestures and four variations. We determined the corresponding virtual reality (VR) actions for these gestures through brainstorming. We achieved an accuracy of 93.8% in recognizing the five basic mouth gestures using the built-in sensors of the head-mounted display devices. We compared GazePuffer with two baseline methods in target selection tasks, demonstrating that GazePuffer is on par with Gaze&Pinch in throughput and speed, slightly outperforming Gaze&Dwell. Finally, we showcased the applicability of GazePuffer in real VR interaction tasks, with users generally finding it usable and effortless.
Due to the limitations of physical imaging, acquiring high-resolution hyperspectral images (HR-HSIs) has always been a significant challenge. Single hyperspectral image super-resolution (SHSR) technology aims to gener...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Due to the limitations of physical imaging, acquiring high-resolution hyperspectral images (HR-HSIs) has always been a significant challenge. Single hyperspectral image super-resolution (SHSR) technology aims to generate corresponding HR-HSIs by processing low-resolution hyperspectral images (LR-HSIs). Compared to multi-source data fusion methods, SHSR relies solely on a single low-resolution image and does not require additional auxiliary information or multimodal data, making it more flexible and efficient in data acquisition. Recently, Kolmogorov–Arnold Networks (KAN), which derive from the Kolmogorov–Arnold representation theorem, show great potential in modeling long-range dependencies. In this paper, we further investigate the potential of KAN for hyperspectral image restoration. Specifically, we propose a spatial-spectral attention block (SSAB) module, which includes a KAN-based spatial attention module (KAN-SpaAB) and a KAN-based spectral attention module (KAN-SpeAB), designed for the restoration of spatial and spectral information, respectively. Experimental results demonstrate that KSSANet outperforms existing methods in both quantitative evaluation and image generation quality, achieving state-of-the-art (SOTA) performance. Our code is available at: https://***/Baisonm-Li/KSSANet.
Alzheimer's disease (AD) is progressive and gets worse with time. Early detection is an effective treatment for the disease, which can timely implement effective interventions. The multi-modal medical image contai...
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Spectral super-resolution, which reconstructs hyperspectral images (HSI) from a single RGB image, has garnered increasing attention. Due to the limitations of CNN structures in spectral modeling and the high computati...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Spectral super-resolution, which reconstructs hyperspectral images (HSI) from a single RGB image, has garnered increasing attention. Due to the limitations of CNN structures in spectral modeling and the high computational cost of Transformer structures, existing deep learning (DL)-based methods struggle to balance spectral reconstruction quality and computational efficiency. Recently, Mamba methods base on state-space models (SSM) show great potential in modeling long-range dependencies with linear complexity. Therefore, we introduce the Mamba model into spectral super-resolution (SSR) task. Specifically, we propose a three-stage SSR network base on Mamba, called SSRMamba. We design SpaMamba, SSMamba, and SpeMamba modules for shallow spatial information extraction, mixed information encoding, and spectral information reconstruction, respectively. Extensive experimental results demonstrate that SSRMamba not only surpasses existing methods in terms of quantification and quality, achieving state-of-the-art (SOTA) performance, but also significantly reduces model size and computational cost. The source code of SSRMamba is available at: https://***/Baisonm-Li/SSRMamba.
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