This study proposes a method for uniformly revolving swarm robots to entrap multiple targets,which is based on a gene regulatory network,an adaptive decision mechanism,and an improved *** the gene regulatory network m...
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This study proposes a method for uniformly revolving swarm robots to entrap multiple targets,which is based on a gene regulatory network,an adaptive decision mechanism,and an improved *** the gene regulatory network method,the robots can generate entrapping patterns according to the environmental input,including the positions of the targets and ***,an adaptive decision mechanism is proposed,allowing each robot to choose the most well-adapted capture point on the pattern,based on its *** robots employ an improved Vicsek-model to maneuver to the planned capture point smoothly,without colliding with other robots or *** proposed decision mechanism,combined with the improved Vicsek-model,can form a uniform entrapment shape and create a revolving effect around targets while entrapping *** study also enables swarm robots,with an adaptive pattern formation,to entrap multiple targets in complex *** robots can be deployed in the military field of unmanned aerial vehicles’(UAVs)entrapping multiple *** experiments demonstrate the feasibility and superiority of the proposed gene regulatory network method.
We present a novel spiking neural network approach to building 3D LiDAR images from temporal information alone. Our method uses the "spike" events from individually detected photons without the need to const...
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In recent years, research in automatic Sign Language Recognition (SLR) has undergone significant progress, serving as a founda-tional base for developing applications that aim to promote the integration of deaf indivi...
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Nowadays, with aging of the human society, the 'ratio of family care givers and elderly' is not equivalent and cannot give enough caring to the elderly in some countries. Therefore, automatic health monitoring...
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Self-supervised learning has produced impressive results in multimedia domains of audio, vision and speech. This paradigm is equally, if not more, relevant for the domain of biosignals, owing to the scarcity of labell...
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Audio classification is paramount in a variety of applications including surveillance, healthcare monitoring, and environmental analysis. Traditional methods frequently depend on intricate signalprocessing algorithms...
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This paper investigates a new stability criterion for generalized (Q,S,R) — α dissipativity for externally interfered discrete systems with saturation overflow nonlinearity. The proposed criterion is capable of esta...
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This paper investigates a new stability criterion for generalized (Q,S,R) — α dissipativity for externally interfered discrete systems with saturation overflow nonlinearity. The proposed criterion is capable of establishing various specific criteria for H ∞ , mixed H ∞ /passivity, and passivity performances in an integrated system through manipulation in weight matrices. Further, an exponential stability criterion for the discrete system with generalized dissipativity is also presented. To facilitate the ease of traceability, the proposed conditions are built in terms of linear matrix inequality. Appropriate numerical examples are used to support the validity of the proposed work.
In recent years, research in automatic Sign Language Recognition (SLR) has undergone significant progress, serving as a founda-tional base for developing applications that aim to promote the integration of deaf indivi...
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In recent years, research in automatic Sign Language Recognition (SLR) has undergone significant progress, serving as a founda-tional base for developing applications that aim to promote the integration of deaf individuals into society. Most of this progress is owed to the recent developments in deep learning. However, the deployment of conventional Artificial Neural Networks (ANNs) can be hindered by their requirements in terms of computational power and energy consumption. Therefore, to improve the ef-ciency of current SLR systems, in this work, we propose the use of the increasingly popular Spiking Neural Networks (SNNs), which, on the one hand, provide more energy-efficient computations than conventional ANNs and, on the other hand, are able to process temporal sequences with simpler architectures thanks to their temporal dynamics. To evaluate our method, we utilize WLASL300, the 300-word (300 classes of signs) dataset from Word-Level American Sign Language, and achieve an improvement in accuracy with the SNN (+2.70%) over the previous state-of-the-art, when working with energy-efficient spiking neurons. Furthermore, we construct a non-spiking version of the same network and evaluate it in a similar manner. Our results demonstrate how the SNN has sparser activations (25% less), thanks to the use of spiking neurons, and therefore can be implemented with a lower power requirement than an ANN version of the same architecture. This work thus demonstrates the possibility of performing SLR in a very effective and efficient way, thus opening up the development of applications that span from the automatic real-time translation of dynamic signs to remote control utilizing sign languages.
Self-supervised learning has produced impressive results in multimedia domains of audio, vision and speech. This paradigm is equally, if not more, relevant for the domain of biosignals, owing to the scarcity of labell...
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ISBN:
(数字)9798350371499
ISBN:
(纸本)9798350371505
Self-supervised learning has produced impressive results in multimedia domains of audio, vision and speech. This paradigm is equally, if not more, relevant for the domain of biosignals, owing to the scarcity of labelled data in such scenarios. The ability to leverage large-scale unlabelled data to learn robust representations could help improve the performance of numerous inference tasks on biosignals. Given the inherent domain differences between multimedia modalities and biosignals, the established objectives for self-supervised learning may not translate well to this domain. Hence, there is an unmet need to adapt these methods to biosignal analysis. In this work we propose a self-supervised model for EEG, which provides robust performance and remarkable parameter efficiency by using state space-based deep learning architecture. We also propose a novel knowledge-guided pre-training objective that accounts for the idiosyncrasies of the EEG signal. The results indicate improved embedding representation learning and downstream performance compared to prior works on exemplary tasks. Also, the proposed objective significantly reduces the amount of pre-training data required to obtain performance equivalent to prior works.
This paper introduces a novel deep learning framework for the discovery of breast cancer stages, which integrates GAN-generated synthetic images with multi-omics data. By employing StyleGAN3 for the generation of real...
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ISBN:
(数字)9798331529819
ISBN:
(纸本)9798331529826
This paper introduces a novel deep learning framework for the discovery of breast cancer stages, which integrates GAN-generated synthetic images with multi-omics data. By employing StyleGAN3 for the generation of realistic histopathological images and Swin Transformer for classification, the model draws upon both visual and biological data to enhance the accuracy of cancer staging predictions. The proposed methodology entails the generation of high-quality synthetic images using StyleGAN3, with a Fréchet Inception Distance (FID) score of 35, indicating a reasonable degree of similarity to real images. The images, in conjunction with RNA, miRNA, and clinical data, are integrated into a Swin Transformer-based classifier, resulting in an accuracy of 95.03 %, a precision of 95.00 %, and an F1 score of 95.00 %. A threshold-based softmax probability analysis was employed during the inference stage to explore the potential discovery of new cancer stages. The preliminary observation-based threshold of 30 % may be optimized through further experimentation. In the event that the model exhibited a confidence level for a given class below the specified threshold, the image was identified as a potential candidate for a previously unidentified stage. This study underscores the potential of multimodal data integration in enhancing breast cancer staging and offers insights into leveraging deep learning models for generating and classifying histopathological data, alongside identifying novel disease stages.
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