Online streaming feature selection is an effective approach for handling large-scale streaming data in real-world applications. However, many existing online streaming feature selection studies do not effectively leve...
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This paper investigates a novel optimal control scheme for affine nonlinear systems. With the complexity of Hamilton-Jacobi-Bellman function and the linear differential inclusion based on neural network model is used ...
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Continuous-Flow Microfluidic Biochip (CFMB), with their integrated features, bring traditional biochemical experiments on a single chip to accomplish complex operations and reactions through precise control, efficient...
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An ultra-wideband filter with wide stopband performance with single notch using E-shaped resonator is proposed. Based on a U-shaped MMR structure loaded by an open-circuited stub connected at its center, the loaded ba...
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An ultra-wideband filter with wide stopband performance with single notch using E-shaped resonator is proposed. Based on a U-shaped MMR structure loaded by an open-circuited stub connected at its center, the loaded basic open-stub structure can generate transmission zeros on both sides of the passband to improve the in-band selectivity. Meanwhile, the symmetrical U-shaped open stub introduced not only strengthens the out-of-band suppression ability, but also optimizes the lower stopband. To suppress the in-band interference phenomenon, the proposed UWB BPF used E-shaped resonator coupled to the main transmission line of the basic UWB BPF to generate a notch band at 8GHz (Satellite communications X band). The filter has passband from 2.74 GHz to 11.5 GHz, the insertion loss is lower than 0.6dB and the filter has a relative high selectivity (30dB skirt factor is 0.891), Meanwhile, the filter has a wide stopband of up to 20.8GHz ($S21 \lt -20$dB).
In this paper, a novel and comprehensive signal denoising method is proposed by combining Symplectic Geometric Modal Decomposition (SGMD) and Block Thresholding denoising. The proposed approach involves a three-step p...
In this paper, a novel and comprehensive signal denoising method is proposed by combining Symplectic Geometric Modal Decomposition (SGMD) and Block Thresholding denoising. The proposed approach involves a three-step process: first, the signal is decomposed into a set of Symplectic Geometric Components (SGCs) using SGMD. Subsequently, each SGC is subjected to Block-Thresholding denoising. Finally, the denoised SGCs are recombined to obtain the denoised linear frequency modulation (LFM) signal. The experimental verification demonstrates the effectiveness of the SGMD-BT method in denoising LFM signals. This novel approach offers a fresh solution for the processing and analysis of LFM signals, holding significant application potential and research importance.
A compact coplanar waveguide multiband antenna based on an open resonant ring is proposed in this paper. The proposed antenna is fabricated on FR-4 substrate with a dielectric constant of 4.4 and a small size of 23 ...
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A compact coplanar waveguide multiband antenna based on an open resonant ring is proposed in this paper. The proposed antenna is fabricated on FR-4 substrate with a dielectric constant of 4.4 and a small size of 23 × 23 × 1.6mm 3 . In the design process of the antenna, the deformed circular split ring metamaterial (RSRR) is introduced to realize the broadband and multi frequency characteristics of the antenna. The two frequency bands of the antenna are 2.91-3.63GHz (relative bandwidth 22%), 4.19-9.32GHz (relative bandwidth 75.9%), covering WiMax (3.4/5.5GHz), lower C-band (4.41GHz), upper C-band (6.9GHz), WLAN (5.4/5.8GHz), lower X-band (8.7GHz) wireless standards, S11 ≤ -10dB. High frequency structure simulator (HFSS) is used to simulate the antenna structure, which has uniform radiation characteristics.
Inference acceleration of large language models (LLMs) has been put forward in many application scenarios and speculative decoding has shown its advantage in addressing inference acceleration. Speculative decoding usu...
ISBN:
(纸本)9798331314385
Inference acceleration of large language models (LLMs) has been put forward in many application scenarios and speculative decoding has shown its advantage in addressing inference acceleration. Speculative decoding usually introduces a draft model to assist the base LLM where the draft model produces drafts and the base LLM verifies the draft for acceptance or rejection. In this framework, the final inference speed is decided by the decoding speed of the draft model and the acceptance rate of the draft provided by the draft model. Currently the widely used draft models usually generate draft tokens for the next several positions in a non-autoregressive way without considering the correlations between draft tokens. Therefore, it has a high decoding speed but an unsatisfactory acceptance rate. In this paper, we focus on how to improve the performance of the draft model and aim to accelerate inference via a high acceptance rate. To this end, we propose a CTC-based draft model which strengthens the correlations between draft tokens during the draft phase, thereby generating higher-quality draft candidate sequences. Experiment results show that compared to strong baselines, the proposed method can achieve a higher acceptance rate and hence a faster inference speed.
In the field of intelligent digital healthcare, Continuous-flow microfluidic biochip (CFMB) has become a research direction of widespread concern. CFMB integrates a large number of microvalves and large-scale microcha...
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CNNs(Convolutional Neural Networks) have a good performance on most classification tasks,but they are vulnerable when meeting adversarial *** and design of highly aggressive adversarial examples can help enhance the s...
CNNs(Convolutional Neural Networks) have a good performance on most classification tasks,but they are vulnerable when meeting adversarial *** and design of highly aggressive adversarial examples can help enhance the security and robustness of *** transferability of adversarial examples is still low in black-box ***,an adversarial example method based on probability histogram equalization,namely HE-MI-FGSM(Histogram Equalization Momentum Iterative Fast Gradient Sign Method) is *** each iteration of the adversarial example generation process,the original input image is randomly histogram equalized,and then the gradient is calculated to generate adversarial perturbations to mitigate overfitting in the adversarial *** effectiveness of the method is verified on the ImageNet *** with the advanced method I-FGSM(Iterative Fast Gradient Sign Method) and MI-FGSM(Momentum I-FGSM),the attack success rate in the adversarial training network increased by 27.9% and 7.7% on average,respectively.
Object goal navigation requires an agent to navigate to a specified object in an unseen environment based on visual observations and user-specified goals. Human decision-making in navigation is sequential, planning a ...
ISBN:
(纸本)9798331314385
Object goal navigation requires an agent to navigate to a specified object in an unseen environment based on visual observations and user-specified goals. Human decision-making in navigation is sequential, planning a most likely sequence of actions toward the goal. However, existing ObjectNav methods, both end-to-end learning methods and modular methods, rely on single-step planning. They output the next action based on the current model input, which easily overlooks temporal consistency and leads to myopic planning. To this end, we aim to learn sequence planning for ObjectNav. Specifically, we propose trajectory diffusion to learn the distribution of trajectory sequences conditioned on the current observation and the goal. We utilize DDPM and automatically collected optimal trajectory segments to train the trajectory diffusion. Once the trajectory diffusion model is trained, it can generate a temporally coherent sequence of future trajectory for agent based on its current observations. Experimental results on the Gibson and MP3D datasets demonstrate that the generated trajectories effectively guide the agent, resulting in more accurate and efficient navigation. The code is available at https://***/sx-zhang/***.
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