The analog frontend(AFE)coupling circuit is a crucial processing element for data acquisition systems based on analog-to-digital converters(ADCs).Currently,high-speed and high-resolution ADCs are predominantly designe...The analog frontend(AFE)coupling circuit is a crucial processing element for data acquisition systems based on analog-to-digital converters(ADCs).Currently,high-speed and high-resolution ADCs are predominantly designed with differential input stages.Conventional highspeed ADC drivers are mainly AC-coupled by employing transformers(Baluns)or fully differential amplifiers(FDAs)with blocking capacitors.However,the results of this study indicate that a certain degree of DC offset error exists and manifests itself as the baseline error in the presence of power dividers connecting several DC-coupled channels that implement high-dynamic-range(HDR)signal conditioning.The study involves a theoretical analysis and explanation of the baseline offset error.The offset error can potentially lead to unexpected out-of-range issues for sampling devices,including high-speed ADCs and switched capacitor array ASICs.High-performance FDAs are adopted,and an offset-free DC-coupled AFE circuit is proposed to address the aforementioned issue using twostage amplification and a resistive attenuator.The proposed methodology is verified via circuit simulations and hardware design.Thus,the baseline offset problem can be accurately solved using the proposed circuit by minimizing the neglectable error.The proposed circuit facilitates improvements in the high-precision measurement of HDR signals in many nuclear physics experiments and some applications in the DC-coupling scheme with FDAs involving resistive power dividers.展开更多
Event cameras,with their significantly higher dynamic range and sensitivity to intensity variations compared to frame cameras,provide new possibilities for 3D reconstruction in high-dynamic-range(HDR)scenes.However,th...Event cameras,with their significantly higher dynamic range and sensitivity to intensity variations compared to frame cameras,provide new possibilities for 3D reconstruction in high-dynamic-range(HDR)scenes.However,the binary event data stream produced by event cameras presents significant challenges for achieving high-precision and efficient 3D reconstruction.In addressing these issues,we observe that the binary projection inherent to Gray-code-based 3D reconstruction naturally aligns with the event camera's imaging mechanism.However,achieving high-accuracy 3D reconstruction using a Gray code remains hindered by two key factors:inaccurate boundary extraction and the degradation of high-order dense Gray code patterns due to spatial blurring.For the first challenge,we propose an inverted Gray code strategy to improve region segmentation and recognition,achieving more precise and easily identifiable Gray code boundaries.For the second challenge,we introduce a spatial-shifting Gray-code encoding method.By spatially shifting Gray code patterns with lower encoding density,a combined encoding is achieved,enhancing the depth resolution and measurement accuracy.Experimental validation across general and HDR scenes demonstrates the effectiveness of the proposed methods.展开更多
The SDR-to-HDR translation technique can convert the abundant standard-dynamic-range (SDR) media resources to high-dynamic-range (HDR) ones, which can represent high-contrast scenes, providing more realistic visual ex...The SDR-to-HDR translation technique can convert the abundant standard-dynamic-range (SDR) media resources to high-dynamic-range (HDR) ones, which can represent high-contrast scenes, providing more realistic visual experiences. While recent vision Transformers have achieved promising performance in many low-level vision tasks, there are few works attempting to leverage Transformers for SDR-to-HDR translation. In this paper, we are among the first to investigate the performance of Transformers for SDR-to-HDR translation. We find that directly using the self-attention mechanism may involve artifacts in the results due to the inappropriate way to model long-range dependencies between the low-frequency and high-frequency components. Taking this into account, we advance the self-attention mechanism and present a dual frequency attention (DFA), which leverages the self-attention mechanism to separately encode the low-frequency structural information and high-frequency detail information. Based on the proposed DFA, we further design a multi-scale feature fusion network, named dual frequency Transformer (DFT), for efficient SDR-to-HDR translation. Extensive experiments on the HDRTV1K dataset demonstrate that our DFT can achieve better quantitative and qualitative performance than the recent state-of-the-art methods. The code of our DFT is made publicly available at https://github.com/CS-GangXu/DFT.展开更多
文摘The analog frontend(AFE)coupling circuit is a crucial processing element for data acquisition systems based on analog-to-digital converters(ADCs).Currently,high-speed and high-resolution ADCs are predominantly designed with differential input stages.Conventional highspeed ADC drivers are mainly AC-coupled by employing transformers(Baluns)or fully differential amplifiers(FDAs)with blocking capacitors.However,the results of this study indicate that a certain degree of DC offset error exists and manifests itself as the baseline error in the presence of power dividers connecting several DC-coupled channels that implement high-dynamic-range(HDR)signal conditioning.The study involves a theoretical analysis and explanation of the baseline offset error.The offset error can potentially lead to unexpected out-of-range issues for sampling devices,including high-speed ADCs and switched capacitor array ASICs.High-performance FDAs are adopted,and an offset-free DC-coupled AFE circuit is proposed to address the aforementioned issue using twostage amplification and a resistive attenuator.The proposed methodology is verified via circuit simulations and hardware design.Thus,the baseline offset problem can be accurately solved using the proposed circuit by minimizing the neglectable error.The proposed circuit facilitates improvements in the high-precision measurement of HDR signals in many nuclear physics experiments and some applications in the DC-coupling scheme with FDAs involving resistive power dividers.
基金supported by the Sichuan Science and Technology Program(No.2023NSFSC0496)the National Natural Science Foundation of China(No.62075143)。
文摘Event cameras,with their significantly higher dynamic range and sensitivity to intensity variations compared to frame cameras,provide new possibilities for 3D reconstruction in high-dynamic-range(HDR)scenes.However,the binary event data stream produced by event cameras presents significant challenges for achieving high-precision and efficient 3D reconstruction.In addressing these issues,we observe that the binary projection inherent to Gray-code-based 3D reconstruction naturally aligns with the event camera's imaging mechanism.However,achieving high-accuracy 3D reconstruction using a Gray code remains hindered by two key factors:inaccurate boundary extraction and the degradation of high-order dense Gray code patterns due to spatial blurring.For the first challenge,we propose an inverted Gray code strategy to improve region segmentation and recognition,achieving more precise and easily identifiable Gray code boundaries.For the second challenge,we introduce a spatial-shifting Gray-code encoding method.By spatially shifting Gray code patterns with lower encoding density,a combined encoding is achieved,enhancing the depth resolution and measurement accuracy.Experimental validation across general and HDR scenes demonstrates the effectiveness of the proposed methods.
基金supported by National Natural Science Foundation of China(Nos.61922046 and 62276145)the National Key Research and Development Program of China(No.2018AAA0100400)Fundamental Research Funds for Central Universities,China(No.63223049).
文摘The SDR-to-HDR translation technique can convert the abundant standard-dynamic-range (SDR) media resources to high-dynamic-range (HDR) ones, which can represent high-contrast scenes, providing more realistic visual experiences. While recent vision Transformers have achieved promising performance in many low-level vision tasks, there are few works attempting to leverage Transformers for SDR-to-HDR translation. In this paper, we are among the first to investigate the performance of Transformers for SDR-to-HDR translation. We find that directly using the self-attention mechanism may involve artifacts in the results due to the inappropriate way to model long-range dependencies between the low-frequency and high-frequency components. Taking this into account, we advance the self-attention mechanism and present a dual frequency attention (DFA), which leverages the self-attention mechanism to separately encode the low-frequency structural information and high-frequency detail information. Based on the proposed DFA, we further design a multi-scale feature fusion network, named dual frequency Transformer (DFT), for efficient SDR-to-HDR translation. Extensive experiments on the HDRTV1K dataset demonstrate that our DFT can achieve better quantitative and qualitative performance than the recent state-of-the-art methods. The code of our DFT is made publicly available at https://github.com/CS-GangXu/DFT.
