A 0.1–5.0 GHz flexible SDR receiver with digitally assisted calibration in 65 nm CMOS

© 2017 Elsevier Ltd. All rights reserved.A 0.1–5.0 GHz flexible software-defined radio (SDR) receiver with digitally assisted calibration is presented, employing a zero-IF/low-IF reconfigurable architecture for both wideband and narrowband applications. The receiver composes of a main-path based on a current-mode mixer for low noise, a high linearity sub-path based on a voltage-mode passive mixer for out-of-band rejection, and a harmonic rejection (HR) path with vector gain calibration. A dual feedback LNA with “8” shape nested inductor structure, a cascode inverter-based TCA with miller feedback compensation, and a class-AB full differential Op-Amp with Miller feed-forward compensation and QFG technique are proposed. Digitally assisted calibration methods for HR, IIP2 and image rejection (IR) are presented to maintain high performance over PVT variations. The presented receiver is implemented in 65 nm CMOS with 5.4 mm2 core area, consuming 9.6–47.4 mA current under 1.2 V supply. The receiver main path is measured with +5 dB m/+5dBm IB-IIP3/OB-IIP3 and +61dBm IIP2. The sub-path achieves +10 dB m/+18dBm IB-IIP3/OB-IIP3 and +62dBm IIP2, as well as 10 dB RF filtering rejection at 10 MHz offset. The HR-path reaches +13 dB m/+14dBm IB-IIP3/OB-IIP3 and 62/66 dB 3rd/5th-order harmonic rejection with 30–40 dB improvement by the calibration. The measured sensitivity satisfies the requirements of DVB-H, LTE, 802.11 g, and ZigBee.Peer reviewedFinal Accepted Versio

Feature-aware uniform tessellations on video manifold for content-sensitive supervoxels

Over-segmenting a video into supervoxels has strong potential to reduce the complexity of computer vision applications. Content-sensitive supervoxels (CSS) are typically smaller in content-dense regionsand larger in content-sparse regions. In this paper, we propose to compute feature-aware CSS (FCSS) that are regularly shaped 3D primitive volumes well aligned with local object/region/motion boundaries in video.To compute FCSS, we map a video to a 3-dimensional manifold, in which the volume elements of video manifold give a good measure of the video content density. Then any uniform tessellation on manifold can induce CSS. Our idea is that among all possible uniform tessellations, FCSS find one whose cell boundaries well align with local video boundaries. To achieve this goal, we propose a novel tessellation method that simultaneously minimizes the tessellation energy and maximizes the average boundary distance.Theoretically our method has an optimal competitive ratio O(1). We also present a simple extension of FCSS to streaming FCSS for processing long videos that cannot be loaded into main memory at once. We evaluate FCSS, streaming FCSS and ten representative supervoxel methods on four video datasets and two novel video applications. The results show that our method simultaneously achieves state-of-the-art performance with respect to various evaluation criteria

Mitigating algorithmic errors in quantum optimization through energy extrapolation

Quantum optimization algorithms offer a promising route to finding the ground states of target Hamiltonians on near-term quantum devices. Nonetheless, it remains necessary to limit the evolution time and circuit depth as much as possible, since otherwise decoherence will degrade the computation. Even when this is done, there always exists a non-negligible error in estimates of the ground state energy. Here we present a scalable extrapolation approach to mitigating this algorithmic error, which significantly improves estimates obtained using three well-studied quantum optimization algorithms: quantum annealing (QA), the variational quantum eigensolver, and the quantum imaginary time evolution at fixed evolution time or circuit depth. The approach is based on extrapolating the annealing time to infinity or the variance of estimates to zero. The method is reasonably robust against noise. For Hamiltonians which only involve few-body interactions, the additional computational overhead is an increase in the number of measurements by a constant factor. Analytic derivations are provided for the quadratic convergence of estimates of energy as a function of time in QA, and the linear convergence of estimates as a function of variance in all three algorithms. We have verified the validity of these approaches through both numerical simulation and experiments on IBM quantum machines. This work suggests a promising new way to enhance near-term quantum computing through classical post-processing.journal articl

Ranking-preserving cross-source learning for image retargeting quality assessment

Image retargeting techniques adjust images into different sizes and have attracted much attention recently. Objective quality assessment (OQA) of image retargeting results is often desired to automatically select the best results. Existing OQA methods train a model using some benchmarks (e.g., RetargetMe), in which subjective scores evaluated by users are provided. Observing that it is challenging even for human subjects to give consistent scores for retargeting results of different source images (diff-source-results), in this paper we propose a learning-based OQA method that trains a General Regression Neural Network (GRNN) model based on relative scores - which preserve the ranking - of retargeting results of the same source image (same-source-results). In particular, we develop a novel training scheme with provable convergence that learns a common base scalar for same-source-results. With this source specific offset, our computed scores not only preserve the ranking of subjective scores for same-source-results, but also provide a reference to compare the diff-source-results. We train and evaluate our GRNN model using human preference data collected in RetargetMe. We further introduce a subjective benchmark to evaluate the generalizability of different OQA methods. Experimental results demonstrate that our method outperforms ten representative OQA methods in ranking prediction and has better generalizability to different datasets

Grain boundary migration in polycrystalline α\alpha-Fe

High energy x-ray diffraction microscopy was used to image the microstructure of $\alpha$-Fe before and after a 600 $^\circ$C anneal. These data were used to determine the areas, curvatures, energies, and velocities of approximately 40,000 grain boundaries. The measured grain boundary properties depend on the five macroscopic grain boundary parameters. The velocities are not correlated with the product of the mean boundary curvature and grain boundary energy, usually assumed to be the driving force. Boundary migration is made up of area changes (lateral motion) and translation (normal motion) and both contribute to the total migration. Through the lateral motion component of the migration, low energy boundaries tend to expand in area while high energy boundaries shrink, reducing the average energy through grain boundary replacement. The driving force for this process is not related to curvature and might disrupt the expected curvature-velocity relationship.Comment: 33 pages, double spaced, accepted for publication in Acta Materiali

GAN-based multi-style photo cartoonization

Cartoon is a common form of art in our daily life and automatic generation of cartoon images from photos is highly desirable. However, state-of-the-art single-style methods can only generate one style of cartoon images from photos and existing multi-style image style transfer methods still struggle to produce high-quality cartoon images due to their highly simplified and abstract nature. In this paper, we propose a novel multi-style generative adversarial network (GAN) architecture, called MS-CartoonGAN, which can transform photos into multiple cartoon styles. We develop a multi-domain architecture, where the generator consists of a shared encoder and multiple decoders for different cartoon styles, along with multiple discriminators for individual styles. By observing that cartoon images drawn by different artists have their unique styles while sharing some common characteristics, our shared network architecture exploits the common characteristics of cartoon styles, achieving better cartoonization and being more efficient than single-style cartoonization. We show that our multi-domain architecture can theoretically guarantee to output desired multiple cartoon styles. Through extensive experiments including a user study, we demonstrate the superiority of the proposed method, outperforming state-of-the-art single-style and multi-style image style transfer methods

JourneyDB: A Benchmark for Generative Image Understanding

While recent advancements in vision-language models have had a transformative impact on multi-modal comprehension, the extent to which these models possess the ability to comprehend generated images remains uncertain. Synthetic images, in comparison to real data, encompass a higher level of diversity in terms of both content and style, thereby presenting significant challenges for the models to fully grasp. In light of this challenge, we introduce a comprehensive dataset, referred to as JourneyDB, that caters to the domain of generative images within the context of multi-modal visual understanding. Our meticulously curated dataset comprises 4 million distinct and high-quality generated images, each paired with the corresponding text prompts that were employed in their creation. Furthermore, we additionally introduce an external subset with results of another 22 text-to-image generative models, which makes JourneyDB a comprehensive benchmark for evaluating the comprehension of generated images. On our dataset, we have devised four benchmarks to assess the performance of generated image comprehension in relation to both content and style interpretation. These benchmarks encompass prompt inversion, style retrieval, image captioning, and visual question answering. Lastly, we evaluate the performance of state-of-the-art multi-modal models when applied to the JourneyDB dataset, providing a comprehensive analysis of their strengths and limitations in comprehending generated content. We anticipate that the proposed dataset and benchmarks will facilitate further research in the field of generative content understanding. The dataset is publicly available at https://journeydb.github.io.Comment: Accepted to the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023