High-speed Low-voltage CMOS Flash Analog-to-Digital Converter for Wideband Communication System-on-a-Chip

With higher-level integration driven by increasingly complex digital systems and downscaling CMOS processes available, system-on-a-chip (SoC) is an emerging technology of low power, high cost effectiveness and high reliability and is exceedingly attractive for applications in high-speed data conversion wireless and wideband communication systems. This research presents a novel ADC comparator design methodology; the speed and performance of which is not restricted by the supply voltage reduction and device linearity deterioration in scaling-down CMOS processes. By developing a dynamic offset suppression technique and a circuit optimization method, the comparator can achieve a 3 dB frequency of 2 GHz in 130 nanometer (nm) CMOS process. Combining this new comparator design and a proposed pipelined thermometer-Gray- binary encoder designed by the DCVSPG logic, a high-speed, low-voltage clocked-digital- comparator (CDC) pipelined CMOS flash ADC architecture is proposed for wideband communication SoC. This architecture has advantages of small silicon area, low power, and low cost. Three CDC-based pipelined CMOS flash ADCs were implemented in 130 nm CMOS process and their experimental results are reported: 1. 4-b, 2.5-GSPS ADC: SFDR of 21.48-dB, SNDR of 15.99-dB, ENOB of 2.4-b, ERBW of 1-GHz, power of 7.9-mW, and area of 0.022-mm2. 2. 4-b, 4-GSPS ADC: SFDR of 25-dB, SNDR of 18.6-dB, ENOB of 2.8-b, ERBW of 2-GHz, power of 11-mW. 3. 6-b, 4-GSPS ADC: SFDR of 48-dB at a signal frequency of 11.72-MHz, SNDR of 34.43-dB, ENOB of 5.4-b, power of 28-mW. An application of the proposed CDC-based pipelined CMOS flash ADC is 1-GHz bandwidth, 2.5-GSPS digital receiver on a chip. To verify the performance of the receiver, a mixed-signal block-level simulation and verification flow was built in Cadence AMS integrated platform. The verification results of the digital receiver using a 4-b 2.5-GSPS CDC-based pipelined CMOS ADC, a 256-point, 12-point kernel function FFT and a frequency detection logic show that two tone signals up to 1125 MHz can be detected and discriminated. A notable contribution of this research is that the proposed ADC architecture and the comparator design with dynamic offset suppression and optimization are extremely suitable for future VDSM CMOS processes and make all-digital receiver SoC design practical

A novel fault location method for a cross-bonded hv cable system based on sheath current monitoring

In order to improve the practice in the operation and maintenance of high voltage (HV) cables, this paper proposes a fault location method based on the monitoring of cable sheath currents for use in cross-bonded HV cable systems. This method first analyzes the power–frequency component of the sheath current, which can be acquired at cable terminals and cable link boxes, using a Fast Fourier Transform (FFT). The cable segment where a fault occurs can be localized by the phase difference between the sheath currents at the two ends of the cable segment, because current would flow in the opposite direction towards the two ends of the cable segment with fault. Conversely, in other healthy cable segments of the same circuit, sheath currents would flow in the same direction. The exact fault position can then be located via electromagnetic time reversal (EMTR) analysis of the fault transients of the sheath current. The sheath currents have been simulated and analyzed by assuming a single-phase short-circuit fault to occur in every cable segment of a selected cross-bonded high voltage cable circuit. The sheath current monitoring system has been implemented in a 110 kV cable circuit in China. Results indicate that the proposed method is feasible and effective in location of HV cable short circuit faults

GLOBER: Coherent Non-autoregressive Video Generation via GLOBal Guided Video DecodER

Video generation necessitates both global coherence and local realism. This work presents a novel non-autoregressive method GLOBER, which first generates global features to obtain comprehensive global guidance and then synthesizes video frames based on the global features to generate coherent videos. Specifically, we propose a video auto-encoder, where a video encoder encodes videos into global features, and a video decoder, built on a diffusion model, decodes the global features and synthesizes video frames in a non-autoregressive manner. To achieve maximum flexibility, our video decoder perceives temporal information through normalized frame indexes, which enables it to synthesize arbitrary sub video clips with predetermined starting and ending frame indexes. Moreover, a novel adversarial loss is introduced to improve the global coherence and local realism between the synthesized video frames. Finally, we employ a diffusion-based video generator to fit the global features outputted by the video encoder for video generation. Extensive experimental results demonstrate the effectiveness and efficiency of our proposed method, and new state-of-the-art results have been achieved on multiple benchmarks

Pattern Recognition for Steam Flooding Field Applications based on Hierarchical Clustering and Principal Component Analysis

Steam flooding is a complex process that has been considered as an effective enhanced oil recovery technique in both heavy oil and light oil reservoirs. Many studies have been conducted on different sets of steam flooding projects using the conventional data analysis methods, while the implementation of machine learning algorithms to find the hidden patterns is rarely found. In this study, a hierarchical clustering algorithm (HCA) coupled with principal component analysis is used to analyze the steam flooding projects worldwide. The goal of this research is to group similar steam flooding projects into the same cluster so that valuable operational design experiences and production performance from the analogue cases can be referenced for decision-making. Besides, hidden patterns embedded in steam flooding applications can be revealed based on data characteristics of each cluster for different reservoir/fluid conditions. In this research, principal component analysis is applied to project original data to a new feature space, which finds two principal components to represent the eight reservoir/fluid parameters (8D) but still retain about 90% of the variance. HCA is implemented with the optimized design of five clusters, Euclidean distance, and Ward\u27s linkage method. The results of the hierarchical clustering depict that each cluster detects a unique range of each property, and the analogue cases present that fields under similar reservoir/fluid conditions could share similar operational design and production performance

VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset

Vision and text have been fully explored in contemporary video-text foundational models, while other modalities such as audio and subtitles in videos have not received sufficient attention. In this paper, we resort to establish connections between multi-modality video tracks, including Vision, Audio, and Subtitle, and Text by exploring an automatically generated large-scale omni-modality video caption dataset called VAST-27M. Specifically, we first collect 27 million open-domain video clips and separately train a vision and an audio captioner to generate vision and audio captions. Then, we employ an off-the-shelf Large Language Model (LLM) to integrate the generated captions, together with subtitles and instructional prompts into omni-modality captions. Based on the proposed VAST-27M dataset, we train an omni-modality video-text foundational model named VAST, which can perceive and process vision, audio, and subtitle modalities from video, and better support various tasks including vision-text, audio-text, and multi-modal video-text tasks (retrieval, captioning and QA). Extensive experiments have been conducted to demonstrate the effectiveness of our proposed VAST-27M corpus and VAST foundation model. VAST achieves 22 new state-of-the-art results on various cross-modality benchmarks. Code, model and dataset will be released at https://github.com/TXH-mercury/VAST.Comment: 23 pages, 5 figure

Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation

Federated learning (FL) enables multiple client medical institutes collaboratively train a deep learning (DL) model with privacy protection. However, the performance of FL can be constrained by the limited availability of labeled data in small institutes and the heterogeneous (i.e., non-i.i.d.) data distribution across institutes. Though data augmentation has been a proven technique to boost the generalization capabilities of conventional centralized DL as a "free lunch", its application in FL is largely underexplored. Notably, constrained by costly labeling, 3D medical segmentation generally relies on data augmentation. In this work, we aim to develop a vicinal feature-level data augmentation (VFDA) scheme to efficiently alleviate the local feature shift and facilitate collaborative training for privacy-aware FL segmentation. We take both the inner- and inter-institute divergence into consideration, without the need for cross-institute transfer of raw data or their mixup. Specifically, we exploit the batch-wise feature statistics (e.g., mean and standard deviation) in each institute to abstractly represent the discrepancy of data, and model each feature statistic probabilistically via a Gaussian prototype, with the mean corresponding to the original statistic and the variance quantifying the augmentation scope. From the vicinal risk minimization perspective, novel feature statistics can be drawn from the Gaussian distribution to fulfill augmentation. The variance is explicitly derived by the data bias in each individual institute and the underlying feature statistics characterized by all participating institutes. The added-on VFDA consistently yielded marked improvements over six advanced FL methods on both 3D brain tumor and cardiac segmentation.Comment: 28th biennial international conference on Information Processing in Medical Imaging (IPMI 2023): Oral Pape

Luminescent LaF₃:Ce-doped Organically Modified Nanoporous Silica Xerogels

Organically modified silica compounds (ORMOSILs) were synthesized by a sol-gel method from amine-functionalized 3-aminopropyl triethoxylsilane and tetramethylorthosilicate and were doped in situ with LaF3:Ce nanoparticles, which in turn were prepared either in water or in ethanol. Doped ORMOSILs display strong photoluminescence either by UV or X-ray excitation and maintain good transparency up to a loading level of 15.66% w/w. The TEM observations demonstrate that ORMOSILs remain nanoporous with pore diameters in the 5-10 nm range. LaF3:Ce nanoparticles doped into the ORMOSILs are rod-like, 5 nm in diameter and 10-15 nm in length. Compression testing indicates that the nanocomposites have very good strength, without significant lateral dilatation and buckling under quasi-static compression. LaF3:Ce nanoparticle-doped ORMOSILs have potential for applications in radiation detection and solid state lighting