Miracle-dz/UniFAR-FTUs huggingface dataset:
4.8 An Area and Energy Efficient 0.12nJ/Pixel 8K 30fps AV1 Video Decoder in 5nm CMOS Process

Major content providers such as YouTube and Netflix use AV1, a state-of-art video coding standard that was finalized in 2018. This paper presents a video processor that supports the AV1 standard via a Multi-Format video Decoder (MFD). An MFD can decode various video coding standards, such as H.264/AVC, HEVC and AV1. The video decoder can decode up to 8K Ultra HD (8K-UHD) video at 30fps at 468MHz core clock frequency. The MFD is fabricated using a state-of-the-art 5nm CMOS process with a 5,835k NAND2 equivalent gate count and 518kB SRAM. For AV1, MFD dissipates 116mW for decoding AV1 $7680 \times4320p$ video at 30fps (0.12nJ/pixel) with core operating voltage at 0.7V.
Cognitive Sonar Intelligence Countermeasures Technology

The active battle sonar abroad adopts low frequency, high power, large aperture array and adaptive processing technology, which integrates active/passive, multiband and multi-function, and presents the development trend of fully adaptive intelligent cognition. Therefore, the research on how to develop effective countermeasures should be carried out. Firstly, the basic structure of cognitive sonar and its challenges to confrontation are introduced on this paper. Secondly, the artificial intelligence technology is applied to the field of countermeasure, and the architecture of cognitive sonar intelligent countermeasure system is proposed. Finally, the key technologies of cognitive sonar intelligent countermeasure are analyzed, which lays a foundation for the research of cognitive sonar intelligent countermeasure technology.
A Hierarchical Centralized Community Energy Management System Using a Model Predictive Controller

This paper presents a Hierarchical Centralized Community Energy Management Systems (CCEMS) which facilitates the energy trading between prosumers in a community by coordinating the operation of distributed Home Battery Storage Systems (HBSSs) and shiftable home appliances in a centralized way using a Model Predictive Controller (MPC). The CCEMS achieves a further reduction in the annual household energy costs for each house, compared to being operated individually (not being a part of the community). The results obtained are based on real historic data of several prosumers in a real community system. The results show that the proposed hierarchical CCEMS guarantees a further reduction in the annual household energy costs for each house (up to 15.89%) when being operated as a part of a community, compared to being operated individually.
Near-field MIMO-VLC System with Display-Camera Smartphones

This paper presents an angular-diversity transceiver design for a near-field (NF) multiple-input-multiple-output (MIMO) visible light communication (VLC) system using a pair of mobile devices. To considerably enlarge the field of view of the imaging receiver, a fisheye lens with a wide viewing angle $(185^{\circ})$ is considered to significantly supplement angular diversity. Besides, a polynomial model for the compound lens which are integrated with ordinary and fisheye lenses is adopted to accurately project a light signal on a smaller receiving area. Therefore, the proposed system can resist interference from different light sources in the receiving part, especially in shortdistance scenarios. Additionally, the precoder and equalizer in the MIMO VLC transceiver are iteratively optimized using VLC characteristics. Experimental results revealed that the optical MIMO transceivers could be effectively used for NF (2 cm) applications. Compared with the traditional scheme (zero forcing and minimum mean square error equalizer), the optimized MIMO VLC system with compound lens can gain transmission efficiency with almost 6.2 times.
Adaptive Cooperative NOMA with Energy Harvesting

In the two-user non-orthogonal multiple access (NOMA) networks, the transmission requirements of weak-user are sometimes not satisfied. To solve the issue, we propose a novel adaptive cooperative NOMA protocol to facilitate the information transfer of weak-user. In the protocol, base station (BS) can adaptively adjust the working mode of the networks according the requirement of the weak-user. Specifically, if the direct-link capacity can meet the transmission demand of weak-user, the networks operates in the traditional NOMA protocol. If the direct-link capacity cannot accomplish the information transfer of weak-user, strong-user, as a full-duplex (FD) relay, captures energy from the received signals to relay weak-user’s messages guaranteeing its own transmission requirement. Under the protocol, the outage probabilities of two users are characterized and their closed-form expressions are derived to evaluate the reliability of the networks. Finally, numerical results are provided to validate the superiority of our proposed scheme.
Non-parallel Sequence-to-Sequence Voice Conversion for Arbitrary Speakers

Voice conversion (VC) aims to modify the speaker’s tone while preserving the linguistic information. Recent works show that voice conversion has made great progress on non-parallel data by introducing phonetic posteriorgrams (PPGs). However, once the prosody of source and target speaker differ significantly, it causes noticeable quality degradation of the converted speech. To alleviate the impact of the prosody of the source speaker, we propose a sequence-to-sequence voice conversion (Seq2SeqVC) method, which utilizes connectionist temporal classification PPGs (CTC-PPGs) as inputs and models the non-linear length mapping between CTC-PPGs and frame-level acoustic features. CTC-PPGs are extracted by the CTC based automatic speech recognition (CTC-ASR) model and used to replace time-aligned PPGs. The blank token is introduced in CTC-ASR outputs to identify fewer information frames and get around consecutive repeating characters. After removing blank tokens, the left CTC-PPGs only contain linguistic information, and the phone duration information of the source speech is removed. Thus, phone durations of the converted speech are more faithful to the target speaker, which means higher similarity to the target and less interference from different source speakers. Experimental results show our Seq2Seq-VC model achieves higher scores in similarity and naturalness tests than the baseline method. What’s more, we expand our seq2seqVC approach to voice conversion towards arbitrary speakers with limited data. The experimental results demonstrate that our Seq2Seq-VC model can transfer to a new speaker using 100 utterances (about 5 minutes).
The effects of temperature, water‐filled pore space and land use on N2O emissions from an imperfectly drained gleysol

To investigate the effect of soil physical conditions and land use on emissions of nitrous oxide (N2O) to the atmosphere, soil cores of an imperfectly drained gleysol were taken from adjacent fields under perennial ryegrass and winter wheat. The cores were fertilized with ammonium nitrate and incubated at three different temperatures and water‐filled pore space (WFPS) values, and N2O emissions were measured by gas chromatography. Emissions showed a very large response to temperature. Apparent values of Q10 (emission rate at (T + 10)°C/emission rate at T°C) for the arable soil were about 50 for the 5–12°C interval and 8.9 for 12–18°C; the corresponding Q10s for the grassland soil were 3.7 and 2.3. Emissions from the grassland soil were always greater than those from the arable soil, although the ratio narrowed with increasing temperature. Changes in soil WFPS also had a profound effect on emissions. Those from the arable soil increased about 30‐fold as the WFPS increased from 60 to 80%, while that from the grassland soil increased 12‐fold. This latter response was similar to earlier field measurements. The N2O emissions were considered to be produced primarily by denitrification. We concluded that the impacts of temperature and WFPS on emissions could both be explained on the basis of existing models relating increasing respiration or decreased oxygen diffusivity, or both, to the development of anaerobic zones within the soil.
Application of Long-Short Term Memory Network and its Variants in Short-term Power Load Time Series Forecasting

Accurate and effective short-term power load forecasting (STLF) is very important for power systems Considering the temporal and non-linear characteristics of power load, this paper studies the application of standard long-short term memory (LSTM) network and its two typical variants, the Gated Recurrent Unit (GRU) and the Just Another NETwork (JANET) in STLF. So as to evaluate performance of the LSTMs, this paper compares three networks from prediction accuracy and training time. Furthermore, the hyperparameters of all LSTMs and the selection of training samples and inputs in the standard LSTM are discussed. The results of two actual examples show the LSTMs are all able to achieve accuracy of over 92% for the day-ahead hourly load prediction, but the standard LSTM and GRU have higher accuracy (over 94%), and the JANET has higher efficiency. Also, the optimal input window size is closely related to the power load period, and the prediction accuracy of LSTMs for STLF can be significantly improved through filtering training samples based on day-type or adding the loads of previous day to the inputs.
An Adaptive Strategy for DC-Link control of Active Front End for Four Quadrant Drives

The conventional voltage-oriented control strategy for Active Front Ends (AFE) is being modified by using an adaptive outer loop control in this paper. The control strategy suggested aims to provide a constant and steady DC link voltage with minimal ripple to the load in both motoring as well as regenerating modes. The adaptive PI controller as name suggests has proportional and integral gains which adjust themselves depending upon the error between reference voltage and DC link voltage and attempts to achieve a better control in overall loading conditions. Furthermore, unity power factor operation along with better transient response, and grid current THD’s within standard specified limits in all quadrants of operation are essential requirements for AFE’s which help in testing the effectiveness of the strategy. Additionally, comparison of the technique with well-established technique like symmetrical optimum method is been provided in the paper. The simulation and experimental results conducted on 60 kW back to back converter system used for driving a load-controlled induction motor is being presented in this paper which validate the proposed control strategy.
A Common Drain Operational Amplifier Using Positive Feedback Integrated by Metal-Oxide TFTs

This paper presents a common-drain-based operational amplifier (OPAMP) fabricated by mono n-type indium-zinc-oxide (IZO) thin-film transistors (TFTs). Positive feedback technology is employed to the load TFTs by cross-coupled connection in order to boost the voltage gain of the common-drain differential pair. The OPAMP exhibits an open-loop voltage gain (A<sub>v</sub>) of 27 dB over a −3 dB bandwidth (BW) of 8.4 kHz at a DC supply voltage of 10 V. The measured unity-gain frequency (UGF), phase margin (PM) and DC power consumption are 119.4 kHz, 36° and 0.96 mW, respectively. Moreover, the chip area of the proposed OPAMP is as small as 0.37 mm <inline-formula> <tex-math notation="LaTeX">$\times0.3$ </tex-math></inline-formula> mm since this concise topology needs only 10 TFTs.
Inpainting for Saturation Artifacts in Optical Coherence Tomography Using Dictionary-Based Sparse Representation

Saturation artifacts in optical coherence tomography (OCT) occur when received signal exceeds the dynamic range of spectrometer. Saturation artifact shows a streaking pattern and could impact the quality of OCT images, leading to inaccurate medical diagnosis. In this paper, we automatically localize saturation artifacts and propose an artifact correction method via inpainting. We adopt a dictionary-based sparse representation scheme for inpainting. Experimental results demonstrate that, in both case of synthetic artifacts and real artifacts, our method outperforms interpolation method and Euler's elastica method in both qualitative and quantitative results. The generic dictionary offers similar image quality when applied to tissue samples which are excluded from dictionary training. This method may have the potential to be widely used in a variety of OCT images for the localization and inpainting of the saturation artifacts.
DC Microgrid Planning, Operation, and Control: A Comprehensive Review

In recent years, due to the wide utilization of direct current (DC) power sources, such as solar photovoltaic (PV), fuel cells, different DC loads, high-level integration of different energy storage systems such as batteries, supercapacitors, DC microgrids have been gaining more importance. Furthermore, unlike conventional AC systems, DC microgrids do not have issues such as synchronization, harmonics, reactive power control, and frequency control. However, the incorporation of different distributed generators, such as PV, wind, fuel cell, loads, and energy storage devices in the common DC bus complicates the control of DC bus voltage as well as the power-sharing. In order to ensure the secure and safe operation of DC microgrids, different control techniques, such as centralized, decentralized, distributed, multilevel, and hierarchical control, are presented. The optimal planning of DC microgrids has an impact on operation and control algorithms; thus, coordination among them is required. A detailed review of the planning, operation, and control of DC microgrids is missing in the existing literature. Thus, this article documents developments in the planning, operation, and control of DC microgrids covered in research in the past 15 years. DC microgrid planning, operation, and control challenges and opportunities are discussed. Different planning, control, and operation methods are well documented with their advantages and disadvantages to provide an excellent foundation for industry personnel and researchers. Power-sharing and energy management operation, control, and planning issues are summarized for both grid-connected and islanded DC microgrids. Also, key research areas in DC microgrid planning, operation, and control are identified to adopt cutting-edge technologies. This review explicitly helps readers understand existing developments on DC microgrid planning, operation, and control as well as identify the need for additional research in order to further contribute to the topic.
Construction and Application of Knowledge Graph of Government Policy Based on Deep Neural Network

With the advent of the era of big data, the development of Deep learning technology has driven the research of smart government. The introduction of Knowledge Graph provides a new way for the development of smart government, and also provides a new way to understand the relevant objects of government policy texts, which has important academic and practical value. Based on the principle of solving the actual needs of smart government, we use the framework of Bert-BiLSTM-CRF model constructs the knowledge graph of government policy and discuss the whole process of the construction of government policy Knowledge Graph from the acquisition and processing of policy document data, the identification of government policy entities, the knowledge fusion of policy entities and the storage of Knowledge Graph. The Knowledge Graph of government policy based on practical problems lays a foundation for the in-depth study of smart government, which is an important application of deep learning technology and knowledge representation technology in the field of policy text research. At the same time, it can provide practical services for demand groups (government, enterprises, individuals, etc.).
Analytical Approach for MRI RF Array Coils Decoupling by Using Counter-Coupled Passive Resonators

We introduce an analytical approach to design decoupling filters for MRI radiofrequency array elements, adopting counter-coupled passive resonators as unit-cells. Specifically, our method is based on a magneto-static hypothesis, thus a deep comprehension of the physical interactions between all the elements in the system and design guidelines can be achieved. In particular, the couplings between adjacent and next-nearest neighbors coils pairs are both modeled, hence addressing the requirements for MRI arrays. The analytically-obtained filter solution is subsequently refined resorting to targeted full-wave simulations, reducing the computational effort. To prove the validity of the proposed approach, we conceived a test-case consisting of three planar RF coils, tuned at the 7T proton Larmor frequency. We demonstrated through full-wave simulations that the analytical design method is accurate and effective. Moreover, we fabricated a prototype and we performed benchtop measurements, both in unloaded conditions and in the presence of a biological phantom, resulting in excellent agreement with simulations. The developed analytical framework can be useful to model and control the mutual interactions between the various elements of an RF MRI system. In addition, the possibility to print the decoupling elements and the RF coils on the same dielectric substrate leads to a mechanically robust prototype.
New concept of silicon photonic MEMS switch based on total internal reflection

A novel silicon photonic MEMS switch based on total internal reflection (TIR) is proposed for the first time. The key element is a multimode interference (MMI) waveguide crossing with a switchable gap. This photonic switch works very well with a low excess loss of ~0.2 dB and a high extinction ratio of >23 dB in an ultra-broad wavelength range of 1250–1650 nm in the OFF state. The excess loss is still as low as 0.18 dB and the extinction ratio is as high as 58 dB in the ON state. © 2020 The Author(s)
Knowledge Extraction via Decentralized Knowledge Graph Aggregation

In many industrial manufacturing processes, human operators play a central role when it comes to parameterizing the involved machinery and dealing with errors in the process. However, large parts of the acquired process knowledge are tacit, leading to difficulties sharing the knowledge between operators. Therefore, knowledge extraction is a necessary but time and cost intensive process, requiring both specially trained personnel and experienced operators. In contrast, we propose that by gathering insights into what influenced operators' actual parameter choices, tacit process knowledge can be extracted during production in an example-based manner. This decentralized knowledge-decentralized in regards to who holds knowledge and where it was extracted—is then aggregated to a coherent knowledge graph. We showcase our methodology on a real-world dataset in the domain of fused deposition modeling (FDM), which is generated by operators providing their insights without additional assistance using extended human machine interfaces. Furthermore, we compare rules extracted from the aggregated knowledge graph against an established FDM knowledge base showing the viability of our approach even with limited amounts of data.
Graph Matching for Marker Labeling and Missing Marker Reconstruction With Bone Constraint by LSTM in Optical Motion Capture

Optical motion capture (MOCAP) is a commonly used technology to record the motion of non-rigid objects with high accuracy in 3D space. However, the MOCAP data has to be processed further before it can be used. The scattered reconstructed motion data must constitute a human configuration by labelling process according to the predefined template, and the missing markers have to be reconstructed to produce a stable motion trajectory. In this work, we propose a novel labelling method for motion sequences. First, a novel graph matching method is employed to determine the connection relationship of the scattered motion data for a single frame. Then, Kalman filtering is used for tracking in the motion sequence. As for the challenge coming from missing markers, we propose a new motion data preprocessing method considering the bone length constraint, which represents the information of variation in the relative position of adjacent markers. The processed motion data is input into a Long-Short Term Memory (LSTM) model to recover the missing markers and de-noise the motion data. The experiment conducted on our own dataset proves that our labelling method achieves a similar effect to Cortex, which is a commonly used commercial motion data analysis software. The experiment on CMU dataset demonstrates that our missing marker reconstruction method can achieve an art-of-state result. The labelling code will be pulished on https://github.com/Lijianfang6930/Graph-Matching-for-Marker-Labelling
Vital Sign Detection and Radar Self-Motion Cancellation Through Clutter Identification

This article presents a novel technique to remove the radar self-motion effects (RSMs) for an accurate detection of human vital signs. As opposed to the commonly used techniques, the proposed approach does not require any additional sensor, and instead, it extracts the RSM from the signals reflected by stationary clutters. Since the proposed technique requires to accurately identify the clutter range, two procedures for its automatic identification are proposed, aimed to detect both small and large radar motions. Besides allowing precise and reliable vital sign detection, it provides a compact, lightweight, comfortable, and cost-effective solution since potentially intrusive additional sensors are not required. Simulations have been carried out for validating the proposed approach, with an insight on the influence of different clutter radar cross sections on the sensitivity. Moreover, the effectiveness of the RSM cancellation has been experimentally demonstrated, showing its suitability for different applications, e.g., radar on moving platforms, vibrating tools, handheld devices, unmanned aerial vehicles, and cars.
A Generic FCN-Based Approach for the Road-Network Extraction From VHR Remote Sensing Images – Using OpenStreetMap as Benchmarks

As one road network is often the backbone in a geo-spatial dataset, capturing and/or updating the road networks using remote sensing imagery play an important role in traffic management, urban planning, vehicle navigation, and emergency management. Along with the progress of remote sensing launching technologies and the successful applications of deep learning in the field of computer vision, it has become more and more efficient and economical to employ deep learning methods for road-features extractions from very high-resolution (VHR) remote sensing imagery. Meanwhile, as one of the most significant and popular volunteer geographic information data sources, the OpenStreetMap (OSM) including the complete road networks in the wide world has been accumulated in the past decades. In this article, a generic and automatic approach for extracting road networks from VHR remote sensing images has been proposed based on fully convolutional neural network, in which the road centerlines from OSM have been employed to construct the labels for the model training and validation. In the conducted experiments on various VHR image datasets with two different spatial resolutions of 0.3 and 1 m, the proposed model has demonstrated quite satisfactory results – the overall completeness and correctness of the roads extraction from VHR remote sensing images exceed 94.0% and 98.0%, respectively.