bluuebunny/crossref_metadata_2025_split huggingface dataset:
Composite control for trajectory tracking of wheeled mobile robots with NLESO and NTSMC
Authors: Wang Haoyu , Zuo Zhiqiang , Wang Yijing , Yang Hongjiu 
Published: April 2021

AbstractThis paper proposes a control strategy integrating the non‐linear extended state observer (NLESO) and the non‐singular terminal sliding mode control (NTSMC) for the trajectory tracking of wheeled mobile robots subject to bounded disturbances. A new transformation method of chained model in terms of Lie derivative is presented to simplify the controller design. A specific NLESO combining linear term and non‐linear term is designed to estimate the disturbances with a faster convergence performance. A scheme for determining the gain range of NLESO is explicitly given to facilitate the tuning of experimental parameters. Meanwhile, the NTSMC achieves finite time convergence of the tracking error system and the chattering phenomenon in NTSMC is dramatically alleviated with the compensation from NLESO. The experimental results validate the strong robustness and good performance of the proposed control strategy.
Noise radar range doppler imaging via 2D generalized smoothed‐l0
Authors: Lu Xingyu , Gu Hong , Su Weimin 
Published: April 2021

AbstractThis paper presents an algorithm to enhance the range‐Doppler imaging performance for noise radar. Traditional matched filtering based method suffers from high range and Doppler sidelobes, which makes the weak targets overwhelmed by the sidelobes of strong targets or clutters in the range‐Doppler map. Sparse recovery based methods have been widely used to suppress such sidelobes, but most of them assume a repetitive transmit waveform, which cannot be applied to noise radar applications. In this letter, the range‐Doppler imaging problem for noise radar is formulated as a sparse recovery model and solved by a 2D generalized smoothed‐l0 algorithm. The proposed method can deal with the random waveforms varying among different pulses in noise radar. The robustness of the proposed method in strong noise and clutters is validated by simulation results.
Blind source separation‐based IVA‐Xception model for bird sound recognition in complex acoustic environments
Authors: Dai Yusheng , Yang Jin , Dong Yiwei , Zou Haipeng , Hu Mingzhi , Wang Bin 
Published: April 2021

AbstractIdentification of bird species from audio recordings has been a major area of interest within the field of ecological surveillance and biodiversity conservation. Previous studies have successfully identified bird species from given recordings. However, most of these studies are only adaptive to low‐noise acoustic environments and the cases where each recording contains only one bird's sound simultaneously. In reality, bird audios recorded in the wild often contain overlapping signals, such as bird dawn chorus, which makes audio feature extraction and accurate classification extremely difficult. This study is the first to focus on applying a blind source separation method to identify all foreground bird species contained in overlapping vocalization recordings. The proposed IVA‐Xception model is based on independent vector analysis and convolutional neural network. Experiments on 2020 Bird Sound Recognition in Complex Acoustic Environments competition (BirdCLEF2020) dataset show that this model could achieve a higher macro F1‐score and average accuracy compared with state‐of‐the‐art methods.
Novel 900 nm diode lasers with epitaxially stacked multiple active regions and tunnel junctions
Authors: Wenzel Hans , Maaßdorf Andre , Zink Christof , Martin Dominik , Weyers Markus , Knigge Andrea 
Published: April 2021

AbstractA multi‐active‐region bipolar‐cascade edge‐emitting laser emitting at nearly 900 nm is presented. The three active regions and two tunnel junctions located in a single waveguide core share the same third‐order vertical mode. A slope efficiency of 3.6 W/A was measured with a threshold current density of 230 A/cm2. The epitaxial layer stack developed features with very low internal optical losses of 0.7 cm−1. The voltage extrapolated to vanishing current is only 0.3 V larger than 3 times the voltage of 1.4 V originating from the photon energy.
Low‐complexity BER computation for coherent detection of orthogonal signals
Authors: Rugini L. , Baruffa G. 
Published: April 2021

AbstractThe bit error rate (BER) computation for coherently detected orthogonal signals in additive white Gaussian noise requires numerical integration, which can be cumbersome in low‐complexity devices. Here, a BER computation approach that circumvents the need for repeated numerical integration is proposed. First, a low‐complexity BER approximation formula is selected, with unknown parameters to be determined. Second, this letter determines the unknown parameters by fitting the BER approximation to a few BER values known in advance. The BER comparison with the exact results shows that the accuracy of the proposed low‐complexity approach is satisfactory for several constellation sizes of interest. This enables the possibility of BER self‐computation in low‐complexity devices that use orthogonal signals, like those used in the Internet of Things (IoT).
A 7.5 mW –43 dB LO leakage source‐driven wideband CMOS millimeter‐wave mixer
Authors: Lin Jiafu , Boon Chirn Chye , Berenguer Roc , Liu Gui 
Published: April 2021

AbstractThis paper presents a millimeter‐wave (mm‐wave) V‐band mixer. The source‐driven transformer‐coupled up‐converter topology can alleviate the local oscillator (LO) leakage problem. The proposed V‐band up‐converter has been implemented on 65 nm CMOS technology. The experimental results show that the LO leakage level can be reduced to –43.1 dB. The up‐converter occupies 0.11 mm2 in area excluding signal pads.
Modelling of the packet delivery rate in an actual LoRaWAN network
Authors: Abdelghany Ahmed , Uguen Bernard , Moy Christophe , Lemur Dominique 
Published: April 2021

AbstractThe Internet of things (IoT) paradigm is paving the way to ensure connectivity and monitoring within different market fields. Thus, it is critical to understand the packet transmission performance in Low Power Wide Area Network (LPWAN), especially LoRaWAN. Packet delivery rate (PDR) is considered as a dominant factor of the network performance and its dependency on the signal power. However, previous works do not model the PDR as a function of the received signal power. An in‐depth investigation of the PDR is done by performing an outdoor measurement campaign in the area of the Campus Beaulieu in Rennes. For each received LoRa packet, the effective signal power (ESP) values are obtained as well as their high influence on PDR is proven. With the given results, modelling the PDR using an ESP‐parameterized beta distribution function is proposed. Feasibility of the proposed model is assured by simulating PDR against ESP, hence, the simulated PDR values follow the distribution of the measured ones well. This PDR model gives important guidelines for future LoRaWAN network regulation and optimization.
Reweighting neural network examples for robust object detection at sea
Authors: Becktor J. , Boukas E. , Blanke M. , Nalpantidis L. 
Published: April 2021

AbstractDeep neural networks have had profound significance in addressing visual object detection and classification tasks. However, though with the caveat of needing large amounts of annotated training data. Furthermore, the possibility of neural networks overfitting to the biases and faults included in their respective datasets. In this work, methods for achieving robust neural networks, able to tolerate untrusted and possibly erroneous training data, are explored. The proposed method is shown to improve performance and help neural networks learn from untrusted data, provided a thoroughly annotated subset.
A novel frequency response measurement method for wideband ADCs' system
Authors: Zhou Yi , Ye Peng , Yang Kuojun , Zhang Yijiao 
Published: April 2021

AbstractThe parallel analogue to digital converter (ADC) structure with mixers such as digital bandwidth‐interleaving ADC (DBI‐ADC) is a practical structure for realising the wideband data acquisition system. The system calibration relies on accurate system frequency response measurement. However, existing measurement methods require complex synchronous circuits or are susceptible to noise. This paper proposes a new frequency response measurement method, which can be realised by a simple circuit without any synchronisation measures. At the same time, frequency response measurement can ensure sufficient accuracy. Simulation and experiment results show that the method is effective and feasible.
Segment attention‐guided part‐aligned network for person re‐identification
Authors: Wang Wen , Liu Yongwen , An Gaoyun 
Published: April 2021

AbstractPart misalignment of the human body caused by complex variations in viewpoint and pose poses a fundamental challenge to person re‐identification. This letter examines Res2Net as the backbone network to extract multi‐scale appearance features. At the same time, it uses the human parsing model to extract part features, which can be used as an attention stream to guide part features re‐calibration from the spatial dimension. Additionally, in order to ensure the diversity of features, SAG‐PAN effectively integrates the global appearance features of person image with part fine‐grained features. The experimental results on the Market‐1501, DukeMTMC‐reID and CUHK03 datasets show that the proposed SAG‐PAN achieved superior performance against the existing state‐of‐the‐art methods.
Face illumination processing via dense feature maps and multiple receptive fields
Authors: Ling Shenggui , Fu Keren , Lin Ye , You Di , Cheng Peng 
Published: April 2021

AbstractRecently, illumination processing of facial image based on generative adversarial networks has made favourable progress. However, the image quality is not so satisfactory and the recognition accuracy is low when the face image under extreme illumination conditions. For these reasons, an elaborately‐designed architecture based on convolutional neural network and generative adversarial networks for processing face illumination is presented. A novel dense feature maps loss that computes loss by using the varisized feature maps extracted from different convolutional layers of pre‐trained feature network is put forward. Moreover, multiple‐receptive‐fields‐based generator that uses multiple encoders during encoding phase is also proposed, and these encoders have the same structure with different kernel size. A variety of experimental results demonstrate that the method is superior to the state‐of‐the‐art methods under various illumination challenges. Code will be available soon at https://github.com/ling20cn/IP‐GAN
Simultaneous regression‐based spatial coverage estimation and object detection with deep learning
Authors: Andersen Rasmus Eckholdt , Nalpantidis Lazaros , Ravn Ole , Boukas Evangelos 
Published: April 2021

AbstractObject detection has been in the focus of researchers within varying applications propelled by the recent advances in deep learning and neural networks. Many applications require both detection of class instances as well as a quantification of the spatial coverage of the class instances. While the performance of deep learning approaches for these tasks has been extensively studied there has not been much effort into creating a unified network structure to achieve both goals. The purpose of this paper is to present a regressor to the faster R‐CNN architecture that can help quantify the spatial coverage estimation of some detected object. The goal of the regressor is to provide a reproducible result of the spatial coverage. To demonstrate the developed architecture, an example use‐case of land cover estimation is used. The experiments conducted in this paper show that the network does not sacrifice object detection accuracy, and indicate that the network is able to estimate the spatial coverage of six different types of land.
Reliability evaluation of regional energy Internet considering electricity–gas coupling and coordination between energy stations
Authors: Liu Hong , Zhao Yue , Ge Shaoyun , Zhang Peng , Liu Wei , Qi Xiaoguang , Hu Ping 
Published: April 2021

AbstractIn view of the issues that the current comprehensive energy supply reliability evaluation mainly focuses on the distributed energy system and rarely involves the mutual coupling of energy supply networks and the coordination of energy stations in the regional energy Internet, an evaluation method of energy supply reliability considering electricity–gas coupling and coordination between energy stations was proposed. First, the framework of regional energy Internet (REI) is established, the energy flow relationship among the main parts in the REI and the influence of electricity–gas coupling on reliability are analysed; second, considering building virtual energy storage and demand response, the rigid and flexible reliability evaluation indexes are proposed from the energy and time levels; third, the operation strategies of normal, failure and repair periods of each energy station are put forward, and the optimal load reduction model of REI aiming at the minimum total rigid energy shortage is established. Moreover, the method and process of energy supply reliability evaluation combining fault mode effect analysis (FMEA) and Monte Carlo simulation are proposed. Finally, through a practical example, the impact of demand response, multiple energy storage resources, electricity–gas coupling and inter‐station coordination on reliability is analysed.
A new scheme for fault location of three‐terminal parallel transmission lines without transposer
Authors: Chen Gonggui , Cai Daiying , Long Hongyu , Long Yi , Zeng Xianjun , Zhou Ping , Kang Peng 
Published: April 2021

AbstractThis paper proposes a new scheme for locating the faults of three‐terminal double‐circuit transmission line without transposer in the phase domain that is not affected by type of fault. Synchronous voltage and current were measured by phasor measurement units (PMUs) installed at three busbars. Considering the coupling effect of parallel lines, the distribution line model was used to obtain more accurate results and third‐order Taylor series expansion to decouple distributed line model. The phase and branch of all faults on the transmission lines were effectively identified, and various types of faults were accurately located including normal shunt fault, evolving fault and cross‐country fault. The proposed algorithm was tested under different fault locations, resistances, initial angles and types (including cross‐country faults and evolving faults). In addition, the effects of different sampling rates, measurement errors, earth resistivities and transmission line parameter errors were also considered. The simulation calculation was performed in MATLAB software and three‐terminal transmission lines were simulated in PSCAD/EMTP software. The results indicate that maximum estimated error was less than 1.8631%.
Ensuring data integrity of healthcare information in the era of digital health
Authors: Zarour Mohammad , Alenezi Mamdouh , Ansari Md Tarique Jamal , Pandey Abhishek Kumar , Ahmad Masood , Agrawal Alka , Kumar Rajeev , Khan Raees Ahmad 
Published: April 2021

AbstractData integrity continues to be a persistent problem in the current healthcare sector. It ensures that the data is correct and has not even in any manner been improperly changed. Incorrect data might become significant health threats for patients and a big responsibility for clinicians, resulting in problems such as scam, misconduct, inadequate treatment and data theft. This sort of endangering scenario causes tremendous difficulty in handling healthcare data. This research intends to describe the threat plot of data integrity in healthcare through numerous attack statistics from around the world and Saudi Arabia and identify the criticality in Saudi Arabia in particular. A literature review by descriptive analysis, unit analysis and rating analysis to achieve the planned systematic literature review goal is outlined. The outcome of ranking analysis using a fuzzy analytical hierarchy process methodology offers a route for Saudi Arabian researchers to promote medical records or data security in Arabic healthcare. It is suggested that blockchain is the most prioritized method for regular use and adaptation across Saudi Arabia in all data integrity management techniques. To address the challenges of data integrity and future path, the authors critically examine the challenges posed by data integrity in the healthcare sector.
A robust photo‐based PM2.5$_{2.5}$ monitoring method by combining linear and non‐linear learning
Authors: Xia Zhifang 
Published: April 2021

AbstractGood health is pursued by people all over the world. However, the continual industrialisation has led to more and more atmospheric contamination, and PM has caused serious harm to our life safety and living environment. Without increasing the cost of sustainable industrial production, more and more attention has been paid to the related researches on improving PM monitoring, prevention and control level. Therefore, it is extremely urgent to establish a robust PM monitoring model that can adapt to a variety of scenarios, not only in local places like campuses but also in wide area like city. Existing work has proven that PM monitoring can be achieved by means of photos. But experiments show that the stated‐of‐the‐art methods are far from ideal for PM monitoring when the author tested the performance in two public datasets. To solve the aforesaid issue, this paper ulteriorly proposes a novel photo‐based PM monitoring model, which fuses the results of existing methods by firstly using the weighted average based on the least absolute shrinkage and selection operator regression for learning the basic linear component, secondly using the support vector regression for learning the non‐linear residual component, and finally incorporating the above two outputs to infer the final PM concentration. The main contributions and innovations of this paper are embodied in: (1) the innovative use of image quality assessment model to extract 9 features for PM monitoring, (2) separately extract macro information and micro information from PM2.5 pictures, (3) two newly‐established large‐scaled datasets are applied to verify the effectiveness and robustness of the proposed PM monitoring model. Experiments show that on the latest PM datasets (local and wide), the proposed model has achieved high performance and demonstrated strong robustness.
Research on fatigue detection based on visual features
Authors: Zhao Guangzhe , He Yanqing , Yang Hanting , Tao Yong 
Published: April 2021

AbstractThe high incidence of traffic accidents brings immeasurable losses to life. In order to avoid such crises, researchers and automakers have used many methods to solve this problem. Among them, technology based on visual features is widely used in driver fatigue detection. As fatigue detection plays a vital role in the driving process, the high accuracy of fatigue monitoring is very important. This paper focuses on the method based on convolutional neural network to detect driver fatigue. First, in the face detection part, the Single‐Shot Multi‐Box Detector algorithm is used to improve the speed and accuracy of face detection to extract the eye and mouth regions; second, the VGG16 network is used to learn fatigue features, which is performed on the NTHU‐Drowsy Driver Detection (NTHU‐DDD) data set and the other two modified data sets Training test. The main result of this work is that the accuracy of fatigue monitoring is higher than other methods including the original method, with an accuracy rate of over 90%. And it has better generalization ability than the multi‐physical feature fusion detection method. At the same time, we propose the fatigue detection method based on convolutional neural network to improve the advanced driver assistance system (ADAS) to make it more robust and reliable decision making.
A novel image quality assessment method and coefficient of quality for digital solutions of colour blindness
Authors: S Meenakshi , Singla Anshu 
Published: April 2021

AbstractEyesight is one of the primary senses that human beings have. Reports show that colour blindness, a form of colour vision deficiency (CVD), affects about 8% of the male population and 0.5% of female population. The Assistive Technology Act of 2004 lays focus on technologies that help individuals with disabilities and deficiencies. With the rapid advancement in technologies, several assistive solutions are available for visually impaired or CVD patients. Such solutions involve simulation and compensation of conflicting colours to help the colour blind in the visual perception of colours. Given the increased usage of the web, post the pandemic, these solutions improve the quality of life for the colour blind. Defining the image quality assessment criteria for such digital solutions becomes imperative. The study proposes a novel method for image quality assessment of digital solutions aimed at assisting the colour blind users. The proposed coefficient of quality (CQ) would be useful to rank colour compensation and recolouring algorithms. Experiments were conducted with a novel questionnaire set designed for this quality measurement. The results affirm the efficiency of the assessment method proposed. This will also provide objective feedback to the researchers and experts in this area to improve their solutions for CVD patients.
An efficient way to use MS‐CLEAN associated with Shannon's entropy
Authors: Campos Ramon G. , Salles Evandro O. T. 
Published: April 2021

AbstractThis article covers deconvolution methods in the context of radio astronomical images. A new formulation is proposed to deal with negative brightness, deconvoluting separately the positive and negative brightness of the sky. The positive brightness is physically possible, but negative brightness is a degradation product. At the same time, the paper presents Shannon's entropy's behaviour in the context of the Multi‐Scale CLEAN (MS‐CLEAN) algorithm, defining the measured brightness as information in the scope of Shannon's entropy. The knowledge acquired is used in an example of information monitoring at scales, which automatically reduces the search space of MS‐CLEAN, and reduces the computational cost. The proposed algorithm, called Relevant Component Multi‐Scale CLEAN (RC‐CLEAN), can be up to 4 times faster than the classic MS‐CLEAN without prejudice to the identification of structures and noise reduction. Here, Structural Similarity Index (SSIM) and Peak Signal to Noise Ratio (PSNR) used to quantify the results, respectively, showed the same quality for the SSIM and gains of up to 11 dB for the PSNR. RC‐CLEAN also shows a result similar to that obtained by the standard software of large astronomical laboratories using real data.