OpenPARF: An Open-Source Placement and Routing Framework for Large-Scale Heterogeneous FPGAs with Deep Learning Toolkit

This paper proposes OpenPARF, an open-source placement and routing framework for large-scale FPGA designs. OpenPARF is implemented with the deep learning toolkit PyTorch and supports massive parallelization on GPU. The framework proposes a novel asymmetric multi-electrostatic field system to solve FPGA placement. It considers fine-grained routing resources inside configurable logic blocks (CLBs) for FPGA routing and supports large-scale irregular routing resource graphs. Experimental results on ISPD 2016 and ISPD 2017 FPGA contest benchmarks and industrial benchmarks demonstrate that OpenPARF can achieve 0.4-12.7% improvement in routed wirelength and more than $2\times$ speedup in placement. We believe that OpenPARF can pave the road for developing FPGA physical design engines and stimulate further research on related topics

An image recognition method for gear fault diagnosis in the manufacturing line of short filament fibres

The manufacturing line is a fundamental element in short filament fibre production, in which the gearbox is the key mechanical part. Any faults in the gearbox will greatly affect the quality of the short filament fibres. However, due to the harsh working environment, the gearbox is vulnerable to failure. Due to the complexity of the manufacturing line, effective and efficient feature extraction of gear faults is still a challenge. To this end, a new fault diagnosis method based on image recognition is proposed in this paper for gear fault detection in fibre manufacturing lines. In this method, wavelet packet bispectrum analysis (WPBA) is proposed to process the gear vibration signals. The bispectrum texture is obtained and then analysed by an image fusion algorithm for texture feature extraction. The grey-level co-occurrence matrix is used in the image fusion and the extracted texture features are four parameters of the grey-level co-occurrence matrix. Finally, a support vector machine (SVM) is adapted to recognise the gear fault type and location. Experimental data acquired from a real-world manufacturing line of short filament fibres are used to evaluate the performance of the proposed image-based gear fault detection method. The analysis results demonstrate that the newly proposed method is capable of accurate gear fault det ection in fibre manufacturing lines

Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition

Hilbert–Huang transform (HHT) is a popular method to analyze nonlinear and non-stationary data. It has been widely used in geophysical prospecting. This paper analyzes the mode mixing problems of empirical mode decomposition (EMD) and introduces the noncontact measurement and detection of instantaneous seismic attributes using complementary ensemble empirical mode decomposition (CEEMD). Numerical simulation testing indicates that the CEEMD can effectively solve the mode mixing problems of EMD and can provide stronger anti-noise ability. The decomposed results of the synthetic seismic record show that CEEMD has a better ability to decompose seismic signals. Then, CEEMD is applied to extract instantaneous seismic attributes of 3D seismic data in a real-world coal mine in Inner Mongolia, China. The detection results demonstrate that instantaneous seismic attributes extracted by CEEMD are helpful to effectively identify the undulations of the top interfaces of limestone

An image recognition method for gear fault diagnosis in the manufacturing line of short filament fibres

The manufacturing line is a fundamental element in short filament fibre production, in which the gearbox is the key mechanical part. Any faults in the gearbox will greatly affect the quality o f the short filament fibres. However, due to the harsh working environment, the gearbox is vulnerable to failure. Due to the complexity o f the manufacturing line, effective and efficient feature extraction o f gear faults is still a challenge. To this end, a new fault diagnosis method based on image recognition is proposed in this paper for gear fault detection in fibre manufacturing lines. In this method, wavelet packet bispectrum analysis (WPBA) is proposed to process the gear vibration signals. The bispectrum texture is obtained and then analysed by an image fusion algorithm for texture feature extraction. The grey-level co-occurrence matrix is used in the image fusion and the extracted texture features are four parameters o f the grey-level co-occurrence matrix. Finally, a support vector machine (SVM) is adapted to recognise the gear fault type and location. Experimental data acquired from a real-world manufacturing line o f short filament fibres are used to evaluate the performance o f the proposed image-based gear fault detection method. The analysis results demonstrate that the newly proposed method is capable o f accurate gear fault detection in fibre manufacturing lines.The Key Laboratory of Expressway Construction Machinery of Shaanxi Province (No 310825161123), NSFC (No 51505475), Yingcai Project of CUMT (YC2017001), Priority Academic Program Development of Jiangsu Higher Education Institutions and the UOW Vice-Chancellor’s Postdoctoral Research Fellowship.http://www.bindt.org/publications/insight-journalam2019Electrical, Electronic and Computer Engineerin

An image recognition method for gear fault diagnosis in the manufacturing line of short filament fibres

The manufacturing line is a fundamental element in short filament fibre production, in which the gearbox is the key mechanical part. Any faults in the gearbox will greatly affect the quality o f the short filament fibres. However, due to the harsh working environment, the gearbox is vulnerable to failure. Due to the complexity o f the manufacturing line, effective and efficient feature extraction o f gear faults is still a challenge. To this end, a new fault diagnosis method based on image recognition is proposed in this paper for gear fault detection in fibre manufacturing lines. In this method, wavelet packet bispectrum analysis (WPBA) is proposed to process the gear vibration signals. The bispectrum texture is obtained and then analysed by an image fusion algorithm for texture feature extraction. The grey-level co-occurrence matrix is used in the image fusion and the extracted texture features are four parameters o f the grey-level co-occurrence matrix. Finally, a support vector machine (SVM) is adapted to recognise the gear fault type and location. Experimental data acquired from a real-world manufacturing line o f short filament fibres are used to evaluate the performance o f the proposed image-based gear fault detection method. The analysis results demonstrate that the newly proposed method is capable o f accurate gear fault detection in fibre manufacturing lines.The Key Laboratory of Expressway Construction Machinery of Shaanxi Province (No 310825161123), NSFC (No 51505475), Yingcai Project of CUMT (YC2017001), Priority Academic Program Development of Jiangsu Higher Education Institutions and the UOW Vice-Chancellor’s Postdoctoral Research Fellowship.http://www.bindt.org/publications/insight-journalam2019Electrical, Electronic and Computer Engineerin

A Perspective on the performance of the CFOSAT rotating fan beam scatterometer

China France Oceanography SATellite (CFOSAT) pre-launch workshop, 8-9 October 2018, Brest, FrancePeer Reviewe

Assessment of the CFOSAT scatterometer backscatter and wind quality

International Ocean Vector Winds Science Team Workshop (2018 IOVWST), 24-26 April 2018, BarcelonaPeer Reviewe

A Perspective on the Performance of the CFOSAT Rotating Fan-Beam Scatterometer

13 pages. 10 figures. 2 tables.-- © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The China-France Oceanography Satellite (CFOSAT) to be launched in October 2018 will carry two innovative payloads, i.e., the surface wave investigation and monitoring instrument and the rotating fan-beam scatterometer [CFOSAT scatterometer (CFOSCAT)]. Both instruments, operated in Ku-band microwave frequency, are dedicated to the measurement of sea surface wave spectra and wind vectors, respectively. This paper provides an overview of the system definition and characteristics of the CFOSCAT instrument. A prelaunch analysis is carried out to estimate the scatterometer backscatter and wind quality based on the developed CFOSCAT simulator prototype. The overall simulation includes two parts: first, a forward model is developed to simulate the ocean backscatter signals, accounting for both instrument and geophysical noise. Second, a wind inversion processor is used to retrieve wind vectors from the outputs of the forward model. The benefits and challenges of the novel observing geometries are addressed in terms of the CFOSCAT wind retrieval. The simulations show that the backscatter accuracy and the retrieved wind quality of CFOSCAT are quite promising and meet the CFOSAT mission requirementsPeer Reviewe