Multiprobe microwave multimeter signals iterative processing

As industrial research in automated driving is rapidly advancing, it is of paramount importance to analyze field data from extensive road tests. This paper investigates the design and development of a toolchain to process and manage experimental data to answer a set of research questions about the evaluation of automated driving functions at various levels, from technical system functioning to overall impact assessment. We have faced this challenge in L3Pilot, the first comprehensive test of automated driving functions (ADFs) on public roads in Europe. L3Pilot is testing ADFs in vehicles made by 13 companies. The tested functions are mainly of Society of Automotive Engineers (SAE) automation level 3, some of them of level 4. In this context, the presented toolchain supports various confidentiality levels, and allows cross-vehicle owner seamless data management, with the efficient storage of data and their iterative processing with a variety of analysis and evaluation tools. Most of the toolchain modules have been developed to a prototype version in a desktop/cloud environment, exploiting state-of-the-art technology. This has allowed us to efficiently set up what could become a comprehensive edge-to-cloud reference architecture for managing data in automated vehicle tests. The project has been released as open source, the data format into which all vehicular signals, recorded in proprietary formats, were converted, in order to support efficient processing through multiple tools, scalability and data quality checking. We expect that this format should enhance research on automated driving testing, as it provides a shared framework for dealing with data from collection to analysis. We are confident that this format, and the information provided in this article, can represent a reference for the design of future architectures to implement in vehicles.

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High-Throughput and Low-Latency Digital Baseband Architecture for Energy-Efficient Wireless VR Systems

Electronics ◽

10.3390/electronics8070815 ◽

2019 ◽

Vol 8 (7) ◽

pp. 815 ◽

Cited By ~ 1

Author(s):

Seokha Hwang ◽

Seungsik Moon ◽

Dongyun Kam ◽

Inn-Yeal Oh ◽

Youngjoo Lee

Keyword(s):

High Speed ◽

Near Field ◽

Rf Circuits ◽

60 Ghz ◽

Video Streams ◽

Iterative Processing ◽

Block Level ◽

Quality Of Transmission ◽

Processing Steps

This paper presents a novel baseband architecture that supports high-speed wireless VR solutions using 60 GHz RF circuits. Based on the experimental observations by our previous 60 GHz transceiver circuits, the efficient baseband architecture is proposed to enhance the quality of transmission. To achieve a zero-latency transmission, we define an (106,920, 95,040) interleaved-BCH error-correction code (ECC), which removes iterative processing steps in the previous LDPC ECC standardized for the near-field wireless communication. Introducing the block-level interleaving, the proposed baseband processing successfully scatters the existing burst errors to the small-sized component codes, and recovers up to 1080 consecutive bit errors in a data frame of 106,920 bits. To support the high-speed wireless VR system, we also design the massive-parallel BCH encoder and decoder, which is tightly connected to the block-level interleaver and de-interleaver. Including the high-speed analog interfaces for the external devices, the proposed baseband architecture is designed in 65 nm CMOS, supporting a data rate of up to 12.8 Gbps. Experimental results show that the proposed wireless VR solution can transfer up to 4 K high-resolution video streams without using time-consuming compression and decompression, successfully achieving a transfer latency of 1 ms.

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A Scale-Adaptive Matching Algorithm for Underwater Acoustic and Optical Images

Sensors ◽

10.3390/s20154226 ◽

2020 ◽

Vol 20 (15) ◽

pp. 4226 ◽

Cited By ~ 1

Author(s):

Jun Liu ◽

Benyuan Li ◽

Wenxue Guan ◽

Shenghua Gong ◽

Jiaxin Liu ◽

...

Keyword(s):

Estimation Method ◽

Scale Space ◽

Optical Data ◽

Enhancement Effect ◽

Underwater Acoustic ◽

Iterative Processing ◽

Similarity Estimation ◽

Optical Images ◽

Acoustic Images ◽

Scale Detection

Underwater acoustic and optical data fusion has been developed in recent decades. Matching of underwater acoustic and optical images is a fundamental and critical problem in underwater exploration because it usually acts as the key step in many applications, such as target detection, ocean observation, and joint positioning. In this study, a method of matching the same underwater object in acoustic and optical images was designed, consisting of two steps. First, an enhancement step is used to enhance the images and ensure the accuracy of the matching results based on iterative processing and estimate similarity. The acoustic and optical images are first pre-processed with the aim of eliminating the influence of contrast degradation, contour blur, and image noise. A method for image enhancement was designed based on iterative processing. In addition, a new similarity estimation method for acoustic and optical images is also proposed to provide the enhancement effect. Second, a matching step is used to accurately find the corresponding object in the acoustic images that appears in the underwater optical images. In the matching process, a correlation filter is applied to determine the correlation for matching between images. Due to the differences of angle and imaging principle between underwater optical and acoustic images, there may be major differences of size between two images of the same object. In order to eliminate the effect of these differences, we introduce the Gaussian scale-space, which is fused with multi-scale detection to determine the matching results. Therefore, the algorithm is insensitive to scale differences. Extensive experiments demonstrate the effectiveness and accuracy of our proposed method in matching acoustic and optical images.

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Error-weighted maximum likelihood (EWML): a new statistically based method to cluster quantitative micropaleontological data

Journal of Paleontology ◽

10.1017/s0022336000036921 ◽

1993 ◽

Vol 67 (3) ◽

pp. 475-486 ◽

Cited By ~ 59

Author(s):

Evan Fishbein ◽

R. Timothy Patterson

Keyword(s):

Maximum Likelihood ◽

Rare Species ◽

Degrees Of Freedom ◽

Statistical Hypothesis ◽

Data Set ◽

Clustering Techniques ◽

Iterative Processing ◽

Clustering Model ◽

Species Abundances ◽

Computer Based

The advent of readily available computer-based clustering packages has created some controversy in the micropaleontological community concerning the use and interpretation of computer-based biofacies discrimination. This is because dramatically different results can be obtained depending on methodology. The analysis of various clustering techniques reveals that, in most instances, no statistical hypothesis is contained in the clustering model and no basis exists for accepting one biofacies partitioning over another. Furthermore, most techniques do not consider standard error in species abundances and generate results that are not statistically relevant. When many rare species are present, statistically insignificant differences in rare species can accumulate and overshadow the significant differences in the major species, leading to biofacies containing members having little in common.A statistically based “error-weighted maximum likelihood” (EWML) clustering method is described that determines biofacies by assuming that samples from a common biofacies are normally distributed. Species variability is weighted to be inversely proportional to measurement uncertainty. The method has been applied to samples collected from the Fraser River Delta marsh and shows that five distinct biofacies can be resolved in the data. Similar results were obtained from readily available packages when the data set was preprocessed to reduce the number of degrees of freedom. Based on the sample results from the new algorithm, and on tests using a representative micropaleontological data set, a more conventional iterative processing method is recommended. This method, although not statistical in nature, produces similar results to EWML (not commercially available yet) with readily available analysis packages. Finally, some of the more common clustering techniques are discussed and strategies for their proper utilization are recommended.

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