scholarly journals Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Huan Yin ◽  
Xuecheng Xu ◽  
Yue Wang ◽  
Rong Xiong
Keyword(s):  
Source Code ◽  
Light Detection And Ranging ◽  
Place Recognition ◽  
Learning Stage ◽  
Joint Learning ◽  
Adverse Conditions ◽  
Testing Stage ◽  
Single Sensor ◽  
Public Datasets

Place recognition is critical for both offline mapping and online localization. However, current single-sensor based place recognition still remains challenging in adverse conditions. In this paper, a heterogeneous measurement based framework is proposed for long-term place recognition, which retrieves the query radar scans from the existing lidar (Light Detection and Ranging) maps. To achieve this, a deep neural network is built with joint training in the learning stage, and then in the testing stage, shared embeddings of radar and lidar are extracted for heterogeneous place recognition. To validate the effectiveness of the proposed method, we conducted tests and generalization experiments on the multi-session public datasets and compared them to other competitive methods. The experimental results indicate that our model is able to perform multiple place recognitions: lidar-to-lidar (L2L), radar-to-radar (R2R), and radar-to-lidar (R2L), while the learned model is trained only once. We also release the source code publicly: https://github.com/ZJUYH/radar-to-lidar-place-recognition.

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

scholarly journals A Century of French Railways: The Value of Remote Sensing and VGI in the Fusion of Historical Data

2021 ◽  
Vol 10 (3) ◽  
pp. 154
Author(s):  
Robert Jeansoulin
Keyword(s):  
Remote Sensing ◽  
Meta Analysis ◽  
Computer Assisted ◽  
Railway Network ◽  
Reconstruction Process ◽  
Railway Stations ◽  
Eu Member States ◽  
Public Data ◽  
Public Datasets

Providing long-term data about the evolution of railway networks in Europe may help us understand how European Union (EU) member states behave in the long-term, and how they can comply with present EU recommendations. This paper proposes a methodology for collecting data about railway stations, at the maximal extent of the French railway network, a century ago.The expected outcome is a geocoded dataset of French railway stations (gares), which: (a) links gares to each other, (b) links gares with French communes, the basic administrative level for statistical information. Present stations are well documented in public data, but thousands of past stations are sparsely recorded, not geocoded, and often ignored, except in volunteer geographic information (VGI), either collaboratively through Wikipedia or individually. VGI is very valuable in keeping track of that heritage, and remote sensing, including aerial photography is often the last chance to obtain precise locations. The approach is a series of steps: (1) meta-analysis of the public datasets, (2) three-steps fusion: measure-decision-combination, between public datasets, (3) computer-assisted geocoding for ‘gares’ where fusion fails, (4) integration of additional gares gathered from VGI, (5) automated quality control, indicating where quality is questionable. These five families of methods, form a comprehensive computer-assisted reconstruction process (CARP), which constitutes the core of this paper. The outcome is a reliable dataset—in geojson format under open license—encompassing (by January 2021) more than 10,700 items linked to about 7500 of the 35,500 communes of France: that is 60% more than recorded before. This work demonstrates: (a) it is possible to reconstruct transport data from the past, at a national scale; (b) the value of remote sensing and of VGI is considerable in completing public sources from an historical perspective; (c) data quality can be monitored all along the process and (d) the geocoded outcome is ready for a large variety of further studies with statistical data (demography, density, space coverage, CO2 simulation, environmental policies, etc.).

Distinguishable Brain Activation Networks for Short- and Long-Term Motor Skill Learning

2005 ◽  
Vol 94 (1) ◽  
pp. 512-518 ◽  
Author(s):  
A. Floyer-Lea ◽  
P. M. Matthews
Keyword(s):  
Motor Skill ◽  
Isometric Force ◽  
Brain Activation ◽  
Skill Learning ◽  
Anterior Cingulate ◽  
Motor Skill Learning ◽  
Short Term ◽  
Learning Stage ◽  
The Right

The acquisition of a new motor skill is characterized first by a short-term, fast learning stage in which performance improves rapidly, and subsequently by a long-term, slower learning stage in which additional performance gains are incremental. Previous functional imaging studies have suggested that distinct brain networks mediate these two stages of learning, but direct comparisons using the same task have not been performed. Here we used a task in which subjects learn to track a continuous 8-s sequence demanding variable isometric force development between the fingers and thumb of the dominant, right hand. Learning-associated changes in brain activation were characterized using functional MRI (fMRI) during short-term learning of a novel sequence, during short-term learning after prior, brief exposure to the sequence, and over long-term (3 wk) training in the task. Short-term learning was associated with decreases in activity in the dorsolateral prefrontal, anterior cingulate, posterior parietal, primary motor, and cerebellar cortex, and with increased activation in the right cerebellar dentate nucleus, the left putamen, and left thalamus. Prefrontal, parietal, and cerebellar cortical changes were not apparent with short-term learning after prior exposure to the sequence. With long-term learning, increases in activity were found in the left primary somatosensory and motor cortex and in the right putamen. Our observations extend previous work suggesting that distinguishable networks are recruited during the different phases of motor learning. While short-term motor skill learning seems associated primarily with activation in a cortical network specific for the learned movements, long-term learning involves increased activation of a bihemispheric cortical-subcortical network in a pattern suggesting “plastic” development of new representations for both motor output and somatosensory afferent information.

scholarly journals How long can we build it? Ensuring usability of a scientific code base

2021 ◽  
Vol 16 (1) ◽  
pp. 11
Author(s):  
Klaus Rechert ◽  
Jurek Oberhauser ◽  
Rafael Gieschke
Keyword(s):  
Data Management ◽  
Source Code ◽  
Research Data ◽  
Scientific Contribution ◽  
Scientific Publications ◽  
Research Data Management ◽  
Code Base

Software and in particular source code became an important component of scientific publications and henceforth is now subject of research data management.  Maintaining source code such that it remains a usable and a valuable scientific contribution is and remains a huge task. Not all code contributions can be actively maintained forever. Eventually, there will be a significant backlog of legacy source-code. In this article we analyse the requirements for applying the concept of long-term reusability to source code. We use simple case study to identify gaps and provide a technical infrastructure based on emulator to support automated builds of historic software in form of source code.  

scholarly journals Long-Term Clinical Outcomes of Gastric MALT Lymphoma: A Nationwide Multicenter Study in Korea

2021 ◽  
Vol 11 ◽  
Author(s):  
Joon Sung Kim ◽  
Jun Chul Park ◽  
Jong Yeul Lee ◽  
Ji Yong Ahn ◽  
Sun Hyung Kang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Malt Lymphoma ◽  
Case Series ◽  
Stage Iii ◽  
Gastric Malt Lymphoma ◽  
University Hospitals ◽  
Initial Stage ◽  
Testing Stage ◽  
H Pylori

BackgroundTreatment recommendations for gastric mucosa-associated lymphoid tissue (MALT) lymphoma are based on case series and expert opinions. Only a few previous studies have focused on the long-term outcomes of gastric MALT lymphoma, especially according to stage.MethodsPatients diagnosed with gastric MALT lymphoma from January 2000 to December 2018 at nine university hospitals in Korea were included. Clinical data of medical history, endoscopic features, histological diagnosis, results of Helicobacter pylori (H. pylori) testing, stage, treatment conditions, and outcomes were collected.ResultsA total of 1,163 patients was enrolled, and 97.6% (n=1,038) of patients were diagnosed as stage IE. 10-year overall survival (OS) for the entire population was 99.1% and was better for patients in stage IE compared with patients in stage III/IV (p=0.002). The 10-year OS for H. pylori-positive patients was better than that of H. pylori-negative patients (p=0.022). Multivariate analyses revealed initial stage III/IV as a prognostic factor associated with over-all survival.ConclusionThe majority of gastric MALT lymphoma patients are diagnosed at an early localized stage in Korea. The overall survival rate of gastric MALT lymphoma is excellent and is associated with the initial stage of the disease.

scholarly journals A portable three-dimensional LIDAR-based system for long-term and wide-area people behavior measurement

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141984153 ◽  
Author(s):  
Kenji Koide ◽  
Jun Miura ◽  
Emanuele Menegatti
Keyword(s):  
Preliminary Analysis ◽  
Three Dimensional ◽  
Light Detection And Ranging ◽  
Wide Area ◽  
Elderly Persons ◽  
Tracking Systems ◽  
People Tracking ◽  
Design Systems ◽  
Environmental Map

It is important to measure and analyze people behavior to design systems which interact with people. This article describes a portable people behavior measurement system using a three-dimensional LIDAR. In this system, an observer carries the system equipped with a three-dimensional Light Detection and Ranging (LIDAR) and follows persons to be measured while keeping them in the sensor view. The system estimates the sensor pose in a three-dimensional environmental map and tracks the target persons. It enables long-term and wide-area people behavior measurements which are hard for existing people tracking systems. As a field test, we recorded the behavior of professional caregivers attending elderly persons with dementia in a hospital. The preliminary analysis of the behavior reveals how the caregivers decide the attending position while checking the surrounding people and environment. Based on the analysis result, empirical rules to design the behavior of attendant robots are proposed.

scholarly journals Research on Singing Voice Detection Based on a Long-Term Recurrent Convolutional Network with Vocal Separation and Temporal Smoothing

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1458
Author(s):  
Xulong Zhang ◽  
Yi Yu ◽  
Yongwei Gao ◽  
Xi Chen ◽  
Wei Li
Keyword(s):  
Time Domain ◽  
Short Term Memory ◽  
Block Size ◽  
Detection Algorithm ◽  
Singing Voice ◽  
Convolutional Network ◽  
Frame Size ◽  
Voice Detection ◽  
Public Datasets

Singing voice detection or vocal detection is a classification task that determines whether a given audio segment contains singing voices. This task plays a very important role in vocal-related music information retrieval tasks, such as singer identification. Although humans can easily distinguish between singing and nonsinging parts, it is still very difficult for machines to do so. Most existing methods focus on audio feature engineering with classifiers, which rely on the experience of the algorithm designer. In recent years, deep learning has been widely used in computer hearing. To extract essential features that reflect the audio content and characterize the vocal context in the time domain, this study adopted a long-term recurrent convolutional network (LRCN) to realize vocal detection. The convolutional layer in LRCN functions in feature extraction, and the long short-term memory (LSTM) layer can learn the time sequence relationship. The preprocessing of singing voices and accompaniment separation and the postprocessing of time-domain smoothing were combined to form a complete system. Experiments on five public datasets investigated the impacts of the different features for the fusion, frame size, and block size on LRCN temporal relationship learning, and the effects of preprocessing and postprocessing on performance, and the results confirm that the proposed singing voice detection algorithm reached the state-of-the-art level on public datasets.

scholarly journals Witchcraft and Anti-Witchcraft in Nupe Society

Africa ◽  
1935 ◽  
Vol 8 (4) ◽  
pp. 423-447 ◽  
Author(s):  
S. F. Nadel
Keyword(s):  
Political Issues ◽  
Adverse Conditions ◽  
Death Sentences ◽  
Opening Paragraph ◽  
The Town

Opening ParagraphMy investigations into witchcraft among the Nupe were carried out under extremely adverse conditions. In February 1932 Bida, the capital of Nupe Emirate, had seen an outbreak of witchcraft which threw the whole country into a state of gravest unrest. Three women, an alleged witch with her daughter and granddaughter, were stoned to death by the enraged people of Bida when they tried to obtain justice against their accusers. The house of one of the town notables who was involved in the case was set fire to. The town was in turmoil, and the ensuing trial before the European authorities, complicated as it was by political issues and violent party feuds, was carried out under great difficulties. It lasted three months, and ended with two death sentences and two sentences of long-term imprisonment. When I arrived in Nupe country in January 1934, these happenings were still alive in every one's memory.

Specification and Manufacturing of Pipes for the X100 Operational Trial

2008 ◽  
Author(s):  
James Johnson ◽  
Mark Hudson ◽  
Nobuaki Takahashi ◽  
Makoto Nagase ◽  
Akio Yamamoto
Keyword(s):  
Mechanical Properties ◽  
Large Diameter ◽  
Operational Performance ◽  
Gas Pipelines ◽  
Adverse Conditions ◽  
The Past ◽  
Design Factors ◽  
Grade Steel ◽  
External Coating

Over the past decade BP and Sumitomo have been developing and promoting the use of X100 grade steel pipe for large diameter high pressure onshore gas pipelines. In 2006, Sumitomo supplied 0.5km of X100 linepipe to BP’s X100 Operational Trial. The X100 Operational Trial is a 48-inch, 180barg X100 pipeline situated in Northumbria, UK, built specifically to advance industry acceptance of the use of X100 in onshore gas pipelines and to demonstrate the long term operational performance of the material under normal and adverse conditions and at higher design factors. In this paper the mechanical properties of the X100 pipes supplied are summarised and background to the linepipe specification and manufacturing process provided. In addition, the effect of specimen type and external coating on mechanical properties is also discussed.

Sign in / Sign up

Export Citation Format

Share Document