broad area
Recently Published Documents





Drones ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 2
Jannette Quino ◽  
Joe Mari Maja ◽  
James Robbins ◽  
James Owen ◽  
Matthew Chappell ◽  

Accurate inventory allows for more precise forecasting, including profit projections, easier monitoring, shorter outages, and fewer delivery interruptions. Moreover, the long hours of physical labor involved over such a broad area and the effect of inefficiencies could lead to less accurate inventory. Unreliable data and predictions, unannounced stoppages in operations, production delays and delivery, and a considerable loss of profit can all arise from inaccurate inventory. This paper extends our previous work with drones and RFID by evaluating: the number of flights needed to read all tags deployed in the field, the number of scans per pass, and the optimum drone speed for reading tags. The drone flight plan was divided into eight passes from southwest to northwest and back at a horizontal speed of 2.2, 1.7, and 1.1 m per second (m/s) at a vertically fixed altitude. The results showed that speed did not affect the number of new tags scanned (p-value > 0.05). Results showed that 90% of the tags were scanned in less than four trips (eight passes) at 1.7 m/s. Based on these results, the system can be used for large-scale nursery inventory and other industries that use RFID tags in outdoor environments. We presented two novel measurements on evaluating RFID reader efficiency by measuring how fast the reader can read and the shortest distance traveled by the RFID reader over tag.

Istvan Hargittai

AbstractJack D. Dunitz (1923–2021) was Professor of Chemical Crystallography at the Swiss Federal Institute of Technology, Zurich. He received his degrees from Glasgow University, was at the ETH Zurich since 1957, and retired in 1990. His research interests included crystal structure analysis as a tool for solving chemical problems, polymorphism, solid state reactions, and a broad area of structural variations during chemical events under the umbrella term of structure correlation.

2021 ◽  
Vol 11 (21) ◽  
pp. 10162
Jiamiao Wang ◽  
Ling Chen ◽  
Lei Li ◽  
Xindong Wu

While most of the existing topic models perform a full analysis on a set of documents to discover all topics, it is noticed recently that in many situations users are interested in fine-grained topics related to some specific aspects only. As a result, targeted analysis (or focused analysis) has been proposed to address this problem. Given a corpus of documents from a broad area, targeted analysis discovers only topics related with user-interested aspects that are expressed by a set of user-provided query keywords. Existing approaches for targeted analysis suffer from problems such as topic loss and topic suppression because of their inherent assumptions and strategies. Moreover, existing approaches are not designed to address computation efficiency, while targeted analysis is supposed to provide responses to user queries as soon as possible. In this paper, we propose a coreBiTerms-basedTopicModel (BiTTM). By modelling topics from core biterms that are potentially relevant to the target query, on one hand, BiTTM captures the context information across documents to alleviate the problem of topic loss or suppression; on the other hand, our proposed model enables the efficient modelling of topics related to specific aspects. Our experiments on nine real-world datasets demonstrate BiTTM outperforms existing approaches in terms of both effectiveness and efficiency.

2021 ◽  
Stefan Bittner ◽  
Kyungduk Kim ◽  
Yongquan Zeng ◽  
Stefano Guazzotti ◽  
Ortwin Hess ◽  

2021 ◽  
Paul Crump ◽  
Md. Jarez Miah ◽  
Jorg Fricke ◽  
Martin Wilkens ◽  
Sabrina Kreutzmann ◽  

2021 ◽  
Lukas Uhlig ◽  
Dominic J. Kunzmann ◽  
Ulrich T. Schwarz

2021 ◽  
Anissa Zeghuzi ◽  
Jan-Philipp Koester ◽  
Mindaugas Radziunas ◽  
Heike Christopher ◽  
Hans Wenzel ◽  

Sign in / Sign up

Export Citation Format

Share Document