Efficient wireless location detection system

2010 ◽  
Author(s):  
M. S. Alam ◽  
S. Alsharif ◽  
N. Haq
Keyword(s):  
Detection System ◽  
Wireless Location ◽  
Location Detection

LORE: a model for the detection of fine-grained locative references in tweets

2021 ◽  
Author(s):  
Nicolás José Fernández-Martínez ◽  
◽  
Carlos Periñán-Pascual ◽  
Keyword(s):  
Emergency Services ◽  
Situational Awareness ◽  
State Of The Art ◽  
Detection System ◽  
Linguistic Knowledge ◽  
Terrorist Attacks ◽  
Fine Grained ◽  
Points Of Interest ◽  
Car Accidents ◽  
Location Detection

Extracting geospatially rich knowledge from tweets is of utmost importance for location-based systems in emergency services to raise situational awareness about a given crisis-related incident, such as earthquakes, floods, car accidents, terrorist attacks, shooting attacks, etc. The problem is that the majority of tweets are not geotagged, so we need to resort to the messages in the search of geospatial evidence. In this context, we present LORE, a location-detection system for tweets that leverages the geographic database GeoNames together with linguistic knowledge through NLP techniques. One of the main contributions of this model is to capture fine-grained complex locative references, ranging from geopolitical entities and natural geographic references to points of interest and traffic ways. LORE outperforms state-of-the-art open-source location-extraction systems (i.e. Stanford NER, spaCy, NLTK and OpenNLP), achieving an unprecedented trade-off between precision and recall. Therefore, our model provides not only a quantitative advantage over other well-known systems in terms of performance but also a qualitative advantage in terms of the diversity and semantic granularity of the locative references extracted from the tweets.

Wi-Fi RSSI-Based Indoor Location Detection System with IoT Device

2017 ◽  
Vol 42 (12) ◽  
pp. 2346-2349
Author(s):  
Jingjun Chen ◽  
Rashmi Sharan Sinha ◽  
Sang-Moon Lee ◽  
Seung-Hoon Hwang
Keyword(s):  
Detection System ◽  
Indoor Location ◽  
Location Detection

Temporal Order Detection and Coding in Nervous Systems

2013 ◽  
Vol 25 (2) ◽  
pp. 510-531 ◽  
Author(s):  
Klaus M. Stiefel ◽  
Jonathan Tapson ◽  
André van Schaik
Keyword(s):  
Cortical Neurons ◽  
Temporal Order ◽  
Detection System ◽  
Temporal Coding ◽  
Visual Direction ◽  
Nervous Systems ◽  
Somatosensory Stimulus ◽  
Direction Detection ◽  
Location Detection ◽  
Stimulus Features

This letter discusses temporal order coding and detection in nervous systems. Detection of temporal order in the external world is an adaptive function of nervous systems. In addition, coding based on the temporal order of signals can be used as an internal code. Such temporal order coding is a subset of temporal coding. We discuss two examples of processing the temporal order of external events: the auditory location detection system in birds and the visual direction detection system in flies. We then discuss how somatosensory stimulus intensities are translated into a temporal order code in the human peripheral nervous system. We next turn our attention to input order coding in the mammalian cortex. We review work demonstrating the capabilities of cortical neurons for detecting input order. We then discuss research refuting and demonstrating the representation of stimulus features in the cortex by means of input order. After some general theoretical considerations on input order detection and coding, we conclude by discussing the existing and potential use of input order coding in neuromorphic engineering.

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