Localization performance of hum-noise-based touch sensor (HumTouch) with unknown participants

2021 ◽  
Author(s):  
Tzu-Hsuan Hsia ◽  
Shogo Okamoto ◽  
Yasuhiro Akiyama ◽  
Yoji Yamada
Keyword(s):  
Touch Sensor ◽  
Localization Performance

Role of pinnae and head movements in localizing pure tones 1The authors would like to thank Mr. Remi Humbert for implementing the experiment in Turbo Pascal and for his further assistance.

1999 ◽  
Vol 58 (3) ◽  
pp. 170-179 ◽  
Author(s):  
Barbara S. Muller ◽  
Pierre Bovet
Keyword(s):  
Sound Source ◽  
Head Movement ◽  
Movement Analysis ◽  
Head Movements ◽  
Sound Sources ◽  
Localization Accuracy ◽  
Sound Effects ◽  
Posterior Quadrant ◽  
Localization Performance ◽  
Pure Tones

Twelve blindfolded subjects localized two different pure tones, randomly played by eight sound sources in the horizontal plane. Either subjects could get information supplied by their pinnae (external ear) and their head movements or not. We found that pinnae, as well as head movements, had a marked influence on auditory localization performance with this type of sound. Effects of pinnae and head movements seemed to be additive; the absence of one or the other factor provoked the same loss of localization accuracy and even much the same error pattern. Head movement analysis showed that subjects turn their face towards the emitting sound source, except for sources exactly in the front or exactly in the rear, which are identified by turning the head to both sides. The head movement amplitude increased smoothly as the sound source moved from the anterior to the posterior quadrant.

Perceptual characteristics of computerized program visualizations can differentially affect detection and localization performance

2006 ◽  
Author(s):  
Elizabeth T. Davis ◽  
Kenneth Hailston ◽  
Eileen Kraemer ◽  
Ashley Hamilton-Taylor ◽  
Philippa Rhodes ◽  
...  
Keyword(s):  
Affect Detection ◽  
Localization Performance ◽  
Perceptual Characteristics ◽  
Detection And Localization

Evaluation of the Integrated In-cell Electromagnetic Resonance Sensor and Capacitive Touch Sensor

2019 ◽  
pp. 1644
Author(s):  
Yuji Suzuki ◽  
Satoshi Uchino ◽  
Kohei Azumi ◽  
Tadayoshi Katsuta ◽  
Daichi Suzuki ◽  
...  
Keyword(s):  
Touch Sensor ◽  
Electromagnetic Resonance ◽  
Resonance Sensor

scholarly journals Development of a Smartphone-Based University Library Navigation and Information Service Employing Wi-Fi Location Fingerprinting

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 432
Author(s):  
Guenther Retscher ◽  
Alexander Leb
Keyword(s):  
Performance Improvement ◽  
Low Cost ◽  
Information Service ◽  
Raspberry Pi ◽  
University Library ◽  
Stop And Go ◽  
Promising Solution ◽  
Localization Performance ◽  
The University ◽  
Suitable Location

A guidance and information service for a University library based on Wi-Fi signals using fingerprinting as chosen localization method is under development at TU Wien. After a thorough survey of suitable location technologies for the application it was decided to employ mainly Wi-Fi for localization. For that purpose, the availability, performance, and usability of Wi-Fi in selected areas of the library are analyzed in a first step. These tasks include the measurement of Wi-Fi received signal strengths (RSS) of the visible access points (APs) in different areas. The measurements were carried out in different modes, such as static, kinematic and in stop-and-go mode, with six different smartphones. A dependence on the positioning and tracking modes is seen in the tests. Kinematic measurements pose much greater challenges and depend significantly on the duration of a single Wi-Fi scan. For the smartphones, the scan durations differed in the range of 2.4 to 4.1 s resulting in different accuracies for kinematic positioning, as fewer measurements along the trajectories are available for a device with longer scan duration. The investigations indicated also that the achievable localization performance is only on the few meter level due to the small number of APs of the University own Wi-Fi network deployed in the library. A promising solution for performance improvement is the foreseen usage of low-cost Raspberry Pi units serving as Wi-Fi transmitter and receiver.

scholarly journals Hybrid Deep Learning Model Based Indoor Positioning Using Wi-Fi RSSI Heat Maps for Autonomous Applications

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 2
Author(s):  
Alwin Poulose ◽  
Dong Seog Han
Keyword(s):  
Deep Learning ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Learning Model ◽  
Signal Interference ◽  
Positioning System ◽  
Heat Maps ◽  
Positioning Systems ◽  
Localization Performance ◽  
Deep Learning Model

Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user’s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.

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