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Nassibi A, Papavassiliou C, Atashzar SF. Depression diagnosis using machine intelligence based on spatiospectrotemporal analysis of multi-channel EEG. Med Biol Eng Comput 2022; 60:3187-3202. [DOI: 10.1007/s11517-022-02647-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 07/28/2022] [Indexed: 11/30/2022]
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Chi TK, Chen HC, Chen SL, Abu PAR. A High-Accuracy and Power-Efficient Self-Optimizing Wireless Water Level Monitoring IoT Device for Smart City. SENSORS 2021; 21:s21061936. [PMID: 33801852 DOI: 10.3390/s21061936] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 02/24/2021] [Accepted: 03/04/2021] [Indexed: 11/16/2022]
Abstract
In this paper, a novel self-optimizing water level monitoring methodology is proposed for smart city applications. Considering system maintenance, the efficiency of power consumption and accuracy will be important for Internet of Things (IoT) devices and systems. A multi-step measurement mechanism and power self-charging process are proposed in this study for improving the efficiency of a device for water level monitoring applications. The proposed methodology improved accuracy by 0.16-0.39% by moving the sensor to estimate the distance relative to different locations. Additional power is generated by executing a multi-step measurement while the power self-optimizing process used dynamically adjusts the settings to balance the current of charging and discharging. The battery level can efficiently go over 50% in a stable charging simulation. These methodologies were successfully implemented using an embedded control device, an ultrasonic sensor module, a LORA transmission module, and a stepper motor. According to the experimental results, the proposed multi-step methodology has the benefits of high accuracy and efficient power consumption for water level monitoring applications.
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Affiliation(s)
- Tsun-Kuang Chi
- Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320314, Taiwan
| | - Hsiao-Chi Chen
- Department of Business Administration, Chung Yuan Christian University, Taoyuan City 320314, Taiwan
| | - Shih-Lun Chen
- Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320314, Taiwan
| | - Patricia Angela R Abu
- Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City 1108, Philippines
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Schulze-Bonhage A, Böttcher S, Glasstetter M, Epitashvili N, Bruno E, Richardson M, V Laerhoven K, Dümpelmann M. [Mobile seizure monitoring in epilepsy patients]. DER NERVENARZT 2019; 90:1221-1231. [PMID: 31673723 DOI: 10.1007/s00115-019-00822-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Wearables are receiving much attention from both epilepsy patients and treating physicians, for monitoring of seizure frequency and warning of seizures. They are also of interest for the detection of seizure-associated risks of patients, for differential diagnosis of rare seizure types and prediction of seizure-prone periods. Accelerometry, electromyography (EMG), heart rate and further autonomic parameters are recorded to capture clinical seizure manifestations. Currently, a clinical use to document nocturnal motor seizures is feasible. In this review the available devices, data on the performance in the documentation of seizures, current options for clinical use and developments in data analysis are presented and critically discussed.
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Affiliation(s)
- A Schulze-Bonhage
- Epilepsiezentrum, Universitätsklinikum Freiburg, Breisacher Str. 64, 79106, Freiburg, Deutschland.
| | - S Böttcher
- Epilepsiezentrum, Universitätsklinikum Freiburg, Breisacher Str. 64, 79106, Freiburg, Deutschland
| | - M Glasstetter
- Epilepsiezentrum, Universitätsklinikum Freiburg, Breisacher Str. 64, 79106, Freiburg, Deutschland
| | - N Epitashvili
- Epilepsiezentrum, Universitätsklinikum Freiburg, Breisacher Str. 64, 79106, Freiburg, Deutschland
| | - E Bruno
- Institute of Psychiatry, Psychology & Neuroscience, Division of Neuroscience, King's College, London, Großbritannien
| | - M Richardson
- Institute of Psychiatry, Psychology & Neuroscience, Division of Neuroscience, King's College, London, Großbritannien
| | - K V Laerhoven
- Department Elektrotechnik und Informatik, Universität Siegen, Siegen, Deutschland
| | - M Dümpelmann
- Epilepsiezentrum, Universitätsklinikum Freiburg, Breisacher Str. 64, 79106, Freiburg, Deutschland
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