Schizophrenia, a severe neurological disorder, is linked to aberrations in dopamine (DA) and glucose levels. While numerous methods have been developed for the sensitive and selective detection of either DA or glucose...
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Schizophrenia, a severe neurological disorder, is linked to aberrations in dopamine (DA) and glucose levels. While numerous methods have been developed for the sensitive and selective detection of either DA or glucose individually, there remains a challenge in achieving simultaneous detection of both with a single platform. To address this, we designed a graphene field effect transistor (GFET) based electrochemical sensor, augmented with 11-mercaptoundecanoic acid-gold nanoclusters (MUA-AuNCs) and pyrene-1-boronic acid (PBA) for enhanced DA and glucose detection. MUA-AuNCs and PBA improved selectivity and sensitivity towards DA and glucose respectively. Moreover, we incorporated a unique data fusion algorithm based on a least squares method. Utilizing a dual-channel sensor setup, this algorithm corrects interference by leveraging accurate DA readings from one channel to compute precise glucose concentrations in mixed solutions, thus ensuring high detection accuracy. This sensor displayed exceptional performance in simultaneous DA and glucose detection, addressing prevalent issues in current methods. Our approach not only has potential to improve schizophrenia monitoring but also provides insights for electrochemical sensor design and optimization.
The stock market is developing rapidly and the study of its investment behavior is indispensable. In this paper, based on the framework of fusionalgorithm, information on various investor behaviors is federated based...
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The stock market is developing rapidly and the study of its investment behavior is indispensable. In this paper, based on the framework of fusionalgorithm, information on various investor behaviors is federated based on IoT technology. Psychological experimental method is used to collect experimental sample data in the form of questionnaires. And based on the theoretical knowledge of behavioral finance, some investment recommendations on individual investors' investment behavior are proposed. The results of this empirical study show that when individual investors face investment risks, the psychological bias of their perceptions is unfavorable to most of them. And the results prove that more than 50% of them are influenced by their own limited cognition.
A method for performing 3D motion tracking of the shoulder joint with respect to the thorax, using MARC sensors and a data fusion algorithm, is proposed. Two tests were done: 1) qualitative and quantitative analysis o...
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A method for performing 3D motion tracking of the shoulder joint with respect to the thorax, using MARC sensors and a data fusion algorithm, is proposed. Two tests were done: 1) qualitative and quantitative analysis of the response of the sensors, static position and during motion, with and without the proposed data fusion algorithm;2) motion tracking of the shoulder joint with the upper arm, the thorax, and the shoulder joint respect to the thorax. Qualitative analysis of experimental results showed that despite slight variations regarding the evaluated motion, these variations did not have repercussions on the estimated orientation. Quantitative analysis showed that the estimated orientation did not exhibit significant variations, in five minutes, such as drift errors (about 0.1 degrees in static position and less than 1.8 degrees during motion), variations due to noise or magnetic disturbances (RMSE less than 0.04 degrees static position and less than 1 degrees during motion);no singularity problems were reported. The main contributions of this research are a multisensor data fusion algorithm, which combines the complementary properties of gyroscopes, accelerometers, and magnetometers in order to estimate the 3D orientation of two body segments separately and with respect to another body segment considering the spatial relationship between them;and a method for performing 3D motion tracking of two body segments, based on the estimation of their orientation, including motion compensation. The proposed method is applicable to monitoring devices based on IMU/MARG sensors;the performance was evaluated using a customized motion analysis system. (C) 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
To deal with the lack of accuracy and generalization ability in some single models, grain output models were built with lots of relevant data, based on the powerful non-linear reflection of the back-propagation (BP) n...
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To deal with the lack of accuracy and generalization ability in some single models, grain output models were built with lots of relevant data, based on the powerful non-linear reflection of the back-propagation (BP) neural network. Three kinds of grain output models were built and took advantage of - particle swarm optimization algorithm, mind evolutionary algorithm, and genetic algorithm - to optimize the BP neural network. By the use of data fusion algorithm, the outcomes of different models can be modified and fused together, and the combination-predicted outcome can be obtained finally. Taking advantage of this combination model to predict the total grain output of China, the results showed that the total grain output in 2015 was a bit larger than the actual value of about 0.0115%. It was much more accurate than the three single models. The experimental results verify the feasibility and validity of the combination model.
This paper presents a data-fusionalgorithm for respiration rate (RR) extraction by employing the ultra-wideband (UWB) transversal propagation measurement method. In the experimental scenario the UWB transmitter and r...
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ISBN:
(纸本)9781728144610;9781728144603
This paper presents a data-fusionalgorithm for respiration rate (RR) extraction by employing the ultra-wideband (UWB) transversal propagation measurement method. In the experimental scenario the UWB transmitter and receiver units were placed on the front and back sides of the thoracic wall. The measurement principle is based on the fact that periodic movements of lung affect the communication channel properties that can be measured. We measured the energy attenuation variations on the receiver side caused by tissue movements from which we extracted the information about the respiration rate. For testing purpose, a custom developed UWB platform suitable for on-body placement was used, with an integrated inertial motion unit (IMU) sensor. UWB and IMU signals were combined by means of a data fusion algorithm. data fusion algorithms based on Extended Kalman filtering (EKF) and Naive Bayes inference show better estimation performance than an estimation from individual signal sources. The obtained error rate of RR estimation by means of the proposed datafusion method is lower than 0.2 respiration per minute (rpm) in comparison to the reference respiration system. Our results show that the proposed method of UWB and IMU sensor fusion is a promising candidate for reliable RR monitoring by compact wearable units under relatively non-stationary body movement conditions.
There are still quite a few problems in river ice environment monitoring by remote monitoring network, such as the increase of the various sensor nodes and network capacity overload due to the dense deployment, and th...
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There are still quite a few problems in river ice environment monitoring by remote monitoring network, such as the increase of the various sensor nodes and network capacity overload due to the dense deployment, and the decrease of the life expectance of the network. In view of this, the improved K-means data fusion algorithm was proposed to fuse a variety of data by a novel self-developed sensor. Furthermore, the resilient BP network was employed to build a data model database, so as to identify the overall river ice environment at the back-end remotely in real time. Simulation verification and comparison test results show that the proposed algorithm can achieve efficient and accurate data transmission by various sensors and the existing sensor network during the real-time monitoring of the river ice environment.
The attitude detection system based on MEMS and magnetoresistive sensors is widely used in a variety areas. The magnetoresistive sensor acts as an auxiliary sensor to correct the measured value of MEMS inertial sensor...
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ISBN:
(纸本)9781538636282
The attitude detection system based on MEMS and magnetoresistive sensors is widely used in a variety areas. The magnetoresistive sensor acts as an auxiliary sensor to correct the measured value of MEMS inertial sensor. It is necessary to design an appropriate data fusion algorithm to process multiple sensor data. According to the output characteristics of each sensor, the data fusion algorithm take advantage of each sensor and reduce the unreliability of a single sensor in the measurement process to obtain the optimal attitude estimation. The performance of data fusion algorithm will affect the accuracy and stability of whole system. Focused and reviewed in this paper are the present situation and development trend of data fusion algorithm for attitude detection system based on MEMS and magnetoresistive sensors.
The main constraints of a wireless sensor network are limited battery power and short lifetime. One of the main reasons of energy consumption is the data transmission. Each node senses the data and sends them over to ...
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ISBN:
(纸本)9781479987924
The main constraints of a wireless sensor network are limited battery power and short lifetime. One of the main reasons of energy consumption is the data transmission. Each node senses the data and sends them over to the base station. The sensor datafusion reduces the volume of message transmission and makes the network energy efficient. In this paper, we have presented a data fusion algorithm which minimizes the computation cost, communication cost and in the same way it reduces the consumption of energy. The algorithm derives the state of the network using the concept of priority of the sensors. The proposed algorithm gives a better false alarm rate than the existing data fusion algorithm used in the coal mine.
The main constraints of a wireless sensor network are limited battery power and short lifetime. One of the main reasons of energy consumption is the data transmission. Each node senses the data and sends them over to ...
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ISBN:
(纸本)9781479987931
The main constraints of a wireless sensor network are limited battery power and short lifetime. One of the main reasons of energy consumption is the data transmission. Each node senses the data and sends them over to the base station. The sensor datafusion reduces the volume of message transmission and makes the network energy efficient. In this paper, we have presented a data fusion algorithm which minimizes the computation cost, communication cost and in the same way it reduces the consumption of energy. The algorithm derives the state of the network using the concept of priority of the sensors. The proposed algorithm gives a better false alarm rate than the existing data fusion algorithm used in the coal mine.
Multi source fusion of data collected by various sensors to realize accurate perception is the key basic technology of the Internet of things. At present, there are many problems in the fusion of various kinds of data...
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Multi source fusion of data collected by various sensors to realize accurate perception is the key basic technology of the Internet of things. At present, there are many problems in the fusion of various kinds of data collected by sensors, such as more noise and more null values. In this paper, the fuzzy neural network algorithm is proposed to establish the model, combined with the Delphi method and the null value estimation method based on the prediction value to construct the datafusion system. This method has rich application scenarios in the construction of IOT system in the field of power and energy.
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