scholarly journals Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network

2020 ◽  
Vol 10 (1) ◽  
pp. 321 ◽  
Author(s):  
Yifan Wang ◽  
Jingxiang Gao ◽  
Zengke Li ◽  
Long Zhao
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Markov Models ◽  
Low Cost ◽  
Posteriori Probability ◽  
Recognition Method ◽  
Location Recognition ◽  
Wide Availability ◽  
Signal Fluctuation ◽  
Fingerprint Database

Currently, indoor locations based on the received signal strength (RSS) of Wi-Fi are attracting more and more attention thanks to the technology’s low cost, low power consumption and wide availability in mobile devices. However, the accuracy of Wi-Fi positioning is limited, due to the signal fluctuation and indoor multipath interference. In order to overcome this problem, this paper proposes a robust and accurate Wi-Fi fingerprint location recognition method based on a deep neural network (DNN). A stacked denoising auto-encoder (SDAE) is used to extract robust features from noisy RSS to construct a feature-weighted fingerprint database offline. We use the combination of the weights of posteriori probability and geometric relationship of fingerprint points to calculate the coordinates of unknown points online. In addition, we use constrained Kalman filtering and hidden Markov models (HMM) to smooth and optimize positioning results and overcome the influence of gross error on positioning results, combined with characteristics of user movement in buildings, both dynamic and static. The experiment shows that the DNN is feasible for position recognition, and the method proposed in this paper is more accurate and stable than the commonly used Wi-Fi positioning methods in different scenes.

scholarly journals RGB-D Object Recognition Using Multi-Modal Deep Neural Network and DS Evidence Theory

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 529 ◽  
Author(s):  
Hui Zeng ◽  
Bin Yang ◽  
Xiuqing Wang ◽  
Jiwei Liu ◽  
Dongmei Fu
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Deep Neural Network ◽  
Low Cost ◽  
Evidence Theory ◽  
Feature Learning ◽  
Decision Fusion ◽  
Support Vector ◽  
Depth Sensors ◽  
Depth Images

With the development of low-cost RGB-D (Red Green Blue-Depth) sensors, RGB-D object recognition has attracted more and more researchers’ attention in recent years. The deep learning technique has become popular in the field of image analysis and has achieved competitive results. To make full use of the effective identification information in the RGB and depth images, we propose a multi-modal deep neural network and a DS (Dempster Shafer) evidence theory based RGB-D object recognition method. First, the RGB and depth images are preprocessed and two convolutional neural networks are trained, respectively. Next, we perform multi-modal feature learning using the proposed quadruplet samples based objective function to fine-tune the network parameters. Then, two probability classification results are obtained using two sigmoid SVMs (Support Vector Machines) with the learned RGB and depth features. Finally, the DS evidence theory based decision fusion method is used for integrating the two classification results. Compared with other RGB-D object recognition methods, our proposed method adopts two fusion strategies: Multi-modal feature learning and DS decision fusion. Both the discriminative information of each modality and the correlation information between the two modalities are exploited. Extensive experimental results have validated the effectiveness of the proposed method.

scholarly journals Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Panyawut Sri-iesaranusorn ◽  
Attawit Chaiyaroj ◽  
Chatchai Buekban ◽  
Songphon Dumnin ◽  
Ronachai Pongthornseri ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Low Cost ◽  
Sampling Rate ◽  
Assessment Procedure ◽  
Semg Signal ◽  
Neural Network Approach ◽  
Assessment Protocol ◽  
Wide Range

Surface electromyography (sEMG) is a non-invasive and straightforward way to allow the user to actively control the prosthesis. However, results reported by previous studies on using sEMG for hand and wrist movement classification vary by a large margin, due to several factors including but not limited to the number of classes and the acquisition protocol. The objective of this paper is to investigate the deep neural network approach on the classification of 41 hand and wrist movements based on the sEMG signal. The proposed models were trained and evaluated using the publicly available database from the Ninapro project, one of the largest public sEMG databases for advanced hand myoelectric prosthetics. Two datasets, DB5 with a low-cost 16 channels and 200 Hz sampling rate setup and DB7 with 12 channels and 2 kHz sampling rate setup, were used for this study. Our approach achieved an overall accuracy of 93.87 ± 1.49 and 91.69 ± 4.68% with a balanced accuracy of 84.00 ± 3.40 and 84.66 ± 4.78% for DB5 and DB7, respectively. We also observed a performance gain when considering only a subset of the movements, namely the six main hand movements based on six prehensile patterns from the Southampton Hand Assessment Procedure (SHAP), a clinically validated hand functional assessment protocol. Classification on only the SHAP movements in DB5 attained an overall accuracy of 98.82 ± 0.58% with a balanced accuracy of 94.48 ± 2.55%. With the same set of movements, our model also achieved an overall accuracy of 99.00% with a balanced accuracy of 91.27% on data from one of the amputee participants in DB7. These results suggest that with more data on the amputee subjects, our proposal could be a promising approach for controlling versatile prosthetic hands with a wide range of predefined hand and wrist movements.

Metal Label Pressed Protuberant Characters Recognition Based on Hidden Markov Model

2011 ◽  
Vol 187 ◽  
pp. 667-671
Author(s):  
Wei Chen
Keyword(s):  
Neural Network ◽  
Markov Model ◽  
Hidden Markov Models ◽  
State Transition ◽  
Markov Models ◽  
Hidden Markov ◽  
Shape Index ◽  
Surface Curvature ◽  
Word Choice ◽  
Recognition Method

A recognition method of pressed protuberant characters based on Hidden Markov models and Neural Network is applied, which the surface curvature properties and the relation of metal label characters are analyzed in detail. The shape index of the characters is extracted. A neural network is used to estimate probabilities for the characters depended on the surface curvature properties, then deriving the best word choice from a sequence of state transition. It is shown in test that the proposed method can be used to recognize the pressed protuberant on metal label.

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