Autonomous Human Flow Counting Service With Deep Neural Network

2018 ◽  
Vol 8 (3) ◽  
pp. 18-36
Author(s):  
Shing Hwang Doong
Keyword(s):  
Neural Network ◽  
Time Window ◽  
Classification Algorithms ◽  
Channel State ◽  
Counting Problem ◽  
State Information ◽  
Space Management ◽  
Final Layer ◽  
The Mean ◽  
Human Flow

Human flow counting has many applications in space management. This study applied channel state information (CSI) available in IEEE 802.11n networks to characterize the flow count. Raw features including the mean, standard deviation and five-number summary were extracted from CSI magnitudes in a time window. Due to the large number of raw features, stacked denoising autoencoders were used to extract higher level features and a final layer of softmax regression was used to classify the flow count. The resulting neural network beat many popular classification algorithms in predicting the correct flow size. In addition to CSI magnitudes, this study also explored the feasibility of using CSI phase-based features. It is found that the magnitude neural network provided a better prediction result than the phase neural network, and combining both networks yielded an even better solution to the flow counting problem.

scholarly journals Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach

2021 ◽  
Vol 7 ◽  
pp. e682
Author(s):  
Mohamed Hassan Essai Ali ◽  
Ibrahim B.M. Taha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Channel State Information ◽  
Short Term Memory ◽  
Superior Performance ◽  
Short Term ◽  
Channel State ◽  
State Information ◽  
Term Memory ◽  
Long Short Term Memory

In this study, a deep learning bidirectional long short-term memory (BiLSTM) recurrent neural network-based channel state information estimator is proposed for 5G orthogonal frequency-division multiplexing systems. The proposed estimator is a pilot-dependent estimator and follows the online learning approach in the training phase and the offline approach in the practical implementation phase. The estimator does not deal with complete a priori certainty for channels’ statistics and attains superior performance in the presence of a limited number of pilots. A comparative study is conducted using three classification layers that use loss functions: mean absolute error, cross entropy function for kth mutually exclusive classes and sum of squared of the errors. The Adam, RMSProp, SGdm, and Adadelat optimisation algorithms are used to evaluate the performance of the proposed estimator using each classification layer. In terms of symbol error rate and accuracy metrics, the proposed estimator outperforms long short-term memory (LSTM) neural network-based channel state information, least squares and minimum mean square error estimators under different simulation conditions. The computational and training time complexities for deep learning BiLSTM- and LSTM-based estimators are provided. Given that the proposed estimator relies on the deep learning neural network approach, where it can analyse massive data, recognise statistical dependencies and characteristics, develop relationships between features and generalise the accrued knowledge for new datasets that it has not seen before, the approach is promising for any 5G and beyond communication system.

scholarly journals NLOS Identification in WLANs Using Deep LSTM with CNN Features

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4057 ◽  
Author(s):  
Viet-Hung Nguyen ◽  
Minh-Tuan Nguyen ◽  
Jeongsik Choi ◽  
Yong-Hwa Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Channel State Information ◽  
Recurrent Neural Network ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Line Of Sight ◽  
Channel State ◽  
State Information ◽  
Channel Conditions

Identifying channel states as line-of-sight or non-line-of-sight helps to optimize location-based services in wireless communications. The received signal strength identification and channel state information are used to estimate channel conditions for orthogonal frequency division multiplexing systems in indoor wireless local area networks. This paper proposes a joint convolutional neural network and recurrent neural network architecture to classify channel conditions. Convolutional neural networks extract the feature from frequency-domain characteristics of channel state information data and recurrent neural networks extract the feature from time-varying characteristics of received signal strength identification and channel state information between packet transmissions. The performance of the proposed methods is verified under indoor propagation environments. Experimental results show that the proposed method has a 2% improvement in classification performance over the conventional recurrent neural network model.

scholarly journals Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sung Mo Ryu ◽  
Sung Wook Seo ◽  
Sun-Ho Lee
Keyword(s):  
Neural Network ◽  
Time Window ◽  
Time Windows ◽  
Test Group ◽  
Population Level ◽  
Training Data ◽  
Receiver Operating Characteristic Curves ◽  
Level Data ◽  
Test Sets ◽  
The Mean

Abstract Background We used the Surveillance, Epidemiology, and End Results (SEER) database to develop and validate deep survival neural network machine learning (ML) algorithms to predict survival following a spino-pelvic chondrosarcoma diagnosis. Methods The SEER 18 registries were used to apply the Risk Estimate Distance Survival Neural Network (RED_SNN) in the model. Our model was evaluated at each time window with receiver operating characteristic curves and areas under the curves (AUCs), as was the concordance index (c-index). Results The subjects (n = 1088) were separated into training (80%, n = 870) and test sets (20%, n = 218). The training data were randomly sorted into training and validation sets using 5-fold cross validation. The median c-index of the five validation sets was 0.84 (95% confidence interval 0.79–0.87). The median AUC of the five validation subsets was 0.84. This model was evaluated with the previously separated test set. The c-index was 0.82 and the mean AUC of the 30 different time windows was 0.85 (standard deviation 0.02). According to the estimated survival probability (by 62 months), we divided the test group into five subgroups. The survival curves of the subgroups showed statistically significant separation (p < 0.001). Conclusions This study is the first to analyze population-level data using artificial neural network ML algorithms for the role and outcomes of surgical resection and radiation therapy in spino-pelvic chondrosarcoma.

Sign in / Sign up

Export Citation Format

Share Document