scholarly journals Movement Path Data Generation from Wi-Fi Fingerprints for Recurrent Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2823
Author(s):  
Hong-Gi Shin ◽  
Yong-Hoon Choi ◽  
Chang-Pyo Yoon
Keyword(s):  
Indoor Environment ◽  
Indoor Positioning ◽  
Data Generation ◽  
Received Signal Strength Indicator ◽  
Data Set ◽  
Movement Path ◽  
Data Movement ◽  
Learning Network ◽  
Deep Learning Network ◽  
Continuous Movements

The recurrent neural network (RNN) model, which is a deep-learning network that can memorize past information, is used in this paper to memorize continuous movements in indoor positioning to reduce positioning error. To use an RNN model in Wi-Fi-fingerprint based indoor positioning, data set must be sequential. However, Wi-Fi fingerprinting only saves the received signal strength indicator for a location, so it cannot be used as RNN data. For this reason, we propose a movement path data generation technique that generates data for an RNN model for sequential positioning from Wi-Fi fingerprint data. Movement path data can be generated by creating an adjacency list for Wi-Fi fingerprint location points. However, creating an adjacency matrix for all location points requires a large amount of computation. This problem is solved by dividing indoor environment by K-means clustering and creating a cluster transition matrix based on the center of each cluster.

scholarly journals 2D–3D reconstruction of distal forearm bone from actual X-ray images of the wrist using convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryoya Shiode ◽  
Mototaka Kabashima ◽  
Yuta Hiasa ◽  
Kunihiro Oka ◽  
Tsuyoshi Murase ◽  
...  
Keyword(s):  
Wrist Joint ◽  
High Accuracy ◽  
Training Data ◽  
Small Data ◽  
Data Set ◽  
Accuracy Estimation ◽  
X Ray ◽  
Learning Network ◽  
Forearm Bone ◽  
Deep Learning Network

AbstractThe purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy. The data used were 173 computed tomography (CT) images and 105 actual X-ray images of a healthy wrist joint. To compensate for the small size of the dataset, digitally reconstructed radiography (DRR) images generated from CT were used as training data instead of actual X-ray images. The DRR-like images were generated from actual X-ray images in the test and adapted to the network, and high-accuracy estimation of a 3D bone model from a small data set was possible. The 3D shape of the radius and ulna were estimated from actual X-ray images with accuracies of 1.05 ± 0.36 and 1.45 ± 0.41 mm, respectively.

Research on an Adaptive Algorithm for Indoor Bluetooth Positioning

2018 ◽  
Vol 32 (06) ◽  
pp. 1854014 ◽  
Author(s):  
Heng Luo ◽  
Xiaobo Niu ◽  
Junchen Li ◽  
Jianping Chen ◽  
Youmin Zou ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Indoor Positioning ◽  
Relative Difference ◽  
Adaptive Model ◽  
Received Signal Strength Indicator ◽  
Positioning Systems ◽  
Coefficient Optimization ◽  
Performance Deterioration ◽  
The Difference ◽  
Environment Experiment

Building structure and other factors lead to the performance deterioration of global postioning system (GPS) positioning systems indoors. An adaptive model for Bluetooth-based indoor positioning is proposed in this paper, targeting at the complex indoor environment, to improve the performance of Bluetooth-oriented indoor positioning systems. More accurate Received Signal Strength Indicator (RSSI) calibration which is optimized via Gaussian filtering, together with the environment-dependent attenuation coefficient optimization, results in a more precise hybrid model in the complicated indoor environment. Experiment results show that the difference between the estimated results and the measured samples is less than 0.25[Formula: see text]m as the target node and reference node is less than 1.5[Formula: see text]m far from each other. As the distance increases to more than 1.5[Formula: see text]m, the relative difference between the estimated values and the measured ones decreases to 7.8% at most, satisfying the requirements for indoor positioning applications.

scholarly journals An Improved Deep Learning Network Structure for Multitask Text Implication Translation Character Recognition

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiaoli Ma ◽  
Hongyan Xu ◽  
Xiaoqian Zhang ◽  
Haoyong Wang
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Good Description ◽  
Rapid Development ◽  
Extreme Value ◽  
Text Detection ◽  
Data Set ◽  
Learning Network ◽  
Coarse Filter ◽  
Deep Learning Network

With the rapid development of artificial intelligence technology, multitasking textual translation has attracted more and more attention. Especially after the application of deep learning technology, the performance of multitask translation text detection and recognition has been greatly improved. However, because multitasking contains the interference problem faced by the translated text, there is a big gap between recognition performance and actual application requirements. Aiming at multitasking and translation text detection, this paper proposes a text localization method based on multichannel multiscale detection of the largest stable extreme value region and cascade filtering. This paper selects the appropriate color channel and scale to extract the maximum stable extreme value area as the character candidate area and designs a cascaded filter from coarse to fine to remove false detections. The coarse filter is based on some simple morphological features and stroke width features, and the fine filter is trained by a two-recognition convolutional neural network. The remaining character candidate regions are merged into horizontal or multidirectional character strings through the graph model. The experimental results on the text data set prove the effectiveness of the improved deep learning network character model and the feasibility of the textual implication translation analysis method based on this model. Among them, the text contains translation character recognition results prove that the model has good description ability. The characteristics of the model determine that this method is not sensitive to the scale of the sliding window, so it performs better than the existing typical methods in retrieval tasks.

scholarly journals Predicting Intentions of Pedestrians from 2D Skeletal Pose Sequences with a Representation-Focused Multi-Branch Deep Learning Network

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 331
Author(s):  
Joseph Gesnouin ◽  
Steve Pechberti ◽  
Guillaume Bresson ◽  
Bogdan Stanciulescu ◽  
Fabien Moutarde
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Driving ◽  
Urban Traffic ◽  
Data Set ◽  
Learning Network ◽  
Starting Point ◽  
Good Starting Point ◽  
Deep Learning Network ◽  
Pedestrian Crossing

Understanding the behaviors and intentions of humans is still one of the main challenges for vehicle autonomy. More specifically, inferring the intentions and actions of vulnerable actors, namely pedestrians, in complex situations such as urban traffic scenes remains a difficult task and a blocking point towards more automated vehicles. Answering the question “Is the pedestrian going to cross?” is a good starting point in order to advance in the quest to the fifth level of autonomous driving. In this paper, we address the problem of real-time discrete intention prediction of pedestrians in urban traffic environments by linking the dynamics of a pedestrian’s skeleton to an intention. Hence, we propose SPI-Net (Skeleton-based Pedestrian Intention network): a representation-focused multi-branch network combining features from 2D pedestrian body poses for the prediction of pedestrians’ discrete intentions. Experimental results show that SPI-Net achieved 94.4% accuracy in pedestrian crossing prediction on the JAAD data set while being efficient for real-time scenarios since SPI-Net can reach around one inference every 0.25 ms on one GPU (i.e., RTX 2080ti), or every 0.67 ms on one CPU (i.e., Intel Core i7 8700K).

scholarly journals Robust Trilateration Based Algorithm for Indoor Positioning Systems

2021 ◽  
Vol 47 (3) ◽  
pp. 1195-1210
Author(s):  
Simeon Pande ◽  
Kwame S Ibwe
Keyword(s):  
Indoor Environment ◽  
Path Loss ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Received Signal Strength Indicator ◽  
Position Information ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Distance Relationship ◽  
Indoor Positioning System

Abstract Indoor Positioning Systems (IPS) plays crucial roles in indoor environment items positioning used in self-navigating robots and helping hands. To obtain position information, positioning algorithms employing Received Signal Strength Indicator (RSSI) are of great benefits since they reuse the existing radio wireless infrastructures for indoor positioning. However, the changes in the indoor environment decrease the overall accuracy of the developed indoor positioning algorithms. To cope with the challenge of environmental dependency in indoor positioning, a robust algorithm using radio signal identification was developed. The algorithm uses circle expansion and reduction mechanism to achieve better RSSI-Distance relationship. The distances from RSSI-Distance relationship are used in trilateration algorithm for position estimation. Experiments were performed to compare position accuracy of the basic RSSI-Based and the proposed algorithm. Simulation results showed that proposed algorithm showed less average positioning errors by 11.2066% and 3.7279% at path loss coefficients of 3.11 and 3.21, respectively compared to the existing algorithms. Likewise, the proposed algorithm showed 2.7282% increase in positioning error when environment was changed from that of path loss coefficient 3.11 to 3.21. The existing basic algorithms show error fluctuation of 10% with the same environment changes. Keywords: Indoor Positioning System; RFID; RSSI; Trilateration

Forecasting Monthly Rainfall using Bio-Inspired Artificial Algae Deep Learning Network

2021 ◽  
Author(s):  
A. Kala ◽  
S. Ganesh Vaidyanathan
Keyword(s):  
Deep Learning ◽  
Time Series Data ◽  
Short Term Memory ◽  
Monthly Rainfall ◽  
Series Data ◽  
Learning Approaches ◽  
Rainfall Forecasting ◽  
Data Set ◽  
Learning Network ◽  
Deep Learning Network

Rainfall forecasting is the most critical and challenging task because of its dependence on different climatic and weather parameters. Hence, robust and accurate rainfall forecasting models need to be created by applying various machine learning and deep learning approaches. Several automatic systems were created to predict the weather, but it depends on the type of weather pattern, season and location, which leads in maximizing the processing time. Therefore, in this work, significant artificial algae long short-term memory (LSTM) deep learning network is introduced to forecast the monthly rainfall. During this process, Homogeneous Indian Monthly Rainfall Data Set (1871–2016) is utilized to collect the rainfall information. The gathered information is computed with the help of an LSTM approach, which is able to process the time series data and predict the dependency between the data effectively. The most challenging phase of LSTM training process is finding optimal network parameters such as weight and bias. For obtaining the optimal parameters, one of the Meta heuristic bio-inspired algorithms called Artificial Algae Algorithm (AAA) is used. The forecasted rainfall for the testing dataset is compared with the existing models. The forecasted results exhibit superiority of our model over the state-of-the-art models for forecasting Indian Monsoon rainfall. The LSTM model combined with AAA predicts the monsoon from June–September accurately.

Tool Condition Monitoring Method Based on Generative Adversarial Network for Data Augmentation

2021 ◽  
Author(s):  
Yongqing Wang ◽  
Mengmeng Niu ◽  
Kuo Liu ◽  
Honghui Wang ◽  
Mingrui Shen ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Data Augmentation ◽  
Tool Condition Monitoring ◽  
Generative Adversarial Network ◽  
Monitoring Method ◽  
Data Set ◽  
Learning Network ◽  
Adversarial Network ◽  
Tool Condition ◽  
Deep Learning Network

Abstract In the process of parts processing, due to the real working conditions and data acquisition equipment, the collected working data of tools are actually limited. Meanwhile, the tool usually works in the normal state, so it is prone to cause the problem of unbalanced data set, which restricts the accuracy of tool condition monitoring. Aiming at this problem, this paper proposes a tool condition monitoring method based on generative adversarial network (GAN) for data augmentation. Specifically, first collect original samples data during processing in different tool conditions, then the collected sample data is input into GAN, and the generator of GAN can generate new samples which has similar distribution with original samples from tool condition signals data, finally the real samples and generated samples are combined to train deep learning network to predict tool conditions. Experimental results show that the proposed method can significantly improve the accuracy of tool condition monitoring. This paper compares and visualizes the impact of the training data set on the classification ability of the deep learning network model. In addition, some traditional methods are used for comparison, and F1 measure is introduced to evaluate the quality of the results. The results show that this method is better than the Adaptive Synthetic Sampling (Adasyn), add-noise, and resampling.

scholarly journals Identification Algorithm Based on Key-Point Detection Network for Vital Parts of Infrared Aerial Target

2020 ◽  
Vol 38 (6) ◽  
pp. 1154-1162
Author(s):  
Kai Zhang ◽  
Hao Liu ◽  
Xi Yang ◽  
Shaoyi Li ◽  
Xiaotian Wang
Keyword(s):  
Network Architecture ◽  
Recognition Rate ◽  
Identification Algorithm ◽  
Data Set ◽  
Learning Network ◽  
Average Recognition Rate ◽  
Image Processing Algorithms ◽  
Deep Learning Network ◽  
Point Detection ◽  
Traditional Image

The precision strike capability of an infrared-guided air-to-air missile to target the vital parts of a fighter is key to precision-guidance weapons. The traditional image processing algorithms select features and designs classifiers according to human prior knowledge, but this has some limitations. Therefore we propose an algorithm for identifying the vital parts of an infrared aerial target based on key-point detection networks. The algorithm uses the end-to-end deep learning network architecture and combines illumination with texture. The data set is augmented and enhanced in terms of lighting, texture and deformation. The entire image information is preprocessed simply as input, and a loss function with constraints is constructed and iterated with an optimization algorithm. Compared with the conventional algorithms with the same training, the average recognition rate of the trained network model increases by 10%. The vital parts of the infrared aerial target are identified at the speed of ≤ 10 ms/frame. The accuracy of recognition of the 4 vital parts proposed by us is more than 80%.

scholarly journals Applying Movement Constraints to BLE RSSI-Based Indoor Positioning for Extracting Valid Semantic Trajectories

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 527 ◽  
Author(s):  
Hani Ramadhan ◽  
Yoga Yustiawan ◽  
Joonho Kwon
Keyword(s):  
Real World ◽  
Indoor Environment ◽  
Experimental Studies ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Low Energy ◽  
Hotspot Detection ◽  
Received Signal Strength Indicator ◽  
Positioning Technique ◽  
Short Time

Indoor positioning techniques, owing to received signal strength indicator (RSSI)-based sensors, can provide useful trajectory-based services. These services include user movement analytics, next-to-visit recommendation, and hotspot detection. However, the value of RSSI is often disturbed due to obstacles in indoor environment, such as doors, walls, and furnitures. Therefore, many indoor positioning techniques still extract an invalid trajectory from the disturbed RSSI. An invalid trajectory contains distant or impossible consecutive positions within a short time, which is unlikely in a real-world scenario. In this study, we enhanced indoor positioning techniques with movement constraints on BLE (Bluetooth Low Energy) RSSI data to prevent an invalid semantic indoor trajectory. The movement constraints ensure that a predicted semantic position cannot be far apart from the previous position. Furthermore, we can extend any indoor positioning technique using these movement constraints. We conducted comprehensive experimental studies on real BLE RSSI datasets from various indoor environment scenarios. The experimental results demonstrated that the proposed approach effectively extracts valid indoor semantic trajectories from the RSSI data.

scholarly journals Fast Signals of Opportunity Fingerprint Database Maintenance with Autonomous Unmanned Ground Vehicle for Indoor Positioning

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3419 ◽  
Author(s):  
Yitang Peng ◽  
Xiaoji Niu ◽  
Jian Tang ◽  
Dazhi Mao ◽  
Chuang Qian
Keyword(s):  
Indoor Environment ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Kriging Interpolation ◽  
Unmanned Ground Vehicle ◽  
Received Signal Strength Indicator ◽  
Ground Vehicle ◽  
Fingerprint Database ◽  
Signals Of Opportunity

Indoor positioning technology based on Received Signal Strength Indicator (RSSI) fingerprints is a potential navigation solution, which has the advantages of simple implementation, low cost and high precision. However, as the radio frequency signals can be easily affected by the environmental change during its transmission, it is quite necessary to build location fingerprint database in advance and update it frequently, thereby guaranteeing the positioning accuracy. At present, the fingerprint database building methods mainly include point collection and line acquisition, both of which are usually labor-intensive and time consuming, especially in a large map area. This paper proposes a fast and efficient location fingerprint database construction and updating method based on a self-developed Unmanned Ground Vehicle (UGV) platform NAVIS, called Automatic Robot Line Collection. A smartphone was installed on NAVIS for collecting indoor Received Signal Strength Indicator (RSSI) fingerprints of Signals of Opportunity (SOP), such as Bluetooth and Wi-Fi. Meanwhile, indoor map was created by 2D LiDAR-based Simultaneous Localization and Mapping (SLAM) technology. The UGV automatically traverse the unknown indoor environment due to a pre-designed full-coverage path planning algorithm. Then, SOP sensors collect location fingerprints and generates grid map during the process of environment-traversing. Finally, location fingerprint database is built or updated by Kriging interpolation. Field tests were carried out to verify the effectiveness and efficiency of our proposed method. The results showed that, compared with the traditional point collection and line collection schemes, the root mean square error of the fingerprinting-based positioning results were reduced by 35.9% and 25.0% in static tests and 30.0% and 21.3% respectively in dynamic tests. Moreover, our UGV can traverse the indoor environment autonomously without human-labor on data acquisition, the efficiency of the automatic robot line collection scheme is 2.65 times and 1.72 times that of the traditional point collection and the traditional line acquisition, respectively.

Sign in / Sign up

Export Citation Format

Share Document