Indoor Simulated Environment Research Articles

Multi-band polarization imaging and image processing in sea fog environment

Considering the advancement in marine research in recent years, studies on the identification of offshore scenery are becoming popular globally. In this study, multiband polarimetric imaging is presented to address the deficiencies of the previous single-band study. Polarization imaging experiments of sea fog and non-sea fog in an indoor simulated environment are carried out and compared and analyzed by establishing an artificial simulation system to characterize the sea fog concentration by optical thickness with different concentrations of sea fog environment as the medium. The polarization information of each waveband converted by Stokes parametric is then brought into the two-dimensional discrete wavelet algorithm for image fusion processing. The findings indicate that when the optical thickness of sea fog increases, the polarized light in the chaotic medium recedes and the effect of the image blurs. Finally, after the image fusion process, the contrast of the image is improved and the detail of the target contour is obvious, which proves that the method has good robustness under the low signal-to-noise ratio of the sea fog environment. This provides a solid platform for targeted surveys and civic operations under dense marine fog conditions.

A networked smart home system based on recurrent neural networks and reinforcement learning

With the widespread application of smart home systems, the optimal design of smart home systems has received considerable research attention. This paper puts forward a network smart home system design scheme based on the analysis of the indoor environment and the forecast of the future indoor environment. By building a multi-level network model, an integrated model system from analysis, prediction to decision-making is formed. The swarm intelligent decision-making ability of the networked smart home system is realized by applying a recurrent neural network and a reinforcement learning method. Meanwhile, the indoor simulation environment is built, the indoor environment variables are simulated and the performance of the system is verified by the simulation environment. The simulation results show that the networked smart home system has advantages over the single smart home equipment in the performance of indoor comfort improvement.

Assessment of Extreme Communication Environment With Ultralow SNR: A Benchmark

Accurate estimation of subjective assessment plays an essential role in not only speech quality perception, but also communication environment assessment. Traditional speech quality perception is almost always with an environment, in which the content of speech can be heard clearly. Unlike speech quality assessment, in extreme communication environment with ultralow SNR, like short wave channel with active jamming, speech intelligibility is impaired. Under this condition, subjective assessment as absolute category ranking (ACR) whose scores are made by experienced staffs, and many objective measurements cannot handle this situation. In this paper, we propose information damage level (IDL) to replace subjective ACR as subjective assessment. IDL is the average of scores which is marked on the subjective recognition rate. Under extreme communication environment, it can effectively avoid excessive differences from people to people to some extent. We also provide a new dataset collected in an environment with active jamming, whose speech files are recoded under three different environments, named as indoor simulation environment (EN1), outdoor simulation environment (EN2), and outdoor real environment (EN3). We also benchmark a novel open framework on random forest for direct predicting subjective assessment by combining all possible objective measurements. Experiment results prove the effectiveness of our open framework together with our dataset.

Indoor High Precision Three-Dimensional Positioning System Based on Visible Light Communication Using Improved Hybrid Bat Algorithm

To solve problems of low positioning accuracy and long time-consuming in indoor visible light three-dimensional positioning system, a visible light three-dimensional positioning method based on the improved hybrid bat algorithm (IHBA) is proposed in this paper. Firstly, some beacon points are set at the beginning of the IHBA to reduce the number of iterations. Secondly, weight coefficient is defined to improve positioning accuracy when the fitness function is constructed. Thirdly, aiming at controlling the search speed reasonably, an adaptive search factor is introduced while the bat individual update formula is designed. Finally, the chaotic perturbation operation is used to avoid the algorithm falling into local optimum. In indoor simulation environment, the size of which is 5m × 5m × 3m, the average positioning error of the IHBA is 1.16 cm and the positioning time is 1.85s. To verify the actual effectiveness of IHBA, a receiver is placed in the experimental environment, the size of which is 1.5m × 1.2m × 2m. Through the optical power detected by the receiver, the processor calculate its coordinates with IHBA. The average positioning error of the IHBA is 3.64 cm and the positioning time is 0.89 s. The proposed algorithm has practical application value in existing indoor positioning methods.

Indoor simulation training system for brain-controlled wheelchair based on steady-state visual evoked potentials

Brain-controlled wheelchair (BCW) is one of the important applications of brain-computer interface (BCI) technology. The present research shows that simulation control training is of great significance for the application of BCW. In order to improve the BCW control ability of users and promote the application of BCW under the condition of safety, this paper builds an indoor simulation training system based on the steady-state visual evoked potentials for BCW. The system includes visual stimulus paradigm design and implementation, electroencephalogram acquisition and processing, indoor simulation environment modeling, path planning, and simulation wheelchair control, etc. To test the performance of the system, a training experiment involving three kinds of indoor path-control tasks is designed and 10 subjects were recruited for the 5-day training experiment. By comparing the results before and after the training experiment, it was found that the average number of commands in Task 1, Task 2, and Task 3 decreased by 29.5%, 21.4%, and 25.4%, respectively ( P < 0.001). And the average number of commands used by the subjects to complete all tasks decreased by 25.4% ( P < 0.001). The experimental results show that the training of subjects through the indoor simulation training system built in this paper can improve their proficiency and efficiency of BCW control to a certain extent, which verifies the practicability of the system and provides an effective assistant method to promote the indoor application of BCW.

Indoor Simulated Training Environment for Brain-Controlled Wheelchair Based on Steady-State Visual Evoked Potentials.

Brain-controlled wheelchair (BCW) has the potential to improve the quality of life for people with motor disabilities. A lot of training is necessary for users to learn and improve BCW control ability and the performances of BCW control are crucial for patients in daily use. In consideration of safety and efficiency, an indoor simulated training environment is built up in this paper to improve the performance of BCW control. The indoor simulated environment mainly realizes BCW implementation, simulated training scenario setup, path planning and recommendation, simulated operation, and scoring. And the BCW is based on steady-state visual evoked potentials (SSVEP) and the filter bank canonical correlation analysis (FBCCA) is used to analyze the electroencephalography (EEG). Five tasks include individual accuracy, simple linear path, obstacles avoidance, comprehensive steering scenarios, and evaluation task are designed, 10 healthy subjects were recruited and carried out the 7-days training experiment to assess the performance of the training environment. Scoring and command-consuming are conducted to evaluate the improvement before and after training. The results indicate that the average accuracy is 93.55% and improves from 91.05% in the first stage to 96.05% in the second stage (p = 0.001). Meanwhile, the average score increases from 79.88 in the first session to 96.66 in the last session and tend to be stable (p < 0.001). The average number of commands and collisions to complete the tasks decreases significantly with or without the approximate shortest path (p < 0.001). These results show that the performance of subjects in BCW control achieves improvement and verify the feasibility and effectiveness of the proposed simulated training environment.

Global Monocular Indoor Positioning of a Robotic Vehicle with a Floorplan.

This paper presents a global monocular indoor positioning system for a robotic vehicle starting from a known pose. The proposed system does not depend on a dense 3D map, require prior environment exploration or installation, or rely on the scene remaining the same, photometrically or geometrically. The approach presents a new way of providing global positioning relying on the sparse knowledge of the building floorplan by utilizing special algorithms to resolve the unknown scale through wall–plane association. This Wall Plane Fusion algorithm presented finds correspondences between walls of the floorplan and planar structures present in the 3D point cloud. In order to extract planes from point clouds that contain scale ambiguity, the Scale Invariant Planar RANSAC (SIPR) algorithm was developed. The best wall–plane correspondence is used as an external constraint to a custom Bundle Adjustment optimization which refines the motion estimation solution and enforces a global scale solution. A necessary condition is that only one wall needs to be in view. The feasibility of using the algorithms is tested with synthetic and real-world data; extensive testing is performed in an indoor simulation environment using the Unreal Engine and Microsoft Airsim. The system performs consistently across all three types of data. The tests presented in this paper show that the standard deviation of the error did not exceed 6 cm.

Chlorophyll-a Predicting Model Based on Dynamic Neural Network

Algal blooms are one of the most prevalent global problems. Studying the Chlorophyll-a (Chl-a) predicting model helps to control algal blooms. Predicting the behavior of algae is difficult because of the complex physical, chemical, and biological processes involved. Artificial neural network (ANN) models have been determined to be useful and efficient, especially for such problems for which the characteristics of the processes are difficult to describe using numerical models. An indoor simulated environment is designed for algal cultivation to analyze the temporal change in the algae biomass of Taihu Lake during summer. A Chl-a prediction model based on a nonlinear autoregressive neural network with exogenous inputs (NARX) that can detect and consider within the time dependency is proposed. The NARX model is compared to a static neural network and a dynamic neural network: feedforward neural network (FNN) and Elman recurrent neural network (ERNN). The performance of the proposed NARX model was examined with experimental data collected over 3 months in 2010. The results showed that the NARX model outperformed the other ANN models and significantly enhance the accuracy of Chl-a prediction.