Indoor localization based on wireless sensor networks (WSNs) is an important field of research with numerous applications, such as elderly care, miner security, and smart buildings. In this paper, we present a localization method based on the received signal strength difference (RSSD) to determine a target on a map with unknown transmission information. To increase the accuracy of localization, we propose a confidence value for each anchor node to indicate its credibility for participating in the estimation. An automatic calibration device is designed to help acquire the values. The acceleration sensor and unscented Kalman filter (UKF) are also introduced to reduce the influence of measuring noise in the application. Energy control is another key point in WSN systems and may prolong the lifetime of the system. Thus, a quadtree structure is constructed to describe the region correlation between neighboring areas, and the unnecessary anchor nodes can be detected and set to sleep to save energy. The localization system is implemented on real-time Texas Instruments CC2430 and CC2431 embedded platforms, and the experimental results indicate that these mechanisms achieve a high accuracy and low energy cost.
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http://dx.doi.org/10.3390/s16060788 | DOI Listing |
Environ Pollut
December 2024
Xiamen Key Laboratory of Indoor Air and Health, Center for Excellence in Regional Atmospheric Environment, Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China.
PLoS One
December 2024
School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan, China.
Vernacular architecture, optimized over centuries to create comfortable thermal environments using sustainable design strategies and local materials, can offer valuable insights for contemporary eco-friendly architectural design. This research investigates the sustainable design strategies of vernacular architecture in southwest Hubei, focusing on the First Granary of Xuan'en County as a representative case study. Through field investigations of indoor environments, this study explores how traditional architectural practices have addressed the region's complex mountainous terrain and hot, humid climate.
View Article and Find Full Text PDFArch Public Health
December 2024
Department of Environmental Sciences, Faculty of Natural Resources, University of Guilan, Someh Sara, Guilan, Iran.
Background: This study evaluated the prevalence of sick building syndrome (SBS) in Rasht, Iran, a subtropical climate with wetter cold season city, during the autumn and winter months of 2020, focusing on the effects of noise and ventilation.
Methods: A total of 420 residents completed the indoor air climate questionnaire (MM040EA), and a walk-through survey of 45 randomly selected residential units assessed environmental noise, ventilation rate, and luminous conditions.
Results: Approximately 38.
Front Vet Sci
December 2024
Clinic for Ruminants and Pigs, Clinic for Reproduction and Large Animals, Veterinary Faculty, University of Ljubljana, Ljubljana, Slovenia.
Introduction: Measurement of hair cortisol concentration (HCC) is a useful tool for assessing the activity of the hypothalamic-pituitary-adrenal axis and thus evaluating the long-term adrenocortical response in different animal species and breeds. Robust indigenous pig breeds are highly adapted to the local environment and are preferred for organic farming, compared to the commercial breeds. We investigated whether seasonality, breeding system (indoor or outdoor) and sex influence HCC of pigs reared on organic farms.
View Article and Find Full Text PDFSci Rep
December 2024
School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar, 161006, China.
A prediction model of the pig house environment based on Bayesian optimization (BO), squeeze and excitation block (SE), convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed to improve the prediction accuracy and animal welfare and take control measures in advance. To ensure the optimal model configuration, the model uses a BO algorithm to fine-tune hyper-parameters, such as the number of GRUs, initial learning rate and L2 normal form regularization factor. The environmental data are fed into the SE-CNN block, which extracts the local features of the data through convolutional operations.
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