Toxic gases pose different risks to human health. Exposure to these toxic gases can be through inhalation of mists, fumes, aerosols, and dust. These inhaled gases can cause injuries, including skin burns, respiratory distress, and death. Nowadays, these harmful toxic agents have become more common in domestic environments, increasing the chances of toxic inhalation in daily life. In a domestic environment, gas toxicity is detected through the odor of toxic gases, as equipment for gas sensing is not readily available and expensive, requiring technical expertise for usage. This situation demands novel methods for avoiding toxic inhalation injuries. Furthermore, most toxic gases affect the breathing rate; thus, constant detection of breathing rate may provide an early warning for toxic gas inhalation and avoid injury. This research study develops a software-defined radio (SDR) system using radio frequency (RF) sensing to monitor abnormal breathing rates continuously by observing channel state information (CSI) variations in the domestic environment. This research work can effectively detect abnormal breathing rates for multi-person in the same environment. The Fast Fourier Transform (FFT) frequency domain analysis-based method is used to detect the abnormal breathing rates of multiple individuals. This study faithfully detects normal, slow, and fast breathing rates for multiple expected cases. The developed system shows noteworthy performance even for five persons in the same environment. This research work is notable because it can be deployed in domestic and occupational settings.

Many Geographic Information Systems (GIS) handle large geospatial datasets stored in raster representation. Spatial joins over raster data are important queries in GIS for data analysis and decision support. However, evaluating spatial joins can be very time intensive due to the size of these datasets. In this paper we propose a new interactive framework that allows users to get approximate answers in near instantaneous time, thus allowing for truly interactive data exploration. Our method utilizes two proposed statistical approaches: probabilistic join and sampling based join. Our probabilistic join method provides speedup of two orders of magnitude with no correctness guarantee, while our sampling based method provides an order of magnitude improvement over the full quad-tree join and also provides running confidence intervals. We propose a framework that combines the two approaches to allow end users to tradeoff speed versus bounded accuracy. The two approaches are evaluated empirically with real and synthetic datasets.