IMPLEMENTATION OF LEBESGUE SAMPLING METHOD AND DIGITAL SENSORS FOR CONTROLLING THE LEVEL VARIABLE IN A CONTINUOUS SYSTEM

This study aims to show that the continuous control from a level system can be efficiently measured and controlled using capacitive digital binary sensors, which in this case, replace the measurement signal from an analog differential pressure transmitter in a level control system. The binary sensors low cost and the digital output they process allow the reproduction of a correct signal and the estimation of a variable for controlling the water level inside the process tank through a proportional pneumatic level control valve, which receives the control signal from the Lebesgue sampling estimation algorithm applied herein for processing digital measurements. In this particular case, the Lebesgue algorithm is applied to reproduce the estimation of values obtained from the continuous signal in the real level process for the measurement and control. Also, are compared both, simulated and real outputs obtained using the Lebesgue algorithm and digital sensors, which were applied to a state observer controller that relates digital signals for controlling the real level system output. The application of the Lebesgue algorithm in the real level process concludes that the analog level signal can be efficiently reproduced using this method. In addition, the controller enables the system to smoothly conduct digital output processing using digital sensors to control the system output correctly, validating that not only analog sensors should be applied for controlling the output of proportional actuators, because it is shown that digital binary signals can be used for controlling and emulating continuous signals, which were processed and applied to the pneumatic valve. Keywords: Lebesgue sampling, estimation, binary sensor, observer controller, finite state machine, continuous system, control, LTI systems, identification, state variable, estimated output, proportional actuator

The binary oxide NiO-CuO nanocomposite based thick film sensor for the acute detection of Hydrogen Sulphide gas vapours

The present research deals with the synthesis of copper oxide and nickel oxide nanoparticles. The nano powder of both NiO-CuO was utilized to fabricate the thick films.Thick films fabricated by screen printing method on glass substrate. The ex-situ doping method was followed for mixing the concentration of nickel oxide in copper oxide lattice. Calculated stoichiometric amount of NiO was loaded during thick film synthesis of CuO.The structure morphology of prepared CuO-NiO nanocomposite thick films was confirmed from x-ray diffraction technique, whichapproves cubic and crystalline CuO-NiO binary nanocomposite. The surface characteristics of the prepared films investigated byscanning electron microscopy that shows homogeneous, porous CuO-NiO nanoparticles with varying dimensions.The prepared thick films of CuO-NiO nanoparticles were analysed for electrical parameter, that assured the prepared material has a semiconducting nature. Further, these thick films promoted for gas sensing interpretation of H2S gas at various temperature and varied gas concentration. Here exclusive reports for hydrogen sulphide gas are reported. The binary CuO-NiO was thoroughly investigated for hydrogen sulphide gas concentration from 50 ppm to 500 ppm at the different temperature. The binary oxide sensor is found to be very sensitive at room temperature and maximum sensitivity response was 75.01 % for H2S gas. Furthermore the response and recovery times are also reported for binary sensor in the present research. The sensor reproducibility cycle was performed forbinary oxide sensor at hydrogen sulphide gas (H2S).

Bayesian algorithms for multiple target tracking with binary sensors on particle filter

In this paper, we propose an approximate Bayesian computation approach to perform a multiple target tracking within a binary sensor network. The nature of the binary sensors (\emph{getting closer - moving away} information) do not allow the use of the classical tools (e.g. Kalman Filter, Particle Filer), because the exact likelihood is intractable. To overcome this, we use the particular feature of the likelihood-free algorithms to produce an efficient multiple target tracking methodology.

Mean Shift-Based Multisource Localization Method in Wireless Binary Sensor Network

Source localization is one of the major research contents in the localization research of wireless sensor networks, which has attracted considerable attention for a long period. In recent years, the wireless binary sensor network (WBSN) has been widely used for source localization due to its high energy efficiency. A novel method which is based on WBSN for multiple source localization is presented in this paper. Firstly, the Neyman-Pearson criterion-based sensing model which takes into account the false alarms is utilized to identify the alarmed nodes. Secondly, the mean shift and hierarchical clustering method are performed on the alarmed nodes to obtain the cluster centers as the initial locations of signal sources. Finally, some voting matrices which can improve the localization accuracy are constructed to decide the location of each acoustic source. The simulation results demonstrate that the proposed method can provide a desirable performance superior to some traditional methods in accuracy and efficiency.

Real-time Recognition of Interleaved Activities Based on Ensemble Classifier of Long Short-Term Memory with Fuzzy Temporal Windows

In this paper, we present a methodology for Real-Time Activity Recognition of Interleaved Activities based on Fuzzy Logic and Recurrent Neural Networks. Firstly, we propose a representation of binary-sensor activations based on multiple Fuzzy Temporal Windows. Secondly, an ensemble of activity-based classifiers for balanced training and selection of relevant sensors is proposed. Each classifier is configured as a Long Short-Term Memory with self-reliant detection of interleaved activities. The proposed approach was evaluated using well-known interleaved binary-sensor datasets comprised of activities of daily living.