Implementation of Compressed Sensing in Telecardiology Sensor Networks

Mobile solutions for patient cardiac monitoring are viewed with growing interest, and improvements on current implementations are frequently reported, with wireless, and in particular, wearable devices promising to achieve ubiquity. However, due to unavoidable power consumption limitations, the amount of data acquired, processed, and transmitted needs to be diminished, which is counterproductive, regarding the quality of the information produced. Compressed sensing implementation in wireless sensor networks (WSNs) promises to bring gains not only in power savings to the devices, but also with minor impact in signal quality. Several cardiac signals have a sparse representation in some wavelet transformations. The compressed sensing paradigm states that signals can be recovered from a few projections into another basis, incoherent with the first. This paper evaluates the compressed sensing paradigm impact in a cardiac monitoring WSN, discussing the implications in data reliability, energy management, and the improvements accomplished by in-network processing

Underwater Acoustic Source Localization and Sounds Classification in Distributed Measurement Networks

info:eu-repo/semantics/publishedVersio

Analog to Digital Conversion Methods for Smart Sensing Systems

The new capabilities of smart sensing systems namely, adaptability, reconfiguration, lowenergy consumption and cost, between others, require a wisely selection of the methods that are use to perform analog to digital conversion. It is very important to optimize the trade-offs between, resolution, accuracy, conversion rate, and energy consumption, between others, and above all to adapt dynamically the conversion parameters for different signals characteristics and applications\\u27 purposes. Establishing the best trade-offs are even more important when signals to be digitized have different signal-to-noise ratios (S/N) ratios, different requirements of measuring accuracy and acquisition rate, their characteristics are time-variant and above all if they are sharing the same digitalization device. Very low resolution or conversion rate of data acquisition (DAQs) systems are generally not compliant with measurement systems\\u27 requirements since signal information is lost without any possible recovery procedure. Otherwise, if resolution or data acquisition rate are excessively high that means the sampling rate is much higher than its minimum value (Nyquist rate), the excessive amplitude and time resolutions provided by A/D conversion or frequency-to-digital conversion (FDC) does not improve measurements system\\u27s performance. Moreover, the excessive resolution or data acquisition rate implies an increase of hardware and software complexity, data processing load and a higher implementation cost, without any benefits. So, for any A/D or FDC conversion method the best trade-off between different conversion characteristics must be established considering applications\\u27 purposes. For example, in wireless sensing and actuating networks (WSAN) energy wastes are particularly important because a wrong choice of conversion method can affect deeply measurement system autonomy. Whenever possible, classical A/D conversion methods are being replaced by discrete A/D conversion methods that are supported by low cost microcontroller (C) (Microchip, 2010) connected to a few external resistive or capacitive components. This solution takes full advantage of Cs benefits, namely specific hardware and software capabilities and it provides a conversion rate that can be higher that several hundreds of kHz that is sufficien

Multimodal Approach for Emotion Recognition Based on Simulated Flight Experiments

The present work tries to fill part of the gap regarding the pilots’ emotions and their bio-reactions during some flight procedures such as, takeoff, climbing, cruising, descent, initial approach, final approach and landing. A sensing architecture and a set of experiments were developed, associating it to several simulated flights ( N f l i g h t s = 13 ) using the Microsoft Flight Simulator Steam Edition (FSX-SE). The approach was carried out with eight beginner users on the flight simulator ( N p i l o t s = 8 ). It is shown that it is possible to recognize emotions from different pilots in flight, combining their present and previous emotions. The cardiac system based on Heart Rate (HR), Galvanic Skin Response (GSR) and Electroencephalography (EEG), were used to extract emotions, as well as the intensities of emotions detected from the pilot face. We also considered five main emotions: happy, sad, angry, surprise and scared. The emotion recognition is based on Artificial Neural Networks and Deep Learning techniques. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were the main methods used to measure the quality of the regression output models. The tests of the produced output models showed that the lowest recognition errors were reached when all data were considered or when the GSR datasets were omitted from the model training. It also showed that the emotion surprised was the easiest to recognize, having a mean RMSE of 0.13 and mean MAE of 0.01; while the emotion sad was the hardest to recognize, having a mean RMSE of 0.82 and mean MAE of 0.08. When we considered only the higher emotion intensities by time, the most matches accuracies were between 55% and 100%

FM-CW radar sensors for vital signs and motor activity monitoring

The article summarizes on-going research on vital signs and motor activity monitoring based on radar sensors embedded in wheelchairs, walkers and crutches for in home rehabilitation. Embedded sensors, conditioning circuits, real-time platforms that perform data acquisition, auto-identification, primary data processing and data communication contribute to convert daily used objects in home rehabilitation into smart objects that can be accessed by caregivers during the training sessions through human–machine interfaces expressed by the new generation of smart phones or tablet computers running Android OS or iOS operating systems. The system enables the management of patients in home rehabilitation by providing more accurate and up-to-date information using pervasive computing of vital signs and motor activity records

Sensors for everyday life : environmental and food engineering

This book offers an up-to-date overview of the concepts, modeling, technical and technological details and practical applications of different types of sensors, and discusses the trends of next generation of sensors and systems for environmental and food engineering. This book is aimed at researchers, graduate students, academics and industry professionals working in the field of environmental and food engineering, environmental monitoring, precision agriculture and food quality control.Preface; How This Book is Organized; About the Editors; Contents; 1 Determination of NOX and Soot Concentrations Using a Multi-wavelength Opacimeter; Abstract; 1 Introduction; 2 The Opacimeter; 3 Absorption of Particles and Gases; 3.1 Absorption of NO2; 3.2 Extinction of Soot Particles; 4 The Multi-wavelength Approach; 5 Technical Realization; 5.1 Physical Setup; 5.2 Electronic Measurement Circuitry; 5.3 Mathematical Model; 6 Results and Discussion; 6.1 Sensitivity Measurements; 6.2 Mathematical Simulations; 6.3 Discussion; 7 Conclusion; References. 2 Development of the Atomic Emission Spectroscopy System Using Helium-Microwave-Induced Plasma for Fine Particles on Environmental MonitoringAbstract; 1 Introduction; 2 Motivation; 3 Device Designs; 4 Calibration and Evaluation; 5 Measurement and Analysis of Suspended Particles in Atmosphere Using MIP System for Environmental Monitoring; 6 Conclusions; Acknowledgment; References; 3 Real-Time HVAC Sensor Monitoring and Automatic Fault Detection System; Abstract; 1 Introduction; 2 Overview on HVAC Systems, Sensor Monitoring and Fault Detection. 3 Sensor Monitoring and Fault Detection Approach in Detail4 Experimental Results; 5 Conclusion and Discussion; References; 4 High Sensitivity Optical Structures for Relative Humidity Sensing; Abstract; 1 Introduction; 2 Materials; 3 Conventional Methods; 4 Optical Fiber Humidity Sensors; 4.1 Evanescent Wave Sensors; 4.2 Lossy Mode Resonances; 4.2.1 Plastic Cladding Removed Multimode Optical Fiber; 4.2.2 Single Mode Optical Fiber; 4.3 Interferometric Sensors; 4.3.1 Fabry-Pèrot Interferometer as Humidity Sensor; 4.4 Long Period Fiber Gratings; 5 Conclusions; References. 5 Oxygen Gas Sensing Technologies Application: A Comprehensive ReviewAbstract; 1 Introduction; 2 Zirconia Potentiometric Oxygen Gas Sensing Technology; 3 Tunable Diode Laser Spectroscopy (TDLS); 4 Paramagnetic Oxygen Gas Sensing Technology; 5 Amperometric Oxygen Gas Sensing Technology with Liquid Electrole (Clark Cell); Acknowledgment; References; 6 Application of Practical Nitrate Sensor Based on Electrochemical Impedance Spectroscopy; Abstract; 1 Introduction; 2 Motivation; 2.1 Present Status of Water Quality Study; 2.2 Current Laboratory Measurement System. 2.3 Laboratory Based Measurement Technique2.4 Market Research; 3 Operating Principle of Interdigital Sensors; 3.1 Interdigital Sensors; 3.2 Novel Planer Interdigital Sensors; 3.3 Electrochemical Impedance Spectroscopy (EIS); 3.4 Basic Principles of EIS; 3.5 Data Presentation in the Form of Nyquist Plot and Bode Plot; 3.6 Randle's Electrochemical Cell Equivalent Circuit Model; 4 Experimental Setup, Results and Discussion; 4.1 Temperature Measurement; 4.1.1 Experimental Setup; 4.1.2 Results and Discussion; 4.2 Humidity Experiment and Result.326 page(s