This paper presents a network for indoor and outdoor air quality monitoring. Each node is installed in a different room and includes tin dioxide sensor arrays connected to an acquisition and control system. The nodes are hardwired or wirelessly connected to a central monitoring unit. To increase the gas concentration measurement accuracy and to prevent false alarms, two gas sensor influence quantities, i.e., temperature and humidity, are also measured. Advanced processing based on multiple-input-single-output neural networks is implemented at the network sensing nodes to obtain temperature and humidity compensated gas concentration values. Anomalous operation of the network sensing nodes and power consumption are also discussed.

Due to recent technological improvements, namely in the field of piezoelectric sensors, ballistocardiography – an almost forgotten physiological measurement – is now being object of a renewed scientific interest.Transcending the initial purposes of its development, ballistocardiography has revealed itself to be a useful informative signal about the cardiovascular system status, since it is a non-intrusive technique which is able to assess the body’s vibrations due to its cardiac, and respiratory physiological signatures.Apart from representing the outcome of the electrical stimulus to the myocardium – which may be obtained by electrocardiography – the ballistocardiograph has additional advantages, as it can be embedded in objects of common use, such as a bed or a chair. Moreover, it enables measurements without the presence of medical staff, factor which avoids the stress caused by medical examinations and reduces the patient’s involuntary psychophysiological responses.Given these attributes, and the crescent number of systems developed in recent years, it is therefore pertinent to revise all the information available on the ballistocardiogram’s physiological interpretation, its typical waveform information, its features and distortions, as well as the state of the art in device implementations.

This paper presents a multisensing system with wireless communication capabilities embedded on a smart wheelchair that can measure physiological parameters such as heart rate and respiratory rate in an unobtrusive way. Ballistocardiography (BCG) sensors and a three-axis inertial microelectromechanical system accelerometer are embedded on the seat or in the backrest of the wheelchair and the acquired data are transmitted by Wi-Fi to a laptop computer for advanced data processing and logging. In addition, a 3-D accelerometer with ZigBee communication capability is used to extract information about the user's posture. Considering the static and dynamic use of the wheelchair, an extended set of measurements for different utilization scenarios was analyzed. An important part of this paper is focused on BCG noise and artifacts removal and heart rate and respiratory rate accurate estimation from BCG signal using wavelet-based filtering and independent component analysis algorithms. A study on wavelet-based filtering considering different types of mother wavelets and different levels of decomposition was also carried out. In the future, other signals will also be acquired to improve the system capabilities and flexibility.

This paper presents a combination of two techniques for high-resolution ultrasonic distance measurements in air. The first is the cross correlation between transmitted and received signals to determine the time of flight (TOF), and the second is sine fitting of other transmitted and received signals to determine the phase shift (PS) between them. Sine fitting can estimate the PS with very high accuracy. The result of this synergy is a very accurate distance measurement. For example, the final uncertainty is less than 100 for distances up to 1200 mm.

There are no rules to select the best curve-fitting method for a given set of data. This problem is of great importance in measurement applications. Optimizing analog and digital methods for a transducer's characteristic interpolation or linearization is a field where constant research is being done, particularly since auto-calibration and self-test of intelligent transducers is a topic of major interest. We present an overview of classical methods for data interpolation and least mean squares regression. We make a comparative evaluation of the relative performance of polynomial and artificial neural networks approximations to measurement data with particular attention paid to the reduction of the required calibration set dimension to obtain a given accuracy.