Pedro Silva Girao

Bio: Pedro Silva Girao is an academic researcher from Instituto Superior Técnico. The author has contributed to research in topics: Wireless sensor network & Data acquisition. The author has an hindex of 25, co-authored 202 publications receiving 2534 citations. Previous affiliations of Pedro Silva Girao include ALFA & ISCTE – University Institute of Lisbon.

Smart Sensors Network for Air Quality Monitoring Applications

Abstract: 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.

Smart Sensor Network for Air Quality Monitoring Applications

Abstract: The work presents a network for indoor and outdoor air quality monitoring whose nodes include tin dioxide sensor arrays connected to an acquisition and control system with WiFi communication capabilities. To increase the gas concentration measurement accuracy and to prevent false alarms, two gas sensor-influencing quantities, temperature and humidity, are also measured. Advanced processing based on multi-input single-output neural networks is implemented at the network sensing nodes in order to obtain temperature and humidity compensated gas concentration values. Anomalous operation of network nodes and power consumption are also discussed

Theory and Developments in an Unobtrusive Cardiovascular System Representation: Ballistocardiography

Abstract: 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.

Physiological Parameters Measurement Based on Wheelchair Embedded Sensors and Advanced Signal Processing

Abstract: 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.

The Environmental Protection Agency

Abstract: The Environmental Protection Agency (EPA) provides access to information on a variety of topics related to the environment and strives to inform citizens of health risks. The EPA also has an extensive library network that consists of 26 libraries throughout the United States, which provide access to a plethora of information to EPA employees, scientists, and researchers. The EPA implemented a reorganization project to digitize their materials so they would be more accessible to a wider range of users, but this plan was drastically accelerated when the EPA was threatened with a budget cut. It chose to close and reduce the hours and services of some of their libraries. As a result, the agency was accused of denying users the “right to know” by making information unavailable, not providing an adequate strategic plan, and discarding vital materials. This case study explores the background of the digitization project, the practices implemented, and the critiques of the project.

Smart Homes that Monitor Breathing and Heart Rate

Abstract: The evolution of ubiquitous sensing technologies has led to intelligent environments that can monitor and react to our daily activities, such as adapting our heating and cooling systems, responding to our gestures, and monitoring our elderly. In this paper, we ask whether it is possible for smart environments to monitor our vital signs remotely, without instrumenting our bodies. We introduce Vital-Radio, a wireless sensing technology that monitors breathing and heart rate without body contact. Vital-Radio exploits the fact that wireless signals are affected by motion in the environment, including chest movements due to inhaling and exhaling and skin vibrations due to heartbeats. We describe the operation of Vital-Radio and demonstrate through a user study that it can track users' breathing and heart rates with a median accuracy of 99%, even when users are 8~meters away from the device, or in a different room. Furthermore, it can monitor the vital signs of multiple people simultaneously. We envision that Vital-Radio can enable smart homes that monitor people's vital signs without body instrumentation, and actively contribute to their inhabitants' well-being.

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Abstract: Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002–2013 of machine learning methods that were used to address common issues in WSNs. The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.

Automatic License Plate Recognition (ALPR): A State-of-the-Art Review

Abstract: Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image or a sequence of images. The extracted information can be used with or without a database in many applications, such as electronic payment systems (toll payment, parking fee payment), and freeway and arterial monitoring systems for traffic surveillance. The ALPR uses either a color, black and white, or infrared camera to take images. The quality of the acquired images is a major factor in the success of the ALPR. ALPR as a real-life application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. It should also be generalized to process license plates from different nations, provinces, or states. These plates usually contain different colors, are written in different languages, and use different fonts; some plates may have a single color background and others have background images. The license plates can be partially occluded by dirt, lighting, and towing accessories on the car. In this paper, we present a comprehensive review of the state-of-the-art techniques for ALPR. We categorize different ALPR techniques according to the features they used for each stage, and compare them in terms of pros, cons, recognition accuracy, and processing speed. Future forecasts of ALPR are given at the end.