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.

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The Environmental Protection Agency

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.

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Smart Homes that Monitor Breathing and Heart Rate

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.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3963&context=sis_research

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

Photoplethysmography and its application in clinical physiological measurement

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.

https://romisatriawahono.net/lecture/rm/survey/computer%20vision/Du%20-%20Automatic%20License%20Plate%20Recognition%20-%202013.pdf

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

A survey of wireless sensor network solutions for gait assessment is presented including own developed solution gait assessment based on smart insoles nodes ZigBee compatible characterized by multichannel force measurement and MEMS inertial measurement IMU. The system was developed to measure ground reaction force, acceleration and direction of feet in order to provide information to physiotherapists for an objective evaluation of rehabilitation effectiveness. Based on acquired data from the sensing channels a set of gait feature extraction such as walking speed stride length, swing time are calculated as part of gait analysis. Software for WSN node control, gait feature calculation and primary analysis of rehabilitation effectiveness developed for the physiotherapist was designed and implemented and preliminary tests were carried out for normal and simulated anomalous gait.

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WSN gait monitoring for objective evaluation of rehabilitation process

Method for unobtrusive measurement of indoor air effects on the cardio-respiratory functions

An hybrid smart sensor network that includes a set of sensing nodes designed and implemented according with the specifications of the Smart Transducer Interface Standard, IEEE 1451, is presented. The communication between the nodes and the host computer that is used for advanced air quality data processing and data storage is performed through a wireless connection that was developed using a low cost, high performance radio device. Regarding the IEEE 1451 implementation the nodes put together a minimal Dot4 Network Capable Application Processor (NCAP) with a data acquisition unit that reads the analog signals from an IEEE 1451.4 Transducer Unit with air quality, temperature and relative humidity sensors using a Mixed-Mode Interface (MMI). The LabVIEW program implemented on the computer performs the automatic identification of the node analog sensors through TEDS reading, air quality data correction using the temperature and relative humidity information, data logging and pollution alarm generation. The use of Virtual TEDS solution for the sensing nodes is also considered.

Embedding IEEE 1451.4 smart sensing nodes in a Wireless Air Quality Monitoring Network

Accuracy, error compensation and simplicity of transducer's communication and interfacing are three important topics in the design and development of any measurement system. Nowadays, there are a substantial number of transducers and actuators that generate or receive, respectively, frequency modulated signals. The main advantages associated with frequency transducers include its high noise immunity, high output signal power, wide dynamic range and simplicity of signal interfacing and coding [1-2]. The frequency-to-digital conversion (FDC) is easily performed by any microcontroller, or circuits based on commercial off-the- shelf (COTS) components, without need of an analog-to-digital converter (ADC), and the same easiness exists when frequency signals are required for actuators. Eliminating the need of ADCs and DACs reduces the cost of instrumentation and measurement systems and eliminates a large number of error sources associated with these conversion devices. This paper is dedicated to FDC based measurement systems, giving particular attention to calibration issues and self- adaptive measurement capabilities that can be used to select a suitable conversion accuracy for a given signal-to-noise ratio. Some simulation and experimental results for a temperature and humidity measurement system will be included as application examples.

A Self-Adaptable Method to Optimize the Performance of Frequency-To-Code Conversion Based Measurement Systems

Many modular platforms have been developed to facilitate the design and implementation of smart sensing systems. Modular platforms can be of great benefit for system prototyping but the way they were initially designed can also limit the possibilities of building and testing new concepts. In a time where smart and ubiquitous sensor systems are of increasing interest, allowing to have the sensor's data available always and anywhere, it is also of great importance knowing what are the main features a sensing platform should have to allow the development and fast prototyping of new innovative sensor based systems. This article focuses on the architectures commonly used by modular and portable sensor platforms, discussing some practical implementations. At the same time is presented a new architecture characterized by high flexibility avoiding smart sensor's redesign each time a new communication paradigm arises.

Pedro Silva Girao

Papers

WSN gait monitoring for objective evaluation of rehabilitation process

Method for unobtrusive measurement of indoor air effects on the cardio-respiratory functions

Embedding IEEE 1451.4 smart sensing nodes in a Wireless Air Quality Monitoring Network

A Self-Adaptable Method to Optimize the Performance of Frequency-To-Code Conversion Based Measurement Systems

Architectures for modular smart sensor systems