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

Analysis of highway addressable remote transducer (HART) protocol is important in reliability evaluation of HART based systems. We propose a virtual instrument based solution for HART signal analysis that includes a digital oscilloscope with GPIB interface, a HART modem, a PC with a GPIB interface board and a RS232 port, and a software component developed in LabVIEW. The HART analyser permits the visualisation of signals that correspond to the HART protocol, gives access to data coming from field instruments and has the ability to analyse and modify field instrument performance

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HART protocol analyser based in LabVIEW

Nowadays, the aquaculture of oysters represents an important economical activity in coastal and estuarine areas. It is known that the growth rate of oysters is affected by several water parameters, including temperature, salinity, turbidity, pH and dissolved oxygen. In this paper, a cloud-based platform is proposed to acquire water parameters that affect oysters' growth. The paper includes the hardware and software description of the measurement system, details about the storage and processing of the acquired data, and some experimental results about the above-mentioned parameters.

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IoT applied to Environmental Monitoring in Oysters' Farms

The chapter presents a set of smart sensing systems, compatible with IEEE1451 standard, intended to unobtrusively monitor the vital signs and motor activity of a wheelchair user. The IEEE 1451 standard was considered to add smartness to transducers: clause 1451.4 adds plug-and-play and self-identification capabilities to the sensors, and clause 1451.1 provides the information model used to share data and functionalities across the network. The .NET Framework was considered to implement the 1451.1 information model as it provides a rich development environment. The .NET’s implementation of the 1451.1 information model, including the customization efforts to meet application-specific requirements, is described. Referring to the measurement channels considered in the present approach, photo-plethysmography (PPG), skin conductivity (SKC), ballistocardiography (BCG), and electrocardiography (ECG) were chosen to monitor cardiorespiratory activity, while a simple 3D accelerometer was used to monitor motor activity.

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IEEE1451 Smart Sensors Architectures for Vital Signs and Motor Activity Monitoring

This paper reports the implementation of a neural processing structure as a component of an intelligent measuring system that uses ion selective electrodes (ISEs) as sensing elements of heavy metal ions (Pb/sup +2/, Cd/sup +2/) concentration. The neural network (NN), designed and implemented to reduce errors due to ion interference and to pH and temperature variations, is of the multiple-input multiple-output Multilayer Perception (MLP-NN) type The NN is a component of a virtual instrument that includes a PC laptop, a PCMCI data acquisition board with associated conditioning circuits and the specific ISE sensors. A practical approach concerning the optimal neural processing solution (number of NN structures, number of neurons, neuron transfer functions) to increase the performance of low cost ISEs is presented. Results are presented to evaluate the performance of the NN intelligent ISE system and to discuss the possibility of transferring the acquisition and processing task to a low cost acquisition and control unit such as a microcontroller.

/pdf/increasing-ion-selective-electrodes-performance-using-neural-i3qx25ow7g.pdf

Increasing ion selective electrodes performance using neural networks

Tele-monitoring associated with vital signs and motor activity represents nowadays an important issue considering the necessity to increase the number of medical services for the aged people living in their houses. The latest developments in the field of mobile devices, wireless communications and Internet access promote these types of solutions. The chapter presents a critical analysis of smart sensing solutions existing in the market and proposes a novel architecture designed for fast prototyping of sensing systems for human motor and cardiac activity monitoring. An extended description of the hardware, firmware, and software associated with proposed smart sensing platform is described in the chapter.

Pedro Silva Girao

Papers

HART protocol analyser based in LabVIEW

IoT applied to Environmental Monitoring in Oysters' Farms

IEEE1451 Smart Sensors Architectures for Vital Signs and Motor Activity Monitoring

Increasing ion selective electrodes performance using neural networks

A Novel Smart Sensing Platform for Vital Signs and Motor Activity Monitoring