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.

/pdf/the-environmental-protection-agency-3gx4opzrjm.pdf

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.

/pdf/smart-homes-that-monitor-breathing-and-heart-rate-425fzf6qc1.pdf

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

The paper presents a practical approach to ion selective electrodes (ISE). The behaviour of Pb +2 ISE (ELIT8231) and Cd +2 ISE (ELIT8241) for pure (only Cd +2 or Pb +2 ) and mixed solutions (Cd +2 and Pb +2 ) in static and dynamic conditions is studied. The pH variation of ISE test solution is considered in order to perform the pH correction of the ISE potentiometric selectivity coefficient determined under static conditions using a neural network processing structure. The dynamic test is performed in order to evaluate sensor's transient response and time constant as well as ion interference and pH influence on sensor's output. The experimental set-up is expressed by a multisensor virtual measurement system that acquires the voltage of the sensor channels, controls the ISE dynamic test and performs the data processing using a software implemented in LabVIEW.

A practical approach to ion selective electrodes performance in static and dynamic conditions

One of the newest targets of public health is management of obesity-hypertension. In this paper is presented the use of an artificial neural network based model for objective classification of obesity-hypertension. Different neural network architectures as part of hybrid processing scheme including comparators and competitive processing blocks were developed and tested. The neural network functionality is the classification of the individuals according to the obesity risks. The results show that the neural network classifier is consistent with the standard criteria suggested by the obesity and hypertension guidelines.

Artificial neural network approach for obesity-hypertension classification

This paper describes a computer based measuring system dedicated to the determination of both the probability distribution function and autocorrelation function of a noise signal. The system, conceived as a sequential one, is hybrid, with most of the necessary manipulation of signals performed in the analog domain. Details of both hardware and software implementation, as well as results obtained for a Gaussian band limited noise generator, are presented. The system is particularly suited not only to calculate the power spectral density of a signal (using Wiener-Khintchine theorem) and cross-correlation between two signals, but also to recover periodic signals buried in noise. >

http://ieeexplore.ieee.org/iel2/1120/8083/00351881.pdf

Characterization of noise generators: automated measuring system for the determination of the probability distribution and autocorrelation functions

Sampling rate and resolution are two main competing interests in digital data acquisition systems design High sampling rate data acquisition systems, usually based on flash converters, exhibits low-resolution, and high resolution systems, usually based on sigma-delta or dual-slope converters, are generally slow However, a compromise can be obtained if digital signal processing techniques are used after analog-to-digital conversion This paper presents a solution that can be used to optimize the performance of data acquisition systems based on analog-to-digital data acquisition boards A dynamic self-adjusted sampling rate and dithering are proposed in order to improve the signal-to-noise relation and measurement accuracy Simulation and experimental results are presented to evaluate the digitizing system performance gains when the proposed solution is applied

Adaptive analog-to-digital conversion using self-dithering in data acquisition systems

The paper presents a displacement measuring system based on one optical emitter (laser diode), two optical sensors and on a smart structure used for sensor data processing. The system is designed around the PIC16F877 8-bit microcontroller that powers the sensors, acquires data from the sensors and performs the intelligent processing based on a neural algorithm. Communication between the smart sensor and a personal computer (PC) is possible using a RS232 interface. A practical approach concerning the laser diode behavior, the on-line correction of the tilt angle influences, and the microcontroller implementation of the neural processing algorithm are also presented.

Pedro Silva Girao

Papers

A practical approach to ion selective electrodes performance in static and dynamic conditions

Artificial neural network approach for obesity-hypertension classification

Characterization of noise generators: automated measuring system for the determination of the probability distribution and autocorrelation functions

Adaptive analog-to-digital conversion using self-dithering in data acquisition systems

Laser based smart displacement sensor