This paper not only seeks to motivate the people of Puerto Rico to consider the purchase of electric vehicles but also, to pursue the implementation of solar recharging stations. An evaluation of the energetic consumption of internal combustion vehicles versus Electric Vehicles (EV) was made. As well, the damage to the planet Earth produced by conventional vehicles is contrasted. The calculations necessary for the construction of a solar charging station were demonstrated based on the energy consumption. Some future research has been highlighted.
Pregnant women with conditions such as hypertension, diabetes, anemia, obesity, among others; have more possibility to be diagnosed with a high-risk pregnancy. Women with this type of pregnancy should visit their gynecologist up to two times a week depending on their condition, to monitor contractions and the fetal heartbeat, just to know the wellbeing of the fetus. Mothers have no other way to monitor the health and the contractions because the equipment is very limited and involves high costs. Constant Monitoring of the fetus could help to detect early symptoms and anomalies that can be a sign of premature childbirth and other fetal complications that could be of major concern. By creating a low-cost contraction monitor that measures the duration and frequency of contractions we could detect some symptoms or abnormalities that may indicate symptoms of early miscarriage or some other problem with the fetus. This device will save the data and would be able to alert the patient if an anomaly in the contractions occurs. When the abnormality is detected the doctor receives a text message with the information of the patient so he can give her a recommendation on what to do. The device will also save all the data so the doctor can analyze and determine the status of the fetus. The idea of this device is to help detect early symptoms of possible complications during pregnancy and so that both the mother and the fetus can enjoy a healthier pregnancy. The data recollected can also be useful to support investigations related to fetal conditions and abnormalities.
High blood pressure has been one of the main causes for cardiovascular health problems like heart attacks, aneurysms, or even strokes. About 32% American adults, have high blood pressure and only about half (54%) of people have their condition under control . The main objective of this project is to analyze, design, implement and test a blood pressure monitor which can transmit data in real time via radio frequencies. The paper includes all the analysis performed for each of the subsystems in the block diagram. Also, a diagram of each of the electronic circuits with the values obtained during the analysis. Results of the implementation and testing were included in the report.
This paper proposes a new approach on the Holter monitor by creating a portable Electrocardiogram (ECG) Holter monitor that will alert the user by detecting abnormal heart beats using a digital signal processing software. The alarm will be triggered when the patient experiences arrhythmias such as bradycardia and tachycardia. The equipment is simple, comfortable and small in size that fit in the hand. It can be used at any time and any moment by placing three leads to the person’s chest which is connected to an electronic circuit. The ECG data will be transmitted via Bluetooth to the memory of a selected mobile phone using an application that will store the collected data for up to 24 hrs. The arrhythmia is identified by comparing the reference signals with the user’s signal. The diagnostic results demonstrate that the ECG Holter monitor alerts the user when an arrhythmia is detected thru the Holter monitor and mobile application.
This work describes a novel method of estimating statistically optimum pixel sizes for classification. Historically
more resolution, smaller pixel sizes, are considered better, but having smaller pixels can cause difficulties in
classification. If the pixel size is too small, then the variation in pixels belonging to the same class could be very
large. This work studies the variance of the pixels for different pixel sizes to try and answer the question of how
small, (or how large) can the pixel size be and still have good algorithm performance. Optimum pixel size is defined
here as the size when pixels from the same class statistically come from the same distribution. The work first derives
ideal results, then compares this to real data. The real hyperspectral data comes from a SOC-700 stand mounted
hyperspectral camera. The results compare the theoretical derivations to variances calculated with real data in order
to estimate different optimal pixel sizes, and show a good correlation between real and ideal data.
An approach to incorporate spatial information in unmixing using the nonnegative matrix factorization is presented.
We call this method the spectrally adaptive constrained NMF (sacNMF). The spatial information is incorporated by
partitioning hyperspectral images into spectrally homogeneous regions using quadtree region partitioning.
Endmembers for each region are extracted using the nonnegative matrix factorization and then clustered in spectral
endmembers classes. The endmember classes better account for the variability of spectral endmembers across the
landscape. Abundances are estimated using all spectral endmembers. Experimental results using AVIRIS data from
Indian Pines is used to demonstrate the potential of the proposed approach. Comparisons with other published
approaches are presented.
An approach for unsupervised unmixing using quadtree region partitioning is studied. Images are partitioned in
spectrally homogeneous regions using quadtree region partitioning. Unmixing is performed in each individual
region using the positive matrix factorization and extracted endmembers are the clustered in endmembers classes
which account for the variability of spectral endmembers across the scene. The proposed method lends itself to an
unsupervised approach. In the paper, the effect of different spectral variability metrics in the splitting of the image
using quadtree partitioning is studied. Experimental results using the AVIRIS AP Hill image show that the Shannon
entropy produces the image partitioning that agrees with published ground truth.
In hyperspectral imaging, the radiation represented by a single pixel rarely comes from the interaction with a single
homogeneous material. However, the high spectral resolution of imaging spectrometers enables the detection,
identification, and classification of subpixel objects from their contribution to the measured spectral signal. Unmixing is
a hyperspectral image processing approach where the measured spectral signature is decomposed into a collection of
constituent spectra, or endmembers, and a set of corresponding fractions or abundances which correspond to the
fractional area occupied by the particular endmember in that pixel. The use of a single spectrum to represent an
endmember class does not take into account the variability of spectral signatures caused by natural factors. Simple
spectral mixture analysis can, by itself, provide suitable accuracies in some relatively homogeneous environments, but
because of the spectral complexity of many landscapes, the use of fixed endmember spectra may results in inaccurate
unmixing analysis for complex regions over large landscapes. This paper addresses the question of how to perform
unsupervised unmixing where local information is used to extract local endmember information and merged at a global
level to extract endmembers classes for developing an accurate description of the scene under study using the nonnegative
matrix factorization. Preliminary results using AVIRIS data are presented. Results show that this approach
better captures local structures that are not possible with global unmixing approach. Furthermore, they show that spatial
information allows the identification of more spectral endmembers than is it possible with just spectral-only methods.