In this paper we want to illustrate the potential estimation for installing photovoltaic systems on rooftop or horizontal building surfaces in residential areas. Different areas will be measured for residential users, and these will be measured using Google Earth and PVWatts software from NREL. The kWh per Year were calculated from the area in PVWatts and with the real consumption of 6 user’s examples. The results showed for all users the areas on their rooftops have the potential to generate three to four times more than what was calculated from the real consumption of each user. With these results and these six residential random data, it is observed that the basic consumption of each user and a surplus could be generated to share with the network microgrids and other residential users. The next step would be quantifying and modeling the benefits of the use of photovoltaic electrical energy arrangements on the rooftops of the users, as a complementary value in supporting the current grid in Puerto Rico. Also, at a global level, to reduce economic dependence on the consumption of fossil fuels and the emission of greenhouse gases, there has been the development of the use of renewable energies.
This paper wants to show the concept of a system that has been designed to automatically detect any voltage fluctuation (high or low) and/or power outage in the electrical network, thus allowing a continuous and more stable supply of electrical energy. The control module will provide a signal to the automatic transfer switch in the event of a power outage and/or voltage fluctuation (≥30 seconds), allowing the emergency generator to provide a constant flow of power to meet utility demand. load. If a voltage variation greater than 30 seconds occurs, the circuit will switch the load to emergency mode. Most commercially available automatic transfer switches are stand-alone units, with voltage monitoring systems purchased separately, whereas here in the engineered system both systems have been combined into a single, very economical control module. The concept designed were similar to those obtained in the simulations, the behavior of the system in the face of changes in the potential difference and electron flow was higher than expected, providing a constant output signal that controlled the devices connected to the system. without hesitation. These results demonstrated what could be achieved if the control module is deployed and used in the areas most affected by power outages and voltage fluctuations. In conclusion, the operation of the designed control module will provide a fundamental advantage, which will be the total independence of the human factor in the event of an interruption and/or fluctuation (low or high) in the energy service.
According to studies, today, we have seen an exponential increase in forest fires, on the island, in the United States and other places in the world. Due to climate change and extreme weather conditions, wildfires have started to appear in parts of the world that have never experienced this before. Many solutions are already being used to monitor forest fires such as: GPS, weather balloons, aerial drones, and many others. Since all these approaches use imaging sensors, they must wait until wildfires are large enough to be detected. This situation becomes a problem because forest fires would be too large to be easily controlled. This research proposes a solution that aims to attack this problem by using a microphone in the device located on the ground. Additional sensors will be incorporated, such as a digital thermometer to register humidity and temperature, a gas sensor to detect different types of gases, and long-range communications that would help our device to communicate with a network of other similar devices. Also, Internet of Things (IoT) will be implemented to send live sensor data to a central command. By focusing on the detection of forest fires, we can not only detect their occurrence in a timelier manner, but also the proposed system will have the ability to predict fires by monitoring meteorological data through smart networks.
KEYWORDS: Electrocardiography, Machine learning, Heart, Signal processing, Signal detection, Neural networks, Education and training, Data modeling, Cardiovascular disorders, Distortion
The heart is an important organ in the human body. The heart pumps blood throughout the body. This mechanical action is generated by electrical signals which can be measured. Measuring this electrical signal, we can perform a series of diagnostics to examine different functions of the heart. The electrocardiogram is a tool to record this activity with the purpose of examining the condition of the conductive system in terms of the timing of the activity of the cardiac muscle. The activity recorded by the ECG is the net electric activity between different points around the body. Using the ECG and the radial pulse we can find the cardiac rhythm in a given situation. Variations in the parameters of these signals could mean possible malfunctions of the conduction system. Most of these variations are known and have been related to heart diseases. Arrhythmias can also be determined using the ECG. In this paper, we will record the ECG of each person in the group, and we will determine a variety of parameters of the cardiac system, including the cardiac vector, the cardiac rate, and the P-R interval. Using the ECG recordings, we will train a neural network, in an unsupervised way, to learn the different types of signals for different individuals. We will also investigate the possible sources of distortion of the signal as well as the effect of inspiration and expiration on the recording of the ECG. In preliminary data obtained, the ECG signals have frequency averages between 0.38, 0.39 and 0.4 Hertz, for individuals between 20 and 25 years old.
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.
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 [9]. 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.
KEYWORDS: Fetus, Sensors, Global system for mobile communications, Electrodes, Microcontrollers, Resistance, LCDs, Computer engineering, Signal processing
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.
KEYWORDS: Electrocardiography, Heart, Beam propagation method, LCDs, Diagnostics, Electrodes, Data storage, Signal processing, Digital signal processing, Analog electronics
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.
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.
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 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.
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