Paper Published

19/11/2021 10:48

Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN

 

Scheme of the Wireless Sensor Network in the Hydroelectric Plant.

 

One of the most important challenges in Wireless Sensor Networks (WSN) is the extension of the sensors lifetime, which are battery-powered devices, through a reduction in energy consumption. Using data prediction to decrease the amount of transmitted data is one of the approaches to solve this problem. This paper provides a comparison of deep learning methods in a dual prediction scheme to reduce transmission. The structures of the models are presented along with their parameters. A comparison of the models is provided using different performance metrics, together with the percent of points transmitted per threshold, and the errors between the final data received by Base Station (BS) and the measured values. The results show that the model with better performance in the dataset was the model with Attention, saving a considerable amount of data in transmission and still maintaining a good representation of the measured data.

M.Sc. Dissertation: Carlos Morales

19/11/2021 10:41

Analysis of a Wireless Sensor Network behavior using Machine Learning Techniques

 

This work presents a review of the main concepts of Wireless Sensor Network (WSN)s and tackles one of their main problems, which is energy consumption. This is done using the data collected by a network deployed at the Hydropower power plant in Cachoeira Dourada. First, an exploratory data analysis of the WSN using statistical and machine learning methods was performed to discover insights about the current state of the network. The analysis provided information about which nodes are more stable, correlations between the data that can be exploited to optimize transmissions, and information about the stability of the links. The work also proposes the use of a Deep Learning model, in a dual prediction scheme, to reduce the transmissions between devices in the network, reduce congestion, and save energy. To do so, a review of data prediction strategies in WSNs is performed. Different neural network based models are introduced and compared using different error metrics in prediction. Finally, a measure of the reduction in data transmission is given, considering different error thresholds. Results show that the model can save a considerable amount of data in transmission, from 70% to 90%, and still maintain a good representation of the measured data.

Tags: Machine LearningRedes de Sensores sem FiosWireless Sensor Network

M.Sc. dissertation presented: Victor Preuss

19/11/2021 10:35

A software package for the steady-state simulation of autonomous circuits using the harmonic balance method.

The main goal of this work is the development of an open-source software package for steady-state simulation of autonomous circuits using the Harmonic Balance method. Oscillators are autonomous circuits of great interest in radiofrequency applications and a main component of transceivers. The periodic steady-state response of an oscillator is very important to designers, to verify parameters such as oscillating frequency, output power and power consumption. Transient simulations are not efficient to evaluate the periodic steady-state of an oscillator, as a large amount of computation is wasted during the initial startup period. The alternative explored in this work is the usage of the Harmonic Balance method with the Auxiliary Generator Technique to solve directly for the steady-state response of oscillators. To achieve that, a full circuit simulation engine was implemented in the Python programming language, with support to DC, AC, transient and harmonic balance analysis. The circuit netlists are described in code using a simple API. Several device models are available for simulation, such as RLC elements, current and voltage sources, Diode, BJT and MOSFET. Multiple examples are presented and the simulation results are compared to commercial engines to validate the implementations. Advantages of simulating circuits inside a Python environment are presented, involving easiness of data-processing and integration with other libraries

LRF@HEPA IEEE/MTT-S Contest

20/10/2021 19:13

LRF graduate student (André Murilo) participated of the IEEE/MTT-S High Efficiency Power Amplifier Student Design Competition 2021 contest.

 

Paper Published

19/05/2020 10:54

Exploiting the RSSI Long-Term Data of a WSN for the RF Channel Modeling in EPS Environments

 

 

One of the most important challenges in Wireless Sensor Networks (WSN) is the extension of the sensors lifetime, which are battery-powered devices, through a reduction in energy consumption. Using data prediction to decrease the amount of transmitted data is one of the approaches to solve this problem. This paper provides a comparison of deep learning methods in a dual prediction scheme to reduce transmission. The structures of the models are presented along with their parameters. A comparison of the models is provided using different performance metrics, together with the percent of points transmitted per threshold, and the errors between the final data received by Base Station (BS) and the measured values. The results show that the model with better performance in the dataset was the model with Attention, saving a considerable amount of data in transmission and still maintaining a good representation of the measured data.

 

PhD thesis defense: Roddy Romero

15/09/2018 23:35

Contribuições ao projeto de leitores e dispositivos sensores baseados em retroespalhamento sem chip, de baixo custo. 

Chipless tags for objects identification have been proposed as a low-cost alternative to the well-known chipped Radio Frequency IDentification (RFID) tags technology. More recently, chipless sensors havealso been indicated for specific applications in which RFID-based sensor are not very suitable, such as in harsh environments or ultra-low cost item monitoring. This research have focused on the study of a chipless monitoring system. It was identified that the reading range is one of the most important concerns of these systems, specifically when both the sensor and the reader are size restricted due to a specific application. Consequently, in this work, a new sensor design is proposed to tackle this issue. Several prototypes of the sensor have been implemented in low-cost substrates, such as common PCB glass epoxy-based laminates, and flexible substrates such as plastic and paper. These have shown comparable results to the reported sensors in literature in terms of sensitivity and, more importantly, a superior radar cross section level considering its reduced size. In addition to the sensor, a theoretical analysis and some tests were conducted to prove the need of implementing self-interference cancellation techniques in single-antenna monostatic chipless readers for improving the readability of the tag. Both proposals, on the sensor and on the reader side, can contribute to the reading range enhancement in frequency coded chipless systems.

Master Thesis defense: Rafael Licursi

25/05/2018 23:03

The MSc. candidate Rafael Licursi successfully defended his thesis, in which he proposed novel Techniques To Locate And Identify Radars With Low Volume, Weight, Costs And Processing Capabilities.

This research proposes signal processing techniques that look at  radiofrequency peculiarities in order to soften the processing workload and to allow the design of radar detectors that present low volume, weight, costs and available computational power. Experiments were carried out with a prototype of an Electronic Support Measures (ESM)  system with tablet processing, based on SoftwareDefined Radio (SDR). Results show that the performance of the proposed pulse measurement  technique degrades significantly only when the pulse amplitudes are  3.7 mV at the input of the used SDR. They also show that the suggested directionfinding method presents Gaussian distributions for the measurements, with a standard deviation as high as the lower the amplitude of incoming pulses, which allows, according to the latter, to make inferences about the former. Benefiting from this, the developed pulseclustering algorithm defined, in front of 6 distributions, clusters of pulses with enclosure rates 92.71 %. Finally, the results show that the proposed pattern recognition algorithm, when it received a cluster of pulses with patterns of 4 simulated radars, with different types of antenna scan, deinterleaved the 4 patterns, with rates of correct assignment of the pulse repetition interval 96.30%, besides estimating the pulse repetition interval with errors of the order of 10-5. This study is recommended for the areas of Radar Signal Processing, Direction  Finding, Pattern Recognition and Electronic Warfare.

 

LRF team wons third place in the national final of the contest “IDEAS FOR MILK” promoted by EMBRAPA

31/12/2016 22:29

logo_ideas

Work under development at LRF  won the third place in the national  final of the  contest “IDEAS FOR MILK” promoted by EMBRAPA. The project is  the result of more than six  years of research, involving undergraduate and graduate students.  Initially, the focus was on the petroleum  extraction sites, where the knowledge of the  water-oil fraction during the oil  extraction carried relevant information about the well. merchanDivulgaUFSC1A microwave resonant cavity based sensor has been used in this context.  More recently, the group started looking for new applications for the sensor, in particular those where there is a correlation between  the electrical permittivity and a property of interest. This is the case of  milk,  where the total of solids (mainly fat) defines the quality of the product and is submitted to specific regulations.  Fortunately, we can correlate the amount of total solids (or fat) with the electrical permittivity of the milk using our microwave sensor. For the contest, we proposed to use it to  improve the accuracy  of the milk  production process in real time.  The research is conducted by  prof. Fernando Rangel and  prof. Daniel  Pagano, by the PhD students Heron Ávila e Roddy Romero and  by the undergraduate student Celso Leite.

 

 

Goodbye lunch

14/12/2016 14:20

 This week, our members gathered for the goodbye lunch of our dear colleague Juan Moya.

After recently obtaining his MSc. degree, he is heading back to his mother country Colombia.
We wish him the best in his future endeavours. Good luck Juan!
 

IMG_20161208_134323855

Conference on ECT by Prof. Wuqiang (IEEE Fellow)

17/09/2016 10:42


Prof. Wuqiang Yang  is visiting us during this week. We are discussing research collaboration in the field of  RF/Microwave Instrumentation and Measurement.
Prof. Wuqiang Yang is an IEEE Fellow and he is professor at the School of Electrical and Electronic Engineering at The University of Manchester, UK.

During his stay he will offer the  lecture:
“Current status of industrial tomography and applications of ECT “
Date: 19/09/2016   at 15h30

Place: PGEEL02 – EEL/CTC/UFSC

Further information can be found here.

 

Paper accepted at IEEE T-MTT!

19/08/2016 22:29

The paper “Achieving Optimal Efficiency in Energy Transfer to a CMOS Fully-Integrated Wireless Power Receiver,” was accepted  for publication in IEEE Transactions on Microwave Theory and Techniques. (read it here)

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