3,770 research outputs found
Effective-SNR estimation for wireless sensor network using Kalman filter
No full textIn many Wireless Sensor Network (WSN) applications, the availability of a simple yet accurate estimation of the RF channel quality is vital. However, due to measurement noise and fading effects, it is usually estimated through probe or learning based methods, which result in high energy consumption or high overheads. We propose to make use of information redundancy among indicators provided by the IEEE 802.15.4 system to improve the estimation of the link quality. A Kalman filter based solution is used due to its ability to give an accurate estimate of the un-measurable states of a dynamic system subject to observation noise. In this paper we present an empirical study showing that an improved indicator, termed Effective-SNR, can be produced by combining Signal to Noise Ratio (SNR) and Link Quality Indicator (LQI) with minimal additional overhead. The estimation accuracy is further improved through the use of Kalman filtering techniques. Finally, experimental results demonstrate that the proposed algorithm can be implemented on resource constraints devices typical in WSNs
Estimating the Dynamics of Weak Efficiency on the Prague Stock Exchange Using the Kalman Filter
No full textThe paper builds on the martingale representation of the market efficiency hypothesis and, with the use of an E-GARCH model of the volatility of the PX and PX-GLOBAL daily returns, a state-space model is formulated. Using the Kalman filter, the time-varying dependency of the daily returns on their lagged values is estimated. The estimation of this parameter shows how quickly the Prague Stock Exchange, represented by its PX index and PX-GLOBAL index, has gradually moved toward the condition of weak efficiency.GARCH, Kalman filter, martingale, weak-efficiency
A novel design for an RF MEMS resistive switch on PCB substrate
No full textCopyright @ 2008 Stimulation Action on MEM
Algoritmos de análise de cena para localização indoor via redes IEEE 802.11
No full textDissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2015.Sistemas de localização global já fazem parte do cotidiano das pessoas. O GPS é usado diariamente para guiar condutores, localizar equipamentos e monitorar atividades externas. Porém, a localização indoor ainda se mostra como um problema. Tecnologias distintas possuem vantagens em determinadas situações, porém nenhuma sobressai como solução definitiva. As redes IEEE 802.11 são frequentemente utilizadas para desempenhar essa função sendo que o algoritmo de análise de cena, que mapeia previamente os níveis de sinais do ambiente, é o que vem fornecendo os melhores resultados. O presente estudo apresenta um sistema de posicionamento indoor utilizando um algoritmo de análise de cena, acrescido de estratégias para minimizar algumas de suas desvantagens. Modificações foram propostas e avaliadas, como a atribuição de fator de qualidade para os pontos coletados, o uso de filtro de Kalman e a utilização do histórico de movimentação na estimativa. Os resultados mostram as melhorias obtidas em termos de precisão na estimativa de posição, em particular para o algoritmo baseado no histórico de movimentação.Abstract : Global Positioning Systems are part of daily life. GPS is used everyday to guide drivers, find equipments and track outdoor activities. However Indoor Location is still an open topic. Distinct technologies are being analyzed and each one has its own advantages, although no one is considered the best solution. IEEE 802.11 networks are frequently chosen for this task and fingerprinting algorithm is providing the best results. This study describes a fingerprinting based indoor location system where modifications were introduced in order to mitigate some of its drawbacks. A quality factor was attributed to each collected point, Kalman filter was used and historical location was taken in consideration during estimation process. The results show a more accurate location estimation, especially when considering the historical location
Automated detection of prostate cancer using wavelet transform features of ultrasound RF time series
No full textThe aim of this research was to investigate the performance of wavelet transform based features of ultrasound radiofrequency (RF) time series for automated detection of prostate cancer tumors in transrectal ultrasound images. Sequential frames of RF echo signals from 35 extracted prostate specimens were recorded in parallel planes, while the ultrasound probe and the tissue were fixed in position in each imaging plane. The sequence of RF echo signal samples corresponding to a particular spot in tissue imaging plane constitutes one RF time series. Each region of interest (ROI) of ultrasound image was represented by three groups of features of its time series, namely, wavelet, spectral and fractal features. Wavelet transform approximation and detail sequences of each ROI were averaged and used as wavelet features. The average value of the normalized spectrum in four quarters of the frequency range along with the intercept and slope of a regression line fitted to the values of the spectrum versus normalized frequency plot formed six spectral features. Fractal dimension (FD) of the RF time series were computed based on the Higuchi's approach. A support vector machine (SVM) classifier was used to classify the ROIs. The results indicate that combining wavelet coefficient based features with previously proposed spectral and fractal features of RF time series data would increase the area under ROC curve from 93.1% to 95.0%, respectively. Furthermore, the accuracy, sensitivity, and specificity increases to 91.7%, 86.6%, and 94.7%, from 85.7%, 85.2%, and 86.1%, respectively, using only spectral and fractal features. [ABSTRACT FROM AUTHOR]Peer reviewedFinal article publishe
Hybrid RF mapping and Kalman filtered spring relaxation for sensor network localization
No full textAn accurate and low-cost hybrid solution to the problem of autonomous self-localization in wireless sensor networks (WSN) is presented. The solution is designed to perform robustly under challenging radio propagation conditions in mind, while requiring low deployment efforts, and utilizing only low-cost hardware and light-weight distributed algorithms for location computation. Our solution harnesses the strengths of two approaches for environments with complex propagation characteristics: RF mapping to provide an initial estimate of each sensor's position based on a coarse-grain RF map acquired with minimal efforts; and a cooperative light-weight spring relaxation technique for each sensor to refine its estimate using Kalman filtered inter-node distance measurements. Using Kalman filtering to pre-process noisy distance measurements inherent in complex propagation environments, is found to have significant positive impacts on the subsequent accuracy and the convergence of our spring relaxation algorithm. Through extensive simulations using realistic settings and real data set, we show that our approach is a practical localization solution which can achieve sub-meter accuracy and fast convergence under harsh propagation conditions, with no specialized hardware or significant efforts required to deploy
Hybrid RF mapping and Kalman filtered spring relaxation for sensor network localization
No full textAn accurate and low-cost hybrid solution to the problem of autonomous self-localization in wireless sensor networks (WSN) is presented. The solution is designed to perform robustly under challenging radio propagation conditions in mind, while requiring low deployment efforts, and utilizing only low-cost hardware and light-weight distributed algorithms for location computation. Our solution harnesses the strengths of two approaches for environments with complex propagation characteristics: RF mapping to provide an initial estimate of each sensor's position based on a coarse-grain RF map acquired with minimal efforts; and a cooperative light-weight spring relaxation technique for each sensor to refine its estimate using Kalman filtered inter-node distance measurements. Using Kalman filtering to pre-process noisy distance measurements inherent in complex propagation environments, is found to have significant positive impacts on the subsequent accuracy and the convergence of our spring relaxation algorithm. Through extensive simulations using realistic settings and real data set, we show that our approach is a practical localization solution which can achieve sub-meter accuracy and fast convergence under harsh propagation conditions, with no specialized hardware or significant efforts required to deploy. © 2012 IEEE
POD–Kalman filtering for improving noninvasive 3D temperature monitoring in MR-guided hyperthermia
No full textBackground: During resonance frequency (RF) hyperthermia treatment, the temperature of the tumor tissue is elevated to the range of 39–44°C. Accurate temperature monitoring is essential to guide treatments and ensure precise heat delivery and treatment quality. Magnetic resonance (MR) thermometry is currently the only clinical method to measure temperature noninvasively in a volume during treatment. However, several studies have shown that this approach is not always sufficiently accurate for thermal dosimetry in areas with motion, such as the pelvic region. Model-based temperature estimation is a promising approach to correct and supplement 3D online temperature estimation in regions where MR thermometry is unreliable or cannot be measured. However, complete 3D temperature modeling of the pelvic region is too complex for online usage. Purpose: This study aimed to evaluate the use of proper orthogonal decomposition (POD) model reduction combined with Kalman filtering to improve temperature estimation using MR thermometry. Furthermore, we assessed the benefit of this method using data from hyperthermia treatment where there were limited and unreliable MR thermometry measurements. Methods: The performance of POD–Kalman filtering was evaluated in several heating experiments and for data from patients treated for locally advanced cervical cancer. For each method, we evaluated the mean absolute error (MAE) concerning the temperature measurements acquired by the thermal probes, and we assessed the reproducibility and consistency using the standard deviation of error (SDE). Furthermore, three patient groups were defined according to susceptibility artifacts caused by the level of intestinal gas motion to assess if the POD–Kalman filtering could compensate for missing and unreliable MR thermometry measurements. Results: First, we showed that this method is beneficial and reproducible in phantom experiments. Second, we demonstrated that the combined method improved the match between temperature prediction and temperature acquired by intraluminal thermometry for patients treated for locally advanced cervical cancer. Considering all patients, the POD–Kalman filter improved MAE by 43% (filtered MR thermometry = 1.29°C, POD–Kalman filtered temperature = 0.74°C). Moreover, the SDE was improved by 47% (filtered MR thermometry = 1.16°C, POD–Kalman filtered temperature = 0.61°C). Specifically, the POD–Kalman filter reduced the MAE by approximately 60% in patients whose MR thermometry was unreliable because of the great amount of susceptibilities caused by the high level of intestinal gas motion. Conclusions: We showed that the POD–Kalman filter significantly improved the accuracy of temperature monitoring compared to MR thermometry in heating experiments and hyperthermia treatments. The results demonstrated that POD–Kalman filtering can improve thermal dosimetry during RF hyperthermia treatment, especially when MR thermometry is inaccurate.RST/Applied Radiation & Isotope
An RF-Powered DLL-Based 2.4-GHz Transmitter for Autonomous Wireless Sensor Nodes
No full textThis paper presents the system and circuit design of a compact radio frequency (RF)-powered 2.4-GHz CMOS transmitter (TX) to be used for autonomous wireless sensor nodes (WSNs). The proposed TX utilizes the received dedicated RF signal for both energy harvesting as well as frequency synthesis. A TX RF carrier is derived from the received RF signal by means of a delay locked loop and XOR-based frequency multiplier. The 50-Ω load is subsequently driven by a tuned switching RF power amplifier (PA) with 25% duty cycle input for high global efficiency. The design is fabricated in 40-nm CMOS technology and occupies a die area of 0.16 mm2. Experimental results show a rectifier with 36.83% peak efficiency and power management circuit with 120-nA current consumption that enables a low start-up power of -18.4 dBm. The TX outputs a continuous 2.44-GHz RF signal at -2.57 dBm with 36.5% PA drain efficiency and 23.9% global efficiency from a 915-MHz RF input and supports ON-OFF keying modulation.Accepted Author ManuscriptBio-Electronic
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