Parallel Cyclostationarity-Exploiting Algorithm for Energy-Efficient Spectrum Sensing

The evolution of wireless communication systems leads to Dynamic Spectrum Allocation for Cognitive Radio, which requires reliable spectrum sensing techniques. Among the spectrum sensing methods proposed in the literature, those that exploit cyclostationary characteristics of radio signals are particularly suitable for communication environments with low signal-to-noise ratios, or with non-stationary noise. However, such methods have high computational complexity that directly raises the power consumption of devices which often have very stringent low-power requirements. We propose a strategy for cyclostationary spectrum sensing with reduced energy consumption. This strategy is based on the principle that p processors working at slower frequencies consume less power than a single processor for the same execution time. We devise a strict relation between the energy savings and common parallel system metrics. The results of simulations show that our strategy promises very significant savings in actual devices.

Parallel Cyclostationarity-Exploiting Algorithm for Energy-Efficient Spectrum Sensing

The evolution of wireless communication systems leads to Dynamic Spectrum Allocation for Cognitive Radio, which requires reliable spectrum sensing techniques. Among the spectrum sensing methods proposed in the literature, those that exploit cyclostationary characteristics of radio signals are particularly suitable for communication environments with low signal-to-noise ratios, or with non-stationary noise. However, such methods have high computational complexity that directly raises the power consumption of devices which often have very stringent low-power requirements. We propose a strategy for cyclostationary spectrum sensing with reduced energy consumption. This strategy is based on the principle that p processors working at slower frequencies consume less power than a single processor for the same execution time. We devise a strict relation between the energy savings and common parallel system metrics. The results of simulations show that our strategy promises very significant savings in actual devices.

Análise de DFA e de agrupamento do perfil de densidade de poços de petróleo

In recent years, the DFA introduced by Peng, was established as an important tool capable of detecting long-range autocorrelation in time series with non-stationary. This technique has been successfully applied to various areas such as: Econophysics, Biophysics, Medicine, Physics and Climatology. In this study, we used the DFA technique to obtain the Hurst exponent (H) of the profile of electric density profile (RHOB) of 53 wells resulting from the Field School of Namorados. In this work we want to know if we can or not use H to spatially characterize the spatial data field. Two cases arise: In the first a set of H reflects the local geology, with wells that are geographically closer showing similar H, and then one can use H in geostatistical procedures. In the second case each well has its proper H and the information of the well are uncorrelated, the profiles show only random fluctuations in H that do not show any spatial structure. Cluster analysis is a method widely used in carrying out statistical analysis. In this work we use the non-hierarchy method of k-means. In order to verify whether a set of data generated by the k-means method shows spatial patterns, we create the parameter Ω (index of neighborhood). High Ω shows more aggregated data, low Ω indicates dispersed or data without spatial correlation. With help of this index and the method of Monte Carlo. Using Ω index we verify that random cluster data shows a distribution of Ω that is lower than actual cluster Ω. Thus we conclude that the data of H obtained in 53 wells are grouped and can be used to characterize space patterns. The analysis of curves level confirmed the results of the k-means

Bio-óleo e biogás da degradação termoquímica de lodo de esgoto doméstico em cilindro rotativo

The objective of this study was to produce biofuels (bio-oil and gas) from the thermal treatment of sewage sludge in rotating cylinder, aiming industrial applications. The biomass was characterized by immediate and instrumental analysis (elemental analysis, scanning electron microscopy - SEM, X-ray diffraction, infrared spectroscopy and ICP-OES). A kinetic study on non-stationary regime was done to calculate the activation energy by Thermal Gravimetric Analysis evaluating thermochemical and thermocatalytic process of sludge, the latter being in the presence of USY zeolite. As expected, the activation energy evaluated by the mathematical model "Model-free kinetics" applying techniques isoconversionais was lowest for the catalytic tests (57.9 to 108.9 kJ/mol in the range of biomass conversion of 40 to 80%). The pyrolytic plant at a laboratory scale reactor consists of a rotating cylinder whose length is 100 cm with capable of processing up to 1 kg biomass/h. In the process of pyrolysis thermochemical were studied following parameters: temperature of reaction (500 to 600 ° C), flow rate of carrier gas (50 to 200 mL/min), frequency of rotation of centrifugation for condensation of bio-oil (20 to 30 Hz) and flow of biomass (4 and 22 g/min). Products obtained during the process (pyrolytic liquid, coal and gas) were characterized by classical and instrumental analytical techniques. The maximum yield of liquid pyrolytic was approximately 10.5% obtained in the conditions of temperature of 500 °C, centrifugation speed of 20 Hz, an inert gas flow of 200 mL/min and feeding of biomass 22 g/min. The highest yield obtained for the gas phase was 23.3% for the temperature of 600 °C, flow rate of 200 mL/min inert, frequency of rotation of the column of vapor condensation 30 Hz and flow of biomass of 22 g/min. The non-oxygenated aliphatic hydrocarbons were found in greater proportion in the bio-oil (55%) followed by aliphatic oxygenated (27%). The bio-oil had the following characteristics: pH 6.81, density between 1.05 and 1.09 g/mL, viscosity between 2.5 and 3.1 cSt and highest heating value between 16.91 and 17.85 MJ/ kg. The main components in the gas phase were: H2, CO, CO2 and CH4. Hydrogen was the main constituent of the gas mixture, with a yield of about 46.2% for a temperature of 600 ° C. Among the hydrocarbons formed, methane was found in higher yield (16.6%) for the temperature 520 oC. The solid phase obtained showed a high ash content (70%) due to the abundant presence of metals in coal, in particular iron, which was also present in bio-oil with a rate of 0.068% in the test performed at a temperature of 500 oC.

DFA e análise de agrupamento aplicadas a perfis de porosidade neutrônico em poços de petróleo

Peng was the first to work with the Technical DFA (Detrended Fluctuation Analysis), a tool capable of detecting auto-long-range correlation in time series with non-stationary. In this study, the technique of DFA is used to obtain the Hurst exponent (H) profile of the electric neutron porosity of the 52 oil wells in Namorado Field, located in the Campos Basin -Brazil. The purpose is to know if the Hurst exponent can be used to characterize spatial distribution of wells. Thus, we verify that the wells that have close values of H are spatially close together. In this work we used the method of hierarchical clustering and non-hierarchical clustering method (the k-mean method). Then compare the two methods to see which of the two provides the best result. From this, was the parameter � (index neighborhood) which checks whether a data set generated by the k- average method, or at random, so in fact spatial patterns. High values of � indicate that the data are aggregated, while low values of � indicate that the data are scattered (no spatial correlation). Using the Monte Carlo method showed that combined data show a random distribution of � below the empirical value. So the empirical evidence of H obtained from 52 wells are grouped geographically. By passing the data of standard curves with the results obtained by the k-mean, confirming that it is effective to correlate well in spatial distribution

Análise de DFA e de agrupamento do perfil de densidade de poços de petróleo

In recent years, the DFA introduced by Peng, was established as an important tool capable of detecting long-range autocorrelation in time series with non-stationary. This technique has been successfully applied to various areas such as: Econophysics, Biophysics, Medicine, Physics and Climatology. In this study, we used the DFA technique to obtain the Hurst exponent (H) of the profile of electric density profile (RHOB) of 53 wells resulting from the Field School of Namorados. In this work we want to know if we can or not use H to spatially characterize the spatial data field. Two cases arise: In the first a set of H reflects the local geology, with wells that are geographically closer showing similar H, and then one can use H in geostatistical procedures. In the second case each well has its proper H and the information of the well are uncorrelated, the profiles show only random fluctuations in H that do not show any spatial structure. Cluster analysis is a method widely used in carrying out statistical analysis. In this work we use the non-hierarchy method of k-means. In order to verify whether a set of data generated by the k-means method shows spatial patterns, we create the parameter Ω (index of neighborhood). High Ω shows more aggregated data, low Ω indicates dispersed or data without spatial correlation. With help of this index and the method of Monte Carlo. Using Ω index we verify that random cluster data shows a distribution of Ω that is lower than actual cluster Ω. Thus we conclude that the data of H obtained in 53 wells are grouped and can be used to characterize space patterns. The analysis of curves level confirmed the results of the k-means

Aspectos epidemiológicos, cognitivo-comportamentais e neurofisiológicos do sonho lúcido

Lucid dreaming (LD) is a mental state in which the subject is aware of being dreaming while dreaming. The prevalence of LD among Europeans, North Americans and Asians is quite variable (between 26 and 92%) (Stepansky et al., 1998; Schredl & Erlacher, 2011; Yu, 2008); in Latin Americans it is yet to be investigated. Furthermore, the neural bases of LD remain controversial. Different studies have observed that LD presents power increases in the alpha frequency band (Tyson et al., 1984), in beta oscillations recorded from the parietal cortex (Holzinger et al., 2006) and in gamma rhythm recorded from the frontal cortex (Voss et al., 2009), in comparison with non-lucid dreaming. In this thesis we report epidemiological and neurophysiological investigations of LD. To investigate the epidemiology of LD (Study 1), we developed an online questionnaire about dreams that was answered by 3,427 volunteers. In this sample, 56% were women, 24% were men and 20% did not inform their gender (the median age was 25 years). A total of 76.5% of the subjects reported recalling dreams at least once a week, and about two-thirds of them reported dreaming always in the first person, i.e. when the dreamer observes the dream from within itself, not as another dream character. Dream reports typically depicted actions (93.3%), known people (92.9%), sounds/voices (78.5%), and colored images (76.3%). The oneiric content was related to plans for upcoming days (37.8%), and memories of the previous day (13.8%). Nightmares were characterized by general anxiety/fear (65.5%), feeling of being chased (48.5%), and non-painful unpleasant sensations (47.6%). With regard to LD, 77.2% of the subjects reported having experienced LD at least once in their lifetime (44.9% reported up to 10 episodes ever). LD frequency was weakly correlated with dream recall frequency (r = 0.20, p <0.001) and was higher in men (χ2=10.2, p=0.001). The control of LD was rare (29.7%) and inversely correlated with LD duration (r=-0.38, p <0.001), which is usually short: to 48.5% of the subjects, LD takes less than 1 minute. LD occurrence is mainly associated with having sleep without a fixed time to wake up (38.3%), which increases the chance of having REM sleep (REMS). LD is also associated with stress (30.1%), which increases REMS transitions into wakefulness. Overall, the data suggest that dreams and nightmares can be evolutionarily understood as a simulation of the common situations that happen in life, and that are related to our social, psychological and biological integrity. The results also indicate that LD is a relatively common experience (but not recurrent), often elusive and difficult to control, suggesting that LD is an incomplete stationary stage (or phase transition) between REMS and wake state. Moreover, despite the variability of LD prevalence among North Americans, Europeans and Asians, our data from Latin Americans strengthens the notion that LD is a general phenomenon of the human species. To further investigate the neural bases of LD (Study 2), we performed sleep recordings of 32 non-frequent lucid dreamers (sample 1) and 6 frequent lucid dreamers (sample 2). In sample 1, we applied two cognitive-behavioral techniques to induce LD: presleep LD suggestion (n=8) and light pulses applied during REMS (n=8); in a control group we made no attempt to influence dreaming (n=16). The results indicate that it is quite difficult but still possible to induce LD, since we could induce LD in a single subject, using the suggestion technique. EEG signals from this one subject exhibited alpha (7-14 Hz) bursts prior to LD. These bursts were brief (about 3s), without significant change in muscle tone, and independent of the presence of rapid eye movements. No such bursts were observed in the remaining 31 subjects. In addition, LD exhibited significantly higher occipital alpha and right temporo-parietal gamma (30-50 Hz) power, in comparison with non-lucid REMS. In sample 2, LD presented increased frontal high-gamma (50-100 Hz) power on average, in comparison with non-lucid REMS; however, this was not consistent across all subjects, being a clear phenomenon in just one subject. We also observed that four of these volunteers showed an increase in alpha rhythm power over the occipital region, immediately before or during LD. Altogether, our preliminary results suggest that LD presents neurophysiological characteristics that make it different from both waking and the typical REMS. To the extent that the right temporo-parietal and frontal regions are related to the formation of selfconsciousness and body internal image, we suggest that an increased activity in these regions during sleep may be the neurobiological mechanism underlying LD. The alpha rhythm bursts, as well as the alpha power increase over the occipital region, may represent micro-arousals, which facilitate the contact of the brain during sleep with the external environment, favoring the occurrence of LD. This also strengthens the notion that LD is an intermediary state between sleep and wakefulness