Number Of SUs Research Articles

Self-reported hypertension prevalence in Brazilian capitals and correlated factors, 2008-2019

This study aims to analyze the prevalence of self-reported arterial hypertension and correlated factors, in the Brazilian capitals and the Federal District, from 2008 to 2019. This is an ecological study. Data on the self-reported hypertension prevalence was collected from Surveillance of Risk and Protective Factors for Chronic Diseases by Telephone Survey (VIGITEL). The significance level used was 5%. A total of 621,689 people were interviewed. Rio de Janeiro was the capital with the highest prevalence of arterial hypertension in most of the years analyzed, ranging from 28.0% to 31.7%. Palmas had the lowest prevalence in most years, ranging from 12.9% to 18.6%. A positive and significant correlation was found between the prevalence of self-reported hypertension and the percentage of elderly people (60 years or older), as well with the number of SUS and non-SUS Basic Clinics/10,000 inhabitants, the number of SUS and non-SUS physicians/1,000 inhabitants and the number of SUS and non-SUS Nurses/1,000 inhabitants. Public health policies must be prioritized with a view to primary, secondary and tertiary prevention hypertension.

The impact of M-ary rates on various quadrature amplitude modulation detection

The 5G system-based cognitive radio network is promised to meet the requirements of huge data applications with spectrum. However, the M-ary effect on the detection has not been thoroughly investigated. In this paper, an M-ary of quadrature amplitude modulation detection system is studied. Many rates are used in this study 4, 16, 64, and 256 constellation points. The detection system is applied to cooperative spectrum sensing to enhance the performance of detection for various rates of M-ary with low signal-to-noise ratio (SNR). Further, three kinds of signals based 5G system are sensed: filtered-orthogonal frequency division multiplexing (F-OFDM), filter bank multi-carrier (FBMC), and universal filtered multi-carrier (UFMC). The best detection performance is obtained when the M-ary=4 and number of SUs=50 user, whereas the worst detection performance is obtained when the M-ary=256 and number of SUs=10 user, as revealed in the simulation results. In addition, the detection performance for the F-OFDM signal is better than that of UFMC and FBMC signals for SNR <0 dB.

Optimization Analysis of Cooperative Spectrum Sensing System over Generalized $$\kappa -\mu $$ and $$\eta -\mu $$ Fading Channels

This paper presents, optimization analysis of energy detection based cooperative spectrum sensing system (CSSS) with hard-decision combining. Several system parameters are optimized to evaluate an optimal performance theoretically over noisy and generalized fading channels. In particular, wireless environments with noise plus $$\kappa -\mu $$ and $$\eta -\mu $$ fading are considered in the sensing channels. More precisely, each secondary user (SU, also called as cognitive radio user) depend on an energy detector (ED). The SU collects the signal from the primary user (PU), is given as input to the ED, and the energy of the signal is calculated for making a binary decision locally. The locally obtained decisions are combined using hard-decision combining and a final decision about position of the PU is made. In this work, the novel mathematical expressions for detection probability of a single SU is derived first, subject to noise plus fading and validated by using Monte Carlo simulations. Next, we develop theoretical frame works for optimization analysis of CSSS using derived mathematical expressions. The channel error probability is considered in both sensing and reporting channels. Further, we derive closed-form optimal expressions of number of SUs and detection threshold subject to generalized fading and optimal values are calculated. Through receiver operating characteristics (ROC), complementary ROC and total error rate, system performance is evaluated for the significant influence of channel and network parameters. Finally, the influence of the generalized fading severity parameters, the signal-to-noise ratio (SNR), the number of SUs, the detection threshold, and the channel error probability on the performance of CSSS is also investigated.

Size and Shape of Sample Units in Native Forests and Plantations

The size and form of sampling units-SU have always been variables considered in planning and structuring forest inventories, being performed in forests or in plantations. The experimental work outlined to deal with the problem was conducted in an area of araucaria forest, in Sao Joao de Triunfo, PR, Brazil. The forms of sampling units circle, square and rectangular were evaluated, whose areas ranged from 200 m2 to 1000 m2. Time was recorded using a stopwatch and computed separately for locomotion and measurement. The power model was used to adjust the relations of the times of locomotion and measurement, as well as a hyperbolic one for the coefficient of variation, all taking as function of the SUs sizes. To achieve the analytical solution for the optimum size of the SU, it was necessary simulating the behavior of the three functions until the size of 10,000 m2. By taking the derivative of the combined function it was found the maximum point, which allowed optimizing the size of the SU in 600 m2 for the structured experimental conditions. This result proved the formulated hypothesis, performing even a critical analysis of the inclusion of other relevant variables, such as size of the area to be inventoried, number of SUs performed in one day of work, average distance between the SUs, average speeds for locomotion between SUs and for taking all respective measurements.

Cooperative Sequential Spectrum Sensing Based on Level-Triggered Sampling

We propose a new framework for cooperative spectrum sensing in cognitive radio networks, that is based on a novel class of non-uniform samplers, called the event-triggered samplers, and sequential detection. In the proposed scheme, each secondary user computes its local sensing decision statistic based on its own channel output; and whenever such decision statistic crosses certain predefined threshold values, the secondary user will send one (or several) bit of information to the fusion center. The fusion center asynchronously receives the bits from different secondary users and updates the global sensing decision statistic to perform a sequential probability ratio test (SPRT), to reach a sensing decision. We provide an asymptotic analysis for the above scheme, and under different conditions, we compare it against the cooperative sensing scheme that is based on traditional uniform sampling and sequential detection. Simulation results show that the proposed scheme, using even 1 bit, can outperform its uniform sampling counterpart that uses infinite number of bits under changing target error probabilities, SNR values, and number of SUs.

Throughput Scaling in Cognitive Multiple Access With Average Power and Interference Constraints

This paper derives tight ergodic sum-rate capacity scaling limits for cognitive radio secondary networks under five different communication environments (CoE) for two different network types when secondary users' (SUs) transmission powers are optimally allocated. The network types studied are power-interference limited (PIL) networks and interference limited (IL) networks. In PIL networks, SUs' transmissions are limited by both an average total power constraint and a constraint on the average interference that they cause to primary users (PUs). In IL networks, SUs' transmissions are only limited by an average interference constraint. The capacity scaling results in PIL networks are derived for three different CoEs in which secondary transmitter to secondary base station (STSB) channel gains are Rayleigh distributed while secondary transmitter to primary base station (STPB) channel gains are Rayleigh, Rician or Nakagami distributed. It is shown that secondary network capacity scales according to log log(N) in these three CoEs, where N is the number of SUs. In addition to these three CoEs, two more CoEs are also studied for IL networks: Rician or Nakagami distributed STSB channel gains and Rayleigh distributed STPB channel gains. It is shown that the secondary network capacity scales according to log(N) for all live CoEs in IL networks. This result implies exponential capacity gains in IL networks over PIL networks. The same capacity scaling results are shown to hold even for heterogeneous cognitive radio networks in which different SUs experience statistically different channel conditions. In some cases, our analysis leads to a new notion called effective number of users, which signifies the effective number of users contributing to multiuser diversity in cognitive radio networks. For example, effective number of users is given by K+1/e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</sup> N when STSB channel gains are Rayleigh distributed and STPB channel gains are Rician distributed with a Rician factor K.