Wideband Sequential Spectrum Sensing with Varying Thresholds

In this contribution, time varying threshold sequential detectors are employed for energy detection-based spectrum sensing in low-SNR regimes. Sequential detection is proven to be faster (on average) than any other multi-sample detector for a set of given probabilities of detection and false-alarm. In this report, exact performance of a sequential detector for spectrum sensing is analyzed using the direct method. The theoretical results presented herein are verified with Monte-Carlo simulations. It is shown that for a SNR of −10 dB, among tests with Wald and triangular thresholds with similar probabilities of mis-detection and false-alarm, triangular performs 54% faster in terms of maximum detection time (90 percentile)

On Optimum Causal Cognitive Spectrum Reutilization Strategy

In this paper we study opportunistic transmission strategies for cognitive radios (CR) in which causal noisy observation from a primary user(s) (PU) state is available. PU is assumed to be operating in a slotted manner, according to a two-state Markov model. The objective is to maximize utilization ratio (UR), i.e., relative number of the PU-idle slots that are used by CR, subject to interference ratio (IR), i.e., relative number of the PU-active slots that are used by CR, below a certain level. We introduce an a-posteriori LLR-based cognitive transmission strategy and show that this strategy is optimum in the sense of maximizing UR given a certain maximum allowed IR. Two methods for calculating threshold for this strategy in practical situations are presented. One of them performs well in higher SNRs but might have too large IR at low SNRs and low PU activity levels, and the other is proven to never violate the allowed IR at the price of a reduced UR. In addition, an upper-bound for the UR of any CR strategy operating in the presence of Markovian PU is presented. Simulation results have shown a more than 116% improvement in UR at SNR of -3dB and IR level of 10% with PU state estimation. Thus, this opportunistic CR mechanism possesses a high potential in practical scenarios in which there exists no information about true states of PU

Sensing or Transmission: Causal Cognitive Radio Strategies with Censorship

This paper introduces a novel opportunistic transmission strategy for cognitive radios (CRs). The primary user (PU) is assumed to transmit in a time-slotted manner according to a two-state Markov model, and the CR is either sensing, that is, obtaining a causal, noisy observation of a primary user (PU) state, or transmitting, but not both at the same time. In other words, the CR observations of the PU are censored whenever the CR is transmitting. The objective of the CR transmission strategy is to maximize the utilization ratio (UR), i.e., the relative number of the PU-idle slots that are used by the CR, subject to that the interference ratio (IR), i.e., the relative number of the PU-active slots that are used by the CR, is below a certain level. We introduce an a-posteriori LLR-based CR transmission strategy, called CLAPP, and evaluate this strategy in terms of the achievable UR for different PU model parameters and received signal-to-noise ratios (SNRs). The performance of CLAPP is compared with a simple censored energy detection scheme. Simulation results show that CLAPP has 52% gain in UR over the best censored energy detection scheme for a maximum IR level of 10% and an SNR of -2dB. \ua9 2002-2012 IEEE

An LLR-based Cognitive Transmission Strategy for Higher Spectrum Reutilization

Reutilization of the spectrum licensed to services with low occupancy is of great interest for cognitive radios (CRs). To achieve this goal, we introduce a simple hidden Markov model which captures the primary users activity, signal uncertainties, and noise. For evaluating the performance of any CR, two new criteria are presented entitled spectrum utilization ratio (UR) and interference ratio (IR). Based on this model and new measures, a new a-posterior log-likelihood-ratio based CR is designed and implemented. Its performance is compared with standard energy-detection based spectrum-sensing CR. We demonstrate more than 300% increase in UR for up to 1% allowed interference at the SNR of -5dB

A NLLS based sub-Nyquist rate Spectrum Sensing for Wideband Cognitive Radio

For systems and devices, such as cognitive radio and networks, that need to be aware of available frequency bands, spectrum sensing has an important role. A major challenge in this area is the requirement of a high sampling rate in the sensing of a wideband signal. In this paper a wideband spectrum sensing method is presented that utilizes a sub-Nyquist sampling scheme to bring substantial savings in terms of the sampling rate. The correlation matrix of a finite number of noisy samples is computed and used by a NLLS estimator to detect the occupied and vacant channels of the spectrum. We provide an expression for the detection threshold as a function of sampling parameters and noise power. Also, a sequential forward selection algorithm is presented to find the occupied channels in a low complexity. The method can be applied to both correlated and uncorrelated wideband multichannel signals. A comparison with conventional energy detection using Nyquist-rate sampling shows that the proposed scheme can yield similar performance for SNR above 4 dB with a factor of 3 smaller sampling rate

Study on effect feedings with probiotics in increasing resistance to Aeromonas hydrophila and Changes in gut bacterial communities Sander lucioperca

Introduction: This study evaluated the effect of dietary administration Lactobacillus brevis MF01 on survival rate of total bacteria, lactic acid bacteria intestinal tract fish and changes in gut bacterial communities Sander lucioperca. Materials and methods: Lactobacillus brevis was isolated and identified from intestine of Sander luciopercaby biochemical and molecular tests.Fish were fed with dietary administration containing A1 (L.brevis MF01 10 10 CFU / g), A2 (L. brevis MF01 10 8CFU / g) and the control group (without Lactobacillus) for 45 days. At the end of the feeding period, fish were challenged with 4.5× 10 8CFU /ml Aeromonas hydrophila. The intestinal micro biota includes lactic acid bacteria and total bacteria at different times (15,30, 45 days) and 15 days after stopping feedings with probiotics were performed by using MRS agar, Plate count agar media. Results: The lactic acid bacteria levels were significantly increased compared to the control group following probiotics administration in diet (P,< 0.05). The highest number of intestinal micro biota was observed in Lactobacillus brevis 1010Cfu /g treatment (A1)(P < 0.05). Any lactic acid bacteria of the intestine, was not detected in the control group after 15 days. After the end of feeding, the number of total bacteria and Lactic acid bacteria in all groups was decreased. The highest and lowest survival rate, respectively were treatments A1 (86%) and the control group (60%). Discussion and conclusion: The results showed that addition of Lactobacillusbrevis as an additive in feeding of sander lucioperca, significantly increased the lactic acid bacteria, the intestinal bacteria and the survival rate was compared to the control group (injected)

Sensing or transmission: Causal cognitive radio strategies with censorship

This paper introduces a novel opportunistic transmission strategy for cognitive radios (CRs). The primary user (PU) is assumed to transmit in a time-slotted manner according to a two-state Markov model, and the CR is either sensing, that is, obtaining a causal, noisy observation of a primary user (PU) state, or transmitting, but not both at the same time. In other words, the CR observations of the PU are censored whenever the CR is transmitting. The objective of the CR transmission strategy is to maximize the utilization ratio (UR), i.e., the relative number of the PU-idle slots that are used by the CR, subject to that the interference ratio (IR), i.e., the relative number of the PU-active slots that are used by the CR, is below a certain level. We introduce an a-posteriori LLR-based CR transmission strategy, called CLAPP, and evaluate this strategy in terms of the achievable UR for different PU model parameters and received SNRs. The performance of CLAPP is compared with a simple censored energy detection scheme. Simulation results show that CLAPP has 52% gain in UR over the best censored energy detection scheme for a maximum IR level of 10% and an SNR of -2dB

MRI Brain Abnormality Detection Using Fuzzy Neural Networks

In this paper an expert system for detection of brain abnormalities is proposed. First preceding methods for segmentation of MR images are reviewed and their limitations are discussed. In the proposed method, MR images (three images from one slice: T1, T2 and Proton Density) are acquired from a scanner or directly from MRI system. For noise deletation two filters (median and bandreject lowpass) are used (This stage is optional). They make a clean view of MR images. It is necessary to have precise detections. So by implementing a gray-scale to color transformation algorithm (it is a radially symmetric butterworth band-reject filter), system can recognize the differences between tissues accurately. Now we have three colored images (T1, T2 and Proton Density) from the last section that better represent tissues and it is possible to say that those tissues with the same color in each of these three images may be same tissues. The combination of fuzzy systems and neural Networks make a powerful tool for pattern recognition problems. So a fuzzified neural network with outputs to a back-propagation network for tissues recognition must be used. Therefore a fuzzy neuron and a fuzzified network are introduced. The output of the back-propagation network is the type of tissue under process. The results of the last section are fed into another network that uses a knowledge-base to make a suggestion for treatment (This level is also optional). Because of the time limitation MATLAB 4.0 for Windows is used for expert system simulation. It has several abilities for matrix calculations and graphing that make the work easier (some of the base modules are written in C++)

Model-Based Cognitive Radio Strategies

Many frequency bands for wireless services are severely underutilized by the primary users (PU) to which these bands are assigned. This motivates a new class of wireless communication devices known as cognitive radios (CR), which identify vacant spectrum and transmit accordingly.In this thesis, the PU traffic model knowledge as well as all the observations available to the CR are included in the CR transmission decisions. A transmission strategy is introduced that is based on comparing an a-posterior probability (APP) log-likelihood ratio (LLR) with a threshold. The objective is to maximize the utilization ratio (UR) subject to that the interference ratio (IR) is below a certain level. In papers A and B, we study CR transmission strategies that are based on all noisy observations of the PU activities, even when the CR itself is transmitting. Paper A demonstrates a more than 300% increase in UR over standard energy detection, for the same IR value, at the PU signal to CR noise power ratio (SNR) of -5dB. Then, in paper B, weuse a continuous-output hidden Markov model for the received signal and calculate an APP LLR based on this model. This paper shows that this strategy is the optimum in the sense of maximizing the UR, given a certain maximum allowed IR, among all CRs.Moreover, two practical schemes for calculating the transmission threshold are introduced.Numerical results show that the first method yields a threshold that is close to optimum when the PU use a large fraction of the available spectrum (i.e., when the PU activity level is high).The second method is analytically proven to always give a valid threshold.Simulation results show a 116% improvement in UR with PU state estimation over energy detection, at an SNR of -3dB and IR level of 10%.In paper C, we extend paper B to consider that PU activities cannot be observed when CR is transmitting, in other words they are censored.This new strategy, entitled CLAPP, calculates a new LLR, which is compared with a threshold. This threshold is computed with a bisection search method. Simulation results show that CLAPP has a 52% gain in UR over the best censored energy detection scheme for a maximum IR level of 10% and an SNR of -2dB. In paper D, we introduce new time-varying thresholds for sequential spectrum sensing. These new thresholds, for an SNR of -10dB, in comparison with standard sequential detection with parallel (fixed) thresholds with similar probabilities of misdetection and false alarm, performs 54% faster in terms of maximum detection time (90 percentile)