SACRB-MAC: A High-Capacity MAC Protocol for Cognitive Radio Sensor Networks in Smart Grid.

The Cognitive Radio Sensor Network (CRSN) is considered as a viable solution to enhance various aspects of the electric power grid and to realize a smart grid. However, several challenges for CRSNs are generated due to the harsh wireless environment in a smart grid. As a result, throughput and reliability become critical issues. On the other hand, the spectrum aggregation technique is expected to play an important role in CRSNs in a smart grid. By using spectrum aggregation, the throughput of CRSNs can be improved efficiently, so as to address the unique challenges of CRSNs in a smart grid. In this regard, we proposed Spectrum Aggregation Cognitive Receiver-Based MAC (SACRB-MAC), which employs the spectrum aggregation technique to improve the throughput performance of CRSNs in a smart grid. Moreover, SACRB-MAC is a receiver-based MAC protocol, which can provide a good reliability performance. Analytical and simulation results demonstrate that SACRB-MAC is a promising solution for CRSNs in a smart grid.

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning.

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches.

Hybrid radar emitter recognition based on rough k-means classifier and relevance vector machine.

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this paper, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, namely the primary signal recognition and the advanced signal recognition. In the former step, a novel rough k-means classifier, which comprises three regions, i.e., certain area, rough area and uncertain area, is proposed to cluster the samples of radar emitter signals. In the latter step, the samples within the rough boundary are used to train the relevance vector machine (RVM). Then RVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and presents lower computational complexity than traditional approaches.

A modular spectrum sensing system based on PSO-SVM.

In the cognitive radio system, spectrum sensing for detecting the presence of primary users in a licensed spectrum is a fundamental problem. Energy detection is the most popular spectrum sensing scheme used to differentiate the case where the primary user’s signal is present from the case where there is only noise. In fact, the nature of spectrum sensing can be taken as a binary classification problem, and energy detection is a linear classifier. If the signal-to-noise ratio (SNR) of the received signal is low, and the number of received signal samples for sensing is small, the binary classification problem is linearly inseparable. In this situation the performance of energy detection will decrease seriously. In this paper, a novel approach for obtaining a nonlinear threshold based on support vector machine with particle swarm optimization (PSO-SVM) to replace the linear threshold used in traditional energy detection is proposed. Simulations demonstrate that the performance of the proposed algorithm is much better than that of traditional energy detection.

Outcome-Based Critical Result Thresholds in the Adult Patient Population.

OBJECTIVES: To derive outcome-based critical result thresholds in the adult patient population. METHODS: We extracted deidentified laboratory results and outcomes (death or discharged) of patients 18 years and older from the Medical Information Mart for Intensive Care database. The lower and upper critical result thresholds were obtained from the nearest minimum and maximum laboratory values, which corresponded to predicted probability of death at 90%. RESULTS: The critical value thresholds were sodium (<123, >153 mmol/L), potassium (<2.2, >6.6 mmol/L), bicarbonate (<15, >49 mmol/L), chloride (<82, >121 mmol/L), urea (>20 mmol/L), creatinine (>1,052 µmol/L), glucose (<1.5, >23.8 mmol/L), total calcium (<1.62, >2.95 mmol/L), magnesium (<0.37, >1.48 mmol/L), phosphate (<0.19, >2.52 mmol/L), pH (<7.22, >7.57), lactate (>5.0 mmol/L), hemoglobin (<4.6 g/dL), WBCs (>32 × 103/µL), prothrombin time (>90 seconds), and international normalized ratio (>10). CONCLUSIONS: The indirect approach described in this study is a pragmatic way to obtain threshold values that are clinically and operationally meaningful.

Relative criticalness of common laboratory tests for critical value reporting.

We determined the relative strengths of association between 23 most commonly ordered laboratory tests and the adverse outcome as indicators of relative criticalness. The lowest and highest results for 23 most commonly ordered laboratory tests, 24 hours prior to death during critical care unit (CCU) stay or discharge from CCU were extracted from a publicly available CCU database (Medical Information Mart for Intensive Care-III). Following this, the Random Forest model was applied to assess the association between the laboratory results and the outcomes (death or discharge). The mean decrease in Gini coefficient for each laboratory test was then ranked as an indication of their relative importance to the outcome of a patient. In descending order, the 10 laboratory tests with the strongest association with death were: bicarbonate, phosphate, anion gap, white cell count (total), partial thromboplastin time, platelet, total calcium, chloride, glucose and INR; moreover, the strength of association was different for critically high versus low results.