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In-line inspection (ILI) is a standard industry practice in managing the safety of buried transmission pipelines. It is important to develop a planning strategy for each ILI program to ensure pipeline safety while maintaining cost-efficiency. Defaulting to the maximum inspection interval allowed by regulations without analyzing time-dependent threats affecting a pipeline segment could lead to loss of containment. An optimal inspection interval serves as another barrier in the defense-in-depth safety concept. In this paper, a risk-based data-driven analysis approach is presented to optimize the inspection interval in terms of operational costs while managing the failure risks. The objective function is formulated to consist of cost components (in present value) and weighted residual risk of failure. The cost components are associated with the periodic inline inspections, as well as the expected mitigation actions. The annual mitigation cost is incurred by the number of features that are sentenced for excavation, field inspection, and repair every year according to the risk of failure. A shorter inspection interval would increase the total ILI cost; however, this would also curtail the rising cost of mitigation due to potential feature growth, as well as the residual risk of failure that depends on the mitigation program. The risk of failure is estimated in terms of the probability and consequence of failure. The residual risk is calculated based on features that are either unmitigated or potentially undetected by the in-line inspection tool run. To demonstrate the application of this approach, numerical examples based on external corrosion are provided to show how various conditions of a pipeline segment affect the optimality of inspection interval. The results support further application of the analysis approach in an industrial scale system-wide analysis, such that optimal inspection intervals can be dynamically updated at segment level using the latest ILI data as they become available in an ILI program.

Multimedia streaming applications with stringent QoS requirements in 60GHz mmWave wireless personal area networks (WPANs) demand high rate and low latency data transfer as well as low service disruption. In this paper, we consider the problem of robust relay placement in 60GHz WPANs. Relays forward traffic from transmitter devices to receiver devices facilitating i) the primary communication path for non-line-of-sight (NLOS) transceiver pairs, and ii) secondary (backup) communication path for line-of-sight (LOS) transceiver pairs. We formulate the robust minimum relay placement problem and the robust maximum utility relay placement problem with the objective to minimize the number of relays deployed and maximize the network utility, respectively. Efficient algorithms are developed to solve both problems and have been shown to incur less service disruption in presence of moving subjects that may block the LOS paths in the environment., Updated conference version, 10 pages, 5 figures

Cognitive radio is considered as a promising technique to increase the utilization of limited spectral resource. The key issue in cognitive radio is to design a reliable spectrum sensing method that is able to detect the signal in the target channel as well as to recognize different signals. In this paper, focusing on classifying different OFDM signals, we propose a two-step detection and identification approach. The key parameters to separate different OFDM signals are the subcarrier spacing and guard interval. A simple but reliable peak detection method is adopted in the first step, while a peak searching method is used to determine the length of guard interval. Simulations are carried out in AWGN to verify the validation of the proposed method. It is shown that our method can satisfy the detection and identification requirement with a low false alarm probability.

Cognitive Radio is considered as a potential solution to improve spectrum utilization via opportunistic spectrum sharing. In this paper, we present a software testbed which is developed to implement the cognitive radio system. The basic functions of cognitive radio are explored and implemented, including spectrum sensing and dynamic spectrum management. The testbed is able to verify the overall performance of cognitive radio technology. Furthermore, it provides an important system model to verify the effectiveness of the new algorithms in future study. The simulation on the testbed demonstrates that primary and secondary users can coexist. This is able to convince regulatory authorities as well as primary users to enable the implementation of cognitive radio technology.

For the purpose of increasing the utilization efficiency of spectrum, secondary usage method of the licensed spectrum has been explored. In this paper, based on the concept of spectrum pooling, we propose a spectrum sharing system with opportunistic accesses. In order to satisfy with the realistic applications, we define heterogeneous secondary users (non-real-time and real-time services) with different quality-of-service (QoS) requirements in our system model. Since each primary userpsilas requiring channel may be occupied by different types of secondary user, the primary user uses different schemes to solve the collision. Numerical results show that our model can obtain a better tradeoff between increasing the spectrum utilization and guaranteeing the primary userpsilas access priority.

Cognitive radio is one of the most effective techniques to improve the spectrum utilization efficiency. To implement the cognitive radio, spectrum sensing is considered as the key functionality because secondary users should identify the spectrum holes and utilize them efficiently without causing interference to primary users. In generally, there are three major spectrum sensing methods, including matched filter, energy detection and feature detection. The most common and simplest method is energy detection. However, the conventional energy detection suffers from large degradation in performance under low SNR environment. In this paper, we proposed alternative energy detection which can estimate the exact power of primary user without the noise effect and lead to the better performance. In order to reduce the effect of noise fluctuation, the sliding window function is utilized to assist the proposed detector. The simulation result shows that our proposed method outperform conventional one.

Cognitive radio is an advanced enabling technology for efficient utilization of under-utilized spectrum since it is able to sense the spectrum and use the frequency when the primary users are absent. Recent investigation suggests that spectrum sensing is compromised when a user experiences fading or shadowing. In order to combat such effects, collaborative sensing is presented. However, the conventional collaborative sensing is not effective when users suffer from different fading environments. In this paper, we propose a weighted-collaborative scheme to improve the spectrum sensing performance under fading environment. The analysis of the simulation results proves that the weighted-collaborative scheme improves sensing performance obviously.