Recently, broadband analog network coding (ANC) was introduced for high-speed transmission over the wireless (frequency-selective fading) channel. However, ANC requires the knowledge of channel state information (CSI) for self-information removal and coherent signal detection. In ANC, the users' pilot signals interfere during the first slot, which renders the relay unable to estimate CSIs of different users, and, consequently, four time-slot pilot-assisted channel estimation (CE) is required to avoid interference. Naturally, this will reduce the capacity of ANC scheme. In this paper, we theoretically analyze the bit error rate (BER) performance of bi-directional broadband ANC communication based on orthogonal frequency division multiplexing (OFDM) radio access. We also theoretically analyze the performance of the channel estimator's mean square error (MSE). The analysis is based on the assumption of perfect timing and frequency synchronization. The achievable BER performance and the estimator's MSE for broadband ANC is evaluated by numerical and computer simulation. We discuss how, and by how much, the imperfect knowledge of CSI affects the BER performance of broadband ANC. It is shown that the CE scheme achieves a slightly higher BER in comparison with ideal CE case for a low and moderate mobile terminal speed in a frequency-selective fading channel.

We read with great interest the article by Vege et al published in issue 34 of World J Gastroenterol 2010. The article evaluates the ability of contrast-enhanced computerized tomography (CECT) to characterize the nature of peripancreatic collections found at surgery. The results of their study indicate that most of the peripancreatic collections seen on CECT in patients with severe acute pancreatitis who require operative intervention contain necrotic tissue and CECT has a limited role in differentiating various types of collections. However, there are some points that need to be addressed, including data about the stage of acute pancreatitis in which CECT was done and the time span between CECT examination and surgery.

The applications of robot are very extended and have already become classic in different branches of mass industrial production such as welding, painting by spraying, antirust protection, etc. Though the operations performed by robots in these fields are very complex, the operations of assembly are even more complex. In fact, robot assembly operations involve the process of direct solving the conflicting situations being not within the classic repetitive work. Investigations treating typical assembly duties started forty years ago (Bohman, 1994). In the meantime, it was offered a series of control mechanism of mating date. Performing assemblies depends on sensation of and appropriate reaction to the forces of contact between mating components date (Wei, 2001). It is shown that with the intelligent techniques, example components can be assembled faster, gentle and more reliably. In order to create robot behaviours that are similarly intelligent, we seek inspiration from human strategies date (Chan, 1995). The working theory is that the human accomplishes an assembly in phases, with a defined behaviour and a subgoal in each phase. The human changes behaviours according to events that occur during the assembly and the behaviour is consistent between the events. The human’s strategy is similar to a discrete event system in that the human progresses through a series of behavioural states separated by recognizable physical events. In achieving acceptably fast robot behavior with assuring contact stability, many promising intelligent-control methods have been investigated in order to learn unstructured uncertainties in robot manipulators date (Chan, 1995), (Miyazaki et al., 1993), (Brignone et al., 2001). For example, (Newman et al., 2001) work describes intelligent mechanical assembly system. First phase for assembly is blind search. In this phase multiple parameters are assigned to rotational search attractor. If sensors register force values higher then thresholds, new parameters are assigned. Intelligent layer is represented on 22-dimensional space of trajectories, and based on blind search parameters (correct and incorrect) neural network is made. Correct assembly path is chosen by using form of Genetic algorithm search, so the new vectors are evolved from most successful “parents”. Using this process, the robot was allowed to generate and test its own program modifications. The primary source of difficulty in automated assembly is the uncertainty in the relative position of the parts being assembled (Vaaler, 1991). The crucial thing in robot assembly is how to enable a robot to accomplish a task successfully in spite of the inevitable uncertainties