Neighborhood area network is the most important part of smart grid communication system, where there is a variety of wired and wireless information transmission technologies to achieve information exchanged with other networks. Since Power Line Communications (PLC) technology is a natural candidate to build the neighborhood area network, we consider the use of PLC to connect network nodes (smart meters) through multi-hop transmission. In consideration of the strict requirements of transmission delay and packet-loss rate in smart grid, we proposed a hybrid multi-hop routing algorithm based on ant colony optimization and simulated annealing. Simulation results demonstrate that the hybrid algorithm can provide an optimal route in the neighborhood area network with delay and reliability guarantee compared with traditional ant colony algorithm. 

In this paper, the power allocation problem for Orthogonal frequency division multiplexing(OFDM)-based relay cognitive radio systems is investigated. An optimization power allocation algorithm is developed. In the algorithm, objective function is studied and simplified. The channel capacity of secondary users is maximized while keeping the total interference introduced to primary user less than a given threshold with a specified power. Compared to the optimal algorithm and the average power allocation algorithm, the proposed algorithm is close to the optimal algorithm and has a better performance than average power allocation algorithm, and has low complexity.

In this paper, a suboptimal subcarrier and power allocation algorithm has been developed for the orthogonal frequency division multiplexing(OFDM)-based relay cognitive radio systems. The channel capacity of secondary users is maximized while keeping the total interference introduced to primary user less than a given threshold with a specified power. Compared with the other power allocation algorithm, the proposed method can achieve greater channel capacity and has a lower complexity.

In this paper, we develop an optimal power allocation algorithm for the orthogonal frequency division multiplexing(OFDM)-based relay cognitive radio systems. The channel capacity of secondary users is maximized while keeping the total interference introduced to primary user less than a given threshold with a specified power. Compared with the other power allocation algorithm, the proposed method can achieve greater channel capacity and has a lower complexity.

In this paper, a multifunction radar system architecture has been introduced. Multifunction radar is a phased array radar system. A phased array radar system can adapt its parameters on a near-instantaneous basis according to the way in which it perceives its operating environments. This allows the combination of function such as tracking, surveillance, and weapon guidance, which were traditionally performed by dedicated individual radars. Multifunction radar has clear advantages of being able to instaneously and adaptively position and control the beam, but it also brings a new set of challenges. Radar resource management is the problem of how to allocate finite available resources in an optimal way to carry out a chosen mission.

The radar performs three main functions: search, tracing and weapon engagement. Each of functions occupies amount of time, energy and computation resource of the radar system. For cognitive radar, it should have the basic function which is learning. The radar needs to manage its resources dynamically and interactively between the setting of radar parameters to optimize the tasks to be carried out and perceive environment highlights the role in which knowledge and intelligence will be central in cognitive radar performance. The problem discussed here is the time allocation of cognitive radars in a multitarget environment. In this paper, we develop the optimization criterion based on the detection probabilities based on dynamic programming algorithm(DPA).

One of the important issues for cognitive radar is how to optimally decide or select the radar waveform for the next transmission based on the observation of past radar returns. In this paper, with the stochastic dynamic programming model of waveform scheduling, the optimal algorithm of waveform scheduling is proposed. In order to solve the problem that we can not compute the exact value function under some circumstances, we step forward in time using an approximation of the value function from the previous iteration. The simulation results testify validity of our algorithm.

For cognitive radar, it should have the basic function which is learning. The radar needs to manage its resources dynamically and interactively between the setting of radar parameters to optimize the tasks the setting of radar parameters to optimize the tasks to be carried out and perceive environment highlights the role in which knowledge and intelligence will be central in cognitive radar performance. The problem discussed here is the time allocation of cognitive radars in a multitarget environment. Radars are used to detect, to locate and to identify target. In this paper, we develop the optimization criterion based on the detection probabilities

The taxget detection in image sequences is part of multidimensional signal detection. Dim point target from an image sequence has been merged into the clutter. So the image has a low SNR and the detection of the dim point target under the low conditions becomes the sticking point. We introduce a novel tracking system based on dynamic programming algorithm(DPA). This algorithm can detect the dim point targets under the condition of low SNR. The dynamic programming technique can detect the trace of the point target which is moving with straight line and reduce the calculation amount. With the result of simulation test, it can be shown that the introduced algorithm can effectively detect moving point target trajectory in image sequences than previously developed algorithm.

Dim point target from an image sequence has been merged into the clutter. So the image has a low SNR and the detection of the dim point target under the low conditions becomes the sticking point. We introduce a novel tracking system based on dynamic programming algorithm (DPA). This algorithm can detect the dim point targets under the condition of low SNR. The dynamic programming technique can detect the trace of the point target which is moving with straight line and reduce the calculation amount. With the result of simulation test, it can be shown that the introduced algorithm can effectively detect moving point target trajectory in image sequences than previously developed algorithm.

A novel waveform named double-slope symmetrical saw-tooth wave is presented and its corresponding algorithm is also introduced to resolve the problem of multiple targets detection for automotive anti-collision radar. The method by using the proposed waveform and algorithm improves the response characteristic of radar system and reduces false targets in detection process. Computer simulation results and theoretical analysis prove that the method is effective and practical for multiple targets detection in intelligence transportation system