In this paper we present a novel approach for evaluating the routing efficiency of clustered architectures, as compared to flat ones in mobile scenarios. For multihnp ad hoc networks there have been much research done on clustered architectures due to their advantages, such as the spatial reuse of resources, the easier update of hierarchical topology and the smaller overheads on routing procedures, see [2]. However, the efficiency of using a clustered architecture depends on many factors such as the network size, how the clusters are formed, type of traflic, connection data rates, network connectivity and topology changes due to in particular node mobility. O u r results show that the clustered architecture need to he very carefully designed in order to he more efficient than a flat architecture for small to medium sized networks of up to 400 nudes. That is, a clusterhead needs to have good knowledge of the h k s in its cluster such that a good route through the cluster can he selected.

In an electronic warfare environment, important equipment or facilities of the friendlies are placed in protective facilities to protect against external Electronic Attacks (EA). No matter how well shielded the facility is, some external electromagnetic waves may penetrate through various paths such as power lines or fans, and the electromagnetic waves may be fatal to certain devices due to the structural resonance of the protective facilities. This paper introduced a real-time electromagnetic canceling technique that removes the resonance field inside the protection facility caused by the intended electronic attack from the enemy. The method makes it possible to cancel the process much faster than the conventional ones that have applied the Matrix Pencil Method (MPM). This is because the internal resonant field can be predicted in a closed-form under the assumption that the external electromagnetic wave is a complex exponential function. Longer exposure to Intended Electromagnetic Interference (IEMI) could be fatal for some devices. Therefore, it is imperative to attenuate the noise within a short time, and a method of reducing internal noise in real-time is beneficial for Electronic Protection (EP). The proposed method could be applied as a new technique to protect important protection facilities, rather than the more traditional method called wrapping using the Faraday cage effect.

In many cases, the study of DOA estimation techniques is developed based on ideal condition of signal sources and array sensor antennas. But, there are much more errors as a result of signal shadow effects from noise contribution and interference of installation environment in real system. In this paper, the DOA estimation algorithm using the de-noising pre-processing based on time-frequency conversion analysis was proposed, and the performance was analyzed. This is focused on the improvement of DOA estimation at a lower SNR and interference environment.

Source enumeration is an important procedure for radio direction-of-arrival finding in the multiple signal classification (MUSIC) algorithm. The most widely used source enumeration approaches are based on the eigenvalues themselves of the covariance matrix obtained from the received signal. However, they have shortcomings such as the imperfect accuracy even at a high signal-to-noise ratio (SNR), the poor performance at low SNR, and the limited detection number of sources. This paper proposestwo source enumeration approaches using the ratio of eigenvalue gaps and the threshold trained by a machine learning based clustering algorithm for gaps of normalized eigenvalues, respectively. In the first approach, a criterion formula derived with eigenvalue gaps is used to determine the number of sources, where the formula has maximum value. In the second approach, datasets of normalized eigenvalue gaps are generated for the machine learning based clustering algorithm and the optimal threshold for estimation of the number of sources are derived, which minimizes source enumeration error probability. Simulation results show that our proposed approaches are superior to the conventional approaches from both the estimation accuracy and numerical detectability extent points of view. The results demonstrate that the second proposed approach has the feasibility to improve source enumeration performance if appropriate learning datasets are sufficiently provided.

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