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The water-dropping scheme plays an important role in the aerial firefighting mission. When a wildfire occurs, the main problem faced by the aircrew is how to choose the appropriate water-dropping scheme (speed, height and trajectory) to achieve the optimal firefighting effect. However, so far, the crew members can only rely on the existing experience to generate the scheme, which lacks the corresponding theory method support. To solve above problem, this paper proposes a fast optimization method of water-dropping scheme for fixed-wing firefighting aircraft. Through the feature extraction of grid distribution, the agent model between aircraft water-dropping scheme and water distribution polygon is built taking advantage of neural network. Meanwhile, the corresponding extinguishing efficiency evaluation model is established with discrete points of fire area. Based on these researches, the combined genetic algorithm is used for searching the optimal water-dropping scheme. Finally, the feasibility and rapidity of the method are demonstrated through the application of examples. It is also shown that the method can optimize the water-dropping scheme quickly instead of aircrew experience and play a role of auxiliary decision support for the firefighting mission of fixed-wing firefighting aircraft.

The conventional broadband impedance matching network in low-pass structure cannot present adequate broadband impedance rotation for the optimum broadband operation of the power amplifier (PA), resulting in performance variations and degradations. The underlying constraint is the phenomenon of opposite impedance rotation. Against such a backdrop, this paper presents a methodology using the band-pass topology to compensate the frequency-variable response of the PA. The CGH40010F from Cree is used to verify the effectiveness of this compensation method. The experimental results are in good agreement with the simulation. The proposed PA yields good gain flatness (±0.5 dB), input/output return loss (>13 dB), and high power added efficiency (>60 %) over the band from 1.7 to 2.7 GHz. After digital predistortion, the measured adjacent channel power ratio is about -55 and -50 dBc at ±5 and ±10 MHz offset when the PA is driven by 5- and 10-MHz WCDMA signals at 6.5 ~ 7 dB power back-off.

The traditional Cancellable biometrics scheme needs another key or token to generate the revocable template, which usually suffers from the token-stolen problem. To solve this problem, a one-factor cancellable palmprint biometric recognition scheme based on Orthogonal Index of Maximum (OIOM) hash and Minimum Signature Hash (MSH) is proposed to generate pseudonymous identifier. Firstly, to improve the efficiency and effectiveness, a parallel structure is designed to obtain Orthogonal Gaussian random projection(GRP)matrices, which is employed to generate the OIOM hash code. Secondly, a random binary string is XOR the binary OIOM hash code to construct the helper data and it is stored in the database. Meanwhile, this string is hashed by MSH to get the final pseudonymous identifier. Lastly, during matching stage, based on helper data and a palmprint images, another pseudonymous identifier is generated to recognition. This implies that, just one factor, a palmprint of user, is needed in the matching stage, and therefore the privacy of user is preserved. To evaluate the proposed scheme, PolyU database and touchless TJU database are used in experiments. The noninvertible, renewability, unlinkability and several security attacks of the proposed scheme are analyzed. The experiment results and analysis show that the proposed scheme has strong security on the basis of maintaining palmprint recognition performance.

This paper proposes a blind channel estimator based on expectation maximization algorithm and historical information-based basis expansion model for uplink wireless communication systems on high speed railways. The information of basis matrices is obtained from the uplink data of the past trains at the base station (BS). With the known basis matrices at the BS, our suggested estimator can estimate the basis coefficients and recover the channel parameters without requiring training symbols. The modified Cramer-Rao bound is derived for the estimated basis coefficients and the computational complexity of the proposed estimator is analyzed. Numerical results are then provided to corroborate our studies. It is shown that the proposed estimator outperforms existing data-aided estimators, including least square and linear minimum mean square error.