We investigate the Hilbert transform of the products of the integral functions and two classes of distribution: polynomial and exponential functions. A sufficient condition and a sufficient and necessary condition for the Bedrosian identity are presented.

In numerical algebra field, the quadratic system decoupling is researched by preserving Lancaster structure, but it is difficult to find decoupling transformations, which involves the solution of nonlinear equations. A new method of numerical decoupling of quadratic system is proposed in this paper. The decoupled system is identified by the isospectrality of systems, and the nonlinear problem to solve decoupling transformations is converted to the solution of Sylvester equation. The decoupling transformations are given based on the Kronecker product knowledge of matrixes. The method is shown to be feasible by numerical experiments, and it supplies a new point for quadratic system decoupling research.

Based on Domingos bias-variance decomposition framework, on three different data sets, we compared the bias-variance structure of the three classification methods: MCLP, LDA and C5.0. The experimental results showed that, generally speaking, C5.0 has low bias and high variance, LDA has high bias and low variance, and MCLP is in between them but near LDA. When the training set is small, bias and variance of MCLP is comparatively high. However, with the increasing of training set, bias and variance of MCLP obviously decrease and even are lower than those of C5.0 and LDA. This study established the basis for constructing the ensemble suited to MCLP.

Taking Xinjiang as an example, we establish a predicting model by using BP neural network, and make a prediction of construction land in 2007. The model learning samples are from the social-economic statistical data between 1996 and 2006. The results show that the relative error between the predicted and actual value is only 0.06％, and the BP neural network has higher precision and better effectiveness than traditional methods. Some strategic countermeasures are put forward for sustainable land use in Xinjiang.

Emergy analysis was usually used to evaluate eco-economic system without prediction ability. In view of this, after analyzing the trend of Jilin eco-economic system, scenario prediction model was established to forecast the emergy value of nonrenewable resources. The main results could be summarized as follows: (1) All emergy flows increased in various degrees, showing that development level of eco-economic system enhanced remarkably with the increasing environmental pressure; (2) 4 emergy efficiency indicators became worse, and the metabolic efficiency dropped to a certain extent; (3) Sustainable development ability declined steadily throughout the analysis period, indicating that economic growth was at the cost of excessive resource consumption with increase of environmental pressure; (4) The emergy value of nonrenewable resources would maintain an average annual growth rate of more than 10% in the following 4 years, and resources consumption as well as environmental pressure would further increase; (5) In the near future the key factor influencing emergy amount would be resources (excluding energy) whose effect on emergy was greater than that of energy, and it would be more and more important.

In order to meet the precision requirement of digital signal instantaneous frequency estimation, an improved algorithm is proposed based on deep analyses of the principle of wavelet ridge. The major improvements of the algorithm include the arrangements on initial values, analytical wavelet's parameter and estimation precision of iterative process. Experimental signal results show that the improved algorithm is more robust and practical.

At present, there is a growing need for multimedia information authentication, but studies on speech authentication are rare. Based on perceptual characteristics, a robust speech authentication algorithm is proposed to combine the psychoacoustic properties with signature algorithm. It meets the requirements of entity authentication and content authentication, and resists the channel noise as well. Analyzing the perceptual properties, such as masking effect and non-linear effect, speech redundancy in both temporal field and frequency field is eliminated, and perceptual parameters are extracted. Then an improved Rainbow algorithm is used to sign the extracted data. Experiment results demonstrate good robustness and uniqueness of the algorithm when applied to the robust authentication of audio communication.

During the short time of the inverse synthetic aperture radar (ISAR) imaging, for inert maneuvering targets such as aircraft, the echoes of the same range cell can be considered to approximately consist of multi-component linear frequency modulated (LFM) signals with different chirp rate. For these cases, a method based on local wave decomposition and Wigner-Ville transformation is proposed, and thus 2-D imaging of the target is realized. It can not only avoid the cross-term of Wigner-Ville transformation but also decrease the computational load effectively for keeping high time-frequency resolution. Computer simulation results demonstrate the validity of the proposed algorithm.

This paper, aiming at retransmission problem in cooperative communication, proposes a new hybrid ARQ scheme when two users cooperatively communicate with the base station (BS). By sending packets in time-sharing slots, each user stores packets transmitted by its partner last time. After receiving ACK/NACK, each user searches for packets needed to be retransmitted according to indications of the BS. These packets mixed with newly generated ones are then forwarded in the next time slot. This scheme, utilizing space diversity gain introduced by the cooperative communication system, greatly improves the performance of hybrid ARQ. We compared our cooperative system with another system where each user communicates with the BS independently. Through comparisons of throughput, PER, and average delay varying with SNR, we proved that our system could achieve higher throughput and lower PER in the same channel state while increasing the average delay during theoretical analysis and simulation.

We studied the scheme of space-borne synthetic aperture RADAR(SAR) data processing based on intermediate frequency sampling and the implementation method on FPGA. For a traditional space-borne SAR system, the video signal is first demodulated usually by analog method, and then sampled and compressed. This paper employs the intermediate frequency sampling technique to realize the quadrature demodulation and processing of space-borne SAR data. We analyzed the digital quadrature demodulation and the block adaptive quantization algorithms, and accomplished the FPGA design and simulation. The results show that the design scheme is feasible. Compared with the analog method, the employment of intermediate frequency sampling has improved the reliability and performance of the system, and it presents the trend of SAR data processing.

A new multi-categorical object recognition method based on the active contour basis model is proposed. The method builds a class-specific codebook of active contour bases, which is robust to scale variation and pose changes. Probabilistic learning by analyzing contextual information is performed using cascaded frame and boot strap dynamic sampling. A classifier is trained to determine the object categories and exact regions. Experimental results demonstrate that the proposed method achieves high efficiency in extracting manifold and complicated objects.

DS-SS is widely used in LEO satellite communication. Since jamming exists in UHF/VHF wave band, which is commonly selected by LEO communication system, anti-jamming module is necessary in receivers. To control the power consuming, an interpolation-based one-sample-per-chip receiver architecture is developed. Performance analyses show that this architecture cuts off half of anti-jamming module computation burden at the cost of the SNR lose less than 0.1dB. By this approach, computation burden and power consuming can be reduced remarkably.

This paper describes a more efficient real time SVM(support vector machine)-based ultrasonic rail defect detection and classification system. Feature extraction is achieved based on the attribute of ultrasonic rail defect and then SVM classification prediction algorithm and statistical processing are used to realize classification and calculating the size of the rail defect. This machine learning algorithm is tested in DSP and the type, grade and location of the defects are displayed in real-time.

We are motivated to provide better protection for operating system integrity with the help of information flow integrity and trusted computing. Traditional measurement based on trusted computing is poor in dynamic measurement and efficiency, while Biba fails in practical application because of its monotonic behavior. In this paper, we design an information flow integrity architecture called BIFI based on classical integrity model Biba with TPM as root of trust. Experiments show that BIFI protects information flow integrity effectively with only a few changes to existing systems.

DBSCAN is a classic density-based clustering algorithm. It can automatically determine the number of clusters and treat clusters of arbitrary shapes. In the clustering process of DBSCAN, two parameters, Eps and minPts,have to be specified by uses. In this paper an adaptive algorithm named SA-DBSCAN was introduced to determine the two parameters automatically via analysis of the statistical characteristics of the dataset, which enabled clustering process of DBSCAN fully automated. Experimental results indicate that SA-DBSCAN can select appropriate parameters and gain a rather high validity of clustering.

This paper proposes a new clustering algorithm named maximum density clustering algorithm(MDCA). In MDCA the concept of density is introduced to identify the count of clusters automatically.By selecting the densest object as the threshold, densities of those objects around the densest object are reviewed to decide the partition of basic blocks. Then the basic blocks are merged to form clusters of arbitrary shape. Experiments show that the ability and validity of MDCA in processing unknown datasets are all better than traditional partition-based clustering algorithms.

Structural comparison is one of the core components of patch comparison. This paper improves the classic algorithm of structural comparison. Compared with the classic algorithm, the improved algorithm calculates the value of similarity between functions. Therefore, the problem of contradiction between the consistency and exclusiveness of signature is solved. Furthermore, a structural comparison tool named as NBD (NCNIPC binary differ), which gets a higher score compared with other popular tools during the exam towards some patches of microsoft, is constructed based on the improved algorithm.

An improved survivable network requirement analysis framework (ISNA) is proposed. It is required to choose the best suitable software development model as the framework basement from the component-oriented set, which avoids SNAs shortcoming with only one choice of heavy model named spiral. The feedback components, an embedded survivable test and fault-attack library, fix the requirements variability and guarantee its integrity. At last, we analyzed an e-business website with ISNA, proving that ISNA performs better at guiding systems implementation and improving its survivability.

A scheme for realization of word-width configuration in embedded programmable memories using synchronous dual-port memory IP and standard cell is proposed. The method reduces the implementing complexity and further improves the designs efficiency. The structure of embedded programmable memorys word width configuration is optimized by registering the output control signals to improve the readout datas stability. Experiment results show that the optimized structure has the advantage of implementing complex circuits with higher performance. The embedded programmable memory based on this optimized structure has been fabricated in SMIC 0.18μm 1P6M CMOS process. According to the measurement results, the fabricated embedded programmable memorys readout data has good stability and it has comparable performance in readout time with the reference to those full custom commercial embedded programmable memories based on the similar process and memory capacity.

This paper presents our experimental work on preparing and manipulating quantum entangled states in optical quantum information processing systems. The main contents are: (1) Using continuous wave(CW) laser beam to pump nonlinear crystals we successfully prepared two-photon polarization entangled states with high brightness and entanglement degree, and it is tunable in entanglement degree and we use this source to produce one kind of important mixed states——Werner states with tunable entanglement fraction in it. (2) We realized the 1→2 qubits Buek-Hillery cloning machine with linear optical devices and showed that the fidelities between the two output qubits and the original qubit are both 5/6 for arbitrary input pure states. (3) We presented a method to test the maximal violation of the CHSH inequality for non-maximally entangled pure states and experimentally realized it with our entangled state source. (4) We perform an all-or-nothing-type Kochen-Specker experiment to verify whether non-contextual hidden variables (NCHV) or quantum mechanics is right. The results strongly agree with quantum mechanics.