Aiming at the problem of low clinical practicability and accuracy in the clinical diagnosis of myocardial infarction， an auxiliary diagnosis method of myocardial infarction based on 12-lead ElectroCardioGram （ECG） signal was proposed. Firstly， denoising and data enhancement were performed on the 12-lead ECG signals. Secondly， aiming at the ECG signals of each lead， the statistical features including standard deviation， kurtosis coefficient and skewness coefficient were extracted respectively to reflect the morphological characteristics of ECG signals， meanwhile the entropy features including Shannon entropy， sample entropy， fuzzy entropy， approximate entropy and permutation entropy were extracted to characterize the time and frequency spectrum complexity， the new mode generation probability， the regularity and the unpredictability of the ECG signal time series as well as detect the small changes of ECG signals. Thirdly， the statistical features and entropy features of ECG signals were fused. Finally， based on the random forest algorithm， the performance of algorithm was analyzed and verified in both intra-patient and inter-patient modes， and the cross-validation technology was used to avoid over-fitting. Experimental results show that， the accuracy and F1 value of the proposed method in the intra-patient modes are 99.98% and 99.99% respectively， the accuracy and F1 value of the proposed method in the inter-patient mode are 94.56% and 97.05% respectively； and compared with the detection method based on single-lead ECG， the detection of myocardial infarction with 12-lead ECG is more logical for doctors’ clinical diagnosis.

To solve the problem that the antenna resources in heterogeneous network are limited which leads to the unrealizable Interference Alignment (IA), a partial IA scheme for maximizing the utilization of antenna resources was proposed based on the characteristics of heterogeneous network. Firstly, a system model based on partial connectivity in heterogeneous network was built and the feasibility conditions for entire system to achieve IA were analyzed. Then, based on the heterogeneity of network (the difference between transmitted power and user stability), the users were assigned to different priorities and were distributed with different antenna resources according to their different priorities. Finally, with the goal of maximizing total rate of system and the utilization of antenna resources, a partial IA scheme was proposed, in which the high-priority users had full alignment and low-priority users had the maximum interference removed. In the Matlab simulation experiment where antenna resources are limited, the proposed scheme can increase total system rate by 10% compared with traditional IA algorithm, and the received rate of the high-priority users is 40% higher than that of the low-priority users. The experimental results show that the proposed algorithm can make full use of the limited antenna resources and achieve the maximum total system rate while satisfying the different requirements of users.

To eliminate the drawbacks of traditional small feature culling algorithm which processing granularity are triangles and can't efficiently cope with the number of vertexes and triangles up to hundreds of millions in a certain time period, an improved small feature culling algorithm for large scale process plant based on octree was proposed. Based on the component primitive characteristics and spatial characteristics of the process plant model, the value of screen for quantizing the size of component was proposed, and the established octree and the value of screen were combined to estimate the upper limit of the number of pixels, so as to quickly determine whether the component would be culled or not. The experimental results show that the proposed algorithm is simple and effective. Compared with the current popular review software after loading the factory model with 10000 pipelines, the frame rate is increased by at least 50%, which greatly improves the platform's fluency. Process factory industry and graphics platform as a whole to enhance the level of design has a positive meaning.

When the packets are broadcasted with the fixed power in Vehicular Ad-Hoc NETwork (VANET), the wireless channel may not be allocated reasonable. In order to solve this problem, a power control mechanism adapted to the variation of vehicle density was proposed. It is adaptive to the variation of vehicle density. The direct neighbor list of each node was constructed and updated in a power control period, the power that used to transmit the vehicle status message was adjusted according to the location of the direct neighbor to cover all the direct neighbors, thus wireless channel could be allocated more reasonable and the performance of router could also be optimized. The validity of the proposed mechanism was proved by the simulation results. It is also found that the proposed mechanism is useful for adjusting the transmission power according to the vehicular density, reducing channel busy ratio and enhancing the performance of packet delivery ratio among direct neighbors, which can ensure the effective transmission of the security information.

In practical application, Modified Gain Extended Kalman Filter (MGEKF) algorithm generally uses erroneous measured values instead of the real values for calculation, so the modified results also contain errors. To solve this problem, an improved MGEKF algorithm based on Back Propagation Neural Network (BPNN), termed BPNN-MGEKF algorithm, was proposed in this paper. At BPNN training time, measured values were used as the input, and modified results by true values as the output. BPNN-MGEKF was applied to single moving station bearing-only position experiment. The experimental results shows that, BPNN-MGEKF improves the positioning accuracy of more than 10% compared to extended Kalman filter, MGEKF and smoothing modified gain extended Kalman filter algorithm, and it is more stable.

In Mobile Ad Hoc Network (MANET), each node forwards a message in the traditional routing protocol such as flooding and expanding ring search, which results in heavy overhead and long latency of routing. In order to improve the performance of routing protocol, a scheme of probabilistic forwarding algorithm was provided based on directional prediction. The information such as ID and time was extracted from data packets by monitoring network, and a table was established to store these records which can hint the distance to the destination node. Based on these records, the node's forwarding probability was calculated and adaptively adjusted according to the network. Whether some node should forward a packet depended on the forwarding probability, which was high enough only for sustaining the routing process towards the destination. The simulation results show that the routing overhead declined up to 70% compared with flooding algorithm and 20% compared with the classical probabilistic forwarding algorithm. The new scheme significantly improved the performance of the network.

With the development on technology of CAD/CAM, the product design, virtual manufacturing and digital prototyping can all be done in the computer, which makes the design of large and complex assembly an essential part in the product design. As these assembly models tend to have a huge number of data, it is extremely inconvenient to process on ordinary PCs. In order to speed up, the large scale assembly model needs simplifying. On the premise of maintaining the style and facade of the system, two simplification methods were proposed: (1) by removing invisible parts from the assembly, (2) by removing the invisible features from the assembly. The proposed methods were based on an algorithm which can directly detect invisible parts or features by pre-rendering the model from multiple view directions and reading the rendered results from the frame buffer. The experimental results show that our methods can correctly remove the invisible parts or features correctly from assembly for simplification.

This paper proposed a new decomposition scheme for lossless image compression by incorporating edge-directed adaptive prediction with wavelet lifting scheme. A vertical one-Dimension Discrete Wavelet Transform (1D-DWT) was first applied to images by means of lifting scheme. Second, edge-directed adaptive prediction procedure was applied to those high-frequency sub-band coefficients generated by the previous DWT. And then, a similar horizontal decomposition was performed in the low-frequency sub-band generated by vertical decomposition. A multi-resolution representation was thus acquired by an iterative repetition at the produced low-resolution approximation. Unlike the well-known coder CALIC and JPEG-LS, this scheme can provide a resolution scalable code-stream due to DWT. In addition, the experimental results indicate, due to the edge-directed prediction, this decomposition scheme has achieved noticeably better performance of lossless compression than JPEG2000 which supports resolution scalability.

This paper studied the spectrum sensing algorithms mainly from the perspective of multiple cognitive radio users. At present, most decision rules do not take the influence of the trust of spectrum sensing results into consideration. This paper proposed a new sending scheme based on index belief degree function. This scheme is a method of low complexity to improve the performance of spectrum sensing. Considering the security in cognitive radio network, this paper presented a cognitive radio spectrum algorithm based on outlier. This algorithm introduced outlier sensing to the fusion rules and this scheme could improve the robustness of spectrum sensing when some sensing nodes were malfunctioned or malicious. Considering the speed and precision of spectrum sensing in cognitive radio, this paper considered the sensing information to the spectrum sensing algorithm. This algorithm can improve the detection performance of spectrum sensing while accelerating the speed of spectrum sensing.

In order to overcome the shortcomings of using fixed search step and existing redundant search point in UMHexagonS algorithm of H. 264 motion estimation, this paper combined the Fibonacci sequence with center-biased feature to improve it. Firstly, the search step was determined by the progressive relationship of the Fibonacci sequence. Secondly, some search points which lead to redundant computation were deleted. At last, the search template of big hexagon was modified by the center-biased feature. The experimental results show that the new algorithm maintains the bit rate and Peak Signal-to-Noise Ratio (PSNR) of UMHexagonS, and reduces the time of motion estimation. And with the improvement of image elements, image complexity and search range, the time for motion estimation becomes less and less, and it can be reduced by an average of 23. 82% of the motion estimation time of UMHexagonS algorithm when the search range is 64.

A new algorithm based on cyclic spectrum was proposed for classification of communication signals in multipath channel, which solved the problems of fewer identification types, difficults table feature parameters extraction and low recognition rate. Firstly, the features face and projective planes of cyclic spectrum, square cyclic spectrum and the fourth power cyclic spectrum were extracted. Secondly, correlation coefficients of features face and projective planes were used as the characteristic parameters. At last, the suitable decision threshold was chosen and seven signals of BPSK, QPSK, 2FSK, 4FSK, MSK, 16QAM and OFDM were identified automatically. The experimental results show that the characteristic parameters have great ability for multipath interference and high recognition rate is acquired at last. When the Signal-to-Noise Ratio (SNR) is higher than 2dB, its overall recognition rate is up to 94%. Compared with the existing algorithms, the simulation results prove that the algorithm is superior.

Among all of the signal detection algorithms in multiple-input multiple-output systems, the capability of sphere decoding algorithm is most close to the capability of maximum-likelihood algorithm. But the calculation complexity of the sphere decoding algorithm is still very high. To decrease the calculation complexity of sphere algorithm, a new sphere decoding algorithm was proposed. The new algorithm was combined by an improved fast sphere decoding algorithm and the Minimum Mean Square Error (MMSE) algorithm. The improved fast sphere decoding algorithm can increase the decreasing rate of sphere radius via multiplying the contraction process of sphere radius by a constant parameter, so that it can reduce the number of signal points in search process to decrease calculation complexity. Meanwhile, the MMSE algorithm can reduce the interference that caused by noise, so that it can decrease the calculation complexity caused by the process of searching noise points. The channel matrix of the MMSE algorithm was applied to the improved fast sphere decoding algorithm, so these two algorithms can be combined with each other efficiently, and the combined algorithm can further reduce the calculation complexity. The simulation results show that, when Signal-to-Noise Ratio (SNR) is less than 10dB, the proposed algorithm improves average performance by 9% compared with original sphere decoding algorithm. Multiple-Input Multiple-Output (MIMO); signal detection; calculation complexity; Sphere Decoding (SD) algorithm; Minimum Mean Square Error (MMSE) algorithm

In Orthogonal Frequency Division Multiplexing (OFDM) systems, the key point is to improve accuracy and reduce complexity of channel estimation algorithm. First, the traditional Discrete Fourier Transform (DFT) channel estimation algorithm was improved. Furthermore, the impact of channel noise was eliminated through the introduction of the decision threshold, and system performance improved, then the improved channel estimation was compared with LS, SVD and DFT estimation. Finally,the Matlab simulation results show that the proposed algorithm can improve the accuracy of channel estimation without increasing the complexity, and then improve the performance of the whole system.

When using recognition algorithm of high-order cumulants to classify and recognize digital modulation signals, the calculation of six-order and six-order above cumulants are too complex and the signals of 8PSK and Multiple Frequency Shift Keying (MFSK) have the same cumulants, so it is impossible to recognize directly. To solve this problem, a new classification algorithm was proposed in this paper, which made wavelet transform on MFSK and 8PSK at first, and then used four-order cumulants recognition. The simulations show that the characteristic parameters could restrain Gaussian white noise efficiently and simply, and classify and recognize 2ASK/BPSK, 4ASK, 2FSK, 4FSK, QPSK, 8PSK and 16QAM successfully. When SNR (Signal-to-Noise Ratio) is 3dB, the recognition rate reaches as high as 96%. Compared with the existing algorithms, the superiority of the algorithm is proved.

Qualitative Probabilistic Network (QPN) is a powerful knowledge representation tool. However, sub-QPN can only represent sub-domain knowledge. To build a large QPN to represent the whole domain knowledge, an integration method of multiple sub-QPNs that have different nodes was proposed based on rough sets. Specifically, a single variable or a combination of multiple variables in a QPN could be regarded as an attribute in rough sets. First, multiple sub-QPNs were combined into an initial integrated QPN during integrating, then the directed edges and qualitative signs were added into the QPN according to attribute dependency degree, and then some unnecessary edges of which child node had multiple parent nodes could be deleted according to relative necessity of attribute. Thus, a large integrated QPN would be obtained to represent the whole domain knowledge. Finally, the experimental results illustrate that the integration method is feasible and effective.