Aiming at the problem that the computing of the image panoptic segmentation task is not fast enough for the existing network structures in practical applications, a panoptic segmentation algorithm based on grouped convolution for feature fusion was proposed. Firstly, through the bottom-up method, the classic Residual Network structure (ResNet) was selected for feature extraction, and the multi-scale feature fusion of semantic segmentation and instance segmentation was performed on the extracted features by using the Atrous convolutional Spatial Pyramid Pooling operation (ASPP) with different expansion rates. Secondly, a single-channel grouped convolution upsampling method was proposed to integrate the semantics and instance features for performing upsampling feature fusion to a specified size. Finally, a more refined panoptic segmentation output result was obtained by performing loss function on semantic branch, instance branch and instance center point respectively. The model was compared with Attention-guided Unified Network for panoptic segmentation (AUNet), Panoptic Feature Pyramid Network (Panoptic FPN), Single-shot instance Segmentation with Affinity Pyramid (SSAP), Unified Panoptic Segmentation Network (UPSNet), Panoptic-DeepLab and other methods on CityScapes dataset. Compared with the Panoptic-DeepLab model, which is the best-performing model in the comparison models, with the decoding network parameters reduced significantly, the proposed model has the Panoptic Quality (PQ) of 0.565, with a slight decrease of 0.003, and the segmentation qualities of objects such as buildings, trains, bicycles were improved by 0.3-5.5, the Average Precision (AP) and the Average Precision with target IoU (Intersection over Union) threshold over 50% (AP ₅₀) were improved by 0.002 and 0.014 respectively, and the mean IoU (mIoU) value was increased by 0.06. It can be seen that the proposed method improves the speed of image panoptic segmentation, has good accuracy in the three indexes of PQ, AP and mIoU, and can effectively complete the panoptic segmentation tasks.

The ship scheduling affected by severe weather is a very complex optimization problem, and is also one of the key issues needed to pay attention in liner companies. Therefore, based on the premise of obtaining the latest weather forecasting information in the designed multi-stage rescheduling mechanism period and the real-time positions of all the ships in service in one liner company on a shipping network, the restriction of liner shipping schedule was focused on, and the realistic constraints such as the change of ship's speed between different ports and the ship capacity, a nonlinear mathematical model was built to minimize the total shipping cost of all the ships during the fixed planning period. And an improved genetic algorithm embedded with gene repair operator was designed to solve the built model. Then the optimal multi-stage rescheduling scheme, which was integrated by the solution strategies of charting for direct-transport, dispatching ship across different routes, adjusting port reaching order and goods transfer, was given. Experimental results of examples with large, medium, and small scales show that, compared with the traditional waiting method, multi-stage rescheduling saves more than 15% of the total shipping cost, verifying the effectiveness of the proposed model and scheme; compared with Cplex, the improved genetic algorithm has the calculation efficiency greatly improved and all the deviation values within 5%; and compared with Ant Colony Optimization (ACO) algorithm, Tabu Search (TS) algorithm, Quantum Differential Evolution (QDE) algorithm, the improved genetic algorithm has the cost reduced by about 10% in the effective time, proving that the algorithm is scientific. It can be seen that the proposed method can provide the reference for actual ship scheduling of liner companies.

The existence of heterogeneous information systems in colleges and universities hinders data assets integration and information interaction. The emergence of Service Oriented Architecture (SOA) and its widespread adoption in enterprises provide ideas for solving this problem, while it is difficult to implement SOA and form an SOA-based informational ecosystem in universities. In response to these problems, an SOA construction scheme driven by master data management was proposed. Firstly, a master data management platform was used to model and integrate the core data assets at the data level. In order to realize data synchronization and consumption, and solve the problem of protocol conversion and service authentication in the process, an enterprise service bus based solution was proposed. Then, in order to the transform the legacy "information island" systems to SOA, a construction solution driven by master data was proposed. The experimental results show that the average latency with concurrency single user, 10 users, 100 users and 10000 users is 8, 11, 59 and 18 ms respectively, which indicates that the performance of the proposed scheme meets the need in different concurrent scenarios. The implementation results show that the data assets integration and information interaction problems have been solved, which proves that the scheme is feasible.

For the monitoring dead angles of fixed surveillance cameras installed in large quantities and low hardware performance of mobile devices, an urban management case target recognition algorithm that can run on IOS mobile devices with low performance was proposed. Firstly, the number of channels of input and output images and the number of feature maps generated by each channel were optimized by adding new hyperparameters to MobileNet. Secondly, a new recognition algorithm was formed by combining the improved MobileNet with the SSD recognition framework and was transplanted to the IOS mobile devices. Finally, the accurate detection of the common 8 specific urban management case targets was achieved by the proposed algorithm, in which the camera provided by the mobile device was used to capture the scene video. The mean Average Precision (mAP) of the proposed algorithm was 15.5 percentage points and 10.4 percentage points higher than that of the prototype YOLO and the prototype SSD, respectively. Experimental results show that the proposed algorithm can run smoothly on low-performance IOS mobile devices, reduce the dead angles of monitoring, and provide technical support for urban management team to speed up the classification and processing of cases.

The major drawback of existing Differential Chaos Shift Keying (DCSK) communication system is low transmission rate. To solve the problem, a Correlation Delay-Differential Chaos Shift Keying (CD-DCSK) communication scheme without inter-signal interference was proposed. At the transmitting side, two orthogonal chaotic signals were generated by an orthogonal signal generator and normalized by the sign function to keep the energy of the transmitted signal constant. Then, two chaotic signals and their chaotic signals with different delay time intervals were respectively modulated by 1 bit data information to form a frame of transmission signal. At the demodulation side, correlation demodulation was used to extract data information and the information bits were recovered by detecting the sign of correlator output. The theoretical Bit Error Rate (BER) performance of system under Additive White Gaussian Noise (AWGN) channel was analyzed by using Gaussian Approximation (GA) method, and was compared with classical chaotic communication systems. The performance analysis and experimental results indicate that, compared with DCSK system, the transmission rate of CD-DCSK system without inter-signal interference increases by 50 percentage points, and the BER performance of the proposed system is better than that of Correlation Delay Shift Keying (CDSK) system.

In order to improve the transmission performance of chaotic signals, a Multi-User Orthogonal Correlation Delay Shift Keying (MU-OCDSK) scheme was proposed based on Correlation Delay Shift Keying (CDSK) scheme and Multi-Carrier Correlation Delay Shift Keying (MC-CDSK) scheme. The multi-carrier was used to modulate chaotic signals. Compared with CDSK, the proposed scheme not only has higher spectral efficiency, but also improves bit error rate. Theoretical simulation and Monte Carlo simulation show that compared with MC-CDSK, the proposed scheme not only doubles the transmission rate, but also improves the bit error rate. The results of theoretical simulation and Monte Carlo simulation are consistent.

Monaural speech enhancement algorithms obtain enhanced speech by estimating and negating the noise components in speech with noise. However, the over-estimation and the error of the introduction to make up the over-estimation of noise power make detrimental effect on the enhanced speech. To constrain the distortion caused by noise over-estimation, a time-frequency mask estimation and optimization algorithm based on Computational Auditory Scene Analysis (CASA) was proposed. Firstly, Decision Directed (DD) algorithm was used to estimate the priori Signal-to-Noise Ratio (SNR) and calculate the initial mask. Secondly, the Inter-Channel Correlation (ICC) factor between noise and speech with noise in each Gammatone filterbank channel was used to calculate the noise presence probability, the new noise estimation was obtained by the probability combining with the power spectrum of speech with noise, and the over-estimation of the primary estimated noise was decreased. Thirdly, the initial mask was iterated by the optimization algorithm to reduce the error caused by the noise over-estimation and raise the target speech components in the mask, and the new mask was obtained when the iteration stopped with the conditions met. Finally, the optimization method was used to optimize the estimated mask. The enhanced speech was composed by using the new mask. Experimental results demonstrate that the new mask has higher Perceptual Evaluation of Speech Quality (PESQ) and Short-Time Objective Intelligibility measure (STOI) values of the enhanced speech in comparison with the mask before optimization, improving the intelligibility and listening feeling of speech.

Deep neural networks tend to suffer from overfitting problem because of the high complexity of the model. To reduce the adverse eeffects of the problem on the network performance, an improved elastic network model based deep learning optimization method was proposed. Firstly, considering the strong correlation between the variables, the adaptive weights were assigned to different variables of L1-norm in elastic network model, so that the linear combination of the L2-norm and the adaptively weighted L1-norm was obtained. Then, the solving process of neural network parameters under this new regularization term was given by combining improved elastic network model with the deep learning optimization model. Moreover, the robustness of this proposed model was theoretically demonstrated by showing the grouping selection ability and Oracle property of the improved elastic network model in the optimization of neural network. At last, in regression and classification experiments, the proposed model was compared with L1-norm, L2-norm and elastic network regularization term, and had the regression error decreased by 87.09, 88.54 and 47.02 and the classification accuracy improved by 3.98, 2.92 and 3.58 percentage points respectively. Thus, theory and experimental results prove that the improved elastic network model can effectively improve the generalization ability of deep neural network model and the performance of optimization algorithm, and solve the overfitting problem of deep learning.

Concerning the blind estimation of the combination code sequence of Time Division Data Modulation-Binary Offset Carrier (TDDM-BOC) modulation signal under low Signal-to-Noise Ratio (SNR), a new method based on Sanger Neural Network (Sanger NN), a kind of multi-principal component neural network, was proposed. Firstly, the segmented TDDM-BOC signal was used as input signal, and the weight vectors of multi-feature components of the segmented TDDM-BOC signal were adaptively extracted by Sanger NN algorithm. Secondly, the weight vectors were trained repeatedly until convergence by continuously inputing segmented TDDM-BOC signal. Finally, the combination signal code sequence was rebuilt by the symbolic function of each weight vector, thus realizing the blind estimation of the TDDM-BOC signal. Furthermore, an optimal variable step method was used in Sanger NN algorithm to greatly improve the convergence speed. Theoretical analysis and simulation results demonstrate that the Sanger NN algorithm can achieve blind estimation of the TDDM-BOC combined code sequence with low SNR of -20.9~0 dB, and its complexity is significantly lower than that of Singular Value Decomposition (SVD) and on-line unsupervised learning neural network for adaptive feature extraction via principal component analysis (LEAP). Although the number of data group required for the convergence of Sanger NN algorithm is larger than that of LEAP algorithm, but the convergence time of Sanger NN is lower than that of LEAP algorithm.

To estimate pseudo code period of multipath tamed spread spectrum signal, a blind method based on power spectrum reprocessing was proposed to estimate the pseudo code period of the tamed spread spectrum signal in multipath channel. Firstly, the general single path tamed spectrum signal was extended to multipath model. Then, the primary power spectrum of the signal was calculated on the basis of the tamed spread spectrum signal model in multipath environment. Next, the obtained primary power spectrum was used as the input signal to calculate the secondary power spectrum of the signal, and the theoretical analyses showed that the peak line of the secondary power spectrum of the signal would appear in the integral multiple of the pseudo code period. Finally, the pseudo code period of the tamed spread spectrum signal could be estimated by detecting the spacing between the peak spectrum lines. In the comparison experiments with time domain correlation method, the Signal-to-Noise Ratio (SNR) of the proposed method was improved by about 1 dB and 2 dB when the correct rate of pseudo code period was 100% and the length of pseudo code sequence was 127 bits and 255 bits, and the average accumulation times of the proposed method was less under the same condition. The experimental results show that the proposed method not only has less computational complexity, but also improves the estimation correct rate.

In order to improve the accuracy of haze forecast and resolve the time lagging and low accuracy of temporal model, a mixed forecast method based on time series analysis and Karman filter was proposed. Firstly, the stability of time series was tested by graph analysis and eigenvalue analysis (ADF). Unstable time series were converted to stable ones by differential operation. A statistical function was established based on the stable time series. And then, the obtained model equations were used as the state and observation equation for Kalman filtering. Final haze forecast was based on recursion by Karman filtering. The experimental results showed that the accuracy of haze forecast is effectively improved by the mixed forecast method based on time series analysis and Karman filtering.

Two improvement methods were presented to avoid ineffective circulation and invalid waiting state problems when running algorithm Randomized Switching for Maximizing Lifetime (RaSMaLai) and then a new random switching algorithm New Randomized Switching for Maximizing Lifetime (NRaSMaLai) was put forward: the first improvement was to conduct the initialized inspection in the process of traversing tree nodes in order to prevent the tree from entering into invalid waiting state; the second improvement was to do the state inspection for the maximum load node and all its descendant nodes in the operation process of updating the tree to avoid ineffective circulation. The tree balance was achieved by increasing the load of the minimum node and its descendant nodes with NRaSMaLai. The simulation experiment shows that these two methods can make the tree achieve the balance state or at least get closer to the presupposed state. When the sink node was located in the regional center, the iterative steps which make the tree balanced can be reduced to 1/5 of the original by NRaSMaLai and also it appears little oscillation. This is significant for the data collection tree's rapid convergence and the extension of the network's lifetime.

A runtime error is generated in the course of the program's dynamic execution. When the error occurred, it needs to use traditional debug tools to analyze the cause of the error.For the real execution environment of some exception and multi-thread can not be reproduced, the traditional debug analysis means is not obvious. If the variable information can be captured during the program execution, the runtime error site will be caught, which is used as a basis for analysis of the cause of the error. In this paper, the technology of capture runtime error site based on variable tracking was proposed; it can capture specific variable information according to user needs, and effectively improved the flexibility of access to variable information. Based on it, a tool named Runtime Fault Site Analysis (RFST) was implemented, which could be used to analyze error cause and provide error site and aided analysis approach as well.

Concerning the problem of modulation identification in the Rayleigh channel, a new algorithm based on cumulants was proposed. The method was efficient and could easily classify seven kinds of signals of BPSK (Binary Phase Shift Keying), QPSK (Quadrature Phase Shift Keying), 4ASK (4-ary Amplitude Shift Keying), 16QAM (16-ary Quadrature Amplitude Modulation), 32QAM (32-ary Quadrature Amplitude Modulation), 64QAM (64-ary Quadrature Amplitude Modulation) and OFDM (Orthogonal Frequency Division Multiplexing) by using the decision tree classifier and the feature parameters that were extracted from combination of four-order cumulant and six-order cumulant. Through theoretical derivation and analysis, the algorithm is insensitive to Rayleigh fading and AWGN (Additive White Gaussian Noise). The computer simulation results show that the successful rates are over 90% when SNR (Signal-to-Noise Ratio) is higher than 4dB in Rayleigh channel, which demonstrates the feasibility and effectiveness of the proposed algorithm.

With regard to the advantages and disadvantages of image retrieval algorithm based on edge orientation autocorrelogram, a kind of image retrieval algorithm based on edge direction histogram correlation matching was proposed. Firstly, the salt and pepper noise in image was filtered by using an adaptive median filter, and then Sobel operator was used to extract image edge. After the edge orientation histogram was got through calculating the edge gradient amplitude and angle, the feature vector was constituted. Lastly, Spearman rank correlation coefficient was used to calculate the correlation coefficient between the feature vectors of images, as a measure of image similarity. Compared with the algorithm based on edge orientation autocorrelogram, the average precision and the recall rate of the new image retrieval algorithm increased by 10.5% and 9.7%. And the retrieval time consumption was also reduced by 7.5%. The experimental results verify the effectiveness of the proposed algorithm. The algorithm could be applied in medium to large image retrieval system to improve retrieval effect and raise the system speed.

To solve the problem of the Orthogonal Frequency Division Multiplexing (OFDM) broadband signal processing, an algorithm for the Direction of Arrival (DOA) estimation of OFDM signal based on the broadband focused matrix and higher-order cumulant was introduced. In the former algorithm, broadband array data was broken down into several narrowband signals by Fourier transform, the direction matrices under different frequence bands were transformed to the same reference frequency by a focused matrix, and then with the Multiple Signal Classification (MUSIC) algorithm DOA was estimated. In the higher-order cumulant algorithm, through the focus operation, array output vectors at different frequency bins were transformed to focusing frequency and individual cumulant matrix was got. Each cumulant matrix was made weighted average and eigen-decomposition, and then the MUSIC algorithm was applied to estimate DOA. Theoretical analysis and simulation results show that the two methods are able to accurately estimate DOA of OFDM signal, the spatial resolution of four-order cumulant method is better than the focusing matrix method. The four-order cumulant expanded the array aperture, and it also has good adaptability when the Signal-to-Noise Ratio (SNR) is low.

According to the blind extracting issues within the spread-spectrum watermark, a kind of blind extracting algorithm which could be used in the extraction of the digital audio signals was proposed. In the algorithm, wavelet transform was applied to the audio document, then the Discrete Cosine Transform (DCT) was used to its low-frequency coefficient. Afterwards, the fifth coefficient was got and it was used to hide the watermark information being spectrum spread. As the spread-spectrum sequence and its length were unknown during the extraction, spectrum-reprocessing and Singular Value Decomposition (SVD) were introduced to estimate the spread-spectrum using in the embedding process, and the blind extraction to the spread-spectrum watermark of the given digital signal was fulfilled. The simulation results show that with unknown spread-spectrum parameter, watermark image with Normalized Coefficient (NC) of one can be extracted, and it is of strong robustness. Under the attacks of noises and low-pass filter, the accuracy rate of the estimating spread-spectrum sequence is over 90%, which guarantees the recovery of clear water mark image with normalization coefficient higher than 0.98.

To deal with broadband signal processing, a new algorithm for the Direction Of Arrival (DOA) estimation of wideband Linear Frequency Modulation (LFM) signal was introduced. In this paper, the broadband LFM signal was transformed into a narrowband signal by Discrete Polynomial-phase Transform (DPT). After that, the wideband LFM signal could be transformed into a single sinusoidal signal and the new noise. And the time-varying direction vector in time domain was changed to the time-invariant vector. Then, the conventional narrow band signal processing method, Multiple Signal Classification (MUSIC) algorithm, was used to estimate the DOA. The theoretical analysis and simulation results show that the proposed method can accurately estimate DOA of the signal. The scheme is easy to implement with less computation, and it has good estimation performance.

Concerning the common multi-path transmission and reflection factors in the radio communications, broadband coherent sources must be considered. A new algorithm for the Direction Of Arrival (DOA) estimation of correlation wideband Non-Linear Frequency Modulation (NLFM) signals based on Discrete Polynomial-phase Transform (DPT) and virtual array was introduced. And comparison, analysis and improvement were done on the new algorithm. It can settle the problem of not finding coherent signals in routine MUSIC and ESPRIT algorithm. This approach can more accurately estimate and does not decrease effective array aperture, and it also can improve the utilization of array. Then the angle estimation algorithm of two closely-spaced emitters was proposed. The simulation results verify the correctness and efficiency of the new algorithm.

Based on the theory of kernel generalized optimal feature extracted mode,a new method for the corresponding mode was proposed.Firstly space transform method was used to transform initial kernel between class scatter matrix and kernel total scatter matrix,so the kernel total scatter matrix became positive definition. At the same time,by the means of kernel uncorrelated feature vectors extraction,the feature vectors got were statistical uncorrelated.To verify the effectiveness of this method,experiment was tested on ORL face databases and the result showed that the face recognition method proposed is more available than other methods such as kernel discriminant analysis.

The cepstrum method of speech pitch detection was analyzed carefully, and several shortcomings were pointed out when the method was realized digitally. In order to overcome these shortcomings, a pitch detection algorithm based on digital spectral analysis was presented. It used the power spectrum reprocessing results of speech to extract the pitch contour. The presented method not only overcame the shortcomings of cepstrum method, but also improved the computational speed and the accuracy of the estimated pitch contour.

In traditional graph theory based image segmentation methods,the grayscale value of an image is processed directly to obtain clustering results, but the computing time of these methods is very large. A novel segmentation method based on graph partition on histogram clustering was presented. The proposed algorithm obtained threshold by clustering histogram potential function. Since the input is histogram data, the computation time will not be affected by the image size. Experiment results demonstrate that the computation time can be significantly reduced by the proposed algorithm.