In the Energy Balanced Clustering algorithm for Self-energized wireless sensor network (EBCS), a node has no threshold limit of energy in the cluster-head election, which leads to that a node with low energy might be elected as the cluster-head; and the cluster-head node can only hold one round, which leads to that the node with rich energy can not continue to be reappointed. Meanwhile, the EBCS has no consideration about the election mechanism after the death node was resurrected based on the self-energized characteristic. In order to solve the problems, a new Clustering routing algorithm based on Cluster-head Optimization for Self-energized wireless sensor network (CCOS) was proposed. Firstly, the energy threshold of cluster-head election was optimized, which limited the election of the node with incompetent energy for the cluster-head. Secondly, the cluster-head reappointment mechanism was introduced and improved, which made that the cluster-head can decide whether to be the cluster-head in the next round with its own level of energy harvesting. What's more, a threshold sensitive node resurrection mechanism was proposed, soft and hard resurrection thresholds were set to let the death node resurrected when its harvesting energy reached the corresponding energy threshold. The experimental results show that, under different energy harvesting scenes, compared with EBCS, the number of available nodes of the proposed CCOS in the current network is increased by about 8% and its success ratio of data transmission is increased by about 5%. The proposed CCOS can make more rational use of renewable energy and is helpful to the deployment of self-energized sensor network.

In view of the problem of computational redundancy due to the repeated computation of resource mapping positions in the traditional de-resource mapping method of Long Term Evolution-Advanced (LTE-A) physical layer, a new architecture of Physical Downlink Shared channel (PDSCH) de-resource mapping method was proposed, which provides support for the related physical layer processing of the LTE-A air interface analyzer. Firstly, before to the mapping of the physical downlink signal and the channel de-resource, the resource indexes of each signal and channel in single antenna port 0 mode, transmit diversity mode, single-stream beamforming, and dual-stream beamforming were generated; and then, the time-frequency location of the resource was directly located according to the resource index; finally, the PDSCH de-resource mapping module was put in the entire LTE-A link level simulation platform, and the simulations were given in four transmission modes, and the corresponding bit error rate and throughput comparison chart was obtained, which provides a theoretical reference to final hardware implementation. At the same time, compared with the de-resource mapping module under the traditional architecture, it shows that the de-resource mapping module under the new architecture costs 33.33% less time than the traditional computation mapping simulation, which reduces the de-resources and device resource consumption when de-resources mapping.

In order to improve the quality of speech signal and adaptability of cochlear implant under strong noise background, an improved method was proposed based on the combination of spectral subtraction and variable-step Least Mean Square error (LMS) adaptive filtering algorithm, and a speech enhancement hardware system for cochlear implant was constructed with this method. Concerning the problem of slow convergence rate and big steady-state error, the squared term of output error was used to adjust the step size of variable-step LMS adaptive filtering algorithm; besides, the combination of fixed and changed values of step was also considered, thus improved the adaptability and quality of speech signal. The speech enhancement hardware system for cochlear implant was composed of TMS320VC5416 and audio codec chip TLV320AIC23B, high-speed acquisition and real-time processing of voice data between TMS320VC5416 and TLV320AIC23B were realized by the interface of Muti-channel Buffered Serial Port (McBSP) and Serial Peripheral Interface (SPI).The Matlab simulation and test results prove that the proposed method has good performance in eliminating noise, the Signal-to-Noise Ratio (SNR) can be increased by about 10 dB in the case of low input SNR, and Perceptual Evaluation of Speech Quality (PESQ) score can be also greatly enhanced, the quality of the voice signal is improved effectively, and the system based on the proposed algorithm has stable performance which further improves the clarity and intelligibility of voice in cochlear implant.

In the era of big data, data intensive computing always relies on distributed Heterogeneous Computing System (HCS), and an effective task scheduling method can improve the efficiency of a HCS. Based on a Directed Acyclic Graph (DAG) model, a task scheduling algorithm for heterogeneous computing named HSFT (Heterogeneous scheduling algorithm with immediate Successor Finish Time) was proposed. In the heterogeneous environment, especially when the computation cost and communication cost vary largely, the balance between them was considered and a more reasonable method was adopted, the product of the computation cost standard deviation and mean value was taken as the computation weight, and the ratio between the out degree communication cost weight and out degree was taken as the communication weight. Furthermore, based on the consideration of Earliest Finish Time (EFT), the immediate Successor Finish Time (SFT) was used for processor selection strategy. The experimental results on randomly generated DAGs show that the proposed algorithm performs better than HEFT (Heterogeneous Earliest Finish Time), SDBATS (Standard Deviation-Based Algorithm for Task Scheduling) and PEFT (Predict Earliest Finish Time) in terms of makespan, schedule length ratio, and efficiency without increasing time complexity.

The real-time performance of Scale Invariant Feature Transform (SIFT) algorithm is excessively bad. To solve the problem, a parallel optimized SIFT algorithm using the Open Computing Language (OpenCL) was proposed. Firstly, all steps of the original algorithm were split and combined; in addition, the indexing method of feature points in memory was restructured. Thus the middle calculation results could be made completely to finish interaction in the memory. Then, each step of the original algorithm was designed in parallel to improve the efficiency of data reading and reduce the transmission delay by multiplexing global memory object, sharing local memory and optimizing memory access. Finally, a fine-grained parallel accelerated SIFT algorithm was completed on Graphics Processing Unit (GPU) platform using OpenCL and the transplant was completed on the Central Processing Unit (CPU) platform. The parallel algorithm speeded up 10.51-19.33 and 2.34-4.74 times in feature extraction on GPU and CPU platform when the registration result was close to the original algorithm. The experimental results show that the parallel accelerated SIFT algorithm using OpenCL can improve the real-time performance of image registration and overcome the disadvantages of that Compute Unified Device Architecture (CUDA) is difficult to be transplanted so that it can not make full use of the multiple computing cores in heterogeneous systems.

Greatest Common Divisor (GCD) is one of the basic subjects in computational number theory. It has a wide application in encryption and analysis of cryptography. For inputing B and C, an algorithm based on right-shift k-ary reduction proposed by Sorenson was presented for finding the integers x and y which satisfy the least significant bits of Bx-Cy were 0,i.e., Bx-Cy=0(mod2^e) where positive integer e was a constant. It could do a lot of right shifts and reduce a large number of cycles with taking advantage of the algorithm for finding the integers x and y. A fast GCD algorithm was proposed combined with modulus algorithm. When the size of the input was n bits, the worst complexity of the fast GCD algorithm was still O(n²).In the best case, the complexity of the proposed algorithm could achieve O(nlog² nlog logn). The experimental data show that actual implementations given input about more than 200000 bits, the fast GCD algorithm is faster than the Binary GCD algorithm, and the fast GCD algorithm is twice as fast as the Binary GCD algorithm for 1 million bits of input.

In order to assist in the fast and efficient diagnosis of breast cancer and provide the prognosis information for pathologists, a computer-aided diagnosis approach for automatically grading breast pathological images was proposed. In the proposed algorithm,cells of pathological images were first automatically detected by deep convolutional neural network and sliding window. Then, the algorithms of color separation based on sparse non-negative matrix factorization, marker controlled watershed, and ellipse fitting were integrated to get the boundary of each cell. A total of 203-dimensional image-derived features, including architectural features of tumor, texture and shape features of epithelial cells were extracted from the pathological images based on the detected cells and the fitted boundary. A Support Vector Machine (SVM) classifier was trained by using the extracted features to realize the automated grading of pathological images. In order to verify the proposed algorithm, a total of 49 Hematoxylin & Eosin (H&E)-stained breast pathological images obtained from 17 patients were considered. The experimental results show that,for 100 ten-fold cross-validation trials, the features with the cell shape and the spatial structure of organization of pathological image set successfully distinguish test samples of low, intermediate and high grades with classification accuracy of 90.20%. Moreover, the proposed algorithm is able to distinguish high grade, intermediate grade, and low grade patients with accuracy of 92.87%, 82.88% and 93.61%, respectively. Compared with the methods only using texture feature or architectural feature, the proposed algorithm has a higher accuracy. The proposed algorithm can accurately distinguish the grade of tumor for pathological images and the difference of accuracy between grades is small.

An access control scheme for cloud storage proposed by Tang et al. (TANG Y，LEE P，LUI J，〖WTBX〗et al.〖WTBZ〗 Secure overlay cloud storage with access control and assured deletion. IEEE Transactions on Dependable and Secure Computing，2012，9(6)：903-916） was analyzed and proved to be vulnerable to collusion attacks, and a detailed attack method was given. To address this problem of the given scheme, this paper took the advantage of the property of collusion attack resistant of the attribute based encryption algorithm and improved a ciphertext-policy attribute-based encryption (CP-ABE) algorithm which used the access tree. Then the improved algorithm could be used seamlessly into the access control and assured deletion scheme for cloud storage without changing the cloud server. Finally, the security against the chosen plaintext attack was proved under the Decision Bilinear Diffie-Hellman (DBDH) assumption, and the ability of resisting the collusion attack of the scheme was proved by analyzing a practical cloud situation.

As a new intelligent spectrum sharing technology, Cognitive Radio (CR) technology could effectively improve the spectrum utilization. It allows the cognitive user to use the idle spectrum of licensed user under the premise of free interference. Because of the high sidelobe attenuation, the traditional physical layer transmission technology in cognitive radio system, that is Orthogonal Frequency Division Multiplexing (OFDM) multi-carrier modulation technology, suffers from the the interference between the adjacent subbands and the interference among licensed users and cognitive users, an alternative multi-carrier modulation technology, filter bank based multi-carrer technology, was introduced in this paper to eliminate the interferences. An indirect design method for designing the prototype filter in filter bank was proposed while formulating the filter bank as an unconstrained linear optimization problem of prototype filter.Firstly,two low-order and linear lowpass filters were designed by using the Parks-McClellan algorithm, and then the prototype filter was optimally designed through the interpolation and cascade operation. Compared to the conventional direct method, the simulation results show that the modulated filter banks designed by the proposed method in this paper present better performance in the length of filter coefficients and the alias errors.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) demonstrates that malignant tumors generally show faster and higher levels of enhancement than they are seen in benign or normal tissue, after an intravenous injection of the contrast agent Gd-DTPA, DCE-MRI has played important roles in diagnosis and detecting malignant tumor. However, it is still a challenge on the fast reconstruction of DCE-MR images. Based on the idea of group sparse and the theory of Compressed Sensing (CS), a conjugate gradient algorithm combined with variable density random sampling method was employed to get samples from the local k-spaces (Fourier coefficient) sampling data. Then traditional l1 norm conjugate gradient descent algorithm was extended to l2,1 norm to jointly reconstruct multiple DCE-MR images simultaneously. Compared with conventional Multi-Measurement Vector (MMV) algorithm, the proposed approach yields a faster and more accurate reconstruction result. The experimental results show that when the sampling rate is less than 40%, the joint reconstruction time based on conjugate gradient algorithm almost decreased by 30% compared with the MMV algorithm. In addition, compared with the uniform random sampling, the variable density random sampling method improves the accuracy rate about 70%.

In order to improve the poor frequency selectivity and spectral leakage of traditional Orthogonal Frequency Division Multiplexing (OFDM) multi-carrier transceiver, multi-carrier transceiver based on modified Discrete Fourier Transform (DFT) filter bank was introduced. The design problem of it was formulated as nonconstrained minimized optimization of perfect reconstruction error of modulated filter banks, and the modified window method was taken to design the prototype filter in the modulated filter banks and linear optimization was used to optimize the objective function. Compared to the traditional window method, a spline function was exploited in the transition band of ideal filter of the modified window to eliminate the Gibbs effect. The simulation results show that the designed filter bank with the proposed method gives a better performance in terms of stopband attenuation, and reconstruction error, and the resulted multicarrier transceiver a better Symbol Error Rate (SER) performance in Quadrature Phase Shift Keying (QPSK) modulation and the 3GPP TS 25.104 vehicular multipath and one-tap frequency-domain equalization.

Concerning the deployment of virtual machines in the cloud computing, an algorithm on the deployment of virtual machines with clustering method based on frame load awareness was proposed. First of all, the load of each layer in datacenter was computed and the clustering of physical machines in each layer was constructed. In the process of deploying virtual machines, the clustering of virtual machines was first done according to some rules and then the frame with lower load was chosen preferentially. The last step was to match the virtual machines cluster and physical machines cluster in order to deploy the optimal physical machines cluster. The performance of the algorithm was validated with the experiments in CloudSim. The result was compared to that of the greedy algorithm and basic deployment algorithm with the frame load awareness, which shows that the algorithm proposed in this article has evident priority, and improves the utilization rate of network resources.

At present, most of the privacy protection schemes are based on sophisticate zero-knowledge protocol or bilinear mapping computation, so their efficiency is low. In order to address this problem, an anonymous negotiation credentials scheme was proposed based on ring signature. On the foundation of anonymous credentials framework, an efficient discrete logarithm based ring signature scheme was constructed to protect the negotiation credentials, compared with two schemes proposed by ZHANG, et al. (ZHANG MING-WU, YANG BO, ZHU SHENG-LIN, et al. Policy-Based Signature Scheme for Credential Privacy Protecting in Trust Negotiation. Journal of Electronics & Information Technology, 2009(1): 224-227) and LIU, et al. (LIU BAILING, LU HONGWEI, ZHAO YIZHU. An efficient automated trust negotiation framework supporting adaptive policies. Proceedings of the Second International Workshop on Education Technology and Computer Science. Washington, DC: IEEE Computer Society, 2010: 96-99) the proposed scheme is more efficient.

Real-time rendering based training system for fire includes three parts: Modeling and displaying for 3-Dimensional architecture map and interaction with users, fire hazards simulation, supervising for life saving and decision-making for succor. This paper presents approaches to the implementation of virtual training system for fire using some algorithms of real-time rendering, AI and PR, combining with hazards model of fire. On second thoughts, we advance a method, which uses virtual reality method, to implement a software system of fire hazard and training. We combine OpenGL with DirectX to realize C-S based virtual training system for fire in Windows.

This paper analysed the functions of all parts of the USB transfer subsystem in real-time human motion capture device, and introduced its detailed designing methods of this subsystem. The USB transfer subsystem can transfer exact data in real time while the device is running and collecting human motion data.