Abstract:As an emerging non-invasive surgical technique,high intensity focused ultrasound (HIFU) has been used clinically in the treatment of various benign and malignant sol id tumors.The focusing efficiency of HIFU transducer still need to be improved to achieve safer and more effective tumor treatment.This paper reviews the principle of the extraordinary acoustic transmission,as well as the recent research on HIFU transducers that utilizing extraordinary acoustic transmission.Two aspects of work are involved:(1) The artificially acoustic structure is introduced into the design of concave acoustic lens,and a corrugated lens is designed and manufactured to improve the acoustic lens of the lens-focused transducer,which has achieved the effect of reducing side lobe and improved the safety of HIFU treatment;(2) Based on spherically periodic slot array,a metamaterial focusing transducer is designed and manufactured to enhance the focusing efficiency of the HIFU transducer.Enhanced acoustic focusing is demonstrated by both measuring and simulating sound pressure distribution of metamaterial focusing transducer and traditional concave transducer,as well as temperature elevation generated in the tissue.The results of this paper can further promote the wide application of HIFU in clinical treatment.

Abstract:Molecular docking technology is an effective approach to protein-ligand interactions and recognition. The near-native conformations generated in the interaction process are the lowest energy conformations, so it is pivotal for the protein-ligand docking to searching these conformations quickly and accurately. Here we reviewed the main conformational searching methods in protein-ligand docking, including the exhaustive searching, heuristic searching and other searching strategies. Meanwhile, we listed the representative docking programs by using these different searching strategies. Furthermore, we introduced the international assessment of predicted interaction, the widely used testing benchmark and the evaluation indexes in protein-ligand docking. Finally, we pointed out the existing major problems of the search strategies in protein-ligand docking and gave a prospect for such field in the near future.

Abstract:The human center of gravity is the basic parameter reflecting the human body's morphological structure and the quality distribution characteristics. In order to obtain the accurate position of the center of gravity flexibly in a somatosensory interactive equipment environment, this study obtains the human body real-time joint point coordinate data by using the somatosensory interactive device Kinect, and constructs the calculation of the human center of gravity. Combining the multiplicative coefficient method, a real-time linear calculation method to calculate the human center of gravity is proposed. At the same time, in order to verify the accuracy of the center of gravity, this method is tested by the data of the center of gravity provided by the plate pressure test system Footscan as the standard center of gravity data. The results show that the method has similar results in the center of gravity calculation and has better accuracy in the action of large scale.

Abstract:Molecular docking is an effective method to predict protein complexes. The optimization of molecular docking algorithm is aimed at accelerating the efficiency of molecular docking, reducing the computational cost, and making full use of computer resources. This paper mainly optimizes the semi flexible docking algorithm using three schemes:(1) The first scheme is optimized on the CPU side. (2) The second scheme, on the base of the first scheme, uses the CUFFT transplantation tool CUFFTW to provide partial GPU parallel interface for the solution. (3) The third scheme is based on the compute unified device architecture (CUDA) parallel structure, which is achieved by a pure CUDA interface and the coordinated operation of CPU and GPU. The three schemes use four different proteins to test the bound docking and unbound docking. The proteins' PDB code is 1PEE, 1B6C, 4HX3 and 2SNI, respectively. The correctness of test is verified based on the minimum root mean square deviation L_RMSD, and its value is less than 5 Å, which satisfies the CAPRI medium precision structural standard. Then, under the premise of ensuring the correctness of the results, the running speed of different proteins under different schemes is tested. Finally, under the premise of ensuring the same efficiency of different protein docking, we take 1PPE as the final object to point out the rate of docking under different schemes. Experimental results show that under the same rotation step and the correctness of the program, the final optimization effect can increase the speed of the algorithm by nearly 10 times, thus greatly improving the speed of the algorithm.

Abstract:Promising application of high intensity focused ultrasound (HIFU) has been shown in the field of clinical treatment of tumors, but the effects of cavitation have not been considered in current treatment plans. In this paper, the impact of cavitation effects on the tissue lesion in HIFU therapy in vitro is discussed. Firstly, the KZK nonlinear equation and the Pennes biological heat transfer model are used to theoretically simulate the tissue lesion caused by the HIFU thermal effect with different ultrasonic excitation parameters. Then in the experiments, we change the pulse lengths and sound pressures to compare the tissue lesion experimentally and theoretically. Finally the B-type imaging system is used to quantitatively monitor acoustic cavitation, so as to quantitatively analyze the effects of cavitation on the tissue lesion, thus providing better guidance for the design of HIFU treatment.

Abstract:Since the demand for functional clothing becomes more and more pressing, wearable electrocardiogram system is designed here. The electrocardiogram signal is collected by three flexible fabric electrodes with high sensitivity and transmitted through the textile lines with good conductivity. Then, the signal is processed by the simple circuit with AD8232 and STM32F103C8T6. Finally, it is sent to the upper computer via bluetooth, and displays with the electrocardiogram waveform in real time. To remove the motion artifact interference, resulting from the great range of movement, a motion artifact interference suppression algorithm is proposed by combining the empirical mode decomposition with the principal component analysis. Experimental results show that the system can monitor the human body's electrocardiogram in different states effectively.

Abstract:To clarify the side effect of multi-bubble cavitation at pre-focus and post-focus in high intensity focused ultrasound (HIFU) treatment and further optimize HIFU treatment, this paper records the spatial distribution of multi-bubble and multi-bubble sonoluminescence (MBSL) at different acoustic intensities in the focused ultrasound field mediated with degassed water using high-speed camera system and digital camera, respectively. Besides, the cavitation characteristics of multiple bubbles and the overall luminous intensity of sound field are separately detected by passive cavitation detection (PCD) technology and photomultiplier tube (PMT) system. It is found that when acoustic intensity is low (such as 3 047 W/cm²), bubbles distribute as a typical standing wave pattern. With the increase of acoustic intensity, a cluster of bubbles gathers at focus, then grows towards post-focus, and 3-6 MHz broadband emissions increase monotonically. Moreover, MBSL firstly occurrs pre-focus and is enlarged towards the surface of the transducer, and the overall luminous intensity of the sound field is proportional to acoustic intensity. When acoustic intensity is sufficient (such as 21 328 W/cm²), the cavitation bubbles at focus collapse tempestuously with a continuous hiss, and an elliptical bright spot appears at focus. The accumulation of bubbles sharply weaken the overall luminous intensity of the sound field.

Abstract:There are no extant high-quality gene structures for newly sequenced genomes to train ab initio gene prediction algorithms. In the study, we present the building reliable training gene set(BRTGS) computational method for building reliable training gene set from RNA-seq assembly. Firstly, the initial gene structures are obtained from RNA-seq assembly. Then, the gene structures with complete and correct coding region are identified with the alignments of transcripts against homology protein. Finally, the sites of start and stop codon are determined according to the homology evidences and RNA-seq assembly structures. Experimental results show that BRTGS can build high-quality of training gene set for various genomes and ab initio algorithms trained on the gene sets can obtain good prediction performance.

Abstract:In order to solve the problems that the intrinsic mode function(IMF) components are difficult to select and the noise components are always eliminated directly when removing the noise of the electrocardiogram(ECG) signal by using the ensemble empirical mode decomposition(EEMD) method, an adaptive thresholding algorithm based on EEMD is proposed. Firstly, the noisy ECG signal is decomposed to obtain the IMFs by the EEMD method, and then the noise IMFs and the siginal IMFs are judged according to the Mahalanobis distance. After that, the thresholding of the niose IMF is determined using the fruit fly optimization algorithm(FOA). The denoised ECG signals are reconstructed by the new IMFs and the rest of IMFs after thresholding denoising. Finally, the method is applied to ECG data in MIT-BIH database. The experimental results indicate that the method can preserve the signal details while denoising.

Abstract:Colorectal cancer is one of the common malignant tumors in digestive system, and its mortality rate is third of malignant tumor mortality in developed countries. The aim of this paper is to identify the pathogenic gene of colorectal cancer through biological analysis and data mining. Firstly, the expression spectrum dataset GSE9348 is downloaded from GEO database, and 339 differentially expressed genes are screened with P<0.05 and Fold change>2 in colorectal cancer by using LIMMA Package in R language. Secondly, based on the known disease genes of colorectal cancer, OMIM database and STRING database, the PPI network composed by the differentially expressed genes and known disease genes is obtained. Furthermore, the network module analysis is performed through ClusterONE plugin of Cytoscape software, and a subnetwork containing 53 genes is obtained. Finally, through network topology analysis, 5 candidate genes of colorectal cancer are considered to be candidate disease genes of colorectal cancer, including CCND1, EGR1, FOS, CEBPB and NOS3. Simultaneously, the newly discovered genes are verified by using the functional enrichment analysis and literature mining.

Abstract:Bionic optimization algorithm is a kind of bionic algorithm that imitates biological behaviors and natural phenomenon, whose aim is to find out the optimal solution of the target problem. In the paper, the origin and basic principles of various bionic optimization algorithms are first introduced, including ant colony algorithm, particle swarm algorithm, bacteria foraging algorithm, bee colony algorithm, fish swarm algorithm, glowworm swarm algorithm, wolf pack algorithm, bat algorithm, chicken swarm algorithm, evolutionary algorithm, immune algorithm, clonal selection algorithm, small-world network, and so on. Then, the state of the art of bionic optimization algorithms is reviewed, and some typical applications of bionic optimization algorithms in signal processing, image processing, speech processing and communication network are given. Finally, the features of bionic optimization algorithms are summarized, the basic ideas on how to extend their applications and explore new bionic optimization algorithms are presented, and the future developments of bionic optimization algorithms are also prospected.

Abstract:Aiming at the motion features of high speed moving target in space, by analyzing the range migration trajectory (RMT) and the equivalent motion model of the target, a RMT-based joint motion compensation algorithm is proposed in this paper. In the algorithm, the translational parameters of the target are estimated from RMT according to the global entropy minimization principle. Then the range profile offset is compensated and one dimensional range alignment distortion is corrected by using these translational parameters, which can realize the joint motion compensation of range alignment and intra pulse moving for the space target echo. Simulational and experimental results show that the proposed algorithm has high accuracy. What's more, ransom offset error and phase error cannot be introduced in the range alignment procedure, which is the precondition of high-resolution imaging method application.

Abstract:The co-location pattern mining discovers the subsets of spatial features which are located together frequently in geography. Instance independence has been taken as a major assumption in the co-location mining based on prevalence framework. However, in real-world spatial data sources, spatial instances are more or less correlated with each other. Prevalence-based framework can do limited work in spatial instance correlated analysis. For reducing the co-location mining results and promoting the usability of co-location patterns, this paper proposed a new framework to identify the co-location patterns with key features and extract the key features from a large collection of prevalent co-location patterns. We first give the definitions of significant co-location patterns; secondly, we design a series of metrics to evaluate significance of co-location patterns and extract the key features; thirdly, an efficient algorithm is proposed to mine the significant co-location pattern with key features. The experiments evaluate the method both on real data sets and synthetic data sets. The results show that our method can effectively identify the significant co-location patterns with key features.

Abstract:Aiming at the universal problem of data sparsity and cold start in recommendation system, a social network recommendation method based on the combination of social label and trust relation is proposed in the paper. The proposed method uses the probabilistic factorization technique to integrate the social trust, item marking information and user item score matrix. The interconnection of users and potential item feature space is realized from different dimensions. On this basis, the realization of dimension reduction by the factorization of probability matrix can achieve effective social recommendation. Experimental results on Epinions and Movielens data sets show that the proposed method is superior to traditional social recommendation and social label recommendation algorithms, especially in the case of less user score data.

Abstract:In the fuzzy decision table, the tolerance relation is built based on the ordered weighted averaging (OWA) operatorand, and OWA operator is a basic tool of information integration. Based on the OWA operator and its induced tolerance relation of cut sets, attribute reductions of the positive region, negative region and boundary region are discussed. Firstly, an attribute contribution degree distinguishing objects is defined by the weight of the OWA operator, to describe the reduction possibility of attributes. Consequently, a method of three-way attribute reduction based on the reduction possibility is proposed in the fuzzy decision table. The method reduces the search space and avoids reduction blindness. Finally, an example is analyzed to show the influencing causes of attribute reductions and the validity of the proposed method. The proposed measurement method of attribute contribution degree and the heuristic method of attribute reduction based on the three regions will reduce the searching space of attribute reduction, and thus they are benefit for applications of the fuzzy decision table.

Abstract:Considering that the conventional associations may not be appropriate due to the special characteristics of heterogeneous cellular networks (HCNs), some novel associations that can balance the network loads should be designed. To reflect the resource consumption levels of users and balance loads, a QoS-aware user association is proposed for load balancing in HCNs. In the association, the load is defined as the amount of consumed resources, which can reflect the resource consumption levels of users. Finally, this scheme is formulated as a network-wide weighted utility maximization problem. That is a nonlinear mixed-integer optimization problem, and obtaining its optimal solutions is very challenging (especially for large-scale problems). To solve the problem, a distributed algorithm is designed using dual decomposition. Numerical results show that, compared with the best power association and range expansion association, the proposed scheme has higher load balancing levels and lower call blocking probabilities.

Abstract:For the dual-hop relay system of unmanned aerial vehicle (UAV), a propagation loss model of dual-hop relay link is established. The influence of fuselage feature, antenna characteristics, climate condition and channel fading are considered. Based on the model, the calculating methods of propagation loss are analyzed. Then the statistical characteristics of propagation loss with random fluctuation caused by composite fading including shadowing and multipath fading are derived. Furthermore, the theoretical expression of the outage probability and bit error rate (BER) of dual-hop UAV relay system is developed. Finally, through the numerical simulations in urban, mountainous and sea, the theoretical results accuracy obtained is verified and the impact of flight altitude, ground node position and communication scenarios on the performance of the dual-hop relay system is discussed.

Abstract:The ability of image features expression with the dictionary designed by artificial shallow features is limited in dictionary based single image super-resolution reconstruction algorithm. For the reason, an image super resolution reconstruction algorithm based on deep learning and feature dictionary is proposed. Firstly, deep-level feature learning is carried out in high and low resolution training sample images by using deep network. Secondly, the feature dictionary is trained with the combination of sparse coding under the sparse dictionary super resolution frame. Finally, a low resolution image is put in and the super resolution reconstruction is realized by using the dictionary. Theoretical analysis shows that the combination of image deep-level feature extraction and dictionary training by using deep network is more beneficial to high frequency information supplement for low resolution image. Experimental results show that compared with bicubic interpolation and other general artificial feature dictionary based super resolution reconstruction algorithms, the proposed algorithm has better subjective visual and objective evaluation indices.

Abstract:The lack of much labeled data in the real world affects the training of supervised model for pedestrian re-identification. Besides, applications of pedestrian re-identification in pedestrian retrieving and criminal tracking are limited because of the lack of semantic representation. This paper presents a pedestrian re-identification method based on the combination of deep learning and attributes learning, which extracts essential features with unsupervised deep learning model and enhances the semantic representation of features with ‘attributes’. Firstly, a convolutional auto-encoder (CAE) is used to extract features of unlabeled pedestrian images, and the extracted features are then input into several attribute classifiers to judge whether the pedestrian owns the attributes. Lastly, with a table of ‘attributes-classes mapping relations’, we can get the final dassification result. Tests of the proposed algorithm and comparisons with other algorithms on the VIPeR and i-LIDS datasets are shown, and results prove that our algorithm indeed strengthens the semantic representation and improves the accuracy of pedestrian re-identification, achieving good ‘zero-shot’ re-identification performance as well.

Abstract:Since the different parts of multimode data belong to multiple modes and there are complementary relationships between them, the traditional analysis and processing techniques cannot fuse the data from different modes and their relationships. In order to solve the problems of modeling, representation and storage of multimode data, a new data model of modal structure graph is proposed to model the multimode medical data. The model utilizes the graph structure to model and represent the modes and their relationships in the multimode data. And based on the model, the paper proposes a parallel loading algorithm, which can extract the data of different modes and relationships, and store them in a graph database. Experimental results by using batch medical data files show that the proposed algorithm can obtain good data processing efficiency.