Vol.60, No.3, 2019-Table of Contents
  • Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine
  • Abstract An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction… More
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  • Dynamic Analysis of a Horizontal Oscillatory Cutting Brush
  • Abstract Street sweeping is an important public service, as it has an impact on aesthetics and public health. Typically, sweeping vehicles have a gutter brush that sweeps the debris that lies in the road gutter. As most of the debris is located in the gutter, the effective operation of the gutter brush is important. The aim of this work is to study the performance of a type of gutter brush, the cutting brush, through a 3D dynamic (transient), large deflection finite element model developed by the authors. In this brush model, the brush mounting board is modelled as fixed, and, consequently,… More
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  • Localization Based Evolutionary Routing (LOBER) for Efficient Aggregation in Wireless Multimedia Sensor Networks
  • Abstract Efficient aggregation in wireless sensor nodes helps reduce network traffic and reduce energy consumption. The objective of this work Localization Based Evolutionary Routing (LOBER) is to achieve global optimization for aggregation and WMSN lifetime. Improved localization is achieved by a novel Centroid Based Octant Localization (CBOL) technique considering an arbitrary hexagonal region. Geometric principles of hexagon are used to locate the unknown nodes in the centroid positions of partitioned regions. Flower pollination algorithm, a meta heuristic evolutionary algorithm that is extensively applied in solving real life, complex and nonlinear optimization problems in engineering and industry is modified as Enhanced Flower… More
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  • Failure Prediction, Lead Time Estimation and Health Degree Assessment for Hard Disk Drives Using Voting Based Decision Trees
  • Abstract Hard Disk drives (HDDs) are an essential component of cloud computing and big data, responsible for storing humongous volumes of collected data. However, HDD failures pose a huge challenge to big data servers and cloud service providers. Every year, about 10% disk drives used in servers crash at least twice, lead to data loss, recovery cost and lower reliability. Recently, the researchers have used SMART parameters to develop various prediction techniques, however, these methods need to be improved for reliability and real-world usage due to the following factors: they lack the ability to consider the gradual change/deterioration of HDDs; they… More
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  • High Precision SAR ADC Using CNTFET for Internet of Things
  • Abstract A high precision 10-bit successive approximation register analog to digital converter (ADC) designed and implemented in 32nm CNTFET process technology at the supply of 0.6V, with 73.24 dB SNDR at a sampling rate of 640 MS/s with the average power consumption of 120.2 μW for the Internet of things node. The key components in CNTFET SAR ADCs are binary scaled charge redistribution digital to analog converter using MOS capacitors, CNTFET based dynamic latch comparator and simple SAR digital code error correction logic. These techniques are used to increase the sampling rate and precision while ensuring the linearity, power consumption and… More
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  • An Efficient Greedy Traffic Aware Routing Scheme for Internet of Vehicles
  • Abstract A new paradigm of VANET has emerged in recent years: Internet of Vehicles (IoV). These networks are formed on the roads and streets between travellers who have relationships, interactions and common social interests. Users of these networks exchange information of common interest, for example, traffic jams and dangers on the way. They can also exchange files such as multimedia files. IoV is considered as part of the Internet of Things (IoT) where objects are vehicles, which can create a multitude of services dedicated to the intelligent transportation system. The interest is to permit to all connected vehicles to communicate with… More
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  • GaiaWorld: A Novel Blockchain System Based on Competitive PoS Consensus Mechanism
  • Abstract The birth of blockchain has promoted the development of electronic currencies such as Bitcoin and Ethereum. Blockchain builds a financial system based on cryptology instead of credit, which allows parties to complete the transaction on their own without the need for credible third-party intermediaries. So far, the application scenario of blockchain is mainly confined to the peer-to-peer electronic financial system, which obviously does not fully utilize the potential of blockchain.
    In this paper, we introduce GaiaWorld, a new system for decentralized application. To solve the problem of resource waste and mismatch between nodes and computing power in traditional PoW… More
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  • Research on Sensor Network Coverage Enhancement Based on Non-Cooperative Games
  • Abstract Coverage is an important issue for resources rational allocation, cognitive tasks completion in sensor networks. The mobility, communicability and learning ability of smart sensors have received much attention in the past decade. Based on the deep study of game theory, a mobile sensor non-cooperative game model is established for the sensor network deployment and a local information-based topology control (LITC) algorithm for coverage enhancement is proposed. We both consider revenue of the monitoring events and neighboring sensors to avoid nodes aggregation when formulating the utility function. We then prove that the non-cooperative game is an exact potential game in which… More
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  • A Recommendation System Based on Fusing Boosting Model and DNN Model
  • Abstract In recent years, the models combining traditional machine learning with the deep learning are applied in many commodity recommendation practices. It has been proved better performance by the means of the neural network. Feature engineering has been the key to the success of many click rate estimation model. As we know, neural networks are able to extract high-order features automatically, and traditional linear models are able to extract low-order features. However, they are not necessarily efficient in learning all types of features. In traditional machine learning, gradient boosting decision tree is a typical representative of the tree model, which can… More
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  • Research on Public Opinion Propagation Model in Social Network Based on Blockchain
  • Abstract With the emergence and development of blockchain technology, a new type of social networks based on blockchain had emerged. In these social networks high quality content creators, filters and propagators can all be reasonably motivated. Due to the transparency and traceability brought by blockchain technology, the public opinion propagation in such social networks presents new characteristics and laws. Based on the theory of network propagation and blockchain, a new public opinion propagation model for this kind of social network based on blockchain technology is proposed in this paper. The model considers the effect of incentive mechanism produced by reasonably quantifying… More
  •   Views:1407       Downloads:933        Download PDF
  • Non-Contact Real-Time Heart Rate Measurement Algorithm Based on PPG-Standard Deviation
  • Abstract Heart rate is an important physiological parameter for clinical diagnosis, it can infer the health of the human body. Thus, efficient and accurate heart rate measurement is important for disease diagnosis and health monitoring. There are two ways to measure heart rate. One is contact type and the other is non-contact. Contact measurement methods include pulse cutting, electrocardiogram, etc. Because of the inconvenience of this method, a non-contact heart rate method has been proposed. Traditional non-contact measurement method based on image is collecting RGB three-channel signals in continuous video and selecting the average value of the green channel pixels as… More
  •   Views:1321       Downloads:733        Download PDF
  • Attention-Aware Network with Latent Semantic Analysis for Clothing Invariant Gait Recognition
  • Abstract Gait recognition is a complicated task due to the existence of co-factors like carrying conditions, clothing, viewpoints, and surfaces which change the appearance of gait more or less. Among those co-factors, clothing analysis is the most challenging one in the area. Conventional methods which are proposed for clothing invariant gait recognition show the body parts and the underlying relationships from them are important for gait recognition. Fortunately, attention mechanism shows dramatic performance for highlighting discriminative regions. Meanwhile, latent semantic analysis is known for the ability of capturing latent semantic variables to represent the underlying attributes and capturing the relationships from… More
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  • An Integrated Suture Simulation System with Deformation Constraint Under A Suture Control Strategy
  • Abstract Current research on suture simulation mainly focus on the construction of suture line, and existing suture simulation systems still need to be improved in terms of diversity, soft tissue effects, and stability. This paper presents an integrated liver suture surgery system composed of three consecutive suture circumstances, which is conducive to liver suture surgery training. The physically-based models used in this simulation are based on different mass-spring models regulated by a special constrained algorithm, which can improve the model accuracy, and stability by appropriately restraining the activity sphere of the surrounding mass nodes around the suture points. We also studied… More
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  • Few-Shot Learning with Generative Adversarial Networks Based on WOA13 Data
  • Abstract In recent years, extreme weather events accompanying the global warming have occurred frequently, which brought significant impact on national economic and social development. The ocean is an important member of the climate system and plays an important role in the occurrence of climate anomalies. With continuous improvement of sensor technology, we use sensors to acquire the ocean data for the study on resource detection and disaster prevention, etc. However, the data acquired by the sensor is not enough to be used directly by researchers, so we use the Generative Adversarial Network (GAN) to enhance the ocean data. We use GAN… More
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  • Researching the Link Between the Geometric and Rènyi Discord for Special Canonical Initial States Based on Neural Network Method
  • Abstract Quantum correlation which is different to the entanglement and classical correlation plays important role in quantum information field. In our setup, neural network method is adopted to simulate the link between the Rènyi discord (α = 2) and the geometric discord (Bures distance) for special canonical initial states in order to show the consistency of physical results for different quantification methods. Our results are useful for studying the differences and commonalities of different quantizing methods of quantum correlation. More
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  • An Adaptive Superpixel Tracker Using Multiple Features
  • Abstract Visual tracking is a challenging issue in the field of computer vision due to the objects’ intricate appearance variation. To adapt the change of the appearance, multiple channel features which could provide more information are used. However, the low level feature could not represent the structure of the object. In this paper, a superpixel-based adaptive tracking algorithm by using color histogram and haar-like feature is proposed, whose feature is classified into the middle level. Based on the superpixel representation of video frames, the haar-like feature is extracted at the superpixel level as the local feature, and the color histogram feature… More
  •   Views:966       Downloads:746        Download PDF
  • Data Based Violated Behavior Analysis of Taxi Driver in Metropolis in China
  • Abstract Violation probability of taxi drivers in metropolis is far more than that of normal drivers because they are labor-intensive, overconfident of self-driving skill, and always searching potential customers, sometimes even picking up or dropping off passengers randomly. In this paper, four types of violated behavior of taxi drivers in metropolis were first summarized, based on which corresponding scale table was initial designed with social statistical method. Furthermore, with certain samples, relative item analysis, exploratory factor analysis, validity analysis and reliability analysis were conducted to verify validity of the initial scale table, based on which some improvements were made, and we… More
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  • Analyzing Dynamic Change in Social Network Based on Distribution-Free Multivariate Process Control Method
  • Abstract Social organizations can be represented by social network because it can mathematically quantify and represent complex interrelated organizational behavior. Exploring the change in dynamic social network is essential for the situation awareness of the corresponding social organization. Social network usually evolves gradually and slightly, which is hard to be noticed. The statistical process control techniques in industry field have been used to distinguish the statistically significant change of social network. But the original method is narrowed due to some limitation on measures. This paper presents a generic framework to address the change detection problem in dynamic social network and introduces… More
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  • An Efficient Crossing-Line Crowd Counting Algorithm with Two-Stage Detection
  • Abstract Crowd counting is a challenging task in crowded scenes due to heavy occlusions, appearance variations and perspective distortions. Current crowd counting methods typically operate on an image patch level with overlaps, then sum over the patches to get the final count. In this paper we describe a real-time pedestrian counting framework based on a two-stage human detection algorithm. Existing works with overhead cameras is mainly based on visual tracking, and their robustness is rather limited. On the other hand, some works, which focus on improving the performances, are too complicated to be realistic. By adopting a line sampling process, a… More
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  • A Method of Obtaining Catchment Basins with Contour Lines for Foam Image Segmentation
  • Abstract Foam image segmentation, represented by watershed algorithm, is wildly used in the extraction of bubble morphology features. H-minima transformation was proved to be effective in locating the catchment basins in the traditional watershed segmentation method. To further improve the accuracy of watershed segmentation, method of top-bottom-cap filters and method of morphological reconstruction were implied to marking the catchment basins. In this paper, instead of H-minima transformation, a method of contour lines is specially proposed to obtain the catchment basins for foam image segmentation by using top-bottom-cap filters and less morphological reconstruction. Experimental results in foam segmentation show that the proposed… More
  •   Views:1097       Downloads:782        Download PDF
  • YATA: Yet Another Proposal for Traffic Analysis and Anomaly Detection
  • Abstract Network traffic anomaly detection has gained considerable attention over the years in many areas of great importance. Traditional methods used for detecting anomalies produce quantitative results derived from multi-source information. This makes it difficult for administrators to comprehend and deal with the underlying situations. This study proposes another method to yet determine traffic anomaly (YATA), based on the cloud model. YATA adopts forward and backward cloud transformation algorithms to fuse the quantitative value of acquisitions into the qualitative concept of anomaly degree. This method achieves rapid and direct perspective of network traffic. Experimental results with standard dataset indicate that using… More
  •   Views:1006       Downloads:667        Download PDF
  • Three-Dimensional Numerical Analysis of Blast-Induced Damage Characteristics of the Intact and Jointed Rockmass
  • Abstract This article reports numerical results investigating the damage evolution and spatial distribution characteristics of intact and jointed rockmass subjected to blast loading. The behaviors of rock material are described by the Holmquist- Johnson-Cook (HJC) constitutive model incorporated in the finite element software LS-DYNA. Results indicate that the damage distribution shows a reverse S-shape attenuation with the increase of the distance from borehole, and a better goodness of fit with the Logistic function is observed. In the single-hole blasting of jointed rockmass, there are two types of regions around the intersection of borehole and joint in which the damage degree is… More
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  • Sentiment Analysis Method Based on Kmeans and Online Transfer Learning
  • Abstract Sentiment analysis is a research hot spot in the field of natural language processing and content security. Traditional methods are often difficult to handle the problems of large difference in sample distribution and the data in the target domain is transmitted in a streaming fashion. This paper proposes a sentiment analysis method based on Kmeans and online transfer learning in the view of fact that most existing sentiment analysis methods are based on transfer learning and offline transfer learning. We first use the Kmeans clustering algorithm to process data from one or multiple source domains and select the data similar… More
  •   Views:1106       Downloads:2256        Download PDF
  • Tibetan Multi-Dialect Speech and Dialect Identity Recognition
  • Abstract Tibetan language has very limited resource for conventional automatic speech recognition so far. It lacks of enough data, sub-word unit, lexicons and word inventories for some dialects. And speech content recognition and dialect classification have been treated as two independent tasks and modeled respectively in most prior works. But the two tasks are highly correlated. In this paper, we present a multi-task WaveNet model to perform simultaneous Tibetan multi-dialect speech recognition and dialect identification. It avoids processing the pronunciation dictionary and word segmentation for new dialects, while, in the meantime, allows training speech recognition and dialect identification in a single… More
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  • Privacy-Preserving Quantum Two-Party Geometric Intersection
  • Abstract Privacy-preserving computational geometry is the research area on the intersection of the domains of secure multi-party computation (SMC) and computational geometry. As an important field, the privacy-preserving geometric intersection (PGI) problem is when each of the multiple parties has a private geometric graph and seeks to determine whether their graphs intersect or not without revealing their private information. In this study, through representing Alice’s (Bob’s) private geometric graph GA (GB) as the set of numbered grids SA (SB), an efficient privacy-preserving quantum two-party geometric intersection (PQGI) protocol is proposed. In the protocol, the oracle operation OA (OB) is firstly utilized… More
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  • Distant Supervised Relation Extraction with Cost-Sensitive Loss
  • Abstract Recently, many researchers have concentrated on distant supervision relation extraction (DSRE). DSRE has solved the problem of the lack of data for supervised learning, however, the data automatically labeled by DSRE has a serious problem, which is class imbalance. The data from the majority class obviously dominates the dataset, in this case, most neural network classifiers will have a strong bias towards the majority class, so they cannot correctly classify the minority class. Studies have shown that the degree of separability between classes greatly determines the performance of imbalanced data. Therefore, in this paper we propose a novel model, which… More
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  • A DDoS Attack Situation Assessment Method via Optimized Cloud Model Based on Influence Function
  • Abstract The existing network security situation assessment methods cannot effectively assess the Distributed denial-of-service (DDoS) attack situation. In order to solve these problems, we propose a DDoS attack situation assessment method via optimized cloud model based on influence function. Firstly, according to the state change characteristics of the IP addresses which are accessed by new and old user respectively, this paper defines a fusion feature value. Then, based on this value, we establish a V-Support Vector Machines (V-SVM) classification model to analyze network flow for identifying DDoS attacks. Secondly, according to the change of new and old IP addresses, we propose… More
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  • An Improved End-to-End Memory Network for QA Tasks
  • Abstract At present, End-to-End trainable Memory Networks (MemN2N) has proven to be promising in many deep learning fields, especially on simple natural language-based reasoning question and answer (QA) tasks. However, when solving some subtasks such as basic induction, path finding or time reasoning tasks, it remains challenging because of limited ability to learn useful information between memory and query. In this paper, we propose a novel gated linear units (GLU) and local-attention based end-to-end memory networks (MemN2N-GL) motivated by the success of attention mechanism theory in the field of neural machine translation, it shows an improved possibility to develop the ability… More
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