Vol.63, No.1, 2020-Table of Contents
  • A Multi-Level Threshold Method for Edge Detection and Segmentation Based on Entropy
  • Abstract The essential tool in image processing, computer vision and machine vision is edge detection, especially in the fields of feature extraction and feature detection. Entropy is a basic area in information theory. The entropy, in image processing field has a role associated with image settings. As an initial step in image processing, the entropy is always used the image’s segmentation to determine the regions of image which is used to separate the background and objects in image. Image segmentation known as the process which divides the image into multiple regions or sets of pixels. Many applications have been development to… More
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  • Left or Right Hand Classification from Fingerprint Images Using a Deep Neural Network
  • Abstract Fingerprint security technology has attracted a great deal of attention in recent years because of its unique biometric information that does not change over an individual’s lifetime and is a highly reliable and secure way to identify a certain individuals. AFIS (Automated Fingerprint Identification System) is a system used by Korean police for identifying a specific person by fingerprint. The AFIS system, however, only selects a list of possible candidates through fingerprints, the exact individual must be found by fingerprint experts. In this paper, we designed a deep learning system using deep convolution network to categorize fingerprints as coming from… More
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  • A Mobile Cloud-Based eHealth Scheme
  • Abstract Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, it is uploaded into the… More
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  • Agglomeration Effects on Static Stability Analysis of Multi-Scale Hybrid Nanocomposite Plates
  • Abstract We propose a multiscale approach to study the influence of carbon nanotubes’ agglomeration on the stability of hybrid nanocomposite plates. The hybrid nanocomposite consists of both macro- and nano-scale reinforcing fibers dispersed in a polymer matrix. The equivalent material properties are calculated by coupling the Eshelby-Mori-Tanaka model with the rule of mixture accounting for effects of CNTs inside the generated clusters. Furthermore, an energy based approach is implemented to obtain the governing equations of the problem utilizing a refined higher-order plate theorem. Subsequently, the derived equations are solved by Galerkin’s analytical method to predict the critical buckling load. The influence… More
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  • Numerical Analysis of Stochastic Vector Borne Plant Disease Model
  • Abstract We are associating the solutions of stochastic and deterministic vector borne plant disease model in this manuscript. The dynamics of plant model depends upon threshold number P. If P <1 then condition helpful to eradicate the disease in plants while P >1 explains the persistence of disease. Inappropriately, standard numerical systems do not behave well in certain scenarios. We have been proposed a structure preserving stochastic non-standard finite difference system to analyze the behavior of model. This system is dynamical consistent, positive and bounded as defined by Mickens. More
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  • Enhanced Portable LUT Multiplier with Gated Power Optimization for Biomedical Therapeutic Devices
  • Abstract Digital design of a digital signal processor involves accurate and high-speed mathematical computation units. DSP units are one of the most power consuming and memory occupying devices. Multipliers are the common building blocks in most of the DSP units which demands low power and area constraints in the field of portable biomedical devices. This research works attempts multiple power reduction technique to limit the power dissipation of the proposed LUT multiplier unit. A lookup table-based multiplier has the advantage of almost constant area requirement’s irrespective to the increase in bit size of multiplier. Clock gating is usually used to reduce… More
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  • A Cryptographic-Based Approach for Electricity Theft Detection in Smart Grid
  • Abstract In order to strengthen their security issues, electrical companies devote particular efforts to developing and enhancing their fraud detection techniques that cope with the information and communication technologies integration in smart grid fields. Having been treated earlier by several researchers, various detection schemes adapted from attack models that benefit from the smart grid topologies weaknesses, aiming primarily to the identification of suspicious incoming hazards. Wireless meshes have been extensively used in smart grid communication architectures due to their facility, lightness of conception and low cost installation; however, the communicated packets are still exposed to be intercepted maliciously in order either… More
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  • Cooperative Relay Selection Mechanism in Multi-Hop Networks
  • Abstract In this paper, we consider a three-hop relay system based on interference cancellation technique in Underlay cognitive radio (CR) network. Although underlay CR has been shown as a promising technique to better utilize the source of primary users (PUs), its secondary performance will be severely degraded. On one hand, by adapting the Underlay spectrum sharing pattern, secondary users (SUs) would observe the strict power constraints and be interfered by primary users. On the other hand, limited transmit power results in limited transmission range, which greatly degrade the secondary transmission capacity. To solve the problems above, we propose an interference cancellation… More
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  • A DDoS Attack Information Fusion Method Based on CNN for Multi-Element Data
  • Abstract Traditional distributed denial of service (DDoS) detection methods need a lot of computing resource, and many of them which are based on single element have high missing rate and false alarm rate. In order to solve the problems, this paper proposes a DDoS attack information fusion method based on CNN for multi-element data. Firstly, according to the distribution, concentration and high traffic abruptness of DDoS attacks, this paper defines six features which are respectively obtained from the elements of source IP address, destination IP address, source port, destination port, packet size and the number of IP packets. Then, we propose… More
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  • Coal Rock Condition Detection Model Using Acoustic Emission and Light Gradient Boosting Machine
  • Abstract Coal rock mass instability fracture may result in serious hazards to underground coal mining. Acoustic emissions (AE) stimulated by internal structure fracture should carry lots of favorable information about health condition of rock mass. AE as a sensitive non-destructive test method is gradually utilized to detect anomaly conditions of coal rock. This paper proposes an improved multi-resolution feature to extract AE waveform at different frequency resolutions using Coilflet Wavelet Transform method (CWT). It is further adopt an efficient Light Gradient Boosting Machine (LightGBM) by several cascaded sub weak classifier models to merge AE features at different views of frequency for… More
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  • Predicting Simplified Thematic Progression Pattern for Discourse Analysis
  • Abstract The pattern of thematic progression, reflecting the semantic relationships between contextual two sentences, is an important subject in discourse analysis. We introduce a new corpus of Chinese news discourses annotated with thematic progression information and explore some computational methods to automatically extracting the discourse structural features of simplified thematic progression pattern (STPP) between contextual sentences in a text. Furthermore, these features are used in a hybrid approach to a major discourse analysis task, Chinese coreference resolution. This novel approach is built up via heuristic sieves and a machine learning method that comprehensively utilizes both the top-down STPP features and the… More
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  • A Novel Steganography Algorithm Based on Instance Segmentation
  • Abstract Information hiding tends to hide secret information in image area where is rich texture or high frequency, so as to transmit secret information to the recipient without affecting the visual quality of the image and arousing suspicion. We take advantage of the complexity of the object texture and consider that under certain circumstances, the object texture is more complex than the background of the image, so the foreground object is more suitable for steganography than the background. On the basis of instance segmentation, such as Mask R-CNN, the proposed method hides secret information into each object's region by using the… More
  •   Views:836       Downloads:390        Download PDF
  • Performance Analysis of Relay Based NOMA Cooperative Transmission under Cognitive Radio Network
  • Abstract This paper proposes a hybrid spectrum accessing mechanism by using NOMA-based cooperative transmission and beam-forming technology. In this mechanism, the secondary user employs spectrum-sensing technology to detect the existence of the primary user. If the primary user does not exist, the secondary source user directly transmits data to the destination user. If the primary user exists, the secondary source user finds the optimal relay according to certain selection principle before transmitting data to the destination user through the chosen relay node. For the signal receiving stage, the secondary user takes use of beam-forming technology to receive the signal from both… More
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  • Efficient Heavy Hitters Identification over Speed Traffic Streams
  • Abstract With the rapid increase of link speed and network throughput in recent years, much more attention has been paid to the work of obtaining statistics over speed traffic streams. It is a challenging problem to identify heavy hitters in high-speed and dynamically changing data streams with less memory and computational overhead with high measurement accuracy. In this paper, we combine Bloom Filter with exponential histogram to query streams in the sliding window so as to identify heavy hitters. This method is called EBF sketches. Our sketch structure allows for effective summarization of streams over time-based sliding windows with guaranteed probabilistic… More
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  • A Differentially Private Data Aggregation Method Based on Worker Partition and Location Obfuscation for Mobile Crowdsensing
  • Abstract With the popularity of sensor-rich mobile devices, mobile crowdsensing (MCS) has emerged as an effective method for data collection and processing. However, MCS platform usually need workers’ precise locations for optimal task execution and collect sensing data from workers, which raises severe concerns of privacy leakage. Trying to preserve workers’ location and sensing data from the untrusted MCS platform, a differentially private data aggregation method based on worker partition and location obfuscation (DP-DAWL method) is proposed in the paper. DP-DAWL method firstly use an improved K-means algorithm to divide workers into groups and assign different privacy budget to the group… More
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  • Human Action Recognition Based on Supervised Class-Specific Dictionary Learning with Deep Convolutional Neural Network Features
  • Abstract Human action recognition under complex environment is a challenging work. Recently, sparse representation has achieved excellent results of dealing with human action recognition problem under different conditions. The main idea of sparse representation classification is to construct a general classification scheme where the training samples of each class can be considered as the dictionary to express the query class, and the minimal reconstruction error indicates its corresponding class. However, how to learn a discriminative dictionary is still a difficult work. In this work, we make two contributions. First, we build a new and robust human action recognition framework by combining… More
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  • Reliability Analysis of Slope Stability Considering Temporal Variations of Rock Mass Properties
  • Abstract Temporal variation of rock mass properties, especially the strength degradation due to drying-wetting cycles as well as the acidic wetting fluid (rainfall or reservoir water) is crucial to stability of reservoir rock slopes. Based on a series of drying-wetting cycling and experiments considering the influences of pH values, the degradation degree models of the reduced cohesion c′, friction angle φ′ are developed. 2D stability analysis of the slope is subsequently carried out to calculate the factor of safety (Fs) via limit equilibrium method (LEM) and a predictive model of Fs is built using multivariate adaptive regression splines (MARS), revealing the… More
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  • A Rub-Impact Recognition Method Based on Improved Convolutional Neural Network
  • Abstract Based on the theory of modal acoustic emission (AE), when the convolutional neural network (CNN) is used to identify rotor rub-impact faults, the training data has a small sample size, and the AE sound segment belongs to a single channel signal with less pixel-level information and strong local correlation. Due to the convolutional pooling operations of CNN, coarse-grained and edge information are lost, and the top-level information dimension in CNN network is low, which can easily lead to overfitting. To solve the above problems, we first propose the use of sound spectrograms and their differential features to construct multi-channel image… More
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  • An Efficient and Practical Quantum Blind Signature Protocol with Relaxed Security Model
  • Abstract Blind signature has a wide range of applications in the fields of E-commerce and block-chain because it can effectively prevent the blind signer from getting the original message with its blindness. Owing to the potential unconditional security, quantum blind signature (QBS) is more advantageous than the classical ones. In this paper, an efficient and practical quantum blind signature scheme relaxed security model is presented, where quantum superposition, decoy qubits and hash function are used for the purpose of blindness. Compared with previous QBS scheme, the presented scheme is more efficient and practical with a relaxed security model, in which the… More
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  • TdBrnn: An Approach to Learning Users’ Intention to Legal Consultation with Normalized Tensor Decomposition and Bi-LSTM
  • Abstract With the development of Internet technology and the enhancement of people’s concept of the rule of law, online legal consultation has become an important means for the general public to conduct legal consultation. However, different people have different language expressions and legal professional backgrounds. This phenomenon may lead to the phenomenon of different descriptions of the same legal consultation. How to accurately understand the true intentions behind different users’ legal consulting statements is an important issue that needs to be solved urgently in the field of legal consulting services. Traditional intent understanding algorithms rely heavily on the lexical and semantic… More
  •   Views:843       Downloads:341        Download PDF
  • Cold Start Problem of Vehicle Model Recognition under Cross-Scenario Based on Transfer Learning
  • Abstract As a major function of smart transportation in smart cities, vehicle model recognition plays an important role in intelligent transportation. Due to the difference among different vehicle models recognition datasets, the accuracy of network model training in one scene will be greatly reduced in another one. However, if you don’t have a lot of vehicle model datasets for the current scene, you cannot properly train a model. To address this problem, we study the problem of cold start of vehicle model recognition under cross-scenario. Under the condition of small amount of datasets, combined with the method of transfer learning, load… More
  •   Views:810       Downloads:339        Download PDF
  • An Energy Based Dynamic AODV Routing Protocol in Wireless Ad Hoc Networks
  • Abstract In recent years, with the rapid development of the Internet and wireless communication technology, wireless Ad hoc networks have received more attention. Due to the limited transmission range and energy of nodes in Ad hoc networks, it is important to establish a reliable and energy-balanced transmission path in Ad hoc networks. This paper proposes an energy-based dynamic routing protocol based on the existing AODV routing protocol, which has the following two aspects of improvement: (1) In the route discovery process, a node selects a suitable route from the minimum energy consumption route and the energy-balanced route designed in this paper… More
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  • Measurement Device Independent Quantum Key Distribution Based on Orbital Angular Momentum under Parametric Light Source
  • Abstract On the one hand, existing measurement device independent quantum key distribution (MDI-QKD) protocols have usually adopted single photon source (SPS) and weak coherent photon (WCP), however, these protocols have suffered from multi-photon problem brought from photon splitter number attacks. On the other hand, the orbital angular momentum (OAM)-MDI-QKD protocol does not need to compare and adjust the reference frame, solving the dependency of the base in the MDI-QKD protocol. Given that, we propose the OAM-MDI-QKD protocol based on the parametric light sources which mainly include single-photon-added-coherent (SPACS) and heralded single-photon sources (HSPS). Due to the stability of OAM and the… More
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  • An Encrypted Image Retrieval Method Based on SimHash in Cloud Computing
  • Abstract With the massive growth of images data and the rise of cloud computing that can provide cheap storage space and convenient access, more and more users store data in cloud server. However, how to quickly query the expected data with privacy-preserving is still a challenging in the encryption image data retrieval. Towards this goal, this paper proposes a ciphertext image retrieval method based on SimHash in cloud computing. Firstly, we extract local feature of images, and then cluster the features by K-means. Based on it, the visual word codebook is introduced to represent feature information of images, which hashes the… More
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  • Analysis of Underlay Cognitive Radio Networks Based on Interference Cancellation Mechanism
  • Abstract In this paper, we investigate the performance of secondary transmission scheme based on Markov ON-OFF state of primary users in Underlay cognitive radio networks. We propose flexible secondary cooperative transmission schemewith interference cancellation technique according to the ON-OFF status of primary transmitter. For maximal ratio combining (MRC) at destination, we have derived exact closed-form expressions of the outage probability in different situations. The numerical simulation results also reveal that the proposed scheme improve the secondary transmission performance compared with traditional mechanism in terms of secondary outage probability and energy efficiency. More
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  • Intent Inference Based Trajectory Prediction and Smooth for UAS in Low-Altitude Airspace with Geofence
  • Abstract In order to meet the higher accuracy requirement of trajectory prediction for Unmanned Aircraft System (UAS) in Unmanned Aircraft System Traffic Management (UTM), an Intent Based Trajectory Prediction and Smooth Based on Constrained State-dependent-transition Hybrid Estimation (CSDTHE-IBTPS) algorithm is proposed. Firstly, an intent inference method of UAS is constructed based on the information of ADS-B and geofence system. Moreover, a geofence layering algorithm is proposed. Secondly, the Flight Mode Change Points (FMCP) are used to define the relevant mode transition parameters and design the guard conditions, so as to generate the mode transition probability matrix and establish the continuous state-dependent-transition… More
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  • Detection of Number of Wideband Signals Based on Support Vector Machine
  • Abstract In array signal processing, number of signals is often a premise of estimating other parameters. For the sake of determining signal number in the condition of strong additive noise or a little sample data, an algorithm for detecting number of wideband signals is provided. First, technique of focusing is used for transforming signals into a same focusing subspace. Then the support vector machine (SVM) can be deduced by the information of eigenvalues and corresponding eigenvectors. At last, the signal number can be determined with the obtained decision function. Several simulations have been carried on verifying the proposed algorithm. More
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  • Method to Appraise Dangerous Class of Building Masonry Component Based on DC-YOLO Model
  • Abstract This DC-YOLO Model was designed in order to improve the efficiency for appraising dangerous class of buildings and avoid manual intervention, thereby making the appraisal results more objective. It is an automated method designed based on deep learning and target detection algorithms to appraise the dangerous class of building masonry component. Specifically, it (1) adopted K-means clustering to obtain the quantity and size of the prior boxes; (2) expanded the grid size to improve identification to small targets; (3) introduced in deformable convolution to adapt to the irregular shape of the masonry component cracks. The experimental results show that, comparing… More
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  • Sliding-Mode PID Control of UAV Based on Particle Swarm Parameter Tuning
  • Abstract Due to the coupled motion between the rotor unmanned aerial vehicle (UAV) and the manipulator, the underactuation characteristics of the system itself, and the influence of external uncertainties, the stability of the rotor UAV’s manipulator control system is difficult to control. Based on the dynamic model of the rotor UAV, the stability of the whole UAV manipulator control system is improved by using the piecewise cost function, the compression factor particle swarm optimization (PSO) algorithm and the sliding mode PID to establish the sliding mode PID control stability method based on the PSO. Compared with the sliding mode PID control… More
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  • Recommender System Combining Popularity and Novelty Based on One-Mode Projection of Weighted Bipartite Network
  • Abstract Personalized recommendation algorithms, which are effective means to solve information overload, are popular topics in current research. In this paper, a recommender system combining popularity and novelty (RSCPN) based on one-mode projection of weighted bipartite network is proposed. The edge between a user and item is weighted with the item’s rating, and we consider the difference in the ratings of different users for an item to obtain a reasonable method of measuring the similarity between users. RSCPN can be used in the same model for popularity and novelty recommendation by setting different parameter values and analyzing how a change in… More
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  • A Fast Filling Algorithm for Image Restoration Based on Contour Parity
  • Abstract Filling techniques are often used in the restoration of images. Yet the existing filling technique approaches either have high computational costs or present problems such as filling holes redundantly. This paper proposes a novel algorithm for filling holes and regions of the images. The proposed algorithm combines the advantages of both the parity-check filling approach and the region-growing inpainting technique. Pairing points of the region’s boundary are used to search and to fill the region. The scanning range of the filling method is within the target regions. The proposed method does not require additional working memory or assistant colors, and… More
  •   Views:795       Downloads:301        Download PDF
  • Self-Certificating Root: A Root Zone Security Enhancement Mechanism for DNS
  • Abstract As a critical Internet infrastructure, domain name system (DNS) protects the authenticity and integrity of domain resource records with the introduction of security extensions (DNSSEC). DNSSEC builds a single-center and hierarchical resource authentication architecture, which brings management convenience but places the DNS at risk from a single point of failure. When the root key suffers a leak or misconfiguration, top level domain (TLD) authority cannot independently protect the authenticity of TLD data in the root zone. In this paper, we propose self-certificating root, a lightweight security enhancement mechanism of root zone compatible with DNS/DNSSEC protocol. By adding the TLD public… More
  •   Views:822       Downloads:446        Download PDF
  • Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity
  • Abstract The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel disease and b) resource limitations… More
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