Showing papers in "Journal of Electrical and Computer Engineering in 2017"
TL;DR: This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly.
Abstract: The Internet of Things (IoT) is defined as a paradigm in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. In this paper, we survey state-of-the-art methods, protocols, and applications in this new emerging area. This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly. As compared to similar survey papers in the area, this paper is far more comprehensive in its coverage and exhaustively covers most major technologies spanning from sensors to applications.
1,025 citations
TL;DR: A machine learning approach based on six years of meteorological and pollution data analyses is proposed to predict the concentrations of PM2.5 from wind (speed and direction) and precipitation levels and demonstrates that the use of statistical models based on machine learning is relevant to predict PM 2.5 concentrations from meteorological data.
Abstract: Outdoor air pollution costs millions of premature deaths annually, mostly due to anthropogenic fine particulate matter (or PM2.5). Quito, the capital city of Ecuador, is no exception in exceeding the healthy levels of pollution. In addition to the impact of urbanization, motorization, and rapid population growth, particulate pollution is modulated by meteorological factors and geophysical characteristics, which complicate the implementation of the most advanced models of weather forecast. Thus, this paper proposes a machine learning approach based on six years of meteorological and pollution data analyses to predict the concentrations of PM2.5 from wind (speed and direction) and precipitation levels. The results of the classification model show a high reliability in the classification of low ( 25 µg/m3) and low (<10 µg/m3) versus moderate (10–25 µg/m3) concentrations of PM2.5. A regression analysis suggests a better prediction of PM2.5 when the climatic conditions are getting more extreme (strong winds or high levels of precipitation). The high correlation between estimated and real data for a time series analysis during the wet season confirms this finding. The study demonstrates that the use of statistical models based on machine learning is relevant to predict PM2.5 concentrations from meteorological data.
131 citations
TL;DR: A spine-leaf Fog computing network (SL-FCN) is presented for reducing latency and network congestion issues in a highly distributed and multilayer virtualized IoT datacenter environment and is cost-effective as it maximizes bandwidth while maintaining redundancy and resiliency against failures in mission critical applications.
Abstract: With the Internet of Everything (IoE) paradigm that gathers almost every object online, huge traffic workload, bandwidth, security, and latency issues remain a concern for IoT users in today’s world. Besides, the scalability requirements found in the current IoT data processing (in the cloud) can hardly be used for applications such as assisted living systems, Big Data analytic solutions, and smart embedded applications. This paper proposes an extended cloud IoT model that optimizes bandwidth while allowing edge devices (Internet-connected objects/devices) to smartly process data without relying on a cloud network. Its integration with a massively scaled spine-leaf (SL) network topology is highlighted. This is contrasted with a legacy multitier layered architecture housing network services and routing policies. The perspective offered in this paper explains how low-latency and bandwidth intensive applications can transfer data to the cloud (and then back to the edge application) without impacting QoS performance. Consequently, a spine-leaf Fog computing network (SL-FCN) is presented for reducing latency and network congestion issues in a highly distributed and multilayer virtualized IoT datacenter environment. This approach is cost-effective as it maximizes bandwidth while maintaining redundancy and resiliency against failures in mission critical applications.
73 citations
TL;DR: A system model is designed to clean and dig into the educational data and also the students’ learning attitude and the duration of learning behavior to establish student profile and presents the intelligent guide model to guide both E-Learning platform and learners to improve learning things.
Abstract: With the development of mobile platform, such as smart cellphone and pad, the E-Learning model has been rapidly developed. However, due to the low completion rate for E-Learning platform, it is very necessary to analyze the behavior characteristics of online learners to intelligently adjust online education strategy and enhance the quality of learning. In this paper, we analyzed the relation indicators of E-Learning to build the student profile and gave countermeasures. Adopting the similarity computation and Jaccard coefficient algorithm, we designed a system model to clean and dig into the educational data and also the students’ learning attitude and the duration of learning behavior to establish student profile. According to the E-Learning resources and learner behaviors, we also present the intelligent guide model to guide both E-Learning platform and learners to improve learning things. The study on student profile can help the E-Learning platform to meet and guide the students’ learning behavior deeply and also to provide personalized learning situation and promote the optimization of the E-Learning.
48 citations
TL;DR: A multi-input convolutional neural network is designed for large scale flower grading and achieves a satisfactory accuracy of 89.6% on the BjfuGloxinia after data augmentation, demonstrating that single-input CNN is a promising model for flower grading.
Abstract: Flower grading is a significant task because it is extremely convenient for managing the flowers in greenhouse and market. With the development of computer vision, flower grading has become an interdisciplinary focus in both botany and computer vision. A new dataset named BjfuGloxinia contains three quality grades; each grade consists of 107 samples and 321 images. A multi-input convolutional neural network is designed for large scale flower grading. Multi-input CNN achieves a satisfactory accuracy of 89.6% on the BjfuGloxinia after data augmentation. Compared with a single-input CNN, the accuracy of multi-input CNN is increased by 5% on average, demonstrating that multi-input convolutional neural network is a promising model for flower grading. Although data augmentation contributes to the model, the accuracy is still limited by lack of samples diversity. Majority of misclassification is derived from the medium class. The image processing based bud detection is useful for reducing the misclassification, increasing the accuracy of flower grading to approximately 93.9%.
47 citations
TL;DR: A DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning and a heuristic detection algorithm based on Singular Value Decomposition (SVD) to construct the detection system are proposed and Experimental results show that the detection method is excellent in TNR, accuracy, and precision.
Abstract: The explosive growth of network traffic and its multitype on Internet have brought new and severe challenges to DDoS attack detection. To get the higher True Negative Rate (TNR), accuracy, and precision and to guarantee the robustness, stability, and universality of detection system, in this paper, we propose a DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning and design a heuristic detection algorithm based on Singular Value Decomposition (SVD) to construct our detection system. Experimental results show that our detection method is excellent in TNR, accuracy, and precision. Therefore, our algorithm has good detective performance for DDoS attack. Through the comparisons with Random Forest, -Nearest Neighbor ( -NN), and Bagging comprising the component classifiers when the three algorithms are used alone by SVD and by un-SVD, it is shown that our model is superior to the state-of-the-art attack detection techniques in system generalization ability, detection stability, and overall detection performance.
39 citations
TL;DR: The proposed key frame extraction method for video copyright protection has advantage in computation complexity and robustness on several video formats, video resolution, and so on.
Abstract: The paper proposes a key frame extraction method for video copyright protection. The fast and robust method is based on frame difference with low level features, including color feature and structure feature. A two-stage method is used to extract accurate key frames to cover the content for the whole video sequence. Firstly, an alternative sequence is got based on color characteristic difference between adjacent frames from original sequence. Secondly, by analyzing structural characteristic difference between adjacent frames from the alternative sequence, the final key frame sequence is obtained. And then, an optimization step is added based on the number of final key frames in order to ensure the effectiveness of key frame extraction. Compared with the previous methods, the proposed method has advantage in computation complexity and robustness on several video formats, video resolution, and so on.
32 citations
TL;DR: The novel ferrography wear particle classifier is founded based on ELM, which has been attracting attentions for its faster learning speed and better generalization performance than those of traditional gradient-based learning algorithms.
Abstract: The morphology of wear particles reflects the complex properties of wear processes involved in particle formation. Typically, the morphology of wear particles is evaluated qualitatively based on microscopy observations. This procedure relies upon the experts’ knowledge and, thus, is not always objective and cheap. With the rapid development of computer image processing technology, neural network based on traditional gradient training algorithm can be used to recognize them. However, the feedforward neural network based on traditional gradient training algorithms for image segmentation creates many issues, such as needing multiple iterations to converge and easy fall into local minimum, which restrict its development heavily. Recently, extreme learning machine (ELM) for single-hidden-layer feedforward neural networks (SLFN) has been attracting attentions for its faster learning speed and better generalization performance than those of traditional gradient-based learning algorithms. In this paper, we propose to employ ELM for ferrography wear particles image recognition. We extract the shape features, color features, and texture features of five typical kinds of wear particles as the input of the ELM classifier and set five types of wear particles as the output of the ELM classifier. Therefore, the novel ferrography wear particle classifier is founded based on ELM.
27 citations
TL;DR: A method of fast image segmentation using two-dimensional Otsu based on estimation of distribution algorithm that can effectively preserve details of the target, improve the segmentation precision, and reduce the running time of algorithms is proposed.
Abstract: Traditional two-dimensional Otsu algorithm has several drawbacks; that is, the sum of probabilities of target and background is approximate to 1 inaccurately, the details of neighborhood image are not obvious, and the computational cost is high. In order to address these problems, a method of fast image segmentation using two-dimensional Otsu based on estimation of distribution algorithm is proposed. Firstly, in order to enhance the performance of image segmentation, the guided filtering is employed to improve neighborhood image template instead of mean filtering. Additionally, the probabilities of target and background in two-dimensional histogram are exactly calculated to get more accurate threshold. Finally, the trace of the interclass dispersion matrix is taken as the fitness function of estimation of distributed algorithm, and the optimal threshold is obtained by constructing and sampling the probability model. Extensive experimental results demonstrate that our method can effectively preserve details of the target, improve the segmentation precision, and reduce the running time of algorithms.
27 citations
TL;DR: A novel image fusion algorithm that combines nonlinear approximation of contourlet transform with image regional features to improve the visual effects of medical image fusion and image quality, image denoising, and enhancement is proposed.
Abstract: According to the pros and cons of contourlet transform and multimodality medical imaging, here we propose a novel image fusion algorithm that combines nonlinear approximation of contourlet transform with image regional features. The most important coefficient bands of the contourlet sparse matrix are retained by nonlinear approximation. Low-frequency and high-frequency regional features are also elaborated to fuse medical images. The results strongly suggested that the proposed algorithm could improve the visual effects of medical image fusion and image quality, image denoising, and enhancement.
17 citations
TL;DR: The experimental results show that the proposed algorithm can greatly improve the efficiency of protection of the data in the interaction with others in the incompletely open manner, without reducing the quality of anonymization and enhancing the information loss.
Abstract: In order to enhance the enthusiasm of the data provider in the process of data interaction and improve the adequacy of data interaction, we put forward the concept of the ego of data and then analyzed the characteristics of the ego of data in the Internet of Things (IOT) in this paper. We implement two steps of data clustering for the Internet of things; the first step is the spatial location of adjacent fuzzy clustering, and the second step is the sampling time fuzzy clustering. Equivalent classes can be obtained through the two steps. In this way we can make the data with layout characteristics to be classified into different equivalent classes, so that the specific location information of the data can be obscured, the layout characteristics of tags are eliminated, and ultimately anonymization protection would be achieved. The experimental results show that the proposed algorithm can greatly improve the efficiency of protection of the data in the interaction with others in the incompletely open manner, without reducing the quality of anonymization and enhancing the information loss. The anonymization data set generated by this method has better data availability, and this algorithm can effectively improve the security of data exchange.
TL;DR: A three-dimensional autonomous chaotic system with high fraction dimension based on the continuous chaos and the discrete wavelet function map and an image encryption algorithm is put forward to demonstrate its robustness against various types of attacks.
Abstract: This paper presents a three-dimensional autonomous chaotic system with high fraction dimension. It is noted that the nonlinear characteristic of the improper fractional-order chaos is interesting. Based on the continuous chaos and the discrete wavelet function map, an image encryption algorithm is put forward. The key space is formed by the initial state variables, parameters, and orders of the system. Every pixel value is included in secret key, so as to improve antiattack capability of the algorithm. The obtained simulation results and extensive security analyses demonstrate the high level of security of the algorithm and show its robustness against various types of attacks.
TL;DR: It is shown that, due to the application, sometimes a simple and not very famous topology is more effective than a famous one, and this comparison on Cockcroft-Walton voltage multipliers shows that.
Abstract: Decades after invention of the Cockcroft-Walton voltage multiplier, it is still being used in broad range of high voltage and ac to dc applications. High voltage ratio, low voltage stress on components, compactness, and high efficiency are its main features. Due to the problems of original circuit, reduction of output ripple and increase of accessible voltage level were the motivations for scientist to propose new topologies. In this article a comparative study on these voltage multipliers was presented. By simulations and experimental prototypes, characteristics of the topologies have been compared. In addition to the performances, components count, voltage stress on the components, and the difficulty and cost of construction are other factors which have been considered in this comparison. An easy to use table which summarized the characteristics of VMs was developed, which can be used as a decision mean for selecting of a topology based on the requirements. It is shown that, due to the application, sometimes a simple and not very famous topology is more effective than a famous one.
TL;DR: An algebraic cryptanalysis scheme of AES-256 using Gr&#-10;bner basis is proposed and analysis shows that the complexity of the scheme is lower than that of the exhaustive attack.
Abstract: The zero-dimensional Gr&#-10;bner basis construction is a crucial step in Gr&#-10;bner basis cryptanalysis on AES-256. In this paper, after performing an in-depth study on the linear transformation and the system of multivariate polynomial equations of AES-256, the zero-dimensional Gr&#-10;bner basis construction method is proposed by choosing suitable term order and variable order. After giving a detailed construction process of the zero-dimensional Gr&#-10;bner basis, the necessary theoretical proof is presented. Based on this, an algebraic cryptanalysis scheme of AES-256 using Gr&#-10;bner basis is proposed. Analysis shows that the complexity of our scheme is lower than that of the exhaustive attack.
TL;DR: The proposed configuration of voltage-mode quadrature sinusoidal oscillator employs two voltage differencing current conveyors, two resistors, and two grounded capacitors and the experimental results agree well with theoretical anticipation.
Abstract: A new configuration of voltage-mode quadrature sinusoidal oscillator is proposed. The proposed oscillator employs two voltage differencing current conveyors (VDCCs), two resistors, and two grounded capacitors. In this design, the use of multiple/dual output terminal active building block is not required. The tuning of frequency of oscillation (FO) can be done electronically by adjusting the bias current of active device without affecting condition of oscillation (CO). The electronic tuning can be done by controlling the bias current using a digital circuit. The amplitude of two sinusoidal outputs is equal when the frequency of oscillation is tuned. This makes the sinusoidal output voltages meet good total harmonic distortions (THD). Moreover, the proposed circuit can provide the sinusoidal output current with high impedance which is connected to external load or to another circuit without the use of buffer device. To confirm that the oscillator can generate the quadrature sinusoidal output signal, the experimental results using VDCC constructed from commercially available ICs are also included. The experimental results agree well with theoretical anticipation.
TL;DR: This work proposes a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach and shows the improved detection rates of this system compared to reference technique.
Abstract: In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.
TL;DR: This paper has further reduced the cost of network coding mechanism by reducing the size of data used for permutation and proposed algorithm for key generation and random permutation confusion key calculation achieves better performance in throughput, encryption time, and energy consumption.
Abstract: Mobile Ad Hoc Networks (MANETs) are composed of a large number of devices that act as dynamic nodes with limited processing capabilities that can share data among each other. Energy efficient security is the major issue in MANETs where data encryption and decryption operations should be optimized to consume less energy. In this regard, we have focused on network coding which is a lightweight mechanism that can also be used for data confidentiality. In this paper, we have further reduced the cost of network coding mechanism by reducing the size of data used for permutation. The basic idea is that source permutes only global encoding vectors (GEVs) without permuting the whole message symbols which significantly reduces the complexity and transmission cost over the network. We have also proposed an algorithm for key generation and random permutation confusion key calculation. The proposed scheme achieves better performance in throughput, encryption time, and energy consumption as compared to previous schemes.
TL;DR: A novel approach for abnormal event detection in wireless sensor networks that considers not only spatiotemporal correlations but also the correlations among observed attributes, and proposes a new concept named attribute correlation confidence to evaluate the fitting degree between the sensor reading and the abnormal event pattern.
Abstract: Abnormal event detection is one of the vital tasks in wireless sensor networks. However, the faults of nodes and the poor deployment environment have brought great challenges to abnormal event detection. In a typical event detection technique, spatiotemporal correlations are collected to detect an event, which is susceptible to noises and errors. To improve the quality of detection results, we propose a novel approach for abnormal event detection in wireless sensor networks. This approach considers not only spatiotemporal correlations but also the correlations among observed attributes. A dependency model of observed attributes is constructed based on Bayesian network. In this model, the dependency structure of observed attributes is obtained by structure learning, and the conditional probability table of each node is calculated by parameter learning. We propose a new concept named attribute correlation confidence to evaluate the fitting degree between the sensor reading and the abnormal event pattern. On the basis of time correlation detection and space correlation detection, the abnormal events are identified. Experimental results show that the proposed algorithm can reduce the impact of interference factors and the rate of the false alarm effectively; it can also improve the accuracy of event detection.
TL;DR: A fully automated algorithm was developed to segment drusen area and volume from SD-OCT images and was evaluated in 20 publically available volumetric scans acquired by using Bioptigen spectral-domain ophthalmic imaging system.
Abstract: Spectral-Domain Optical Coherence Tomography (SD-OCT) is a widely used interferometric diagnostic technique in ophthalmology that provides novel in vivo information of depth-resolved inner and outer retinal structures. This imaging modality can assist clinicians in monitoring the progression of Age-related Macular Degeneration (AMD) by providing high-resolution visualization of drusen. Quantitative tools for assessing drusen volume that are indicative of AMD progression may lead to appropriate metrics for selecting treatment protocols. To address this need, a fully automated algorithm was developed to segment drusen area and volume from SD-OCT images. The proposed algorithm consists of three parts: (1) preprocessing, which includes creating binary mask and removing possible highly reflective posterior hyaloid that is used in accurate detection of inner segment/outer segment (IS/OS) junction layer and Bruchźs membrane (BM) retinal layers; (2) coarse segmentation, in which 3D curvelet transform and graph theory are employed to get the possible candidate drusenoid regions; (3) fine segmentation, in which morphological operators are used to remove falsely extracted elongated structures and get the refined segmentation results. The proposed method was evaluated in 20 publically available volumetric scans acquired by using Bioptigen spectral-domain ophthalmic imaging system. The average true positive and false positive volume fractions (TPVF and FPVF) for the segmentation of drusenoid regions were found to be 89.15%ź± 3.76 and 0.17%ź± .18%, respectively.
TL;DR: The results of the experiments show that the proposed model can obtain good performance in clear and noisy environment and be insensitive to the low-quality speech, but the time cost of the model is high.
Abstract: Today, more and more people have benefited from the speaker recognition. However, the accuracy of speaker recognition often drops off rapidly because of the low-quality speech and noise. This paper proposed a new speaker recognition model based on wavelet packet entropy (WPE), i-vector, and cosine distance scoring (CDS). In the proposed model, WPE transforms the speeches into short-term spectrum feature vectors (short vectors) and resists the noise. I-vector is generated from those short vectors and characterizes speech to improve the recognition accuracy. CDS fast compares with the difference between two i-vectors to give out the recognition result. The proposed model is evaluated by TIMIT speech database. The results of the experiments show that the proposed model can obtain good performance in clear and noisy environment and be insensitive to the low-quality speech, but the time cost of the model is high. To reduce the time cost, the parallel computation is used.
TL;DR: A chattering-free nonsingular terminal sliding-mode controller is proposed to achieve the rudder angle tracking with a chattering elimination and tracking dynamic performance improvement and a Lyapunov-based proof ensures the asymptotic stability and finite-time convergence of the closed-loop system.
Abstract: Considering the backlash nonlinearity and parameter time-varying characteristics in electromechanical actuators, a chattering-free sliding-mode control strategy is proposed in this paper to regulate the rudder angle and suppress unknown external disturbances. Different from most existing backlash compensation methods, a special continuous function is addressed to approximate the backlash nonlinear dead-zone model. Regarding the approximation error, unmodeled dynamics, and unknown external disturbances as a disturbance-like term, a strict feedback nonlinear model is established. Based on this nonlinear model, a chattering-free nonsingular terminal sliding-mode controller is proposed to achieve the rudder angle tracking with a chattering elimination and tracking dynamic performance improvement. A Lyapunov-based proof ensures the asymptotic stability and finite-time convergence of the closed-loop system. Experimental results have verified the effectiveness of the proposed method.
TL;DR: This paper proposes a particular solution for QR code detection in uncontrolled environments using a binary large object- (BLOB-) based algorithm with subsequent iterative filtering QR symbol position detection patterns that do not require complex processing and training of classifiers frequently used for these purposes.
Abstract: Quick Response QR barcode detection in nonarbitrary environment is still a challenging task despite many existing applications for finding 2D symbols. The main disadvantage of recent applications for QR code detection is a low performance for rotated and distorted single or multiple symbols in images with variable illumination and presence of noise. In this paper, a particular solution for QR code detection in uncontrolled environments is presented. The proposal consists in recognizing geometrical features of QR code using a binary large object- (BLOB-) based algorithm with subsequent iterative filtering QR symbol position detection patterns that do not require complex processing and training of classifiers frequently used for these purposes. The high precision and speed are achieved by adaptive threshold binarization of integral images. In contrast to well-known scanners, which fail to detect QR code with medium to strong blurring, significant nonuniform illumination, considerable symbol deformations, and noising, the proposed technique provides high recognition rate of 80%–100% with a speed compatible to real-time applications. In particular, speed varies from 200 ms to 800 ms per single or multiple QR code detected simultaneously in images with resolution from 640 × 480 to 4080 × 2720, respectively.
TL;DR: Critical constituent gates in math circuits are detected and graded based on the impact of an error in the output of a circuit, reducing the occurrence of critical errors.
Abstract: Hardware redundancy at different levels of design is a common fault mitigation technique, which is well known for its efficiency to the detriment of area overhead. In order to reduce this drawback, several fault-tolerant techniques have been proposed in literature to find a good trade-off. In this paper, critical constituent gates in math circuits are detected and graded based on the impact of an error in the output of a circuit. These critical gates should be hardened first under the area constraint of design criteria. Indeed, output bits considered crucial to a system receive higher priorities to be protected, reducing the occurrence of critical errors. The 74283 fast adder is used as an example to illustrate the feasibility and efficiency of the proposed approach.
TL;DR: The method integrates the advantages of the two methods and the accuracy of the method is verified by comparing with the methods based on traditional Bayesian network and BP neural network, respectively, when the historical data is sufficient.
Abstract: According to the characteristics of fault diagnosis for pumping station, such as the complex structure, multiple mappings, and numerous uncertainties, a new approach combining T-S fuzzy gate fault tree and Bayesian network (BN) is proposed. On the one hand, traditional fault tree method needs the logical relationship between events and probability value of events and can only represent the events with two states. T-S fuzzy gate fault tree method can solve these disadvantages but still has weaknesses in complex reasoning and only one-way reasoning. On the other hand, the BN is suitable for fault diagnosis of pumping station because of its powerful ability to deal with uncertain information. However, it is difficult to determine the structure and conditional probability tables of the BN. Therefore, the proposed method integrates the advantages of the two methods. Finally, the feasibility of the method is verified through a fault diagnosis model of the rotor in the pumping unit, the accuracy of the method is verified by comparing with the methods based on traditional Bayesian network and BP neural network, respectively, when the historical data is sufficient, and the results are more superior to the above two when the historical data is insufficient.
TL;DR: A method of keeping good communication performance even for a short GI is presented and a method of data selective rake reception (DSRake) is proposed, which is discussed by received signal distribution and confirmed by simulation results.
Abstract: In underwater acoustic communication (UAC), very long delay waves are caused by reflection from water surfaces and bottoms and obstacles Their waves interfere with desired waves and induce strong multipath interference Use of a guard interval (GI) is effective for channel compensation in OFDM However, a GI tends to be long in shallow-water environment because a guard time is determined by a delay time of multipath A long GI produces a very long OFDM frame in several seconds, which is disadvantageous to a response speed of communication This paper presents a method of keeping good communication performance even for a short GI We discuss influence of intercarrier interference (ICI) in OFDM demodulation and propose a method of data selective rake reception (DSRake) The effectiveness of the proposed method is discussed by received signal distribution and confirmed by simulation results
TL;DR: Results show that the method combined with MBQPSO and objective function, which is formulated based on the gap length between region and background, has the best performance in accuracy and efficiency.
Abstract: Subpixel mapping technology can determine the specific location of different objects in the mixed pixel and effectively solve the uncertainty of the ground features spatial distribution in traditional classification technology. Existing methods based on linear optimization encounter the premature and local convergence of the optimization algorithm. This paper proposes a subpixel mapping method based on modified binary quantum particle swarm optimization (MBQPSO) to solve the above issues. The initial subpixel mapping imagery is obtained according to spectral unmixing results. We focus mainly on the discretization of QPSO, which is implemented by modifying the discrete update process of particle location, to minimize the objective function, which is formulated based on different connected regional perimeter calculating methods. To reduce time complexity, a target optimization strategy of global iteration combined with local iteration is performed. The MBQPSO is tested on standard test functions and results show that MBQPSO has the best performance on global optimization and convergent rate. Then, we analyze the proposed algorithm qualitatively and quantitatively by simulated and real experiment; results show that the method combined with MBQPSO and objective function, which is formulated based on the gap length between region and background, has the best performance in accuracy and efficiency.
TL;DR: Experiments suggested that the continuous operating performance of the ECU is robust and capable of saving 15% of the total electricity comparing with ordinary fan speed control method.
Abstract: A hybrid electrical bus employs both a turbo diesel engine and an electric motor to drive the vehicle in different speed-torque scenarios. The cooling system for such a vehicle is particularly power costing because it needs to dissipate heat from not only the engine, but also the intercooler and the motor. An electronic control unit (ECU) has been designed with a single chip computer, temperature sensors, DC motor drive circuit, and optimized control algorithm to manage the speeds of several fans for efficient cooling using a nonlinear fan speed adjustment strategy. Experiments suggested that the continuous operating performance of the ECU is robust and capable of saving 15% of the total electricity comparing with ordinary fan speed control method.
TL;DR: Extended Kalman filtering algorithm had been put forward in consideration of the nonlinear characteristic of the tire and was verified by the Carsim and Simulink joint simulation, such as the simulation on the wet cement road and the ice and snow road with double lane change.
Abstract: The sideslip angle plays an extremely important role in vehicle stability control, but the sideslip angle in production car cannot be obtained from sensor directly in consideration of the cost of the sensor; it is essential to estimate the sideslip angle indirectly by means of other vehicle motion parameters; therefore, an estimation algorithm with real-time performance and accuracy is critical. Traditional estimation method based on Kalman filter algorithm is correct in vehicle linear control area; however, on low adhesion road, vehicles have obvious nonlinear characteristics. In this paper, extended Kalman filtering algorithm had been put forward in consideration of the nonlinear characteristic of the tire and was verified by the Carsim and Simulink joint simulation, such as the simulation on the wet cement road and the ice and snow road with double lane change. To test and verify the effect of extended Kalman filtering estimation algorithm, the real vehicle test was carried out on the limit test field. The experimental results show that the accuracy of vehicle sideslip angle acquired by extended Kalman filtering algorithm is obviously higher than that acquired by Kalman filtering in the area of the nonlinearity.
TL;DR: A new algorithm for harmonic detection and compensation based on synchronous reference frame (SRF) is proposed, in which a band-pass filter with center frequency of th harmonic is designed in fundamental frequency SRF to extract random harmonic current.
Abstract: Algorithms for harmonic detection and compensation are important guarantees for an active power filter (APF) to achieve the harmonic control function and directly determine the overall performance. Existing algorithms usually need a large amount of computation, and the compensation effect of specified order harmonic is also limited. DC side capacitor voltage at sudden change of load is affected by the algorithm as well. This paper proposes a new algorithm for harmonic detection and compensation based on synchronous reference frame (SRF), in which a band-pass filter with center frequency of th harmonic is designed in fundamental frequency SRF to extract random harmonic current with two different frequencies of ( )th harmonic in stationary reference frame. This new algorithm can rapidly detect any specified harmonic, and it can adjust the power factor to compensate reactive power. Meanwhile, it has few impacts on DC side capacitor voltage under complicated operating conditions such as sudden change of load. The correctness and effectiveness of this new algorithm are verified by simulation and experiment.
TL;DR: Experimental results show that the proposed algorithm can significantly improve the encryption time compared with the traditional AES algorithm.
Abstract: A new type of student information management system is designed to implement student information identification and management based on fingerprint identification. In order to ensure the security of data transmission, this paper proposes a data encryption method based on an improved AES algorithm. A new -box is cleverly designed, which can significantly reduce the encryption time by improving ByteSub, ShiftRow, and MixColumn in the round transformation of the traditional AES algorithm with the process of look-up table. Experimental results show that the proposed algorithm can significantly improve the encryption time compared with the traditional AES algorithm.