Search results for: structural vector autoregression

Authors: Yanjun Chen, Chuanping Hu, Jie Shao, Lin Mei, Chongyang Zhang

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Traditional image classification is based on encoding scheme (e.g. Fisher Vector, Vector of Locally Aggregated Descriptor) with low-level image features (e.g. SIFT, HoG). Compared to these low-level local features, deep convolutional features obtained at the mid-level layer of convolutional neural networks (CNN) have richer information but lack of geometric invariance. For scene classification, there are scattered objects with different size, category, layout, number and so on. It is crucial to find the distinctive objects in scene as well as their co-occurrence relationship. In this paper, we propose a method to take advantage of both deep convolutional features and the traditional encoding scheme while taking object-centric and scene-centric information into consideration. First, to exploit the object-centric and scene-centric information, two CNNs that trained on ImageNet and Places dataset separately are used as the pre-trained models to extract deep convolutional features at multiple scales. This produces dense local activations. By analyzing the performance of different CNNs at multiple scales, it is found that each CNN works better in different scale ranges. A scale-wise CNN adaption is reasonable since objects in scene are at its own specific scale. Second, a fisher kernel is applied to aggregate a global representation at each scale and then to merge into a single vector by using a post-processing method called scale-wise normalization. The essence of Fisher Vector lies on the accumulation of the first and second order differences. Hence, the scale-wise normalization followed by average pooling would balance the influence of each scale since different amount of features are extracted. Third, the Fisher vector representation based on the deep convolutional features is followed by a linear Supported Vector Machine, which is a simple yet efficient way to classify the scene categories. Experimental results show that the scale-specific feature extraction and normalization with CNNs trained on object-centric and scene-centric datasets can boost the results from 74.03% up to 79.43% on MIT Indoor67 when only two scales are used (compared to results at single scale). The result is comparable to state-of-art performance which proves that the representation can be applied to other visual recognition tasks.

Keywords: deep convolutional features, Fisher Vector, multiple scales, scale-specific normalization

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Authors: J. S. Kim, N. P. Hong

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In this paper, a CMOS vector modulator designed for wireless backhaul system based on 802.11ac is presented. A poly phase filter and sign select switches yield two orthogonal signal paths. Two variable gain amplifiers with strongly reduced phase shift of only ±5 ° are used to weight these paths. It has a phase control range of 360 ° and a gain range of -10 dB to 10 dB. The current drawn from a 1.2 V supply amounts 20.4 mA. Using a 0.13 mm technology, the chip die area amounts 1.47x0.75 mm².

Keywords: CMOS, phase shifter, backhaul, 802.11ac

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Authors: Amna Khan, Zareena Kausar, Saad Malik

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Bio Mechatronics has extended applications in the field of rehabilitation. It has been contributing since World War II in improving the applicability of prosthesis and assistive devices in real life scenarios. In this paper, classification accuracies have been compared for two classifiers against five positions of elbow. Electromyography (EMG) signals analysis have been acquired directly from skeletal muscles of human forearm for each of the three defined positions and at modified extreme positions of elbow flexion and extension using 8 electrode Myo armband sensor. Features were extracted from filtered EMG signals for each position. Performance of two classifiers, support vector machine (SVM) and linear discriminant analysis (LDA) has been compared by analyzing the classification accuracies. SVM illustrated classification accuracies between 90-96%, in contrast to 84-87% depicted by LDA for five defined positions of elbow keeping the number of samples and selected feature the same for both SVM and LDA.

Keywords: classification accuracies, electromyography, linear discriminant analysis (LDA), Myo armband sensor, support vector machine (SVM)

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Authors: Ming Liu, Chong Wu, Bingquan Liu, Lei Chen

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Internet users have to face the massive amount of textual data every day. Organizing texts into categories can help users dig the useful information from large-scale text collection. Clustering, in fact, is one of the most promising tools for categorizing texts due to its unsupervised characteristic. Unfortunately, most of traditional clustering algorithms lose their high qualities on large-scale text collection. This situation mainly attributes to the high- dimensional vectors generated from texts. To effectively and efficiently cluster large-scale text collection, this paper proposes a vector reconstruction based clustering algorithm. Only the features that can represent the cluster are preserved in cluster’s representative vector. This algorithm alternately repeats two sub-processes until it converges. One process is partial tuning sub-process, where feature’s weight is fine-tuned by iterative process. To accelerate clustering velocity, an intersection based similarity measurement and its corresponding neuron adjustment function are proposed and implemented in this sub-process. The other process is overall tuning sub-process, where the features are reallocated among different clusters. In this sub-process, the features useless to represent the cluster are removed from cluster’s representative vector. Experimental results on the three text collections (including two small-scale and one large-scale text collections) demonstrate that our algorithm obtains high quality on both small-scale and large-scale text collections.

Keywords: vector reconstruction, large-scale text clustering, partial tuning sub-process, overall tuning sub-process

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Authors: Chen-Yin Kuo, Yung-Hsin Lee

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Previous studies applying residual income valuation (RIV) model generally use panel data and single-equation model to forecast stock prices. Unlike these, this paper uses Taiwan longitudinal data to estimate multi-equation time-series models such as Vector Autoregressive (VAR), Vector Error Correction Model (VECM), and conduct out-of-sample forecasting. Further, this work assesses their forecasting performance by two instruments. In favor of extant research, the major finding shows that VECM outperforms other three models in forecasting for three stock sectors over entire horizons. It implies that an error correction term containing long-run information contributes to improve forecasting accuracy. Moreover, the pattern of composite shows that at longer horizon, VECM produces the greater reduction in errors, and performs substantially better than VAR.

Keywords: residual income valuation model, vector error correction model, out of sample forecasting, forecasting accuracy

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Authors: Rama Krishna Supramanian, Marzuki Isahak, Noran Naqiah Hairi

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Noise-induced hearing loss (NIHL) is one of the highest recorded occupational diseases, despite being preventable. Hearing Conservation Program (HCP) is designed to protect workers hearing and prevent them from developing hearing impairment due to occupational noise exposures. However, there is still a lack of evidence regarding the effectiveness of this program. The purpose of this study was to determine the effectiveness of a Hearing Conservation Program (HCP) in preventing or reducing audiometric threshold changes among vector control workers. This study adopts a cluster randomized controlled trial study design, with district health offices as the unit of randomization. Nine district health offices were randomly selected and 183 vector control workers were randomized to intervention or control group. The intervention included a safety and health policy, noise exposure assessment, noise control, distribution of appropriate hearing protection devices, training and education program and audiometric testing. The control group only underwent audiometric testing. Audiometric threshold changes observed in the intervention group showed improvement in the hearing threshold level for all frequencies except 500 Hz and 8000 Hz for the left ear. The hearing threshold changes range from 1.4 dB to 5.2 dB with largest improvement at higher frequencies mainly 4000 Hz and 6000 Hz. Meanwhile for the right ear, the mean hearing threshold level remained similar at 4000 Hz and 6000 Hz after 3 months of intervention. The Hearing Conservation Program (HCP) is effective in preserving the hearing of vector control workers involved in fogging activity as well as increasing their knowledge, attitude and practice towards noise-induced hearing loss (NIHL).

Keywords: adult, hearing conservation program, noise-induced hearing loss, vector control worker

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Authors: Alireza Taghdiri, Sara Ghanbarzade Ghomi

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With gravity load reduction in the structural and non-structural components, the lightweight construction will be achieved as well as the improvement of efficiency and functional specifications. The advantages of lightweight construction can be examined in two levels. The first is the mass reduction of load bearing structure which results in increasing internal useful space and the other one is the mass reduction of building which decreases the effects of seismic load as a result. In order to achieve this goal, the essential building materials specifications and also optimum load bearing geometry of structural systems and elements have to be considered, so lightweight materials selection particularly with lightweight aggregate for building components will be the first step of lightweight construction. In the next step, in addition to selecting the prominent samples of Iran's traditional architecture, the process of these works improvement is analyzed through the viewpoints of structural efficiency and lightweighting and also the practical methods of lightweight construction have been extracted. The optimum design of load bearing geometry of structural system has to be considered not only in the structural system elements, but also in their composition and the selection of dimensions, proportions, forms and optimum orientations, can lead to get a maximum materials efficiency for loads and stresses bearing.

Keywords: gravity load, lightweighting structural system, load bearing geometry, seismic behavior

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Authors: K. Yahia, A. Ghoggal, A. Titaouine, S. E. Zouzou, F. Benchabane

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This paper deals with the problem of stator faults diagnosis in induction motors. Using the discrete wavelet transform (DWT) for the current Park’s vector modulus (CPVM) analysis, the inter-turn short-circuit faults diagnosis can be achieved. This method is based on the decomposition of the CPVM signal, where wavelet approximation and detail coefficients of this signal have been extracted. The energy evaluation of a known bandwidth detail permits to define a fault severity factor (FSF). This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. Simulation, as well as experimental, results show the effectiveness of the used method.

Keywords: Induction Motors (IMs), Inter-turn Short-Circuits Diagnosis, Discrete Wavelet Transform (DWT), Current Park’s Vector Modulus (CPVM)

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Authors: Amir H. Alavi, Kaveh Barri, Qianyun Zhang

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This study presents a new generation of lightweight and mechanically tunable structural composites with sensing and energy harvesting functionalities. This goal is achieved by integrating metamaterial and triboelectric energy harvesting concepts. Proof-of-concept polymeric beam prototypes are fabricated using 3D printing methods based on the proposed concept. Experiments and theoretical analyses are conducted to quantitatively investigate the mechanical and electrical properties of the designed multifunctional beams. The results show that these integrated structural elements can serve as nanogenerators and distributed sensing mediums without a need to incorporating any external sensing modules and electronics. The feasibility of design self-sensing and self-powering structural elements at multiscale for next generation infrastructure systems is further discussed.

Keywords: multifunctional structures, composites, metamaterial, triboelectric nanogenerator, sensors, structural health monitoring, energy harvesting

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Authors: Aditi Viswanathan, Shree Ranjani, Aruna Govada

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With data sizes constantly expanding, and with classical machine learning algorithms that analyze such data requiring larger and larger amounts of computation time and storage space, the need to distribute computation and memory requirements among several computers has become apparent. Although substantial work has been done in developing distributed binary SVM algorithms and multi-class SVM algorithms individually, the field of multi-class distributed SVMs remains largely unexplored. This research seeks to develop an algorithm that implements the Support Vector Machine over a multi-class data set and is efficient in a distributed environment. For this, we recursively choose the best binary split of a set of classes using a greedy technique. Much like the divide and conquer approach. Our algorithm has shown better computation time during the testing phase than the traditional sequential SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of the data set grows. This approach also classifies the data with higher accuracy than the traditional multi-class algorithms.

Keywords: distributed algorithm, MapReduce, multi-class, support vector machine

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Authors: Iqbaldeep Kaur, Amarjeet Kaur

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Software Reusability is important part of software development. So component based software development in case of software testing has gained a lot of practical importance in the field of software engineering from academic researcher and also from software development industry perspective. Finding test cases for efficient reuse of test cases is one of the important problems aimed by researcher. Clustering reduce the search space, reuse test cases by grouping similar entities according to requirements ensuring reduced time complexity as it reduce the search time for retrieval the test cases. In this research paper we proposed approach for re-usability of test cases by unsupervised approach. In unsupervised learning we proposed k-mean and Support Vector Machine. We have designed the algorithm for requirement and test case document clustering according to its tf-idf vector space and the output is set of highly cohesive pattern groups.

Keywords: software testing, reusability, clustering, k-mean, SVM

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Authors: Reza Shakoori

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This paper presents a structural correction of Arabic and Persian strokes using manipulation of their chain codes in order to improve the rate and performance of Persian and Arabic handwritten word recognition systems. It collects pure and effective features to represent a character with one consolidated feature vector and reduces variations in order to decrease the number of training samples and increase the chance of successful classification. Our results also show that how the proposed approaches can simplify classification and consequently recognition by reducing variations and possible noises on the chain code by keeping orientation of characters and their backbone structures.

Keywords: Arabic, chain code normalization, OCR systems, image processing

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Authors: Osama Al-Sehail

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This paper argues for sustainability as a necessity in the evolution of tall architecture. It provides a different mode for dealing with sustainability in tall architecture, taking into consideration the speciality of its typology. To this end, the article develops a Biomimetic Structural Form as a paradigm to attain Vital Sustainability. A Biomimetic Structural Form, which is derived from the amalgamation of biomimicry as an approach for sustainability defining nature as source of knowledge and inspiration in solving humans’ problems and a Structural Form as a catalyst for evolving tall architecture, is a dynamic paradigm emerging from a conceptualizing and morphological process. A Biomimetic Structural Form is a flow system whose different forces and functions tend to be “better”, more "fit", to “survive”, and to be efficient. Through geometry and function—the two aspects of knowledge extracted from nature—the attributes of the Biomimetic Structural Form are formulated. Vital Sustainability is the survival level of sustainability in natural systems through which a system enhances the performance of its internal working and its interaction with the external environment. A Biomimetic Structural Form, in this context, is a medium for evolving tall architecture to emulate natural models in their ways of coexistence with the environment. As an integral part of this article, the sustainable super tall building 3Ts is discussed as a case study of applying Biomimetic Structural Form.   

Keywords: biomimicry, design in nature, high-rise buildings, sustainability, structural form, tall architecture, vital sustainability

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Authors: Hyunchul Ahn, William X. S. Wong

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Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: corporate credit rating prediction, Feature selection, genetic algorithms, instance selection, multiclass support vector machines

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Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos

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In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.

Keywords: anomaly detection, back-propagation neural networks, network intrusion detection systems, support vector machines

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Authors: S. Siddharth, Saravana Kumar

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All Metros across the world tend to have a large number of bridges and there have been concerns about the safety of these bridges. As the traffic in most cities in India is heterogeneous, Trucks and Heavy vehicles traverse on our roads on an everyday basis this will lead to structural damage on the long run. All bridges are designed with a maximum Load limit and this limit is seldom checked. We have hence come up with an idea to check the load of all the vehicles entering the bridge and block the bridge with barricades if the vehicle surpasses the maximum load , this is done to catch hold of the perpetrators. By doing this we can avoid further structural damage and also provide an effective way to enforce the law. If our solution is put in place structural damage and accidents would be reduced to a great deal and it would also make the law enforcement job easier.

Keywords: heterogeneous, structural, load, law, heavy, vehicles

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Authors: Geraldine Giraldo

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In the research of advanced materials, ceramics based on KNN are an important topic, especially for multifunctional applications. In this work, the physical, structural, and microstructural properties of the (KNN-CaLi-xSm) system were analyzed by varying the concentration of samarium, which was prepared using the conventional solid-state reaction method by mixing oxides. It was found that the increase in Sm+3 concentration led to higher porosity in the sample and, consequently, a decrease in density, which is attributed to the structural vacancies at the A-sites of the perovskite-type structure of the ceramic system. In the structural analysis, a coexistence of Tetragonal (T) and Orthorhombic (O) phases were observed at different rare-earth ion contents, with a higher content of the T phase at xSm=0.010. Furthermore, the structural changes in the calcined powders at different temperatures were studied using the results of DTA-TG, which allowed for the analysis of the system's composition. It was found that the lowest total decomposition temperature occurred when xSm=0.010 at 770°C.

Keywords: perovskite, piezoelectric, multifunctional, Structure, ceramic

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Authors: Amir Abbas Fatemi, Zahra Tabrizian, Kabir Sadeghi

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To find the location and severity of damage that occurs in a structure, characteristics changes in dynamic and static can be used. The non-destructive techniques are more common, economic, and reliable to detect the global or local damages in structures. This paper presents a non-destructive method in structural damage detection and assessment using GA and static data. Thus, a set of static forces is applied to some of degrees of freedom and the static responses (displacements) are measured at another set of DOFs. An analytical model of the truss structure is developed based on the available specification and the properties derived from static data. The damages in structure produce changes to its stiffness so this method used to determine damage based on change in the structural stiffness parameter. Changes in the static response which structural damage caused choose to produce some simultaneous equations. Genetic Algorithms are powerful tools for solving large optimization problems. Optimization is considered to minimize objective function involve difference between the static load vector of damaged and healthy structure. Several scenarios defined for damage detection (single scenario and multiple scenarios). The static damage identification methods have many advantages, but some difficulties still exist. So it is important to achieve the best damage identification and if the best result is obtained it means that the method is Reliable. This strategy is applied to a plane truss. This method is used for a plane truss. Numerical results demonstrate the ability of this method in detecting damage in given structures. Also figures show damage detections in multiple damage scenarios have really efficient answer. Even existence of noise in the measurements doesn’t reduce the accuracy of damage detections method in these structures.

Keywords: damage detection, finite element method, static data, non-destructive, genetic algorithm

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Authors: J. I. Aguwa, S. Sadiku, M. Abdullahi

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This study examined the structural reliability of the Nigerian grown Opepe timber as bridge beam material. The strength of a particular specie of timber depends so much on some factors such as soil and environment in which it is grown. The steps involved are collection of the Opepe timber samples, seasoning/preparation of the test specimens, determination of the strength properties/statistical analysis, development of a computer programme in FORTRAN language and finally structural reliability analysis using FORM 5 software. The result revealed that the Nigerian grown Opepe is a reliable and durable structural bridge beam material for span of 5000mm, depth of 400mm, breadth of 250mm and end bearing length of 150mm. The probabilities of failure in bending parallel to the grain, compression perpendicular to the grain, shear parallel to the grain and deflection are 1.61 x 10^-7, 1.43 x 10^-8, 1.93 x 10^-4 and 1.51 x 10^-15 respectively. The paper recommends establishment of Opepe plantation in various Local Government Areas in Nigeria for structural applications such as in bridges, railway sleepers, generation of income to the nation as well as creating employment for the numerous unemployed youths.

Keywords: bending and deflection, bridge beam, compression, Nigerian Opepe, shear, structural reliability

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Authors: Govindaraj Ramanathan

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The time delay and the structural stability are major issues in big size projects due to several factors. Improper planning and poor coordination lead to delay in construction, which sometimes result in reworking or rebuilding. This definitely increases the cost and time of project. This situation stresses the structural engineers to plan out of the limits of contemporary technology utilizing non-chronological approach with creative ideas. One of the strategies to solve this issue is through structural integrity solutions in a cost-effective way. We have faced several problems in a project worth 470 million USD, and one such issue is crane girder installation with composite steel beams. We have applied structural integrity approach with the proper and revised planning schedule to solve the problem efficiently with minimal expenses.

Keywords: construction management, delay, non-chronological approach, composite beam, structural integrity

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Authors: Hussin K. Ragb, Vijayan K. Asari

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In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: pedestrian detection, phase congruency, local phase, LBP features, CSLBP features, FST descriptor

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Authors: J. Lamilla-Monje, C. Solano-Puerto, L. Franco-Lara

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Trees play an essential role in cities due to their ability to provide multiple ecosystem goods and services. Bogota trees are threatened by factors such as pests, pathogens, contamination, among others. Among the pathogens, phytoplasmas are a potential risk for urban trees, generating symptoms that affect the ecosystem services that these trees provide in Bogota, an example of this is the affectation of Q. humboldtii by phytoplasmas, these bacteria are transmitted for insects of the order Hemiptera, this is why the objective of this work was to know if the climatic variables (humidity, precipitation, and temperature) and environmental variables (PM10 and PM2.5) could be related to the distribution of the Oak Quercus entomofauna and specifically with the phytoplasma vector insects in Bogota. For this study, the sampling points were distributed in areas of the city with contrasting variables in two types of locations: parks and streets. A total of 68 trees were sampled in which the associated insects were collected using two methodologies: jameo and agitation traps. The results show that insects of the order Hemiptera were the most abundant, including a total of 1682 individuals represented by 29 morphotypes, within this order individuals from eight families were collected (Aphidae, Aradidae, Berytidae, Cicadellidae, Issidae, Membracidae, Miridae, and Psyllidae), finding as possible vectors the families Cicadellidae, Membracidae, and Psyllidae with 959, 8 and 14 individuals respectively. Within the Cicadellidae family, 21 morphotypes were found, being reported as vectors in the literature: Amplicephalus, Exitianus atratus, Haldorus sp., Xestocephalus desertorum, Idiocerinae sp., Scaphytopius sp., the Membracidae family was represented by two morphotypes and the Psyllidae by one. Results that suggest that there is no correlation between climatic and environmental variables with the diversity of insects associated with oak. Knowing the vector insects of phytoplasmas in oak trees will complete the pathosystem and generate effective vector control.

Keywords: vector insects, diversity, phytoplasmas, Cicadellidae

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Authors: Djamila Benhaddouche, Abdelkader Benyettou

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In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.

Keywords: biomedical data, learning, classifier, algorithms decision tree, knowledge extraction

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Authors: Abdelsalam A. Ahmed

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Fast speed drives for Permanent Magnet Synchronous Motor (PMSM) is a crucial performance for the electric traction systems. In this paper, PMSM is drived with a Model-based Predictive Control (MPC) technique. Fast speed tracking is achieved through optimization of the DC source utilization using MPC. The technique is based on predicting the optimum voltage vector applied to the driver. Control technique is investigated by comparing to the cascaded PI control based on Space Vector Pulse Width Modulation (SVPWM). MPC and SVPWM-based FOC are implemented with the TMS320F2812 DSP and its power driver circuits. The designed MPC for a PMSM drive is experimentally validated on a laboratory test bench. The performances are compared with those obtained by a conventional PI-based system in order to highlight the improvements, especially regarding speed tracking response.

Keywords: permanent magnet synchronous motor, model-based predictive control, DC source utilization, cascaded PI control, space vector pulse width modulation, TMS320F2812 DSP

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Authors: Alireza Derambakhsh

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The objective of this examination is to explore the capability of utilizing Google Earth for Internet GIS applications. The study particularly analyzes the utilization of vector and characteristic information and the capability of showing and preparing this information in new ways utilizing the Google Earth stage. It has progressively been perceived that future improvements in GIS will fixate on Internet GIS, and in three noteworthy territories: GIS information access, spatial data scattering and GIS displaying/preparing. Google Earth is one of the group of geobrowsers that offer a free and simple to utilize administration that empower information with a spatial part to be overlain on top of a 3-D model of the Earth. This examination makes a methodological structure to accomplish its objective that comprises of three noteworthy parts: A database level, an application level and a customer level. As verification of idea a web model has been produced, which incorporates a differing scope of datasets and lets clients direst inquiries and make perceptions of this custom information. The outcomes uncovered that both vector and property information can be successfully spoken to and imagined utilizing Google Earth. In addition, the usefulness to question custom information and envision results has been added to the Google Earth stage.

Keywords: Google earth, internet GIS, vector, characteristic information

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Authors: Ke Ren, Huiruo Shi, Linsen Li, Baoshuai Wang, Yu Zhou

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Since some discriminative features are required for ground moving targets classification, we propose a new feature extraction method based on micro-Doppler signature. Firstly, the time-frequency analysis of measured data indicates that the time-frequency spectrograms of the three kinds of ground moving targets, i.e., single walking person, two people walking and a moving wheeled vehicle, are discriminative. Then, a three-dimensional time-frequency feature vector is extracted from the time-frequency spectrograms to depict these differences. At last, a Support Vector Machine (SVM) classifier is trained with the proposed three-dimensional feature vector. The classification accuracy to categorize ground moving targets into the three kinds of the measured data is found to be over 96%, which demonstrates the good discriminative ability of the proposed micro-Doppler feature.

Keywords: micro-doppler, time-frequency analysis, feature extraction, radar target classification

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Authors: Yung Gi Wu

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With the growing of computer and network, digital data can be spread to anywhere in the world quickly. In addition, digital data can also be copied or tampered easily so that the security issue becomes an important topic in the protection of digital data. Digital watermark is a method to protect the ownership of digital data. Embedding the watermark will influence the quality certainly. In this paper, Vector Quantization (VQ) is used to embed the watermark into the image to fulfill the goal of data hiding. This kind of watermarking is invisible which means that the users will not conscious the existing of embedded watermark even though the embedded image has tiny difference compared to the original image. Meanwhile, VQ needs a lot of computation burden so that we adopt a fast VQ encoding scheme by partial distortion searching (PDS) and mean approximation scheme to speed up the data hiding process. The watermarks we hide to the image could be gray, bi-level and color images. Texts are also can be regarded as watermark to embed. In order to test the robustness of the system, we adopt Photoshop to fulfill sharpen, cropping and altering to check if the extracted watermark is still recognizable. Experimental results demonstrate that the proposed system can resist the above three kinds of tampering in general cases.

Keywords: data hiding, vector quantization, watermark, color image

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Authors: Khin Mar Yee, Mu Mu Than, Kyi Lint, Aye Aye Oo, Chan Mya Hmway, Khin Zar Chi Winn

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The dynamic changes of Land Use and Land Cover (LULC) changes in Yangon have generally resulted the improvement of human welfare and economic development since the last twenty years. Making map of LULC is crucially important for the sustainable development of the environment. However, the exactly data on how environmental factors influence the LULC situation at the various scales because the nature of the natural environment is naturally composed of non-homogeneous surface features, so the features in the satellite data also have the mixed pixels. The main objective of this study is to the calculation of accuracy based on change detection of LULC changes by Support Vector Machines (SVMs). For this research work, the main data was satellite images of 1996, 2006 and 2015. Computing change detection statistics use change detection statistics to compile a detailed tabulation of changes between two classification images and Support Vector Machines (SVMs) process was applied with a soft approach at allocation as well as at a testing stage and to higher accuracy. The results of this paper showed that vegetation and cultivated area were decreased (average total 29 % from 1996 to 2015) because of conversion to the replacing over double of the built up area (average total 30 % from 1996 to 2015). The error matrix and confidence limits led to the validation of the result for LULC mapping.

Keywords: land use and land cover change, change detection, image processing, support vector machines

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Authors: ShuHui Yang, ChingChing Lu

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Measuring event-related potentials (ERPs) has been fundamental to our understanding of how people process language. One specific ERP component, a P600, has been hypothesized to be associated with syntactic reanalysis processes. We, however, propose that the P600 is not restricted to reanalysis processes, but is the index of the structural pattern processing. To investigate the structural pattern processing, we utilized the effects of stimulus degradation in structural priming. To put it another way, there was no P600 effect if the structure of the prime was the same with the structure of the target. Otherwise, there would be a P600 effect if the structure were different between the prime and the target. In the experiment, twenty-two participants were presented with four sentences of Tang poetry. All of the first two sentences, being prime, were conducted with SVO+VP. The last two sentences, being the target, were divided into three types. Type one of the targets was SVO+VP. Type two of the targets was SVO+VPVP. Type three of the targets was VP+VP. The result showed that both of the targets, SVO+VPVP and VP+VP, elicited positive-going brainwave, a P600 effect, at 600~900ms time window. Furthermore, the P600 component was lager for the target’ VP+VP’ than the target’ SVO+VPVP’. That meant the more dissimilar the structure was, the lager the P600 effect we got. These results indicate that P600 was the index of the structure processing, and it would affect the P600 effect intensity with the degrees of structural heterogeneity.

Keywords: ERPs, P600, structural pattern, structural priming, Tang poetry

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Authors: Ju-Hong Lee, Ching-Wei Liao

Abstract:

Adaptive beamforming using antenna array of sensors is useful in the process of adaptively detecting and preserving the presence of the desired signal while suppressing the interference and the background noise. For conventional adaptive array beamforming, we require a prior information of either the impinging direction or the waveform of the desired signal to adapt the weights. The adaptive weights of an antenna array beamformer under a steered-beam constraint are calculated by minimizing the output power of the beamformer subject to the constraint that forces the beamformer to make a constant response in the steering direction. Hence, the performance of the beamformer is very sensitive to the accuracy of the steering operation. In the literature, it is well known that the performance of an adaptive beamformer will be deteriorated by any steering angle error encountered in many practical applications, e.g., the wireless communication systems with massive antennas deployed at the base station and user equipment. Hence, developing effective signal processing techniques to deal with the problem due to steering angle error for array beamforming systems has become an important research work. In this paper, we present an effective signal processing technique for constructing an adaptive beamformer against the steering angle error. The proposed array beamformer adaptively estimates the actual direction of the desired signal by using the presumed steering vector and the received array data snapshots. Based on the presumed steering vector and a preset angle range for steering mismatch tolerance, we first create a matrix related to the direction vector of signal sources. Two projection matrices are generated from the matrix. The projection matrix associated with the desired signal information and the received array data are utilized to iteratively estimate the actual direction vector of the desired signal. The estimated direction vector of the desired signal is then used for appropriately finding the quiescent weight vector. The other projection matrix is set to be the signal blocking matrix required for performing adaptive beamforming. Accordingly, the proposed beamformer consists of adaptive quiescent weights and partially adaptive weights. Several computer simulation examples are provided for evaluating and comparing the proposed technique with the existing robust techniques.

Keywords: adaptive beamforming, robustness, signal blocking, steering angle error

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