• Journal of information and communication convergence engineering
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• v.7 no.1
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• pp.41-45
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• 2009

In mobile GIS environments, a client needs to receive the isogram data with a topographical map from a server, if the mobile client wants to display map with elevation information. Because the elevation data size is normally quite large, the client will suffer some problems to receive the elevation data from server. The main reason is a resource limitation of the mobile devices. To overcome these problems, this paper proposes new data structures and algorithms. They are designed for efficient transmission of contour data to a client. Because of the contour data are generated as a vector style from elevation information stored at a server, the proposed algorithms are focused to minimize the transmission data volume and time.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.761-762
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• 2008

This paper presents a hardware accelerator for accelerating vector graphics applications based on the OpenVG standard. Since our design mainly targets embedded applications, we focus on efficient uses of limited resources, especially the memory bandwidth. The designed accelerator can process the images of $640{\times}240$ pixels with moderate complexity at the rate of 30 frames per second.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.213-219
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• 2010

MCU(Micro Controller Unit) used for the automobiles has been required for improving of the safety and high reliability. Also, the necessity of high performance MCU equipped with high fuel-efficiency has been risen according to increased requests of high fuel-efficiency and improving the occupants safety with the development of intelligent vehicles and future vehicles. The MPC5554 32-bit embedded controller, made by Freescale Semiconductor, specialized in the part of the power train provides the high reliability, fast interrupt process and real-time control. In This paper, the investigation on IPMSM using MPC5554 has been performed. Also SVPWM(Space Vector Pulse Width Modulation) is implemented to the servo system.

• Transactions on Electrical and Electronic Materials
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• v.18 no.6
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• pp.320-322
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• 2017

In this paper, a high-performance, low-area gradient-magnitude calculator architecture is proposed, based on approximate image processing. To reduce the computational complexity of the gradient-magnitude calculation, vector properties, the symmetry axis, and common terms were applied in a hardware-resource-shared architec-ture. The proposed gradient-magnitude calculator was implemented using an Altera Cyclone IV FPGA (EP4CE115F29) and the Quartus II v.16 device software. It satisfied the output-data quality while reducing the logic elements by 23% and the embedded multipliers by 76%, compared with previous work.

• Journal of Information Processing Systems
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• v.6 no.4
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• pp.435-452
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• 2010

This paper proposes a novel reversible data hiding scheme based on a Vector Quantization (VQ) codebook. The proposed scheme uses the principle component analysis (PCA) algorithm to sort the codebook and to find two similar codewords of an image block. According to the secret to be embedded and the difference between those two similar codewords, the original image block is transformed into a difference number table. Finally, this table is compressed by entropy coding and sent to the receiver. The experimental results demonstrate that the proposed scheme can achieve greater hiding capacity, about five bits per index, with an acceptable bit rate. At the receiver end, after the compressed code has been decoded, the image can be recovered to a VQ compressed image.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.528-537
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• 2021

Face recognition has gained significant notice because of its application in many businesses: security, healthcare, and marketing. In this paper, we will present the recognition method using the combination of correlation filters (CF) and Support Vector Machine (SVM). Firstly, we evaluate the performance and compared four different correlation filters: minimum average correlation energy (MACE), maximum average correlation height (MACH), unconstrained minimum average correlation energy (UMACE), and optimal-tradeoff (OT). Secondly, we propose the machine learning approach by using the OT correlation filter for features extraction and SVM for classification. The numerical results on National Cheng Kung University (NCKU) and Pointing'04 face database show that the proposed method OT-SVM gets higher accuracy in face recognition compared to other machine learning methods. Our approach doesn't require graphics card to train the image. As a result, it could run well on a low hardware system like an embedded system.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.17-30
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• 2021

This paper develops a convenient approach for deterministic and probabilistic evaluations of tunnel face stability using support vector machine classifiers. The proposed method is comprised of two major steps, i.e., construction of the training dataset and determination of instance-based classifiers. In step one, the orthogonal design is utilized to produce representative samples after the ranges and levels of the factors that influence tunnel face stability are specified. The training dataset is then labeled by two-dimensional strength reduction analyses embedded within OptumG2. For any unknown instance, the second step applies the training dataset for classification, which is achieved by an ad hoc Python program. The classification of unknown samples starts with selection of instance-based training samples using the k-nearest neighbors algorithm, followed by the construction of an instance-based SVM-KNN classifier. It eventually provides labels of the unknown instances, avoiding calculate its corresponding performance function. Probabilistic evaluations are performed by Monte Carlo simulation based on the SVM-KNN classifier. The ratio of the number of unstable samples to the total number of simulated samples is computed and is taken as the failure probability, which is validated and compared with the response surface method.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1250-1260
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• 2020

This study proposes a novel deep neural network model that can accurately detect objects and their relationships in an image and represent them as a scene graph. The proposed model utilizes several multimodal features, including linguistic features and visual context features, to accurately detect objects and relationships. In addition, in the proposed model, context features are embedded using graph neural networks to depict the dependencies between two related objects in the context feature vector. This study demonstrates the effectiveness of the proposed model through comparative experiments using the Visual Genome benchmark dataset.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.315-321
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• 2011

The CRT monitor display system for SONAR signal that are commonly used in ships or naval vessels uses vector scanning method. Therefore the processing circuits of the system is complex. Also because production had been shut down, the supply of parts is difficult as well as high-cost. FPGA -based embedded processor system is flexible to adapting to various applications because it makes simple processing circuits and its core is easily reconfigurable, and provides high speed performance in low-cost. In this paper, we describe an implementation of SONAR signal LCD display system using the FPGA embedded processor system to overcome some weakness of existing CRT system. By changing X-Y Deflection and CRT control blocks of current system into FPGA embedded processor system, our system provides the simplicity, flexibility and low-cost of system configuration, and also real-time acquisition and display of SONAR signal.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.5
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• pp.277-286
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• 2019

In this paper, we propose an efficient hyperplane generation technique to classify human activity from combination of events and sequence information obtained from multiple-event sensors. By generating hyperplane efficiently, our machine learning algorithm classify with less memory and run time than the LSVM (Linear Support Vector Machine) for embedded system. Because the fact that light weight and high speed algorithm is one of the most critical issue in the IoT, the study can be applied to smart home to predict human activity and provide related services. Our approach is based on reducing numbers of hyperplanes and utilizing robust string comparing algorithm. The proposed method results in reduction of memory consumption compared to the conventional ML (Machine Learning) algorithms; 252 times to LSVM and 34,033 times to LSTM (Long Short-Term Memory), although accuracy is decreased slightly. Thus our method showed outstanding performance on accuracy per hyperplane; 240 times to LSVM and 30,520 times to LSTM. The binarized image is then divided into groups, where each groups are converted to binary number, in order to reduce the number of comparison done in runtime process. The binary numbers are then converted to string. The test data is evaluated by converting to string and measuring similarity between hyperplanes using Levenshtein algorithm, which is a robust dynamic string comparing algorithm. This technique reduces runtime and enables the proposed algorithm to become 27% faster than LSVM, and 90% faster than LSTM.