• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.45 no.2
• /
• pp.1-9
• /
• 2008

As the security requirements of mobile phones have been increasing, there have been extensive researches using one biometric feature (e.g., an iris, a fingerprint, or a face image) for authentication. Due to the limitation of uni-modal biometrics, we propose a method that combines face and iris images in order to improve accuracy in mobile environments. This paper presents four advantages and contributions over previous research. First, in order to capture both face and iris image at fast speed and simultaneously, we use a built-in conventional mega pixel camera in mobile phone, which is revised to capture the NIR (Near-InfraRed) face and iris image. Second, in order to increase the authentication accuracy of face and iris, we propose a score level fusion method based on SVM (Support Vector Machine). Third, to reduce the classification complexities of SVM and intra-variation of face and iris data, we normalize the input face and iris data, respectively. For face, a NIR illuminator and NIR passing filter on camera are used to reduce the illumination variance caused by environmental visible lighting and the consequent saturated region in face by the NIR illuminator is normalized by low processing logarithmic algorithm considering mobile phone. For iris, image transform into polar coordinate and iris code shifting are used for obtaining robust identification accuracy irrespective of image capturing condition. Fourth, to increase the processing speed on mobile phone, we use integer based face and iris authentication algorithms. Experimental results were tested with face and iris images by mega-pixel camera of mobile phone. It showed that the authentication accuracy using SVM was better than those of uni-modal (face or iris), SUM, MAX, NIN and weighted SUM rules.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.45 no.4
• /
• pp.94-104
• /
• 2008

In this paper, we propose a new architecture of multiplier-and-accumulator (MAC) for high speed multiplication and accumulation arithmetic. By combining multiplication with accumulation and devising a hybrid type of carry save adder (CSA), the performance was improved. Since the accumulator which has the largest delay in MAC was removed and its function was included into CSA, the overall performance becomes to be elevated. The proposed CSA tree uses 1's complement-based radix-2 modified booth algorithm (MBA) and has the modified array for the sign extension in order to increase the bit density of operands. The CSA propagates the carries by the least significant bits of the partial products and generates the least significant bits in advance for decreasing the number of the input bits of the final adder. Also, the proposed MAC accumulates the intermediate results in the type of sum and carry bits not the output of the final adder for improving the performance by optimizing the efficiency of pipeline scheme. The proposed architecture was synthesized with $250{\mu}m,\;180{\mu}m,\;130{\mu}m$ and 90nm standard CMOS library after designing it. We analyzed the results such as hardware resource, delay, and pipeline which are based on the theoretical and experimental estimation. We used Sakurai's alpha power low for the delay modeling. The proposed MAC has the superior properties to the standard design in many ways and its performance is twice as much than the previous research in the similar clock frequency.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.1
• /
• pp.59-69
• /
• 2018

In this paper, we present a robust beamforming design to tackle the weighted sum-rate maximization (WSRM) problem in a multicell multiple-input multiple-output (MIMO) - non-orthogonal multipleaccess (NOMA) downlink system for 5G wireless communications. This work consider the imperfectchannel state information (CSI) at the base station (BS) by adding uncertainties to channel estimation matrices as the worst-case model i.e., singular value uncertainty model (SVUM). With this observation, the WSRM problem is formulated subject to the transmit power constraints at the BS. The objective problem is known as on-deterministic polynomial (NP) problem which is difficult to solve. We propose an robust beam forming design which establishes on majorization minimization (MM) technique to find the optimal transmit beam forming matrix, as well as efficiently solve the objective problem. In addition, we also propose a joint user clustering and power allocation (JUCPA) algorithm in which the best user pair is selected as a cluster to attain a higher sum-rate. Extensive numerical results are provided to show that the proposed robust beamforming design together with the proposed JUCPA algorithm significantly increases the performance in term of sum-rate as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme.

• The KIPS Transactions:PartB
• /
• v.13B no.1 s.104
• /
• pp.53-62
• /
• 2006

Object recognition techniques using principal component analysis are disposed to be decreased recognition rate when lighting change of image happens. The purpose of this thesis is to propose an object recognition technique using new PCA analysis method that discriminates an object in database even in the case that the variation of illumination in training images exists. And the object recognition algorithm proposed here represents more enhanced recognition rate using improved k-Nearest Neighbor. In this thesis, we proposed an object recognition algorithm which creates object space by pre-processing and being learned image using histogram equalization and median filter. By spreading histogram of test image using histogram equalization, the effect to change of illumination is reduced. This method is stronger to change of illumination than basic PCA method and normalization, and almost removes effect of illumination, therefore almost maintains constant good recognition rate. And, it compares ingredient projected test image into object space with distance of representative value and recognizes after representative value of each object in model image is made. Each model images is used in recognition unit about some continual input image using improved k-Nearest Neighbor in this thesis because existing method have many errors about distance calculation.

• Proceedings of the KSRS Conference
• /
• 1998.09a
• /
• pp.286-291
• /
• 1998

SAR has provided weather independent images on land and sea surface, which can be used for extracting various useful informations. Recently attempts to estimate wind field parameters from SAR images over the oceans have been made by various groups over the world. Although scatterometer loaded in ERS-1 and ERS-2 observes the global wind vector field at spatial resolution of 50 Km with accuracies of $\pm$2m/s in speed, the spatial resolution may not be good enough for applications in coastal regions. It is weil known the sea surface roughness is closely correlated to the wind field, but the wind retrieval algorithms from SAR images are yet in developing stage. Since the radar backscattering properties of the SAR images are principally the same as that of scatterometer, some previous studies conducted by other groups report the success in mesoscale coastal wind field retrievals using ERS SAR images. We have tested SWA (SAR Wind Algorithm) and CMOD4 model for estimation of wind speed using an ERS-1 SAR image acquired near Cheju Island, Korea, in October 11, 1994. The precise estimation of sigma nought and the direction of wind are required for applying the CMOD4 model to estimate wind speed. The wind speed in the test sub-image is estimated to be about 10.5m/s, which relatively well agrees to the observed wind speed about 9.0m/s at Seoguipo station. The wind speed estimation through the SWA is slightly higher than that of CMOD4 model. The sea surface condition may be favorable to SWA on the specific date. Since the CMOD4 model requires either wind direction or wind speed to retrieve the wind field, we should estimate the wind speed first using other algorithm including SWA. So far, it is not conclusive if the SWA can be used to provide input wind speed data for CMOD4 model or not. Since it is only initial stage of implementing the wind field retrieval algorithms and no in-situ observed data is currently avaliable, we are not able to evaluate the accuracy of the results at the moment. Therefore verification studies should be followed in the future to extract reliable wind field information in the coastal region using ERS SAR images.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.38 no.3
• /
• pp.304-315
• /
• 2001

In this paper, we peformed the real-time implementation of AMR(Adaptive Multi-Rate) speech coding algorithm which is adopted for IMT-2000 service using TMS320C6201, i.e., a Texas Instrument´s fixed-point DSP. With the ANSI C source code released from ETSI, optimization is performed to make it run in real-time with memory as small as possible using the C compiler and assembly language. Implemented AMR speech codec has the size of 32.06 kWords program memory, 9.75 kWords data RAM memory, and 19.89 kWords data ROM memory. And, The time required for processing one frame of 20 ms length speech data is about 4.38 ms, and it is short enough for real-time operation. It is verified that the decoded result of the implemented speech codec on the DSP is identical with the PC simulation result using ANSI C code for test sequences. Also, actual sound input/output test using microphone and speaker demonstrates its proper real-time operation without distortions or delays.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.40 no.6
• /
• pp.613-622
• /
• 2020

The South Korean government plans for a 37 % reduction in CO2 emissions against business as usual by 2030. Subsequently, the Ministry of Land, Infrastructure and Transport declared a 26.9 % reduction target in greenhouse gas emissions from buildings by 2020 and established the Green Standard for Energy and Environmental Design (G-SEED) to help improve the environmental performance of buildings. Construction companies often work with consulting firms to prepare for G-SEED certification. In the process, owing to inefficient data sharing and work connections, it is difficult to achieve economic efficiency and obtain certification. The objective of this study was to develop an economic model to assist contractors in achieving the required G-SEED scores for materials and resources. To do this, we automated the process for material comparison and selection on the basis of an analysis of actual consulting data, and developed a model that selects material alternatives that can meet the required scores at a minimum cost. Information on materials is input by applying a genetic algorithm to the optimization of alternatives. When the model was applied to actual data, the construction cost could be lowered by 79.3 % compared with existing methods. The economical material selection model is expected to not only reduce construction costs for owners desiring G-SEED certification but also shorten the project design time.

• The Journal of Bigdata
• /
• v.6 no.2
• /
• pp.125-137
• /
• 2021

The number of CCTV installed in Korea is over 1.3 million, increasing by more than 15% annually. However, due to the limited budget compared to the installation demand, the infrastructure is composed of 500,000 pixel low-quality CCTV, and there is a limits on identification of objects in the video. Public CCTV has high utility in various fields such as crime prevention, traffic information collection (control), facility management, and fire prevention. Especially, since installed in high height, it works as its role in solving diverse crime and is in increasing trend. However, the current public CCTV field is operated with potential problems such as inability to identify due to environmental factors such as fog, snow, and rain, and the low-quality of collected images due to the installation of low-quality CCTV. Therefore, in this study, in order to remove the typical low-quality elements of public CCTV, the method of attenuating scattered light in the image caused by dust, water droplets, fog, etc and algorithm application method which uses deep-learning algorithm to improve input video into videos over quality over 4K are suggested.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.6B
• /
• pp.597-603
• /
• 2006

The purpose of this study is to improve the short term rainfall forecast skill using neural network model that can deal with the non-linear behavior between satellite data and ground observation, and minimize the flood damage. To overcome the geographical limitation of Korean peninsula and get the long forecast lead time of 3 to 6 hour, the developed rainfall forecast model took satellite imageries and wide range AWS data. The architecture of neural network model is a multi-layer neural network which consists of one input layer, one hidden layer, and one output layer. Neural network is trained using a momentum back propagation algorithm. Flood was estimated using rainfall forecasts. We developed a dynamic flood inundation model which is associated with 1-dimensional flood routing model. Therefore the model can forecast flood aspect in a protected lowland by levee failure of river. In the case of multiple levee breaks at main stream and tributaries, the developed flood inundation model can estimate flood level in a river and inundation level and area in a protected lowland simultaneously.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.40 no.5
• /
• pp.312-321
• /
• 2003

In this paper, we implement robot which are ability to recognize obstacles and moving automatically to destination. we present two results in this paper; hardware implementation of image processing board and software implementation of visual feedback algorithm for a self-controlled robot. In the first part, the mobile robot depends on commands from a control board which is doing image processing part. We have studied the self controlled mobile robot system equipped with a CCD camera for a long time. This robot system consists of a image processing board implemented with DSPs, a stepping motor, a CCD camera. We will propose an algorithm in which commands are delivered for the robot to move in the planned path. The distance that the robot is supposed to move is calculated on the basis of the absolute coordinate and the coordinate of the target spot. And the image signal acquired by the CCD camera mounted on the robot is captured at every sampling time in order for the robot to automatically avoid the obstacle and finally to reach the destination. The image processing board consists of DSP (TMS320VC33), ADV611, SAA7111, ADV7l76A, CPLD(EPM7256ATC144), and SRAM memories. In the second part, the visual feedback control has two types of vision algorithms: obstacle avoidance and path planning. The first algorithm is cell, part of the image divided by blob analysis. We will do image preprocessing to improve the input image. This image preprocessing consists of filtering, edge detection, NOR converting, and threshold-ing. This major image processing includes labeling, segmentation, and pixel density calculation. In the second algorithm, after an image frame went through preprocessing (edge detection, converting, thresholding), the histogram is measured vertically (the y-axis direction). Then, the binary histogram of the image shows waveforms with only black and white variations. Here we use the fact that since obstacles appear as sectional diagrams as if they were walls, there is no variation in the histogram. The intensities of the line histogram are measured as vertically at intervals of 20 pixels. So, we can find uniform and nonuniform regions of the waveforms and define the period of uniform waveforms as an obstacle region. We can see that the algorithm is very useful for the robot to move avoiding obstacles.