Showing papers in "Journal of the Institute of Electronics Engineers of Korea in 2010"

Adaptive Energy Detection for Spectrum Sensing in Cognitive Radio

Abstract: Energy detection based spectrum sensing compares the energy of a received signal from a primary user with a detection threshold and decides whether it is active or not in the frequency band of interest. Here the detection threshold depends on not only a target false alarm probability but also the level of the noise energy in the band. So, if the noise energy changes, the detection threshold must be adjusted accordingly to maintain the given false alarm probability. Most previous works on energy detection for spectrum sensing are based on the assumption that noise energy is known a priori. In this paper, we present a new energy detection scheme updating its detection threshold under the assumption that the noise is white, and analyze its detection performance. Analytic results show that the proposed scheme can maintain a target false alarm rate without regard to the noise energy level and its spectrum sensing performance gets better as the time bandwidth product of the signal used to estimate the noise energy increases.

표면 근전도를 이용한 이두박근의 운동단위 위치 추정에 관한 연구

Abstract: 본 논문에서는 표면 근전도(surface electromyogram, SEMG)를 이용하여 운동단위(motor unit, MU)의 위치(location)를 추정하는 새로운 방법을 제안하였다. 운동단위의 위치에 따라 운동단위 활동전위(motor unit action potential, MUAP), 나아가서는 표면 근전도의 크기(amplitude)가 변화하므로 운동단위의 위치 추정은 근력 추정에 있어서 중요하다. 제안된 방법은 표면근전도 시뮬레이션을 통해 취득한 기준 신호와 3 채널 표면전극을 이용하여 검출한 표면 근전도 신호를 비교하여 운동단위의 위치를 추정하는 방법이다. 운동단위 위치 추정의 정확도를 파악하기 위하여 컴퓨터 시뮬레이션을 통하여 취득한 MUAP를 본 연구에서 제안한 방법 및 기존 방법들을 적용하여 확인하였다. 시뮬레이션 결과 8[㎜] 위치에 운동단위가 위치할 경우 본 논문에서 제안한 운동단위 위치 추정 방법은 0.01[㎜]의 평균 추정 오차를 보였다. 반면에 Roeleveld가 제안한 추정 방법은 2.33[㎜]의 평균 추정 오차를 보였으며 Akazawa가 제안한 추정 방법은 1.70[㎜]의 평균 추정 오차를 보여 본 연구에서 제안한 운동단위 위치 추정 방법이 기존의 방법들에 비하여 더 정확한 위치 추정이 가능하였다.

Low Power Consumption Technology for Streaming Data Playback in the IPTV Set-top Box

Abstract: The hard disk is one of the most frequently used storage in IPTV sep-top box. It has large storage capacity and provides fast I/O speed compared to its price whereas it causes high power consumption due to mechanical characteristics of spindle motor. In order to play streaming data in the set-top box, spindle motor of hard disk keeps active mode and it causes high power consumption. In this paper, We propose an offset-buffering and multi-mode spin-down method to reduce power consumption for streaming data playback. The offset-buffering inspects the user's viewing pattern and performs buffering based on the analysis of viewing pattern. So, it can maintain the status of spindle motor as idle mode for long time. Besides, it can reduce power consumption by spinning down according to offset-buffer size. The experimental result shows that proposed offset-buffering and multi mode spin-down method is about 28.3% and 12.5% lower than the full-Buffering method in terms of the power consumption and spin-down frequency, respectively.

Face and Iris Detection Algorithm based on SURF and circular Hough Transform

Abstract: The paper presents a novel algorithm for face and iris detection with the application for driver iris monitoring. The proposed algorithm consists of the following major steps: Skin-color segmentation, facial features segmentation, and iris positioning. For the skin-segmentation we applied a multi-layer perceptron to approximate the statistical probability of certain skin-colors, and filter out those with low probabilities. The next step segments the face region into the following categories: eye, mouth, eye brow, and remaining facial regions. For this purpose we propose a novel segmentation technique based on estimation of facial class probability density functions (PDF). Each facial class PDF is estimated on the basis of salient features extracted from a corresponding facial image region. Then pixels are classified according to the highest probability selected from four estimated PDFs. The final step applies the circular Hough transform to the detected eye regions to extract the position and radius of the iris. We tested our system on two data sets. The first one is obtained from the Web and contains faces under different illuminations. The second dataset was collected by us. It contains images obtained from video sequences recorded by a CCD camera while a driver was driving a car. The experimental results are presented, showing high detection rates.

Difference Edge Acquisition for B-spline Active Contour-Based Face Detection

Abstract: This paper proposes a method for enhancing detection performance and reducing computational cost when detecting a human face by applying B-spline active contour to the frame difference of consecutive images. Firstly, the method estimates amount of user's motion using kurtosis. If the kurtosis is smaller than a pre-defined threshold, it is considered that the amount of user's motion is insufficient and thus the contour fitting is not applied. Otherwise, the contour fitting is applied by exploiting the fact that the amount of motion is sufficient. Secondly, for the contour fitting, difference edges are detected by combining the distance transformation of the binarized frame difference and the edges of current frame. Lastly, the face is located by assigning the contour fitting process to the detected difference edges. Kurtosis-based motion amount estimation can reduce a computational cost and stabilize the results of the contour fitting. In addition, distance transformation-based difference edge detection can enhance the problems of contour lag and discontinuous difference edges. Experimental results confirm that the proposed method can reduce the face localization error caused by the contour lag and discontinuity of edges, and decrease the computational cost by omitting approximately 39% of the contour fitting.

The Hardware Architecture of Efficient Intra Predictor for H.264/AVC Decoder

Abstract: In this paper, we described intra prediction which is the one of techniques to be used for higher compression performance in H.264/AVC and proposed the design of intra predictor for efficient intra prediction mode processing. The proposed system is consist of processing elements, precomputation processing elements, an intra prediction controller, an internal memory and a register controller. The proposed system needs the reduced the computation cycles by using processing elements and precomputation processing element and also needs the reduced the number of access time to external memory by using internal memory and registers architecture. We designed the proposed system with Verilog-HDL and verified with suitable test vectors which are encoded YUV files. The proposed architecture belongs to the baseline profile of H.264/AVC decoder and is suitable for portable devices such as cellular phone with the size of . As a result of experiment, the performance of the proposed intra predictor is about 60% higher than that of the previous one.

Moving Object Detection using Clausius Entropy and Adaptive Gaussian Mixture Model

Abstract: A real-time detection and tracking of moving objects in video sequences is very important for smart surveillance systems. In this paper, we propose a novel algorithm for the detection of moving objects that is the entropy-based adaptive Gaussian mixture model (AGMM). First, the increment of entropy generally means the increment of complexity, and objects in unstable conditions cause higher entropy variations. Hence, if we apply these properties to the motion segmentation, pixels with large changes in entropy in moments have a higher chance in belonging to moving objects. Therefore, we apply the Clausius entropy theory to convert the pixel value in an image domain into the amount of energy change in an entropy domain. Second, we use an adaptive background subtraction method to detect moving objects. This models entropy variations from backgrounds as a mixture of Gaussians. Experiment results demonstrate that our method can detect motion object effectively and reliably.

Perceptual Localization of a phantom sound image for Ultrahigh-Definition TV

Abstract: This paper presents a localization perception of a phantom sound image for ultrahigh-definition TV with respect to various loudspeaker configurations; two-horizontal, two-vertical and triplet loudspeakers. Vector base amplitude panning algorithm with modification for non-equidistant loudspeaker setup is applied to create the phantom sound image. In order to practically study the localization performance in real situation, the listening tests were conducted at the on-axis and off-axis positions of TV in normal listening room. A method of adjustment which can reduce the ambiguity of a perceived angle is exploited to evaluate the angles of octave-band signals. The subjects changed the panning angle until the real sound source and virtually panned source were coincident. A spatial blurring can be measured by examining the differences of the panning angles perceived with respect to each band. The listening tests show that the triplet panning method has better performance than vertical panning in view of perceptual localization and spatial blurring at both on-axis and off-axis positions.

Adaptive Hard Decision Aided Fast Decoding Method in Distributed Video Coding

Abstract: Recently distributed video coding (DVC) is spotlighted for the environment which has restriction in computing resource at encoder Wyner-Ziv (WZ) coding is a representative scheme of DVC The WZ encoder independently encodes key frame and WZ frame respectively by conventional intra coding and channel code WZ decoder generates side information from reconstructed two key frames (t-1, t+1) based on temporal correlation The side information is regarded as a noisy version of original WZ frame Virtual channel noise can be removed by channel decoding process So the performance of WZ coding greatly depends on the performance of channel code Among existing channel codes, Turbo code and LDPC code have the most powerful error correction capability These channel codes use stochastically iterative decoding process However the iterative decoding process is quite time-consuming, so complexity of WZ decoder is considerably increased Analysis of the complexity of LPDCA with real video data shows that the portion of complexity of LDPCA decoding is higher than 60% in total WZ decoding complexity Using the HDA (Hard Decision Aided) method proposed in channel code area, channel decoding complexity can be much reduced But considerable RD performance loss is possible according to different thresholds and its proper value is different for each sequence In this paper, we propose an adaptive HDA method which sets up a proper threshold according to sequence The proposed method shows about 62% and 32% of time saving, respectively in LDPCA and WZ decoding process, while RD performance is not that decreased

Multi-target Classification Method Based on Adaboost and Radial Basis Function

Abstract: Adaboost is well known for a representative learner as one of the kernel methods. Adaboost which is based on the statistical learning theory shows good generalization performance and has been applied to various pattern recognition problems. However, Adaboost is basically to deal with a two-class classification problem, so we cannot solve directly a multi-class problem with Adaboost. One-Vs-All and Pair-Wise have been applied to solve the multi-class classification problem, which is one of the multi-class problems. The two methods above are ones of the output coding methods, a general approach for solving multi-class problem with multiple binary classifiers, which decomposes a complex multi-class problem into a set of binary problems and then reconstructs the outputs of binary classifiers for each binary problem. However, two methods cannot show good performance. In this paper, we propose the method to solve a multi-target classification problem by using radial basis function of Adaboost weak classifier.

경사도로에서 다중 센서를 이용한 이동로봇의 위치추정 성능 개선

Abstract: 본 논문에서는 실외환경에서 주행하는 이동로봇의 위치를 추정하는 알고리즘을 제안한다. 실외환경은 실내와 다르게 바닥이 고르지 않고, 경사진 지형 등 지면에 대한 불확실성을 포함한다. 이러한 환경에서 로봇의 진행 방향을 추정하기 위해 magnetic 센서 또는 IMU(Inertial Measurement Unit)가 예전부터 많이 사용되어 왔다. Magnetic 센서는 진행방향에 대한 절대 각도를 알려주며, IMU는 센서 내부에서 자이로스코프와 가속도계, 전자 나침반을 사용하여 각도 정보를 제공한다. 하지만 본 연구에 사용된 이동로봇은 전기자동차로써 자기장의 영향을 많이 받기 때문에 위 두 센서를 사용할 수가 없는 실정이다. 그래서 자기장의 영향을 받지 않는 1축 자이로 센서 3개를 이용한 자이로 모듈을 구성하여 진행방향을 추정하는 알고리즘을 구현하였다. GPS와 엔코더, 자이로 센서 모듈 등을 통해 얻은 정보를 융합하여 확장 칼만 필터 알고리즘에 의한 이동로봇의 위치추정 알고리즘을 개발하였고 실험을 통하여 제안한 알고리즘의 성능을 검증하였다.

A Gate Delay Model Considering Temporal Proximity of Multiple Input Switching

Abstract: Conventional cell characterization does not consider Multiple Input Switching(MIS). Since the impact of MIS on gate delay variation is large, it is not possible to predict the accurate gate delay with the conventional cell characterization. We observed the maximum 46% difference in gate delay due tn MIS. In this paper, we propose a gate delay model considering the delay variation caused by the temporal proximity of MIS. The proposed model calculates the delay variation using the Radial Basis Function. The experimental results show that the proposed method can more accurately predict the gate delay when MIS occurs.

Design of Computer Vision Interface by Recognizing Hand Motion

Abstract: As various interfacing devices for computational machines are being developed, a new HCI method using hand motion input is introduced. This interface method is a vision-based approach using a single camera for detecting and tracking hand movements. In the previous researches, only a skin color is used for detecting and tracking hand location. However, in our design, skin color and shape information are collectively considered. Consequently, detection ability of a hand increased. we proposed primary orientation edge descriptor for getting an edge information. This method uses only one hand model. Therefore, we do not need training processing time. This system consists of a detecting part and a tracking part for efficient processing. In tracking part, the system is quite robust on the orientation of the hand. The system is applied to recognize a hand written number in script style using DNAC algorithm. Performance of the proposed algorithm reaches 82% recognition ratio in detecting hand region and 90% in recognizing a written number in script style.

A Design and Implementation of Music & Image Retrieval Recommendation System based on Emotion

Abstract: Emotion intelligence computing is able to processing of human emotion through it's studying and adaptation. Also, Be able more efficient to interaction of human and computer. As sight and hearing, music & image is constitute of short time and continue for long. Cause to success marketing, understand-translate of humanity emotion. In this paper, Be design of check system that matched music and image by user emotion keyword(irritability, gloom, calmness, joy). Suggested system is definition by 4 stage situations. Then, Using music & image and emotion ontology to retrieval normalized music & image. Also, A sampling of image peculiarity information and similarity measurement is able to get wanted result. At the same time, Matched on one space through pared correspondence analysis and factor analysis for classify image emotion recognition information. Experimentation findings, Suggest system was show 82.4% matching rate about 4 stage emotion condition.

Laser Speckle Imaging Using Adaptive Windowing Method

Abstract: A laser speckle is a random pattern that has a granular appearance produced by reflected light when a coherent laser illuminates an irregular course surface. Most important property of laser speckle is detecting micro-vascular. Speckle image needs image processing to detect micro-vascular. This paper proposes a new image processing method for laser speckle, adaptive window method that adaptively processes laser speckle images in the spatial. Conventional fixed window based LASCA has shortcoming in that it uses the same window size regardless of target areas. Inherently laser speckle contains undesired noise. Thus a large window is helpful for removing the noise but it results in low resolution of image. Otherwise a small window may detect micro vascular but it has limits in noise removal. To overcome this trade-off, we newly introduce the concept of adaptive window method to conventional laser speckle image analysis. We have compared conventional LASCA and its variants with the proposed method in terms of image quality and processing complexity.

Texture Descriptor for Texture-Based Image Retrieval and Its Application in Computer-Aided Diagnosis System

Abstract: Texture information plays an important role in object recognition and classification. To perform an accurate classification, the texture feature used in the classification must be highly discriminative. This paper presents a novel texture descriptor for texture-based image retrieval and its application in Computer-Aided Diagnosis (CAD) system for Emphysema classification. The texture descriptor is based on the combination of local surrounding neighborhood difference and centralized neighborhood difference and is named as Combined Neighborhood Difference (CND). The local differences of surrounding neighborhood difference and centralized neighborhood difference between pixels are compared and converted into binary codewords. Then binomial factor is assigned to the codewords in order to convert them into high discriminative unique values. The distribution of these unique values is computed and used as the texture feature vectors. The texture classification accuracies using Outex and Brodatz dataset show that CND achieves an average of 92.5%, whereas LBP, LND and Gabor filter achieve 89.3%, 90.7% and 83.6%, respectively. The implementations of CND in the computer-aided diagnosis of Emphysema is also presented in this paper.

Loading/Unloading Decision System of Ship Block in the Shipyard

Abstract: It is an important element increasing ship production to manage an accurate position of transporters(TP) and ship blocks in a shipyard. However, most works are presently being performed by judgment of a system manager and skilled workers. This paper introduced about the system for tracking an accurate position of the transporters and the blocks which are main mobile objects in the shipyard, and proposed a method to decide whether or not a loading/unloading state of the blocks, which is one of the most important functions of the tracking system. Three sensors were used in order to implement the method. One is a RFID reader to identify a target block, another is a RFID reader to estimate a position of the TP as it recognizes a underground tag. The other is a ultrasonic sensor to detect an object. Two experiments were carried out in the shipyard. After correcting errors found on the first experiment. we confirmed that the result could be applied to the shipbuilding yard from the final experiment.

KNN/PFCM Hybrid Algorithm for Indoor Location Determination in WLAN

Abstract: For the indoor location, wireless fingerprinting is most favorable because fingerprinting is most accurate among the technique for wireless network based indoor location which does not require any special equipments dedicated for positioning. As fingerprinting method,k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighborsk and positions of reference points(RPs). So possibilistic fuzzy c-means(PFCM) clustering algorithm is applied to improve KNN, which is the KNN/PFCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN,k RPs are firstly chosen as the data samples of PFCM based on signal to noise ratio(SNR). Then, thek RPs are classified into different clusters through PFCM based on SNR. Experimental results indicate that the proposed KNN/PFCM hybrid algorithm generally outperforms KNN and KNN/FCM algorithm when the locations error is less than 2m.

Design and Verification of Efficient On-Chip Debugger for Core-A

Abstract: Nowadays, the SoC is watched by all over the world with interest. The design trend of the SoC is hardware and software co-design which includes the design of hardware structure in RTL level and the development of embedded software. Also the technology is toward deep-submicron and the observability of the SoC's internal state is not easy. Because of the above reasons, the SoC debug is very difficult and time-consuming. So we need a reliable debugger to find the bugs in the SoC and embedded software. In this paper, we developed a hardware debugger named OCD. It is based on IEEE 1140.1 JTAG standard. In order to verify the operation of OCD, it is integrated into the 32bit RISC processor - Core-A (Core-A is the unique embedded processor designed by Korea) and is tested by interconnecting with software debugger. When embedding the OCD in Core-A, there is 14.7% gate count overhead. We can modify the DCU which occupies 2% gate count in OCD to adapt with other processors as a debugger.

Cooperative Spectrum Sensing with Distance Based Weight for Cognitive Radio Systems

Abstract: In this paper, we analysis the performance of cooperative spectrum sensing with distance based weight for cognitive radio (CR) systems and CR systems sense the spectrum of the licensed user by using a energy detection method. Threshold is determined in accordance with the constant false alarm rate (CFAR) algorithm for energy detection. The signal of licensed user is OFDM signal and the wireless channel between a licensed user and CR systems is modeled as Gaussian channel. From the simulation results, the cooperative spectrum sensing with distance based weight combining (DWC) and equal gain combing (EGC) methods shows higher spectrum sensing performance than single spectrum sensing does. And the detection probability performance with the DWC is higher than that with the EGC.

소스 피드백을 이용한 고이득 W-band MMIC 증폭기 설계

Abstract: 본 논문에서는 70 nm mHEMT MMIC 기술을 이용한 고이득 W-band 증폭기를 제시한다. W-band에서 고이득 특성을 얻기 위하여 공통 소스 FET의 소스 피드백 라인의 길이를 조절하면 설계 주파수에서 이득이 최대가 되도록 하였다. 이 라인의 길이를 조절하여 94 GHz에서 MAG를 0.8 dB 향상 시킬 수 있음을 시뮬레이션에서 확인하였다. 뿐만 아니라, 이 소스 피드백 라인은 FET의 입력 임피던스도 변화시켜 입력 정합을 용이하게 한다. 이 현상을 이용하여 공통 소스 FET 4단으로 이루어진 w-band 증폭기를 CPW로 설계하였다. 제작된 W-band 증폭기는 측정 결과 70~103 GHz에서 22.0 dB 이상의 아주 우수한 이득 특성을 보였다. 【In this paper, a high gain W-band amplifier is presented using 70 run mHEMT MMIC technology. The length of source feedback line of common-source FET is carefully determined to maximize the gain at a design frequency. Simulation shows that MAG can be increased by 0.8 dB by optimizing the length of this line. In addition, this feedback line changes the input impedance of the common-source FET in a way that the input match can be made easier. In this work, 4-stage amplifier is designed on CPW using the source feedback. The measurement shows the excellent gain performance higher than 22.0 dB across 70~103 GHz.】

Subpixel Shift Estimation in Noisy Image Using Iterative Phase Correlation of A Selected Local Region

Abstract: In this paper, we propose a subpixel shift estimation method using phase correlation with a local region for the registration of noisy images. Phase correlation is commonly used to estimate the subpixel shift between images, which is derived from analyzing shifted and downsampled images. However, when the images are affected by additive white Gaussian noise and aliasing artifacts, the estimation error is increased. Thus, instead of using the whole image, the proposed method uses a specific local region that is less affect by noises. In addition, to improve the estimation accuracy, iterative phase correlation is applied between selected local regions rather than using a fitting function. the restricted range is determined by analyzing the maximum peak and the two adjacent values of the inverse Fourier transform of the normalized cross power spectrum. In the experiments, the proposed method shows higher accuracy in registering noisy images than the other methods. Thus, the edge-sharpness and clearness in the super-resolved image is also improved.

Design of Traffic Generator Based on Modeling of Characteristic of Multimedia Data

Abstract: A study on network traffic analysis and modeling has been exclusively done due to its importance. However, conventional studies on network traffic analysis and modeling only focus on transmitting simple packet stream or traffic features of specific application, such as HTTP. In this paper, we propose a network traffic generator, which reflects the characteristics of multimedia data. To analyze the traffics of online game, which is one of the most popular multimedia contents, we modeled the distribution according to the time between packets and packet size random variable and designed the traffic generator which has the model for input. We generated the traffics of L4D(Left4Dead), WoW(World of Warcraft) with proposed network traffic generator and we found that the generated traffics have similar distributions with real data.

Implementation of Intelligent Image Surveillance System based Context

Abstract: This paper is a study on implementation of intelligent image surveillance system using context information and supplements temporal-spatial constraint, the weak point in which it is hard to process it in real time. In this paper, we propose scene analysis algorithm which can be processed in real time in various environments at low resolution video(320*240) comprised of 30 frames per second. The proposed algorithm gets rid of background and meaningless frame among continuous frames. And, this paper uses wavelet transform and edge histogram to detect shot boundary. Next, representative key-frame in shot boundary is selected by key-frame selection parameter and edge histogram, mathematical morphology are used to detect only motion region. We define each four basic contexts in accordance with angles of feature points by applying vertical and horizontal ratio for the motion region of detected object. These are standing, laying, seating and walking. Finally, we carry out scene analysis by defining simple context model composed with general context and emergency context through estimating each context's connection status and configure a system in order to check real time processing possibility. The proposed system shows the performance of 92.5% in terms of recognition rate for a video of low resolution and processing speed is 0.74 second in average per frame, so that we can check real time processing is possible.

Lane and Curvature Detection Algorithm based on the Curve Template Matching Method using Top View Image

Abstract: In this paper, lane and curvature detection algorithm based on the curve template matching method is proposed. To eliminate the perspective effect of the original image, the input image is transformed to a top view image. From this top view image, its edge image is created. To increase the accuracy of detection, a novel edge detection method, which shows a strength in lane detection, is proposed. In the first step, straight lanes are detected from the edge image, and then the Curve Template Matching(CTM) method is applied to detect the curved lanes and to find their curvatures. Since the proposed CTM method uses only the simple equations, such as line and circle equations, to detect the curved lane, the algorithm is simple. Moreover, we used the detected lane information in the previous frames to detect the current frame's lanes, the detection results become more reliable. The proposed algorithm has been tested in various road conditions (highway, urban street, night time highway, etc.). Experimental results show that the proposed algorithm can process about 70 frames per second with the successful lane detection rate over 95% and curvature detection rate about 90%.