Search results for: Adaptive Video Streaming

Authors: Ouadoudi Zytoune, Youssef Fakhri, Driss Aboutajdine

Abstract:

This paper focuses on reducing the power consumption of wireless sensor networks. Therefore, a communication protocol named LEACH (Low-Energy Adaptive Clustering Hierarchy) is modified. We extend LEACHs stochastic cluster-head selection algorithm by a modifying the probability of each node to become cluster-head based on its required energy to transmit to the sink. We present an efficient energy aware routing algorithm for the wireless sensor networks. Our contribution consists in rotation selection of clusterheads considering the remoteness of the nodes to the sink, and then, the network nodes residual energy. This choice allows a best distribution of the transmission energy in the network. The cluster-heads selection algorithm is completely decentralized. Simulation results show that the energy is significantly reduced compared with the previous clustering based routing algorithm for the sensor networks.

Keywords: Wireless Sensor Networks, Energy efficiency, WirelessCommunications, Clustering-based algorithm.

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Authors: El Falaki Brahim, Khalidi Idrissi Mohammed, Bennani Samir

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E-learning is not restricted to the use of new technologies for the online content, but also induces the adoption of new approaches to improve the quality of education. This quality depends on the ability of these approaches (technical and pedagogical) to provide an adaptive learning environment. Thus, the environment should include features that convey intentions and meeting the educational needs of learners by providing a customized learning path to acquiring a competency concerned In our proposal, we believe that an individualized learning path requires knowledge of the learner. Therefore, it must pass through a personalization of diagnosis to identify precisely the competency gaps to fill, and reduce the cognitive load To personalize the diagnosis and pertinently measure the competency gap, we suggest implementing the formative assessment in the e-learning environment and we propose the introduction of a pre-regulation process in the area of formative assessment, involving its individualization and implementation in e-learning.

Keywords: Competency-Based-Approach, E-learning, Formative assessment, learner model, Modeling, pre-regulation process

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Authors: Linnea Stenliden

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The aim of this paper is to understand emerging learning conditions, when a visual analytics is implemented and used in K 12 (education). To date, little attention has been paid to the role visual analytics (digital media and technology that highlight visual data communication in order to support analytical tasks) can play in education, and to the extent to which these tools can process actionable data for young students. This study was conducted in three public K 12 schools, in four social science classes with students aged 10 to 13 years, over a period of two to four weeks at each school. Empirical data were generated using video observations and analyzed with help of metaphors within Actor-network theory (ANT). The learning conditions are found to be distinguished by broad complexity, characterized by four dimensions. These emerge from the actors’ deeply intertwined relations in the activities. The paper argues in relation to the found dimensions that novel approaches to teaching and learning could benefit students’ knowledge building as they work with visual analytics, analyzing visualized data.

Keywords: Analytical reasoning, complexity, data use, problem space, visual analytics, visual storytelling, translation.

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Authors: Robert F. Kenny, Glenda A. Gunter

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Current technological advances pale in comparison to the changes in social behaviors and 'sense of place' that is being empowered since the Internet made it on the scene. Today-s students view the Internet as both a source of entertainment and an educational tool. The development of virtual environments is a conceptual framework that needs to be addressed by educators and it is important that they become familiar with who these virtual learners are and how they are motivated to learn. Massively multiplayer online role playing games (MMORPGs), if well designed, could become the vehicle of choice to deliver learning content. We suggest that these games, in order to accomplish these goals, must begin with well-established instructional design principles that are co-aligned with established principles of video game design. And have the opportunity to provide an instructional model of significant prescriptive power. The authors believe that game designers need to take advantage of the natural motivation player-learners have for playing games by developing them in such a way so as to promote, intrinsic motivation, content learning, transfer of knowledge, and naturalization.

Keywords: serious games, endogenous fantasy, intrinsic motivation, online learning.

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Authors: R. Sridevi, G. Jagajothi

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Artificial Immune Systems (AIS), inspired by the human immune system, are algorithms and mechanisms which are self-adaptive and self-learning classifiers capable of recognizing and classifying by learning, long-term memory and association. Unlike other human system inspired techniques like genetic algorithms and neural networks, AIS includes a range of algorithms modeling on different immune mechanism of the body. In this paper, a mechanism of a human immune system based on apoptosis is adopted to build an Intrusion Detection System (IDS) to protect computer networks. Features are selected from network traffic using Fisher Score. Based on the selected features, the record/connection is classified as either an attack or normal traffic by the proposed methodology. Simulation results demonstrates that the proposed AIS based on apoptosis performs better than existing AIS for intrusion detection.

Keywords: Apoptosis, Artificial Immune System (AIS), Fisher Score, KDD dataset, Network intrusion detection.

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Authors: Iman Iraei, Mina Sharifi

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A method for object tracking in motion blurred images is proposed in this article. This paper shows that object tracking could be improved with this approach. We use mean shift algorithm to track different objects as a main tracker. But, the problem is that mean shift could not track the selected object accurately in blurred scenes. So, for better tracking result, and increasing the accuracy of tracking, wavelet transform is used. We use a feature named as blur extent, which could help us to get better results in tracking. For calculating of this feature, we should use Harr wavelet. We can look at this matter from two different angles which lead to determine whether an image is blurred or not and to what extent an image is blur. In fact, this feature left an impact on the covariance matrix of mean shift algorithm and cause to better performance of tracking. This method has been concentrated mostly on motion blur parameter. transform. The results reveal the ability of our method in order to reach more accurately tracking.

Keywords: Mean shift, object tracking, blur extent, wavelet transform, motion blur.

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Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani

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Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.

Keywords: Color space, neural network, random forest, skin detection, statistical feature.

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Authors: Lae-Jeong Park, Jung-Ho Moon

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People detection from images has a variety of applications such as video surveillance and driver assistance system, but is still a challenging task and more difficult in crowded environments such as shopping malls in which occlusion of lower parts of human body often occurs. Lack of the full-body information requires more effective features than common features such as HOG. In this paper, new features are introduced that exploits global self-symmetry (GSS) characteristic in head-shoulder patterns. The features encode the similarity or difference of color histograms and oriented gradient histograms between two vertically symmetric blocks. The domain-specific features are rapid to compute from the integral images in Viola-Jones cascade-of-rejecters framework. The proposed features are evaluated with our own head-shoulder dataset that, in part, consists of a well-known INRIA pedestrian dataset. Experimental results show that the GSS features are effective in reduction of false alarmsmarginally and the gradient GSS features are preferred more often than the color GSS ones in the feature selection.

Keywords: Pedestrian detection, cascade of rejecters, feature extraction, self-symmetry, HOG.

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Authors: L. McAllister, A. Dudau

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This paper explores gender related barriers to interagency collaboration in statutory children safeguard partnerships against a theoretical framework that considers individuals, professions and organisations interacting as part of a complex adaptive system. We argue that gender-framed obstacles to effective communication between culturally discrepant agencies can ultimately impact on the effectiveness of policy delivery,. We focused our research on three partnership structures in Sefton Metropolitan Borough in order to observe how interactions occur, whether the agencies involved perceive their occupational environment as being gender affected and whether they believe this can hinder effective collaboration with other biased organisations. Our principal empirical findings indicate that there is a general awareness amongst professionals of the role that gender plays in each of the agencies reviewed, that gender may well constitute a barrier to effective communication, but there is a sense in which there is little scope for change in the short term. We aim to signal here, however, the need to change against the risk of service failure.

Keywords: Children's safeguard, gender, gendered professions, inter-agency collaboration, partnerships.

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Authors: Mário A. R. Talaia

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As it is known, buoyancy and drag forces rule bubble's rise velocity in a liquid column. These forces are strongly dependent on fluid properties, gravity as well as equivalent's diameter. This study reports a set of bubble rising velocity experiments in a liquid column using water or glycerol. Several records of terminal velocity were obtained. The results show that bubble's rise terminal velocity is strongly dependent on dynamic viscosity effect. The data set allowed to have some terminal velocities data interval of 8.0 ? 32.9 cm/s with Reynolds number interval 1.3 -7490. The bubble's movement was recorded with a video camera. The main goal is to present an original set data and results that will be discussed based on two-phase flow's theory. It will also discussed, the prediction of terminal velocity of a single bubble in liquid, as well as the range of its applicability. In conclusion, this study presents general expressions for the determination of the terminal velocity of isolated gas bubbles of a Reynolds number range, when the fluid proprieties are known.

Keywords: Bubbles, terminal velocity, two phase-flow, vertical column.

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Authors: N. M. Nawi, R. S. Ransing, M. R. Ransing

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The conjugate gradient optimization algorithm is combined with the modified back propagation algorithm to yield a computationally efficient algorithm for training multilayer perceptron (MLP) networks (CGFR/AG). The computational efficiency is enhanced by adaptively modifying initial search direction as described in the following steps: (1) Modification on standard back propagation algorithm by introducing a gain variation term in the activation function, (2) Calculation of the gradient descent of error with respect to the weights and gains values and (3) the determination of a new search direction by using information calculated in step (2). The performance of the proposed method is demonstrated by comparing accuracy and computation time with the conjugate gradient algorithm used in MATLAB neural network toolbox. The results show that the computational efficiency of the proposed method was better than the standard conjugate gradient algorithm.

Keywords: Adaptive gain variation, back-propagation, activation function, conjugate gradient, search direction.

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Authors: Abdalla Kablan, Joseph Falzon

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This paper is motivated by the aspect of uncertainty in financial decision making, and how artificial intelligence and soft computing, with its uncertainty reducing aspects can be used for algorithmic trading applications that trade in high frequency. This paper presents an optimized high frequency trading system that has been combined with various moving averages to produce a hybrid system that outperforms trading systems that rely solely on moving averages. The paper optimizes an adaptive neuro-fuzzy inference system that takes both the price and its moving average as input, learns to predict price movements from training data consisting of intraday data, dynamically switches between the best performing moving averages, and performs decision making of when to buy or sell a certain currency in high frequency.

Keywords: Financial decision making, High frequency trading, Adaprive neuro-fuzzy systems, moving average strategy.

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Authors: Nariman O. Kandil, Ehab K. Abou-Elkheir, Amr M. Kotb

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Effective IT governance guarantees the strategic alignment of IT and business goals, risk mitigation control, and better IT and business performance. This study seeks to examine empirically the extent of IT governance implementation within the firms listed on the Egyptian Stock Exchange (EGX30) and its determinants. Accordingly, 18 semi-structured interviews, face to face, phone, and video-conferencing, using various tools (e.g., WebEx, Zoom, and Microsoft Teams) were undertaken at the interviewees’ offices in Egypt between the end of November 2019 and the end of August 2020. Results suggest that there are variances in the extent of IT Governance (ITG) implementation within the firms listed on the EGX30, mainly caused by the industry type and internal and external triggers. The results also suggest that the organization size, the type of auditor, the criticality of the industry, the effective processes & key performance indicators (KPIs), and the information intensity expertise of the chief information officers (CIOs) have a significant impact on ITG implementation within the firms.

Keywords: Effective IT governance, Egyptian Market, information security, risk controls.

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Authors: Xiaofei Hu, Zhiyu Cai, Weian Yao

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V-notch problem under dynamic loading condition is considered in this paper. In the time domain, the precise time domain expanding algorithm is employed, in which a self-adaptive technique is carried out to improve computing accuracy. By expanding variables in each time interval, the recursive finite element formulas are derived. In the space domain, a Symplectic Analytical Singular Element (SASE) for V-notch problem is constructed addressing the stress singularity of the notch tip. Combining with the conventional finite elements, the proposed SASE can be used to solve the dynamic stress intensity factors (DSIFs) in a simple way. Numerical results show that the proposed SASE for V-notch problem subjected to dynamic loading condition is effective and efficient.

Keywords: V-notch, dynamic stress intensity factor, finite element method, precise time domain expanding algorithm.

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Authors: P. Acharjee, S. K. Goswami

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Particle Swarm Optimization (PSO) with elite PSO parameters has been developed for power flow analysis under practical constrained situations. Multiple solutions of the power flow problem are useful in voltage stability assessment of power system. A method of determination of multiple power flow solutions is presented using a hybrid of Particle Swarm Optimization (PSO) and local search technique. The unique and innovative learning factors of the PSO algorithm are formulated depending upon the node power mismatch values to be highly adaptive with the power flow problems. The local search is applied on the pbest solution obtained by the PSO algorithm in each iteration. The proposed algorithm performs reliably and provides multiple solutions when applied on standard and illconditioned systems. The test results show that the performances of the proposed algorithm under critical conditions are better than the conventional methods.

Keywords: critical conditions, ill-conditioned systems, localsearch technique, multiple power flow solutions, particle swarmoptimization.

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Authors: Ching-Fen Jiang

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Edge detection is usually the first step in medical image processing. However, the difficulty increases when a conventional kernel-based edge detector is applied to ultrasonic images with a textural pattern and speckle noise. We designed an adaptive diffusion filter to remove speckle noise while preserving the initial edges detected by using a Sobel edge detector. We also propose a genetic algorithm for edge selection to form complete boundaries of the detected entities. We designed two fitness functions to evaluate whether a criterion with a complex edge configuration can render a better result than a simple criterion such as the strength of gradient. The edges obtained by using a complex fitness function are thicker and more fragmented than those obtained by using a simple fitness function, suggesting that a complex edge selecting scheme is not necessary for good edge detection in medical ultrasonic images; instead, a proper noise-smoothing filter is the key.

Keywords: edge detection, ultrasonic images, speckle noise

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Authors: Zenaide Carvalho da Silva, Andrey Ricardo Pimentel, Leandro Rodrigues Ferreira

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Learning styles (LSs) correspond to the individual preferences of a person regarding the modes and forms in which he/she prefers to learn throughout the teaching/learning process. The content presentation of learning objects (LOs) using knowledge about the students’ LSs offers them digital educational resources tailored to their individual learning preferences. In this context, the most relevant characteristics of the LSs along with the most appropriate forms of LOs' content presentation were mapped and associated. Such was performed in order to define the composition of an adaptive model of LO's content presentation considering the LSs, which was called Adaptation of Content Presentation of Learning Objects Considering Learning Styles (ACPLOLS). LO prototypes were created with interfaces that were adapted to students' LSs. These prototypes were based on a model created for validation of the approaches that were used, which were established through experiments with the students. The results of subjective measures of students' emotional responses demonstrated that the ACPLOLS has reached the desired results in relation to the adequacy of the LOs interface, in accordance with the Felder-Silverman LSs Model.

Keywords: Adaptation, interface, learning styles, learning objects, students.

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Authors: Nuha Omran Abokhdair, Azizah Bt Abdul Manaf

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Reversible watermarking is a special branch of image watermarking, that is able to recover the original image after extracting the watermark from the image. In this paper, an adaptive prediction-based reversible watermarking scheme is presented, in order to increase the payload capacity of MRI medical images. The scheme divides the image into two parts, Region of Interest (ROI) and Region of Non-Interest (RONI). Two bits are embedded in each embeddable pixel of RONI and one bit is embedded in each embeddable pixel of ROI. The experimental results demonstrate that the proposed scheme is able to achieve high embedding capacity. This is mainly caused by two reasons. First, the pixels that were excluded from data embedding due to overflow/underflow are used for data embedding. Second, large location map that need to be added to watermark data as overhead is eliminated and thus lower data embedding capacity is prevented. Moreover, the scheme provides good visual quality to the watermarked image.

Keywords: Medical image watermarking, reversible watermarking, Difference Expansion, Prediction-Error Expansion.

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Authors: C. Treerattanaphan, P. Boonpramuk, P. Singla

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Frequent, continuous speech training has proven to be a necessary part of a successful speech therapy process, but constraints of traveling time and employment dispensation become key obstacles especially for individuals living in remote areas or for dependent children who have working parents. In order to ameliorate speech difficulties with ample guidance from speech therapists, a website has been developed that supports speech therapy and training for people with articulation disorders in the standard Thai language. This web-based program has the ability to record speech training exercises for each speech trainee. The records will be stored in a database for the speech therapist to investigate, evaluate, compare and keep track of all trainees’ progress in detail. Speech trainees can request live discussions via video conference call when needed. Communication through this web-based program facilitates and reduces training time in comparison to walk-in training or appointments. This type of training also allows people with articulation disorders to practice speech lessons whenever or wherever is convenient for them, which can lead to a more regular training processes.

Keywords: Web-Based Remote Training Program, Thai Speech Therapy, Articulation Disorders.

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Authors: I. R. Farah, I. B. Ismail, M. B. Ahmed

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The huge development of new technologies and the apparition of open communication system more and more sophisticated create a new challenge to protect digital content from piracy. Digital watermarking is a recent research axis and a new technique suggested as a solution to these problems. This technique consists in inserting identification information (watermark) into digital data (audio, video, image, databases...) in an invisible and indelible manner and in such a way not to degrade original medium-s quality. Moreover, we must be able to correctly extract the watermark despite the deterioration of the watermarked medium (i.e attacks). In this paper we propose a system for watermarking satellite images. We chose to embed the watermark into frequency domain, precisely the discrete wavelet transform (DWT). We applied our algorithm on satellite images of Tunisian center. The experiments show satisfying results. In addition, our algorithm showed an important resistance facing different attacks, notably the compression (JEPG, JPEG2000), the filtering, the histogram-s manipulation and geometric distortions such as rotation, cropping, scaling.

Keywords: Digital data watermarking, Spatial Database, Satellite images, Discrete Wavelets Transform (DWT).

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Authors: Inês Ramalhete, Hugo Farias, Rui da Silva Pinto

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Adequate housing has been a widely discussed theme in academic circles related to low-cost housing, whereas its physical features are easy to deal with, overcrowding (related to social, cultural and economic aspects) is still ambiguous, particularly regarding the set of indicators that can accurately reflect and measure it. This paper develops research on low-cost housing models for developing countries and what is the best method to embed overcrowding as an important parameter for adaptability. A critical review of international overcrowding indicators and their application in two developing countries, Cape Verde and Angola, is presented. The several rationales and the constraints for an accurate assessment of overcrowding are considered, namely baseline data (statistics), which can induce misjudgments, as well as social and cultural factors (such as personal choices of residents). This paper proposes a way to tackle overcrowding through housing adaptability, considering factors such as physical flexibility, functional ambiguity, and incremental expansion schemes. Moreover, a case-study is presented to establish a framework for the theoretical application of the proposed approach.

Keywords: Adaptive housing, low-cost housing, overcrowding.

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Authors: S. Sarumathi, M. Vaishnavi, S. Geetha, P. Ranjetha

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Machine learning is a new and exciting area of artificial intelligence nowadays. Machine learning is the most valuable, time, supervised, and cost-effective approach. It is not a narrow learning approach; it also includes a wide range of methods and techniques that can be applied to a wide range of complex realworld problems and time domains. Biological image classification, adaptive testing, computer vision, natural language processing, object detection, cancer detection, face recognition, handwriting recognition, speech recognition, and many other applications of machine learning are widely used in research, industry, and government. Every day, more data are generated, and conventional machine learning techniques are becoming obsolete as users move to distributed and real-time operations. By providing fundamental knowledge of machine learning tools and research opportunities in the field, the aim of this article is to serve as both a comprehensive overview and a guide. A diverse set of machine learning resources is demonstrated and contrasted with the key features in this survey.

Keywords: Artificial intelligence, machine learning, deep learning, machine learning algorithms, machine learning tools.

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Authors: A. Pedram, M. R. Jamali, T. Pedram, S. M. Fakhraie, C. Lucas

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Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.

Keywords: LOLIMOT, hardware, neurofuzzy systems, reconfigurable, parallel.

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Authors: Shiuh-Jer Huang, Ming-Tien Su

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Since the driving speed and control accuracy of commercial optical disk are increasing significantly, it needs an efficient controller to monitor the track seeking and following operations of the servo system for achieving the desired data extracting response. The nonlinear behaviors of the actuator and servo system of the optical disk drive will influence the laser spot positioning. Here, the model-free fuzzy control scheme is employed to design the track seeking servo controller for a d.c. motor driving optical disk drive system. In addition, the sliding model control strategy is introduced into the fuzzy control structure to construct a 1-D adaptive fuzzy rule intelligent controller for simplifying the implementation problem and improving the control performance. The experimental results show that the steady state error of the track seeking by using this fuzzy controller can maintain within the track width (1.6 μm ). It can be used in the track seeking and track following servo control operations.

Keywords: Fuzzy control, gain tuning and optical disk drive.

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Authors: Lu Yang, Meiyang Song, Xiang Yu, Wenhao Zhou, Chuntao Feng

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To address the problem that the FM continuous-wave (FMCW) radar extracts human vital sign signals which are susceptible to noise interference and low reconstruction accuracy, a detection scheme for the sign signals is proposed. Firstly, an improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) algorithm is applied to decompose the radar-extracted thoracic signals to obtain several intrinsic modal functions (IMF) with different spatial scales, and then the IMF components are optimized by a backpropagation (BP) neural network improved by immune genetic algorithm (IGA). The simulation results show that this scheme can effectively separate the noise, accurately extract the respiratory and heartbeat signals and improve the reconstruction accuracy and signal to-noise ratio of the sign signals.

Keywords: Frequency modulated continuous wave radar, ICEEMDAN, BP Neural Network, vital signs signal.

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Authors: Sun-Ki Hong, Yong-Ho Cho, Ki-Seok Kim, Tae-Sam Kang

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Piezoelectric actuator is treated as RC load when it is modeled electrically. For some piezoelectric actuator applications, arbitrary voltage is required to actuate. Especially for unidirectional arbitrary voltage driving like as sine wave, some special inverter with circuit that can charge and discharge the capacitive energy can be used. In this case, the difference between power supply level and the object voltage level for RC load is varied. Because the control gain is constant, the controlled output is not uniform according to the voltage difference. In this paper, for charge and discharge circuit for unidirectional arbitrary voltage driving for piezoelectric actuator, the controller gain is controlled according to the voltage difference. With the proposed simple idea, the load voltage can have controlled smoothly although the voltage difference is varied. The appropriateness is proved from the simulation of the proposed circuit.

Keywords: Analog voltage inverter, Capacitive load, Gain control, DC-DC converter, Piezoelectric, Voltage waveform.

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Authors: M. Polanský

Abstract:

This paper introduces a new method called ARPDC (Advanced Robust Parallel Distributed Compensation) for automatic control of nonlinear systems. This method improves a quality of robust control by interpolating of robust and optimal controller. The weight of each controller is determined by an original criteria function for model validity and disturbance appreciation. ARPDC method is based on nonlinear Takagi-Sugeno (T-S) fuzzy systems and Parallel Distributed Compensation (PDC) control scheme. The relaxed stability conditions of ARPDC control of nominal system have been derived. The advantages of presented method are demonstrated on the inverse pendulum benchmark problem. From comparison between three different controllers (robust, optimal and ARPDC) follows, that ARPDC control is almost optimal with the robustness close to the robust controller. The results indicate that ARPDC algorithm can be a good alternative not only for a robust control, but in some cases also to an adaptive control of nonlinear systems.

Keywords: Robust control, optimal control, Takagi–Sugeno (TS) fuzzy models, linear matrix inequality (LMI), observer, Advanced Robust Parallel Distributed Compensation (ARPDC).

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Authors: A. Elyousfi, A. Tamtaoui, E. Bouyakhf

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The H.264/AVC standard uses an intra prediction, 9 directional modes for 4x4 luma blocks and 8x8 luma blocks, 4 directional modes for 16x16 macroblock and 8x8 chroma blocks, respectively. It means that, for a macroblock, it has to perform 736 different RDO calculation before a best RDO modes is determined. With this Multiple intra-mode prediction, intra coding of H.264/AVC offers a considerably higher improvement in coding efficiency compared to other compression standards, but computational complexity is increased significantly. This paper presents a fast intra prediction algorithm for H.264/AVC intra prediction based a characteristic of homogeneity information. In this study, the gradient prediction method used to predict the homogeneous area and the quadratic prediction function used to predict the nonhomogeneous area. Based on the correlation between the homogeneity and block size, the smaller block is predicted by gradient prediction and quadratic prediction, so the bigger block is predicted by gradient prediction. Experimental results are presented to show that the proposed method reduce the complexity by up to 76.07% maintaining the similar PSNR quality with about 1.94%bit rate increase in average.

Keywords: Intra prediction, H.264/AVC, video coding, encodercomplexity.

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Authors: Zhongzhen Luo, Saeid Habibi, Martin v. Mohrenschildt

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Self-driving vehicle require a high level of situational awareness in order to maneuver safely when driving in real world condition. This paper presents a LiDAR based real time perception system that is able to process sensor raw data for multiple target detection and tracking in dynamic environment. The proposed algorithm is nonparametric and deterministic that is no assumptions and priori knowledge are needed from the input data and no initializations are required. Additionally, the proposed method is working on the three-dimensional data directly generated by LiDAR while not scarifying the rich information contained in the domain of 3D. Moreover, a fast and efficient for real time clustering algorithm is applied based on a radially bounded nearest neighbor (RBNN). Hungarian algorithm procedure and adaptive Kalman filtering are used for data association and tracking algorithm. The proposed algorithm is able to run in real time with average run time of 70ms per frame.

Keywords: LiDAR, real-time system, clustering, tracking, data association.

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Authors: Seungsuk Ha, Youngjoon Han, Hernsoo Hahn

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This paper proposes a method of adaptively generating a gait pattern of biped robot. The gait synthesis is based on human's gait pattern analysis. The proposed method can easily be applied to generate the natural and stable gait pattern of any biped robot. To analyze the human's gait pattern, sequential images of the human's gait on the sagittal plane are acquired from which the gait control values are extracted. The gait pattern of biped robot on the sagittal plane is adaptively generated by a genetic algorithm using the human's gait control values. However, gait trajectories of the biped robot on the sagittal plane are not enough to construct the complete gait pattern because the biped robot moves on 3-dimension space. Therefore, the gait pattern on the frontal plane, generated from Zero Moment Point (ZMP), is added to the gait one acquired on the sagittal plane. Consequently, the natural and stable walking pattern for the biped robot is obtained.

Keywords: Biped robot, gait pattern, genetic algorithm.

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