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Signal Processing and Information Technology (ISSPIT), 2010 IEEE International Symposium on

Date 15-18 Dec. 2010

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Displaying Results 1 - 25 of 103
  • [Copyright notice]

    Page(s): 1
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  • Table of contents

    Page(s): 1 - 4
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    Freely Available from IEEE
  • Keynotes speakers

    Page(s): 1 - 3
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    Provides an abstract for each of the keynote presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings. View full abstract»

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  • Organizers

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  • Program

    Page(s): 1 - 8
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  • Sponsors

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  • Technical Program Committee

    Page(s): 1 - 2
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  • Welcome message from the technical program chair

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  • First-Slot scheduling with wavelength conversion for distributed file transfers

    Page(s): 42 - 47
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (990 KB) |  | HTML iconHTML  

    As more flexible and self-adaptive network connections are expected between cloud users and service vendors, the requests for more sophisticated bandwidth scheduling schemes are emerging. In this paper, we conduct a thorough study on the a specific type of bandwidth reservation: First-Slot scheduling in this scenario. We introduce a model for the optical networks with sparse wavelength conversion, which are now widely served as dedicated networks between the users and the cloud. We evaluate the performances of EBF and KDP algorithms under various topologies and workloads. Our results show that increasing the fraction of nodes with wavelength converters reduces the blocking probability, but have only marginal effects on requests' average start time. However, both metrics are heavily affected by network topology and traffic load. We also observe that accepting requests greedily, like EBF does, yields better performance when the workload is low, but the more conservative approach, KDP, is superior when the workload is high. View full abstract»

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  • Computer networks resilience challenges: Routing protocols

    Page(s): 28 - 33
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2026 KB) |  | HTML iconHTML  

    Computer networks are the core of the cyberinfrastucture and are endangered by malicious activities and natural disasters. These threats stress the need for a resilient computer networks to ensure business operational continuity and optimum performance. Several aspects affect computer network resilience level such as network topology, routing protocols, and others. This paper demonstrates Redundancy, Fault Tolerance and Resilience concepts within the computer network domain to illustrate the computer networks resilience goals. It also proposes a set of parameters to evaluate routing protocols' resilience. Results showed that the compounded use of throughput rate, convergence time, and routing protocol table size can be used as to evaluate the routing protocols behavior towards the computer networks resilience. View full abstract»

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  • Task clustering & scheduling with duplication using recursive critical path approach (RCPA)

    Page(s): 34 - 41
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1014 KB) |  | HTML iconHTML  

    This paper address the problem of static task clustering and scheduling of a parallel program given as a Directed Acyclic Graph(DAG) with duplication. Scheduling of task graphs onto multiprocessors is known to be an NP-complete in most cases leading to solutions based on heuristics. In this paper, a novel task-duplication scheduling heuristic is presented. Our approach differs from other heuristics found in literature since task clustering and scheduling are handled during the same phase. This technique omit the need of the independent clustering and scheduling phases (i.e., creating a cluster for every single task in the given DAG followed by the scheduling process) as in. Preliminarily results shows that our algorithm outperforms PY, LWB, PLW, TCSD and LG algorithms in terms of the overall makespan, and total number of processors used. View full abstract»

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  • An enhanced e-learning ecosystem based on an integration between cloud computing and Web2.0

    Page(s): 48 - 55
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (795 KB) |  | HTML iconHTML  

    What we know is less important than our capacity to continue to learn more until e-learning appeared. While e-learning technology has matured considerably since its inception, there are still many problems that practitioners find when come to implementing e-learning. Today's knowledge society of the 21st century requires a flexible learning environment which is capable to adapt according to teaching and learning objectives, students' profiles and preferences for information and communication technologies and services. Advances in technology offer new opportunities in enhancing teaching and learning. Many advances in learning technologies are taking place throughout the world. The new technologies enable individuals to personalize the environment in which they work or learn, utilizing a range of tools to meet their interests and needs. Research community has believed that an e-learning ecosystem is the next generation e-learning but has faced challenges in optimizing resource allocations, dealing with dynamic demands on getting information and knowledge anywhere and anytime, handling rapid storage growth requirements, cost controlling and greater flexibility. Additionally, e-learning ecosystems need to improve its infrastructure, which can devote the required computation and storage resources for e-learning ecosystems. So, we need flourish, growing, up-to-date and strong infrastructure e-learning ecosystems in a productive and cost-effective way to be able to face rapidly-changing environments. In this paper, an e-learning ecosystem (ELES) which supports modern technologies is introduced and implemented. An integration between cloud computing and Web 2.0 technologies and services will be used to support the development of e-learning ecosystems; cloud computing as an adoptable technology for many of the organizations with its dynamic scalability and usage of virtualized resources as a service through the Internet. Web 2.0 brings new instruments help building dynamic- - e-learning ecosystem on the web. View full abstract»

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  • SCAMRA: Simulated Context-Aware Meeting Room Application

    Page(s): 56 - 60
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (657 KB) |  | HTML iconHTML  

    This paper deals with how to adapt the context architecture defined in [1] to a context-aware application called Simulated Context-Aware Meeting Room Application (SCAMRA). The main goal is to test and experiment the CFM and the proposed architecture done in [1]. SCAMRA can be used to manage any holding meeting on offices, universities, organizations or any other location by managing sessions, attendees and generating overall meeting report. It uses a large variety of context including user location, favorite subjects, preferred setting display, questions/notes that users ask/take, time, presentations, schedules, reports, and many activities and/or services. Primary context used in SCAMRA is about person, device, time, location, activity and services. Those contexts are grouped under more general categories related to meeting state and time-context, which are as before meeting, start meeting, end meeting, during meeting and after meeting context. SCAMRA has two main activities, person arrival and ask/take question/note activity. Other activities are based on the previous meeting states. These activities enforce special services such as greeting arrivals, change desktop setting, send meeting presentations to members, display meeting schedule, open presentation in its session, save attendee's questions/notes, switch between Bluetooth and barcode as needed, register new visitors, format/create/open/print/send overall meeting report. View full abstract»

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  • Analyses on kernel-specific generalization ability for kernel regressors with training samples

    Page(s): 61 - 66
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (567 KB) |  | HTML iconHTML  

    Theoretical analyses on generalization error of a model space for kernel regressors with respect to training samples are given in this paper. In general, the distance between an unknown true function and a model space tends to be small with a larger set of training samples. However, it is not clarified that a larger set of training samples achieves a smaller difference at each point of the unknown true function and the orthogonal projection of it onto the model space, compared with a smaller set of training samples. In this paper, we show that the upper bound of the squared difference at each point of these two functions with a larger set of training samples is not larger than that with a smaller set of training samples. We also give some numerical examples to confirm our theoretical result. View full abstract»

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  • Singer and music discrimination based threshold in polyphonic music

    Page(s): 445 - 450
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1567 KB) |  | HTML iconHTML  

    Song and music discrimination play a significant role in multimedia applications such as genre classification and singer identification. Song and music discrimination play a significant role in multimedia applications such as genre classification and singer identification. The problem of identifying sections of singer voice and instrument signals is addressed in this paper. It must therefore be able to detect when a singer starts and stops singing. In addition, it must be efficient in all circumstances that the interpreter is a man or a woman or that he or she has a different register (soprano, alto, baritone, tenor or bass), different styles of music and independent of the number of instruments. Our approach does not assume a priori knowledge of song and music segments. We use simple and efficient threshold-based distance measurements for discrimination. Linde-Buzo-Gray vector quantization algorithm and Gaussian Mixture Models (GMMs) are used for comparison purposes. Our approach is validated on a large experimental dataset from the music genre database RWC that includes many styles (25 styles and 272 minutes of data). View full abstract»

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  • Optical watermarking technique robust to geometrical distortion in image

    Page(s): 67 - 72
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1671 KB) |  | HTML iconHTML  

    This paper presents an optical watermarking technique that is robust against geometrical distortion in images by using spatially modulated illumination. It can protect “analog” objects like pictures painted by artists from having photographs taken of them illegally in museums. Illegally captured images in practical situations may contain various distortions, and embedded watermarks may be incorrectly detected. Geometrical distortion caused by the shooting angle that the objects are captured at is a particularly major problem. We carried out experiments to evaluate the robustness of watermarking images that were geometrically distorted, in which distortions were intentionally created by moving the position of the projector and the digital camera from right in front of the object. The accuracy of the extracted watermarking data was almost 100%, even if the shooting angle was inclined by about 20 degrees between the projector and digital camera, in both cases when a Discrete Cosine Transform (DCT) and a Walsh-Hadamard Transform (WHT) were used as the methods of embedding watermarks. We introduced rectangular mesh fitting and a technique of “bi-linear interpolation” based on the four nearest points to correct the distortions. View full abstract»

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  • A framework to strengthen password authentication using mobile devices and browser extensions

    Page(s): 73 - 78
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (654 KB) |  | HTML iconHTML  

    Shoulder-surfing, phishing and keylogging are widely used by attackers to obtain users' sensitive credentials. In this paper, we propose a framework to strengthen password authentication using mobile devices and browser extensions. This approach provides a relatively high resilience against shoulder-surfing, phishing and keylogging attacks while requires no change on the server side. A prototype implementation of the proposed approach and its security analysis are also provided. View full abstract»

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  • Human activity recognition via temporal moment invariants

    Page(s): 79 - 84
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (758 KB) |  | HTML iconHTML  

    Temporal invariant shape moments intuitively seem to provide an important visual cue to human activity recognition in video sequences. In this paper, an SVM based method for human activity recognition is introduced. With this method, the feature extraction is carried out based on a small number of computationally-cheap invariant shape moments. When tested on the popular KTH action dataset, the obtained results are promising and compare favorably with that reported in the literature. Furthermore our proposed method achieves real-time performance, and thus can provide latency guarantees to real-time applications and embedded systems. View full abstract»

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  • Multi-objective optimization of NoC standard architectures using Genetic Algorithms

    Page(s): 85 - 90
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1238 KB) |  | HTML iconHTML  

    One of the challenging problems in Networks-on-Chip (NoC) design is optimizing the architectural structure of the on-chip network in order to maximize the network performance while minimizing corresponding costs. In this paper, a methodology for multi-objective optimization of NoC standard architectures using Genetic Algorithms is presented. The methodology considers two cost metrics, power and area, and two performance metrics, delay and reliability. Moreover, our methodology combines the best selection of NoC standard topology, the optimum mapping of application cores onto that topology, and the best routing of application traffic traces over the generated network. The methodology is evaluated by applying it to an NoC benchmark application as a case study. Results show that the architectures generated by our methodology outperform those of other standard architectures customization techniques with respect to power, area, delay, reliability, and the combination of the four metrics. View full abstract»

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  • Improving performance of fading channel simulators by use of uniformly distributed random numbers

    Page(s): 91 - 96
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (919 KB) |  | HTML iconHTML  

    Filter-based fading channel simulators universally use White Gaussian Noise (WGN) to generate complex tap coefficients. In this work, we will show that replacing WGN source by Uniform Random Number Generator (URNG) results in improved simulation speed in case of software simulator; and reduced area/power in case of hardware simulator. We will verify, both analytically and through extensive simulations, that use of URNG does not cause any degradation in important simulator performance parameters like statistical properties, spectral shape, level crossing rate and bit error rate. To validate our analysis, we have designed fading channel simulators both in software and hardware. We will show that use of URNG causes 6 percent improvement in simulation time in case of software simulator. Similarly, we will demonstrate that for a hardware simulator, we obtain an improvement of 30 and 40 percent in area and power consumption respectively. View full abstract»

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  • Hierarchical clustering of 3-D line segments for building detection

    Page(s): 388 - 393
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1161 KB) |  | HTML iconHTML  

    A novel approach for an efficient extraction of rectangular boundaries from aerial image data is proposed in this paper. In this approach, a Centroid Neural Network (CNN) with a metric of line segments is utilized for connecting low-level linear structures or grouping similar objects. The proposed an approach, called hierarchical clustering method, utilizes the fact that rooftops of a building are about the same height and perform clustering process with candidate 3-D line segments with similar heights. Experiments are performed with a set of high resolution satellite image data. The results show that the proposed hierarchical clustering method can remove noisy segments such as shade lines efficiently and find more accurate rectangular boundaries. View full abstract»

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  • Content-based retrieval of audio data using a Centroid Neural Network

    Page(s): 394 - 398
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (771 KB) |  | HTML iconHTML  

    A classification scheme for content-based audio signal retrieval is proposed in this paper. The proposed scheme uses the Centroid Neural Networks (CNN) with a Divergence Measure called Divergence-based Centroid Neural Network (DCNN) to perform clustering of Gaussian Probability Density Function (GPDF) data. In comparison with other conventional algorithms, the DCNN designed for probability data has the robustness advantages of utilizing a audio data representation method in which each audio data is represented by a Gaussian distribution feature vector. Experiments and results for several audio data sets have shown that the DCNN-based classification algorithm has accuracy improvements over models employing the conventional k-means and Self Organizing Map (SOM) algorithms. View full abstract»

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  • Fuzzy self-tuning inverse dynamics control of 3 DOF planar robot manipulators

    Page(s): 439 - 444
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1275 KB) |  | HTML iconHTML  

    Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. Inverse dynamics control is a well-known conventional motion control strategy for manipulators which ensures global asymptotic stability for fixed symmetric positive definite (proportional and derivative) gain matrices. In this paper, a supervisory hierarchical fuzzy controller (SHFC) for tuning the Proportional and Derivative gains according to the actual tracking position error and the actual tracking velocity error. Numerical simulations using the dynamic model of three DOF planar rigid robot with uncertainties show the effectiveness of the approach in trajectory tracking problems. Performance indices i.e. integral absolute error (IAE) and integral time multiplied absolute error (ITAE) are used for comparison. View full abstract»

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  • On the security of image encryption schemes based on Multiple Parameters Transforms

    Page(s): 97 - 101
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1363 KB) |  | HTML iconHTML  

    Recent developments of generalized forms of signal processing transforms with a large number of independent parameters, such as the Multiple Parameter Fractional Fourier Transform and the Discrete Fractional Cosine Transform, have encouraged many researchers to propose image encryption algorithms based on a single or multiple applications of these transforms. In order to claim a high level of security of these parameterized transforms-based schemes, their authors usually use the argument that the encrypted image is visually indistinguishable from random noise. In this paper, we show that these algorithms represent typical textbook examples of insecure ciphers; all the building blocks of these schemes are linear, and hence, breaking these scheme, using a known plaintext attack, is equivalent to solving a set of linear equations. We also invalidate the argument of relying on the visual quality of the encrypted image ciphertext by presenting an example for a trivially insecure system that produces ciphertext images with the same property. An agreement against the claimed efficiency of these schemes is also provided. View full abstract»

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  • Pupil location and movement measurement for efficient emotional sensibility analysis

    Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (821 KB) |  | HTML iconHTML  

    With advances in intelligent technologies, e.g. ambient intelligence, context-aware, and pervasive systems, much research is now devoted to a computational paradigm that senses and perceives changes in human emotion. This paper presents a context-aware architecture for adaptive emotional sensibility analysis called CAF-ESA (a Context-Aware Framework based Emotional Sensibility Analysis) with adaptive capability for use under both diverse changes in both human emotion and the illumination environment. Our proposed system implements context-awareness by a system that identifies working situations as usage contexts. An unsupervised learning algorithm models usage context while a supervised learning algorithm identifies the usage context. A genetic algorithm explores the emotional sensibility space for each identified usage context to determine human eye movement face images. The framework is validated for locating the pupil under changing illumination environments, and for pupil movement that is associated with emotional sensibility such as for both a positive and a negative emotion. We have achieved encouraging experimental results in the real time detection of pupil location and measurement of pupil movement. View full abstract»

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