Catálogo de publicaciones - libros

Lee et al.’s presented a Verifier-based key agreement protocol in 2004. They claimed that their protocol was secure against Stolen-verifier attack in the case of server compromise. However, it is not really a secure protocol. In this paper, we briefly review this scheme and demonstrate the flaw, which once was pointed out by Shim and Seo. Subsequently, we propose a novel Verifier-based authenticated key agreement protocol and show that it withstands Stolen-verifier attack, Dictionary attack, and man-in-middle attack.

Because of the fact that mobile communication channel changes by time, it is necessary to employ adaptive channel equalizers in order to combat the distorting effects of the channel. Least Mean Squares (LMS) algorithm is one of the most popular channel equalization algorithms and is preferred over other algorithms such as the Recursive Least Squares (RLS) and Maximum Likelihood Sequence Estimation (MLSE) when simplicity is a dominant decision factor. However, LMS algorithm suffers from poor performance and convergence speed within the training period specified by most of the standards. The aim of this study is to improve the convergence speed and performance of the LMS algorithm by adjusting the step size using fuzzy logic. The proposed method is compared with the Channel Matched Filter-Decision Feedback Equalizer (CMF-DFE) [1] which provides multi path propagation diversity by collecting the energy in the channel, Minimum Mean Square Error-Decision Feedback Equalizer (MMSE-DFE) [2] which is one of the most successful equalizers for the data packet transmission, normalized LMS-DFE (N-LMS-DFE) [3], variable step size (VSS) LMS-DFE [4], fuzzy LMS-DFE [5,6] and RLS-DFE [7]. The obtained simulation results using HIPERLAN/1 standards have demonstrated that the proposed LMS-DFE algorithm based on fuzzy logic has considerably better performance than others.

In this paper, we propose a bandwidth efficient cooperative transmission protocol for WSNs (Wireless Sensor Networks). Orthogonal frequency division multiplexing (OFDM) modulation is a bandwidth efficient technique. However, in a multi-path fading channel, these sub-carriers will experience different fading levels. Therefore cooperative communication protocol across the sub-carriers is essential to get spatial diversity effect. In cooperative transmission, a partner node assists one sensor node to transmit their data to cluster-head. Instead of using M partners for M sensor nodes, we propose 1 partner for M sensor nodes. Proposed protocol offers similar diversity order as conventional one with much less bandwidth. A variety of numerical results reveal that the proposed protocol can save the networks power up to 7dB over direct transmission at BER of 10 -2.

In this paper, we propose an optimum power allocation method (OPA) which to minimize the the symbol-error-rate (SER) performance for a two-user amplify-and-forward cooperative system. With this optimum and equal power allocation (EPA) methods, we also discuss the impact of relay location to the SER performance and ergodic capacity by making use of two characteristic rectangle and ellipse topologies. With the EPA method, the ergodic capacity and SER curves show an interesting symmetric characteristic and the optimum relay location is just in the middle with respect to the source and destination. While a different conclusion is drown with the OPA method, the optimum relay location is near the destination in this case.

The optimal multiuser detection for communication systems can be characterized as an NP-hard optimization problem. In this paper, as a new heuristic intelligent optimization algorithm, Artificial Fish Swarm Algorithm (AFSA) is employed for the detection problem, the results show that it has better performances such as good global convergence, strong robustness, insensitive to initial values, simplicity of implementation and faster convergent speed with random initial values compared with genetic algorithm (GA). With the increase of fish or iteration number, the AFSA has only the linear increment of complexity and maintain a superior performance; its improved methods are also proposed and analysed in the end.

In 2005, Sun-Cao pointed out that Peinado’s key authentication scheme does not achieve non-repudiation of the user’s public key, like its previous versions. Then, they proposed an improved public key authentication scheme, which is proved to realize the non-repudiation service. However, in this paper, we will show that Sun-Cao’s improved public key authentication scheme for non-repudiation is still vulnerable to public key substitution attack, i.e., an intruder can substitute a fake public key for the genuine one. Furthermore, we propose an improved public key authentication scheme for non-repudiation. In our scheme, we resolve the problems appeared in Sun-Cao’s scheme as the public key substitution attack. As a result, our scheme is highly secure than Sun-Cao’s scheme.

Most people say that home network will become one of the most important things in the future industries and create high profit. Home network will grow to the direction that improves human life more conveniently. In the near future, an intelligent home environment that e diverse home services are provided, there are various devices connected to the internet and people can roam between homes will emerge in the human life. However, due to such a complexity of the home network, it will become more difficult for us to find an appropriate security mechanism for home network. Furthermore, some home devices like home appliances, AV systems and sensors may not be able to process the public key mechanism due to their low computation capability. So we think that an appropriate security mechanism is required for authentication and authorization in the case that devices need to be used within their own home. In this paper, we present the efficient authentication and authorization architecture for intelligent home network. To achieve our goal, we consider the performance of devices, secure multi-home roaming, the efficient security architecture and so forth.

In this paper, we present a realistic 3-D face construction system using either a still image or real-time moving images. The synthesized 3-D face is useful in communicating among peoples and fashion industry (hire dress, makeup and accessory). The system consists of three parts. The first part is to detect a face, and the second part extracts feature information of face such as eye, nose and mouth. Finally, the third part constructs 3-D face, and then animates it for application. We evaluate the performance of face detection and feature extraction with the Korean face database and standard Cohn-Kanade Facial Expression database separately.

This paper presents a grid-based approach for robustly extracting head and hand regions of a moving human in a varying distance from the camera. First, our method applies background subtraction scheme to the sequence of images based on hue and saturation information to classify the foreground and background pixels. While the original given images are with 648×468 resolutions, the background subtracted image is partitioned into grid patches, where each grid consists of 8 × 8 pixels. The grid patches are classified into background, non-skin foreground and skin foreground classes based on the histogram analysis of patch feature values. The histogram analysis of patch feature values makes patch classification be robust regardless of the distance from the camera. Then, the connected component labeling is applied to the grid image which consists of the classified patches. By using the grid image, we can effectively extract the skin regions of human head and hands, and we also can reduce unexpected labeling results from the noises in detecting the skin regions.

We present a vehicle color classification method from outdoor vehicle images. Although the vehicle color recognition is important especially for the newest applications including ITS (intelligent transportation system), we have no significant previous results at least to our knowledge. In this paper, we started from converting the vehicle image into an HSV(hue-saturation-value) color model-based image, to eliminate distortions due to the intensity changes. Then, we construct the feature vector, which is a two-dimensional histogram for the hue and saturation pairs. We use the SVM(support vector machine) method to classify these feature vectors into five vehicle color classes: black, white, red, yellow and blue. Our implementation result shows 94.92% of success rate for 500 outdoor vehicle images.