Communication technology of future networks is predicted to provide a large variety of services including WiFi service in vehicular network. In this paper, we assume that vehicles are embedded with WiMAX antenna and in-vehicle terminals receive WiMAX traffic through WiFi interface. This assumption will impose severe performance degradation due to interference among mobile BSSs when WiFi access points (APs) are densely located. Existing interference avoidance techniques cannot properly resolve the above problems and do not cope with dynamically moving vehicular scenario since they focus only on the fixed network topology. In this paper, we propose a mobility-aware interference avoidance scheme for WiFi services. The proposed scheme computes the interference duration by exploiting mobility vector and location information of neighboring APs. If the interference duration is not negligible, our scheme searches for another channel in order to avoid interference. However, if the interference duration is negligible, our scheme continues to use the channel to reduce switching overhead. To measure the effectiveness of the proposed scheme against other existing techniques, we evaluated performance by using OPNET simulator. Through the simulation, we obtained about 60% reduction in the maximum interference frequency and about 67% improvement in throughput. Furthermore, our scheme provides fair channel usage.

The key issue in providing fast and reliable access on cloud services is the effective management of resources in a cloud system. However, the high variation in cloud service access rates affects the system performance considerably when there are no default routines to handle this type of occurrence. Adaptive techniques are used in resource management to support robust systems and maintain well-balanced loads within the servers. This paper presents an adaptive resource management for cloud systems which supports the integration of intelligent methods to promote quality of service (QoS) in provisioning of cloud services. A technique of dynamically assigning cloud services to a group of cloud servers is proposed for the adaptive resource management. Initially, cloud services are collected based on the excess cloud services load and then these are deployed to the assigned cloud servers. The assignment function uses the proposed proportional ordering which efficiently assigns cloud services based on its resource consumption. The difference in resource consumption rate in all nodes is analyzed periodically which decides the execution of service assignment. Performance evaluation showed that the proposed dynamic service assignment (DSA) performed best in throughput performance compared to other resource allocation algorithms.

We propose a novel reduced-state maximum-likelihood sequence detection (MLSD) structure using the Viterbi algorithm based on the second-order Volterra kernel modeling nonlinear distortion due to square law detection of multipath channels commonly occurring in chromatic dispersion (CD) or polarization mode dispersion (PMD) in optical communication systems. While all existing MLSD methods for square-law detection receivers are based on direct computation of branch metrics, the proposed algorithm provides an efficient and structured way to implement reduced-state MLSD with almost the same complexity of a MLSD for linear channels. As a result, the proposed algorithm reduces the number of parameters to be estimated and the complexity of computation.

To reduce the implementation complexity of the Vertical Bell Labs layered space-time (V-BLAST) systems with respect to the zero-forcing (ZF) criterion, a computationally efficient recursive algorithm is proposed. A fast implementation of the proposed algorithm is developed and its complexity is analyzed in detail. The proposed algorithm matches the ZF-OSIC detection well, and its three significant advantages can be demonstrated by analyses and simulations. Firstly, its speedups over the conventional ZF-OSIC with norm-based ordering, the original fast recursive algorithm (FRA) and the fastest known algorithm (FKA) in the number of flops are 1.58, 2.33 and 1.22, respectively. Secondly, a much simpler implementation than FRA and FKA can be expected. Finally, the storage requirements are lower than those of FRA and FKA. These advantages make the proposed algorithm more efficient and practical.

A new and secure image steganographic scheme based on nonuniform discrete Fourier transform (NDFT) is proposed in this paper. First, the chaotic system is introduced to select embedding points randomly in NDFT domain suitable range, and NDFT is implemented on every non-overlapping block of eight consecutive pixels. Second, the secret messages are scrambled by chaotic systems, and embedded into frequency coefficients by quantization method. The stego-image is obtained by inverse NDFT (INDFT). Besides, in order to discriminate tampers, the low frequency wavelet coefficients of 7 most significant bits (MSBs) of the stego-image are converted into the binary sequence after nonuniform scalar quantization. Then the obtained binary sequence is scrambled by the chaotic systems, and embedded into the least significant bit (LSB) of the stego-image. Finally, the watermarked stego-image can be obtained by a new improved LSB steganographic method. The embedded secret messages can be extracted from the watermarked stego-image without the original cover image. Experimental results show the validity of the proposed scheme, and dual statistics attacks are also conducted to indicate the security.

Vision-based vehicle detector systems are becoming increasingly important in ITS applications. Real-time operation, robustness, precision, accurate estimation of traffic parameters, and ease of setup are important features to be considered in developing such systems. Further, accurate vehicle detection is difficult in varied complex traffic environments. These environments include changes in weather as well as challenging traffic conditions, such as shadow effects and jams. To meet real-time requirements, the proposed system first applies a color background to extract moving objects, which are then tracked by considering their relative distances and directions. To achieve robustness and precision, the color background is regularly updated by the proposed algorithm to overcome luminance variations. This paper also proposes a scheme of feedback compensation to resolve background convergence errors, which occur when vehicles temporarily park on the roadside while the background image is being converged. Next, vehicle occlusion is resolved using the proposed prior split approach and through reasoning for rule-based tracking. This approach can automatically detect straight lanes. Following this step, trajectories are applied to derive traffic parameters; finally, to facilitate easy setup, we propose a means to automate the setting of the system parameters. Experimental results show that the system can operate well under various complex traffic conditions in real time.

A variety of reconstruction methods has been developed to convert a set of scattered points generated from real models into explicit forms, such as polygonal meshes, parametric or implicit surfaces. In this paper, we present a method to construct multi-scale implicit surfaces from scattered points using multiscale kernels based on kernel and multi-resolution analysis theories. Our approach differs from other methods in that multi-scale reconstruction can be done without additional manipulation on input data, calculated functions support level of detail representation, and it can be naturally expanded for n-dimensional data. The method also works well with point-sets that are noisy or not uniformly distributed. We show features and performances of the proposed method via experimental results for various data sets.

In order to protect some important information communicated through an insecure network, a common hidden key must be used. One can produce the common hidden key using key agreement protocols; and this helps to have high security in modern data networks. Today, the designers of public key cryptography protocols try to set the public identity of a system's users (like their email addresses) as their public key. This not only makes a cryptographic protocol more efficient but also decreases its cost. These protocols are called "identity-based". In this article, an identity-based multiple key agreement scheme will be presented; this scheme uses the challenge-response method to do the verification. While the number of random values produced in our scheme is the same as other schemes, the number of keys generated in this scheme is much more than what many other key agreement schemes produce,. Therefore, we will have less computational complexities campered with other schems. In this paper, we consider the security of our scheme and consequently, we will show that it satisfies many security conditions such as strong security.